E3S Web Conf.
Volume 145, 20202019 International Academic Exchange Conference on Science and Technology Innovation (IAECST 2019)
|Number of page(s)||9|
|Section||International Conference on New Energy Science and Environmental Engineering|
|Published online||06 February 2020|
Understanding Spatial and Temporal Change Patterns of Population in Urban Areas Using Mobile Phone Data
1 Research Institute of Highway Ministry of Transport, Beijing, 100086, China
2 Research Institute of Highway Ministry of Transport, Beijing, 100086, China
3 College of Metropolitan Transportation, Beijing University of Technology, Chaoyang District, Beijing, China
* Corresponding author’s e-mail: email@example.com
The wide application of information computing technology has allowed for the emergence of big data on tracing human activities. Therefore, it provides an opportunity to explore temporal profile of population changes in geographical area subdivisions. In this paper, we present a multi-step method to characterize and approximate temporal changes of population in a geographical area subdivision using eigen decomposition. Datasets in weekday and weekend are decomposed to obtain the principal temporal change profiles in Xiamen, China. The Principal Components are common patterns of temporal population changes shared by most geographical area subdivisions. Its corresponding elements in eigenvectors could be regard as a coefficient to principal components. Then, a measure, which is the similarity of each eigenvector to a basis vector, that could characterize the temporal population change is established. Based on this, the coupling interaction between population changes and land use characteristics is explored using this measure. It shows that it is restricted by land use characteristics and also is a reflection of population changes over time. These results provided an insight on understanding temporal population change patterns and it would help to improve urban planning and establish a job-housing balance.
© The Authors, published by EDP Sciences 2020
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