Disaggregating County-Level Census Data for Population Mapping Using Residential Geo-Objects With Multisource Geo-Spatial Data

Abstract

Accurate spatialization of socioeconomic data is conducive to understand the spatial and temporal distribution of human social development status and, thus, effectively support future scientific decision-making. This study focuses on population mapping, which is a classical spatialization of macroeconomic data of the social economy. Traditional population mapping based on rough grids or administrative divisions such as townships often has deficiencies in the accuracy of spatial pattern and prediction. In this article, hence, we employ residential geo-objects as basic mapping units and formalize the problem as a spatial prediction process using machine-learning (ML) methods with high-spatial-resolution (HSR) satellite remote sensing images and multisource geospatial data. The indicators of population spatial density, including residential geo-objects' area, building existence index, terrain slope, night light intensity, density of point of interest (POI) and road network from Internet electronic maps, and locational factors such as the distances from road and river, are jointly applied to establish the relationship between these multivariable factors and quantitative index of population density using ML algorithms such as Random Forests and XGBoost. The predicated values of population density from the mined nonlinear regression relation are further used to calculate the weights of disaggregation of each unit, and then the population quantity distribution at the scale of residential geo-objects is obtained under the control of the total amount of population statistics. Experiments with a county area

Geoparcel-Based Spatial Prediction Method for Grassland Fractional Vegetation Cover Mapping

“…In such an HSR-RS image, the boundary of the grassland parcel is highlighted by merging a group of adjacent pixels. In addition, visual features, including spectrum, shape features and texture, were extracted from the HSR-RS image [24][25][26][27].…”

Population estimation beyond counts—Inferring demographic characteristics

“…With more than half of the world’s population living in urban areas, and with this trend continuing positive trajectory, urban management, planning and analysis are increasingly important to better understand, manipulate and improve urban systems [ 1 – 3 ]. For effective planning and appropriate measures, data on demographic distributions plays an important role [ 2 , 4 ]. These spatial patterns are essential to gain knowledge about socio-economic and environmental phenomena, which supports both public and private sectors in planning and decision making [ 5 , 6 ].…”

“…These predictors come in different forms and shapes and from different sources [ 22 ]. For example, land use classes and night time lights, derived from remote sensing techniques, are a common set of information that are used in population estimations [ 1 , 4 , 23 – 25 ]. Further examples are many: household counts [ 4 , 6 ], telecommunication data [ 10 , 26 , 27 ], tax parcel information [ 28 ], and social media [ 29 , 30 ].…”

“…For example, land use classes and night time lights, derived from remote sensing techniques, are a common set of information that are used in population estimations [ 1 , 4 , 23 – 25 ]. Further examples are many: household counts [ 4 , 6 ], telecommunication data [ 10 , 26 , 27 ], tax parcel information [ 28 ], and social media [ 29 , 30 ]. The large number of disparate information and wide range of data sources used in the analyses are united in predicting the number of people living in an area, but they do not do much beyond that despite the diversity of input data.…”

Comparison of Twelve Machine Learning Regression Methods for Spatial Decomposition of Demographic Data Using Multisource Geospatial Data: An Experiment in Guangzhou City, China

“…However, the spatial resolution and update frequency of census data are too low to meet the requirements of modern urban governance. Due to the fact that demographic data is usually collected in subdistrict units, the spatial decomposition of demographic data into gridded population data can show population distribution patterns more accurately [7][8][9][10][11]. Therefore, fine-scale and accurate population information is essential for exploring the relationship between urban residents and the built environment [1].…”