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Sanchez-Cespedes LM, Leasure DR, Tejedor-Garavito N, Amaya Cruz GH, Garcia Velez GA, Mendoza AE, Marín Salazar YA, Esch T, Tatem AJ, Ospina Bohórquez M. Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia. POPULATION STUDIES 2024; 78:3-20. [PMID: 36977422 DOI: 10.1080/00324728.2023.2190151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 11/22/2022] [Indexed: 03/30/2023]
Abstract
Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.
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Affiliation(s)
| | - Douglas Ryan Leasure
- Leverhulme Centre for Demographic Science, University of Oxford
- WorldPop, University of Southampton
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2
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Zhang J, Zhao X. Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169499. [PMID: 38128656 DOI: 10.1016/j.scitotenv.2023.169499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/22/2023] [Accepted: 12/17/2023] [Indexed: 12/23/2023]
Abstract
Geospatial big data and remote sensing data are widely used in population spatialization studies. However, the relationship between them and population distribution has regional heterogeneity in different geographic contexts. It is necessary to improve data processing methods and spatialization models in areas with large geographical differences. We used land cover data to extract human activity, nighttime light and point-of-interest (POI) data to represent human activity intensity, and considered differences in geographical context to divide mainland China into northern, southern and western regions. We constructed random forest models to generate gridded population distribution datasets with a resolution of 500 m, and quantitatively evaluated the importance of auxiliary data in different geographical contexts. The street-level accuracy assessment showed that our population dataset is more accurate than WorldPop, with a higher R2 and smaller deviation. The improved datasets provided broad potential for exploring the spatial-temporal changes in grid-level population distribution in China from 2010 to 2020. The results indicated that the population density and settlement area have increased, and the overall pattern of population distribution has remained highly stable, but there are significant differences in population change patterns among cities with different urbanization processes. The importance of the ancillary data to the models varied significantly, with POI contributing the most to the southern region and the least to the western region. Moreover, POI had relatively less influence on model improvement in undeveloped areas. Our study could provide a reference for predicting social and economic spatialized data in different geographical context areas using POI and multisource satellite data.
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Affiliation(s)
- Jinyu Zhang
- College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
| | - Xuesheng Zhao
- College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
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3
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Herfort B, Lautenbach S, Porto de Albuquerque J, Anderson J, Zipf A. A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap. Nat Commun 2023; 14:3985. [PMID: 37414776 PMCID: PMC10326063 DOI: 10.1038/s41467-023-39698-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 06/26/2023] [Indexed: 07/08/2023] Open
Abstract
OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data producers and urban analysts to manage the uneven coverage of OSM data, as well as a framework to support the assessment of completeness biases.
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Affiliation(s)
- Benjamin Herfort
- Heidelberg Institute for Geoinformation Technology, Heidelberg, Germany.
- GIScience Chair, Institute of Geography, Heidelberg University, Heidelberg, Germany.
| | - Sven Lautenbach
- Heidelberg Institute for Geoinformation Technology, Heidelberg, Germany
| | | | | | - Alexander Zipf
- Heidelberg Institute for Geoinformation Technology, Heidelberg, Germany
- GIScience Chair, Institute of Geography, Heidelberg University, Heidelberg, Germany
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Tatem AJ. Small area population denominators for improved disease surveillance and response. Epidemics 2022; 41:100641. [PMID: 36228440 PMCID: PMC9534780 DOI: 10.1016/j.epidem.2022.100641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/12/2022] [Accepted: 10/04/2022] [Indexed: 12/29/2022] Open
Abstract
The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.
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Affiliation(s)
- A J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
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Chamberlain HR, Lazar AN, Tatem AJ. High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa. Sci Data 2022; 9:711. [PMCID: PMC9673897 DOI: 10.1038/s41597-022-01799-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 10/21/2022] [Indexed: 11/19/2022] Open
Abstract
AbstractSocial distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.
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Borkovska O, Pollard D, Hamainza B, Kooma E, Renn S, Schmidt J, Engin H, Heaton M, Miller JM, Psychas P, Riley C, Martin A, Nyirenda J, Bwalya F, Winters A, Sobel C. Developing High-Resolution Population and Settlement Data for Impactful Malaria Interventions in Zambia. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:2941013. [PMID: 36203504 PMCID: PMC9532120 DOI: 10.1155/2022/2941013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 05/11/2022] [Accepted: 06/02/2022] [Indexed: 12/02/2022]
Abstract
Foundational high-resolution geospatial data products for population, settlements, infrastructure, and boundaries may greatly enhance the efficient planning of resource allocation during health sector interventions. To ensure the relevance and sustainability of such products, government partners must be involved from the beginning in their creation, improvement, and/or management, so they can be successfully applied to public health campaigns, such as malaria control and prevention. As an example, Zambia had an ambitious strategy of reaching the entire population with malaria vector control campaigns by late 2020 or early 2021, but they lacked the requisite accurate and up-to-date data on infrastructure and population distribution. To address this gap, the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) program, Akros, and other partners developed maps and planning templates to aid Zambia's National Malaria Elimination Program (NMEP) in operationalizing its strategy.
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Affiliation(s)
- Olena Borkovska
- Geo-Referenced Infrastructure and Demographic Data for Development (GRID3), Center for International Earth Science Information Network (CIESIN), Columbia Climate School, New York, USA
| | | | | | | | - Silvia Renn
- Geo-Referenced Infrastructure and Demographic Data for Development GRID3, African Sun Consulting, Lusaka, Zambia
| | - Jolynn Schmidt
- Geo-Referenced Infrastructure and Demographic Data for Development (GRID3), Center for International Earth Science Information Network (CIESIN), Columbia Climate School, New York, USA
| | - Hasim Engin
- Geo-Referenced Infrastructure and Demographic Data for Development (GRID3), Center for International Earth Science Information Network (CIESIN), Columbia Climate School, New York, USA
| | - Matthew Heaton
- Geo-Referenced Infrastructure and Demographic Data for Development (GRID3), Center for International Earth Science Information Network (CIESIN), Columbia Climate School, New York, USA
| | - John M Miller
- PATH Malaria Control And Elimination Partnership in Africa (MACEPA), Lusaka, Zambia
| | - Paul Psychas
- U.S. President's Malaria Initiative, U.S. Centers for Disease Control and Prevention, Lusaka, Zambia
| | | | | | | | | | | | - Corey Sobel
- Geo-Referenced Infrastructure and Demographic Data for Development (GRID3), Center for International Earth Science Information Network (CIESIN), Columbia Climate School, New York, USA
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Hierink F, Boo G, Macharia PM, Ouma PO, Timoner P, Levy M, Tschirhart K, Leyk S, Oliphant N, Tatem AJ, Ray N. Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa. COMMUNICATIONS MEDICINE 2022; 2:117. [PMID: 36124060 PMCID: PMC9481590 DOI: 10.1038/s43856-022-00179-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/01/2022] [Indexed: 12/04/2022] Open
Abstract
Background Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels. Methods Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min). Results Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels. Conclusions The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.
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Affiliation(s)
- Fleur Hierink
- GeoHealth group, Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
| | - Gianluca Boo
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Small Arms Survey, The Graduate Institute, Geneva, Switzerland
| | - Peter M. Macharia
- Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Paul O. Ouma
- Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme, Nairobi, Kenya
| | - Pablo Timoner
- GeoHealth group, Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
| | - Marc Levy
- CIESIN, The Center for International Earth Science Information Network, Columbia University, Palisades, NY USA
| | - Kevin Tschirhart
- CIESIN, The Center for International Earth Science Information Network, Columbia University, Palisades, NY USA
| | - Stefan Leyk
- Department of Geography, University of Colorado in Boulder, Boulder, CO USA
| | - Nicholas Oliphant
- The Global Fund to Fight AIDS, Tuberculosis and Malaria, Geneva, Switzerland
| | - Andrew J. Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Nicolas Ray
- GeoHealth group, Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
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The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11070403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess its capacity to capture the distribution of population counts in several urban areas, spread across the major world regions. This analysis was performed to assess its suitability for global population modelling. We acquired the most detailed local population data available for several cities and compared this with the GHS-POP layer. Results showed diverse error rates and degrees depending on the geographic context. In general, cities in High-Income (HIC) and Upper-Middle-Income Countries (UMIC) had fewer model errors as compared to cities in Low- and Middle-Income Countries (LMIC). On a global average, 75% of all urban spaces were wrongly estimated. Generally, in central mixed or non-residential areas, the population was overestimated, while in high-density residential areas (e.g., informal areas and high-rise areas), the population was underestimated. Moreover, high model uncertainties were found in low-density or sparsely populated outskirts of cities. These geographic patterns of errors should be well understood when using population models as an input for urban growth models, as they introduce geographic biases.
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Tatem AJ. Small area population denominators for improved disease surveillance and response. Epidemics 2022; 40:100597. [PMID: 35749928 PMCID: PMC9212890 DOI: 10.1016/j.epidem.2022.100597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/13/2022] [Indexed: 11/30/2022] Open
Abstract
The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.
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Affiliation(s)
- A J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
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10
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What we do know (and could know) about estimating population sizes of internally displaced people. J Migr Health 2022; 6:100120. [PMID: 35694420 PMCID: PMC9184859 DOI: 10.1016/j.jmh.2022.100120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/21/2022] [Accepted: 05/28/2022] [Indexed: 11/21/2022] Open
Abstract
The estimation of population denominators of internally displaced people (IDP) and other crisis-affected populations is a foundational step that facilitates all humanitarian assistance. However, the humanitarian system remains somewhat tolerant of irregular and inaccurate estimates of population size and composition, particularly of IDPs. In this commentary, we review how humanitarian organizations currently approach the estimation of IDP populations, and how field approaches and analytical methodologies can be improved and integrated.
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