This paper focuses on finding spatial and temporal criminal hotspots. It
analyses two different real-world crimes datasets for Denver, CO and Los
Angeles, CA and provides a comparison between the two datasets through a
statistical analysis supported by several graphs. Then, it clarifies how we
conducted Apriori algorithm to produce interesting frequent patterns for
criminal hotspots. In addition, the paper shows how we used Decision Tree
classifier and Naive Bayesian classifier in order to predict potential crime
types. To further analyse crimes datasets, the paper introduces an analysis
study by combining our findings of Denver crimes dataset with its demographics
information in order to capture the factors that might affect the safety of
neighborhoods. The results of this solution could be used to raise awareness
regarding the dangerous locations and to help agencies to predict future crimes
in a specific location within a particular time.

通过统计分析以及利用 Apriori 算法找出犯罪热点、利用决策树分类器及朴素贝叶斯分类器预测潜在的犯罪类型，再结合人口统计数据对犯罪数据集进行分析，以识别可能影响社区安全的因素。通过该研究，可以提高人们对危险区域的认识，并帮助机构预测特定地点在特定时间内的犯罪行为。