Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety

• URL: http://arxiv.org/abs/2409.10838v1
• Date: Tue, 17 Sep 2024 02:07:14 GMT
• Title: Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety
• Authors: Sia Gupta, Simeon Sayer,
• Abstract summary: This paper explores the effectiveness of ML techniques to predict spatial and temporal patterns of crimes in urban areas. Research goal is to achieve a high degree of accuracy in categorizing calls into priority levels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, urban safety has become a paramount concern for city planners and law enforcement agencies. Accurate prediction of likely crime occurrences can significantly enhance preventive measures and resource allocation. However, many law enforcement departments lack the tools to analyze and apply advanced AI and ML techniques that can support city planners, watch programs, and safety leaders to take proactive steps towards overall community safety. This paper explores the effectiveness of ML techniques to predict spatial and temporal patterns of crimes in urban areas. Leveraging police dispatch call data from San Jose, CA, the research goal is to achieve a high degree of accuracy in categorizing calls into priority levels particularly for more dangerous situations that require an immediate law enforcement response. This categorization is informed by the time, place, and nature of the call. The research steps include data extraction, preprocessing, feature engineering, exploratory data analysis, implementation, optimization and tuning of different supervised machine learning models and neural networks. The accuracy and precision are examined for different models and features at varying granularity of crime categories and location precision. The results demonstrate that when compared to a variety of other models, Random Forest classification models are most effective in identifying dangerous situations and their corresponding priority levels with high accuracy (Accuracy = 85%, AUC = 0.92) at a local level while ensuring a minimum amount of false negatives. While further research and data gathering is needed to include other social and economic factors, these results provide valuable insights for law enforcement agencies to optimize resources, develop proactive deployment approaches, and adjust response patterns to enhance overall public safety outcomes in an unbiased way.

Related papers

• Revolutionizing Urban Safety Perception Assessments: Integrating Multimodal Large Language Models with Street View Images [5.799322786332704]
  Measuring urban safety perception is an important and complex task that traditionally relies heavily on human resources. Recent advances in multimodal large language models (MLLMs) have demonstrated powerful reasoning and analytical capabilities. We propose a method based on the pre-trained Contrastive Language-Image Pre-training (CLIP) feature and K-Nearest Neighbors (K-NN) retrieval to quickly assess the safety index of the entire city.
  arXiv  Detail & Related papers  (2024-07-29T06:03:13Z)
• Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis [63.532413807686524]
  This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL) We propose a new architecture that handles the trade-off between efficient progress and safety during exploration.
  arXiv  Detail & Related papers  (2023-12-18T16:09:43Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• Safe Deep Reinforcement Learning by Verifying Task-Level Properties [84.64203221849648]
  Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL) The cost is typically encoded as an indicator function due to the difficulty of quantifying the risk of policy decisions in the state space. In this paper, we investigate an alternative approach that uses domain knowledge to quantify the risk in the proximity of such states by defining a violation metric.
  arXiv  Detail & Related papers  (2023-02-20T15:24:06Z)
• 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)
• A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs [0.0]
  One of the most immediate needs of people affected by a disaster is finding shelter. This paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used.
  arXiv  Detail & Related papers  (2021-07-29T18:22:34Z)
• 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)
• Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification [81.32981236437395]
  We present a semi-formal verification approach for decision-making tasks based on interval analysis. Our method obtains comparable results over standard benchmarks with respect to formal verifiers. Our approach allows to efficiently evaluate safety properties for decision-making models in practical applications.
  arXiv  Detail & Related papers  (2020-10-19T11:18:06Z)
• Multi-officer Routing for Patrolling High Risk Areas Jointly Learned from Check-ins, Crime and Incident Response Data [6.295207672539996]
  We formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information. We propose a joint learning and non-random optimisation method for the representation of possible solutions. The performance of the proposed solution is verified and compared with several state-of-art methods using real-world datasets.
  arXiv  Detail & Related papers  (2020-07-31T23:33:14Z)
• 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.