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Correlation between fine-grained neighborhood socioeconomic status distribution and crime rates in New York city based on machine learning
- 1 Edinburgh College of Art, University of Edinburgh, Edinburgh, United Kingdom
* Author to whom correspondence should be addressed.
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.
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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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Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
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