Explaining Crime Diversity with Google Street View

Khorshidi, Samira; Carter, Jeremy; Mohler, George; Tita, George E.

doi:10.1007/s10940-021-09500-1

Cited by 21 publications

(6 citation statements)

References 71 publications

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“…Computational techniques for collecting and analyzing text and image data are becoming increasingly popular in criminology and criminal justice studies (Al-Zaidy et al 2012;Khorshidi et al 2021;Kleck et al 2006;Kuang et al 2017;Pina-Sánchez et al 2019b,a;Snaphaan and Hardyns 2019;Vander Beken et al 2021;Williams and Burnap 2016). Although much of the discussion and application of computational methods to date pertains to quantitative research, there is much to be gained from the advancement and application of computational techniques in support of qualitative research too.…”

Section: The Computational Turn In Criminology and Criminal Justice Studiesmentioning

confidence: 99%

“…Of the images sampled, 67 were logos attached to news releases posted by the Saskatchewan RCMP and were coded as such but are omitted from this analysis. In a larger-scale analysis, one might apply techniques from computer vision (see e.g., Khorshidi et al 2021) to automatically identify and filter out duplicate images such as these to save time. However, automation cannot replace the process of reckoning (Smith 2019) that goes into sorting the images and making meaning from the categories as we have opted to do in this analysis.…”

Section: Analyzingmentioning

confidence: 99%

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Jumpstarting the Justice Disciplines: A Computational-Qualitative Approach to Collecting and Analyzing Text and Image Data in Criminology and Criminal Justice Studies

Luscombe

Duncan

Walby

2022

Journal of Criminal Justice Education

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Computational methods are increasingly popular in criminal justice research. As more criminal justice data becomes available in big data and other digital formats, new means of embracing the computational turn are needed. In this article, we propose a framework for data collection and case sampling using computational methods, allowing researchers to conduct thick qualitative research -analyses concerned with the particularities of a social context or phenomenon -starting from big data, which is typically associated with thinner quantitative methods and the pursuit of generalizable findings. The approach begins by using open-source web scraping algorithms to collect content from a target website, online database, or comparable online source. Next, researchers use computational techniques from the field of natural language processing to explore themes and patterns in the larger data set. Based on these initial explorations, researchers algorithmically generate a subset of data for in-depth qualitative analysis. In this computationally driven process of data collection and case sampling, the larger corpus and subset are never entirely divorced, a feature we argue has implications for traditional qualitative research techniques and tenets. To illustrate this approach, we collect, subset, and analyze three years of news releases from the Royal Canadian Mounted Police website (N = 13,637) using a mix of web scraping, natural language processing, and visual discourse analysis. To enhance the pedagogical value of our intervention and facilitate replication and secondary analysis, we make all data and code available online in the form of a detailed, step-by-step tutorial.

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Section: The Computational Turn In Criminology and Criminal Justice Studiesmentioning

confidence: 99%

Section: Analyzingmentioning

confidence: 99%

Jumpstarting the Justice Disciplines: A Computational-Qualitative Approach to Collecting and Analyzing Text and Image Data in Criminology and Criminal Justice Studies

Luscombe

Duncan

Walby

2022

Journal of Criminal Justice Education

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“…However, when using manual evaluation methods these studies have limited geographic scope, and therefore a recent advance is to utilize machine learning technologies to analyze GSV data using deep neural networks such as AlexNet and Inception-v3 (Kang and Kang 2017;Zhang et al 2019). One study used the Google Vision API to classify various objects in the environment and assessed how this is related to crime diversity on street segments (Khorshidi et al 2021).…”

Section: Measuring the Built Environmentmentioning

confidence: 99%

How concentrated disadvantage moderates the built environment and crime relationship on street segments in Los Angeles

Hipp

Lee

et al. 2022

Criminology & Criminal Justice

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Criminological theories have posited that the built environment impacts where crime occurs; however, measuring the built environment is difficult. Furthermore, it is uncertain whether the built environment differentially impacts crime in high-disadvantage neighborhoods. This study extracts features of the built environment from Google Street View images with a machine learning semantic segmentation strategy to create measures of fences, walls, buildings, and greenspace for over 66,000 street segments in Los Angeles. Results indicate that the presence of more buildings on a segment was associated with higher crime rates and had a particularly strong positive relationship with robbery and motor vehicle theft in low-disadvantage neighborhoods. Notably, fences and walls exhibited different relationships with crime. Walls, which do not allow visibility, were strongly negatively related to crime, particularly for robbery and burglary in high-disadvantage neighborhoods. Fences, which allow visibility, were associated with fewer robberies and larcenies, but more burglaries and aggravated assaults. Fences only exhibited a negative relationship with violent crime when they were located in low-disadvantage neighborhoods. The results highlight the importance of accounting for the built environment and the surrounding level of disadvantage when exploring the micro-location of crime.

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“…Their analysis showed that the visual features of the street environment could distinguish areas with different crime rates. Khorshidi [ 35 ] and Sytsma [ 36 ] also used similar methods to extract street environment information from street view images to study the relationships between street features and crime. However, existing studies mainly applied a single semantic segmentation method to extract street environment elements from street view images.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique

Xie

Liu

Han

2022

IJERPH

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Street crime is a common social problem that threatens the security of people’s lives and property. Understanding the influencing mechanisms of street crime is an essential precondition for formulating crime prevention strategies. Widespread concern has contributed to the development of streetscape environment features as they can significantly affect the occurrence of street crime. Emerging street view images are a low-cost and highly accessible data source. On the other hand, machine-learning models such as XGBoost (eXtreme Gradient Boosting) usually have higher fitting accuracies than those of linear regression models. Therefore, they are popular for modeling the relationships between crime and related impact factors. However, due to the “black box” characteristic, researchers are unable to understand how each variable contributes to the occurrence of crime. Existing research mainly focuses on the independent impacts of streetscape environment features on street crime, but not on the interaction effects between these features and the community socioeconomic conditions and their local variations. In order to address the above limitations, this study first combines street view images, an objective detection network, and a semantic segmentation network to extract a systematic measurement of the streetscape environment. Then, controlling for socioeconomic factors, we adopted the XGBoost model to fit the relationships between streetscape environment features and street crime at the street segment level. Moreover, we used the SHAP (Shapley additive explanation) framework, a post-hoc machine-learning explainer, to explain the results of the XGBoost model. The results demonstrate that, from a global perspective, the number of people on the street, extracted from street view images, has the most significant impact on street property crime among all the street view variables. The local interpretability of the SHAP explainer demonstrates that a particular variable has different effects on street crime at different street segments. The nonlinear associations between streetscape environment features and street crime, as well as the interaction effects of different streetscape environment features are discussed. The positive effect of the number of pedestrians on street crime increases with the length of the street segment and the number of crime generators. The combination of street view images and interpretable machine-learning techniques is helpful in better accurately understanding the complex relationships between the streetscape environment and street crime. Furthermore, the readily comprehensible results can offer a reference for formulating crime prevention strategies.

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Explaining Crime Diversity with Google Street View

Cited by 21 publications

References 71 publications

Jumpstarting the Justice Disciplines: A Computational-Qualitative Approach to Collecting and Analyzing Text and Image Data in Criminology and Criminal Justice Studies

Jumpstarting the Justice Disciplines: A Computational-Qualitative Approach to Collecting and Analyzing Text and Image Data in Criminology and Criminal Justice Studies

How concentrated disadvantage moderates the built environment and crime relationship on street segments in Los Angeles

Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique

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