论文标题
使用Twitter和出租车数据改善犯罪数量的预测
Improving Crime Count Forecasts Using Twitter and Taxi Data
论文作者
论文摘要
犯罪预测对于刑事司法决策者和预防犯罪的努力至关重要。本文评估了从出租车旅行，Twitter和Foursquare数据得出的人类活动模式的解释性和预测价值。对纽约市六个月的犯罪数据分析表明，与仅使用人口统计学数据相比，这些数据源将财产犯罪的预测准确性提高了19％。当新颖的功能一起使用时，这种效果最强，从而产生了对犯罪预测的新见解。值得注意的，并且与社会混乱理论一致，新颖的特征不能改善暴力犯罪的预测。
Crime prediction is crucial to criminal justice decision makers and efforts to prevent crime. The paper evaluates the explanatory and predictive value of human activity patterns derived from taxi trip, Twitter and Foursquare data. Analysis of a six-month period of crime data for New York City shows that these data sources improve predictive accuracy for property crime by 19% compared to using only demographic data. This effect is strongest when the novel features are used together, yielding new insights into crime prediction. Notably and in line with social disorganization theory, the novel features cannot improve predictions for violent crimes.
