This paper focuses on advancing the traditional association rule mining (ARM) approach to capture the rich, multidimensional and multiscalar context that is anticipated to be associated with residential Motor Vehicle Theft (MVT) across urban environments. We tackle the challenge to materialize complex social and spatial components in the mining process and present a novel interactive visualization based on social network analysis of rules and associations to facilitate the analysis of mined rules. The spatial ARM (SARM) findings successfully identify many socio-spatial associations to MVT prevalence and establish their relative influence on crime outcome in a case study. Also, the analysis provides unique insights to understand the interactive relationships between neighborhood characteristics and environmental features to both high and low MVT and underscores the importance of spatial properties of spillover and neighborhood effects on urban residential MVT prevalence. This work follows the tradition of inductive and abductive learning and presents a promising analysis framework using data mining which can be applied to different applications in social sciences.

本文侧重于推进传统的关联规则挖掘 (ARM) 方法，以捕获预计与城市环境中的住宅机动车辆盗窃 (MVT) 相关的丰富、多维和多标量上下文。我们解决了在挖掘过程中实现复杂的社会和空间组件的挑战，并提出了一种基于规则和关联的社交网络分析的新型交互式可视化，以促进对挖掘规则的分析。空间 ARM (SARM) 研究结果成功地确定了与 MVT 流行率的许多社会空间关联，并在案例研究中确定了它们对犯罪结果的相对影响。还，该分析为理解社区特征和环境特征与高低 MVT 之间的交互关系提供了独特的见解，并强调了溢出的空间特性和邻里效应对城市住宅 MVT 流行率的重要性。这项工作遵循归纳和溯因学习的传统，并提出了一个使用数据挖掘的有前途的分析框架，可应用于社会科学的不同应用。