本論文利用Google Maps TimeLine服務，直接使用個人智慧型手機中GPS定位功能，蒐集國立清華大學學士班共168位學生於校園中的數位足跡，運用R語言Shiny工具建構數位足跡視覺化系統以互動式網頁呈現足跡分布，結合五大人格特質以及校園空間，讓研究者可以分析數位足跡、人格特質以及校園空間之間的關連。本論文使用機器學習之分群演算法、社交網路分析進行運算，採用高低分組針對五大人格特質中五種類別進行個別分析，結果發現外向性偏高，其足跡較容易聚集於校園中的社團空間；而外向性偏低、神經質偏高或友善性偏低的學生，其足跡較容易聚集於校園外租屋處或校園外飲食區；開放性偏高的男學生較喜歡在校外用餐、開放性偏低的女學生較喜歡在校內用餐；而嚴謹性偏低的學生卻比嚴謹性偏高的學生更容易在圖書館停留。本論文除了與相關文獻的結果一致外，還可發現過去未知的關連，對於未來研究者可提供在有限的設備以及人力成本的情況下，運用受試者隨身攜帶設備來蒐集資料，進行數位足跡相關研究之參考。

This paper revealed the relationships between GPS-based digital footprints and personality by unsupervised machine learning methods and Social Network Analysis tools. All the data was collected by Google Maps TimeLine service of personal smartphones for two semesters. Participants were 168 undergraduate students of National Tsing Hua University. Meanwhile, a user-friendly interface of data visualization was developed in this paper for exploring the potential patterns of row data.
The participants’ big-five personality was discovered by questionaries before data collection. The results showed that if students’ extraversion was high, their footprints were likely to be gathered near the club offices in campus; if the students with low extraversion, high neuroticism or low agreeableness, their footprints were likely to be gathered in the rental housing or foodcourt outside the campus; male students with high openness to experience prefer eating outside the campus, female students with low openness to experience prefer eating inside the campus; while students with low conscientiousness are more likely to stay in the library than those with high conscientiousness.
This paper discussed the usability of using personal GPS devices of cell phones in digital footprint collection and applying machine learning methods for analyzing the relationships between the footprint and personality, which can be used as a reference for future researchers.

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