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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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