Next Article in Journal
Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images
Previous Article in Journal
Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm
Article

Modeling the Hourly Distribution of Population at a High Spatiotemporal Resolution Using Subway Smart Card Data: A Case Study in the Central Area of Beijing

by 1,2,3, 1,2,3,*, 1,2,3 and 1,2,3
1
Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China
2
Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing Normal University, Beijing 100875, China
3
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editors: Norbert Bartelme and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(5), 128; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6050128
Received: 22 February 2017 / Revised: 16 April 2017 / Accepted: 24 April 2017 / Published: 26 April 2017
The accurate estimation of the dynamic changes in population is a key component in effective urban planning and emergency management. We developed a model to estimate hourly dynamic changes in population at the community level based on subway smart card data. The hourly population of each community in six central districts of Beijing was calculated, followed by a study of the spatiotemporal patterns and diurnal dynamic changes of population and an exploration of the main sources and sinks of the observed human mobility. The maximum daytime population of the six central districts of Beijing was approximately 0.7 million larger than the night-time population. The administrative and commercial districts of Dongcheng and Xicheng had high values of population ratio of day to night of 1.35 and 1.22, respectively, whereas Shijingshan, a residential district, had the lowest value of 0.84. Areas with a high population ratio were mainly concentrated in Dongcheng, Xicheng, West Chaoyang, and Southeast Haidian. The daytime population distribution showed a hierarchical spatial pattern of planar centers and second scattered centers as opposed to multiple scattered centers during the night-time. This was because most people moved inward from the areas with a low–high to high–low population ratio of day to night from night-time to daytime, which can be explained by the process of commuting between residential areas and workplaces. Several distinctive phenomena (e.g., the distribution of new industrial parks, the so-called old residential areas, and colleges and universities) in the development of China are reflected by the spatiotemporal pattern of the distribution of population. The general consistency of the population ratios of day to night, population distribution, population variation of typical communities, and population mobility pattern with previous research suggests that the subway smart card data has potential in analyzing dynamic diurnal variations of urban population. This method can be easily duplicated to calculate hourly dynamic changes in population at the community level. These results can be used to estimate the potential hourly number of evacuees under different temporal scenarios of disasters and to support future urban planning in Beijing. View Full-Text
Keywords: population distribution; hourly; community level; innovative model; subways smart card data; Beijing population distribution; hourly; community level; innovative model; subways smart card data; Beijing
Show Figures

Figure 1

MDPI and ACS Style

Ma, Y.; Xu, W.; Zhao, X.; Li, Y. Modeling the Hourly Distribution of Population at a High Spatiotemporal Resolution Using Subway Smart Card Data: A Case Study in the Central Area of Beijing. ISPRS Int. J. Geo-Inf. 2017, 6, 128. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6050128

AMA Style

Ma Y, Xu W, Zhao X, Li Y. Modeling the Hourly Distribution of Population at a High Spatiotemporal Resolution Using Subway Smart Card Data: A Case Study in the Central Area of Beijing. ISPRS International Journal of Geo-Information. 2017; 6(5):128. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6050128

Chicago/Turabian Style

Ma, Yunjia, Wei Xu, Xiujuan Zhao, and Ying Li. 2017. "Modeling the Hourly Distribution of Population at a High Spatiotemporal Resolution Using Subway Smart Card Data: A Case Study in the Central Area of Beijing" ISPRS International Journal of Geo-Information 6, no. 5: 128. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6050128

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop