Street Egohood: An Alternative Perspective of Measuring Neighborhood and Spatial Patterns of Crime

Abstract

Objectives

The current study proposes an approach that accounts for the importance of streets while at the same time accounting for the overlapping spatial nature of social and physical environments captured by the egohood approach. Our approach utilizes overlapping clusters of streets based on the street network distance, which we term street egohoods.

Methods

We used the street segment as a base unit and employed two strategies in clustering the street segments: (1) based on the First Order Queen Contiguity; and (2) based on the street network distance considering physical barriers. We utilized our approaches for measuring ecological factors and estimated crime rates in the Los Angeles metropolitan area.

Results

We found that whereas certain socio-demographics, land use, and business employee measures show stronger relationships with crime when measured at the smaller street based unit, a number of them actually exhibited stronger relationships when measured using our larger street egohoods. We compared the results for our three-sized street egohoods to street segments and two sizes of block egohoods proposed by Hipp and Boessen (Criminology 51(2):287–327, 2013) and found that two egohood strategies essentially are not different at the quarter mile egohood level but this similarity appears lower when looking at the half mile egohood level. Also, the street egohood models are a better fit for predicting violent and property crime compared to the block egohood models.

Conclusions

A primary contribution of the current study is to develop and propose a new perspective of measuring neighborhood based on urban streets. We empirically demonstrated that whereas certain socio-demographic measures show the strongest relationship with crime when measured at the micro geographic unit of street segments, a number of them actually exhibited the strongest relationship when measured using our larger street egohoods. We hope future research can use egohoods to expand understanding of neighborhoods and crime.

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Notes

  1. 1.

    For crime incident data with exact street address, Southern California Crime Study (SCCS) researchers geocoded crime incidents to longitude and latitude points using the address information. If 100 block addresses rather than exact street addresses were provided, SCCS geocoded at the random street addresses within the 100 block, which is essentially similar to geocoding to the center of the 100 block. We believe that this process should not affect to the aggregation to the various units because our base unit is the street segment which is identical to 100 block in the geocoding process.

  2. 2.

    We decided to use one factor that had an eigenvalue of 1.00 or higher. Eigenvalues of the concentrated disadvantage index across all units are greater than 2.

  3. 3.

    We include population as an offset variable (with coefficient constrained to 1) and also include it in the model. This is a straightforward way to handle to possibility that population does not have a 1:1 relationship with crime (which is the assumption when creating crime rates). We prefer our approach as the provided t test from Stata 13 is for the difference from 1 (which is a reasonable test, given the common assumption of a 1:1 relationship with crime in crime rates), rather than testing whether an estimate is different than zero (which would assume no relationship with population, which is a less interesting test).

  4. 4.

    McFadden’s pseudo R-squared is calculated as 1 − (log likelihood value for the fitted model/log likelihood of the null model). Values closer to 1 indicate better model fit, while conversely closer to 0 suggest less predictive ability.

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Appendix

Appendix

See Tables 8 and 9.

Table 8 Negative binomial models of various street based units (standardized coefficients)—property crime
Table 9 Negative binomial models of various street based units (standardized coefficients)—violent crime

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Kim, Y., Hipp, J.R. Street Egohood: An Alternative Perspective of Measuring Neighborhood and Spatial Patterns of Crime. J Quant Criminol 36, 29–66 (2020). https://doi.org/10.1007/s10940-019-09410-3

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Keywords

  • Streets
  • Neighborhoods
  • Level of aggregation
  • Units of analysis
  • Egohood
  • Crime