方法证据记录
Kernel Density Crime Mapping
Kernel density crime mapping turns a scatter of geocoded crime points into a smooth, continuous surface that shows where incidents concentrate. Each event is spread out over a small neighborhood by a kernel function, and the overlapping contributions are summed across a fine grid so that areas with many nearby crimes glow as peaks. Chainey, Tompson, and Uhlig (2008) showed that, among common hot-spot mapping techniques, kernel density estimation is one of the most accurate at predicting where future crime will occur, which is why it became the default crime-mapping surface in policing.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Kernel Density Estimation for Crime Mapping
分类方法记录 · process-pipeline / criminology
- Chainey, S., Tompson, L., & Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1–2), 4–28. · DOI 10.1057/palgrave.sj.8350066
- Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall. · ISBN 9780412246203
精选声明
声明已持久化到证据分类账中，每个声明都有自己的评估。
尚无精选声明
当分类账中没有声明时，此视图不会自行创建声明评估。
相关方法
从方法图中生成，显示为机器建议的关系 — 不推断任何证据声明。