特征向量空间滤波方法在室外盗窃空间分析中的应用

Application of Eigenvector Spatial Filtering in Spatial Analysis of Larceny-theft

在犯罪空间分析和空间建模过程中,模型残差中的空间自相关问题对模型参数估计的准确度和犯罪相关因素的分析构成了极大的阻碍,模型残差存在显著空间自相关会导致模型的有偏估计及误导性的推断,甚至导致错误的研究结论。本研究采用一种较为新颖的空间统计方法,即特征向量空间滤波方法(Eigenvector Spatial Filtering),来消除犯罪回归模型估计过程中的残差自相关问题,以及由此引发的模型参数有偏估计问题。以此为基础,立足犯罪模式理论和社会解组理论,以浙江省海宁市主城区为研究区,采用2018年1月—2021年9月室外盗窃警情立案数据、海宁市建成环境数据、珞珈一号夜间灯光遥感数据以及WorldPop人口网格数据,在网格尺度上构建基于特征向量空间滤波的泊松回归模型,在消除模型残差自相关、纠正模型参数估计偏误的基础上,识别出海宁室外盗窃犯罪的影响因素。研究发现:(1)基于特征向量空间滤波的泊松回归模型提取出了模型残差中的自相关部分,确保残差无显著空间自相关,将显著的空间滤波加入回归模型,较大幅度提升了模型拟合优度,纠正了系数估计偏误问题,缓解了过度离散问题,并找回了遗漏变量。该方法可推广至其他计数模型和广义线性回归分析场景中,有助于提升模型参数的准确估计水平,找回因自相关等原因而被遗漏的变量;(2)新兴时空热点分析显示,室外盗窃绝对数量随疫情到来呈递减趋势,室外盗窃热点持续于海宁市主城区中部,冷点呈多点分布;(3)人均夜间灯光所表征的城市相对剥夺水平对室外盗窃有显著正向影响;(4)由各类建成环境所刻画的犯罪吸引地、产生地、促成地对室外盗窃有显著影响,本文同时也对与以往研究结论不一致的地方进行了讨论。

In spatial analysis and modeling of urban crime,the spatial autocorrelation of model residuals poses an significant obstacle to model parameter estimation and produces deviations in analysis of the determinants of urban crime.The presence of significant spatial autocorrelation of model residuals and overdispersion of the model could lead to biased estimates and misleading inferences,even resulting in wrong conclusions.This study employed a new spatial regression method,namely Poisson regression with Eigenvector Spatial Filtering,to solve the problem of model residual spatial autocorrelation and model overdispersion to avoid subsequent biased estimation in model results.To explain the spatial variation of urban crime,we used two theories in spatial crime analysis:crime pattern theory and social disorganization theory.The case study focused on the main urban area of the Haining city in Zhejiang province,China,and the crime data that we used were larceny-theft over a fouryear period,from January 2018 to September 2021.Other datasets that we employed for generating covariates included POI data of various facilities in Haining,the Luojia 1-01 nighttime light data,and the WorldPop global population data.We established a Poisson regression model with eigenvector spatial filtering and further identified several important determinants of larceny-theft with unbiased model parameters.The major findings are as follows:(1) The Poisson regression with eigenvector spatial filtering identified the spatial autocorrelation of model residuals,ensuring no significant spatial autocorrelation issue in model residuals.This can improve the model's goodness of fit,correct model parameter estimation,alleviate the impact of overdispersion,and retrieve omitted variables.More importantly,the eigenvector spatial filtering method could be applied to other generalized linear models such as Poisson regression;(2) The results of Emerging Hot Spot Analysis showed that the absolute number of larceny-theft decreased during the period of COVID-19 pandemic,and crime hot spots occurred in the central places of the main urban area of Haining while the cold spots exhibited a trend of multipoint distribution;(3) The level of relative deprivation measured by per capita nighttime light had a significant impact on larceny-theft in the unbiased model with eigenvector spatial filtering;(4) The crime generator,attractor and enabler in various built environment of interest had a significant impact on larceny-theft.The inconsistencies with the conclusions of previous studies were also discussed.