| Author: | Guo, Zijian |
| Title: | Modeling activity space using travel big data |
| Advisors: | Liu, Xin Tao (LSGI) |
| Degree: | M.Sc. |
| Year: | 2019 |
| Subject: | Invasive plants -- China - Hong Kong Vegetation monitoring -- Remote sensing Lamma Island (Hong Kong, China) Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Construction and Environment Department of Land Surveying and Geo-Informatics |
| Pages: | viii, 70 pages : color illustrations |
| Language: | English |
| Abstract: | The activity space is important in the fields of studying human behaviors and urban development. In the past, traditional approaches have paid little attention to directionality and trends of human activity, and many of them build the space based on the distribution of point elements. In my work, combined with the characteristics of Wi-Fi big data in Macau, I designed a vector field model based on clients' trajectory weighted by distance, time and POI. I developed a vector field construction solution that can be used for big data, including data acquisition, preprocessing, vector field construction and visualization. In the case study, I built a vector field with simulated Wi-Fi data in Macau, based on which I qualitatively analyzed the geometric characteristics of the vector field itself and the relationship between this vector field and the activity space. Finally, I compared this method with other methods of activity space to discuss the advantages of this method. As a result, for individuals, we can see the inclinations and habits of individuals. From the overall perspective, we can see the trend of population movement over a period. If we divide activity space into different groups, we can also differentiate their behavioral characteristics. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022385545403411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.29 MB | Adobe PDF | View/Open |
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