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Examining the Relationship Between the Structural Characteristics of Place and Crime by Imputing Census Block Data in Street Segments: Is the Pain Worth the Gain?

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Abstract

Objective

The current study proposes unique methods for apportioning existing census data in blocks to street segments and examines the effects of structural characteristics of street segments on crime. Also, this study tests if the effects of structural characteristics of street segments are similar with or distinct from those of blocks.

Methods

This study compiled a unique dataset in which block-level structural characteristics are apportioned to street segments utilizing the 2010 U.S. Census data of the cities of Anaheim, Santa Ana, and Huntington Beach in Orange County, California. Negative binomial regression models predicting crime that include measures of social disorganization and criminal opportunities in street segments and blocks were estimated.

Results

The results show that whereas some of the coefficients tested at the street segment level are similar to those aggregated to blocks, a few were quite different (most notably, racial/ethnic heterogeneity). Additional analyses confirm that the imputation methods are generally valid compared to data actually collected at the street segment level.

Conclusions

The results from the street segment models suggest that the structural characteristics from social disorganization and criminal opportunities theories at street segments may operate as crucial settings for crime. Also the results indicate that structural characteristics have generally similar effects on crime in street segments and blocks, yet have some distinct effects at the street segment level that may not be observable when looking at the block level. Such differences underscore the necessity of serious consideration of the issues of level of aggregation and unit of analysis when examining the structural characteristics-crime nexus.

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Notes

  1. This study excluded Amtrak/rail lines, shorelines, rivers, private streets, and highways. All other types of streets having address ranges in which addresses can be geocoded were included. There exist random breaks that often divide long street segments in Census TIGER line shape files. To deal with this issue, I dissolved the lines by the street names to remove all the random breaks before splitting the streets at intersections to get the street segments. A street segment is defined as a street from true intersection to intersection or when a street changes names without a true intersection. Although random breaks were removed by dissolving the street centerline file using street name, this process would not be a major concern for the data and analysis because for the most of cases, street names change when crossing intersections, which means it is very unusual that a street name just changes in the middle of a street.

  2. The standard width of local streets in Orange County ranges from 40-56 feet, roadway shall be 50 feet, and parkway may be reduced to 5 feet.

  3. For both of SA and SWA methods, each block was used multiple times to impute some of its characteristics to the street segments. For example, as shown in Fig. 1, a typical block (Block A) is associated with four street segments (a, b, c, and d) that border on the block. Therefore, in this case, the block was used four times for data imputation to the street segments. For the SWA method, some residential parcels (about 10 % of total 32,851), mostly those at the corners of the blocks, were double counted. Models using data in which the parcels are not inflated in the totals (spatially joining the parcels to the nearest street segment) were run and the results showed that the double-counting of parcels does not affect the overall findings.

  4. Block-level estimates of housing values were not obtained by joining points to streets and then aggregating the street segment level data to blocks. Point-level data of home values were spatially joined to the street segments and blocks respectively.

  5. Geocoding was done in ArcGIS 10.2 using a specific geocoding locator for Orange County using 2013 Census TIGER line shape file. The geocoding locater used the following parameters: spelling sensitivity = 75, minimum candidate score = 10, minimum match score = 10, side offset = 0, end offset 1 %, and Match if candidates tie = no. I used MapQuest open geocoding service and Google Earth Pro to geocode not matched incidents after the geocoding process using ArcGIS 10.2. Geocoding hit rates for crime incidents are 96.8 % (6038 of 5845) for Santa Ana, 96.8 % (9315 of 9621) for Anaheim, and 94.0 % (4725 of 5025) for Huntington Beach.

  6. Census data provide only the percent single-parent households variable at block level. To have other variables for the concentrated disadvantage measure, I used an ecological inference technique. The variables used in the imputation model were: percent owners, racial composition, percent divorced households, percent households with children, percent vacant units, population density, and age structure (percent aged: 0–4, 5–14, 15–19, 20–24, 25–29, 30–44, 45–64, 65 and up). See (Kubrin and Hipp 2014).

  7. To test whether the independent variables have similar or different relationships across 3 cities, I estimated separate models for each city employing three different aggregation strategies (SA, SWA, and Block). I present the results in Appendix Table 8, 9, and 10. The results show that the social disorganization components (i.e., concentrated disadvantage, average length of residence, and percent occupied) and the criminal opportunities measures (e.g., number of retail employees) have similar effects on crime at both of the street segment and block levels. The findings suggest that the effects of independent variables on crime are generally similar across the three cities. To systematically assess the difference of coefficients across three cities, I performed a series of joint tests on pairs of the cities. For example, I estimated a model with just Anaheim and Santa Ana, and included a dummy variable for one city (e.g., Anaheim) and included interactions between this city dummy variable and all other variables in the model. Given the large sample size and thus statistical power, I employ an information criterion rather than a \(\chi^{2}\) test. For all models, the Akaike Information Criterion (AIC) values were higher when allowing the coefficients to differ across the two cities compared to the model constraining them to be equal, suggesting that there are not systematic differences in the coefficients across cities. Thus, the three-city-pooled models presented in the current study are appropriate.

  8. To empirically check whether distance matters for the spatially lagged independent variables, I ran the models with 0.5 mile buffer measures. I found no substantial difference between the models with 0.25 mile buffer measures and those with 0.5 mile buffers.

  9. Including spatially lagged independent variables in the models is a conventional and valid way to account for spatial effects if theoretically justified. Anselin (2002) stated that it “does not require specialized estimation methods and ordinary least squares remains unbiased” (p.251). Florax and Folmer (1992) argued that omission of spatially lagged independent variables is an important cause for spatially correlated residuals. They empirically tested and revealed that the spatially dependent residuals can be remedied by incorporating the omitted spatially lagged predictor variables into the model. Many studies in the field address spatial dependence by including spatially lagged exogenous variables (Anselin 2003; Bernasco and Block 2011; Elffers 2003; Haberman and Ratcliffe 2015; Hipp 2010; Kubrin and Hipp 2014; Morenoff 2003; Sampson et al. 1999; Wo 2014; Wo and Boessen 2016). I follow the lead of these previous studies by including spatially lagged independent variables to account for spatial effects.

  10. Although the Moran’s I values of residuals in all models were very small, the presence of spatial autocorrelation can affect the standard errors in the models. Nevertheless, ignoring correlated spatial errors will still yield consistent coefficient estimates (Anselin 1988: 59).

  11. The lower correlations of percent black and percent occupied units are possibly the results of a “small numbers effect” impacting percentages. To check this, I restricted the data by dropping the extremely low values. I computed the correlations after excluding cases beyond the extreme 1 % of the distribution of each measure. In terms of percent occupied units, the correlation between SA and SWA increased from 0.64 to 0.90, and that of SWA and Block slightly increased from 0.50 to 0.66, while the correlation of SWA-Block remained unchanged. When it comes to the percent black measure, the correlations remained stable. Thus, it is somewhat true that the lower correlations are simply because of a small numbers effect (i.e., the result of percent occupied units between SWA and SA), but it still implies that the relationships between these measures and crime may differ for street segments compared to blocks.

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  1. Department of Criminology, Law and Society, University of California - Irvine, 3331 Social Ecology II, Irvine, CA, 92697-7080, USA

    Young-An Kim

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Correspondence to Young-An Kim.

Appendix

Appendix

See Figs. 3, 4 and 5, Tables 8, 9, 10, 11, 12.

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Kim, YA. Examining the Relationship Between the Structural Characteristics of Place and Crime by Imputing Census Block Data in Street Segments: Is the Pain Worth the Gain?. J Quant Criminol 34, 67–110 (2018). https://doi.org/10.1007/s10940-016-9323-8

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