urban crime essay

America’s Suburban Crime Problem

A fter several years of rising crime, big city mayors and police chiefs across the country are breathing a sigh of relief. Statistics published by the Council of Criminal Justice and other recent analysis show the number of homicides and aggravated assaults fell by a respective 10% and 3% in big cities in 2023 compared to 2022, though the rates remain higher than in pre-pandemic years.

These are hopeful signals. If these trends persisted on a national scale, this could indicate violent crime markets have retracted. But declarations that violent crime is falling miss important—and disturbing—crime trends data that paint a much more complicated picture. While big city crime may be falling, suburban crime may be rising. More surprising still, crime in rural areas appears to be rising even faster—and a much higher share of this crime involves strangers and guns.

These startling findings come from an important but underappreciated nationally representative data source—the National Crime Victimization Survey (NCVS)—that includes crimes not reported to police. Along with last year’s big city estimates, the latest figures from the FBI's 2022 UCR program gathered from police reports, and the Bureau of Justice Statistics' NCVS report drawn from interviews with households, present a complex narrative that does more than merely highlight differences in data collection methods but unveils a nuanced and evolving picture of violent crime in the U.S.

Besides a slight rise in robbery rates from 65.5 to 66.1 per 100,000 residents, the UCR program suggests a national decrease in both the rates of fatal (homicide) and non-fatal felony violence (rape, robbery, and aggravated assault) from 2021 to 2022.  In contrast, the NCVS shows an increase in non-fatal felony violence, with victimizations per 1,000 persons over the age of 12 increasing from 5.6 in 2021 to 9.8 in 2022, primarily due to a doubling of aggravated assault rates. NCVS estimates imply that a substantial portion of crime remains unreported, the so-called " dark figure " of crime that eludes the detection of law enforcement.

A clearer picture of who is at greatest risk for violent victimization emerges when analyzing crime rates by location. The NCVS shows that the traditional boundaries between urban and non-urban violence are dissolving. Suburban and rural areas, once considered safe havens, are now confronting a jump in non-fatal violent crime, fundamentally changing the geography of public safety.

The robbery rate in urban centers increased by 21% over the three years, driven largely by the 78% increase between 2021 and 2022. Looking at the suburbs, the 2022 increase in robbery rates hit 21%, contributing to the 40% increase through 2021. In the rural areas where the American dream of pastoral peace is most cherished, robbery rates rose by 44% in 2022 after a two-year decline.

This shift is further accentuated in the rates of aggravated assault, which have not only risen in urban areas but have skyrocketed in non-urban areas. In urban centers, these assaults have risen by 51% in a single year from 2021 to 2022. Looking back at the suburbs and rural areas, the respective increases were even more pronounced with rates over nearly three times and two times higher in 2022 than in 2021.

Gun violence also increased and spread across geographies. The gun-related victimization rate in urban centers increased by 1.3 per 1,000 in 2022 compared to the previous year, reaching 2019 levels after a decrease. This rate doubled over the past two years in the suburbs and is slightly higher than in 2019, while in rural areas, there was a surge in non-fatal gun violence rates, with approximately 66,000 more reported victimizations between 2021 and 2022, returning to rates last observed in 1997.

Research suggests most violent crimes are committed by someone the victim knows such as friends, acquaintances, and relatives. This remains the case, but estimates indicate strangers are responsible for more violent crimes, especially in non-urban centers. After decreases in the number of violent felony victimizations involving strangers from 2019 to 2021, all areas experienced large increases by 2022. For this one-year period, these types of victimizations climbed by 37% in urban areas, 73% in the suburbs, and more than doubled (102%) in rural locales.

Read More: If We Want to Reduce Deaths at Hands of Police, We Need to Reduce Traffic Stops

Breaking down the data by race adds a layer of complexity to the narrative. White Americans have seen a marked increase in victimization, particularly in urban areas, reversing previous declines. From 2021 to 2022, violent felony victimization for this group rose by 75% in urban areas, 93% in suburban areas, and 62% in America’s countryside.

For Black Americans, the pattern is more complex, with an initial rise in urban victimization rates followed by a 20% decrease from 2021 to 2022. However, the increase in violent felonies in suburban areas paints a troubling picture of the changing risks these communities face. For Black Americans residing in the suburbs, the rate of violent felonies spiked 74% over the three years, with a sharp leap of 172% from 2021 to 2022. Outside the metropolitan centers, the three-year increase is less dramatic (29%) but still alarming.

The rise in violent crime comes at a time of historic domestic migration . During the height of the COVID-19 pandemic and associated lockdowns, people and families relocated from cities to the suburbs and rural locales, motivated by the flexibility of remote work and the desire for safer, more affordable, and more spacious living environments. Studies have found that violent victimization influences residential mobility, but it appears more factors are at play. As people migrate, they not only bring their dreams and aspirations but also create economic tensions and cultural integration challenges that can ferment crime and complicate public safety efforts. It's in this intersection of mobility and security that we must revisit our approach to crime prevention and intervention.

Although the Justice Department's Roadmap provides resources based on the Ten Essential Actions Cities Can Take to Reduce Violence Now , developed by the Council on Criminal Justice, the evidence is mostly from studies conducted in urban areas. Efforts to reduce violent crime in non-urban areas face challenges such as limited resources, large territories that inhibit community engagement and response times, despite initiatives like the BJA’s Rural and Small Department Violent Crime Reduction Program that collaborate with law enforcement agencies (LEAs) to develop strategies addressing these issues and Crime Analyst in Residence program intended to assist LEAs in enhancing their operational and procedural management through the utilization of data analysis and analytics.

This shift observed in the NCVS also calls for examination of the racial differences in victimization rates—particularly the heightened vulnerability of white Americans in urban settings and the complex pattern of rising and then decreasing rates for Black Americans.

Further research is needed to address gaps and uncertainties in the valuable insights provided by NCVS, particularly with regards to how victimization rates influence residential mobility within urban centers, the potential underestimation of victimization among Black people, and the variations within different areas. It is imperative to also examine the challenges posed by response rates to the NCVS, especially among hard-to-reach populations.

Meanwhile, NCVS estimates force us to seriously consider that criminal violence might be evolving rather than declining, necessitating the development and adoption of effective strategies like proven community violence reduction initiatives as well as housing, public health, and employment programs adapted to the particular needs and strengths of suburban and rural communities. Otherwise, there is a danger of resourcing and implementing urban-centric, pre-pandemic strategies in a post-pandemic world that misses the opportunity to improve community safety across racial and geographic divides.

While the recent data suggests a decrease in urban crime, many Americans still feel uneasy. It's conceivable that the pronounced changes in victimization, particularly in suburban and rural areas, have heightened this sense of vulnerability. The discrepancy between the actual numbers and public perception challenges us to consider the changing geography of crime and the impact it has on the nation's sense of security.

More Must-Reads From TIME

• Exclusive: Google Workers Revolt Over $1.2 Billion Contract With Israel
• Jane Fonda Champions Climate Action for Every Generation
• Stop Looking for Your Forever Home
• The Sympathizer Counters 50 Years of Hollywood Vietnam War Narratives
• The Bliss of Seeing the Eclipse From Cleveland
• Hormonal Birth Control Doesn’t Deserve Its Bad Reputation
• The Best TV Shows to Watch on Peacock
• Want Weekly Recs on What to Watch, Read, and More? Sign Up for Worth Your Time

Contact us at [email protected]

You May Also Like

• The Journal
• Contributors

Stay up to date with news, events and the latest thinking on urban policy from Vital City.

Alex Kent / Getty Images

20 Strategies for Reducing Crime in Cities

John k. roman, october 10, 2023.

Complements, not substitutes, to policing

It is easy to despair of crime in cities. But there is much to be learned from recent history. Two decades of research on the almost everywhere, almost all-at-once, Great American Crime Decline of the 1990s —  when violence in America dropped by half in a single decade — finds dozens of evidence-based reasons why crime declined . And overwhelmingly, that research finds that the most effective crime-fighting tools were not explicitly about fighting crime. 

In the 1990s, crime declined, among other reasons , because more people had access to Medicaid , better medicines for behavioral health became available, less cash was in circulation and fewer people were poisoned by environmental toxins . And, more evidence-based programs and practices were used in schools , workforce development and public health . Yes, mass incarceration and new policing strategies played a role, but the strongest evidence suggests they explain perhaps one-quarter of the crime decline . 

What these explanations have in common is strong empirical evidence and a focus on classical prevention based on the idea that supporting people and strengthening communities is the surest path to widespread safety. There are hundreds of solutions — market-based solutions, medical solutions, structural solutions and behavioral nudges — that can meaningfully reduce the risk of crime and violence without expanding the criminal justice system. Instead of responding to problems, these solutions reduce risk factors and risk conditions and promote resiliency, stopping crime and violence before they happen.  

But prevention does not work a la carte and there is no silver bullet, only the hard work of gradual improvements and the challenge of waiting for the longer-term positive outcomes to emerge. Quantity has a quality all its own, and the more of these strategies that are employed, the better the outcomes.

In that spirit, here are 20 crime-reducing strategies that strengthen people and communities and are supported by solid social-science research to reduce crime. The list is here to draw you in.: There are more evidence-based approaches than even this, and even more promising programs being tested. We do not have to settle for 20th-century criminal justice. The vast breadth of available prevention policies and programs should vanquish any one-dimensional view of crime reduction. 

A call for non-criminal justice solutions is not a call to defund the police in disguise. These are complements to, not substitutes for, law-enforcement-led strategies. There are numerous evidence-based law enforcement-focused mechanisms that should be a critical part of any public safety proposal. But, if the arc from Michael Brown to George Floyd taught America anything, it is that we must move beyond law enforcement working in isolation to find justice and safety.

The 20 Strategies 1. Help Victims of Crime  There is far too little support for victims of crime, even though it is the most obvious place to start. Prior victimization — of a person or a place — is the top predictor of future victimization . Supporting people who have been victimized from being victimized again — through social supports and target - hardening — has enormous potential for positive change.   2. Reduce Demand for Law Enforcement A central reason why law enforcement does not prevent more crime or solve more crimes is that they are too busy doing things that accomplish neither objective. If the police were called less often for unproductive reasons, there would be less under-policing — and less over-policing as well. If cities and towns set the explicit goal of having people call the police less often, law enforcement would be more efficient at taking on the tasks that remain. 3. Fixing Distressed Spaces There is a wide body of evidence that shows that places poison people more routinely than people poison places. Crime does not result from “areas” of the “inner city” being high risk, but rather from a few very small, very bad places . Concentrated efforts to improve contagious places can build resiliency across neighborhoods.  4. Making Crime Attractors Less Appealing  Certain places attract and generate crime — schools , the built environment and bars being at the top of the list. More often than not, careful planning and implementation of best practices in situational crime prevention can reduce the harms they unintentionally generate and, in the case of schools and transit, unlock their potential for guardianship. 5. Scientific Supports for Law Enforcement  Police in the United States would benefit from increased reliance on civilians in two realms: translating scientific evidence into practice , and increasing their reliance on civilian analysts to study local policing practices . In particular, if law enforcement was aided by more civilian analysts who were better trained , crime would be reduced while the footprint of policing was reduced.   6. Improving the Job Market and Job Training The relationship between jobs and crime is far more complex than in the popular imagination — higher national-level unemployment rates, for example, do not seem to increase violence . But targeted programs can have large effects. Integrating social and emotional skills training into employment training for young people has solid evidence of effectiveness as does employment planning for people returning from prison and transitional jobs for high risk people .  7. Facilitate Neighborhood Non-Profits In his excellent book ”Uneasy Peace,” Professor Patrick Sharkey reports on a study that found that for each 10 additional nonprofits in a given city, the violent crime rate is reduced by 14% (in the study period between 1990 and 2013). It should come as no surprise that access to more and better services has positive effects. Local government can aid the development of these local assets by providing funding for hyper-local community projects.  8. Make Jails and Prison Less Criminogenic We have overwhelmingly designed our jails and prisons to prevent people from gaining the skills to work and maintain their sobriety when they go home , and cut them off from their most crime-reducing assets, their family and friends. Small investments in humanity yield large returns when jails and prisons are not designed to produce more crime. 9. Better Prepare People to Return Home from Prison People returning from prison need specific supports to facilitate a successful transition – 82% of people released from prison are rearrested within 10 years. And the solutions are simple — leaving facilities with an ID , prescriptions , a place to stay , a way to get started . A goal without a plan is a wish — people should leave prison with a plan and the supports to implement that plan. 10. Fund Community-Based Violence Interruption A growing body of evidence finds that credible messengers — individuals with lived experience — coupled with psychosocial services can prevent retaliatory violence and repeat victimization. But this is a new sector and will need time and space to learn and grow. 11. Use Technology to Reduce Violence Professor Graham Farrell argues convincingly that increases in security technology (such as engine immobilizers and cameras) in the 1990s were the only universal explanation for the universal decline in crime. There is much more that can be done using technology without imposing on civil liberties: text message reminders for court and probation appearances , databases to maintain records on police officers with histories of abuse and anti-crime features on ordinary consumer products are just the start.  12. Tackle the Causes and Consequences of Poverty  Poverty drives crime and violence in numerous ways beyond a simple lack of income, through weakened social bonds . A number of important policies have been successfully piloted but not fully implemented by state and local government. These are the big-ticket items — child poverty tax credits , whole-school anti-bullying programs , expanding Medicaid — that have the biggest crime reduction benefits. But the benefits outweigh the costs for dozens of policies and programs .  13. Fix Long-Standing Problems  Problems often persist because they have high costs, a lack of immediacy and declining political constituency — but these perpetual problems are often the key risk condition causing crime in a place to persist. Unhealthy homes , lead paint and pipes , and under-resourced foster care all promote crime. 14. Shorten the Reach of the Criminal Justice System Too many financial burdens are imposed on people with low risk to public safety, creating a cycle of debt and incarceration , the latter which increases violence through stigma , criminal capital accumulation and a disruption of social bonds . Removing those conditions by clearing old warrants and convictions , reducing toxic fines and fees and ending poverty traps would prevent crime. 15. Help Those with Substance-Use Disorders  In the 1990s and 2000s, with trepidation, the justice system began treating substance-use disorders as a disease rather than a crime. Expansion in the broadest of these interventions – problem-solving courts and in-prison substance use treatment — largely ended more than a decade ago. Many extremely useful ideas have been piloted — trauma-informed care , motivational interviewing , treating withdrawal in prison — but few were ever taken fully to scale. Those foundations are ready-made to build upon.  16. Support Programs for High-Risk Young People and Families A lot of criminology is concerned with bending the criminal trajectory curve — to keep adolescents from accelerating their delinquency or failing to desist as they age — and a huge body of scholarship has contributed to numerous model programs. From prenatal programs , to social and emotional learning , to programs for high-risk adolescents , there is a tremendous base of knowledge. 17. Education Improving education is its own crime-reducing category, but schools can facilitate crime reduction outside of schools. Reducing food insecurity , humanizing discipline and improving the safety of the school commute benefit everyone.  18. Housing Like education, housing is its own category beyond the scope of this essay. But there are housing solutions with specific crime-reducing benefits: permanent, supportive housing ; transitional housing for young people leaving homelessness; and housing programs specifically for people who cycle through emergency services .  19. Policy and Law There are any number of laws and regulations that could be tweaked to meaningfully reduce crime and victimization. For example, higher taxes that specifically target the overuse of criminogenic products like guns and alcohol have been shown to reduce excess demand.  20. Stop the Proliferation of Firearms  The link between firearms and violence is ironclad — the more guns, the more crime. More guns explain much of the difference in rates of violence between the U.S and peer nations. Fixing violence in the U.S. without addressing the gun problem, which is to say ensuring fewer potentially dangerous people have easy access to weapons, is embracing half-measures. Next steps   The next step in strengthening people and communities is for the evidence-making industry to think beyond one intervention at a time. What we need is classical policy analysis that considers the choices faced by lawmakers in the presence of budget constraints. That means embracing cost-effective evidence-based prevention over expensive remediation, and programs that lift as many people as possible and leave behind far fewer than we do today. We need to embrace science and evidence, to think holistically and comprehensively and to stop thinking of crime and violence as a problem that can only be addressed through police and prisons.  In medicine, we learn that our first line of defense is a catchall triage — some exercise, a better diet and more sleep are the cure for a vast array of simple problems before they become serious. In economics, we learn that simple nudges can motivate better choices. In public health, we can learn that a small early change in trend and trajectory today has enormous long-term benefits. All of these lessons await discovery in the public safety sector.  John K. Roman is a senior fellow at NORC at the University of Chicago. He also serves as the co-Director of the National Prevention Science Coalition. Up next...

What new york’s new anti-airbnb law misses.

Alyssa Katz

Find anything you save across the site in your account

The Great Crime Decline

By Adam Gopnik

Audio: Listen to this story. To hear more feature stories, download the Audm app for your iPhone.

Big events go by unseen while we sweat the smaller stuff; things happen underground while we watch the boulevard parades. Truly underground, sometimes: in 1858, the pundits and politicians in Britain were obsessing over the British government’s takeover of India from the East India Company and the intentions of Napoleon III, yet the really big thing was the construction, with the supervisory genius of the great engineer Joseph Bazalgette, of a sewer system to protect London from its own waste, and so arrest the smelly “miasma” that had come to crisis conditions that year. This underground system, along with its visible embankments, would, both directly and by example, save countless lives in the developed world during the next century—making cholera epidemics, for instance, a thing of the distant past. But it got built in relative invisibility.

In the United States over the past three decades, while people argue about tax cuts and terrorism, the wave of social change that has most altered the shape of American life, as much as the new embankments of the Thames changed life then, has been what the N.Y.U. sociologist Patrick Sharkey calls “the great crime decline.” The term, which seems to have originated with the influential Berkeley criminologist Franklin E. Zimring, refers to the still puzzling disappearance from our big-city streets of violent crime, so long the warping force of American life—driving white flight to the suburbs and fuelling the rise of Richard Nixon and Ronald Reagan, not to mention the career of Martin Scorsese. (“Taxi Driver” is the great poem of New York around the height of high crime, with steam coming out of the hellish manholes and violence recumbent in the back seat.) No one saw it coming, and the still odder thing is that, once it came, no one seemed adequately equipped to praise it.

Sharkey, who came of age in that safer era, intends to be its eulogist. He begins his remarkable new book, “Uneasy Peace: The Great Crime Decline, the Renewal of City Life, and the Next War on Violence” (Norton), in the South Bronx, at a city block near Yankee Stadium, and recalls a time in the nineteen-seventies, whose climax was the fearsome blackout riots of 1977, when even the Stadium was sparsely attended. “In some years, night games drew ten thousand fewer fans than day games,” he writes; many New Yorkers were unwilling to make their way into the Bronx after dark. “Spaces that had been created to support public life, to be enjoyed by all—those that define city life in America’s greatest metropolis—were dominated by the threat of violence.” Now, he says, “the calm of Franz Sigel Park reflected the atmosphere of peace through New York City. In the city where more than 2,000 people used to be murdered each year, 328 were killed in 2014, the lowest tally since the first half of the twentieth century.” (Last year, the tally was still lower.) It wasn’t just New York. Violent crime fell in Atlanta, Dallas, Los Angeles, and Washington, and not by a little but by a lot.

More important, the quality of life changed dramatically, particularly for the most vulnerable. Sharkey, studying the crime decline in six American cities, concludes, “As the degree of violence has fallen, the gap between the neighborhoods of the poor and nonpoor has narrowed.” In Cleveland in the eighties, the level of violence in poor neighborhoods was about seventy per cent higher than in the rest of the city; by 2010, that number had dropped to twenty-four per cent. The reduction of fear allowed much else to blossom: “Subway cars, commuter lines, and buses in U.S. cities filled up, as residents and commuters became more willing to leave their cars behind and travel to and from work together. . . . Fans came back to Yankee Stadium in the Bronx, and just as many began to show up for night games as for day games.” The big city was revived. From Portland, Maine, to Portland, Oregon, the transformation of America’s inner cities from wastelands to self-conscious espresso zones became the comedy of our time.

Yet little trace of this transformation troubles our art, or even much of our public discourse. Our pundits either take the great crime decline for granted or focus on the troubles it has helped create, like high housing prices in San Francisco or Brooklyn. Even when we pay attention to the comedy, we rarely look at the cause. Some of our politicians even pretend it hasn’t happened, with Donald Trump continuing to campaign against crime and carnage where it scarcely exists. (If people really thought that urban crime still flourished, of course, he wouldn’t be able to sell condos with his name on them on the far West Side of Manhattan.) Attorney General Jeff Sessions, meanwhile, feels free to tell the outrageous lie that “for the first time in a long time, Americans can have hope for a safer future.”

This lack of appreciation is partly a question of media attention-deficit disorder: if there is little news value in Dog Bites Man, there is none whatever in Dog Does Not Bite Man. It is part of the neutral unseen background of events, even if there had previously been an epidemic of dog bites. But it’s hard for those who didn’t live through the great crime wave of the sixties, seventies, and eighties to fully understand the scale or the horror of it, or the improbability of its end. Every set of blocks had its detours; a new arrival in New York was told always to carry a ten-dollar bill in case of a mugging. Crime ruled Broadway comedies: Neil Simon’s “The Prisoner of Second Avenue” told the tale of people barricaded in their apartments for fear of muggings. My great-aunt and great-uncle lived on 115th and Riverside Drive; an address they boasted of in 1962 had become a neighborhood they were frightened to have company visit by 1975. For those trapped in true low-income, high-crime communities, these circumstances were even worse, with, as Sharkey shows, catastrophic effects not only on life and limb and property but on the fundamental human capacity for hope. In every way, the crime wave had effects far wider-reaching than its emergency-ward casualties. Liberal urbanists, who had been, perhaps mostly by chance, in power when the crime wave began, were discredited for a generation. The neocons gained credibility on foreign policy because they once seemed right about the Upper West Side.

Sharkey, unlike many of his peers on the left, regards the great decline as an unmediated good, benefitting everyone, and, above all, the poorest and most vulnerable. Sharkey’s book, in fact, illustrates why social science, with all its uncertainties—uncertainties built into a field in which you are studying the actions of several million autonomous agents who can alter their actions at a whim, with several thousand outliers guaranteed in advance to be bizarrely atypical—still really is science. What makes it science is what makes it social: an insistence on paying attention to the facts that other people have gathered even when they conflict with the way you want the world to be; a reluctance to tailor the facts to one’s views, instead of one’s views to the facts.

You might wonder that anyone would dispute the notion that the crime decline is a good thing for everyone, but some do, either sentimentally—what ever became of all the lively crack dealers and Forty-second Street prostitutes?—or sententiously: a “cleaned up” city dismissed as merely sanitized, with the social problems pushed to the periphery. Sharkey, a sympathizer with progressive causes, sees the position in which urban crime is taken to be a kind of political violence—an as yet insufficiently organized program of dissent—for the academic indulgence that it is. The view that violent crime is a kind of instinctive form of political protest is not a new one, or entirely outlandish. We take it for granted, thinking of the poverty-stricken thieves, hanged for stealing handkerchiefs in eighteenth-century London, that the argument of the “The Beggar’s Opera” is not wrong: even when not explicitly political, crime can have an implicit politics. But though these arguments—like the parallel ones about when terrorism becomes patriotism and patriotism terrorism—are easy to make, they are hard to use as helpful guides to the real world.

Sharkey’s own research began with a simple experiment by the neuroscientist David Diamond, of the University of South Florida. Diamond placed a cat outside a cage of rats, and found that rats raised in this condition did worse on rat-friendly cognitive tests—running complicated mazes and the like—than did rats kept away from the sight of cats. You might imagine that rats raised in the presence of a predator learn to be shrewder. But this seems not to be true of rats raised in the presence of a predator whom they can do nothing to avoid or outwit —rats that feel helpless in the face of, so to speak, a cat wave. Brains under stress get frozen.

Sharkey’s subsequent research showed that children respond to the stress of community violence in a similar way. When children take a standardized test shortly after a neighborhood murder, their scores suffer. The price of crime is paid, above all, by the trauma of kids whose parents can’t buy their way out of its presence. “Local violence does not make children less intelligent,” Sharkey says. “Rather, it occupies their minds.” Thinking about a threat leaves you less room to think about anything else. The social cost of street crime, therefore, is far higher than the price of lives lost and bodies maimed; it can maim minds, too. Conversely, Sharkey finds that, in places where violence has declined the most, kids do much better at school, and minority kids lag least. Anyone who says that the decline in crime is a white person’s prerogative and pleasure hasn’t been following the facts.

But what made the crime wave happen and what made it halt? As liberal-minded people, we want the real cause of the crime decline to be nice people doing nice things, with no role for nasty people doing nasty things to those still nastier. And Sharkey does make heroes, persuasively, of many nice people doing nice things to stop crime. He is an enthusiast of the hypothesis that local community organizing was a key factor in the crime drop: “It was hard work by residents, organized into community groups and block clubs, that transformed urban neighborhoods.” He thinks that technology—surveillance cameras, LoJack systems—played a part. But he also finds that incarceration accounted for some of the crime decline, and so did more aggressive policing. “Federal funding paid for tens of thousands of new police officers,” he writes. “The tactics they used were sometimes oppressive and sometimes brutal but were also more effective, focusing resources on the precise locations where crime was most intense.”

Here some ambiguity arises. What’s now called stop-and-frisk policing—in which police aggressively sought out suspected minor criminals on the streets, most of them minority kids—was, he suggests, a kind of schismatic variant of what had originally been called, more benignly, “broken windows” policing. As Sharkey notes, broken-windows policing was based on a theory that was offered without any real evidentiary basis, and published in The Atlantic , not in a peer-reviewed journal. What was easily missed was that the broken-windows tactic, as first articulated by the criminologists James Q. Wilson and George L. Kelling in 1982, was not an appeal to the power of policing. It was an appeal to the power of self -policing. At a time when policing had been reduced, in many American cities, to having wary patrolmen drive around in squad cars, waiting for a radio call telling them that something bad had already happened, the new theory insisted on an aggressive pursuit of petty crime, before it could get to be big crime. If the cops led the way—and this was the crucial idea—the community would follow. Blocks left intact, windows repaired by conscientious landlords, would produce the eyes on the street and the small-scale intense local engagement that had in the past assured the safety of local neighborhoods. South Philadelphia or the Bronx’s Grand Concourse in the nineteen-fifties had been largely benign places not because the police were present but because there were so many engaged passersby that the police didn’t need to be present.

This sane theory of self-policing soon became its own opposite. A new, noxious notion grew. It would be all police, all the time. If enough policemen frisked enough young minority kids, they would find enough weed and weaponry to send them away. Once sent away, the potential criminals—known to be so owing to their possession of weed and weaponry—could not be street criminals, by dint of not being on the street. By the time they were back outside, the window of opportunity for committing crimes would have largely passed, crime, like gymnastics, being an occupation of the young. It was an extraordinarily crude reduction of Wilson and Kelling’s view.

Link copied

Liberal-minded people do not merely want mass incarceration to be the moral scandal it obviously is. We want it to be a practical scandal as well—it won’t and can’t do any good. But, Sharkey reports, the facts suggest that, for some period and to some measurable degree, it did contribute to the crime decline. It’s just the most expensive, inefficient, and cruel of all ways to combat the crime wave. And the moral horror thereby incurred is intolerable to a liberal democracy that does not want to have millions of men under permanent penal restraint. The social cost of that mass incarceration is as high, in its way, as the crime wave it was meant to hamper. Sharkey’s climactic thesis is that the real challenge for the decades to come is to take advantage of the decline in crime to engineer a parallel decline in incarceration, sending noncareer criminals back to safer streets.

Sharkey, as good as he is at explaining what happened—whom it helped, what it permitted—isn’t as good at explaining why it happened. The curious truth is that the decline in crime happened across the entire Western world, in East London just as it did in the South Bronx. At the same time, the relative decline in New York was significantly bigger than elsewhere. Sharkey’s guess that the crime decline can be attributed to the uncomfortable but potent intersection of community action and coercive policing seems about as good as any. Here, he echoes Zimring’s earlier conclusion that many small walls are a better barrier to crime than any single big one. Still, the magnitude of the shift remains mystifying. A good comparison might be the contemporaneous war on drunk driving: there, too, the decline in deaths has been impressive, and there, too, there was no one solution but a host of them, ranging from the Mothers Against Drunk Driving campaign to raised ages for legal drinking.

With the crime wave, it would seem, small measures that pushed the numbers down by some noticeable amount engendered a virtuous circle that brought the numbers further and further down. You didn’t have to change the incidence of crime a lot to make people worry less about it. What ended violent crime, in this scenario, was not an edict but a feedback system—created when less crime brought more eyes onto the streets and subways, which in turn reduced crime, leading to people feeling safer, which in turn brought more eyes out. The self-organized response of society to crime was, in effect, to outnumber the muggers on the street before they mugged someone. One has only to get on the New York City subway at 3 a.m. , and recall what 3 a.m. on the New York City subway was like thirty years ago, to sense the presence of this circle.

One wonders if, among those eyes, some important ones belonged, so to speak, to George and Elaine (“Seinfeld” having begun right as the sharp slope of the crime ramp-down began). They represented the kind of people who, in the previous American dispensation, would never have stayed in the city seeking partners through their twenties but would instead have been married and living out in New Rochelle with Rob and Laura Petrie. Sex, to put it bluntly, might have had some role in saving the city. Not long after 9/11, when New York, though increasingly safe from crime, was still on edge, a real-estate tycoon told me that he had made a fortune investing in Manhattan properties since the seventies (when the city seemed doomed), and was optimistic that New York would continue to thrive, for a simple reason: the median age of first children kept going up. It still is: the average age of a mother with a first child is now twenty-eight, and rising, and among women with advanced degrees it is into the thirties. The developer’s theory was that as long as people were spending their twenties looking for mates instead of settling down, they would flock to New York, and the city would continue to flourish in the face of crime, plague, or terrorism.

Anecdotal evidence is not to be taken very seriously, but artistic evidence is the best kind we ever get—Dickens saw far more deeply into that Victorian miasma and its causes than anyone else did. And it is certainly the case that a dominant mode of American entertainment that paralleled the crime decline was comedies about young people in Manhattan looking for love: “Seinfeld” and “Friends” and then the almost too neatly named “Sex and the City.” The people who are generally thought to have merely profited from the new reality may have had something to do with making it happen. The pursuit of small pleasures helps provoke social sanity. Improbable actors perform righteous acts. This was Adam Smith’s actual insight, this time put in motion in small apartments and sofa beds.

The negative side of this change lies in the supposed reduction of once diverse neighborhoods to monotone yuppie dormitories. Sharkey’s take on the process, routinely called “gentrification,” is surprising. He reports that in New York there is “little evidence that gentrification leads to any detectable increase in displacement.” (The fear of cultural displacement is, he thinks, another story.) Research by the sociologist Lance Freeman suggests instead that much good happens for residents when “neighborhoods become more economically diverse and safer, attracting new resources for schools, new businesses and new attention from public agencies.” Sharkey’s is a story without the usual heroes, but also without the usual villains.

A real problem, going forward, is the one identified by Black Lives Matter and associated groups: police violence. As the social cost of stop-and-frisk and mass incarceration has become, rightly, intolerable, we ask if the crime decline, with its unprecedented benefits for the marginalized populations, can survive. Sharkey emphatically thinks it can, and so far there’s no evidence to counter his view. The conservative urbanists at City Journal can point to this or that bump or burp in the numbers, but since the nineties New York has kept lowering both its incarceration rate and its crime rate. The plunge continues under the supposedly soft-on-crime Bill de Blasio as much as it did during ironfisted Rudy Giuliani. Whatever is happening out there seems immune to local politics.

Effects that we don’t normally track are surely related to the crime decline, not least the rise of the Black Lives Matter movement itself. Without a general understanding that crime was no longer the real problem but that the response to crime might be, the movement could not have caught a surprisingly large, sympathetic audience. Sharkey writes, “The videos of police violence have resonated so powerfully because they come at a time when there is no crisis of crime in most of the country, when every other form of violence in society has subsided.” The acts may have been constant, but the anger can only arise in the new social field. (One element that Sharkey perhaps does not sufficiently underline is the American abundance of guns: their existence both frightens police officers and makes them overreact in minor moments, turning routine encounters potentially lethal.)

Ironically, though the urban crime wave is over, it still persists as a kind of zombified general terror, particularly in places where it was never particularly acute. Trump can continue to campaign against crime largely in places where crime never happened much but where, having long been molded to preëxisting bigotry, the spectre of violence still occupies a fetishistic role. He has completely lost the power to frighten New Yorkers with tales of crime, but in rural districts in Wisconsin and Pennsylvania the symbolic depiction of crime, crafted nationally as the great wave rose, is still a bloody shirt that can be effectively waved even if the bloodstains on it are decades old.

Will they stay that way? An upturn in crime in a very few cities is probably a local bump on a bigger, encouraging graph, but no one can know this for certain. Sharkey believes in police “hot-spotting”: most violent crimes take place in a small number of venues. They also, Sharkey says, tend to involve a tiny number of people linked in a tight network. When it comes to gangs in Chicago—where gun violence afflicts a very small percentage of the population, but intensely—he likes the idea of trained “interrupters,” who intervene with members after a violent incident to prevent retaliation. He writes, “The warning is unflinching: If you continue to engage in firearm violence, you will face serious, uncompromising prosecution . But the offer must be equally sincere: If you choose a different path, you will be supported with all resources and assistance that the community and the state can muster. ”

At the intersection of politics and policy, Sharkey imagines that the best path forward lies in a somewhat below-the-radar coalition that enlists right and left in a movement to accept that the end of the crime wave can lead to the end of mass incarceration. He cites a meeting at the right-wing Heritage Foundation, where he found significant agreement about dis-incarceration among otherwise opposed types. Followers of Michelle Alexander, the author of “The New Jim Crow” and the most prominent advocate of the view that mass incarceration is designed to “reinforce a racialized caste system in the United States,” found common ground with Grover Norquist, the government-hating founder of Americans for Tax Reform. Sharkey wants to believe that a tacit truce has been forged between these disparate types, leading toward a new era where the benefits of the crime decline will be enjoyed without mass incarceration to support them.

It seems just as likely, though, that two kinds of illiberalism have joined in an uneasy and unstable alliance. Those in Alexander’s school are inclined to believe that the system can’t be reformed short of revolutionary change—a revolutionary change that is necessarily ill-defined and, given the country’s political demography, essentially impossible. A revolutionary-minded racial politics that produces a reactionary white racial politics will always be frustrated. Meanwhile, the Norquistians don’t just want to stop spending money locking men up. They also want to stop spending money educating kids and assisting neighborhoods and helping communities rebuild. They want to stop spending money on anything. It is safe to say that people who don’t want to spend money on prisons also don’t want to spend it on social programs, let alone reparations. If pressed to a point, they will spend it on clubs and bullets. The leftists want the prisons opened because the people in them shouldn’t be there; the Norquistians want them opened because it costs too much to keep them shut. The two groups have an operational end in common, but they do not remotely share the same social picture, as will become plain when the next, presumably smaller, crime wave hits.

Sharkey ends with a curious formula. We must now fight a “war on violence,” he says, which seems to include a war against police violence, but one fought with a companion understanding that the “police play a crucial role in the effort to maintain social order” and that the most vulnerable among us are those most hurt by the loss of order. “Peace and order” is effectively his platform, as “law and order” was once that of the Republican right that capitalized on the crime wave.

He believes that peace and order—and here his vision does have something in common with that of the libertarians he would like to welcome to the cause—can only emerge from the ground up. As ways of keeping a durable social peace, he returns again and again to small community works, like those community interrupters, or what he calls “community quarterbacks,” or even systems of self-policing by Aboriginal Australians. A paradox emerges here: crime, if immune to party politics, seems enormously sensitive to something as seemingly anodyne as community policing. One thing that the crime decline may force us to do is look again at what we mean by a city. At a time when cities become ever-larger megalopolises, it seems sentimental to insist on the primacy of neighborhoods and communities as units, and yet some evidence suggests that that is where this big change began. Somewhat surprisingly, Sharkey gives a significant and welcome nod to the urban oracle Jane Jacobs, whose rhapsodic evocation of New York streets might, after so many wars, have seemed as dated as the account of Jerusalem before the fall of the Temple. But he italicizes her essential lesson: politics may not all be local—Trump is a refutation of that, winning the allegiance of localities that he can’t or won’t serve—but city life is. “Social cohesion, trust and shared commitment to the community,” Sharkey says, vary from neighborhood to neighborhood. In ones where they are strong, violence almost vanishes. It isn’t that poor neighborhoods produce violent crime. The problem is, rather, “that concentrated poverty tends to slowly tear apart the social fabric of neighborhoods.” Restore the social fabric first, and the crime ends not long after. The city was won back block by block.

How to make the social fabric stronger? Sharkey offers the immensely cheering pictures of the possibility of spending money to rebuild communities and replacing the noxious “warrior” cop with those “community quarterbacks.” It’s hard to imagine a more essential career for a young urbanist, even though such a cadre sounds at least questionable as a source of crime-stopping. But the lesson of wise public works is not, truth be told, always about the benefits of foundational analysis or fundamental change. An epidemic of violence was resolved without addressing what were thought to be its underlying disorders. We cured the crime wave without fixing “the broken black family,” that neocon bugaboo. For that matter, we cured it without greater income equality or even remotely solving the gun problem. The story of the crime decline is about the wisdom of single steps and small sanities. We could end cholera—in London, they did—without really understanding how cholera bacteria work. We have curbed crime without knowing how we did it, perhaps simply by doing it in many ways at once. It is possible to see this as a kind of humanist miracle, a lesson about the self-organizing and, sometimes, self-healing capacities of human communities that’s as humbling, in its way, as any mystery that faith can offer. ♦

Books & Fiction

By signing up, you agree to our User Agreement and Privacy Policy & Cookie Statement . This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

By Ken Auletta

By Jay Caspian Kang

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Urban Poverty and Neighborhood Effects on Crime: Incorporating Spatial and Network Perspectives

Corina graif.

Department of Sociology and Criminology, The Pennsylvania State University

Andrew S. Gladfelter

Stephen a. matthews.

Department of Sociology and Department of Anthropology, The Pennsylvania State University

Research on neighborhoods and crime is on a remarkable growth trajectory. In this article, we survey important recent developments in the scholarship on neighborhood effects and the spatial stratification of poverty and urban crime. We advance the case that, in understanding the impact of neighborhoods and poverty on crime, sociological and criminological research would benefit from expanding the analytical focus from residential neighborhoods to the network of neighborhoods individuals are exposed to during their daily routine activities. This perspective is supported by reemerging scholarship on activity spaces and macro-level research on inter-neighborhood connections. We highlight work indicating that non-residential contexts add variation in criminogenic exposure, which in turn influence offending behavior and victimization risk. Also, we draw on recent insights from research on gang violence, social and institutional connections, and spatial mismatch, and call for advancements in the scholarship on urban poverty that investigates the salience of inter-neighborhood connections in evaluating the spatial stratification of criminogenic risk for individuals and communities.

Introduction

Since the beginning of the 20th century, urban scholars have extensively studied the role of urbanism and poverty in increasing crime. Rapid urban growth and population mobility together with stark socioeconomic differentiations across the urban space were, from the early years of the Chicago School, associated with the breakdown of social control and increased crime ( Zorbaugh 1929 ). Classic ecological studies showed that neighborhoods with high poverty near commercial and industrial districts exhibited the highest levels of delinquency and criminality ( Shaw and McKay 1942 ). These levels persisted over decades even when neighborhood population groups changed dramatically, indicating that structural conditions like neighborhood poverty contributed to delinquency and crime above and beyond individual disposition.

In the late-20 th century, industrial restructuring and suburban flight has exacerbated the spatial differentiation of resources and concentration of unemployment among the low-skilled. In The Truly Disadvantaged , Wilson (1987) noted that unemployment and poverty clustered and that together these ‘concentration effects’ weakened family bonds and institutional ties, undermining the formal and informal capacity for crime control. Scholars today refer to areas of high poverty as areas of concentrated disadvantage . The Great Recession of 2008 added greater strain to struggling low-income urban communities across the country and recent studies increasingly connect economic distress (e.g. foreclosures) to higher crime ( Ellen et al. 2013 ; see Arnio and Baumer 2012 for an exception).

Building on a century old tradition of research, research on neighborhoods and crime in the past decade has shown remarkable growth. More than 250 articles were published on this topic in 2012 alone ( Figure 1 ). The scholarship on place, space, and geography in relation to crime exhibited similar trajectories. Combined, this literature demonstrates that neighborhood poverty and related social and economic conditions are closely related to multiple indices of criminal exposure and offending. Specifically, studies find that neighborhood poverty and associated structural factors continue to predict multiple crime-related outcomes, including: individuals’ exposure to violence ( Bingenheimer et al. 2005 ; Sampson et al. 1997 ); risk of victimization ( Burchfield and Silver 2013 ); adolescent violent crime ( De Coster et al. 2006 ; Zimmerman and Messner 2010 ); aggression ( Molnar et al. 2008 ); arrests for violent behavior (Ludwig et al. 2000); domestic violence ( Benson et al. 2003 ); incarceration ( Rodriguez 2013 ); and recidivism ( Kubrin and Stewart 2006 ). With few exceptions, these patterns tend to hold in multiple cities and in nationally representative studies.

Yearly Publication Count by Keyword Combination for the Past 10 Years Based on ISI Web of Science Search Results

Most studies implicitly assume that exposures to risk (e.g. criminal offending or victimization) are sufficiently represented by attributes of the neighborhood of residence. Chaix (2009) refers to this as the "residential trap." However, the residential focus ignores the fact that individuals' routines, in the aggregate, expose them to different neighborhoods on a daily basis. Few studies have examined the implications of routine exposures to multiple, non-residential neighborhood locations for crime. In this paper, we address this gap and advance a case for the idea that a more complete understanding of neighborhood effects on crime will greatly benefit from moving beyond the traditional focus on residential exposure to research based on an individual’s exposure to networks of neighborhoods . In building our argument, we draw on classic and modern theorizing on neighborhood effects and the spatial differentiation of poverty and crime, and integrate it with re-emerging literature on activity spaces, inter-neighborhood connections, and social and institutional networks.

Neighborhood effects on crime

In this section, we review some of the core mechanisms and associated theoretical perspectives that have been proposed to account for observed neighborhood effects. We discuss issues related to the definition of the neighborhood, scales of spatial exposures, and the spatial clustering of neighborhood disadvantage across urban environments.

Internal neighborhood mechanisms

Several major theoretical perspectives shed light on some of the possible mechanisms underlying the neighborhood effects on crime. First, one of the oldest theoretical perspectives, social disorganization, posits that ecological conditions like socioeconomic disadvantage, racial heterogeneity, and residential mobility, erode neighborhood social control and facilitate crime ( Shaw and McKay 1942 ). Social control largely operates through local ties to other individuals and institutions (Bursik and Grasmick 1993). A later extension of this theory proposes that independent of social ties, collective efficacy—a combination of social cohesion, trust and the ability of neighborhood residents to realize common goals and values—reduces neighborhood crime ( Sampson et al. 1997 ). Second, a perspective that is gaining increasing traction recently, routine activities states that crimes are most common when motivated offenders intersect in time and space with attractive targets in the absence of guardianship ( Cohen and Felson 1979 ). Often, routine activities components are assessed through measures such as unemployment rate (a proxy for motivated offenders) and time spent out of the household (low level of guardianship). Third, subcultural theories propose that local value structures can promote crime ( Fischer 1975 ), the focus being on "urban," "street" and a "southern" culture of violence. Finally, relative deprivation or strain approaches suggest that socioeconomic standing relative to peers or neighbors may influence offending behavior ( Merton 1938 ). In empirical tests of these theories, indicators of social disorganization and routine activities are found to exhibit the strongest and most consistent effects on crime (for a meta-analysis, see Pratt and Cullen 2005 ). Valuable reviews of social disorganization research and related theoretical thinking on neighborhood effects together with important suggestions for future directions are offered by Sampson et al. (2002) and Kubrin and Weitzer (2003) .

The main social mechanisms have been summarized by Sampson and collaborators (2002) under four categories: social ties and local interactions , referring to local interpersonal networks of friends and kin and neighborly exchanges; norms and collective efficacy , based on different dimensions of culture, social cohesion, trust, and social control ( Sampson et al. 1997 ); institutional resources , which include neighborhood organizations, family wellbeing support centers, youth centers and the like; and routine activities ( Cohen and Felson 1979 ), referring to the mix of residential, commercial or industrial land use and also the pattern of daily routine activities which facilitate access to local desirable targets by potential offenders living outside the neighborhood. The latter encompasses spatial mismatch theory ( Kain 1968 ), which highlights the distance between home and workplaces among population subgroups, a phenomenon also understood as institutional isolation (Wilson 1987).

These dimensions of neighborhood processes, while analytically distinct, are empirically related. Yet few studies have examined the nature and extent of these relationships. Sampson and Graif's study (2009b) is an exception that investigated the social networks of friends and kin and reciprocal exchange, collective efficacy (the ability of residents to realize common goals), culture (norms of conduct for different age groups), institutional engagement (neighborhood activism, involvement in local organizations, schools, and churches) and neighborhood leader contacts within and outside the community. They concluded that "as residents seem to disengage and are more cynical in disadvantaged communities, community leaders become more intensely involved in seeking resources, often from afar" ( Sampson and Graif 2009b , p. 1601). Independent of disadvantage, another study found that internal community network structures are positively associated with trust among leaders and among residents ( Sampson and Graif 2009a ). When networks extending outside the community shape the density of internal networks ( Sampson 2012 ), we might expect additional improvements in trust and other dimensions of social order. This literature implies important, yet understudied, relationships between the private or parochial levels of control and the external, public level ( Hunter 1985 , detailed below) with consequences for control of crime especially in disadvantaged communities.

Despite great advancements on the theoretical and empirical testing of neighborhood level mechanisms, we agree with Kubrin and Weitzer's (2003 , p. 387) assessment that "compared to the large number of studies on the effects of intra-neighborhood factors on crime, surprisingly little attention has been given to the role of exogenous determinants, and very little is known about the connections and interactions between internal and external factors. This would be a fruitful avenue for future research, and would rightly expand the scope of social disorganization theory in a more macro direction." Below, we present important recent developments relevant to bridging the internal-external mechanisms gap and offer additional suggestions for the future.

Neighborhood definitions and scales of spatial exposures

Over 40 years ago, Hunter and Suttles (1972) stressed the importance of multiple scales of measurement. They identify four scales: the “face-block," where residents tend to know each other; the " defended neighborhoods, " the smallest areas with distinct identities recognized by outsiders and insiders; the “ community of limited liability, ” where local participation depends on residents' attachment to community; and the “ expanded communities of limited liability,” a large geographic area in which groups of residents come together only when needed to gain larger traction on specific political or economic decisions. Importantly, each of these traditions uses pre-defined, administratively-bounded areas. Since the 1970s much of the measurement of neighborhoods in crime research spanned the meso-to-macro scales, from census tracts ( Graif and Sampson 2009 ) to community areas ( Sampson and Graif 2009a , 2009b ) to counties ( Messner and Anselin 2004 ). More recent studies have made important advances at the micro-level too, illustrating the importance of local network groupings ( Hipp et al. 2012 ), blocks ( Hipp 2007 ) and street segment dynamics ( Weisburd et al. 2004 ) in shaping crime.

A limited but growing number of studies, however, have adopted a different framework altogether—eliminating dependence on administrative boundaries. These researchers define neighborhoods egocentrically, as the geographic context around an individual's residence or around a block independent of neighborhood administrative boundaries ( Hipp and Boessen 2013 ). The features of the surroundings that are closest geographically to the focal residence are assumed to be most influential ( Tobler 1970 ). Work in geography also has used kernel density analyses and routines that treat the world as a continuous surface ( Matthews 2011 ). A major advantage of these analytic frameworks is an acknowledgment that access to resources is often facilitated by geographic proximity (e.g. access to youth services may decrease delinquent behavior) independent of artificially defined neighborhood boundaries.

The bounded neighborhood approach and the respondent-centered approach fed recurrent debates about the "proper" definition of the neighborhood. We believe this is a false dichotomy that may distract from thinking in an integrative way about local social processes. Similarly, the debates over the correct geographic scale of the neighborhood mask an important point: certain features of the surrounding non-residential areas may matter above and beyond the residential neighborhood, however defined. We revisit the four types of mechanisms noted by Sampson and colleagues (2002) with respect to the immediate neighborhoods of residence in the first column of Table 1 . Additionally, we expand further to illustrate how these types of processes may interact in shaping individuals' victimization experiences and offending behavior with features of a) the broader area surrounding the immediate neighborhood of residence (the extended neighborhood, column two) and b) the neighborhoods frequented as part of peoples' daily routine activities (e.g. the neighborhood of workplace or of close friends, column three). These examples may be translated into research hypotheses in future studies.

Examples of Neighborhood Mechanisms from Extended Spatial and Network Perspectives

The spatial embeddedness of neighborhoods

It has long been shown, in multiple cities, that poverty and crime are both associated with each other and exhibit spatial clustering ( Peterson and Krivo 2010 ). In addition, social processes like neighborhood trust and collective efficacy also cluster in space, and the spatial covariation between poverty and neighborhood processes remained strong over the past four decades ( Sampson and Graif 2009a ). Moreover, the associations between neighborhood poverty and crime tend to be similar for multiple neighborhoods that are geographically proximate to each other, even though they vary from one section of the city to another ( Graif and Sampson 2009 ).

Given the progress in highlighting the ecological levels of covariation between poverty and crime, it is surprising that advances in our collective understanding of spatial dynamics at the ecological level have not been integrated into the analytical framework of neighborhood effects on individuals (for an exception, see Sampson et al. 1999 ). This gap is related to the fact that we still know little about the processes underlying observed spatial clustering ( Kubrin and Weitzer 2003 ). These patterns are in part attributed to measurement issues and in part to processes of contagion or diffusion, whereby nearby crime activity spills over neighborhood boundaries ( Anselin et al. 2000 ; Tita and Griffiths 2005 ). Other processes assumed to explain clustering are residents' daily movement and increased exposures to risk factors in nearby neighborhoods. To the extent that effects of spatial proximity are in large part due to overlapping activity spaces, a more general form of interdependence—which transcends geographic proximity while subsuming some aspects of it—may be inter-neighborhood connections forged as a result of individuals’ frequent movement (e.g. daily commuting) across space.

Non-residential neighborhoods and routine activity spaces

Individuals routinely travel outside the neighborhood of residence for leisure and work. Pathways of movement across large distances may increase variability of access to resources, institutions, information, and people in ways that may affect crime. Furthermore, much of the time spent in the neighborhood of residence is spent inside the home, when the objective risk of committing crime or being victimized is arguably low (Bureau of Labor Statistics 2013). Despite increasing calls for definitions of neighborhood context that take into account individuals' daily activity patterns ( Cagney et al. 2013 ; Matthews 2011 ; Matthews and Yang 2013 ), most social science literature still relies on census tract of residence as the operational definition for the neighborhood of influence. However, research on the journey to crime indicates that up to 70 percent of crimes are committed by individuals outside their neighborhood of residence ( Bernasco 2010 ; Wikström 1991 , p. 213-223). Moreover, compared to violent crime, property crimes are committed further away from offenders' neighborhoods of residence (White 1932). Additionally, Bernasco (2010) finds that locations where offenders lived in the past are more likely to be chosen as the location of current offending. Evidence on the importance of non-residential contexts in the study of crime is thus becoming increasingly more salient.

The argument that researchers need to focus on relevant contexts other than the neighborhood of residence is not new to sociology (e.g. Foley 1950 ). McClenahan (1929) was one of the first to argue that urban residents' activities are rarely located within the immediate vicinity of the home. Routine activity patterns have been shown to matter for individuals' outcomes. Inagami and colleagues (2007) suggest that the negative effects on health of living in disadvantaged neighborhoods may be confounded and suppressed by exposure to non-disadvantaged, non-residential neighborhoods in the course of routine daily activities (i.e. grocery shopping). More recently, both qualitative and quantitative research in sociology has highlighted the importance of nonresidential contexts ( Matthews, 2011 ). Other disciplines too have started to adopt activity space approaches and are beginning to focus on nonresidential neighborhoods ( Cagney et al. 2013 ; Zenk et al, 2011 ). To date however, few studies have assessed the impact of individual activity spaces on the propensity to commit crime or become the victim of crime.

One notable exception is a recent study of youth in a UK city ( Wikström et al. 2010 ), which showed that more than 54% of respondents’ awake time was spent outside their home area ( Figure 2 ). Those with higher propensities for crime were exposed more frequently to criminogenic settings outside their home and school areas and, in such settings, were more likely to become involved in criminal behavior. More than half of the respondents’ crimes were committed at locations central to their routine activities. These findings highlight the importance of designing new studies that do not rely on residential contexts as the only purveyor of contextual effects.

Examples of Cross-Neighborhood Activity Spaces

Source: Adapted from Wikström et al. 2010 . With kind permission from Springer.

Networks of neighborhood exposures

The work on the importance of non-residential neighborhoods on crime and victimization provides some evidence for the necessity to study neighborhoods not as isolated, independent places but rather as parts of a larger, interconnected network of places. This type of perspective also has roots in earlier sociology, geography, and planning (see Matthews 2008 ). In the early sixties sociologists wrote about the “community without propinquity” or spatially dispersed communities ( Webber 1963 ) . Later Wellman (1979) discussed “community liberation”, extended social networks, and long distance communications such as “networks in the global village” ( Wellman 1999 ) which provides a bridge to the “new mobilities research” paradigm ( Larsen et al. 2012 ). We reintroduce and emphasize the idea that neighborhoods are part of a larger system of resource exchanges, in the form of networks, between places.

Our network perspective also draws on Hunter’s (1985) discussion of the importance of three core types of relational networks in shaping neighborhood social control. The “private” social order refers to intimate informal primary ties within a neighborhood (e.g. kin and friends) which can control crime through the threat of social disproval or other forms of deprivation. The “parochial” order is given by the broader connections with local institutions such as schools, churches, or community organizations, characterized by weaker attachment than the primary networks but nonetheless important. Finally, the “public” social order describes a community’s connections to external organizations and institutions that facilitate the mobilization of resources, mediate the ability of local networks to control local crime, and sometimes even enable the foundation of local institutions (also Taub et al. 1977 ).

The literature to date has predominantly focused on the private or parochial dimension of crime control, with little attention to the public dimension (Bursik and Grasmick 1993). Interestingly, the private and parochial ties often extend across space to create the foundation for public control. In a creative approach to neighborhoods as inter-related friendship networks, Hipp and colleagues (2012) show that while a high proportion of teens’ friends are predictably spatially clustered, many ties cross neighborhood boundaries over large geographic distances. To the extent that individuals’ contextual exposures are defined through their interactions, these findings underscore that a focus on only the administrative area of residence would miss substantial exposures to many friends’ neighborhoods. 1

The extent of between-neighborhood connections in Chicago are investigated in a recent monograph, Sampson's (2012) Great American City , as a function of residential mobility and nominations of influential people. Sampson (2012 , pp. 309-310) finds it surprising "how little neighborhood networks have actually been studied, as opposed to being invoked in metaphorical terms. … [P]rior research is dominated by a focus on individual connections and an “egocentric” perception of social structure. … [R]arely has social science documented variations between communities in social networks, much less the citywide structure."

A recent article by Slocum and colleagues (2013) addresses in part this gap by showing that organizations whose function it is to bridge to the larger community and secure resources for the local residents (e.g. community boards, political groups, economic development centers) are significantly associated with lower violent and property crime, even after controlling for multiple features of the community. Still, few empirical studies exist that show how neighborhoods are connected and more specifically how these ties matter for crime related outcomes. Just as residential neighborhood contexts matter to individuals through their connections with institutions and organizations within it ( Tran et al. 2013 ), similarly, involvement in non-residential neighborhoods may be consequential for criminal behavior or victimization risk.

Broader social phenomena highlight the importance of the interconnectedness of neighborhoods. For example, economic declines have been found to play a role in increasing violence ( Catalano et al. 2011 ; Ellen et al. 2013 ) but the evidence tends to be mixed and little is understood about the underlying mechanisms. We suggest that through plant closures and mass layoffs, recessions may sever critical interaction pathways (i.e. resource exchange in the form of labor) between neighborhoods. Despite a long tradition of research on spatial mismatch in employment prospects ( Kain 1968 ), understanding violence in the context of a neighborhood's connectivity to or isolation from other particularly influential communities in the city is underexplored.

In sum, as individuals move about space and across neighborhoods within urban contexts, patterns of behavior aggregate to create functional ties between sets of neighborhoods. Such ties may turn out to be as important for neighborhood change as spatial proximity is observed to be. In other words, underlying (or complementing) the spatial clustering of poverty and crime among neighborhoods in a city may be a broader network structure of interdependence governed by how people routinely move through the urban landscape. To the extent that communities are connected to others who are successful in dealing with crime, those strategies and tools may be transmitted through such ties (i.e. innovation diffusion).

Methodological considerations in the study of neighborhood networks

The empirical study of "networks of neighborhoods" is relatively new and underdeveloped. While we cannot offer definite approach to the study of neighborhood networks, we provide some guidance based on prior research and the emergence of data and methods to study complex networks. We highlight five relevant macro-level studies and their commonalities and differences along six dimensions: the nodes (neighborhoods) and the ties (relationships between nodes) as units of analysis; the levels of analysis; the type of analysis; the type of data used; and the questions of interest. This information is intended to provide readers with an overview of the types of methodological choices when designing a study of neighborhood networks.

In the networks of neighborhood approach, the nodes, or units of analysis, are frequently a geographic subdivision. In our selected examples, the operational definition of nodes range from administrative definitions of "neighborhoods" of the Paris Commune in the late nineteen century and community areas in contemporary Chicago ( Gould 1991 ; Sampson 2012 ) to tracts ( Schaefer 2012 ) and more complex units like gang turfs ( Papachristos et al. 2013 ). The definition and measurement of the ties between nodes - arguably the main focus of a networks approach to neighborhoods – vary as a function of the research question. In Gould's (1991) study, ties were represented by the number of men living in a neighborhood serving in the same military units as residents of another neighborhood. Papachristos and colleagues (2013) and Schaefer (2012) represented ties as gang violence and criminal co-offending relationships between places, respectively. Sampson (2012) measured ties as nominations by political leaders of people in the city who they believed they could rely on to "get things done" in their community. Thus, the core requirement of a tie is that it represent a form of meaningful interaction or relationship between nodes (see Table 2 ).

Selected Macro-level Applications of a Network of Neighborhoods Approach

The level of analysis is typically macro because of the interest in inter - neighborhood interactions or how neighborhoods are connected. All the studies we selected examined exchanges between macro-level units defined as a neighborhood. Data sources vary depending on the topic of interest, though some common themes emerge from the types of data used. Three of the studies ( Gould 1991 ; Papachristos et al. 2013 ; Schaefer 2012 ) used archival records whereas Sampson (2012) used a prospectively longitudinal survey format to collect data. Other types of data that link places to other places - including but not limited to resource exchange (e.g. financial exchange; commuting to work), criminal exchange (e.g. court records of co-offenders’ neighborhoods of residence; police reports linking offenders’ or victims’ address and crime location), or political exchange (e.g. nominations of "movers and shakers;" political interactions) - can be used to assess neighborhood networks. As an example, one ongoing study (Author 2013) uses police records and administrative data to connect employers’ location and employees’ neighborhood of residence to examine the extent to which commuting to violent neighborhoods increases victimization rates among the residents of a focal neighborhood ( Figure 3 ).

Inter-Neighborhood Networks and Exposure to Violence

Source: Adapted from Author (2013).

The leftmost map represents Chicago’s 77 community areas while the middle and the rightmost maps are network representations of the communities in the highest thirtile (red nodes) and lowest thirtile of violence (green nodes), respectively. Community areas are represented as nodes and situated in geographic space according to the latitude and longitude coordinates of their centroids. Ties between nodes represent workers living in one neighborhood and commuting to the other. The arrows point toward the neighborhood of work and show the extent to which communities of similar violence level are connected to each other or not. This ecological perspective on networks of communities opens the field to new perspectives on age-old questions related to structural embeddedness, selection and exclusion, displacement of crime, and the diffusion of norms relevant for crime control (see also column 3 of Table 1 ).

With respect to modeling and analytical approaches, all of the selected studies use a combination of GIS mapping, spatial, and network analyses. These methods are used to assess how different types of neighborhoods are distributed over space, to calculate the geographic distance between them, and to assess the association between social and spatial distance on the one hand and prevalence of inter-neighborhood connections on the other. The network analyses use two different approaches: a) a nodal approach ( Gould 1991 ) where the outcome and most covariates are modeled egocentrically at the nodal level while the dyadic relationships are only summarized in the form of a network autocorrelation term, similar to a spatial autocorrelation term, and, b) a dyadic and structural approach, using exponential random graph models (ERGMs), where the outcome is at the tie level as are many covariates, but nodal attributes and structural properties of the overall network are also included ( Papachristos et al 2013 ; Schaefer 2012 ).

Not represented among these examples, but nonetheless an important approach for the future that allows for changes in the network structure over time, is modeling using SIENA ( Snijders 2001 ). A relatively recent development, SIENA is gaining traction in examining longitudinal networks at the individual level but little research so far has made use of it in examining change in a network of neighborhoods. For instance, one type of question that this strategy would help address in the future is whether increases in neighborhood unemployment contribute to subsequent increases in co-offending relationships between any two neighborhoods or whether co-offending occurs before or independent of increases in unemployment. Other types of questions may focus on the diffusion of crime between neighborhoods (how shots fired across neighborhoods may lead to further shootings in retaliation) or on crime displacement (how policing in a neighborhood pushes crime into new places) (Tita and Cohen 2004). In sum, while the macro-level study of networks of neighborhoods is still in its early stages, existing examples are encouraging.

When the primary interest focuses on individual behaviors, experiences, and outcomes related to crime and victimization, studies of neighborhood network effects may combine network analytic tools with more typical approaches to the study of neighborhood effects or peer influences. Just like exposure to a network of delinquent friends affects individuals' attitudes and delinquent behavior, exposure to criminogenic places in which individuals spend considerable time (whether their own neighborhood of residence or outside it) may shape their attitudes and behavior. The mechanisms of peer influence on individual behavior may only in part overlap, if at all, with the mechanisms of place influence. Yet, the methodological advancements in assessing the role of one's network of peers ( Kreager et al. 2011 ) may also be valuable to scholars interested in assessing the role of an individual's network of neighborhoods.

The logic of the typical multilevel approach, for instance, as used in estimating effects of peer groups or of residential neighborhoods on individual attitudes and behavior related to crime and victimization may be also used to estimate the effects of a network of neighborhoods. The core difference consists in assessing criminogenic exposures based not only on where respondents live but based on the neighborhoods they frequent when they hang out with friends, go to school, shop, or commute to work. Exposures to each place can be weighted by the time respondents report (or are observed) to spend there or by another index representing functional ties (e.g. the number of friends they know in each place). GPS, smartphones, and tracking technologies enable collection of data that allows for weighting by the duration of exposure to a place. Alternatively, researchers may account for the time spent in traditional “nodes” such as home, work, and school as captured through activity logs ( Basta et al. 2010 ). To account for individuals' exposures to multiple non-nested places, multiple-membership models may constitute a valuable strategy ( Browne et al. 2001 ).

Related modeling strategies include the use of network lagged variables in hierarchical linear models. This would be similar to the use of spatial lag variables in multilevel analyses (see Crowder and South 2011 ; Sampson et al. 1999 ) but instead of geographic proximity it would model the lag as a function of existing network ties. For different examples of modeling social and spatial networks we direct the reader to Entwisle and colleagues (2007) and Larsen and colleagues (2012) .

Conclusions and directions for the future

In this article, we surveyed classic and recent studies on neighborhood effects and on the spatial stratification of poverty and urban crime. We argue that for a more complete understanding of the impact of neighborhoods and poverty on crime, sociological research would benefit from expanding the analytical focus from the residential neighborhoods to the network of neighborhoods (residential and non-residential) that individuals use during the course of their routine daily activity.

The reemergence of scholarship on activity spaces offers much promise for studies of non-residential contexts and crime. These non-residential contexts may add variation in criminogenic exposure, which would in turn influence their offending behavior ( Wikström et al. 2010 ) and victimization risk. We proposed that non-residential exposures may be thought of as a part of a " network of neighborhood exposures " that includes the neighborhood contexts of the workplace, school, friends' homes, recreation activities, or wherever individuals tend to spend their time on a routine basis.

Our approach is also related to insights on the importance of inter-neighborhood connections over large geographic distances directly or indirectly implied in studies of residential mobility ( Sampson 2012 ), extra-local organizational connections and involvement ( Sampson and Graif 2009a , 2009b ; Slocum et al. 2013 ), daily commuting distances ( Zenk et al. 2011 ), and spatial mismatch ( Holzer 1991 ; Kain 1968 ). We suggest that the criminogenic role of chronic unemployment resulting from the spatial mismatch between the location of jobs and the location of housing may be in part due to the absence of positive externalities of inter-neighborhood connections that may be forged through daily mobility across the urban landscape. We believe that our collective understanding of the causal relationship between neighborhood poverty, inter-neighborhood networks and crime will be greatly advanced by creative designs applied to studies of the recent Great Recession and economic decline more generally. More research is needed on how changes to activity spaces due to plant closures shape neighborhoods and crime and what happens when communities become disconnected as a result of economic restructuring.

Our principal purpose was to highlight the importance of studying how neighborhoods are related across space for advancing our collective understanding of macro-level patterns of neighborhood crime as well as individual attitudes and behavior. However, the study of inter-neighborhood connectivity is important for our understanding of urban stratification across space above and beyond crime. For instance, Krivo and colleagues (2013) , using the Los Angeles Families and Neighborhoods Survey (L.A. FANS) data, found that social inequality is reproduced through daily activities. That is, people living in socioeconomically advantaged neighborhoods similarly tend to conduct activities in (i.e. work, recreation, shopping, dining) neighborhoods that are non-overlapping with those in which disadvantaged populations conduct activities. Individuals from disadvantaged areas rarely enter non-disadvantaged parts of the city.

The comprehensive overview of the state of the field in the last decade and the discussion of the historical and theoretical context of the scholarship on urban poverty, neighborhoods, and crime left little room for addressing other important and recurrent issues in the field such as selection bias, ecological fallacy, and neighborhood change. We recommend several excellent reviews for more detailed discussions on these ( Kim et al. 2013 ; Kirk and Laub 2010 ; Kubrin and Weitzer 2003 ; Matthews and Yang 2013 ; Pratt and Cullen 2005 ; Sampson et al. 2002 ). We differ from previous reviews in our focus on a network approach to understanding neighborhood exposures. We call for new and creative research designs and analytical approaches to understanding urban crime that transcend the typical focus on the neighborhood of residence to include a focus on the broader context of routine activities. We also call for advancements in research on urban poverty that investigate the salience of inter-neighborhood connections in evaluating criminogenic risk for individuals and communities.

Acknowledgments

The first author thanks the Social Science Research Institute and the Population Research Institute at Penn State (NIH grant # R24 HD041025) for support during the writing of this article.

1 While a focus on networks as neighborhoods is valuable, we would also caution that the absence of friendships could alternatively activate criminogenic processes like alienation and anomie.

• Anselin Luc, Cohen Jacqueline, Cook David, Gorr Wilpen, Tita George. Spatial Analyses of Crime. In: Duffee David., editor. Measurement and Analysis of Crime and Justice. National Institute of Justice; Washington, D.C.: 2000. pp. 213–262. [ Google Scholar ]
• Arnio Ashley N., Baumer Eric P. Demography, Foreclosure, and Crime:: Assessing Spatial Heterogeneity in Contemporary Models of Neighborhood Crime Rates. Demographic Research. 2012; 26 :449–488. [ Google Scholar ]
• Basta Luke A., Richmond Therese S., Wiebe Douglas J. Neighborhoods, Daily Activities, and Measuring Health Risks Experienced in Urban Environments. Social Science & Medicine. 2010; 71 (11):1943–1950. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Benson Michael L., Fox Greer L., DeMaris Alfred, Van Wyk Judy. Neighborhood Disadvantage, Individual Economic Distress and Violence Against Women in Intimate Relationships. Journal of Quantitative Criminology. 2003; 19 (3):207–235. [ Google Scholar ]
• Bernasco Wim. A Sentimental Journey to Crime: Effects of Residential History on Crime Location Choice. Criminology. 2010; 48 (2):389–416. [ Google Scholar ]
• Bingenheimer Jeffrey B., Brennan Robert T., Earls Felton J. Firearm Violence Exposure and Serious Violent Behavior. Science. 2005; 308 (5726):1323–1326. [ PubMed ] [ Google Scholar ]
• Browne William J., Goldstein Harvey, Rasbash Jon. Multiple Membership Multiple Classification (MMMC) Models. Statistical Modelling. 2001; 1 (2):103–124. [ Google Scholar ]
• Burchfield Keri B., Silver Eric. Collective Efficacy and Crime in Los Angeles Neighborhoods: Implications for the Latino Paradox. Sociological Inquiry. 2013; 83 (1):154–176. [ Google Scholar ]
• Bursik Robert J., Jr., Grasmick Harold G. Neighborhoods & Crime. Lexington Books; 1999. [ Google Scholar ]
• Cagney Kathleen A., Browning Christopher R., Jackson Aubrey L., Soller Brian. Networks, Neighborhoods, and Institutions: An Integrated 'Activity Space' Approach for Research on Aging. In: Waite Linda J., Plewes Thomas J., editors. New Directions in the Sociology of Aging. The National Academies Press; Washington, D.C.: 2013. pp. 151–174. [ PubMed ] [ Google Scholar ]
• Catalano Ralph, et al. The Health Effects of Economic Decline. Annual Review of Public Health. 2011; 32 :431–450. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Chaix Basile. Geographic Life Environments and Coronary Heart Disease: A Literature Review, Theoretical Contributions, Methodological Updates, and a Research Agenda. Annual Review of Public Health. 2009; 30 :81–105. [ PubMed ] [ Google Scholar ]
• Cohen Lawrence E., Felson Marcus. Social Change and Crime Rate Trends: A Routine Activity Approach. American Sociological Review. 1979; 44 (4):588–608. [ Google Scholar ]
• Crowder Kyle, South Scott J. Spatial and Temporal Dimensions of Neighborhood Effects on High School Graduation. Social Science Research. 2011; 40 (1):87–106. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• De Coster Stacy, Heimer Karen, Wittrock Stacy M. Neighborhood Disadvantage, Social Capital, Street Context, and Youth Violence. Sociological Quarterly. 2006; 47 (4):723–753. [ Google Scholar ]
• Ellen Ingrid Gould, Lacoe Johanna, Sharygin Claudia Ayanna. Do Foreclosures Cause Crime. Journal of Urban Economics. 2013; 74 :59–70. [ Google Scholar ]
• Entwisle Barbara, Faust Katherine, Rindfuss Ronald R., Kaneda Toshiko. Networks and Contexts: Variation in the Structure of Social Ties. American Journal of Sociology. 2007; 112 (5):1495–1533. [ Google Scholar ]
• Fischer Claude S. Toward a Subcultural Theory of Urbanism. American Journal of Sociology. 1975; 80 (6):1319–1341. [ Google Scholar ]
• Foley Donald L. The Use of Local Facilities in a Metropolis. American Journal of Sociology. 1950; 56 (3):238–246. [ Google Scholar ]
• Gould Roger V. Multiple Networks and Mobilization in the Paris Commune, 1871. American Sociological Review. 1991; 56 (6):716–729. [ Google Scholar ]
• Graif Corina, Sampson Robert J. Spatial Heterogeneity in the Effects of Immigration and Diversity on Neighborhood Homicide Rates. Homicide Studies. 2009; 13 (3):242–260. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hipp John R. Block, Tract, and Levels of Aggregation: Neighborhood Structure and Crime and Disorder as a Case in Point. American Sociological Review. 2007; 72 (5):659–680. [ Google Scholar ]
• Hipp John R., Boessen Adam. Egohoods as Waves Washing Across the City: A New Measure of 'Neighborhoods. Criminology. 2013; 51 (2):287–327. [ Google Scholar ]
• Hipp John R., Faris Robert W., Boessen Adam. Measuring ‘Neighborhood’: Constructing Network Neighborhoods. Social Networks. 2012; 34 (1):128–140. [ Google Scholar ]
• Holzer Harry J. The Spatial Mismatch Hypothesis: What Has the Evidence Shown? Urban Studies. 1991; 28 (1):105–122. [ Google Scholar ]
• Hunter Albert J. Private, Parochial, and Public Social Orders: The Problem of Crime and Incivility in Urban Communities. In: Suttles Gerald D., Zald Mayer N., editors. The Challenge of Social Control: Citizenship and Institution Building in Modern Society. Ablex; Norwood, NJ: 1985. pp. 230–242. [ Google Scholar ]
• Hunter Albert J., Suttles Gerald D. The Expanding Community of Limited Liability. In: Suttles Gerald D., editor. The Social Construction of Communities. University of Chicago Press; Chicago, IL: 1972. pp. 44–81. [ Google Scholar ]
• Inagami Sanae, Cohen Deborah A., Finch Brian K. Non-Residential Neighborhood Exposures Suppress Neighborhood Effects on Self-Rated Health. Social Science & Medicine. 2007; 65 (8):1779–1791. [ PubMed ] [ Google Scholar ]
• Kain John F. Housing Segregation, Negro Employment, and Metropolitan Decentralization. The Quarterly Journal of Economics. 1968; 82 (2):175–197. [ Google Scholar ]
• Kim Sangmoon, LaGrange Randy L., Willis Cecil L. Place and Crime Integrating Sociology of Place and Environmental Criminology. Urban Affairs Review. 2013; 49 (1):141–155. [ Google Scholar ]
• Kirk David S., Laub John H. Neighborhood Change and Crime in the Modern Metropolis. Crime and Justice. 2010; 39 (1):441–502. [ Google Scholar ]
• Kreager Derek A., Rulison Kelly, Moody James. Delinquency and the Structure of Adolescent Peer Groups. Criminology. 2011; 49 (1):95–127. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Krivo Lauren J, et al. Social Isolation of Disadvantage and Advantage: The Reproduction of Inequality in Urban Space. Social Forces. 2013; 92 (1):141–164. [ Google Scholar ]
• Kubrin Charis E., Stewart Eric A. Predicting Who Reoffends: The Neglected Role of Neighborhood Context in Recidivism Studies. Criminology. 2006; 44 (1):165–197. [ Google Scholar ]
• Kubrin Charis E., Weitzer Ronald. New Directions in Social Disorganization Theory. Journal of Research in Crime and Delinquency. 2003; 40 (4):374–402. [ Google Scholar ]
• Larsen Jonas, Axhausen Kay, Urry John. Mobilities, Networks, Geographies. Ashgate Publishing, Ltd; Burlington, VT: 2012. [ Google Scholar ]
• Ludwig Jens, Duncan Greg J., Hirschfield Paul. Urban Poverty and Juvenile Crime: Evidence from a Randomized Housing-Mobility Experiment. The Quarterly Journal of Economics. 2001; 116 (2):655–679. [ Google Scholar ]
• Matthews Stephen A. The Salience of Neighborhood: Some Lessons from Sociology. American Journal of Preventive Medicine. 2008; 34 (3):257–259. [ PubMed ] [ Google Scholar ]
• Matthews Stephen A. Spatial Polygamy and the Heterogeneity of Place: Studying People and Place via Egocentric Methods. In: Burton Linda M., Matthews Stephen A., Leung ManChui, Kemp Susan P., Takeuchi David T., editors. Communities, Neighborhoods, and Health, Social Disparities in Health and Health Care. Springer; New York: 2011. pp. 35–55. [ Google Scholar ]
• Matthews Stephen A., Yang Tse-Chuan. Spatial Polygamy and Contextual Exposures (SPACEs) Promoting Activity Space Approaches in Research on Place And Health. American Behavioral Scientist. 2013; 57 (8):1057–1081. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• McClenahan Bessie Averne. The Changing Urban Neighborhood. University of Southern California; Los Angeles, CA: 1929. [ Google Scholar ]
• Merton Robert K. Social Structure and Anomie. American Sociological Review. 1938; 3 (5):672–682. [ Google Scholar ]
• Messner Stephen, Anselin Luc. Spatial Analyses of Homicide with Areal Data. In: Goodchild Michael F., Janelle Donald G., editors. Spatially Integrated Social Science. Oxford University Press; New York: 2004. pp. 127–144. [ Google Scholar ]
• Molnar Beth E., Cerda Magdalena, Roberts Andrea L., Buka Stephen L. Effects of Neighborhood Resources on Aggressive and Delinquent Behaviors Among Urban Youths. American Journal of Public Health. 2008; 98 (6):1086–1093. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Papachristos Andrew V., Hureau David M., Braga Anthony A. The Corner and the Crew: The Influence of Geography and Social Networks on Gang Violence. American Sociological Review. 2013; 78 (3):417–447. [ Google Scholar ]
• Peterson Ruth D., Krivo Lauren J. Divergent Social Worlds: Neighborhood Crime and the Racial-Spatial Divide. Russell Sage Foundation; New York: 2010. [ Google Scholar ]
• Pratt Travis C., Cullen Francis T. Assessing Macro-Level Predictors and Theories of Crime: A Meta-Analysis. Crime and Justice. 2005; 32 :373–450. [ Google Scholar ]
• Radil Steven M., Flint Colin, Tita George E. Spatializing Social Networks: Using Social Network Analysis to Investigate Geographies of Gang Rivalry, Territoriality, and Violence in Los Angeles. Annals of the Association of American Geographers. 2010; 100 (2):307–326. [ Google Scholar ]
• Rodriguez Nancy. Concentrated Disadvantage and the Incarceration of Youth Examining How Context Affects Juvenile Justice. Journal of Research in Crime and Delinquency. 2013; 50 (2):189–215. [ Google Scholar ]
• Sampson Robert J. Great American City: Chicago and the Enduring Neighborhood Effect. University of Chicago Press; Chicago: 2012. [ Google Scholar ]
• Sampson Robert J., Graif Corina. Neighborhood Networks and Processes of Trust. In: Cook Karen S., Levi Margaret, Hardin Russell., editors. Whom Can We Trust? How Groups, Networks, and Institutions Make Trust Possible. Russell Sage Foundation; New York: 2009a. pp. 182–216. [ Google Scholar ]
• Sampson Robert J., Graif Corina. Neighborhood Social Capital as Differential Social Organization Resident and Leadership Dimensions. American Behavioral Scientist. 2009b; 52 (11):1579–1605. [ Google Scholar ]
• Sampson Robert J., Morenoff Jeffrey D., Earls Felton. Beyond Social Capital: Spatial Dynamics of Collective Efficacy for Children. American Sociological Review. 1999; 64 (5):633–660. [ Google Scholar ]
• Sampson Robert J., Morenoff Jeffrey D., Gannon-Rowley Thomas. Assessing ‘Neighborhood Effects’: Social Processes and New Directions in Research. Annual Review of Sociology. 2002; 28 :443–478. [ Google Scholar ]
• Sampson Robert J., Raudenbush Stephen W., Earls Felton. Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy. Science. 1997; 277 (5328):918–924. [ PubMed ] [ Google Scholar ]
• Schaefer David R. Youth Co-Offending Networks: An Investigation of Social and Spatial Effects. Social Networks. 2012; 34 (1):141–149. [ Google Scholar ]
• Shaw Clifford R., McKay Henry D. Juvenile Delinquency and Urban Areas. University of Chicago Press; Chicago, IL: 1942. [ Google Scholar ]
• Slocum Lee Ann, Rengifo Andres F., Choi Tiffany, Herrmann Christopher R. The Elusive Relationship Between Community Organizations and Crime: An Assessment Across Disadvantaged Areas of the South Bronx. Criminology. 2013; 51 (1):167–216. [ Google Scholar ]
• Snijders Tom A. B. The Statistical Evaluation of Social Network Dynamics. Sociological Methodology. 2001; 31 (1):361–395. [ Google Scholar ]
• Taub Richard P., Surgeon George P., Lindholm Sara, Otti Phyllis Betts, Bridges Amy. Urban Voluntary Associations, Locality Based and Externally Induced. American Journal of Sociology. 1977; 83 (2):425–442. [ Google Scholar ]
• Tita George, Griffiths Elizabeth. Traveling to Violence: The Case for a Mobility-Based Spatial Typology of Homicide. Journal of Research in Crime and Delinquency. 2005; 42 (3):275–308. [ Google Scholar ]
• Tobler Waldo R. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography. 1970; 46 :234–240. [ Google Scholar ]
• Tran Van C., Graif Corina, Jones Alison D., Small Mario L., Winship Christopher. Participation in Context: Neighborhood Diversity and Organizational Involvement in Boston. City & Community. 2013; 12 (3):187–210. [ Google Scholar ]
• Webber Melvin M. Order in Diversity: Community without Propinquity. In: Wingo Lowdon., Jr., editor. Cities and Space: The Future Use of Urban Land. Johns Hopkins; Baltimore, MD: 1963. pp. 23–54. [ Google Scholar ]
• Weisburd David, Bushway Shawn, Lum Cynthia, Yang Sue-Ming. Trajectories of Crime at Places: A Longitudinal Study of Street Segments in the City of Seattle. Criminology. 2004; 42 (2):283–322. [ Google Scholar ]
• Wellman Barry. The Community Question: The Intimate Networks of East Yorkers. American Journal of Sociology. 1979; 84 (5):1201–1231. [ Google Scholar ]
• Wellman Barry. Networks In The Global Village: Life In Contemporary Communities. Westview Press; Boulder, CO: 1999. [ Google Scholar ]
• White R. Clyde. The Relation of Felonies to Environmental Factors in Indianapolis. Social Forces. 1931; 10 :498–509. [ Google Scholar ]
• Wikström Per-Olof H. Urban Crime, Criminals, and Victims: The Swedish Experience in an Anglo-American Comparative Perspective. Springer-Verlag Publishing; New York: 1991. [ Google Scholar ]
• Wikström Per-Olof H., Ceccato Vania, Hardie Beth, Treiber Kyle. Activity Fields and the Dynamics of Crime. Journal of Quantitative Criminology. 2010; 26 (1):55–87. [ Google Scholar ]
• Wilson William Julius. The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy. Second University of Chicago Press; Chicago, IL: 2012. [ Google Scholar ]
• Zenk Shannon N, et al. Activity Space Environment and Dietary and Physical Activity Behaviors: A Pilot Study. Health & Place. 2011; 17 (5):1150–1161. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zimmerman Gregory M., Messner Steven F. Neighborhood Context and the Gender Gap in Adolescent Violent Crime. American Sociological Review. 2010; 75 (6):958–980. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zorbaugh Harvey Warren. The Gold Coast and the Slum: A Sociological Study of Chicago’s Near North Side. University of Chicago Press; Chicago: 1929. [ Google Scholar ]

• Undergraduate
• High School
• Architecture
• American History
• Asian History
• Antique Literature
• American Literature
• Asian Literature
• Classic English Literature
• World Literature
• Creative Writing
• Linguistics
• Criminal Justice
• Legal Issues
• Anthropology
• Archaeology
• Political Science
• World Affairs
• African-American Studies
• East European Studies
• Latin-American Studies
• Native-American Studies
• West European Studies
• Family and Consumer Science
• Social Issues
• Women and Gender Studies
• Social Work
• Natural Sciences
• Pharmacology
• Earth science
• Agriculture
• Agricultural Studies
• Computer Science
• IT Management
• Mathematics
• Investments
• Engineering and Technology
• Engineering
• Aeronautics
• Medicine and Health
• Alternative Medicine
• Communications and Media
• Advertising
• Communication Strategies
• Public Relations
• Educational Theories
• Teacher's Career
• Chicago/Turabian
• Company Analysis
• Education Theories
• Shakespeare
• Canadian Studies
• Food Safety
• Relation of Global Warming and Extreme Weather Condition
• Movie Review
• Admission Essay
• Annotated Bibliography
• Application Essay
• Article Critique
• Article Review
• Article Writing

Book Review

• Business Plan
• Business Proposal
• Capstone Project
• Cover Letter
• Creative Essay
• Dissertation
• Dissertation - Abstract
• Dissertation - Conclusion
• Dissertation - Discussion
• Dissertation - Hypothesis
• Dissertation - Introduction
• Dissertation - Literature
• Dissertation - Methodology
• Dissertation - Results
• GCSE Coursework
• Grant Proposal
• Marketing Plan
• Multiple Choice Quiz
• Personal Statement
• Power Point Presentation
• Power Point Presentation With Speaker Notes

Questionnaire

• Reaction Paper
• Research Paper
• Research Proposal
• SWOT analysis
• Thesis Paper
• Online Quiz
• Literature Review
• Movie Analysis
• Statistics problem
• Math Problem
• All papers examples
• How It Works
• Money Back Policy
• Terms of Use
• Privacy Policy
• We Are Hiring

Assessing Urban Crime and Its Control, Essay Example

Pages: 3

Words: 809

Hire a Writer for Custom Essay

Use 10% Off Discount: "custom10" in 1 Click 👇

You are free to use it as an inspiration or a source for your own work.

Problem: Urban Crime

Big cities in the United States continue to be burdened by disproportionately higher crime rates than recorded in rural and suburban areas, a trend that has persisted for centuries. Individuals engage in illicit activity because they reaps fruitful financial benefits, although non-pecuniary crimes like rap and assault make up a percentage of urban crimes. Urban life is characterized by more arrests and lower likelihood of recognition, which accounts for an estimated twenty percent of the “urban crime effect.” This phenomenon often leads to a reduction in crime costs (Gleaser & Sacerdote, 1996, p.1). The remaining types of urban crime is attributed to contingencies that varies by cities in addition to individuals, such as family structure, social influences, and particular tastes (Gleaser & Sacerdote, 1996, p. 1). Urban crime rates in the United States have exponentially declined during the 1990s, and they have remained at such minimal levels throughout the twenty-first century. Statistical evidence suggests that this general downward trend is not related to shifting socioeconomic conditions in various urban spaces. The oscillation of crime rates unequivocally directly affects demographics, residential location, and property values. Policy-makers at the local, state, and national levels have greatly invested in building up a large police force in order to maintain control over urban crime, thereby rendering law enforcement obligated to the community at large to reduce crime rates in the city. There is bountiful evidence that reveals that increasing public funding on the enhancement of law enforcement directly results in lower rates of urban crime. The majority of scholars, however, eschew the capacious role of private and community-level protection and control efforts in addition to the crucial role of “private inputs into police investigations” that go uncompensated (Cook, 2008). Police forces assigned to areas hampered by endemically higher crime rates need to overcome various obstacles they regularly faced such as reluctant witnesses who did not cooperate with the efforts of law enforcement.

Indeed, law enforcement officials retain the function and role as the “gatekeeper” of the U.S. criminal justice system because they are charged with responsibility of enforcing the law and overseeing that citizens comply with laws. They must bring perpetrators of crime to justice, and the foundation upon which this function is built on is the daily interaction between them and regular citizens (Lab, 2001, p. 52). Crime prevention therefore remains a critical part and objective of law enforcement work to ensure community safety by allaying potentially explosive situations prior to culminating into dangerous and violent situations.  Furthermore, if law enforcement does not confront minor offenses such as drunkenness, disorderly conduct in public, and vandalism, more serious criminal offenses are more likely to proliferate and materialize.  (Sridhar, 2006, p. 1842). Ultimately, policy-makers must remain cognizant of the correlation between the size of a city and criminal activity due to the predisposition of those living in the city–especially socioeconomically disadvantaged cities–towards crime. They need to negotiate more effective local and community measures in order to adequately address and mitigate urban crime that threatens to tear communities asunder.

Audience:  The audience is policy-makers at the local and states levels. Urban crime is a rampant problem, especially socioeconomically disadvantaged locales where poverty is endemic. Additional measures must be taken in order to improve the efficacy of the police force. Moreover, measures must be taken to dismantle structural and institutional racism that has resulted in unfair treatment and police brutality.

Counter-Arguments:  This paper argues that the inefficacy of a larger police force renders it obsolete to continue pursuing that strategy as a viable means of combating urban crime. Counter-arguments can be made that larger and more diverse police forces have resulted in lower crime rates. Moreover, a shift in police training could enhance the efficacy of law enforcement in eradicating or mitigating urban crime. Call to Action:  Traditional law enforcement policies geared towards reducing crime rates in the cities have hitherto yielded limited success. Fears continue to permeate the suburbs regarding urban crime, which has resulted in the escalation of police visibility and police presence in metropolitan locales. Incarceration rates have proliferated, and harsher, longer prison sentences continue to be doled out, especially for juvenile criminals. Howeverm crimes rates have remained inert. As such, policymakers need to focus less on law-and-order, traditional approaches to reducing crime in the city and crime prevention efforts. In addition, private expenditures on efficient security systems has hitherto proven to be useful in reducing crimes in urban areas such Los Angeles, which is one of the largest cities hampered by urban crime. Less traditional modes of crime prevention need to be explored, such as the funding of public recreation and parks. There has been a dearth of studies conducted on the direct impact public recreation has on crime, but the nascent studies reify that crime is reduced when public recreation and other green spaces are available for public consumption.

Cook, P. J. (2008, February 1). Assessing urban crime and its control: an overview.  NBER.  Retrieved September 18, 2015, from http://www.nber.org/papers/w13781

Cook, P. J., & Ludwig, J. (2010, November 1). Economical crime control.  NBER.  Retrieved September 18, 2015, from http://www.nber.org/papers/w16513

Cook, P. J., & Macdonald, J. (2010, April 1). Public safety through private action: an economic assessment of BIDs, locks, and citizen cooperation.  NBER . Retrieved September 18, 2015, from http://www.nber.org/papers/w15877

Glaeser, E. L., & Sacerdote, B. (1996, June 1). Why is there more crime in cities?.  NBER.  Retrieved September 18, 2015, from http://www.nber.org/papers/w5430

Stuck with your Essay?

Get in touch with one of our experts for instant help!

The Demographic Winter, Questionnaire Example

The Battle for the Beginning, Book Review Example

Time is precious

don’t waste it!

Plagiarism-free guarantee

Privacy guarantee

Secure checkout

Money back guarantee

Related Essay Samples & Examples

Voting as a civic responsibility, essay example.

Pages: 1

Words: 287

Utilitarianism and Its Applications, Essay Example

Words: 356

The Age-Related Changes of the Older Person, Essay Example

Pages: 2

Words: 448

The Problems ESOL Teachers Face, Essay Example

Pages: 8

Words: 2293

Should English Be the Primary Language? Essay Example

Pages: 4

Words: 999

The Term “Social Construction of Reality”, Essay Example

Words: 371

• Open access
• Published: 01 December 2021

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study

• Marcos Oliveira   ORCID: orcid.org/0000-0003-3407-5361 1 , 2  

Crime Science volume  10 , Article number:  27 ( 2021 ) Cite this article

23k Accesses

7 Citations

67 Altmetric

Metrics details

Crime rates per capita are used virtually everywhere to rank and compare cities. However, their usage relies on a strong linear assumption that crime increases at the same pace as the number of people in a region. In this paper, we demonstrate that using per capita rates to rank cities can produce substantially different rankings from rankings adjusted for population size. We analyze the population–crime relationship in cities across 12 countries and assess the impact of per capita measurements on crime analyses, depending on the type of offense. In most countries, we find that theft increases superlinearly with population size, whereas burglary increases linearly. Our results reveal that per capita rankings can differ from population-adjusted rankings such that they disagree in approximately half of the top 10 most dangerous cities in the data analyzed here. Hence, we advise caution when using crime rates per capita to rank cities and recommend evaluating the linear plausibility before doing so.

Introduction

In criminology, it is generally accepted that crime occurs more often in more populated regions. In one of the first works of modern criminology, Balbi and Guerry examined the crime distribution across France in 1825, revealing that some areas experienced more crime than others (Balbi and Guerry, 1829 ; Friendly, 2007 ). To compare these areas, they realized the need to adjust for population size and analyzed crime rates instead of raw numbers. This method eliminates the linear effect of population size on crime numbers and has been used to measure crime and compare cities almost everywhere—from academia to news outlets (Hall, 2016 ; Park and Katz, 2016 ; Siegel, 2011 ). However, this approach overlooks the potential nonlinear effects of population and, more importantly, exposes our limited understanding of the population–crime relationship.

Though different criminology theories expect a relationship between population size and crime, they tend to disagree on how crime increases with population (Chamlin and Cochran, 2004 ; Rotolo and Tittle, 2006 ). These theories predict divergent population effects, such as linear and superlinear crime growth. Despite these theoretical disputes, however, crime rates per capita are broadly used by assuming that crime increases linearly with the number of people in a region. Crucially, crime rates are often deemed to be a standard means of comparing crime in cities.

Yet the widespread adoption of crime rates is arguably due more to tradition (Boivin, 2013 ) rather than its ability to remove the effects of population size. Many urban indicators, including crime, have already been shown to increase nonlinearly with population size (Bettencourt et al., 2007 ). When we violate the linear assumption and use rates, we deal with quantities that still have population effects, thus introducing an artifactual bias into rankings and analyses.

Despite this inadequacy, we only have a limited understanding of the impact of nonlinearity on crime rates. Although previous works have investigated population–crime relationships extensively (Alves et al., 2013a ; Bettencourt et al. 2010 ; Chang et al. 2019 ; Gomez-Lievano et al., 2012 ; Hanley et al., 2016 ; Yang et al., 2019 ), they have failed to quantify the impact of nonlinear relationships on rankings and restricted their analyses to either specific offenses or countries. The lack of comprehensive systematic studies has limited our knowledge on how the linear assumption influences crime analyses and, more critically, has prevented us from better understanding the effect of population on crime.

In this work, we analyze burglaries and thefts in 12 countries and investigate how crime rates per capita can misrepresent cities in rankings. Instead of assuming that the population–crime relationship is linear, we estimate this relationship from data using probabilistic scaling analysis (Leitão et al., 2016 ). We use our estimates to rank cities while adjusting for population size, and we then examine how these rankings differ from rankings based on rates per capita. In our results, we find that the linear assumption is unjustified. We show that using crime rates to rank cities can lead to rankings that considerably differ from rankings adjusted for population size. Finally, our results reveal contrasting growths of burglaries and thefts with population size, implying that different crime dynamics can produce distinct features at the city level. Our work sheds light on the population–crime relationship and suggests caution in using crime rates per capita.

Crime and population size

Different theoretical perspectives predict the emergence of a relationship between population size and crime. Three main criminology theories expect this relationship: structural, social control, and subcultural (Chamlin and Cochran, 2004 ; Rotolo and Tittle, 2006 ). In general, these perspectives agree that variations in the number of people in a region have an impact on the way people interact with one another. These theories, however, differ in the types of changes in social interaction and how they can produce a population–crime relationship.

From a structural perspective, a higher number of people increases the chances of social interaction, which increases the occurrence of crime. Two distinct rationales can explain such an increase. Mayhew and Levinger ( 1976 ) posit that crime is a product of human contact: more interaction leads to higher chances of individuals being exploited, offended, or harmed. They claim that a larger population size raises the number of opportunities for interaction at an increasing rate, which would lead to a superlinear crime growth with population size (Chamlin and Cochran, 2004 ). In contrast, Blau ( 1977 ) implies a linear population–crime relationship. He posits that population aggregation reduces spatial distance among individuals, thereby promoting different social associations such as victimization. At the same time, as conflictive association increases, other integrative associations also increase, leading to a linear growth of crime (Chamlin and Cochran, 2004 ). Notably, the structural perspective focuses on the quantitative consequences of population growth.

The social control perspective advocates that changes in population size have a qualitative impact on social relations, which weakens informal social control mechanisms that inhibit crime (Groff, 2015 ). From this perspective, crime relates to two aspect of a population: size and stability. A larger population size leads to higher population density and heterogeneity—not only do individuals have more opportunities for social contacts, but they are also often surrounded by strangers (Wirth, 1938 ). This situation makes social integration difficult and promotes a high anonymity, which encourages criminal impulses and harms a community’s ability to socially constrain misbehavior (Freudenburg, 1986 ; Sampson, 1986 ). Similarly, from a systemic viewpoint, any change (i.e., increase or decrease) in population size can have an impact on crime numbers (Rotolo and Tittle, 2006 ). From this viewpoint, the understanding is that regular and sustained social interactions produce community networks with effective mechanisms of social control (Bursik and Webb, 1982 ). Population instability, however, hinders the construction of such networks. In communities with unstable population size, residents avoid socially investing in their neighborhoods, which hurts community organization and weakens social control, thus increasing misbehavior and crime (Miethe et al., 1991 ; Sampson, 1988 ).

Both social-control and structural perspectives solely focus on individuals’ interactions without considering their private interests. These perspectives pay little attention to how unconventional interests increase with urbanization (Fischer, 1975 ) and how these interests relate to misbehavior.

In contrast, the subcultural perspective advocates that population concentration brings together individuals with shared interests, which produces private social networks built around these interests, thereby promoting social support for behavioral choices. Fischer ( 1975 ) posits that population size has an impact on the creation, diffusion, and intensification of unconventional interests. He proposes that large populations have a sufficient number of people with specific shared interests, thus enabling social interaction and lead to the emergence of subcultures. The social networks surrounding a subculture bring normative expectations that increase the likelihood of misbehavior and crime (Fischer, 1975 ,, 1995 ).

These three perspectives—structural, social control, and subcultural—expect that a higher number of people in an area leads to more crime in that area. In the case of cities, we know that population size is indeed a strong predictor of crime (Bettencourt et al., 2007 ) . The existence of a population–crime relationship implies that we must adjust for population size to analyze crime in cities properly.

Crime rates per capita

In the literature, the typical solution for removing the effect of population size from crime numbers is to use ratios such as the following:

This ratio is often used together with a multiplier that contextualizes the quantity (e.g., crime per 100,000 inhabitants; Boivin, 2013 ). However, even though crime rates are popularly used, they present at least two inadequacies. First, the way in which we define population affects crime rates. The common approach is to use resident population (e.g., census data) to estimate rates, but this practice can distort the picture of crime in a place: crime is not limited to residents (Gibbs and Erickson, 1976 ), and cities attract a substantial number of non-residents (Stults and Hasbrouck, 2015 ). Instead, researchers suggest using ambient population (Andresen, 2006 ,, 2011 ) and accounting for criminal opportunities, which depends on the type of crime (Boggs, 1965 ; Clarke, 1984 ; Cohen et al., 1985 ; Harries, 1981 ).

Second, Eq. ( 1 ) assumes that the population–crime relationship is linear. The rationale behind this equation is that we have a relationship of the form

which means that crime can be linearly approximated via population. Given this linear assumption, when we divide crime by population in Eq. ( 1 ), we are trying to cancel out the effect of population on crime. This assumption implies that crime increases at the same pace as population growth. However, not all theoretical perspectives agree with this type of growth, and many urban indicators, including crime, have been shown to increase with population size in a nonlinear fashion (Bettencourt et al., 2007 ).

Cities and scaling laws

Much research has been devoted to understanding urban growth and its impact on indicators such as gross domestic product, total wages, electrical consumption, and crime (Bettencourt et al., 2007 , 2010 ; Bettencourt, 2013 ; Gomez-Lievano et al., 2016 ). Bettencourt et al. ( 2007 ) have found that a city’s population size, denoted by N , is a strong predictor of its urban indicators, denoted by Y , exhibiting the following relationship:

This so-called scaling law tells us that, given the size of a city, we expect certain levels of wealth creation, knowledge production, criminality, and other urban aspects. This expectation suggests general processes underlying urban development (Bettencourt et al., 2013 ) and indicates that regularities exist in cities despite their idiosyncrasies (Oliveira and Menezes, 2019 ). To understand this scaling and the urban processes better, we can examine the exponent \(\beta\) , which describes how an urban indicator grows with population size.

Urban scaling laws and rates per capita. The way in which urban indicators increase with population size depends on the class of the indicator. A Social aspects, such as crime and total wages, increase superlinearly with population size, whereas infrastructural indicators (e.g., road length) increase sublinearly. B  In nonlinear scenarios, rates per capita still depend on population size

Bettencourt et al. ( 2007 ) presented evidence that different categories of urban indicators exhibit distinct growth regimes. They showed that social indicators grow faster than infrastructural ones (see Fig.  1 A). Specifically, social indicators, such as the number of patents and total wages, increase superlinearly with population size (i.e., \(\beta > 1\) ), meaning that these indicators grow at an increasing rate with population. In the case of infrastructural aspects (e.g., road surface, length of electrical cables), an economy of scale exists. As cities grow in population size, these urban indicators increase at a slower pace with \(\beta < 1\) (i.e., sublinearly). In both scenarios, because of nonlinearity, we should be careful with per capita analyses.

When we violate the linear assumption of per capita ratios, we deal with quantities that can misrepresent an urban indicator. To demonstrate this, we use Eq. ( 3 ) to define the per capita rate C of an urban indicator as follows:

which implies that rates are independent from population only when \(\beta\) equals to one—when \(\beta \ne 1\) , population is not cancelled out from the equation. In these nonlinear cases, per capita rates can inflate or deflate the representation of an urban indicator depending on \(\beta\) (see Fig.  1 B). This misrepresentation occurs because population still has an effect on rates. By definition, we expect that per capita rates are higher in larger cities when \(\beta > 1\) , whereas when \(\beta < 1\) , we expect larger cities to have lower rates. When we use rates to compare cities in nonlinear situations, we introduce an artifactual bias. To compare cities properly, previous works have proposed scaled-adjusted indicators that account for population size (Alves et al., 2013a ; Bettencourt et al., 2010 ), supporting the need for population adjustment but failing to quantify the impact of the linear assumption on rankings of urban indicators.

More crime in cities?

In the case of crime, researchers have found a superlinear growth with population size. Bettencourt et al. ( 2007 ) showed that serious crime in the United States exhibits superlinear scaling with exponent \(\beta \approx 1.16\) , and some evidence has confirmed similar superlinearity for homicides in Brazil, Colombia, and Mexico (Alves et al., 2013b ; Gomez-Lievano et al., 2012 ). Previous works have also found that different kinds of crime in the United Kingdom and in the United States present nonlinear scaling relationships (Chang et al., 2019 ; Hanley et al., 2016 ; Yang et al., 2019 ). Remarkably, the existence of these scaling laws of crime suggests fundamental urban processes that relate to crime, independent of cities’ particularities.

This regularity manifests itself in the so-called scale-invariance property of scaling laws. It is possible to show that Eq. ( 3 ) holds the following property:

where \(g(\kappa )\) does not depend on N  (Thurner et al., 2018 ). From a modeling perspective, this relationship reveals two aspects about crime. First, we can predict crime numbers in cities via a populational scale transformation \(\kappa\)  (Bettencourt et al., 2013 ). This transformation is independent of population size but depends on \(\beta\) , which tunes the relative increase in crime such that \(g(\kappa ) = \kappa ^\beta\) . Second, Eq. ( 4 ) implies that crime is present in any city, independent of size. This implication arguably relates to the Durkheimian concept of crime normalcy in that crime is seen as a normal and necessary phenomenon in societies, provided that its numbers are not unusually high (Durkheim, 1895 ). Broadly speaking, the scale-invariance property tells us that crime in cities is associated with population in a somewhat predictable fashion. Crucially, this property might give the impression that such regularity is independent of crime type.

However, different types of crime are connected to social mechanisms in different ways (Hipp and Steenbeek, 2016 ) and exhibit unique temporal (Miethe et al., 2005 ; Oliveira et al., 2018 ) and spatial characteristics (Andresen and Linning, 2012 ; Oliveira et al., 2015 , 2017 ; White et al., 2014 ). It is plausible that the scaling laws of crime depend on crime type. Nevertheless, the literature has mostly focused on either specific countries or crime types. Few studies have systematically examined the scaling of different crime types, and the focus on specific countries has prevented us from better understanding the impact of population on crime. Likewise, the lack of a comprehensive systematic study has limited our knowledge about the impact of the linear assumption on crime rates. We still fail to understand how per capita analyses can misrepresent cities in nonlinear scenarios.

In this work, we characterize the scaling laws of burglary and theft in 12 countries and investigate how crime rates per capita can misrepresent cities in rankings. Instead of assuming that the population–crime relationship is linear, as described in Eq. ( 2 ), we investigate this relationship under its functional form as follows:

Specifically, we examine the plausibility of scaling laws to describe the population–crime relationship. To estimate the scaling laws, we use probabilistic scaling analysis, which enables us to characterize the scaling laws of crime. We use our estimates to rank cities while accounting for the effects of population size. Finally, we compare these adjusted rankings with rankings based on per-capita rates (i.e., with the linear assumption).

We use data from 12 countries to investigate the relationship between population size and crime at the city level (see the appendix for data sources). Specifically, we examine annual data from Belgium, Canada, Colombia, Denmark, France, Italy, Mexico, Portugal, South Africa, Spain, the United Kingdom, and the United States (see Table  1 ). In this work, we characterize how crime increases with population size in each country, focusing on burglary and theft. We analyze both crimes in all considered countries, except Mexico, Portugal, and Spain, where we only have data for one type of offense.

The population–crime relationship in 12 countries. Different criminology theories expect a relationship between population size and crime, predicting divergent population effects, such as linear and superlinear crime growth. Despite these theoretical disputes, however, crime rates per capita are broadly used by assuming that crime increases linearly with population size

The scaling laws of crime in cities

To assess the relationship between crime Y and population size N (see Fig.  2 ), we model \(\mathrm{P}(Y|N)\) using probabilistic scaling analysis (see the Methods section). In our study, we examine whether this relationship follows the general form of \(Y \sim N^\beta\) . First, we estimate \(\beta\) from data, and we then evaluate the plausibility of the model ( \(p>0.05\) ) and the evidence for nonlinearity (i.e., \(\beta \ne 1\) ). Our results reveal that Y and N often exhibit a nonlinear relationship, depending on the type of offense.

The scaling laws of crime. We find evidence for a nonlinear relationship between crime and population size in more than half of the data sets. In most considered countries, theft exhibits superlinearity, whereas burglary tends to display linearity. In the plot, the lines represent the error bars for the estimated \(\beta\) of each country–crime for two consecutive years; circles denote a lack of nonlinearity plausibility; triangles represent superlinearity, and upside-down triangles indicate sublinearity

In most of the considered countries, theft increases with population size superlinearly, whereas burglary tends to increase linearly (see Fig.  3 ). Precisely, in 9 out of 11 countries, we find that \(\beta\) for theft is above one; our results indicate linearity for theft (i.e., absence of nonlinear plausibility) in Canada and South Africa. In the case of burglary, we are unable to reject linearity in 7 out of 10 countries; in France and the United Kingdom, we find superlinearity, and in Canada, sublinearity. In almost all considered data sets, these estimates are consistent over two consecutive years in the countries for which we have data for different years (see Appendix  1 ).

Our results suggest that the general form of \(Y\sim N^\beta\) is plausible in most countries, but that this compatibility depends on the offense. We find that burglary data are compatible with the model ( \(p>0.05\) ) in 80% of the considered countries. In the case of theft, the superlinear models are compatible with data in five out of nine countries. We note that in Canada and South Africa, where we are unable to reject linearity for theft, the linear model also lacks compatibility with data.

We find that the estimates of \(\beta\) for each offense often have different values across countries—for example, the superlinear estimates of \(\beta\) for theft range from 1.10 to 1.67. However, when we analyze each country separately, we find that \(\beta\) for theft tends to be larger than \(\beta\) for burglary in each country, except for France and the United Kingdom.

In summary, we find evidence for a nonlinear relationship between crime and population size in more than half of the considered data sets. Our results indicate that crime often increases with population size at a pace that is different from per capita. This relationship implies that analyses with a linear assumption might create distorted pictures of crime in cities. To understand such distortions, we must examine how nonlinearity influences comparisons of crime in cities, when linearity is assumed.

Bias in crime rates per capita. When crime increases nonlinearly with population size, we have an artifactual bias in crime rates. The linearity in Portugal makes rates independent of size (left). However, in Denmark (right), because of the superlinear growth, we expect larger cities to have higher crime rates, but not necessarily more crime than expected. For example, though Aalborg and Solrød have similar theft rates, less crime occurs in Aalborg than expected for cities of the same size, based on the model, whereas Solrød is above the expectation

The inadequacy of crime rates and per capita rankings

We investigate how crime rates of the form \(C = Y/N\) introduce bias in the comparisons and rankings of cities. To understand this bias, we use Eq. ( 3 ) to rewrite crime rate as \(C \sim N^{\beta - 1}\) . This relationship implies that crime rate depends on population size when \(\beta \ne 1\) . For example, in Portugal and Denmark, this dependency is clear when we analyze burglary and theft numbers (see Fig.  4 ). In the case of burglary in Portugal, linearity makes C independent of population size. In Denmark, since theft increases superlinearly, we expect rates to increase with population size. In this country, based on data, the expected theft rate of a small city is lower than the rates of larger cities. We must account for this tendency in order to compare crime in cities; otherwise, we introduce bias against larger cities.

To account for the population–crime relationship found in data, we compare cities using the model P ( Y | N ) as the baseline. We compare the number of crimes in a city with the expectation of the model. For each city i with population size \(n_i\) , we evaluate the z score of the city with respect to \(P(Y|N=n_i)\) . The z score indicates how much more or less crime a particular city has in comparison to cities with a similar population size, as expected by the model. These z scores enable us to compare cities in a country and rank them while accounting for population size differences. In contrast, crime rates per capita only adjust for population size in the linear scenario. This approach is similar to previously proposed indicators that adjust for population size (Alves et al., 2013a ; Bettencourt et al., 2010 ). In our case, the adjustment also accounts for the variance. We denote this kind of analysis as a comparison adjusted for the population–crime relationship.

For example, in Denmark, the theft rate in the municipality of Aalborg ( \(\approx 0.0186\) ) is almost the same as in Solrød ( \(\approx 0.0188\) ). However, less crime occurs in Aalborg than expected for cities of a similar size, while crime in Solrød is above the model expectation (see Fig.  4 B). This disagreement arises because of the different population sizes. Since Aalborg is more than 10 times larger than Solrød, we expect rates in Aalborg to be larger than in Solrød. When we account for this tendency and evaluate their z scores, we find that the z score of Aalborg is \(-2.47\) , whereas in Solrød the z  score is 2.43.

Such inconsistencies have an impact on the crime rankings of cities. The municipality of Aarhus, in Denmark, for example, is ranked among the top 12 cities with the highest theft rate in the country. However, when we account for population–crime relationship using z scores, we find that Aarhus is only at the end of the top 54 rankings.

The inadequacy of per capita rankings. Per capita ranking can differ substantially from rankings adjusted for population size, depending on the scaling exponent. In Italy and Denmark, for example, A theft ranks (top) diverge considerably more than the ranks for burglary (bottom). Data points represent cities’ positions in the rankings. B In nonlinear cases, these rankings diverge, as measured via rank correlation

To understand these variations systematically, we compare rankings based on crime rates with rankings that account for the population–crime relationship (i.e., adjusted rankings). Our results reveal that these two rankings create distinct representations of cities. For each considered data set, we rank cities based on their z scores and crime rates C , and we then examine the change in the rank of each city. According to our findings, the positions of the cities can change substantially. For instance, in Italy, half of the cities have theft rate ranks that diverge in at least 11 positions from the adjusted ranking (Fig.  5 A). This disagreement means that these rankings disagree for approximately half of the top 10 most dangerous cities.

We evaluate these discrepancies by using the Kendall rank correlation coefficient \(\tau\) to measure the similarity between crime rates and adjusted rankings in the considered countries. We find that these rankings can differ considerably but converge when \(\beta \approx 1\) . The \(\tau\) coefficients for the data sets range from 0.6 to 1.0, exhibiting a dependency on the type of crime; or more specifically, on the scaling (Fig.  5 B). As expected, as \(\beta\) approaches 1, the rankings are more similar to one another. For example, in Italy, in contrast to theft, the burglary rate ranking of half of the cities only differs from the adjusted ranking in a maximum of two positions (Fig.  5 A).

Discussion and conclusion

Despite its popularity, comparing cities via crime rates without accounting for population size has a strong assumption that crime increases at the same pace as the number of people in a region. Though previous works have widely investigated the population–crime relationship, they have failed to quantify the impact of nonlinear relationships on rankings and restricted their analyses to either specific offenses or countries. In this work, we analyze crime in different countries to investigate how crime grows with population size and how the widespread assumption of linear growth influences cities’ rankings.

First, we analyzed crime in cities from 12 countries to characterize the population–crime relationship statistically, examining the plausibility of scaling laws to describe this relationship. Then, we used our estimates to rank cities and compared how those rankings differ from rankings based on rates per capita.

Our results showed that the assumption of linear crime growth is unfounded. In more than half of the considered data sets, we found evidence for nonlinear crime growth—that is, crime often increases with population size at a different pace than per capita. This nonlinearity introduces a population effect into crime rates, influencing rankings. We demonstrated that using crime rates to rank cities substantially differs from ranking cities adjusted for population size.

These findings imply that using crime rates per capita—though deemed a standard measure in criminal justice statistics—can create a distorted view of cities’ rankings. For example, in superlinear scenarios, we expect larger cities to have higher crime rates. In this case, when we use rates to rank cities, we build rankings whereby large cities are at the top. But, these cities might not experience more crime than what we expect from places with a similar population size. It is an artifactual bias introduced by population effects still present in crime rates.

Such effects arise from nonlinear population effects that persist in rates due to the linear assumption. This assumption is more than just a statistical subtlety. By assuming linearity, we essentially overlook cities’ context: we ignore the actual impact of population size on crime and how this impact depends on crime type, country, and aggregation units, among other things. For instance, our results indicate that in thefts, linearity is an exception rather than the rule. The indiscriminate use of crime rates neglects significant population–crime interactions that should be considered in order to compare crime in cities properly.

As a result of this inadequacy, we advise caution when using crime rates per capita to compare cities. We recommend evaluating linear plausibility before comparing crime rates. In general, we suggest comparing cities via the z scores computed using the approach (Leitão et al., 2016 ) discussed in the manuscript, thereby avoiding crime rates. It is important to emphasize that this inadequacy in rates is relevant only when comparing cities of different population sizes. In analyses without comparisons, a place’s crime rate can be seen as a rough indicator that contextualizes crime numbers relative to population size. Additionally, when cities have the same size, comparing crime rates boils down to comparing raw crime numbers.

In summary, in this work, we shed light on the population–crime relationship. The linear assumption is exhausted and expired. We have resounding evidence of nonlinearity in crime, which disallows us from unjustifiably assuming linearity. In light of our results, we also note that the scaling laws are plausible models only for half of the considered data sets. Better models are thus needed—in particular, models that account for the fact that different crime types relate to population size differently. More adequate models will help us better understand the relationship between population and crime.

Limitations

Our work presents limitations related to the way in which we define population, crime, and cities. First, we note that crime rates depend on how we define population; in our study, we define it as the resident population (i.e., census data). However, crime is not limited to residents (Gibbs and Erickson, 1976 ), and cities attract a significant number of non-residents (Stults and Hasbrouck, 2015 ). We highlight that this limitation is not specific to our study, and crime rates are often measured using resident population. Previous works have suggested using ambient population and accounting for the number of targets (Andresen, 2006 , 2011 ; Boggs, 1965 ). Collecting this data, however, is challenging, especially when dealing with different countries. Future research should investigate crime rates and scaling laws using other definitions of population, particularly using social media data (Malleson and Andresen, 2016 ; Pacheco et al., 2017 ).

Second, scaling analyses depend on the definition of what constitutes a city (Arcaute et al., 2014 ). In the literature, definitions include legal divisions (e.g., counties, municipalities) and data-driven delineations based on population density and economic interactions (Cottineau et al., 2017 ). It is possible that different city definitions yield divergent scaling regimes for the same urban indicator (Louf and Barthelemy, 2014 ). In our work, we only have access to crime data regarding specific aggregation units, and we thus define cities based on official legal divisions by using census data. City definitions in our analysis consequently depend on the country. We emphasize that we investigate whether per capita rankings are justified under a given city definition. Nevertheless, we believe that even though the use of other city definitions might change our quantitative results, our qualitative results are robust: the inadequacy of crime rates is independent of city definitions. When analyzing different definitions of cities, future research should examine scaling divergences as an opportunity to understand the population–crime relationship better.

Finally, cross-national crime analyses have methodological challenges due to international differences in crime definitions, police and court practices, and reporting rates, among other things (Takala and Aromaa, 2008 ). Although we avoid direct comparisons of countries’ absolute crime numbers in our work, we compare their growth exponents. In this comparison, we assume that cross-national differences have a negligible impact on how crime increases with population, particularly regarding the crime types we analyzed. We understand that some offenses (e.g., sexual assault, drug trafficking) are more sensitive to cross-national comparisons than the offenses we analyzed here (Harrendorf et al., 2010 ; Harrendorf, 2018 ). Collecting high-quality international comparative data could help future works in disentangling cross-national differences.

Probabilistic scaling analysis

We use probabilistic scaling analysis to estimate the scaling laws of crime. Instead of analyzing the linear form of Eq. ( 3 ), we use the approach developed by Leitão et al. ( 2016 ) to estimate the parameters of a distribution Y | N that has the following expectation:

that is, N scales the expected value of an urban indicator (Bettencourt et al., 2013 ; Gomez-Lievano et al., 2012 ; Leitão et al., 2016 ). Note that this method does not assume that the fluctuations around \(\ln y\) and \(\ln x\) are normally distributed (Leitão et al., 2016 ). Instead, we compare models for \(\mathrm{P}(Y|N)\) that satisfy the following conditional variance:

where typically \(\delta \in [1,2]\) , since urban systems have been previously shown to exhibit non-trivial fluctuations around the mean—the so-called Taylor’s law (Hanley et al., 2014 ). To estimate the scaling laws, we maximize the log-likelihood

since we assume \(y_i\) as an independent realization from \(\mathrm{P}(Y|N)\) . In this work, we use an implementation developed by Leitão et al. ( 2016 ) that maximizes the log-likelihood with the “L-BFGS-B” algorithm. We model \(\mathrm{P}(Y|N)\) using Gaussian and log-normal distributions in order to analyze whether accounting for the size-dependent variance influences the estimation. In the case of the Gaussian, the conditions from Eq. ( 5 ) and Eq. ( 6 ) are satisfied with

whereas in the case of the log-normal distribution,

In the log-normal case, note that, if \(\delta = 2\) , then the fluctuations are independent of N ; thus this would be the same as using the minimum least-squares approach (Leitão et al., 2016 ). With this framework, we compare models that have fixed \(\delta\) against models wherein \(\delta\) is also included in the optimization process. In the case of the Gaussian, we have fixed \(\delta =1\) and free \(\delta \in [1,2]\) , whereas in the case of the log-normal, we have fixed \(\delta =2\) and free \(\delta \in [1,3]\) . In this framework, p -values represent a statistic testing two crucial aspects of the modelling: sample independence and model compatibility with data. The statistic consists of the D’Agostino \(K^2\) test together with Spearman’s rank correlation of residuals, which evaluates compatibility and independence, respectively (Leitão et al., 2016 )

Finally, we compare each of the four models individually against the linear alternative (with fixed \(\beta = 1\) ), to test the nonlinearity plausibility. With the fits of all types of crime and countries, we measure the Bayesian information criterion ( \(\mathrm{BIC}\) ), defined as

where k is the number of free parameters in the model and lower \(\mathrm{BIC}\) values indicate better data description. The \(\mathrm{BIC}\) value of each fit enables us to compare the models’ ability to explain data.

Availability of data and materials

All data and source code are available at https://github.com/macoj/scaling_laws_of_crime/ .

Alves, L. G. A., Ribeiro, H. V., Lenzi, E. K., & Mendes, R. S. (2013a). Distance to the scaling law: A useful approach for unveiling relationships between crime and urban metrics. PLoS ONE, 8 (8), e69580.

Article   Google Scholar  

Alves, L. G. A., Ribeiro, H. V., & Mendes, R. S. (2013b). Scaling laws in the dynamics of crime growth rate. Physica A: Statistical Mechanics and Its Applications, 392 (11), 2672–2679.

Andresen, M. A. (2006). Crime measures and the spatial analysis of criminal activity. The British Journal of Criminology, 46 (2), 258–285.

Andresen, M. A. (2011). The ambient population and crime analysis. The Professional Geographer, 63 (2), 193–212.

Andresen, M. A., & Linning, S. J. (2012). The (in)appropriateness of aggregating across crime types. Applied Geography, 35 (1–2), 275–282.

Arcaute, E., Hatna, E., Ferguson, P., Youn, H., Johansson, A., & Batty, M. (2014). Constructing cities, deconstructing scaling laws. Journal of The Royal Society Interface, 12 (102), 20140745.

Balbi, A., & Guerry, A.-M. (1829). Statistique comparée de l’état de l’instruction et du nombre des crimes dans les divers arrondissements des Académies et des Cours Royales de France . Paris: Everat.

Bettencourt, L. M. A. (2013). The origins of scaling in cities. Science, 340 (6139), 1438–1441.

Bettencourt, L. M. A., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences, 104 (17), 7301–7306.

Bettencourt, L. M. A., Lobo, J., Strumsky, D., & West, G. B. (2010). Urban scaling and its deviations: Revealing the structure of wealth, innovation and crime across cities. PLoS ONE, 5 (11), e13541.

Bettencourt, L. M. A., Lobo, J., & Youn, H. (2013). The hypothesis of urban scaling: Formalization, implications and challenges. Technical Report arXiv:1301.5919 [physics.soc-ph], arXiv.

Blau, P. (1977). Inequality and heterogeneity: A primitive theory of social structure (1st ed.). Free Press.

Google Scholar  

Boggs, S. L. (1965). Urban crime patterns. American Sociological Review, 30 (6), 899.

Boivin, R. (2013). On the use of crime rates. Canadian Journal of Criminology and Criminal Justice, 55 (2), 263–277.

Bursik, R. J., & Webb, J. (1982). Community change and patterns of delinquency. American Journal of Sociology, 88 (1), 24–42.

Chamlin, M. B., & Cochran, J. K. (2004). An excursus on the population size–crime relationship. Western Criminology Review, 5 (2), 119–130.

Chang, Y. S., Kim, H. E., & Jeon, S. (2019). Do larger cities experience lower crime rates? A scaling analysis of 758 cities in the U.S. Sustainability, 11 (11), 3111.

Clarke, R. V. (1984). Opportunity-based crime rates: The difficulties of further refinement. The British Journal of Criminology, 24 (1), 74–83.

Cohen, L. E., Kaufman, R. L., & Gottfredson, M. R. (1985). Risk-based crime statistics: A forecasting comparison for burglary and auto theft. Journal of Criminal Justice, 13 (5), 445–457.

Cottineau, C., Hatna, E., Arcaute, E., & Batty, M. (2017). Diverse cities or the systematic paradox of urban scaling laws. Computers, Environment and Urban Systems, 63 , 80–94.

Durkheim, É. (1895). Les règles de la méthode sociologique . Paris: Flix Alcan.

Fischer, C. S. (1975). Toward a subcultural theory of urbanism. American Journal of Sociology, 80 (6), 1319–1341.

Fischer, C. S. (1995). The subcultural theory of urbanism: A twentieth-year assessment. American Journal of Sociology, 101 (3), 543–577.

Freudenburg, W. R. (1986). The density of acquaintanceship: An overlooked variable in community research? American Journal of Sociology, 92 (1), 27–63.

Friendly, M. (2007). A.-M. Guerry’s Moral statistics of France : Challenges for multivariable spatial analysis. Statistical Science   22 (3), 368–399.

Gibbs, J. P., & Erickson, M. L. (1976). Crime rates of American cities in an ecological context. American Journal of Sociology, 82 (3), 605–620.

Gomez-Lievano, A., Patterson-Lomba, O., & Hausmann, R. (2016). Explaining the prevalence, scaling and variance of urban phenomena. Nature Human Behaviour, 1 (1), 0012.

Gomez-Lievano, A., Youn, H., & Bettencourt, L. M. A. (2012). The Statistics of Urban Scaling and Their Connection to Zipf’s Law. PLoS ONE, 7 (7), e40393.

Groff, E. R. (2015). Informal Social Control and Crime Events. Journal of Contemporary Criminal Justice, 31 (1), 90–106.

Hall, J. (2016). Flint is the most dangerous city in America—But it has nothing to do with the water crisis. The Independent .

Hanley, Q. S., Khatun, S., Yosef, A., & Dyer, R.-M. (2014). Fluctuation scaling, Taylor’s law, and crime. PLoS ONE, 9 (10), e109004.

Hanley, Q. S., Lewis, D., & Ribeiro, H. V. (2016). Rural to urban population density scaling of crime and property transactions in English and Welsh Parliamentary Constituencies. PLOS ONE, 11 (2), e0149546.

Harrendorf, S. (2018). Prospects, problems, and pitfalls in comparative analyses of criminal justice data. Crime and Justice, 47 (1), 159–207.

Harrendorf, S., Heiskanen, M., & Malby, S. (Eds.). (2010). International statistics on crime and justice , Volume 64. Helsinki: European Institute for Crime Prevention and Control, affiliated with the United Nations.

Harries, K. D. (1981). Crime rates and environmental denominators. Journal of Environmental Systems, 11 (1), 3–15.

Hipp, J. R., & Steenbeek, W. (2016). Types of crime and types of mechanisms. Crime & Delinquency, 62 (9), 1203–1234.

Leitão, J. C., Miotto, J. M., Gerlach, M., & Altmann, E. G. (2016). Is this scaling nonlinear? Royal Society Open Science, 3 (7), 150649.

Louf, R., & Barthelemy, M. (2014). Scaling: Lost in the smog. Environment and Planning B: Planning and Design, 41 (5), 767–769.

Malleson, N., & Andresen, M. A. (2016). Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice, 46 , 52–63.

Mayhew, B. H., & Levinger, R. L. (1976). Size and the Density of Interaction in Human Aggregates. American Journal of Sociology, 82 (1), 86–110.

Miethe, T., McCorkle, R., & Listwan, S. J. (2005). Crime profiles: The anatomy of dangerous persons, places, and situations (3rd edn.). Oxford University Press.

Miethe, T. D., Hughes, M., & McDowall, D. (1991). Social change and crime rates: An evaluation of alternative theoretical approaches. Social Forces, 70 (1), 165–185.

Oliveira, M., Barbosa-Filho, H., Yehle, T., White, S., & Menezes, R. (2015). From criminal spheres of familiarity to crime networks. In Complex Networks VI , pp. 219–230. Springer.

Oliveira, M., Bastos-Filho, C., & Menezes, R. (2017). The scaling of crime concentration in cities. PLOS ONE, 12 (8), e0183110.

Oliveira, M., & Menezes, R. (2019). Spatial concentration and temporal regularities in crime. arXiv preprint arXiv:1901.03589.

Oliveira, M., Ribeiro, E., Bastos-Filho, C., & Menezes, R. (2018). Spatio-temporal variations in the urban rhythm: the travelling waves of crime. EPJ Data Science, 7 (1), 29.

Pacheco, D. F., Oliveira, M., & Menezes, R. (2017). Using social media to assess neighborhood social disorganization: A case study in the United Kingdom. In Proceedings of the thirtieth international Florida artificial intelligence research society conference, FLAIRS 2017, Marco Island, Florida, USA, May 22–24, 2017. , pp. 341–346.

Park, H., & Katz, J. (2016). Murder rates rose in a quarter of the nation’s 100 largest cities. The New York Times .

Rotolo, T., & Tittle, C. R. (2006). Population size, change, and crime in U.S. cities. Journal of Quantitative Criminology, 22 (4), 341–367.

Sampson, R. J. (1986). Crime in cities: The effects of formal and informal social control. Crime and Justice, 8, 271–311.

Sampson, R. J. (1988). Local friendship ties and community attachment in mass society: A multilevel systemic model. American Sociological Review, 53 (5), 766.

Siegel, L. J. (2011). Criminology (11th ed.). Wadsworth Publishing.

Stults, B. J., & Hasbrouck, M. (2015). The effect of commuting on city-level crime rates. Journal of Quantitative Criminology, 31 (2), 331–350.

Takala, J.-P., & Aromaa, K. (2008). Victimology. In Encyclopedia of violence, peace, & conflict , pp. 2272–2288. Elsevier.

Thurner, S., Hanel, R., & Klimek, P. (2018). Introduction to the theory of complex systems . Oxford University Press.

Book   Google Scholar  

White, S., Yehle, T., Serrano, H., Oliveira, M., & Menezes, R. (2014). The spatial structure of crime in urban environments. In Social informatics , pp. 102–111. Springer.

Wirth, L. (1938). Urbanism as a Way of Life. American Journal of Sociology, 44 (1), 1–24.

Yang, V. C., Papachristos, A. V., & Abrams, D. M. (2019). Modeling the origin of urban-output scaling laws. Physical Review E, 100 (3), 32306.

Download references

No outside funding was used to support this work.

Author information

Authors and affiliations.

University of Exeter College of Engineering Mathematics and Physical Sciences, Exeter, UK

Marcos Oliveira

GESIS-Leibniz Institute for the Social Sciences, Cologne, Germany

You can also search for this author in PubMed   Google Scholar

Contributions

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Marcos Oliveira .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Appendix 1: Results from the probabilistic scaling analysis

To test the plausibility of a nonlinear scaling, we compare each model against the linear alternative (i.e., \(\beta =1\) ) using the difference \(\Delta \mathrm{BIC}\) between the fits for each data set. We follow Leitão et al. ( 2016 ) and define three outcomes from this comparison. First, if \(\Delta \mathrm{BIC} < 0\) , we say that the model is linear ( \(\rightarrow\) ), since we can consider that the linear model explains the data better. Second, if \(0< \Delta \mathrm{BIC} < 6\) , we consider the analysis of \(\beta \ne 1\) inconclusive because we do not have enough evidence for the nonlinearity. Finally, if \(\Delta \mathrm{BIC} > 6\) , we have evidence in favor of the nonlinear scaling, which can be superlinear ( \(\nearrow\) ) or sublinear ( \(\searrow\) ). We also use \(\Delta \mathrm{BIC}\) to determine the model \(\mathrm{P}(Y|N)\) that describes the data better. In Tables  2 and Table  3 , we summarize the results in that a dark gray cell value indicates the best model based on \(\Delta \mathrm{BIC}\) , a light gray value indicates the best model given a \(\mathrm{P}(Y|N)\) model, and \(*\) indicates that the model is plausible ( p -value \(>0.05\) ).

Appendix 2: Data sources

See Table 4 .

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Oliveira, M. More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study. Crime Sci 10 , 27 (2021). https://doi.org/10.1186/s40163-021-00155-8

Download citation

Received : 29 January 2021

Accepted : 08 September 2021

Published : 01 December 2021

DOI : https://doi.org/10.1186/s40163-021-00155-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Population size
• Urban scaling
• Complex systems

Crime Science

ISSN: 2193-7680

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Current Knowledge on Crime and Safety in Rural Areas

• Open Access
• First Online: 23 June 2022

Cite this chapter

You have full access to this open access chapter

• Vania Ceccato 3 &
• Jonatan Abraham 3  

Part of the book series: SpringerBriefs in Criminology ((BRIEFSCRIMINOL))

2710 Accesses

5 Altmetric

This chapter reports the growing body of literature on crime and safety in rural areas via a systematic review of four decades of publications, from 1980 to 2020. This chapter describes the review approach, which focused on English-language literature (in Scopus, JSTOR, and ScienceDirect) in the form of articles, books, and book chapters, and identified research themes. This chapter characterizes the research on crime and safety in rural areas; highlights some of the most prevalent themes, such as policing, crime prevention, and methodology; and emphasizes the importance of the interdisciplinary nature of the field.

• Rural crime
• Safety perceptions
• English-language literature

You have full access to this open access chapter,  Download chapter PDF

This chapter reports on the growing body of literature on crime and safety in rural areas. The international literature is quite definitive about the complexity of rural areas and how their nature affects crime, safety perceptions, policing, and practices of crime prevention. In order to show evidence of this rich and vast body of research, we have executed a systematic review of four decades of English-language publications (in Scopus, JSTOR, and ScienceDirect) from 1980 to 2020 (Moher et al. 2009 ), Footnote 1 including articles, books, and book chapters, and excluding so-called gray literature as much as possible. We characterize the research on crime and safety in rural areas; highlight some of the most important themes, such as policing and crime prevention; and emphasize the importance of the interdisciplinary nature of the field.

Out of the 840 initially identified publications in total, 410 were found to be eligible publications, of which 78% were journal articles and the remainder were books and book chapters. By assigning themes to each publication, we were able to categorize the research into 12 themes (Fig. 3.1 ). This review in general, and the identified themes in particular, illustrate that rural criminology is a rich field of research that contributes to both criminology and numerous other related disciplines, such as rural studies and policing. In this chapter, we summarize some of the main findings, while in Chap. 4 , we discuss the research in more detail, including examples.

Research on rural crime and safety 1980–2020 collected in Scopus, JSTOR, and ScienceDirect ( N  = 410), where each publication was assigned a maximum of two themes. Source: Based on Abraham and Ceccato ( forthcoming )

Most reviewed publications dealt with trends and patterns of crime, at different degrees, in one or more rural areas (21%), followed by studies on rural police, policing, and the rest of the criminal justice system, including the court system and prison industry (21%) (Fig. 3.1 ).

This theme on crime trends in rural areas covered over a fifth (21%, n  = 85) of the publications reviewed for this book, mainly from North America, followed by British and Australian cases. Most of these studies utilized secondary data and official records (56%), and/or performed statistical analyses (35%). Studies of crime trends in the Global South were often published more recently, for example, China (Cheong and Wu 2015 ), Brazil (Scorzafave et al. 2015 ), Haiti (Brewis et al. 2020 ), Zimbabwe (Mafumbabete et al. 2019 ), and Nigeria (Osakwe and Osakwe 2015 ).

Regarding the theme on policing and the criminal justice system, despite it being one of the earliest and most covered topics of research, systematic studies of rural policing were rare before the 1970s (Payne et al. 2005 ). Some of the more comprehensive studies were published in the late twentieth century, such as the studies by Weisheit et al. ( 1995 ) and Sims ( 1988 ). The methods used in these studies were mainly secondary data (34%), interviews (24%), surveys (17%), and statistical analyses (15%). These studies were affiliated with universities in the United States followed by the United Kingdom, but also a number of other countries, such as Canada, Sweden, Australia, and Tanzania.

Violence is the theme of 15% of the reviewed publications, often focusing on domestic violence and violence against women but some focusing on general “street violence” and other types of assaults in public places. Fear of crime was also a major theme with 57 publications, of which 78% were related to fear of crime in rural contexts, 10% to fear of “others,” and 12% to both or other related anxieties. Rural crime prevention also appeared in a notable number of the studies (10%), of which 46% covered police-based prevention and community efforts, 22% focused on the use of different types of technologies in situational crime prevention, such security alarms and CCTVs, and the rest covered both types. Within our time frame, the earliest study in this theme of crime prevention appeared in 1986 (Shernock 1986 ), while 41% of this research were published after 2015. The most used methods were surveys (37%), followed by secondary data (34%), and interviews and statistical analyses (22%). Crime prevention in the United States was studied the most, followed by Australia, Sweden, and Tanzania.

Another common subject was perceptions of safety and fear of crime in rural contexts, accounting for 14% ( n  = 59) of our reviewed publications. Fear in rural areas has been written about comparably longer than other themes, as more publications were published before 2010 than after. Surveys (53%) followed by interviews (22%) were the most used methods. Although the sample was dominated by the northern hemisphere, studies of safety in rural contexts are also found in Australia and New Zealand, as well as countries of the Global South including India, Mexico and Central America, Pakistan, and Turkey.

Rural crime prevention and interventions for improving safety was identified as a theme in 10% of the publications. The earliest study within our time frame appeared in 1986 (Shernock 1986 ), while 41% of the studies were published after 2015. The most frequently used methods were surveys (37%), followed by secondary data (34%), and interviews and statistical analyses (22%). Most publications focused on preventing crime in rural America, while Australia, Sweden, and Tanzania were present in more studies than the United Kingdom.

The international literature from 1980 to 2020 shows that the theme Environmental and Wildlife Crime (EWC) covered 7% of all the reviewed publications ( n  = 30). Research on environmental crime has mainly been conducted in the past decade, with 94% having been published in 2011–2020. Among the methods identified ( n  = 45), most publications utilized secondary data in their analysis (27%), followed by interviews (19%) as well as reviews of other research and other theoretical pieces (14%). In this theme, the United States is the most studied area (22%), followed by the United Kingdom and Sweden (19% each). Studies on the Global South include examples from Brazil, Indonesia, and Ghana.

Drugs in rural areas were part of 7% of the publications in the literature review. While there have been several comprehensive studies on drugs within rural criminology, findings have to some extent been limited to the context of the United States, providing little perspective on drug behavior, production or markets in rural areas in, for example, Europe or the Global South. Exceptions to this were a handful of studies from Australia, the United Kingdom, Canada, Sweden, Norway, Brazil, Peru, Bolivia, and Thailand. Many of the studies used interviews as their main method (37%), followed by secondary data analysis (33%), and use of statistical analyses such as regression models (26%). The most common topic was substance abuse, especially related to the rural youth, although in studies in the Global South it is more common to examine rural drug production.

Several studies on crime trends may have included data on property crime, but only 5% ( n  = 22) of the reviewed publications covered rural property crime as a distinct theme. Of these, close to 60% were published in the span of 2010-2020. Surveys were the most common method, used in 32% of the studies, while statistical analyses such as regression models were applied in 26% of the articles. The most common study areas were the United States and the United Kingdom, followed by Sweden, Australia, and Malaysia. Studies would often investigate rural rates of property crime overall, but specific types such as burglaries and farm-related thefts were also common.

Among the minor topics, we find hate crime, organized crime, as well as other more emergent topics within rural criminology. Hate crime has been a largely unstudied area in rural criminology with only 18 identified publications (4%) in our selected time period. Secondary data (19%) and interviews (15%) were the most frequent methods among the studies, followed by surveys and literature reviews (11% each). Notably, India and the United Kingdom were the two most studied areas, followed by Brazil and the United States.

Organized crime constituted a similar size as hate crime, with total of 4% ( n  = 17) of the reviewed publications. Roughly two-fifths (41.7%) of the publications used interviews as a method for data collection, while nearly one-fifth (19%) employed field work. The United States was the most frequent studied area, followed by British examples. Other studies were mainly situated in Latin America, including Peru, Brazil, Mexico, and Cuba.

Other topics that could not be classified into the other themes and those considered more emergent comprised 2% of the reviewed publications ( n  = 8). All of these were published in 2011–2020. This research was dominated by examples from the Global South, including India, Bangladesh, Ghana, and China. Here secondary data were utilized in 38% of the publications, with interviews and literature reviews in a quarter each. This theme included rare topics such as rural corruption (Banerjee et al. 2014 ; Cheng and Urpelainen 2019 ) and rural prostitution, as in Scott ( 2016 ).

In most of these studies, there is a recognition that criminology has for decades relied on urban understandings of rural crime and rural offenders. Some recently published studies have expanded the urban-centric framework, calling for theoretical and empirical models that can better explain the mechanisms behind crime in areas on the rural-urban continuum. This research has also focused on understandings of the intersections of demographic, ethnic, and socioeconomic factors; cultural contexts; and situational conditions that are typical to rural areas.

Research findings from multiple studies indicate that, over the years, crime has decreased overall in many parts of the world, but there are major variations between indicators and crime types. Historically, rural areas in most countries exhibited lower crime rates than urban areas (with a few exceptions), but lately rural areas are showing higher increases than (some) urban areas for certain types of crime. During the past decades, there have been signs that rural and urban crime rates are converging (urban decreasing and rural increasing), but crime underreporting and definitional, theoretical, and methodological difficulties in comparing crime rates across geographies still limit the analyses of crime trends (e.g. Ceccato, 2016 ). In quantitative studies, the implementation of “rural” as an analytical category can vary significantly, not only between countries but also between studies in a single country; see examples in the United States, Sweden, and Brazil in the next chapter.

In addition, the reviewed publications show how crime prevention programs have been urban-centric as well, meaning they are often imported from urban areas and directly applied in a way that ignores the uniqueness of rural contexts. Therefore, studies indicate that there is need for comparative analyses based on more than just rural-urban dichotomies, in particular in relation to crime prevention and police practices. A future approach that recognizes rural-urban interlinkages would further cement the complexities of rural areas without the need for comparisons with the urban norm, which may not be an appropriate reference in the first place.

A small set of the reviewed studies examine the spatial and temporal characteristics of rural crime. The literature has also identified some of the typical offenders in rural areas, as well as how deviant behavior may be normalized among the local population. There have been studies on how globalization, organized crime, new ideological trends, and ICT have influenced criminogenic conditions in the countryside (e.g., computer-based fraud, illegal animal rights activism, animal abuse, drugs, wage theft, slavery, racism).

Authors were most frequently affiliated with universities and colleges in the United States (41%), while British institutions came second, followed by Australian, Swedish, and Canadian universities. More recently, book chapters and articles have also been published by authors in and about the Global South, namely, India, Malaysia, Brazil, China, and a few African countries. Publications by female lead authors ( n female  = 143) were most common in the United States, Sweden, the United Kingdom, Australia, and Canada. Of the female lead authors not at universities and colleges in these five countries, most were in the Global South ( n female  = 26) rather than other countries in the Global North ( n female  = 7). Figure 3.2 shows the reviewed publications by (a) university affiliation (when the affiliation of the first author(s) could be identified) and by (b) study area.

( a ) Reviewed publications on rural crime and safety 1980-2020 by university affiliation (first author)Fig. 3.2 ( b ) Reviewed publications on rural crime and safety 1980-2020 by study area. Source: Based on Abraham and Ceccato (forthcoming)

In terms of methods, approximately half the reviewed publications utilized qualitative methods, a third quantitative methods, and the rest a mix of qualitative and quantitative methods (Fig. 3.3 ). Yet, studies showed major variations in methodology.

Methods utilized in the reviewed publications on rural crime and safety 1980–2020 ( N  = 410). Studies were assigned one or more methods. Source: Based on Abraham and Ceccato (forthcoming)

From 1980 to 2000, the number of publications grew slowly, but a major increase occurred after 2011. This review follows and complements the existent compilations of literature by Hubbard et al. ( 1980 ), by Marshall and Johnson ( 2005 ), and on rural policing by Tucker ( 2015 ), as these reviews were neither systematic (Higgins and Green 2011 ) nor comprehensive (see also Weisheit ( 2016 )). Interestingly, one of the earliest publications in our time frame was a compilation of North American literature on rural crime prevention and criminal justice (Hubbard et al. 1980 ) that reviewed also studies dating back to the early nineteenth century on rural-urban differences in crime and victimization. Also of note is an early article written by Laub ( 1981 ) which assessed the variation in crime reporting to the police among victims in urban, suburban, and rural areas. And one of the most recent articles from 2020 by Arisukwu et al. ( 2020 ) reported examples of informal crime prevention practices in rural Nigeria, which showed (via surveys) that poor safety perceptions are linked to crime victimization and poor police presence.

Different theoretical traditions characterize the studies over these four decades. The theme encompassing theory in rural criminology covers 7% of the reviewed publications (Fig. 3.1 ). These studies have expanded on definitions, concepts, and theoretical models. Half of the studies were published between 2015 and 2020, and were often written by authors in the United States and the United Kingdom but also in France, Slovenia, and Australia (Barclay 2017 ; Harris and Harkness 2016 ; Hodgkinson and Harkness 2020 ; Meško 2020 ; Mouhanna 2016 ).

Social disorganization theory is among the most common criminological approaches to explain rural crime. Although contemporary criminology associates social disorganization with urban areas and the Chicago School, the concept actually emerged from studies of rural Europe (for an in-depth discussion, see Rogers and Pridemore ( 2016 )). In its North American version, social disorganization theory suggests that structural disadvantage breeds crime and that offending occurs when impaired social bonds are insufficient to enforce legitimate behavior and discourage offending. The legacy of social disorganization (Bursik 1999 ; Kornhauser 1978 ; Sampson 1986 ; Shaw and McKay 1942 ) has been observed in many contexts. Population size, mobility and instability, income, unemployment, and degree of urbanization are conditions that have consistently been found to relate to crime (Allen and Cancino 2012 ; Barnett and Mencken 2002 ; Fafchamps and Minten 2006 ; Jobes 1999 ; Ukert et al. 2018 ). Although Jobes et al. ( 2004 ) found support for the theory in rural areas, economic factors showed weaker relationships with crime than social factors such as population diversity and family stability.

Other theories in environmental criminology also comprises an integral part of the reviewed rural crime literature, including applications of routine activity theory and situational crime prevention to rural areas in different countries (e.g., Aransiola and Ceccato 2020 ; Ceccato 2015 ; Harkness 2020 ; Harris and Harkness 2016 ). One example includes the analysis of environmental crime. While the first studies in this area were concerned with crime geography and prevention (e.g., Ceccato and Uittenbogaard 2013 ; Cowan et al. 2020 ; Maingi et al. 2012 ; Stassen and Ceccato 2020 ), new research of a more “tactical” nature is emerging, for example, detecting a place’s equivalent to a “fingerprint,” which is important information for crime investigation and prevention (Lega et al. 2014 ).

Finally, critical perspectives on rural criminology and the intersectionality of safety heavily dominated the past two decades of reviewed studies, with numerous contributions from North America, Australia, and the United Kingdom (Carrington et al. 2014 ; DeKeseredy et al. 2007 ; Donnermeyer 2007 , 2012 , 2017 , 2018 ; Donnermeyer and DeKeseredy 2013 ; Donnermeyer et al. 2013 ; Garland and Chakraborti 2006 ; Robinson and Gardner 2012 ; Rogers and Pridemore 2016 ; Smith and McElwee 2013 ; Somerville et al. 2015 ; Yarwood 2010 ; Yarwood and Edwards 1995 ) and also from Green Criminology (White 2013 ; Nurse and Whyatt 2020 ).

In summary, although studies in rural criminology have been testing a diverse set of theoretical traditions, in the next chapter we show examples of how the discipline is slowly expanding to theoretical perspectives beyond the boundaries of criminology. Examples include, for instance, studies involving engineering, computer science but also psychology, architecture, and geography. An example of this trend is analyzing how police and voluntary organizations use social media to engage communities in sparsely populated areas. In Chaps. 4 , 5 , 6 , and 7 , we draw attention to a selection of research fields on crime and safety in areas on the rural-urban continuum.

We adopted the systematic review protocol of type PRISMA-P 2015 (Moher et al. 2015 ) using a vast array of keywords; for more information about the methodology, see Abraham and Ceccato ( forthcoming ). We avoided studies dealing with emergency services overall and focused instead on the governance of safety issues, namely, the role of the police and policing. While we do include rural fears not solely based on crime but also on the “other,” we do not include, for example, farmers’ fear of GMO development, or safety perceptions in terms of fear and anxiety due to natural hazards such as hurricanes, and similar.

Abraham, J., & Ceccato, V. (forthcoming). Crime and safety in rural areas: A systematic review of the English-language literature 1980–2020. Journal of Rural Studies .

Google Scholar  

Allen, J., & Cancino, J. M. (2012, 2012/03/01/). Social disorganization, Latinos and juvenile crime in the Texas borderlands. Journal of Criminal Justice, 40 (2), 152–163. https://doi.org/10.1016/j.jcrimjus.2012.02.007

Aransiola, T. J., & Ceccato, V. (2020). The role of modern technology in rural situational crime prevention. A review of the literature. In Rural Crime Prevention (1st ed., pp. 58–72). Routledge. https://doi.org/10.4324/9780429460135-6

Arisukwu, O., Igbolekwu, C., Oye, J., Oyeyipo, E., Asamu, F., Rasak, B., & Oyekola, I. (2020, 2020/09/01/). Community participation in crime prevention and control in rural Nigeria. Heliyon, 6 (9), e05015. https://doi.org/10.1016/j.heliyon.2020.e05015

Banerjee, A., Green, D. P., McManus, J., & Pande, R. (2014). Are poor voters indifferent to whether elected leaders are criminal or corrupt? A vignette experiment in rural India. Political Communication, 31 (3), 391–407. https://doi.org/10.1080/10584609.2014.914615

Article   Google Scholar  

Barclay, E. M. (2017). Rural crime. In The Palgrave Handbook of Australian and New Zealand Criminology, Crime and Justice (pp. 285–297). https://doi.org/10.1007/978-3-319-55747-2_19

Barnett, C., & Mencken, F. C. (2002). Social disorganization theory and the contextual nature of crime in nonmetropolitan counties. Rural Sociology, 67 (3), 372–393. https://doi.org/10.1111/j.1549-0831.2002.tb00109.x

Brewis, A., Wutich, A., Galvin, M., & Lachaud, J. (2020, 2020/05/15/). Localizing syndemics: A comparative study of hunger, stigma, suffering, and crime exposure in three Haitian communities. Social Science & Medicine , 113031. https://doi.org/10.1016/j.socscimed.2020.113031

Bursik, R. J. (1999, 1999/02/01). The Informal Control of Crime Through Neighborhood Networks. Sociological Focus, 32 (1), 85–97. https://doi.org/10.1080/00380237.1999.10571125

Carrington, K., Donnermeyer, J. F., & DeKeseredy, W. S. (2014). Intersectionality, rural criminology, and re-imaging the boundaries of critical criminology. Critical Criminology, 22 , 463–477.

Ceccato, V. (2016) Rural crime and community safety. London and New York: Routledge.

Ceccato, V. (2015). Rural crime and community safety. Journal of Rural Studies, 39 , 157–159. https://doi.org/10.1016/j.jrurstud.2015.04.001

Ceccato, V., & Uittenbogaard, A. C. (2013). Environmental and Wildlife Crime in Sweden. International Journal of Rural Criminology, 2 (1), 23–50. http://kb.osu.edu/dspace/handle/1811/51122

Cheng, C. Y., & Urpelainen, J. (2019). Criminal Politicians and Socioeconomic Development: Evidence from Rural India. Studies in Comparative International Development, 54 (4), 501–527. https://doi.org/10.1007/s12116-019-09290-5

Cheong, T. S., & Wu, Y. (2015). Crime rates and inequality: a study of crime in contemporary China. Journal of the Asia Pacific Economy, 20 (2), 202–223. https://doi.org/10.1080/13547860.2014.964961

Cowan, D., Moreto, W. D., Burton, C., Nobles, M. R., & Singh, R. (2020, 2020/08/01). Applying Crime Pattern Theory and Risk Terrain Modeling to Examine Environmental Crime in Cambodia. Journal of Contemporary Criminal Justice, 36 (3), 327–350. https://doi.org/10.1177/1043986220923467

DeKeseredy, W. S., Donnermeyer, J. F., Schwartz, M. D., Tunnell, K., & Hall, M. (2007, 2007/12/01). Thinking Critically About Rural Gender Relations: Toward a Rural Masculinity Crisis/Male Peer Support Model of Separation/Divorce Sexual Assault. Critical Criminology, 15 (4), 295-311. https://doi.org/10.1007/s10612-007-9038-0

Donnermeyer, J. F. (2007). Rural Crime: Roots and Restoration. International journal of rural crime, 1 , 2–20. http://dx.doi.org/

Donnermeyer, J. F. (2012). Rural crime and critical criminology. In W. S. DeKeseredy & M. Dragiewicz (Eds.), Routledge Handbook of Critical Criminology . Routledge

Donnermeyer, J. F. (2017). The place of rural in a southern criminology. International Journal for Crime, Justice and Social Democracy, 6 (1), 118.

Donnermeyer, J. F. (2018). The rural dimensions of a southern criminology: Selected topics and general processes. In The Palgrave handbook of criminology and the global south (pp. 105–120). Springer.

Chapter   Google Scholar  

Donnermeyer, J. F., & DeKeseredy, W. S. (2013). Rural criminology . Routledge.

Book   Google Scholar  

Donnermeyer, J. F., Scott, J., & Carrington, K. (2013). How Rural Criminology Informs Critical Thinking in Criminology. International Journal for Crime, Justice and Social Democracy, 2 (3), 69–91.

Fafchamps, M., & Minten, B. (2006). Crime, transitory poverty, and isolation: Evidence from Madagascar. Economic Development and Cultural Change, 54 (3), 579–603. https://doi.org/10.1086/500028

Garland, J., & Chakraborti, N. (2006, June 1, 2006). ‘Race’, Space and Place: Examining Identity and Cultures of Exclusion in Rural England. Ethnicities, 6 (2), 159–177. https://doi.org/10.1177/1468796806063750

Harkness, A. (2020). Rural Crime Prevention: Theory, Tactics and Techniques .

Harris, B., & Harkness, A. (2016). Introduction: Locating Regional, Rural, and Remote Crime in Theoretical and Contemporary Context. In Locating crime in context and place: Perspectives on regional, rural and remote Australia . Federation Press.

Higgins, J. P. T., & Green, S. (2011). Cochrane Handbook for Systematic Reviews of Interventions http://handbook.cochrane.org/

Hodgkinson, T., & Harkness, A. (2020). Introduction: Rural crime prevention in theory and context. In Rural Crime Prevention (pp. 1–16). Routledge.

Hubbard, R. D., Horton, D. M., & Duncan, S. T. S. (1980). Rural crime and criminal justice: A Selected Bibliography . https://www.ojp.gov/pdffiles1/Digitization/69221NCJRS.pdf

Jobes, P. C. (1999). Residential Stability and Crime in Small Rural Agricultural and Recreational Towns. Sociological Perspectives, 42 (3), 499–524. https://doi.org/10.2307/1389700

Jobes, P. C., Barclay, E., Weinand, H., & Donnermeyer, J. F. (2004). A structural analysis of social disorganisation and crime in rural communities in Australia [Review]. Australian and New Zealand Journal of Criminology, 37 (1), 114–140. https://doi.org/10.1375/acri.37.1.114

Kornhauser, R. (1978). Social sources of delinquency . University of Chicago Press.

Laub, J. H. (1981, 1981/01/01/). Ecological considerations in victim reporting to the police. Journal of Criminal Justice, 9 (6), 419–430. https://doi.org/10.1016/0047-2352(81)90088-X

Lega, M., Ferrara, C., Persechino, G., & Bishop, P. (2014, 2014/12/01). Remote sensing in environmental police investigations: aerial platforms and an innovative application of thermography to detect several illegal activities. Environmental Monitoring and Assessment, 186 (12), 8291–8301. https://doi.org/10.1007/s10661-014-4003-3

Mafumbabete, C., Chivhenge, E., Museva, T., Zingi, G. K., & Ndongwe, M. R. (2019). Mapping the spatial variations in crime in rural Zimbabwe using geographic information systems. Cogent Social Sciences, 5 (1), Article 1661606. https://doi.org/10.1080/23311886.2019.1661606

Maingi, J. K., Mukeka, J. M., Kyale, D. M., & Muasya, R. M. (2012). Spatiotemporal patterns of elephant poaching in south-eastern Kenya. Wildlife Research, 39 (3), 234–249. https://doi.org/10.1071/WR11017

Marshall, B., & Johnson, S. (2005). Crime in rural areas: A review of the literature for the rural evidence research centre .

Meško, G. (2020). Rural criminology–A challenge for the future. European journal of crime, criminal law and criminal justice, 28 (1), 3–13.

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA<br>Statement. PLoS Med, 6 (7), Article e1000097. https://doi.org/10.1371/journal.pmed1000097 .

Moher, D., Shamseer, L., Clarke, M., et al. (2015). Preferred reporting items for systematic review and meta-analysis protocol (PRISMA -P) 2015 statement. Syst Rev 4 (1). https://doi.org/10.1186/2046-4053-4-1 .

Mouhanna, C. (2016). From myth to myth: Rural criminology in France. In The routledge international handbook of rural criminology (pp. 65–73). Routledge.

Nurse, A., & Wyatt, T. (2020). Wildlife criminology. Bristol University Press.

Osakwe, E., & Osakwe, M. (2015). Rural decay, resource control, and the dynamics of youth unemployment and crime in Nigeria. International Journal of Interdisciplinary Global Studies, 10 (4), 1–14. https://doi.org/10.18848/2324-755X/CGP/v10i04/53386

Payne, B. K., Berg, B. L., & Sun, I. Y. (2005, 2005/01/01/). Policing in small town America: Dogs, drunks, disorder, and dysfunction. Journal of Criminal Justice, 33 (1), 31-41. https://doi.org/10.1016/j.jcrimjus.2004.10.006

Robinson, V., & Gardner, G. (2012). Unravelling a stereotype: The lived experience of black and minority ethnic in rural Wales. In N. Chakraborti & J. Garland (Eds.), (pp. 85–107). Routledge.

Rogers, E., & Pridemore, W. A. (2016). Research on social disorganization theory and crime in rural communities. In The Routledge international handbook of rural criminology (pp. 23–31). Routledge.

Sampson, R. J. (1986). Crime in cities: the Effects of formal and informal social control. Crime and Justice, 8 , 271–311.

Scorzafave, L. G., Justus, M., & Shikida, P. F. A. (2015, 2015/06/01/). Safety in the global south: Criminal victimization in Brazilian rural areas. Journal of Rural Studies, 39 , 247–261. https://doi.org/10.1016/j.jrurstud.2014.12.002

Scott, J. (2016). Rural prostitution. In J. F. Donnermeyer (Ed.), The Routledge International Handbook of Rural Criminology (pp. 75–82). Routledge.

Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas . University of Chicago Press.

Shernock, S. K. (1986, 1986/01/01/). A profile of the citizen crime prevention activist. Journal of Criminal Justice, 14 (3), 211–228. https://doi.org/10.1016/0047-2352(86)90002-4

Sims, V. H. (1988). Small town and Rural Police. In. C. C. Thomas.

Smith, R., & McElwee, G. (2013). Confronting Social Constructions of Rural Criminality: A Case Story on ‘Illegal Pluriactivity’ in the Farming Community. Sociologia Ruralis, 53 (1), 112-134. https://doi.org/10.1111/j.1467-9523.2012.00580.x

Somerville, P., Smith, R., & McElwee, G. (2015, 2015/06/01/). The dark side of the rural idyll: Stories of illegal/illicit economic activity in the UK countryside. Journal of Rural Studies, 39 , 219–228. https://doi.org/10.1016/j.jrurstud.2014.12.001

Stassen, R., & Ceccato, V. (2020). Environmental and Wildlife Crime (EWC) in Sweden 2000-2017. Journal of Contemporary Criminal Justice .

Tucker, R. D. (2015). R ural crime and policing: Literature Review . http://www.dyfedpowys-pcc.org.uk/media/2694/ruralcrimeandpolicingliteraturereview2015.pdf

Ukert, B., Wiebe, D. J., & Humphreys, D. K. (2018, 2018/10/01/). Regional differences in the impact of the “Stand Your Ground” law in Florida. Preventive Medicine, 115 , 68–75. https://doi.org/10.1016/j.ypmed.2018.08.010

Weisheit, R., Edward, L., & Falcone, D. N. (1995). Crime and Policing in Rural and Small-Town America . Waveland Pr Inc.

Weisheit, R. A. (2016). Rural Crime: A Global Perspective. International Journal of Rural Criminology, 3 (1), 23.

White, R. (2013). The conceptual contours of green criminology. In Emerging issues in green criminology (pp. 17–33). Palgrave Macmillan, London.

Yarwood, R. (2010, 2010/03/01). An exclusive countryside? Crime concern, social exclusion and community policing in two English villages. Policing and Society, 20 (1), 61–78. https://doi.org/10.1080/10439461003611500

Yarwood, R., & Edwards, B. (1995). Voluntary Action in Rural Areas: the Case of Neighbourhood Watch. Journal of Rural Studies, 11 (4), 447–459. https://doi.org/10.1016/0743-0167(95)00030-5

Download references

Author information

Authors and affiliations.

Department of Urban Planning and Environment, KTH Royal Institute of Technology, Stockholm, Sweden

Vania Ceccato & Jonatan Abraham

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2022 The Author(s)

About this chapter

Ceccato, V., Abraham, J. (2022). Current Knowledge on Crime and Safety in Rural Areas. In: Crime and Safety in the Rural. SpringerBriefs in Criminology. Springer, Cham. https://doi.org/10.1007/978-3-030-98290-4_3

Download citation

DOI : https://doi.org/10.1007/978-3-030-98290-4_3

Published : 23 June 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-98289-8

Online ISBN : 978-3-030-98290-4

eBook Packages : Law and Criminology Law and Criminology (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research