Search Results (28)

Accurate demographic data are essential for evaluating flood exposure in urban areas, where heterogeneous environment and localized risks complicate modeling efforts. Gridded population datasets serve as valuable resources for such assessments; however, differences in spatial resolution and methodology can significantly affect flood-exposed population estimates. This study evaluates how various gridded population datasets influence the sensitivity and accuracy of flood exposure estimates in Gangnam District, Seoul. Seven datasets from Statistical Geographic Information Service (SGIS), National Geographic Information Institute (NGII), and Intelligent Dasymetric Mapping (IDM), ranging from 30 m to 1 km in resolution, were evaluated against census data to assess their accuracy and variability in flood exposure estimates. The results indicate that multi-source gridded population datasets with different spatial resolutions and modeling approaches strongly affect both the accuracy and variability of flood-exposed population estimates. IDM 30 m outperformed other datasets, showing the lowest variability (CV = 0.310) and the highest agreement with census data (RMSE = 193.51; R² = 0.9998). Coarser datasets showed greater estimation errors and variability. These findings demonstrate that fine-resolution IDM population dataset yields reliable results for flood exposure estimation in Gangnam, Seoul. They also highlight the need for further comparative evaluations across different hazard and spatial contexts. Full article

This study develops a globally adaptable and scalable methodology for high-resolution, building-level population mapping, integrating Earth observation techniques, geospatial data acquisition, and machine learning to enhance population estimation in rapidly urbanizing cities, particularly in developing countries. Using Bangkok, Thailand, as a case study, this research presents a problem-driven approach that leverages open geospatial data, including Overture Maps and OpenStreetMap (OSM), alongside Digital Elevation Models, to overcome limitations in data availability, granularity, and quality. This study integrates morphological terrain analysis and machine learning-based classification models to estimate building ancillary attributes such as footprint, height, and usage, applying micro-dasymetric mapping techniques to refine population distribution estimates. The findings reveal a notable degree of accuracy within residential zones, whereas performance in commercial and cultural areas indicates room for improvement. Challenges identified in mixed-use and townhouse building types are attributed to issues of misclassification and constraints in input data. The research underscores the importance of geospatial AI and remote sensing in resolving urban data scarcity challenges. By addressing critical gaps in geospatial data acquisition and processing, this study provides scalable, cost-effective solutions in the integration of multi-source remote sensing data and machine learning that contribute to sustainable urban development, disaster resilience, and resource planning. The findings reinforce the transformative role of open-access geospatial data in Earth observation applications, supporting real-time decision-making and enhanced urban resilience strategies in rapidly evolving environments. Full article

Data repositories such as Eurostat and OECD provide important socioeconomic datasets useful to guide decision support towards reaching sustainable development goals. However, socioeconomic data are typically available at a limited spatiotemporal scale. In the Horizon Europe-funded AquaINFRA project, a specific scope is to make EU data more analysis ready. As part of this, transformations of data into common spatial entities are needed to facilitate cross-analysis in, for example, social-ecological modelling. This paper uses CORINE land cover as ancillary data and EUROSTAT population data to investigate binary and weighted dasymetric refinement strategies to arrive at areal interpolation algorithms to estimate population data at smaller spatial scales. Six different algorithms are presented, and their accuracies are tested with quality measures. Their limitations and further development potentials on how to make them more precise and expand their usefulness in the future to other types of socioeconomic data are discussed. Full article

Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps. Full article

Rational delineation of urban–rural boundaries is a foundational prerequisite for holistic urban and rural development planning and rational resource allocation. However, using a single data source for urban–rural boundaries yields non-comprehensive results. To address this problem, the present study proposes a method for extracting urban–rural boundaries using multiple sources such as population data, nighttime light data, land use, and points of interest (POI) data. Considering Guizhou Province for a case study, this study presents a two-step method for identifying urban–rural boundaries. First, the random forest model was combined with the dasymetric mapping method to obtain the province’s population spatialization data with a 30-m resolution. Second, based on the spatialized population, the urban–rural boundary for Guizhou Province in 2020 was extracted using the breaking point method. This method comprehensively integrated the benefits of various data and judiciously extracted the boundaries of the main urban areas and small and medium-sized towns of each city in the study province at the same spatial scale. The stratified random sampling method revealed an average overall accuracy of 88.05%. The proposed method has high universality and application value and can be useful for accurate and practical identification of urban–rural boundaries. Full article

Emerging megacities in the global south face unprecedented transformation dynamics, manifested in rapid demographic, economic, and physical growth. Anticipating the associated sustainability and resilience challenges requires an understanding of future trajectories. Global change models provide consistent high-level urbanization scenarios. City-scale urban growth models accurately simulate complex physical growth. Modeling approaches linking the global and the local scale, however, are underdeveloped. This work introduces a novel approach to inform a local urban growth model by global Shared Socioeconomic Pathways to produce consistent maps of future urban expansion and population density via cellular automaton and dasymetric mapping. We demonstrate the approach for the case of Pune, India. Three scenarios are explored until 2050: business as usual (BAU), high, and low urbanization. After calibration and validation, the BAU scenario yields a 55% growth in Pune’s population and 90% in built-up extent, entailing significant impacts: Pune’s core city densifies further with up to 60,000 persons/km², adding pressure to its strained infrastructure. In addition, 66–70% more residents are exposed to flood risk. Half of the urban expansion replaces agriculture, converting 167 km² of land. The high-urbanization scenario intensifies these impacts. These results illustrate how spatially explicit scenario projections help identify impacts of urbanization and inform long-term planning. Full article

Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings. Full article

Studying the spatiotemporal pattern of urban leisure activities helps us to understand the development and utilization of urban public space, people’s quality of life, and the happiness index. It has outstanding value for improving rational resource allocation, stimulating urban vitality, and promoting sustainable urban development. This study aims at discovering the spatiotemporal distribution patterns and people’s behavioral preferences of urban leisure activities using quantitative technology merging ubiquitous sensing big data. On the basis of modeling individual activity traces using mobile signaling data (MSD), we developed a space-time constrained dasymetric interpolation method to refine the urban leisure activity spatiotemporal distribution. We conducted an empirical study in Nanjing, China. The results indicate that leisure plays an essential role in daily human life, both on weekdays and weekends. Significant differences exist in spatiotemporal and type selection in urban leisure. The weekend afternoon is the breakout period of leisure, and entertainment is the most popular leisure activity. Furthermore, the correlation between leisure resource allocation and leisure activity participation was argued. Our findings confirm that data-driven approaches would be a promising method for analyzing human behavior patterns; therefore, assisting in land planning decisions and promoting social justice and sustainability. Full article

Datasets collecting demographic and socio-economic statistics are widely available. Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming the statistics reported for a set of source zones into values for a set of target zones, with a different geometry and a higher spatial resolution. This article reports on a novel dasymetric disaggregation method that uses encoder–decoder convolutional neural networks, similar to those adopted in image segmentation tasks, to combine different types of ancillary data. Model training constitutes a particular challenge. This is due to the fact that disaggregation tasks are ill-posed and do not entail the direct use of supervision signals in the form of training instances mapping low-resolution to high-resolution counts. We propose to address this problem through self-training. Our method iteratively refines initial estimates produced by disaggregation heuristics and training models with the estimates from previous iterations together with relevant regularization strategies. We conducted experiments related to the disaggregation of different variables collected for Continental Portugal into a raster grid with a resolution of 200 m. Results show that the proposed approach outperforms common alternative methods, including approaches that use other types of regression models to infer the dasymetric weights. Full article

The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets. Full article

The spatial distribution of the population is uneven for various reasons, such as urban-rural differences and geographical conditions differences. As the basic element of the natural structure of the population, the age structure composition of populations also varies considerably across the world. Obtaining accurate and spatiotemporal population age structure maps is crucial for calculating population size at risk, analyzing populations mobility patterns, or calculating health and development indicators. During the past decades, many population maps in the form of administrative units and grids have been produced. However, these population maps are limited by the lack of information on the change of population distribution within a day and the age structure of the population. Urban functional regions (UFRs) are closely related to population mobility patterns, which can provide information about population variation intraday. Focusing on the area within the Beijing Fifth Ring Road, the political and economic center of Beijing, we showed how to use the temporal scaling factors obtained by analyzing the population survey sampling data and population dasymetric maps in different categories of UFRs to realize the intraday variation mapping of elderly individuals and children. The population dasymetric maps were generated on the basis of covariates related to population. In this article, 50 covariates were calculated from remote sensing data and geospatial data. However, not all covariates are associate with population distribution. In order to improve the accuracy of dasymetric maps and reduce the cost of mapping, it is necessary to select the optimal subset for the dasymetric model of elderly and children. The random forest recursive feature elimination (RF-RFE) algorithm was introduced to obtain the optimal subset of different age groups of people and generate the population dasymetric model in this article, as well as to screen out the optimal subset with 38 covariates and 26 covariates for the dasymetric models of the elderly and children, respectively. An accurate UFR identification method combining point of interest (POI) data and OpenStreetMap (OSM) road network data is also introduced in this article. The overall accuracy of the identification results of UFRs was 70.97%, which is quite accurate. The intraday variation maps of population age structure on weekdays and weekends were made within the Beijing Fifth Ring Road. Accuracy evaluation based on sampling data found that the overall accuracy was relatively high—$R^2$ for each time period was higher than 0.5 and root mean square error (RMSE) was less than 0.05. On weekdays in particular, $R^2$ for each time period was higher than 0.61 and RMSE was less than 0.02. Full article

Advanced developments have been achieved in urban human population estimation, however, there is still a considerable research gap for the mapping of remote rural populations. In this study, based on demographic data at the town-level, multi-temporal high-resolution remote sensing data, and local population-sensitive point-of-interest (POI) data, we tailored a random forest-based dasymetric approach to map population distribution on the Qinghai–Tibet Plateau (QTP) for 2000, 2010, and 2016 with a spatial resolution of 1000 m. We then analyzed the temporal and spatial change of this distribution. The results showed that the QTP has a sparse population distribution overall; in large areas of the northern QTP, the population density is zero, accounting for about 14% of the total area of the QTP. About half of the QTP showed a rapid increase in population density between 2000 and 2016, mainly located in the eastern and southern parts of Qinghai Province and the central-eastern parts of the Tibet Autonomous Region. Regarding the relative importance of variables in explaining population density, the variables “Distance to Temples” is the most important, followed by “Density of Villages” and “Elevation”. Furthermore, our new products exhibited higher accuracy compared with five recently released gridded population density datasets, namely WorldPop, Gridded Population of the World version 4, and three national gridded population datasets for China. Both the root-mean-square error (RMSE) and mean absolute error (MAE) for our products were about half of those of the compared products except for WorldPop. This study provides a reference for using fine-scale demographic count and local population-sensitive POIs to model changing population distribution in remote rural areas. Full article

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. Full article

High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density. Full article

High-resolution population data are a necessary basis for identifying affected regions (e.g., natural disasters, accessibility of social infrastructures) and deriving recommendations for policy and planning, but municipalities are, as in Germany, regularly the smallest available reference unit for data. The article presents a dasymetric-based approach for modeling high-resolution population data based on urban density, dispersion, and land cover/use. In addition to common test statistics like MAE or MAPE, the Gini-coefficient and the local Moran’s I are applied and their added value for accuracy assessment is tested. With data on urban density, a relative deviation between the modeled and actual population of 14.1% is achieved. Data on land cover/use reduces the deviation to 12.4%. With 23.6%, the dispersion measure cannot improve distribution accuracy. Overall, the algorithms perform better for urban than for rural areas. Gini-coefficients show that same spatial concentration patterns are achieved as in the actual population distribution. According to local Moran’s I, there are statistically significant underestimations, especially in the highly-dense inner-urban areas. Overestimates are found in the transition to less urbanized areas and the core areas of peripheral cities. Overall, the additional test statistics can provide important insights into the data, which go beyond common methods for evaluation. Full article