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Predicting Crime Using Time and Location Data

Published: 27 July 2019 Publication History

Abstract

To have a better response towards criminal activity, it is very important that one should understand the patterns in crime. We analyze this pattern by taking crime datasets from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. This dataset includes different blocks of the city of Chicago. The major aim of this mission is to expect which category of crime is most probably to take place at a detailed time and places in Chicago. Finally, this paper uses a different algorithm like Random Forest, Decision Tree and different ensemble methods such as Extra Trees, Bagging and AdaBoost to evaluate the accuracy given by each algorithm.

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Cited By

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  • (2024)Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive SurveyACM Transactions on Intelligent Systems and Technology10.1145/369951516:2(1-75)Online publication date: 7-Oct-2024
  • (2024)Improving Robustness of Optimized Parameters Gradient Tree Boosting for Crime Forecast ModelArtificial Intelligence Tools and Applications in Embedded and Mobile Systems10.1007/978-3-031-56576-2_1(1-8)Online publication date: 30-Jun-2024
  • (2023)Intelligent System for Crime and Insecurity Management2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS)10.1109/ICMEAS58693.2023.10429894(1-8)Online publication date: 1-Nov-2023
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Published In

cover image ACM Other conferences
ICCCM '19: Proceedings of the 7th International Conference on Computer and Communications Management
July 2019
260 pages
ISBN:9781450371957
DOI:10.1145/3348445
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Chongqing University of Posts and Telecommunications

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2019

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Author Tags

  1. AdaBoost
  2. Bagging
  3. Decision Tree
  4. Ensemble
  5. Extra Trees
  6. Machine learning
  7. Random Forest

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Cited By

View all
  • (2024)Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive SurveyACM Transactions on Intelligent Systems and Technology10.1145/369951516:2(1-75)Online publication date: 7-Oct-2024
  • (2024)Improving Robustness of Optimized Parameters Gradient Tree Boosting for Crime Forecast ModelArtificial Intelligence Tools and Applications in Embedded and Mobile Systems10.1007/978-3-031-56576-2_1(1-8)Online publication date: 30-Jun-2024
  • (2023)Intelligent System for Crime and Insecurity Management2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS)10.1109/ICMEAS58693.2023.10429894(1-8)Online publication date: 1-Nov-2023
  • (2023)Crime Hotspot Detection using Optimized K-means Clustering and Machine Learning Techniques2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC57686.2023.10193563(787-792)Online publication date: 6-Jul-2023
  • (2023)Machine learning in crime predictionJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-023-04530-y14:3(2887-2913)Online publication date: 2-Feb-2023
  • (2023)A conditional machine learning classification approach for spatio-temporal risk assessment of crime dataStochastic Environmental Research and Risk Assessment10.1007/s00477-023-02420-537:7(2815-2828)Online publication date: 15-Mar-2023
  • (2023)Spatio-Temporal Crime Forecasting: Approaches, Datasets, and Comparative StudyInternational Conference on Advanced Intelligent Systems for Sustainable Development10.1007/978-3-031-26384-2_21(231-251)Online publication date: 10-Jun-2023
  • (2022)Crime Analyses Using Data AnalyticsInternational Journal of Data Warehousing and Mining10.4018/IJDWM.29901418:1(1-15)Online publication date: 1-Jan-2022
  • (2022)Time-Series Models for Crime Prediction in IndiaUsing Computational Intelligence for the Dark Web and Illicit Behavior Detection10.4018/978-1-6684-6444-1.ch004(57-73)Online publication date: 6-May-2022
  • (2022)The Forecast of the Number of Police Cases Based on Time Series and Convolutional Neural Network ModelAutomatic Control and Computer Sciences10.3103/S014641162203004X56:3(230-238)Online publication date: 14-Jul-2022
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