Geostatistical methods have rarely been applied to area‐level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car‐related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area‐to‐area and area‐to‐point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local‐ and regional‐scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car‐related theft at different scales.

中文翻译：

---

将地统计分析应用于犯罪数据：波罗的海国家与汽车相关的盗窃。

地统计方法很少应用于区域级犯罪数据。本文利用先前分析的 2000 年爱沙尼亚、拉脱维亚和立陶宛汽车相关盗窃数据，展示了它们在改善对犯罪模式的解释和理解方面的潜力。变异函数用于了解犯罪、社会和犯罪行为的变化规模。经济数据。区域到区域和区域到点的泊松克里金法用于过滤小数问题引起的噪声。后者还用于生成估计犯罪风险（每 10,000 名居民的预期犯罪数量）的连续地图，从而减少大空间单元的视觉偏差。在寻求检测最可能的犯罪集群时，通过使用随机模拟的局部集群分析来处理与犯罪风险估计相关的不确定性。阶乘克里金分析用于估计犯罪风险和解释变量的局部和区域尺度空间成分。然后使用回归模型来确定哪些因素与不同规模的汽车相关盗窃风险相关。