Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments
Objectives Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro − geographic units, measuring this is difficult. Methods This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street s...
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| Contributors: | ; ; |
| Format: | Electronic Article |
| Language: | English |
| Published: |
2022
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| In: |
Journal of quantitative criminology
Year: 2022, Volume: 38, Issue: 3, Pages: 537-565 |
| Online Access: |
Presumably Free Access Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
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MARC
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| 109 | |a Hipp, John R. |a Hipp, John | ||
| 245 | 1 | 0 | |a Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments |
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| 520 | |a Objectives Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro − geographic units, measuring this is difficult. Methods This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street segment in the city of Santa Ana, CA, and then uses machine learning to detect features of the environment. We capture eleven different features across four main dimensions, and demonstrate that their relative presence across street segments considerably increases the explanatory power of models of five different Part 1 crimes. Results The presence of more persons in the environment is associated with higher levels of crime. The auto − oriented measures—vehicles and pavement—were positively associated with crime rates. For the defensible space measures, the presence of walls has a slowing negative relationship with most crime types, whereas fences did not. And for our two greenspace measures, although terrain was positively associated with crime rates, vegetation exhibited an inverted − U relationship with two crime types. Conclusions The results demonstrate the efficacy of this approach for measuring the built environment. | ||
| 650 | 4 | |a Semantic Segmentation | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Google Street View | |
| 650 | 4 | |a Crime | |
| 650 | 4 | |a Built Environment | |
| 700 | 1 | |a Lee, Sugie |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ki, Donghwan |e VerfasserIn |4 aut | |
| 700 | 1 | |a Kim, Jae Hong |e VerfasserIn |4 aut | |
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