Past studies have failed to address the spatially and temporally varying impacts of environmental factors regarding the uncertain geographic context problem. This study seeks to provide an innovative framework to facilitate the understanding of spatially and temporally varying impacts of multiple contexts on individuals' travel modes using GIS and machine learning techniques. It adopts machine learning techniques to create likelihood maps to predict the spatiotemporal patterns of individual travel behaviors and uses explanatory tools to explore the spatially and temporally varying impacts. The most notable change at a local level in the spatial dimension was that assaults and offenses involving children turned out to be important in two selected communities in Chicago. Regarding the temporally varying impact, batteries, other offenses, and robberies showed negative associations with the walking prediction to some extent at the afternoon peak (5–7:59 p.m.) during weekdays. The proposed approach will enable meaningful interpretation of complex interactions between multiple environmental factors and individual travel behaviors to suggest policies in urban planning and design.

中文翻译：

---

使用解释工具解释上下文影响：使用 GPS 轨迹的旅行模式可能性映射

过去的研究未能解决环境因素对不确定地理背景问题的空间和时间变化影响。本研究旨在提供一个创新框架，以促进使用 GIS 和机器学习技术了解多种环境对个人出行方式的时空变化影响。它采用机器学习技术创建似然图来预测个人旅行行为的时空模式，并使用解释工具来探索时空变化的影响。在空间维度上，地方层面最显着的变化是涉及儿童的攻击和犯罪在芝加哥的两个选定社区中变得很重要。关于随时间变化的影响、电池、其他罪行，在工作日的下午高峰（下午 5 点至 7 点 59 分），抢劫案与步行预测在一定程度上呈负相关。所提出的方法将能够对多种环境因素和个人出行行为之间的复杂相互作用进行有意义的解释，以提出城市规划和设计的政策。