High-resolution Population Research Articles (Page 1)

Urban heat islands (UHIs) intensify thermal stress in cities, particularly in arid and semi-arid regions undergoing rapid urban expansion. The main objectives of this study are to quantify and compare UHI intensity in six major Saudi Arabian cities (Dammam, Makkah, Madinah, Jeddah, Riyadh, and Abha) representing diverse climatic zones and to examine how UHI patterns vary between urban, suburban, and rural zones over a 30-year period. Understanding the magnitude and spatial variability of UHIs across different climatic settings is crucial for developing effective urban planning and climate adaptation strategies in Saudi Arabia’s rapidly expanding cities. Except for Abha, these cities are the five most populous cities in the Kingdom. Each city was categorized into urban (>1500 people km−2), suburban (300–1500 people km−2), and rural (<300 people km−2) zones using high-resolution population density data. Two independent temperature datasets (ERA5-land and CHIRTS-ERA5) were analyzed for the years 1994, 2004, 2014, and 2024. Both datasets revealed consistent spatial patterns and a general warming trend across all zones and cities over the 30-year period. The UHI effect was most pronounced for minimum temperatures, with urban zones warmer than rural zones by 0.85 °C (ERA5-land) and 1.10 °C (CHIRTS-ERA5), likely due to greater heat retention and slower cooling rates in built-up areas. Mean temperature differences were smaller but still indicated positive UHI. Conversely, both datasets exhibited a reversed UHI pattern for maximum temperatures, with rural zones warmer than urban zones by 1.73 °C (ERA5-land) and 1.52 °C (CHIRTS-ERA5). This reversed pattern is attributed to the surrounding desert landscapes with minimal vegetation, indicated by low normalized difference vegetation index (NDVI), while urban areas have increasingly benefited from greening and landscaping initiatives. City-level analysis showed the strongest reversed UHI in maximum temperatures in Abha, while Jeddah exhibited the weakest. These findings highlight the need for localized urban planning strategies, particularly the expansion of vegetation cover and sustainable land use, to mitigate extreme thermal conditions in Saudi Arabia.

High spatiotemporal-resolution population distribution prediction (HSTP) enhances urban management and emergency response. Existing HSTP methods commonly treat geolocated digital footprints (GDFs) as a direct population proxy, ignoring the representation bias caused by the spatiotemporal heterogeneity of geolocated behavior. Therefore, we propose HSTP, a framework that explicitly models this bias. Drawing from behavioral geography, we introduce the per capita triggering frequency of digital footprints (TFDFs), which represents the average rate at which an individual in a specific area and time generates GDF, thus capturing the local intensity of geolocated behavior. HSTP is built on the premise that GDF volume is the product of the true population and its corresponding TFDF. HSTP employs a Transformer encoder to learn geospatial context and a dual-decoder to co-predict both population and TFDF. Enforcing this premise as a training constraint allows HSTP to learn from abundant GDF data even without population labels, enhancing its spatiotemporal generalization. Experiments on hourly, 200-m-grid data from Wuhan showed that HSTP reduces prediction error (symmetric mean absolute percentage error, sMAPE) by over 35.5% compared to state-of-the-art baselines. Thus, HSTP serves as a high-precision population prediction tool and a novel framework for modeling behavioral heterogeneity in urban computing.

Escalating heatwave exposure of the elderly across China.

Recent global temperature increases and extreme heat events have raised concerns about their impact on health, particularly in vulnerable regions like Africa. This study assesses future heat stress and population exposure in the Lake Victoria region under the high-emission SSP5-8.5 climate change scenario, using a convection-permitting climate model, heat stress indices (humidex and heat index), and high-resolution population projections under the high-emission SSP5-8.5 scenario, interpreted here as the high-end of the climate change signal. Results indicate a substantial increase in the duration of dangerous heat stress. By the end of the century, up to 122 million people, or around 44 of the population may experience dangerous heat stress for more than 5 of the time annually (i.e. ∼18 days), compared to 1 of the population or around 1 million people for the period 2005-2016. Up to 28 of the population (∼78 million people) would even experience dangerous heat for 15 of the time (i.e. ∼55 days). 66 of this increased population exposure can be attributed to the combined effect of increasing temperatures and total population in the region. High heat-risk areas include the northern and southern shores of Lake Victoria and urban areas. The study highlights the need to consider both climate and population dynamics when assessing heat stress, and underscores the urgency of adaptation in the Lake Victoria region.

Assessing cold exposure risk during cold waves in Beijing using high spatiotemporal resolution population data and temperature variations.

High-resolution population density mapping in urban areas using a contextualized geographically weighted neural network (CGWNN) model

ABSTRACT We assess the impact of China’s urban spatial structure on innovation using the LandScanTM high-resolution global population data set for 2001 to 2017. Per our findings, while some large cities in China show polycentricity, most cities still show monocentric development. Regression analysis reveals that the impact of the urban spatial structure on innovation shows temporal heterogeneity. Urbanization causes the advantages of monocentricity to gradually decrease or disappear, whereas those of polycentricity are strengthened. Furthermore, cities with higher population densities and more extensive service industries are more suitable for polycentric spatial structures (PSSs). The key to reaping the advantages of polycentricity is to build transportation infrastructure.

Rangifer tarandus (caribou or reindeer) survived periods of abrupt climatic warming during the last deglaciation but are currently in global decline. Using process-explicit models of likely climate-human-Rangifer interactions and inferences of demographic change from radiocarbon-dated fossils and ancient DNA, we reconstruct and decipher 21,000 years of Rangifer population dynamics. These high-resolution population reconstructions pinpoint ecological characteristics and life-history traits that most likely enabled Rangifer to survive rapid warming events following the Last Glacial Maximum. Projecting these process-driven models into the future reveals that these attributes are unlikely to buffer Rangifer against wide-scale population declines from expected 21st Century climatic warming. Our findings highlight a need to boost investments in the management and conservation of Rangifer, particularly in North America, where projected losses are expected to exceed 80%. This will not only support the survival of the species and the vital services it renders in Arctic ecosystems, but also help sustain the socioeconomic, cultural, and emotional well-being of many Rangifer-dependent communities.

Abstract Hybrid zones present a unique framework to study the genetic basis of reproductive isolation and speciation. The European fire-bellied toad (Bombina bombina) and yellow-bellied toad (B. variegata) hybridize in a zone across several thousand kilometres in Central Europe. The Bombina system has been examined for more than a century and its scientific legacy comprises the development of the ‘multilocus cline theory’ and the ‘maintenance of hybrid zones theory’. To set the scene for future genomic research we review the scientific literature on the Bombina hybrid zone, with emphasis on studies that provided insights into genetic processes. Our review emphasises that the two species are highly genetically diverged and reduced hybrid fitness prevents unrestricted gene flow between the two parental species (endogenous selection). However, environmental variables influence the location of hybrid populations (exogenous selection). Depending on region, the transects are described as clinal, tension, mosaic, and residual hybrid zones. The era of genomics has opened a plethora of yet unutilized analytical possibilities in Bombina research, including high resolution population genomics, understanding the genetic basis of reproductive isolation, phenotype-genotype associations, and elucidating the roles of endo- and exogenous selection in maintaining the hybrid zone. Because the hybrid zone occurs across many different environmental settings and genetic backgrounds, it should be possible to distinguish common genetic barriers to gene flow from those arising due to local adaptation. We argue that the Bombina zone offers an excellent opportunity to gain deep genomic insights into the selection processes that maintain the species boundary.

Fine-scale population distribution information is crucial for applications in urban public safety, planning, and management. However, when using machine learning methods for population spatialization, issues such as data overfitting and limited interpretability need to be addressed. This study introduced a combined approach using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) to estimate population spatialization at various grid scales and interpret the key influencing factors, then we applied accuracy evaluation metrics and landscape ecology indices to identify the optimal grid scale. The results showed that the XGBoost model outperformed the WorldPop dataset in accuracy across all grid scales, with determination coefficients (R2) consistently exceeding 0.83. The SHAP analysis revealed that the primary influencing factors were the address, access, and dwelling characteristics of points of interest (POIs). The influence of these factors showed regional variations, with urban centers having a strong positive effect, while the negative influence increased with the distance to suburban areas. The population density estimates across different grid scales consistently exhibited a spatial gradient pattern of decreasing density from the urban center toward suburban areas. Based on comprehensive evaluations of accuracy and spatial heterogeneity, the 100 m grid was identified as the optimal scale for Shanghai’s population spatialization. The proposed XGBoost-SHAP population spatialization method demonstrates high reliability and generalizability, effectively explaining the heterogeneity of population distribution. This approach not only provides critical decision-making support for urban planning but also serves as a methodological reference for high-resolution population spatialization studies in other cities.

Abstract The simultaneous occurrence of both extreme droughts and heatwaves has become more frequent with global warming, resulting in increases in the frequency and potential impact of compound drought and heatwave (CDHW) globally. It is critical to evaluate the impacts of CDHW and assess global socio-economic risks to formulate appropriate risk mitigation strategies. Most studies have focused on projecting the likely variation in the multidimensional hazard of CDHW. However, the discrepancies among global population projection datasets based on shared socioeconomic pathways (SSPs) and their potential impacts on disaster risk assessments remain underexplored. In this study, multiple global high-resolution population projection datasets are used in combination with projected CDHW hazards via the multimodel ensemble from Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate how different sources of population data could affect the assessment of CDHW-exposed populations under SSPs. The results show that at the global scale, the spatial pattern and temporal evolution of the CDHW-exposed population under climate change can be depicted consistently on the basis of different population data. However, at the subcontinental scale, substantial spatial heterogeneity exists in the projected exposure. For regions such as the Mediterranean, South Asia, and western Central Asia, the projections from different datasets are consistent with low uncertainty. In contrast, for regions including the northern hemisphere above 40°N, Oceania, eastern Central Asia, East Asia, the South American monsoon region, western Africa, Central Africa, etc., the uncertainty in the estimated exposed population is higher and is expected to increase from the 2020s to the end of the 21st century. Additional locational socioeconomic data should be collected in these areas to reduce uncertainty in future socioeconomic projections. The findings highlight the critical need to consider different elements-at-risk and choose fit-for-purpose datasets, providing essential guidance for disaster risk assessments that support climate adaptation strategies and sustainable development goals.

High-resolution multi-hazard residential buildings and population exposure model for coastal areas: A case study in northeastern Italy

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.

ABSTRACT High-resolution population data are crucial for various applications, from developing regional plans to disaster risk management. Current population spatialization methods typically apply population mapping relationships established at the regional level to the grid level using multi-source data. However, the significant scale difference between the regional and grid levels, combined with the simple integration of multi-source data features without considering the spatial dependence of the population, results in lower accuracy. To address the scale mismatch issue in the downscaling process, we first construct a spatially heterogeneous population label by combining census data with gridded population datasets. Then, we establish a relationship mapping between population covariates and population at a low-resolution scale (100 m) and apply it to a neighboring high-resolution scale (25 m) to reduce the prediction bias resulting from directly downscaling from the regional level to the grid level. Meanwhile, a deep learning model based on transformer feature attention convolution net (TFACNet) is employed to aggregate each geographic unit's global and local spatial relationships, integrating complementary features learned from multi-source heterogeneous data in an end-to-end manner. The experimental results in Wuhan and Guilin show that our method achieved a more accurate population spatialization (overall R 2 ≈ 0.92) at the street level.

IntroductionThis study investigates the effects of urban polycentricity and city size on total factor productivity (TFP) in Chinese cities.MethodsUsing high-resolution population distribution data from Landscan and applying instrumental variable (IV) estimation to address endogeneity concerns, we construct a novel measure of urban polycentricity.ResultsOur findings show that while expanding city size enhances TFP through increased economies of scale, greater urban polycentricity negatively affects productivity by weakening agglomeration economies and innovation spillovers.DiscussionThe analysis suggests that polycentricity reduces the concentration of economic activities, which hampers knowledge diffusion and innovation, leading to lower productivity. Additionally, we identify the optimal city size for maximizing TFP, where excessive urban growth beyond a certain point becomes counterproductive.

Most low- and middle-income countries face significant public health challenges, exacerbated by the lack of reliable demographic data supporting effective planning and intervention. In such data-scarce settings, statistical models combining geolocated survey data with geospatial datasets enable the estimation of population counts at high spatial resolution in the absence of dependable demographic data sources. This study introduces a Bayesian model jointly estimating building and population counts, combining geolocated survey data and gridded geospatial datasets. The model provides population estimates for the Kasaï-Oriental province, Democratic Republic of the Congo (DRC), at a spatial resolution of approximately one hectare. Posterior estimates are aggregated across health zones and health areas to offer probabilistic insights into their respective populations. The model exhibits a -0.28 bias, 0.47 inaccuracy, and 0.95 imprecision using scaled residuals, with robust 95% credible intervals. The estimated population of Kasaï-Oriental for 2024 is approximately 4.1 million, with a credible range of 3.4 to 4.8 million. Aggregations by health zones and health areas reveal significant variations in population estimates and uncertainty levels, particularly between the provincial capital, Mbuji-Mayi and the rural hinterland. High-resolution Bayesian population estimates allow flexible aggregation across spatial units while providing probabilistic insights into model uncertainty. These estimates offer a unique resource for the public health community working in Kasaï-Oriental, for instance, in support of a better-informed allocation of vaccines to different operational boundaries based on the upper bound of the 95% credible intervals.

ABSTRACT Accurate population mapping is crucial for disaster management, urban planning, etc. However, current methods using nighttime light (NTL) and gridded population datasets are limited by low spatial resolution and insufficient training data for complex models such as deep learning. These models do not adequately utilize spatial information in population mapping. To address these limitations, this study proposes a high-resolution population mapping method using the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) glimmer imager data and deep learning. The method includes a sample generation strategy with multiple regression and multilevel screening to provide sufficient, high-quality samples for deep learning. A Fine Population mapping network (FinePop-net) is also developed to train regression models using image samples, capturing multi-scale features for model training. When applied to the Guangdong-Hong Kong-Macao Greater Bay Area with 40-meter resolution SDGSAT 1 glimmer imagery, the method significantly reduced the average absolute error and root-mean-square error by 9.35% and 11.44%, respectively, compared with those of the pixel-level learning methods. It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.

BackgroundInforming local decision-making, improving service delivery and designing household surveys require having access to high-spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance.MethodsWe leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100-m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest-derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees.ResultsUsing OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100-m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making.ConclusionsGathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need.

Intensifying hydroclimatic changes in North Africa are causing unprecedented floods, droughts, and land degradation patterns that are increasingly associated with human casualties, socioeconomic instabilities, and outflow migrations. These patterns’ and their future forecasts remain largely unquantified, aggravating the impacts on several populous areas. To address this deficiency, we employ pixel-based remote sensing data correlation analysis and soil loss modeling to constrain the uncertainties on the decadal hydroclimatic and ecosystem changes in North Africa. Using cloud-based big data analysis in Google Earth Engine, we establish the convolution between precipitation patterns and surface textural characteristics, evaluating the spatial distribution of soil erosion risks at the continental scale. Our investigation uses a multi-step approach, integrating risk areas derived from soil erosion with high-resolution population data, offering critical insights into zones of different vulnerabilities. Our results unveiled a significant escalation in soil erosion anomalies over the past two decades. In particular, 15 % of the areas receiving precipitation in all of North Africa are currently at medium to high risk of soil erosion versus only 7 % in 2002. These risks are concentrated in urban areas, where each year, ∼29,000 people become highly vulnerable to these hazards, up from ∼22,000 in 2002. These increases are primarily associated with the surge in semi-unformal urban settings and the rise in rain aggressiveness and storminess. These factors, combined with the poor public perception of the imminence of these risks, create hotspots where the impacts are becoming insurmountable, as considered herein for the case of the recent catastrophic floods in Derna, Libya, used as a validation site. We conclude that increased soil erosion will modulate the impacts of upcoming catastrophic floods. As such, a pressing change in urban and land use policies in expansive areas of North Africa is called for to increase their resilience to upcoming hydroclimatic fluctuations.

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed Popcorn, a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number of aggregate population counts over coarse census districts for calibration. Despite the minimal data requirements our approach surpasses the mapping accuracy of existing schemes, including several that rely on building footprints derived from high-resolution imagery. E.g., we were able to produce population maps for Rwanda with 100m GSD based on less than 400 regional census counts. In Kigali, those maps reach an R2 score of 66% w.r.t. a ground truth reference map, with an average error of only ±10 inhabitants/ha. Conveniently, Popcorn retrieves explicit maps of built-up areas and local building occupancy rates, making the mapping process interpretable and offering additional insights, for instance about the distribution of built-up, but unpopulated areas, e.g., industrial warehouses. With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns. Project page: https://popcorn-population.github.io/.