Tropical Geography ›› 2020, Vol. 40 ›› Issue (1): 101-109.

### Population Spatialization Based on Satellite Remote Sensing and POI Data: Guangzhou as an Example

Zhao xin1, Song Yingqiang1a, Liu Yilun1a, Chen Feixiang1a, Hu Yueming1,2,3()

1. 1. a. College of natural resources and environment, South China Agricultural University; b. Key Laboratory of Construction Land improvement, Ministry of Land and Resources; c. Guangdong Province Key Laboratory for Land Use and Consolidation; d. Guangdong Province EngineeringResearch Center for Land Information Technology, Guangzhou 510642, China
2. College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
3. College of natural resources and environment, University of Electronic Science and Technology of China, Chengdu 610054, China
• Received:2019-04-15 Revised:2019-09-04 Online:2020-01-10 Published:2020-02-24
• Contact: Hu Yueming E-mail:yueminghugis@163.com

Abstract:

The fine-scale spatial distribution of a population can provide assistance for decision-making regarding research, such as urban planning, economic decision-making, disaster prevention, and crime management, which is of considerable importance to the study of human activities. Considering Guangzhou as an example, this study constructed a population spatialization index system based on NPP/VIIRS night light intensity, land use, points of interest (POI), and other natural geographical and socio-economic factors. Further, the principal component weighting method was used to calculate the weight of the population distribution. According to which, geographic information system (GIS) technology was used to assign census data to each pixel. Finally, an accuracy test was performed by comparing the result of the population spatialization with the real population of townships of Guangzhou. The result of the population spatialization, considering natural geography and socio-economic factors, is consistent with the real population spatial pattern, and the spatial resolution is 30 m. The accuracy test result showed that the absolute relative error of 62 townships was less than 25%, which accounted for 36.47% of all townships in Guangzhou, indicating that the result of the population spatialization is highly accurate. The population spatialization result without POI data showed that the absolute relative error of 33 townships was less than 25%, which accounted for 20% of all townships in Guangzhou, showing that the accuracy of population of the spatialization result was significantly reduced. Compared with other studies on population spatialization, this study included the following characteristics: 1) a comprehensive consideration of NPP/VIIRS night light intensity, land use, POI, and other natural geography and socio-economic factors, which are helpful for achieving high-precision results of population spatialization and 2) POI data that can reflect surface features, such as urban land use types and population activity preferences. Additionally, POI data are easy to acquire, rich in data volume, have a high positioning accuracy, and have a better resolution regarding micro details. Introducing POI data into population spatialization research can expand the data source of population spatialization and improve the accuracy of population spatialization results.

CLC Number:

• P208