Correlation between fine-grained neighborhood socioeconomic status distribution and crime rates in New York city based on machine learning

Research Article
Open access

Correlation between fine-grained neighborhood socioeconomic status distribution and crime rates in New York city based on machine learning

Xuefei Qin 1*
  • 1 Edinburgh College of Art, University of Edinburgh, Edinburgh, United Kingdom    
  • *corresponding author 6180303353@caa.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230744
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Urban crime poses a serious challenge to urban sustainability and livability. Many studies have been conducted to explore the patterns and causes of urban crime, as well as prevention techniques. Studies have found that neighborhood socioeconomic status affects the incidence of urban crime, but studies on this topic are limited due to data limitations. To fill this gap, this study designed an approach for Brooklyn, USA, that collects publicly available data from housing advertising sites and the Open Street Map and trains a machine learning model to predict fine-grained neighborhood socioeconomic status. The experimental results show that the gradient boosting decision tree regression model has the best prediction accuracy. Then, we verified the predicted significant correlation between fine-grained neighborhood socioeconomic status and criminal activity in the precinct by using a geographically weighted regression model, that is, criminal activity has a higher incidence in disadvantaged neighborhoods. It was also found that neighbourhood socioeconomic status was the best predictor of harassment and burglary.

Keywords:

neighborhood social economic status, machine learning, crime rate, big data analysis, urban design.

Qin,X. (2023). Correlation between fine-grained neighborhood socioeconomic status distribution and crime rates in New York city based on machine learning. Applied and Computational Engineering,6,39-51.
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References

[1]. Mohler G. Marked point process hotspot maps for homicide and gun crime prediction in Chicago. International Journal of Forecasting. 2014 Jul 1;30(3):491–7.

[2]. Pijper LK, Breetzke GD, Edelstein I. Building neighbourhood-level resilience to crime: the case of Khayelitsha, South Africa. South African Geographical Journal. 2021 Jul 6;103(3):342–57.

[3]. Becker GS. Crime and Punishment: an Economic Approach. In: Fielding NG, Clarke A, Witt R, editors. The Economic Dimensions of Crime [Internet]. London: Palgrave Macmillan UK; 2000 [cited 2022 Oct 14]. p. 13–68. Available from: https://doi.org/10.1007/978-1-349-62853-7_2

[4]. Webb S, Janus M, Duku E, Raos R, Brownell M, Forer B, et al. Neighbourhood socioeconomic status indices and early childhood development. SSM - Population Health. 2017 Dec 1;3:48–56.

[5]. Molnar BE, Cerda M, Roberts AL, Buka SL. Effects of Neighborhood Resources on Aggressive and Delinquent Behaviors Among Urban Youths. Am J Public Health. 2008 Jun;98(6):1086–93.

[6]. Fabio A, Tu LC, Loeber R, Cohen J. Neighborhood Socioeconomic Disadvantage and the Shape of the Age–Crime Curve. Am J Public Health. 2011 Dec;101(S1):S325–32.

[7]. Niu T, Chen Y, Yuan Y. Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou. Sustainable Cities and Society. 2020 Mar 1;54:102014.

[8]. Wang L, He S, Su S, Li Y, Hu L, Li G. Urban neighborhood socioeconomic status (SES) inference: A machine learning approach based on semantic and sentimental analysis of online housing advertisements. Habitat International. 2022 Jun 1;124:102572.

[9]. Meng G, Hall GB. Assessing housing quality in metropolitan Lima, Peru. J Housing Built Environ. 2006 Dec 1;21(4):413–39.

[10]. Census Bureau Data [Internet]. Available from: https://data.census.gov/cedsci/

[11]. Yang TC, Kim S, Zhao Y, Choi S won E. Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes. Health & Place. 2021 May 1;69:102574.

[12]. Statistics - NYPD [Internet]. Available from: https://www1.nyc.gov/site/nypd/stats/stats.page

[13]. Smoyer-Tomic KE, Spence JC, Raine KD, Amrhein C, Cameron N, Yasenovskiy V, et al. The association between neighborhood socioeconomic status and exposure to supermarkets and fast food outlets. Health & Place. 2008 Dec 1;14(4):740–54.

[14]. Zhang X, Liu L, Xiao L, Ji J. Comparison of Machine Learning Algorithms for Predicting Crime Hotspots. IEEE Access. 2020;8:181302–10.

[15]. New York Police Department [Internet]. Available from: https://www1.nyc.gov/site/nypd/index.page

Cite this article

Qin,X. (2023). Correlation between fine-grained neighborhood socioeconomic status distribution and crime rates in New York city based on machine learning. Applied and Computational Engineering,6,39-51.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Mohler G. Marked point process hotspot maps for homicide and gun crime prediction in Chicago. International Journal of Forecasting. 2014 Jul 1;30(3):491–7.

[2]. Pijper LK, Breetzke GD, Edelstein I. Building neighbourhood-level resilience to crime: the case of Khayelitsha, South Africa. South African Geographical Journal. 2021 Jul 6;103(3):342–57.

[3]. Becker GS. Crime and Punishment: an Economic Approach. In: Fielding NG, Clarke A, Witt R, editors. The Economic Dimensions of Crime [Internet]. London: Palgrave Macmillan UK; 2000 [cited 2022 Oct 14]. p. 13–68. Available from: https://doi.org/10.1007/978-1-349-62853-7_2

[4]. Webb S, Janus M, Duku E, Raos R, Brownell M, Forer B, et al. Neighbourhood socioeconomic status indices and early childhood development. SSM - Population Health. 2017 Dec 1;3:48–56.

[5]. Molnar BE, Cerda M, Roberts AL, Buka SL. Effects of Neighborhood Resources on Aggressive and Delinquent Behaviors Among Urban Youths. Am J Public Health. 2008 Jun;98(6):1086–93.

[6]. Fabio A, Tu LC, Loeber R, Cohen J. Neighborhood Socioeconomic Disadvantage and the Shape of the Age–Crime Curve. Am J Public Health. 2011 Dec;101(S1):S325–32.

[7]. Niu T, Chen Y, Yuan Y. Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou. Sustainable Cities and Society. 2020 Mar 1;54:102014.

[8]. Wang L, He S, Su S, Li Y, Hu L, Li G. Urban neighborhood socioeconomic status (SES) inference: A machine learning approach based on semantic and sentimental analysis of online housing advertisements. Habitat International. 2022 Jun 1;124:102572.

[9]. Meng G, Hall GB. Assessing housing quality in metropolitan Lima, Peru. J Housing Built Environ. 2006 Dec 1;21(4):413–39.

[10]. Census Bureau Data [Internet]. Available from: https://data.census.gov/cedsci/

[11]. Yang TC, Kim S, Zhao Y, Choi S won E. Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes. Health & Place. 2021 May 1;69:102574.

[12]. Statistics - NYPD [Internet]. Available from: https://www1.nyc.gov/site/nypd/stats/stats.page

[13]. Smoyer-Tomic KE, Spence JC, Raine KD, Amrhein C, Cameron N, Yasenovskiy V, et al. The association between neighborhood socioeconomic status and exposure to supermarkets and fast food outlets. Health & Place. 2008 Dec 1;14(4):740–54.

[14]. Zhang X, Liu L, Xiao L, Ji J. Comparison of Machine Learning Algorithms for Predicting Crime Hotspots. IEEE Access. 2020;8:181302–10.

[15]. New York Police Department [Internet]. Available from: https://www1.nyc.gov/site/nypd/index.page

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