Abstract
Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.
Highlights
High-precision mapping of urban population plays an essential role in urban research, which is supportive for urban planning management [1], optimization of resource allocation [2], and understanding of urban spatial structure [3]
The Grid Voronoi building use regression model proposed by the research can be expanded in the following aspects in the future: (1) further constructions of work time and all-time building regression models with the population derived from mobile phone data to deeply understand the spatiotemporal population dynamic distribution in urban space; (2) with the constantly increasing availability of semantic 3D city models [43], the vertical attribute can be included into the attributes of the building data to study the correlation between different building space volumes and population distribution from a three-dimensional perspective; and (3) empirical researches on the potential combinations of different scale grids with building data and mobile phone data in Building Use Regression (BUR) models, as well as their impact on results
This study contributes a proposition for the application of a Grid Voronoi building use regression model to attain the gridded human spatial population in the development area of Wuhan
Summary
High-precision mapping of urban population plays an essential role in urban research, which is supportive for urban planning management [1], optimization of resource allocation [2], and understanding of urban spatial structure [3]. The dasymetric mapping method [4,5,6,7,8,9,10] has become the mainstream direction for high-precision population mapping, utilizing the correlation between population data and auxiliary data to disaggregate population data into micro-spatial levels through interpolation algorithm. The accuracy of dasymetric mapping is still susceptible to the resolution of source data and the correlation between auxiliary data and micro-scale population distribution. Most traditional studies take census data as the source population, which has the disadvantage of insufficient timeliness and cannot meet the current planning management requirements for dynamic high-precision population data [20]. Attributes of auxiliary data in different regions may be the same or similar, which cannot reflect spatial heterogeneity, and brings challenges to the improvement of method accuracy
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