论文标题
是还是不成为？罗切斯特的空间预测犯罪模型
To be or not to be? A spatial predictive crime model for Rochester
论文作者
论文摘要
该项目使用空间模型（地理位置加权回归）将各种物理和社会特征与犯罪率联系起来。除了从基本数据统计数据中做出有趣的预测外，训练有素的模型还可以用于预测测试数据。然后，该预测对测试数据的高度准确性使我们能够根据位置，人口，财产率，日/年的时间等地预测不同领域的犯罪概率。然后，这进一步使我们想到了可以构建申请，以帮助人们在罗切斯特周围旅行的人们何时以及是否进入犯罪区域。
This project uses a spatial model (Geographically Weighted Regression) to relate various physical and social features to crime rates. Besides making interesting predictions from basic data statistics, the trained model can be used to predict on the test data. The high accuracy of this prediction on test data then allows us to make predictions of crime probabilities in different areas based on the location, the population, the property rate, the time of the day/year and so on. This then further gives us the idea that an application can be built to help people traveling around Rochester be aware when and if they enter crime prone area.
