Journal
JOURNAL OF TRANSPORT GEOGRAPHY
Volume 19, Issue 3, Pages 394-404Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jtrangeo.2010.11.002
Keywords
Time geography; Space-time GIS; Activity diary data
Categories
Ask authors/readers for more resources
Study of human activities in space and time has been an important research topic in transportation research. Limitations of conventional statistical methods for analysis of individual-level human activities have encouraged spatiotemporal analysis of human activity patterns in a space-time context. Based on Hagerstrand's time geography, this study presents a space-time GIS approach that is capable of representing and analyzing spatiotemporal activity data at the individual level. Specifically, we have developed an ArcGIS extension, named Activity Pattern Analyst (APA), to facilitate exploratory analysis of activity diary data. This extension covers a set of functions such as space-time path generation, space-time path segmentation, space-time path filter, and activity distribution/density pattern exploration. It also provides a space-time path based multi-level clustering method to investigate individual-level spatiotemporal patterns. Using an activity diary dataset collected in Beijing, China, this paper presents how this Activity Pattern Analyst extension can facilitate exploratory analysis of individual activity diary data to uncover spatiotemporal patterns of individual activities. (C) 2010 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available