Methods of Population Spatialization Based on the Classification Information of Buildings from China’s First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China

Abstract

Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.

Evaluation of the Equity of Urban Park Green Space Based on Population Data Spatialization: A Case Study of a Central Area of Wuhan, China

“…Because the population distribution is related to many factors, such as geographical location, land cover, road network convenience, water area, and economic development, etc. [19], some scholars have begun to use a variety of data as indicative factors related to population distribution. For example, Wang L. et al [30] combined LULC data and night-light data and simulated the spatial distribution of population in China in 1990, 2000 and 2010.…”

“…The direct use of population data based on administrative division statistics in micro and medium-scale urban research makes it difficult to meet the corresponding precision requirements, and there is a modifiable areal unit problem [18], which means the analysis results are affected by the selected analysis area unit. In order to improve the accuracy of the evaluation, some studies directly use building data or residential committee data [19,20,21] to characterize population distribution. However, such data is often difficult to obtain.…”

Exploring the Spatial Pattern and Influencing Factors of Land Carrying Capacity in Wuhan

“…For a finer-scaling spatial analysis, it was necessary to construct a grid based on demographic data. Li et al [28] indicated that the 250 m * 250 m population grid shows a higher accuracy with the same datasets from the National Earth System Science Data Sharing Infrastructure (http://www.geodata.cn). Therefore, we also used a 250 m * 250 m fishnet of Wuhan City in this study.…”

Evaluation of the Fairness of Urban Lakes’ Distribution Based on Spatialization of Population Data: A Case Study of Wuhan Urban Development Zone

“…(1) With remote sensing image data as the basis, this study considered the effect of land cover types on the spatial distribution of the population but ignored the vertical distribution (high-rise buildings). Research is conducted to explore population spatialization methods based on building information [48]. However, building data from field trips are typically suitable for small-scale research.…”