基于K-means的泗水市犯罪率建模信息系统的应用

S. Supangat, M. M. Sholiq
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引用次数: 0

摘要

本研究旨在利用k均值聚类方法对泗水市的犯罪率进行建模。使用的数据是泗水前几年发生的犯罪数据,包括犯罪类型、犯罪地点和犯罪率。采用k-means聚类方法,对泗水地区2020-2022年的犯罪数据进行分类,包括第3类,即犯罪率中等的地区,覆盖6个街道(1260例),第1类,犯罪率高的地区,12个街道,2363例,第2类,犯罪率低的地区,包括13个地区,2178例,基于犯罪数量的数据。地理空间可视化系统用于可视化地显示建模结果,使有关各方更容易识别犯罪的位置。预计这项研究的结果将为有关方面,如警察和社区,提供有用的资料,以采取预防行动,防止泗水的犯罪率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Utilization of Information System for Crime Rate Modelling in Surabaya Using K-means
This study aims to model the crime rate in the city of Surabaya using the k-means clustering method. The data used is crime data that occurred in Surabaya in previous years, which includes the type of crime, location of crime, and crime rate. The k-means clustering method is used to classify crime data in the Surabaya area for 2020-2022 consisting of cluster 3, namely areas with moderate crime rates covering 6 sub-districts (1,260 cases), cluster 1 with areas with high crime rates, namely 12 sub-districts with 2,363 cases, and cluster 2 areas with low crime rates consisting of 13 districts with 2,178 cases based on data on the number of crimes. The geospatial visualization system is used to visually display modeling results, making it easier for interested parties to identify the location of a crime. The results of this study are expected to provide useful information for interested parties, such as the police and the community, in taking preventive action regarding crime rates in Surabaya.
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