Can we Trust Crime Predictors and Crime Categories? Expansions on the Potential Problem of Generalization

Abstract

City-driven open data initiatives have made spatially referenced crime and risk factor data more readily available online, allowing for significance tests to determine the relationship between environment and crime. This paper uses a variety of open source data to assess risk factors for specific violent crime types (assault, homicide, rape, robbery) in three different cities. The results contribute to our understanding of 1) variation in intra-city risk factors for each violent crime type, 2) the degree of spatial overlap for high-risk places for each violent crime type within a city, and 3) the generalizability of risk factors across crime types and cities. Risk Terrain Modeling (RTM) was used to determine the risk factors associated with each crime type at the micro-level and conjunctive analyses of case configurations (CACC) determined the unique behavior settings at the highest risk for each specific violent crime in each city. The findings indicate that intra-city risk factors vary greatly for each violent crime, highrisk places for different violent crimes tend to not overlap spatially within a city, and risk factors are not generalizable across crime types or across cities. Researchers and law enforcement need to examine local, crime-specific contexts when assessing crime problems and generating solutions.

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Notes

  1. 1.

    A more complete discussion of RTM, including the associated methods and relevant literature, will be highlighted in “Risk Terrain Modeling” section.

  2. 2.

    There are three primary reasons these cities were selected for the present analysis. The first reason was data availability, since this analysis was comprised largely of publicly available data each respective city had to maintain an updated and robust open data portal. Second, the similarities in population size and recent city growth make them more readily amenable for comparison. We often use population size as a proxy measure for general similarity in city-level research. Third, the differing regions rules out the cities having a mutual influence on one another’s functioning that may introduce issues of collinearity. Cities that are geographically closer together may create dependent outcomes.

  3. 3.

    Airport boundaries were included for Denver and Indianapolis because these airports are policed by designated units within the local police department. The crime data obtained from the study is from the same agency that polices the airport for these two cities. In DC, the airport falls outside the boundaries of DC and is not policed by the DC police department (it is policed by the Metropolitan Washington Airport Authority). Resultantly, the airport and its crime data were not included in the DC analyses.

  4. 4.

    http://data.indy.gov/

  5. 5.

    https://www.arcgis.com/home/index.html

  6. 6.

    https://www.google.com/maps/about/mymaps/

  7. 7.

    https://www.denvergov.org/opendata

  8. 8.

    The Infogroup risk factors included in the study were: ATM’s, banks, bars, check-cashing/payday loans, liquor stores, nightclubs/lounges, and restaurants.

  9. 9.

    http://opendata.D.C.gov/

  10. 10.

    The only discrepancy in the risk factor data is the use of three semi-interchangeable proxy measures. Abandoned/foreclosed buildings were used for Indianapolis, the Denver data was limited to foreclosures only, and the Washington, D.C. analysis only included buildings listed as vacant/abandoned.

  11. 11.

    Hotels and motels were operationalized as separate constructs due to the differences in design, with hotels often having more floors and rooms whereas motels generally have one to two floors and may provide longer term living options and often are characterized by lower rates.

  12. 12.

    The result presentation for the intra-city risk factor variation assessments is largely a-contextual due to the primary aim of the study. The present study is interested in determining if the same risk factors are significant when changing the crime type and the city as opposed to delving further into why each risk factor is significant and what the potential relationship between the risk factor and the outcome might be. Future research should examine some of the potential explanations regarding why certain risk factors only influence specific crime types or specific cities, and should strive to create risk narratives (Caplan and Kennedy 2016).

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Correspondence to Nathan T. Connealy.

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Connealy, N.T. Can we Trust Crime Predictors and Crime Categories? Expansions on the Potential Problem of Generalization. Appl. Spatial Analysis 13, 669–692 (2020). https://doi.org/10.1007/s12061-019-09323-5

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Keywords

  • Risk terrain modeling
  • Crime generators and attractors
  • Conjunctive analysis
  • Violent crime
  • Situational crime prevention