Michael Leitner, Philip Glasner, and Ourania Kounadi
The most prominent law in geography is Tobler’s first law (TFL) of geography, which states that “everything is related to everything else, but near things are more related than distant things.” No other law in geography has received more attention than TFL. It is important because many spatial statistical methods have been developed since its publication and, especially since the advent of geographic information system (GIS) and geospatial technology, have been conceptually based on it. These methods include global and local indicators of spatial autocorrelation (SA), spatial and spatial-temporal hotspots and cold spots, and spatial interpolation. All of these are highly relevant to spatial crime analysis, modeling, and mapping and will be discussed in the main part of this text.
The Housing Choice Voucher (HCV) Program is the largest housing subsidy program in the United States, serving over 2.2 million households. Through the program, local public housing authorities (PHAs) provide funds to landlords on behalf of participating households, covering a portion of the household’s rent. Given the reliance on the private market, there are typically many more locational options for HCV households than for traditional public housing, which has a set (and declining) number of units and locations. The growth of this program has been robust in recent decades, adding nearly 1 million vouchers in the last 25 years. This has been a deliberate attempt to move away from the traditional public housing model toward one that emphasizes choice and a diversity of location outcomes through the HCV program.
There are many reasons for these policy and programmatic shifts, but one is undoubtedly the high crime rates that came to be the norm in and near far too many public housing developments. During the mid-20th century, when the vast majority of public housing units were created, they were frequently sited in undesirable areas that offered few amenities and contained high proportions of low-income and minority households. As poverty further concentrated in central cities due to the flight of higher-income (often white) households to the suburbs, many public housing developments became increasingly dangerous places to live. The physical design of public housing developments was also frequently problematic, with entire city blocks being taken up by large high-rises set back from the street, standing out as areas to avoid within their neighborhoods.
There are many quantitative summaries and anecdotal descriptions of the crime and violence present in some public housing developments from sources as diverse as journalists, housing researchers, and architects. Now that the shift to housing vouchers (and the low-income housing tax credit [LIHTC]) has been underway for over two decades, we have a good understanding of how effective these changes have been in reducing exposure to crime for subsidized households. Further, we are beginning to better understand the limitations of these efforts and why households are often unsuccessful in moving from high-crime areas.
In studies of moving housing voucher households away from crime, the following questions are of particular interest: What is the connection between subsidized housing and crime? What mechanisms of the housing voucher program work to allow households to live in lower-crime neighborhoods than public housing? And finally, how successful has this program been in reducing participant exposure to crime, and how do we explain some of the limitations?
While many aspects of the relationship between subsidized housing and crime are not well understood, existing research provides several important insights. First, we can conclude that traditional public housing—particularly large public housing developments—often concentrated crime to dangerously high levels. Second, we know that public housing residents commonly expressed great concern over the presence of crime and drugs in their communities, and this was a frequent motivation for participating in early studies of housing mobility programs such as Gautreaux in Chicago and the Moving to Opportunity experiment. Third, while the typical housing voucher household lives in a lower-crime environment than public housing households, they still live in relatively high-crime neighborhoods, and there is substantial research on the limited nature of moves using vouchers. Finally, while there is research on whether voucher households cause crime in the aggregate, the outcomes are rather ambiguous—some rigorous studies have found that clusters of voucher households increase neighborhood crime and some have found there is no effect. Furthermore, any potential effects on neighborhood crime by vouchers need to be weighed against their effectiveness at reducing exposure to neighborhood crime among subsidized households.
Nick Malleson, Alison Heppenstall, and Andrew Crooks
Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling.
Graham C. Ousey
Immigration and gentrification are two sources of population change that occur in geographic communities. Immigration refers to the inflow of foreign-born residents, while gentrification involves middle- and upper-income residents moving into poor urban communities. Scholars have speculated that both types of population change may be related to crime rates. The nature of those relationships, however, is debated. Classic criminological perspectives, such as Social Disorganization Theory, suggest that these population changes are likely to result in increased crime rates. More recent perspectives proffer an opposing viewpoint: that immigration and gentrification may lower crime rates. Some research suggests that these opposing arguments may each draw empirical support but under differing social conditions or circumstances. Regarding the effects of immigration on crime, one theory is that immigration is most likely to be a crime-protective factor when it occurs in places where there is a well-established immigrant population base and institutional supports. And immigration may contribute to higher crime rates in places that lack the strong preexisting immigrant population and institutions. Study design variations also appear to impact the findings that researchers investigating the immigration-crime relationship have found, with longitudinal studies more consistently reporting the immigration works to reduce (rather than increase) crime. With respect to gentrification, scholars suggest that its effects on crime are likely to hinge on factors such as the racial composition of the place, the timing or stage at which the gentrification process is observed, or the degree to which gentrifying neighborhoods are surrounded by poor non-gentrifying neighborhoods or by other communities that have already progressed through the gentrification process.