research-article

AIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction

Authors:

Tanzima HashemAuthors Info & Claims

ACM Transactions on Spatial Algorithms and Systems, Volume 9, Issue 2

Article No.: 13, Pages 1 - 31

https://doi.org/10.1145/3582274

Published: 12 April 2023 Publication History

Abstract

Accuracy and interpretability are two essential properties for a crime prediction model. Accurate prediction of future crime occurrences along with the reason behind a prediction would allow us to plan the crime prevention steps accordingly. The key challenge in developing the model is to capture the non-linear and dynamic spatial dependency and temporal patterns of a specific crime category, while keeping the underlying structure of the model interpretable. In this article, we develop AIST, an Attention-based Interpretable Spatio Temporal Network for crime prediction. AIST models the dynamic spatio-temporal correlations for a crime category based on past crime occurrences, external features (e.g., traffic flow and point of interest information) and recurring trends of crime. Extensive experiments show that AIST outperforms the state-of-the-art techniques in terms of accuracy (e.g., AIST shows a decrease of 4.1% on mean average error and 7.45% on mean square error for the Chicago 2019 crime dataset) and interpretability.

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Cited By

Roshankar RKeyvanpour M(2025)GeoCrime Analytic Framework (G.C.A.F.): A Comprehensive Framework for Dynamic Spatial Temporal Crime AnalysisApplied Spatial Analysis and Policy10.1007/s12061-025-09640-y18:1Online publication date: 4-Feb-2025
https://doi.org/10.1007/s12061-025-09640-y
Solís MCalvo-Valverde L(2024)Deep Learning for Crime Forecasting of Multiple Regions, Considering Spatial–Temporal Correlations between RegionsITISE 202410.3390/engproc2024068004(4)Online publication date: 28-Jun-2024
https://doi.org/10.3390/engproc2024068004
Kaladevi PGopalraj MHarish KGuna APraveen R(2024)Tweets to Predict: LSTM Model for Crime Analysis Using Twitter Time Series Data2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)10.1109/ICKECS61492.2024.10616955(1-6)Online publication date: 18-Apr-2024
https://doi.org/10.1109/ICKECS61492.2024.10616955
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Index Terms

AIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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cover image ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems Volume 9, Issue 2

June 2023

201 pages

ISSN:2374-0353

EISSN:2374-0361

DOI:10.1145/3592535

Editor:
Walid G. Aref
Purdue University, USA

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Publication History

Published: 12 April 2023

Online AM: 30 January 2023

Accepted: 15 January 2023

Revised: 19 October 2022

Received: 26 September 2021

Published in TSAS Volume 9, Issue 2

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Cited By

Roshankar RKeyvanpour M(2025)GeoCrime Analytic Framework (G.C.A.F.): A Comprehensive Framework for Dynamic Spatial Temporal Crime AnalysisApplied Spatial Analysis and Policy10.1007/s12061-025-09640-y18:1Online publication date: 4-Feb-2025
https://doi.org/10.1007/s12061-025-09640-y
Solís MCalvo-Valverde L(2024)Deep Learning for Crime Forecasting of Multiple Regions, Considering Spatial–Temporal Correlations between RegionsITISE 202410.3390/engproc2024068004(4)Online publication date: 28-Jun-2024
https://doi.org/10.3390/engproc2024068004
Kaladevi PGopalraj MHarish KGuna APraveen R(2024)Tweets to Predict: LSTM Model for Crime Analysis Using Twitter Time Series Data2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)10.1109/ICKECS61492.2024.10616955(1-6)Online publication date: 18-Apr-2024
https://doi.org/10.1109/ICKECS61492.2024.10616955
J JP RS J(2024)Enhancing Cloud Storage Security with Multi-Authority Authentication and File Encryption2024 International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT60155.2024.10544946(1387-1391)Online publication date: 24-Apr-2024
https://doi.org/10.1109/ICICT60155.2024.10544946
Pavithra SMuruganantham B(2024)Utilizing enhanced deep learning methods for residential burglary detection via analysis of human behavior patternsMultimedia Tools and Applications10.1007/s11042-024-19097-9Online publication date: 25-Apr-2024
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Rather IKumar SGandomi A(2024)Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasetsArtificial Intelligence Review10.1007/s10462-024-10859-357:9Online publication date: 2-Aug-2024
https://doi.org/10.1007/s10462-024-10859-3
Du YDing N(2023)A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction MethodsISPRS International Journal of Geo-Information10.3390/ijgi1206020912:6(209)Online publication date: 23-May-2023
https://doi.org/10.3390/ijgi12060209
Fonseca CSilveira I(2023)Um framework baseado em aprendizado de máquina e dados de processos res judicata para análise e previsão de sanções penais referentes a crimes cibernéticosRevista Científica Multidisciplinar Núcleo do Conhecimento10.32749/nucleodoconhecimento.com.br/engenharia-da-computacao/dados-de-processos(36-60)Online publication date: 9-Feb-2023
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Johnson JR CAnusha Bamini A(2023)EGFD-AROMA-based Pattern Forecasting with Crime Rate Level Identification2023 Third International Conference on Smart Technologies, Communication and Robotics (STCR)10.1109/STCR59085.2023.10396860(1-8)Online publication date: 9-Dec-2023
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Tasnim NImam IHashem M(2022)A Novel Multi-Module Approach to Predict Crime Based on Multivariate Spatio-Temporal Data Using Attention and Sequential Fusion ModelIEEE Access10.1109/ACCESS.2022.317184310(48009-48030)Online publication date: 2022
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