Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

Abstract

Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

“…To evaluate the performance of the models and for consistency with previous validation studies ( Zou et al, 2019;Yang, Ye, et al, 2019;Wang et al, 2019), the tenfold CV method is used in our study and four statistical metrics-the coefficient of determination (R 2 ), root-mean-square error (RMSE), mean fractional bias (MB), and correlation coefficient (R)-are used to measure the prediction performance. The four statistical metrics are calculated at each radiation site using the following equations (1)-(4), respectively:…”

“…DGSR is also crucial for the utilization of solar energy through transformation of technologies (Wu et al, 2012(Wu et al, , 2016Tang et al, 2018;Prăvălie et al, 2019;Zou et al, 2019). In recent years, there has been an aggravation of air pollution in China induced by massive fossil fuel consumption and emissions coinciding with unfavorable weather conditions (Guo et al, 2016Lou et al, 2019;Yang, et al, 2018;Yang, Ye, et al, 2019;Zheng et al, 2019). On the one hand, this has significantly modulated the change in surface solar radiation (Che et al, 2005;Guo et al, 2018;Wang et al, 2012Wang et al, , 2013Wang & Wild, 2016;Zheng et al, 2018;He & Wang, 2020;Yang et al, 2020), while on the other hand, it has led to solar radiation becoming one of the fastest-growing and important sources of clean and renewable energy.…”

An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh

“…This is especially important in low-and middle-income countries (LMICs) where resources for conducting surveys are usually limited. Moreover, the accuracy of the population distribution products in urban areas and in the outskirts is lower due to the high heterogeneity of the urban features [16]. Given the increasing urbanization experienced worldwide, the demand for mapping and the understanding of population distribution at a high-resolution is increasing as well.Dasymetric mapping is a particular cartographic representation of data, where census data are disaggregated into spatial units/grids with the aid of ancillary data to produce HGPS [17].…”

Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs

“…Moreover, two raster layers were produced for each POI category. Kernel density estimation (KDE) was applied to each category to generate a smooth and continuous POI density layer [34,47]. KDE is a well-known method to estimate the probability density function of a random variable.…”

“…In the context of dasymetric mapping, the accuracy of the population estimates depends greatly on the covariates used [27]. Remote sensing products and geospatial big data are the most commonly used covariates in dasymetric mapping [1,3,18,[28][29][30][31][32][33][34][35]. Remotely sensed population-related products, such as land use/land cover (LULC) data and nighttime light (NTL) data, could show the actual surface conditions that reflect the physical factors that affect the population distribution.…”