Suraksha: Spatio-Temporal Crime Forecasting and Micro-Location Analysis

被引：0

作者：

Jayawardana, Hiranya ^{[1
]}

Pathmaperuma, Madushi Hasara ^{[2
]}

机构：

[1] Univ Cent Lancashire, Preston, England

[2] Universal Coll Lanka UCL, Sri Jayawardenepura Kotte, Sri Lanka

来源：

JOURNAL OF ELECTRICAL SYSTEMS | 2024年 / 20卷 / 09期

关键词：

Random Forest; Chicago Crime Dataset; Crime Prediction; Location Micro-Analysis; PREDICTION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Suraksha, a spatiotemporal crime prediction system, designed to elevate crime prevention with precise insights, empowering law enforcement for a safer tomorrow. Utilizing vast datasets, machine learning, and GIS, it forecasts crime hotspots by incorporating Chicago's extensive crime statistics. Addressing both precision and ethical considerations, Suraksha achieves RMSE values of 0.0874 (latitude) and 0.0602 (longitude), marking a leap in predictive policing. This pioneering approach aims to transform public safety by proactively combating crime, ensuring community well-being through innovative data-driven strategies.

引用

页码：1635 / 1641

页数：7

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