Community security is the key to sustainable governance: Methods and functions of crime hotspot predictions

Abstract

Forecasting is becoming increasingly important in corporate sustainability governance, as is government governance, and the prediction of police crime hotspots is related to human rights, so transparency is needed. There are many ways to predict hotspots of criminal activity in urban areas. Experts assume that if many crimes occur somewhere, even more, are likely to happen at subsequent times. Such predictions may rely on a state dependency model such as the Poisson distribution algorithm to formulate re-occurrence, its results can provide a visualized hotspot map with Q-GIS maps. Forecasting sets the threshold for re-occurrence and affects the distribution of the forecast. This paper studies the occurrence of criminal activity in urban areas, refers to the metrics set by the NIJ’s crime prediction contest and focuses on the presentation of the results by accumulating different historical data. It was determined that when the amount of cumulative data is greater, its prediction measures by the prediction accuracy index (PAI) insures that accuracy is improved, but the prediction efficiency index (PEI) that efficiency level is worse. Because threshold setting directly affects the performance of the forecast, it can be used differently. Here sets four different indicators, hit rate, useful rate, waste rate, and missing rate. It was determined that the hit rate, missing rate, the PAI value, and the PEI value are directly proportional to the threshold value, while the trend of useful rate and waste rate are inversely related. Concerned policymakers can set different thresholds dependent up the number and budgetary constraints of police forces, and they can work towards achieving crime prevention in urban hotspots. Importantly, Poisson’s approach can be simply implemented with Excel, be conducive to drive by the office practitioner, and elevate the transparency of crime prediction.