Gridded Population Research Articles

Improving the accuracy of extant gridded population maps using multisource map fusion

ABSTRACT Population distribution is the most direct indicator used to describe human activities. Grid-based population distribution maps overcome the drawbacks of statistical data and are thus more suitable for integrated analysis with environmental data. However, current modeling methods seeking to improve accuracy ignore the role of many existing products, resulting in the ineffective use of advantageous information from different gridded population maps. In this study, the multisource map fusion method is developed and combined with population mapping to simply and efficiently achieve improved results accuracy by understanding the uncertainty of different gridded products. Three areas in China with significant environmental differences were used as case studies to validate the results. The case studies use representative Granger-Ramanathan (GR), variance weighted (VW), and random forest (RF) algorithms to implement multisource map fusion for three existing population maps – GPW4, LandScan, and WorldPop. The results of the experiments indicate that fusing multisource population maps can produce a more accurate product than input maps. Compared with the highest accuracy input data, the maximum reduction percentages for the RMSE and MAE of fused maps at the grid scale are 13.66% and 20.39% in the Beijing, Tianjin, and Hebei Region (BTHR); 15.47% and 18.29% in Guangdong Province; and 5.05% and 6.15% in Guizhou Province. This study provides new strategies for producing high-accuracy population distribution maps, and its inexpensive features make it especially suitable for developing countries to produce wide-range gridded population maps that are more accurate than existing products using a few surveys.

Park & Ride facilities and suburban sprawl

ABSTRACT This study investigates the effects of Park & Ride (P&R) facilities, which aim to increase the attractiveness of commuting by public transport, on population growth in suburban areas. We employ a difference-in-differences approach to parking capacity extensions of seven P&R facilities outside the central business district of the city of Vienna between 2012 and 2016. Specifically, using fine-grained grid population data, we compare population growth in close distance to the P&R facility to population growth in surrounding municipalities. We find that the expansion of a P&R facility, which is located at least 20 min away from a main public transport station in Vienna, causes population growth of, on average, 1.6–1.9% in neighbouring municipalities compared to those where the P&R facility is located. This accounts for up to 15% of the total population growth in the respective regions between 2008 and 2019 and highlights the role of P&R facilities in fostering suburbanization and the suburban sprawl.

Combining Conventional Statistics and Big Data to Map Global Tourism Destinations Before COVID-19

World Tourism Organization (UNWTO) is the major source of internationally comparable data on tourism. However, UNWTO data has two drawbacks: it focuses on international trips and ignores differences between regions within individual countries. Alternative sources of big data are increasingly used to enhance tourism statistics. In this paper, we combine traditional information sources with gridded population dataset and Airbnb data to address the limitations of UNWTO statistics. We produce a map of world tourism destinations measured by the number of tourism visits and tourism expenditure in 2019, before the COVID-19 pandemic. We then identify hot spots of tourism and compare the level of spatial concentration of tourism to that of global population and economy. The results illustrate how supply and demand shape the global distribution of tourism, highlight the dominance of domestic travels in global tourism mobility and may help planning tourism policy in the face of current global challenges.

Reconstruction of Residential Land Cover and Spatial Analysis of Population in Bursa Region (Turkey) in the Mid-Nineteenth Century

The historic reconstruction of residential land cover is of significance to uncover the human-environment relationship and its changing dynamics. Taking into account the historical census data and cadastral maps of seven villages, this study generated residential land cover maps for the Bursa Region in the 1850s using a model based on natural constraints, land zoning, socio-economic factors and residential suitability. Two different historical reconstructions were generated; one based on a high density residential model and another based on a low density model. The simulated landcover information was used as an ancillary data to redistribute aggregated census counts to fine scale raster cells. Two different statistical models were developed; one based on probability maps and the other applying regression models including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The regression models were validated with historical census data of the 1840s. From regression models, socio-economic and physical characteristics, accessibility and natural amenities showed significant impacts on the distribution of population. Model validation analysis revealed that GWR is more accurate than OLS models. The generated residential land cover and gridded population datasets can provide a basis for the historical study of population and land use.

Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA)

The release of global gridded population datasets, including the Gridded Population of the World (GPW), Global Human Settlement Population Grid (GHS-POP), WorldPop, and LandScan, have greatly facilitated cross-comparison for ongoing research related to anthropogenic impacts. However, little attention is paid to the consistency and discrepancy of these gridded products in the regions with rapid changes in local population, e.g., Mainland Southeast Asia (MSEA), where the countries have experienced fast population growth since the 1950s. This awkward situation is unsurprisingly aggravated because of national scarce demographics and incomplete census counts, which further limits their appropriate usage. Thus, comparative analyses of them become the priority of their better application. Here, the consistency and discrepancy of the four common global gridded population datasets were cross-compared by combing the 2015 provincial population statistics (census and yearbooks) via error-comparison based statistical methods. The results showed that: (1) the LandScan performs the best both in spatial accuracy and estimated errors, then followed by the WorldPop, GHS-POP, and GPW in MSEA. (2) Provincial differences in estimated errors indicated that the LandScan better reveals the spatial pattern of population density in Thailand and Vietnam, while the WorldPop performs slightly better in Myanmar and Laos, and both fit well in Cambodia. (3) Substantial errors among the four gridded datasets normally occur in the provincial units with larger population density (over 610 persons/km2) and a rapid population growth rate (greater than 1.54%), respectively. The new findings in MSEA indicated that future usage of these datasets should pay attention to the estimated population in the areas characterized by high population density and rapid population growth.

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas.

Measuring the accuracy of gridded human population density surfaces: A case study in Bioko Island, Equatorial Guinea

BackgroundGeospatial datasets of population are becoming more common in models used for health policy. Publicly-available maps of human population make a consistent picture from inconsistent census data, and the techniques they use to impute data makes each population map unique. Each mapping model explains its methods, but it can be difficult to know which map is appropriate for which policy work. High quality census datasets, where available, are a unique opportunity to characterize maps by comparing them with truth.MethodsWe use census data from a bed-net mass-distribution campaign on Bioko Island, Equatorial Guinea, conducted by the Bioko Island Malaria Elimination Program as a gold standard to evaluate LandScan (LS), WorldPop Constrained (WP-C) and WorldPop Unconstrained (WP-U), Gridded Population of the World (GPW), and the High-Resolution Settlement Layer (HRSL). Each layer is compared to the gold-standard using statistical measures to evaluate distribution, error, and bias. We investigated how map choice affects burden estimates from a malaria prevalence model.ResultsSpecific population layers were able to match the gold-standard distribution at different population densities. LandScan was able to most accurately capture highly urban distribution, HRSL and WP-C matched best at all other lower population densities. GPW and WP-U performed poorly everywhere. Correctly capturing empty pixels is key, and smaller pixel sizes (100 m vs 1 km) improve this. Normalizing areas based on known district populations increased performance. The use of differing population layers in a malaria model showed a disparity in results around transition points between endemicity levels.DiscussionThe metrics in this paper, some of them novel in this context, characterize how these population maps differ from the gold standard census and from each other. We show that the metrics help understand the performance of a population map within a malaria model. The closest match to the census data would combine LandScan within urban areas and the HRSL for rural areas. Researchers should prefer particular maps if health calculations have a strong dependency on knowing where people are not, or if it is important to categorize variation in density within a city.

Neighbourhood change and spatial inequalities in Cape Town

AbstractCapturing the dynamics of population change in urban areas necessitates access to geographically fine‐grained and temporally consistent data for several time points. Such data are generally not available and they must be created using standard population data which cannot usually be compared across time periods. In this paper, the focus is on changing spatial inequalities in Cape Town, South Africa. This paper details an approach to generation of gridded population counts (250 m by 250 m) for two census years – 2001 and 2011. Census data for Small Area Layers (SALs), Spot Building Count (SBC) data, and Open Street Map (OSM) landuse data were used to construct a grid of populated cells to which population counts are then reallocated. The reallocation of population counts from SALs to grid cells was undertaken using area‐to‐point kriging – an approach which is informed by the spatial variation in the population groups of interest as measured using the variogram. A case study based on population grids of unemployment rates shows how the grids can be used to chart changes and also to measure spatial inequalities across the city at two time points. The advantages of grids for capturing fine‐scale complexities and correctly accounting for physical separation between communities are demonstrated and the results show, while the broad patterns of inequality are consistent across time, there are pronounced increases in inequalities in some neighbourhoods. These areas – and what leads some areas to fall further behind – should be the subject of attention by policy‐makers.

Accounting for internal migration in spatial population projections—a gravity-based modeling approach using the Shared Socioeconomic Pathways

Gridded population projections constitute an essential input for climate change impacts, adaptation, and vulnerability (IAV) assessments as they allow for exploring how future changes in the spatial distribution of population drive climate change impacts. We develop such spatial population projections, using a gravity-based modeling approach that accounts for rural-urban and inland-coastal migration as well as for spatial development patterns (i.e. urban sprawl). We calibrate the model (called CONCLUDE) to the socioeconomically diverse Mediterranean region, additionally considering differences in socioeconomic development in two geographical regions: the northern Mediterranean and the southern and eastern Mediterranean. We produce high-resolution population projections (approximately 1 km) for 2020–2100 that are consistent with the Shared Socioeconomic Pathways (SSPs), both in terms of qualitative narrative assumptions as well as national-level projections. We find that future spatial population patterns differ considerably under all SSPs, with four to eight times higher urban population densities and three to 16 times higher coastal populations in southern and eastern Mediterranean countries compared to northern Mediterranean countries in 2100. In the South and East, the highest urban density (8000 people km−2) and coastal population (107 million) are projected under SSP3, while in the North, the highest urban density (1500 people km−2) is projected under SSP1 and the highest coastal population (15.2 million) under SSP5. As these projections account for internal migration processes and spatial development patterns, they can provide new insights in a wide range of IAV assessments. Furthermore, CONCLUDE can be extended to other continental or global scales due to its modest data requirements based on freely available global datasets.

Large conservation opportunities exist in >90% of tropic-subtropic coastal habitats adjacent to cities

Large conservation opportunities exist in >90% of tropic-subtropic coastal habitats adjacent to cities

Implications for Tracking SDG Indicator Metrics with Gridded Population Data

Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.

Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya

Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.

Spatially Explicit Population Projections: The case of Copenhagen, Denmark

Abstract. Cities expand rapidly with international migration significantly contributing to urban growth and urban population change. However, cities miss out on a great opportunity of reclaiming valuable knowledge on future population distribution due to the lack of established tools and methodologies to project where it is more likely for people of specific socio-demographic groups to set up home. The present work suggests that spatially explicit projections can play a significant role as a tool for urban planning and for managing diversity creatively, especially when a combination of social, demographic and topographic data is utilized. Machine learning techniques have demonstrated capabilities to capture relationships among this plethora of urban features to estimate future population distribution. We present a flexible, ML-based methodology for high-resolution gridded population projections by demographic characteristics, and specifically by region of origin, for the capital region of Copenhagen, Denmark, by combining various socio-demographic and topographic input layers.

A decision-making process for wave energy converter and location pairing

Global awareness concerning clean energy is increasing worldwide. Wave energy converters harvest energy from waves and help reduce greenhouse gas emissions. This research covers major aspects of the wave energy field including converters and installation-locations, and proposes a Decision-Making Process to identify optimum converter-location pairs for wave energy harvesting. This Process is based on the wave energy converter Selection-Guideline arising from essential features, and the synthesis of previous works so far focused on pinched-point locations. This Process yields a single map, denoting hotspot-areas for energy harvesting, and thereby wave-farm installations, based on multiple input information from the converters (including performance and installation depth), wave climate, resource availability and sustainability, area accessibility, restricted areas, power grid locations, population density, and energy demand, alongside local geographic features including touristic areas. Different spatial-resolution models along the Southeast Australian shelves, and thirty converters showed a high spatial-variation of the pairs nearshore. Devices with high maximum rated power are often, but not necessarily, the best for a given zone. Moreover, this Process is completed by the Energy Demand-Response Index that enables the division and ranking of the area into zones regarding the pairs’ energy response to the energy demand at a reference-point such as a grid station. This research also shows that wave direction can significantly impact energy-production estimations. This Process can be applied to other zones worldwide and has the flexibility to include features that may not have been covered here.

Flexible modulation of sequence generation in the entorhinal-hippocampal system.

Exploration, consolidation, and planning depend on the generation of sequential state representations. However, these algorithms require disparate forms of sampling dynamics for optimal performance. We theorize how the brain should adapt internally generated sequences for particular cognitive functions and propose a neural mechanism by which this may be accomplished within the entorhinal-hippocampal circuit. Specifically, we demonstrate that the systematic modulation along the MEC dorsoventral axis of grid population input into hippocampus facilitates a flexible generative process which can interpolate between qualitatively distinct regimes of sequential hippocampal reactivations. By relating the emergent hippocampal activity patterns drawn from our model to empirical data, we explain and reconcile a diversity of recently observed, but apparently unrelated, phenomena such as generative cycling, diffusive hippocampal reactivations, and jumping trajectory events.

Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates.

Gridded population data is widely used to map fine scale population patterns and dynamics to understand associated human-environmental processes for global change research, disaster risk assessment and other domains. This study mapped gridded population across Germany using weighting layers from building density, building height (both from previous studies) and building type datasets, all created from freely available, temporally and globally consistent Copernicus Sentinel-1 and Sentinel-2 data. We first produced and validated a nation-wide dataset of predominant residential and non-residential building types. We then examined the impact of different weighting layers from density, type and height on top-down dasymetric mapping quality across scales. We finally performed a nation-wide bottom-up population estimate based on the three datasets. We found that integrating building types into dasymetric mapping is helpful at fine scale, as population is not redistributed to non-residential areas. Building density improved the overall quality of population estimates at all scales compared to using a binary building layer. Most importantly, we found that the combined use of density and height, i.e. volume, considerably increased mapping quality in general and with regard to regional discrepancy by largely eliminating systematic underestimation in dense agglomerations and overestimation in rural areas. We also found that building density, type and volume, together with living floor area per capita, are suitable to produce accurate large-area bottom-up population estimates.

High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent

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.

Cellular differential evolutionary algorithm with double-stage external population-leading and its application

This paper aims to address the problem of poor diversity and convergence for traditional evolutionary algorithms in solving multi-objective optimization problems and proposes a cellular differential evolutionary algorithm with double-stage external population-leading. This algorithm divides the maintenance of external population diversity into double stages and introduces new disturbance in the mutation operation to avoid the algorithm falling into a local optimum. In the first stage, the external population is maintained according to the rank and k-nearest neighbor distance. The second stage adopts the external population retention strategy of multi-objective cellular differential algorithm that only retains non-dominated solutions. In both stages, the external population has complete feedback, which means the solutions from the external population randomly replace existing individuals in the two-dimensional grid population after every iteration. Tests on fifteen benchmark functions show that the new algorithm can obtain more uniform Pareto front and competitive convergence results than the other four typical algorithms. Finally, the feasibility and effectiveness of this algorithm are verified by the case study of cycloid speed reducer.

Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions

Clean air is a fundamental necessity for human health and well-being. Anthropogenic emissions that are harmful to human health have been reduced substantially under COVID-19 lockdown. Satellite remote sensing for air pollution assessments can be highly effective in public health research because of the possibility of estimating air pollution levels over large scales. In this study, we utilized both satellite and surface measurements to estimate air pollution levels in 20 cities across the world. Google Earth Engine (GEE) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) application were used for both spatial and time-series assessment of tropospheric Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) statuses during the study period (1 February to May 11, 2019 and the corresponding period in 2020). We also measured Population-Weighted Average Concentration (PWAC) of particulate matter (PM2.5 and PM10) and NO2 using gridded population data and in-situ air pollution estimates. We estimated the economic benefit of reduced anthropogenic emissions using two valuation approaches: (1) the median externality value coefficient approach, applied for satellite data, and (2) the public health burden approach, applied for in-situ data. Satellite data have shown that ~28 tons (sum of 20 cities) of NO2 and ~184 tons (sum of 20 cities) of CO have been reduced during the study period. PM2.5, PM10, and NO2 are reduced by ~37 (μg/m3), 62 (μg/m3), and 145 (μg/m3), respectively. A total of ~1310, ~401, and ~430 premature cause-specific deaths were estimated to be avoided with the reduction of NO2, PM2.5, and PM10. The total economic benefits (Billion US$) (sum of 20 cities) of the avoided mortality are measured as ~10, ~3.1, and ~3.3 for NO2, PM2.5, and PM10, respectively. In many cases, ground monitored data was found inadequate for detailed spatial assessment. This problem can be better addressed by incorporating satellite data into the evaluation if proper quality assurance is achieved, and the data processing burden can be alleviated or even removed. Both satellite and ground-based estimates suggest the positive effect of the limited human interference on the natural environments. Further research in this direction is needed to explore this synergistic association more explicitly.

An evaluation of accessibility to preschool education institutions using geographic information systems

This study aims to evaluate current locations of preschool education institutions and suggest alternative spatial arrangements in the Afyonkarahisar province in Turkey, using heuristic location-allocation modelling approaches to improve overall spatial accessibility. The population totals were generated by supplying a unique code for each spatial unit. The census information was joined with a vector layer to transfer the population values to source units. In order to better predict the school age population distribution structure within the source zones, an intelligent areal interpolation approach was performed. The digital boundaries of the central district and villages of the Afyonkarahisar province were used as the source zones. The population surfaces of the source units were modelled by using the Corine Land use/cover (2018 dated) dataset and dasymetric mapping method. The gridded population surface model with population estimates and 1199 grid points across the province were used to model the preschool aged population in need of preschool educational services. With the current distribution of preschools in Afyonkarahisar, the preschool aged population would have to travel an average distance of 4629.49 m to access the closest preschool to them. The results of this study show that the provision of alternative preschool locations decreased the average travel distance from each residential home to each preschool by 626.82 m, thereby improving overall accessibility to preschools.