Open Access

ARTICLE

Exploring Urban Population Forecasting and Spatial Distribution Modeling with Artificial Intelligence Technology

Yan Zou^1,2,3,*, Shaoliang Zhang¹, Yanhai Min¹

1 School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 22116, China.
2 School of Humanity and Law, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China.
3 School of Global, Urban and Social Studies, RMIT University, Melbourne, VIC 3001, Australia.

* Corresponding Author: Yan Zou. Email: .

(This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)

Computer Modeling in Engineering & Sciences 2019, 119(2), 295-310. https://doi.org/10.32604/cmes.2019.03873

Abstract

The high precision population forecasting and spatial distribution modeling are very important for the theory and application of population sociology, city planning and Geo-Informatics. However, the two problems need to be solved for providing the high precision population information. One is how to improve the population forecasting precision of small area (e.g., street scale); another is how to improve the spatial resolution of urban population distribution model. To solve the two problems, some new methods are proposed in this contribution. (1) To improve the precision of small area population forecasting, a new method is developed based on the fade factor and the slide window. (2) To improve the spatial resolution of urban population distribution model, a new method is proposed based on the land classification, public facility information and the artificial intelligence technology. For validation of the proposed methods, the real population data of 15 streets in Xicheng district, Beijing, China from 2010 to 2016, the remote sensing images and the public facility data are collected and used. A number of experiments are performed. The results show that the spatial resolution of proposed model reaches 30m*30m and the forecasting precision is better than 5% using the proposed method to forecast the population of 15 streets in Xicheng district in the next four years.

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Citations