To examine the influence of street block slope on robbery in Cincinnati, Ohio.
Data visualizations were used to examine how street block slope varies across the city. Negative binomial regression models were used to estimate the influence of street block slope on robbery net of betweenness, facility composition, and socio-demographics.
A 1% increase in street block slope was associated with roughly 4.5% fewer street block robberies per foot of street block length. Street blocks with a higher expected usage potential, measured via betweenness, were also observed to have higher expected robbery levels. In addition, numerous facilities and neighborhood socio-demographic characteristics linked to higher robbery levels.
Steeper street blocks may have fewer robberies because they make the physical costs for committing robberies too high, are too difficult to escape from, and/or provide fewer robbery opportunities due to relatively lower usage. Moreover, more robberies appear to occur on street blocks with higher betweenness due to more potential opportunities there. Finally, the influence of facilities and community characteristics were largely consistent with theoretical expectations and past studies. Future studies should continue to examine how topography influences aggregate crime levels and offender decision making in other settings to bolster the external validity of the present findings.
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As one reviewer noted, a reasonable hypothesis is that busy places will have less crime by increasing guardianship. This hypothesis is often framed using Jacobs’ (1961) notion of “eyes on the street”. The environmental criminology literature reviewed here generally does not support that hypothesis. This may be because guardianship requires more than just the presence of people (Reynald 2009) and many people fail to act when present during crimes and other events (Fischer et al. 2011). Nonetheless, the guardianship hypothesis remains viable for future research, and suggests two-tailed hypothesis tests should be used when estimating facilities-crime relationships.
The least effort principle suggests people will exert the least effort possible to achieve their goals, thus it is hypothesized that offenders also seek to exert the least effort possible during offending (Zipf 1950).
We thank the manuscript’s editor, Arjan Blokland, and the anonymous reviewers for helping us explicate our ideas more clearly here.
Of course, as the editor Arjan Blokland noted during review, the preceding two mechanisms would not apply to robberies where the offender uses an automobile. Given the operationalization of robbery described in the “Data and Method” section, this critique is minimized for the present study.
Due to Cincinnati’s hilly nature, 108 pedestrian-only segments remained in the cleaned dataset. These segments were predominantly staircases connecting street blocks (usually with the same street name). On average, these segments were quite steep (Slope Mean = 13.17). These segments, however, had valid address ranges and two robberies occurred on these segments, thus they were included in the analyses. Effectively identical results were observed when those 108 segments were excluded from the analyses.
Re-estimating the models after calculating the betweenness measure using the edited street network dataset did not impact the results. However, because Cincinnatians might use the edited out street features to travel, we present the results with the betweenness measure calculated for the unedited street network.
We note two nuances to the facilities data. First, the facilities data precede the crime data by 1 year. While this establishes temporal ordering, it is possible some facilities closed and others opened in that time. Weisburd et al. (2012) suggest facilities are relatively stable over time. The legal process for zoning also makes it unlikely locations changed facility types entirely in that time. But readers should keep that point in mind. Second, hypothetically all facilities could be captured with count variables. In reality, multiple facilities of a homogenous type will be located on a street block for only some facilities. Whether a facility was represented by a count or indicator variable is denoted for each facility when it is introduced in the manuscript.
Some readers may be concerned that spatially lagged predictors increase the number of significance tests conducted and the likelihood of making a Type-I error (Bernasco et al. 2017), thus prefering more parsimonious spatially lagged outcome models. A robustness check using a spatially lagged outcome model was also conducted. The results were not substantively different from the more theoretically defensible results shown herein, but are shown in the Online Appendix.
Moran’s I tests were performed in GeoDa 18.104.22.168. GeoDa only accepts point and polygon vector files, so the street block data were converted to mid-points prior estimating the Moran’s I results. In order to ensure the results were robust to different specifications, spatial relationships were specified with different spatial weights matrices: (1) queen contiguity and (2) k-nearest neighbors of orders 4, 5, and 6. The k-orders were chosen because the average number of neighbors per street block were 4.22 and 99% of street blocks had six or fewer total neighbors.
We thank an anonymous reviewers for this important idea.
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Haberman, C.P., Kelsay, J.D. The Topography of Robbery: Does Slope Matter?. J Quant Criminol 37, 625–645 (2021). https://doi.org/10.1007/s10940-020-09451-z
- Crime pattern theory
- Geography of crime