Reducing crime by shaping the built environment with zoning: an empirical study of Los Angeles.

• Author: Anderson, James M.
• Position: III. Empirical Analysis of the Effect of Land Use Law on the Built Environment and Crime through Conclusion, with footnotes, p. 727-756

3. EMPIRICAL ANALYSIS OF THE EFFECT OF LAND USE LAW ON THE BUILT ENVIRONMENT AND CRIME

   The core research question in this Article is whether there exists a relationship between land use zoning and crime, and if so, whether it is observed through visible differences in the built environment. In this Part, we attempt to answer this question in three steps. First, we examine the association between crime and the primary zoning of parcels of land on street blocks within the same geographic areas of the city. Second, we examine the associations between the primary zoning of parcels of land on street blocks and physical order maintenance, territoriality, natural surveillance, and "walkability." Finally, we examine whether the observed association between land use zoning of parcels and crime is mediated by differences in the built environment. This will help ascertain whether land use law affects crime through the built environment or whether some other causal mechanism is at work.

   Our general strategy in this study is to compare the crime rates on and around street blocks with different types of parcel zoning but within the same geographic areas in Los Angeles. Block-to-block comparisons in the same sections of the city reduce the risk that unobserved confounding variables (e.g., demographic differences) will bias the results. (158) While this method helps control for many potential confounding variables, it is possible that some unobserved variables remain that are correlated with both zoning and crime. We are, however, unable to identify any particularly plausible candidates that would affect both crime and zoning at a block-by-block level of analysis within the same sections of a city having a comparable demographic makeup. Zoning changes may be requested by property owners, or as part of a comprehensive redevelopment project, but they require considerable effort and are relatively uncommon. (159) Because of the relative infrequency of zoning changes, the likelihood of reverse causation is low (i.e., crime causing zoning changes).

   One alternative avenue of research, which we did not pursue, involves examining the actual land use rather than the zoning classification of parcels. We decided to use zoning classifications for several reasons. First, changing the zoning of a parcel is easier than changing its actual use. Policymakers can, for example, change zoning by passing a new ordinance. Our focus on zoning thus acknowledges it as a tool within the reach of policymakers. Second, zoning evolves more slowly over time than does land use. Zoning is, therefore, less likely to be affected by crime than actual land use, making our inference stronger. If we examined the effect of actual land use on crime, it would be more difficult to determine whether land use affects crime, or whether crime affects changes in land use.

   1. Sample Construction

      For our study, we selected 205 face blocks (160) in eight Community Plan Areas in Los Angeles. (161) Table 1 below displays the number of blocks in each area. We chose these areas because they have medium or high levels of crime relative to other sections of the city. (162) The 205 blocks were selected based on the following criteria: (1) primary and mix of parcel zoning on observed block; (2) primary and mix of parcel zoning on nearby blocks; (3) proximity to parks, schools, Metro (rail) stations, and freeway on-ramps; and (4) geographical severance of block face by roadway (freeway). (163)

      Our empirical strategy measures the association between zoning and reported crime by comparing the crime rates among blocks within the same areas having different primary forms of parcel zoning. To confirm that differently zoned blocks within the same area did not differ systematically on key variables, we compared data from the 2000 Census (164) for mixed- and single-zoned blocks. Specifically, we compared these blocks using the following variables: (1) percentage of African American residents, (2) percentage of Hispanic residents, (3) percentage of male residents under the age of twenty-five, (4) percentage of employed residents, (5) average housing tenure, (165) and (6) percentage of families receiving welfare. These variables are widely used measures of social disadvantage that are correlated with neighborhood crime rates. (166)

      Table 2 below reveals substantial variability across the eight areas of Los Angeles on the census measures. More importantly, however, Table 2 shows considerable similarity across blocks within each area--regardless of zoning classification. (167) Within areas, there are no meaningful differences between mixed zoning, on the one hand, and commercial and residential zoning, on the other, relative to the percentage of African American residents, the percentage of Hispanic residents, the percentage of males under twenty-five, the percentage of employed residents, and average housing tenure. (168) For the percentage of families receiving welfare, only one area showed any difference across zoning classifications: in Southeast Los Angeles, the percentage of families receiving welfare is lower in mixed-zoned blocks (17.7%) than in other zoned blocks (24%). Even this difference, however, is not particularly large. These findings provide some assurance that differently zoned blocks within each area have similar population attributes.

   2. Data

      We constructed a data set containing data of three types: land use data on zoning, crime data at the street block-level, and observed built-environment data collected by field researchers.

      1. Land Use Data

         The land use zoning classifications for all parcels included in the study's 205 blocks were collected from publicly accessible data from the City of Los Angeles. (170) We employ two different classification schemes for land use.

         First, we classify blocks into four broad categories: residential (n = 122 blocks), commercial (n = 22), mixed-use (residential and commercial parcel zoning) (n = 57), and manufacturing (n = 4). Each block was classified based on the two most common parcel classifications on the block.

         Second, we use an alternative set of categories that is more sensitive to different kinds of residential and commercial usage. This second formulation uses the following categories: multipurpose commercial zoning (containing more than a single kind of commercial zone) (n = 21), single-purpose commercial/industrial zoning (n = 19), single purpose residential zoning (n = 99), and mixed-purpose zoning (containing both residential and commercial zones) (n = 66). A simple way to conceptualize the distinction between multipurpose commercial and mixed-purpose blocks is that multipurpose commercial blocks have only commercial zoning, whereas mixed-purpose blocks have either residential zoning mixed in with commercial zoning or multiple forms of residential zoning. Given that many cities encourage mixed-purpose zoning plans, it is useful to compare multiple forms of residential zoning. One can easily imagine, for example, a zoning plan that allows mixed-purpose uses for residential and commercial zoning, but does not allow exclusionary zoning for a single residential or commercial purpose.

      2. Crime Data

         Crime data were extracted from the Los Angeles Times's open-source data on crime as reported by the Los Angeles Police Department (LAPD) and the Los Angeles Sheriffs Department (LASD). (171) The number and types of reported crimes within 100 and 250 meters of each of the 205 blocks in the study were collected for a six-month interval. (172) Our analyses examine total crime, (173) as well as assaults, burglaries, robberies, and automobile break-ins, because these offenses are most likely to occur in public view and to be influenced by characteristics of the built environment. (174)

      3. Built Environment Data

         Data on the built environment of blocks in the study were collected using systematic social observation. We modified and applied data collection instruments that were developed in a previous study of Los Angeles neighborhoods, (175) Field observers examined blocks in the study over a twelve-week period, from August 19, 2010, to November 13, 2010. (176)

         Following the prior scholarly work on the relationship between the built environment and crime, (177) our systematic observation collected data to construct six measures of the built environment. First, we measured the general condition of a block, which included observers' overall assessment of the condition of street surfaces for driving, the condition of the sidewalks for walking, and the condition of residential, commercial, and industrial buildings. Second, we measured physical disorder through the presence of garbage, litter, and broken glass; signs of illegal drug activity; cigarette butts; feces or urine; and graffiti. Third, we measured territoriality by recording the presence of well-tended yards, flowerboxes or planters, and flags on properties. Fourth, following Jacobs's work on the potential for "eyes on the street" (178) to reduce crime, we measured natural surveillance: observers estimated the number of buildings standing more than fifteen feet from the street and recorded the presence of front porches, driveways, and buildings without windows. (179) Fifth, we captured data on the presence of establishments that attract crime. We included a sum of binary variables measuring the presence of bars, pawnshops, check cashing businesses, liquor stores, and convenience stores on each block. (180) Sixth, we measured the walkability of each block. Walkability may affect crime, either by increasing street traffic and "eyes on the street" or by increasing the number of potential victims of crime. We therefore obtained "Walk Score" data, based on the proximity of nearby amenities that decrease the need for personal automobiles, such as grocery stores, coffee shops, and public transit. (181)

         We computed each measure of the built environment by summing its individual items...