Abstract
How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. <br><br> In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. <br><br> Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the approximate 55% spatiotemporal clusters distributed in different locations can be eventually grouped as the same type of clusters with consideration of semantic aspect.
Highlights
A mechanistic understanding of human activity pattern can aid in contributing to a diversity of urban applications, such as urban planning, traffic forecasting, and epidemic prevention
We propose a clustering method which is able to group geotagged social media data from spatiotemporal perspectives and considering similar semantics patterns raising from each spatiotemporal clusters
A total of 1,569 spatiotemporal clusters was generated from 423 different activity spots
Summary
A mechanistic understanding of human activity pattern can aid in contributing to a diversity of urban applications, such as urban planning, traffic forecasting, and epidemic prevention. We propose a clustering method which is able to group geotagged social media data from spatiotemporal perspectives and considering similar semantics patterns raising from each spatiotemporal clusters. Through analysing this type of clusters, the human activity pattern can be analysed more deeply and reasonably. Different spatiotemporal clusters indicate different types of activities in terms of spatiotemporal dimension By using these activity clusters, human mobility pattern can be inferred but what kind of activities occurred within each cluster is still hard to be uncovered. Topic models are subsequently introduced to infer a semantic pattern within each mobility pattern
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