From top to bottom: gridded human population estimates in data-poor situations.

Where disease risks are heterogeneous across population groups or space, or dependent on transmission between individuals, spatial data on population distributions - human, livestock and wildlife - are required to estimate infectious disease risks, burdens and dynamics. As a result, large-scale, spatially explicit, high-resolution human population data are being increasingly used in a wide range of animal- and public-health planning and policy development scenarios. Official census data, aggregated by administrative unit, provide the only complete enumeration of a country's population. While census data from developed countries are generally up-to-date and of high quality, in resource-poor settings they are often incomplete, out of date, or only available at the country or province level. The challenges associated with producing accurate population estimates in regions that lack high-quality census data have led to the development of census-independent approaches to small-area population estimations. Known as bottom-up models, as opposed to the census-based top-down approaches, these methods combine microcensus survey data with ancillary data to provide spatially disaggregated population estimates in the absence of national census data. This review highlights the need for high-resolution gridded population data, discusses problems associated with using census data as top-down model inputs, and explores census-independent, or bottom-up, methods of producing spatially explicit, high-resolution gridded population data, together with their advantages.

Dans les contextes où les risques de maladie sont hétérogènes et dépendent du groupe de population ou du territoire dont il s'agit, ou des probabilités de transmission entre individus, il est nécessaire de disposer de données spatiales sur la distribution des populations (couvrant la population humaine et les populations d'animaux d'élevage et sauvages) afin d'être en mesure d'évaluer les risques de maladie infectieuse, de calculer le fardeau qu'elles représentent et de mettre en lumière les dynamiques à l'oeuvre. En conséquence, il est fait de plus en plus souvent appel à des données spatialement explicites, à grande échelle et à haute résolution pour construire les scénarios utilisés à des fins de planification et d'élaboration des politiques de santé animale et de santé publique. Les données officielles de recensement agrégées par unité administrative constituent la seule énumération complète de la population d'un pays. Si dans les pays développés ces données de recensement sont généralement actualisées et de bonne qualité, dans les configurations moins dotées en ressources elles sont souvent incomplètes, obsolètes ou n'existent qu'à l'échelle nationale ou provinciale. Les difficultés rencontrées pour produire des estimations suffisamment exactes dans les régions dépourvues de données de recensement de bonne qualité ont conduit à élaborer des méthodes visant à estimer la population de territoires limités, sans passer par le recensement. Ces modèles, qualifiés d'" ascendants " par opposition aux modèles de recensement " descendants ", associent aux données issues d'opérations de micro-recensement un certain nombre de données complémentaires afin de fournir des estimations de population ventilées par territoires, en l'absence de données nationales de recensement. Dans cet article, l'auteure souligne l'importance de disposer de données maillées de population à haute résolution ; après avoir examiné les problèmes associés à l'utilisation des résultats des modèles descendants, elle décrit les méthodes ascendantes non basées sur le recensement et leur capacité à fournir des données maillées de population spatialement explicites et à haute résolution. Elle conclut sur les avantages de ces dernières méthodes.

En circunstancias en que el riesgo de enfermedad varía según el grupo de población o el espacio de que se trate o en que dicho riesgo depende de la transmisión entre individuos, es necesario disponer de datos espaciales sobre la distribución de poblaciones (ya sean humanas, ganaderas o de animales salvajes) para calcular el riesgo y determinar la carga y la dinámica de una enfermedad infecciosa. De ahí que en muy diversas situaciones en las que se elaboran planes o políticas de sanidad animal o salud pública se vengan utilizando, cada vez más, conjuntos de datos a gran escala y alta resolución referidos expresamente a la población humana de un determinado ámbito geográfico. Los datos del censo oficial, agregados por unidad administrativa, ofrecen el único recuento completo de la población de un país. No obstante, si bien los datos censales de países desarrollados suelen estar al día y ser de buena calidad, en condiciones de escasez de recursos esos datos tienden a ser incompletos, estar obsoletos o existir únicamente a nivel de país o de provincia. La dificultad de obtener estimaciones poblacionales exactas en regiones donde no hay datos censales de buena calidad ha llevado a concebir métodos que no dependan del censo para realizar cálculos referidos a la población de pequeños territorios. Estos métodos, llamados modelos "ascendentes", por oposición a los planteamientos "descendentes" basados en el censo, permiten subsanar la falta de datos censales nacionales combinando datos de encuestas microcensales con otros datos complementarios para obtener estimaciones poblacionales desglosadas por espacio geográfico. La autora, tras subrayar la necesidad de disponer de cuadrículas de población de alta resolución, explica los problemas derivados del uso de datos censales como fuente de información en los modelos "descendentes" y expone métodos no dependientes del censo, o "ascendentes", para elaborar cuadrículas de población de alta resolución referidas expresamente a un espacio geográfico, así como las ventajas que ofrecen estos métodos.

Population Aging in India: A Micro-Level Estimate Using Gridded Population Data.

As population aging continues to become a major demographic trend globally, it is essential to examine the demographic shifts at the micro-level to understand the changing scenario of older populations. A lack of adequate data in India on older populations is a hindrance to the government's efforts to provide social security for them. This study uses gridded population data to analyze the spatial patterns, micro-level trends, and the share of older populations in India for 2030 and 2040. The study's findings demonstrate that India has seen a dramatic shift in population aging trends, with large intra-state variability. The micro-level analysis shows that certain districts have a higher percentage of older people. Further, the share of older populations is predicted to rise considerably over the next two decades. The results highlight the need to shift from national and state-level policies to a more localized approach. The findings provide a comprehensive analysis of population aging at the micro-level in India and highlight the need for targeted policies and programs to ensure the well-being of older populations. The results of this study can inform policymakers in their efforts to provide social security for older people and improve their quality of life.

Relationship between freshwater harmful algal blooms and neurodegenerative disease incidence rates in South Korea.

BACKGROUND: Due to anthropogenic activities and global warming, the severity and distribution of harmful algal blooms (HABs) have been increasing steadily worldwide, including in South Korea (S. Korea). Previous studies reported that exposure to HABs could increase the risk of HAB-related diseases. However, very few studies examined the linkage between HABs and disease occurrence, particularly in S. Korea. The objective of this study was to evaluate the potential impact of HABs on neurodegenerative diseases (NDs), including Alzheimer's disease, Parkinson's disease, and motor neuron disease, at a population level. METHODS: Thirteen-year data (2005-2017) for chlorophyll-a (chl-a) concentrations as a bloom-related parameter, annual numbers of NDs, and population information were collected. First, the entire area of S. Korea was divided into a grid of 1 km, and the population number in each 1-km grid was collected using the Statistical Geographic Information Service Plus system. Cross-sectional time series data were analyzed with two statistical models, a generalized linear mixed model and a generalized linear model. RESULTS: The results show a general trend of increasing chl-a concentration and NDs year by year. We observed positive correlations between HAB intensity and the incidence rate of NDs. Particularly, HABs seem to have the most long-term carry-over effect on Parkinson's disease. Another key finding was that a 5-km radius from the HAB location was the boundary that showed the most significant associations with three NDs. CONCLUSIONS: This study provides statistical evidence that supports the potential risk of NDs from the exposure to HAB. Thus, it is recommended to monitor a broad spectrum of cyanotoxins, including neurotoxins, in bloom-affected regions in S. Korea and epidemiological studies in the future.

Addressing Unintentional Exclusion of Vulnerable and Mobile Households in Traditional Surveys in Kathmandu, Dhaka, and Hanoi: a Mixed-Methods Feasibility Study.

The methods used in low- and middle-income countries' (LMICs) household surveys have not changed in four decades; however, LMIC societies have changed substantially and now face unprecedented rates of urbanization and urbanization of poverty. This mismatch may result in unintentional exclusion of vulnerable and mobile urban populations. We compare three survey method innovations with standard survey methods in Kathmandu, Dhaka, and Hanoi and summarize feasibility of our innovative methods in terms of time, cost, skill requirements, and experiences. We used descriptive statistics and regression techniques to compare respondent characteristics in samples drawn with innovative versus standard survey designs and household definitions, adjusting for sample probability weights and clustering. Feasibility of innovative methods was evaluated using a thematic framework analysis of focus group discussions with survey field staff, and via survey planner budgets. We found that a common household definition excluded single adults (46.9%) and migrant-headed households (6.7%), as well as non-married (8.5%), unemployed (10.5%), disabled (9.3%), and studying adults (14.3%). Further, standard two-stage sampling resulted in fewer single adult and non-family households than an innovative area-microcensus design; however, two-stage sampling resulted in more tent and shack dwellers. Our survey innovations provided good value for money, and field staff experiences were neutral or positive. Staff recommended streamlining field tools and pairing technical and survey content experts during fieldwork. This evidence of exclusion of vulnerable and mobile urban populations in LMIC household surveys is deeply concerning and underscores the need to modernize survey methods and practices.

Missing millions: undercounting urbanization in India.

The measurement and characterization of urbanization crucially depends upon defining what counts as urban. The government of India estimates that only 31% of the population is urban. We show that this is an artifact of the definition of urbanity and an underestimate of the level of urbanization in India. We use a random forest-based model to create a high-resolution (~ 100 m) population grid from district-level data available from the Indian Census for 2001 and 2011, a novel application of such methods to create temporally consistent population grids. We then apply a community-detection clustering algorithm to construct urban agglomerations for the entire country. Compared with the 2011 official statistics, we estimate 12% more of urban population, but find fewer mid-size cities. We also identify urban agglomerations that span jurisdictional boundaries across large portions of Kerala and the Gangetic Plain.

Global population datasets overestimate flood exposure in Sweden.

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies.

Spatially explicit downscaling and projection of population in mainland China.

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

A grid-based sample design framework for household surveys.

Traditional sample designs for household surveys are contingent upon the availability of a representative primary sampling frame. This is defined using enumeration units and population counts retrieved from decennial national censuses that can become rapidly inaccurate in highly dynamic demographic settings. To tackle the need for representative sampling frames, we propose an original grid-based sample design framework introducing essential concepts of spatial sampling in household surveys. In this framework, the sampling frame is defined based on gridded population estimates and formalized as a bi-dimensional random field, characterized by spatial trends, spatial autocorrelation, and stratification. The sampling design reflects the characteristics of the random field by combining contextual stratification and proportional to population size sampling. A nonparametric estimator is applied to evaluate the sampling design and inform sample size estimation. We demonstrate an application of the proposed framework through a case study developed in two provinces located in the western part of the Democratic Republic of the Congo. We define a sampling frame consisting of settled cells with associated population estimates. We then perform a contextual stratification by applying a principal component analysis (PCA) and k-means clustering to a set of gridded geospatial covariates, and sample settled cells proportionally to population size. Lastly, we evaluate the sampling design by contrasting the empirical cumulative distribution function for the entire population of interest and its weighted counterpart across different sample sizes and identify an adequate sample size using the Kolmogorov-Smirnov distance between the two functions. The results of the case study underscore the strengths and limitations of the proposed grid-based sample design framework and foster further research into the application of spatial sampling concepts in household surveys.

The changing PM2.5 dynamics of global megacities based on long-term remotely sensed observations.

Satellite observations show that the rapid urbanization and emergence of megacities with 10 million or more residents have raised PM2.5 concentrations across the globe during the past few decades. This study examines PM2.5 dynamics for the 33 cities included on the UN list of megacities published in 2018. These megacities were classified into densely (>1500 residents per km2), moderately (300-1500 residents per km2) and sparsely (<300 residents per km2) populated areas to examine the effect of human population density on PM2.5 concentrations in these areas during the period 1998-2016. We found that: (1) the higher population density areas experienced higher PM2.5 concentrations; and (2) the megacities with high PM2.5 concentrations in these areas had higher concentrations than those in the moderately and sparsely populated areas of other megacities as well. The numbers of residents experiencing poor air quality is substantial: approximately 452 and 163 million experienced average annual PM2.5 levels exceeding 10 and 35 µg/m3, respectively in 2016. We also examined PM2.5 trends during the past 18 years and predict that high PM2.5 levels will likely continue in many of these megacities in the future without substantial changes in their economies and/or pollution abatement practices. There will be more megacities in the highest PM2.5 pollution class and the number of megacities in the lowest PM2.5 pollution class will likely not change. Finally, we analyzed how the PM2.5 pollution burden varies geographically and ranked the 33 megacities in terms of PM2.5 pollution in 2016. The most polluted regions are China, India, and South Asia and the least polluted regions are Europe and Japan. None of the 33 megacities currently fall in the WHO's PM2.5 attainment class (<10 µg/m3) while 9 megacities fall into the PM2.5 non-attainment class (>35 µg/m3). In 2016, the least polluted megacity was New York and most polluted megacity was Delhi whose average annual PM2.5 concentration of 110 µg/m3 is nearly three times the WHO's non-attainment threshold.

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies.

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

Evaluating nighttime lights and population distribution as proxies for mappinganthropogenic CO2 emission in Vietnam, Cambodia and Laos.

Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO2 emissions, this study compares two common proxies used to disaggregate CO2 emission estimates. We use a known gridded CO2 model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO2, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.

Gridded Population Maps Informed by Different Built Settlement Products.

The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.

Modelling changing population distributions: an example of the Kenyan Coast, 1979-2009.

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.