Density of Route Frequency for Enforcement

N. Aziz, R. M. F. R. M. Yusoff
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Abstract

Recently, big data has become one of the hot topics that lead the organization, either public or private, to generate more efficient management solutions. Not for business purposes only, the big data has the potential to create significant value to assist law enforcement or to improve the policy for the decision-making process. The features of big data that have the ability to handle massive data volume and variety at high velocity, can be used in fields of policing to improve organization operation. The data are valuable for Geographic Information System (GIS) based decision-making. With intelligent GIS, information can be updated across the organization immediately. Geospatial Modelling (GM) is to help in the analysis solution in order to improve the decision-making process in the daily operation of the police. The GM technique is designed to discover patterns data involving the location and other spatial related information such as density. The patterns data consist of valuable traffic density will be used to simulate routes planning in the patrol car. This paper presents the approach on how to generate valuable traffic density to accurately dispatch the patrol cars in a geographic environment as well as to reduce criminal cases.
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执行路线频率的密度
最近,大数据已经成为一个热门话题,引导组织,无论是公共还是私人,产生更有效的管理解决方案。大数据不仅可以用于商业目的,还可以为协助执法或改善决策过程中的政策创造巨大价值。大数据具有高速处理海量、多样数据的能力,可应用于警务领域,提高组织运营水平。这些数据对基于地理信息系统(GIS)的决策具有重要价值。有了智能GIS,信息可以在整个组织内立即更新。地理空间建模(GM)是在分析解决方案,以改善决策过程中,警察的日常运作。GM技术旨在发现涉及位置和其他空间相关信息(如密度)的模式数据。这些模式数据由有价值的交通密度组成,将用于模拟巡逻车的路线规划。本文提出了如何产生有价值的交通密度,以便在地理环境中准确调度巡逻车,减少刑事案件的方法。
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