A Comparative Analysis of Multiple Methods for Predicting a Specific
Type of Crime in the City of Chicago
- URL: http://arxiv.org/abs/2304.13464v1
- Date: Wed, 26 Apr 2023 11:35:06 GMT
- Title: A Comparative Analysis of Multiple Methods for Predicting a Specific
Type of Crime in the City of Chicago
- Authors: Deborah Djon, Jitesh Jhawar, Kieron Drumm, and Vincent Tran
- Abstract summary: We aim to answer the question: "How well can we predict theft using spatial and temporal features?"
XBoost showed the best results with an F1-score of 0.86.
- Score: 0.9799637101641152
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Researchers regard crime as a social phenomenon that is influenced by several
physical, social, and economic factors. Different types of crimes are said to
have different motivations. Theft, for instance, is a crime that is based on
opportunity, whereas murder is driven by emotion. In accordance with this, we
examine how well a model can perform with only spatiotemporal information at
hand when it comes to predicting a single crime. More specifically, we aim at
predicting theft, as this is a crime that should be predictable using
spatiotemporal information. We aim to answer the question: "How well can we
predict theft using spatial and temporal features?". To answer this question,
we examine the effectiveness of support vector machines, linear regression,
XGBoost, Random Forest, and k-nearest neighbours, using different imbalanced
techniques and hyperparameters. XGBoost showed the best results with an
F1-score of 0.86.
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