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.

Related papers

• Performative Prediction on Games and Mechanism Design [69.7933059664256]
  We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We show how to achieve better trade-offs and use them for mechanism design.
  arXiv  Detail & Related papers  (2024-08-09T16:03:44Z)
• Spatial-Temporal Meta-path Guided Explainable Crime Prediction [40.03641583647572]
  We present a Spatial-Temporal Metapath guided Explainable Crime prediction (STMEC) framework to capture dynamic patterns of crime behaviours. We show the superiority of STMEC compared with other advancedtemporal models, especially in predicting felonies.
  arXiv  Detail & Related papers  (2022-05-04T05:42:23Z)
• Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction [60.508960752148454]
  This work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework to tackle the label scarcity issue in crime prediction. We propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space. We also design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination.
  arXiv  Detail & Related papers  (2022-04-18T23:46:01Z)
• Spatial-Temporal Sequential Hypergraph Network for Crime Prediction [56.41899180029119]
  We propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns. In particular, to handle spatial-temporal dynamics under the long-range and global context, we design a graph-structured message passing architecture. We conduct extensive experiments on two real-world datasets, showing that our proposed ST-SHN framework can significantly improve the prediction performance.
  arXiv  Detail & Related papers  (2022-01-07T12:46:50Z)
• The effect of differential victim crime reporting on predictive policing systems [84.86615754515252]
  We show how differential victim crime reporting rates can lead to outcome disparities in common crime hot spot prediction models. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas.
  arXiv  Detail & Related papers  (2021-01-30T01:57:22Z)
• Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy [85.20742045853738]
  CNNs are widely known to be vulnerable to adversarial attacks. We propose an empirical study on the class-wise accuracy and robustness of adversarially trained models. We find that there exists inter-class discrepancy for accuracy and robustness even when the training dataset has an equal number of samples for each class.
  arXiv  Detail & Related papers  (2020-10-26T06:32:32Z)
• Socio-economic, built environment, and mobility conditions associated with crime: A study of multiple cities [9.78342936850961]
  We propose a Bayesian model to explore how crime is related to socio-economic factors. We find that the combined use of socio-economic conditions, mobility information and physical characteristics of the neighbourhood effectively explain the emergence of crime. We show that the socio-ecological factors of neighbourhoods relate to crime very differently from one city to another.
  arXiv  Detail & Related papers  (2020-04-13T08:36:59Z)
• Crime Prediction Using Spatio-Temporal Data [8.50468505606714]
  Supervised learning technique is used to predict crimes with better accuracy. The proposed system is feed with a criminal-activity data set of twelve years of San Francisco city.
  arXiv  Detail & Related papers  (2020-03-11T16:19:19Z)
• Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction [48.1813701535167]
  We perform crime prediction exploiting the cross-type and-temporal correlations of urban crimes. We propose a coherent framework to mathematically model these correlations for crime prediction. Further experiments have been conducted to understand the importance of different correlations in crime prediction.
  arXiv  Detail & Related papers  (2020-01-20T00:34:53Z)
• Perfecting the Crime Machine [1.266953082426463]
  This study explores using different machine learning techniques and to predict crime related statistics, specifically crime type in Philadelphia. We use crime location and time as main features, extract different features from the two features that our raw data has, and build models that would work with large number of class labels. We report that the Random Forest as the best performing model to predict crime type with an error log loss of 2.3120.
  arXiv  Detail & Related papers  (2020-01-14T23:25:40Z)
• A Comparative Study on Crime in Denver City Based on Machine Learning and Data Mining [0.0]
  I analyzed a real-world crime and accident dataset of Denver county, USA, from January 2014 to May 2019. This project aims to predict and highlights the trends of occurrence that will, in return, support the law enforcement agencies and government to discover the preventive measures. The outcomes are captured using two popular test methods: train-test split, and k-fold crossvalidation.
  arXiv  Detail & Related papers  (2020-01-09T01:36:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.