An Empirical Analysis of the Link Between Built Environment and Safety in Chicago’s Transit Station Areas

Abstract

Problem, research strategy, and findings

Considering that safety is a key environmental factor that promotes health, understanding the relationship between built environment features around transit station areas and crime may shed light on how to foster healthy communities. Yet, there is limited work that has examined how the combination of different built environment features around transit correlate with different crimes. We addressed this issue in this study using data from Chicago (IL). First, we used cluster analysis to classify stations in Chicago in a spectrum from transit-oriented development (TOD) to transit-adjacent development (TAD) categories depending on their built environment characteristics: central business district (CBD)–TOD, TOD, hybrid, and TAD. Then, we identified the block groups that fell within a 1-mile network distance of each of these station areas and used propensity score matching to find adequate comparison block groups for them. Results from our analyses show that CBD station areas with the highest activity density, land use diversity, amenity richness, accessibility, and walkability (i.e., CBD–TOD) were the safest. In contrast, TOD areas with medium activity density and land use diversity but high amenity richness, walkability, and accessibility appeared to be the least safe. That said, low levels across these built environment features as found in TAD station areas also correlated with higher crime.

Takeaway for practice

These findings suggest the importance of balancing amenity richness and accessibility with density and land use diversity. Areas rich in amenities but with lower levels of land use diversity and density may attract crime victims and offenders while facilitating spaces in which the availability of eyes on the street or guardians is low. As such, these station areas may be poor promoters of healthy communities if high crime rates deter people from engaging in active mobility promoted by greater walkability, connectivity, and amenity richness.

Keywords:

• built environment
• crime
• healthy communities
• safety
• transit-oriented development

SUPPLEMENTAL MATERIAL

Supplemental data for this article can be found on the publisher’s website.

Notes

1 Given the growth of micro-mobility (Elmashhara et al., 2022), we thought it was important to include it in our typology.

2 In 2019 geographic coordinates were missing for less than 1% (0.47%) of crimes.

3 This category includes all forms of theft except financial ID theft.

4 We used the primary type field to identify the type of crime. To identify domestic offenses, we used the domestic offense flag in data. To exclude crimes in residences, hospitals, and schools we used the location of the crime. Crimes inside buildings included elevators and hallways.

5 We calculated that there were 11,891 violent crimes, 7,278 robberies, 47,428 thefts, 1,887 burglaries, and 2,721 incidents of purse-snatching or pickpocketing in 2019.

6 The entropy calculation is: Entropy = − [Office share ∗ ln(Office share) + Education share ∗ ln(Education share) + Commercial share ∗ ln(Commercial share) + Public share ∗ ln(Public share)]/ln(3), where ln is the natural logarithm of the value in parentheses and the shares are measured in terms of total parcel land areas.

7 For more details on the Transit Score calculation, see Walk Score (2022).

8 For more information on Chicago’s e-bike (Divvy) program, see Divvy (2022).

9 Table A6 in the Technical Appendix shows descriptive statistics for the unstandardized values.

10 We found no evidence of multicollinearity. See Table A8 in the Technical Appendix. The Technical Appendix provides step-by-step information on the PSM.

11 We also estimated models without controls and obtained similar results (Tables A13 and A14 in the Technical Appendix).

12 The TAD–CBD–TOD comparison was excluded due to the inability to find good matches.

Additional information

Notes on contributors

Ahoura Zandiatashbar

AHOURA ZANDIATASHBAR ([email protected]) is an assistant professor of urban and regional planning and co-director of the Spatial Analytics and Visualization (SAVi) Center at San Jose State University.

Agustina Laurito

AGUSTINA LAURITO ([email protected]) is an assistant professor in the Department of Public Administration at the University of Illinois at Chicago.