Spatial Distribution Estimates of the Urban Population Using DSM and DEM Data in China

Abstract

Spatial distribution and population density are important parameters in studies on urban development, resource allocation, emergency management, and risk analysis. High-resolution height data can be used to estimate the total or spatial pattern of the urban population for small study areas, e.g., the downtown area of a city or a community. However, there has been no case of population estimation for large areas. This paper tries to estimate the urban population of prefectural cities in China using building height data. Building height in urban population settlement (Mdiffs) was first extracted using the digital surface model (DSM), digital elevation model (DEM), and land use data. Then, the relationships between the census-based urban population density (CPD) and the Mdiffs density (MDD) for different regions were regressed. Using these results, the urban population for prefectural cities of China was finally estimated. The results showed that a good linear correlation was found between Mdiffs and the census data in each type of region, as all the adjusted R 2 values were above 0.9 and all the models passed the significance test (95% confidence level). The ratio of the estimated population to the census population (PER) was between 0.7 and 1.3 for 76% of the cities in China. This is the first attempt to estimate the urban population using building height data for prefectural cities in China. This method produced reasonable results and can be effectively used for spatial distribution estimates of the urban population in large scale areas. necessary to develop alternative techniques and methods to improve the accuracy, time resolution, and spatial resolution.Currently, mobile phone data and social media data, such as mobile call data [4][5][6], WiFi data [7,8], and social networking service software data [9][10][11], which have short acquisition periods and high timeliness, are popular methods to estimate population density, distribution, and mobility. Deville et al. used mobile phone data to estimate the urban population density of Portugal, and the accuracy of the estimation results was increased by comparing the results with census data at an administrative division level and remote sensing data at a 100 m×100 m grid scale [4]. However, the users of mobile phones and social media service software do not cover the whole country [12], so that the estimated total population might be low if only big data sources are used. Due to privacy, the aforementioned data may be difficult to obtain and thus cannot be widely used, which represents another difficulty when estimating population mobility.Remote sensing has become an important method of performing population estimates in the past four decades [13] and many methods have been reported in the GIS and remote sensing literature. Depending on the intended goal and the required information, these methods can be grouped into two categories: Areal interpolation and statistical modeling [14]. The former is primarily designed for zone transformation that involves transforming...

A 30 m global map of elevation with forests and buildings removed

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Assessing Volcanic Hazard and Exposure at Obscure Volcanic Fields: A Case Study from the Bolaven Volcanic Field, Laos

“…In many cases the available results are estimates only (e.g. Freire et al, 2016;Wardrop et al, 2018;Zhang et al, 2018). The BVF is located in the least economically developed country in Asia (Pink, 2016) and one of the least economically developed countries in the world (Delang and Toro, 2011).…”

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