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.
Highlights
Urban population forecasting and spatial distribution can provide important information to local governments, businesses and academics for various purposes
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 four years
To improve the precision of small area population forecasting, a new method is developed based on the fade factor and the slide window; and to improve the spatial resolution of urban population distribution model, a new method is proposed based on the land classification, city public facility information and the artificial intelligence technology
Summary
Urban population forecasting and spatial distribution can provide important information to local governments, businesses and academics for various purposes. The demographic model can obtain the high precise results of the large area population forecasting (such as a country, a province, a state), where Mean Absolute Percentage Error (MAPE) will be less than 6% [Wilson (2016)]. It is not suitable for the small area population forecasting since the small area is lack of the necessary population statistical information, such as birth rate, death rate, migration rate, etc. Tab. 1 shows the merits and demerits of demographic model and pure mathematic model
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