Topography and crime in place: The effects of elevation, slope, and betweenness in San Francisco street segments

ABSTRACT

Few studies have investigated how features of the land surface and the street network affect spatial crime patterns. Accordingly, for the current study, we estimated negative binomial regression models to test for main and moderating effects of elevation, slope, and betweenness on crime across San Francisco street segments. While significant effects were observed for all topography measures assessed, we found that elevation differences in the surrounding ¼ mile (i.e., hilliness) reduces the risk for crime more so than the elevation and slope of the segment itself. In comparison, betweenness based on the street network produced a higher risk for crime. We also determined a conditional effect between elevation differences in the surrounding ¼ mile and betweenness. To supplement the regression analysis, we produce maps that show the predicted values of the different crime outcomes for each segment in our sample, thereby underscoring certain policy and practical implications of our findings.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. https://www.census.gov/quickfacts/fact/table/US/PST045219.

2. We employed the Simple Average (SA) imputation method by proportioning the average values of block level Census data to adjacent street segments as proposed by Kim (2018).

3. Although land use measurement occurs after the measurement of crime, changes in land use, especially a change in one land use category to another land use category, is typically uncommon and would likely unfold over a very long time-period (Stucky & Ottensmann, 2009).

4. Street segments on Treasure Island (an island in the San Francisco Bay) have been dropped.

5. The highest pairwise correlation between the elevation, slope, standard deviation of elevation, and betweenness measures (logged) was 0.46.

6. Haberman and Kelsay (2020) theoretically distinguish between elevation and slope, however, they only link the latter to crime.

7. We produced maps using the geometric interval classification method instead; however, this method did not substantively change the pattern of results when using the quantile method.

Additional information

Notes on contributors

Young-An Kim

Young-An Kim is an Assistant Professor in the College of Criminology and Criminal Justice at Florida State University. His research interests focus on various areas such as neighborhoods and crime, criminology of place, immigration and crime, spatial analysis, and quantitative research methods.

James C. Wo

James C. Wo is an Assistant Professor in the Department of Sociology and Criminology at the University of Iowa, and a Research Fellow at the University of Iowa’s Public Policy Center. His research interests include neighborhoods and crime, local institutions and organizations, land uses, and quantitative research methods.