The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime‐related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non‐game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary‐based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft‐from‐vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.

中文翻译：

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使用推文了解温哥华曲棍球事件的空间犯罪分布变化。

事实证明，在社交事件中使用社交媒体数据进行犯罪模式的空间分析具有指导意义。这项研究考虑了曲棍球比赛日，犯罪行为和Twitter活动，分析了犯罪地域。具体而言，我们考虑与地理位置相关的与犯罪相关的Twitter活动与犯罪之间的关系。通过游戏和非游戏时间分辨率，我们分析了六个曲棍球季节在温哥华的传播区域基本单位中汇总的六种财产犯罪类型。使用相同的方法，将地理定位的Twitter消息和环境变量汇总到传播区域。我们采用空间聚类，基于推文的基于字典的挖掘，空间自相关以及全局和局部回归模型（空间滞后和地理加权回归）。调查结果表明，Twitter数据对偷车和恶作剧具有重要影响，主要是在曲棍球比赛期间。地理加权回归模型的关系表明，推文是一个有价值的自变量，可用于解释和理解犯罪模式。