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Karagiorgos K, Georganos S, Fuchs S, Nika G, Kavallaris N, Grahn T, Haas J, Nyberg L. Global population datasets overestimate flood exposure in Sweden. Sci Rep 2024; 14:20410. [PMID: 39223219 PMCID: PMC11368945 DOI: 10.1038/s41598-024-71330-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024] Open
Abstract
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.
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Affiliation(s)
- Konstantinos Karagiorgos
- Risk and Environmental Studies, Karlstad University, Karlstad, Sweden.
- Centre of Natural Hazards and Disaster Science (CNDS), Uppsala, Sweden.
- Centre for Societal Risk Research (CSR), Karlstad University, Karlstad, Sweden.
| | | | - Sven Fuchs
- Risk and Environmental Studies, Karlstad University, Karlstad, Sweden
- Department of Civil Engineering and Natural Hazards, BOKU University, Vienna, Austria
| | - Grigor Nika
- Mathematics, Karlstad University, Karlstad, Sweden
| | - Nikos Kavallaris
- Centre for Societal Risk Research (CSR), Karlstad University, Karlstad, Sweden
- Mathematics, Karlstad University, Karlstad, Sweden
| | - Tonje Grahn
- Risk and Environmental Studies, Karlstad University, Karlstad, Sweden
- Centre for Societal Risk Research (CSR), Karlstad University, Karlstad, Sweden
| | - Jan Haas
- Centre for Societal Risk Research (CSR), Karlstad University, Karlstad, Sweden
- Geomatics, Karlstad University, Karlstad, Sweden
| | - Lars Nyberg
- Risk and Environmental Studies, Karlstad University, Karlstad, Sweden
- Centre of Natural Hazards and Disaster Science (CNDS), Uppsala, Sweden
- Centre for Societal Risk Research (CSR), Karlstad University, Karlstad, Sweden
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2
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Sarif N, Anil Kumar AHS, Chakraborty A, Jagannath Yadav N. Population Aging in India: A Micro-Level Estimate Using Gridded Population Data. J Aging Soc Policy 2023; 35:882-900. [PMID: 37712574 DOI: 10.1080/08959420.2023.2255490] [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: 01/17/2023] [Accepted: 06/08/2023] [Indexed: 09/16/2023]
Abstract
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.
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Affiliation(s)
- Nawaj Sarif
- Department of Migration and Urban Studies, International Institute for Population Sciences, Mumbai, India
| | - A H Sruthi Anil Kumar
- Department of Family and Generations, International Institute for Population Sciences, Mumbai, India
| | - Aditi Chakraborty
- Department of Biostatistics and Demography, International Institute for Population Sciences, Mumbai, India
| | - Nilesh Jagannath Yadav
- Department of Biostatistics and Demography, International Institute for Population Sciences, Mumbai, India
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Aden MA, Bashiru G. HOW MISUSE OF ANTIMICROBIAL AGENTS IS EXACERBATING THE CHALLENGES FACING SOMALIA'S PUBLIC HEALTH. Afr J Infect Dis 2022; 16:26-32. [PMID: 36124330 PMCID: PMC9480883 DOI: 10.21010/ajid.v16i2s.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/13/2022] [Accepted: 07/13/2022] [Indexed: 11/23/2022] Open
Abstract
Background In contrast to most developed countries, antimicrobial resistance (AMR) has continued to be a serious challenge to public health in the majority of resource-limited countries in Africa. Materials and method A comprehensive review of all available literature reporting on antimicrobial resistance patterns, antimicrobial drug usage in both human and animals, as well as national AMR regulations in Somalia was undertaken. Results The review observed that successful AMR control and surveillance among resource-poor nations are affected by a lack of infrastructural and institutional capacities, poor investment in human and material resources, as well as non-adherence to available policies. The humanitarian crisis affecting Somalia has persisted for too long, leading to loss of lives, productivity and dilapidation of public health infrastructures. Somalia like most countries has adopted the One Health approach in developing their soon-to-be gazetted National Action Plan on AMR, which covers both human health, animal health and the environment. Although there are many other similar policy documents and guidelines regulating the usage and administration of antimicrobials in the country, evidence of the implementation indicates there is still a need for more effort. Conclusion AMR constitute a significant public health problem in Somali, and there is urgent need for gazetting and enforcement of the newly developed national policy. In addition, there is also the need for collaboration with the major stakeholders to develop workable solutions to combat the hazards posed by AMR in the country.
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Affiliation(s)
- Moussa Ayan Aden
- Institute for Medical Research, SIMAD University, Mogadishu, Somalia
| | - Garba Bashiru
- Institute for Medical Research, SIMAD University, Mogadishu, Somalia,Department of Veterinary Public Health and Preventive Medicine, Faculty of Veterinary Medicine, Usmanu Danfodiyo University, Sokoto, Nigeria,Corresponding Author’s E-Mail:
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Li C, Chen K, Yang K, Li J, Zhong Y, Yu H, Yang Y, Yang X, Liu L. Progress on application of spatial epidemiology in ophthalmology. Front Public Health 2022; 10:936715. [PMID: 36033806 PMCID: PMC9399620 DOI: 10.3389/fpubh.2022.936715] [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: 05/05/2022] [Accepted: 07/19/2022] [Indexed: 01/25/2023] Open
Abstract
Most ocular diseases observed with cataract, chlamydia trachomatis, diabetic retinopathy, and uveitis, have their associations with environmental exposures, lifestyle, and habits, making their distribution has certain temporal and spatial features based essentially on epidemiology. Spatial epidemiology focuses on the use of geographic information systems (GIS), global navigation satellite systems (GNSS), and spatial analysis to map spatial distribution as well as change the tendency of diseases and investigate the health services status of populations. Recently, the spatial epidemic approach has been applied in the field of ophthalmology, which provides many valuable key messages on ocular disease prevention and control. This work briefly reviewed the context of spatial epidemiology and summarized its progress in the analysis of spatiotemporal distribution, non-monitoring area data estimation, influencing factors of ocular diseases, and allocation and utilization of eye health resources, to provide references for its application in the prevention and control of ocular diseases in the future.
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Affiliation(s)
- Cong Li
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kang Chen
- Department of Ophthalmology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Kaibo Yang
- Department of Ophthalmology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiaxin Li
- Department of Graduate, China Medical University, Shenyang, China
| | - Yifan Zhong
- Department of Ophthalmology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yajun Yang
- Department of Cataract, Baotou Chaoju Eye Hospital, Baotou, China,*Correspondence: Yajun Yang
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,Xiaohong Yang
| | - Lei Liu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,Department of Ophthalmology, Jincheng People's Hospital, Jincheng, China,Lei Liu
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Everhart AR, Ferguson L, Wilson JP. Construction and validation of a spatial database of providers of transgender hormone therapy in the US. Soc Sci Med 2022; 303:115014. [PMID: 35594740 DOI: 10.1016/j.socscimed.2022.115014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 04/04/2022] [Accepted: 05/04/2022] [Indexed: 11/26/2022]
Abstract
What little data on transgender healthcare is available often focuses on transgender people's negative experiences in accessing healthcare. However, no research has been conducted that illustrates where gender-affirming hormone therapy, one part of transgender-specific medical care, is available. Without these data, large scale research to discern patterns of availability of and access to gender-affirming medical care is nearly impossible. Community-based organizations, and even trans individuals themselves have constructed repositories and databases of healthcare providers to inform other care seekers where they can access transition-related care providers, but their data are often incomplete, and usually formatted to be user-facing rather than streamlined for research purposes. To fill this gap, this article outlines the methodology for the construction of a spatial database of providers of gender-affirming hormone therapy for transgender people in the US, which is available on GitHub, created from existing community-based resources and the accompanying verification process. The completeness of the database is tested via comparison to data from the US Transgender Survey in which respondents reported travel distance to access transgender-specific care providers. The database accounted for all but 7.5% of respondents who may have accessed unknown facilities based on self-reported travel distance. Results indicate that existing methodologies for database construction regarding healthcare providers are difficult to apply when working with transgender-specific medical care and that tests for replicability and validation often take for granted the wide availability of relevant data and information. While the database unto itself can only demonstrate where care is available, it will enable future research into why these geographic patterns in care availability exist. Finally, the methodology can be replicated to produce databases for other kinds of specialized or politicized medical care such as abortion, gender-affirming surgery, or HIV treatment.
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Affiliation(s)
- Avery R Everhart
- University of Southern California, Dornsife College of Letters, Arts, and Sciences, Spatial Sciences Institute, 3616 Trousdale Parkway, AHF B55, Los Angeles, CA, 90089, USA; Center for Applied Transgender Studies, Chicago, IL, USA.
| | - Laura Ferguson
- University of Southern California, Keck School of Medicine, Institute on Inequalities in Global Health, 2001 N Soto St, Los Angeles, CA, 90032, USA.
| | - John P Wilson
- University of Southern California, Dornsife College of Letters, Arts, and Sciences, Spatial Sciences Institute, 3616 Trousdale Parkway, AHF B55, Los Angeles, CA, 90089, USA.
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Harvey B, Dalal W, Amin F, McIntyre E, Ward S, Merrill RD, Mohamed A, Hsu CH. Planning and implementing a targeted polio vaccination campaign for Somali mobile populations in Northeastern Kenya based on migration and settlement patterns. ETHNICITY & HEALTH 2022; 27:817-832. [PMID: 33126830 PMCID: PMC10120329 DOI: 10.1080/13557858.2020.1838455] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Supporting the global eradication of wildpoliovrisu (WPV), this project aimed to provide polio and measles vaccines to a population frequenty missed by immunization services and campaigns, ethnic Somali children living among mobile populations within Kenya's Northeastern Region. Additionally, nutritional support, albendazole (for treatment of intestinal parasites) and vitamin A were provided to improve children's health and in accordance with regional vaccination campaign practices. To better understand movement patterns and healthcare-seeking behaviors within this population, we trained community-based data collectors in qualitative and geospatial data collection methods. Data collectors conducted focus group and participatory mapping discussions with ethnic Somalis living in the region. Qualitative and geospatial data indicated movement patterns that followed partially definable routes and temporary settlement patterns with an influx of ethnic Somali migrants into Kenya at the start of the long rainy season (April-June). Community members also reported concerns about receiving healthcare services in regional health facilities. Using these data, an 8-week vaccination campaign was planned and implemented: 2196 children aged 0-59 months received polio vaccine (9% had not previously received polio vaccine), 2524 children aged 9-59 months received measles vaccine (27% had not previously received measles vaccine), 113 were referred for the treatment of severe acute malnourishment, 150 were referred to a supplementary feeding program due to moderate acute malnourishment, 1636 children aged 12-59 months were provided albendazole and 2008 children aged 6-59 months were provided with vitamin A. This project serves as an example for how community-based data collectors and local knowledge can help adapt public health programming to the local context and could aid disease eradication in at-risk populations.
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Affiliation(s)
- Bonnie Harvey
- Global Immunization Division, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Warren Dalal
- International Organization for Migration, Geneva, Switzerland
| | - Farah Amin
- International Organization for Migration, Geneva, Switzerland
| | - Elvira McIntyre
- Division of Toxicology and Human Health Sciences (DTHHS), Agency for Toxic Substance and Disease Registry (ATSDR), Atlanta, GA, USA
| | - Sarah Ward
- Division of Global Migration and Quarantine, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rebecca D. Merrill
- Division of Global Migration and Quarantine, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Christopher H. Hsu
- Global Immunization Division, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA
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Huo D, Zhang X, Cai Y, Hung K. Distribution Network for the Last Mile of Cross-Border E-business in a Smart City at Emerging Market in Response to COVID-19: A Key Node Analysis Based on a Vision of Fourth Party Logistics. Front Public Health 2021; 9:765087. [PMID: 34708021 PMCID: PMC8542771 DOI: 10.3389/fpubh.2021.765087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 09/15/2021] [Indexed: 11/15/2022] Open
Abstract
This research studies the development of distribution networks for the last mile distribution for cross-border E-business based on a vision of fourth party logistics (4PL) in smart cities in emerging markets in response to COVID-19. This research analyzes the distribution centers of distribution companies in Beijing city using fuzzy cluster analysis as a case study of smart cities. The location decision for distribution centers to serve cross-border E-business is further analyzed by considering the local conditions of the distribution centers. The solutions to the location decisions for distribution centers in different cases are further visualized by 2-mode networks. The key nodes in the distribution network of the last mile for cross-border E-business are further studied based on fourth-party logistics by a immune algorithm. Cross-border E-business value creation based on the development of distribution networks using fourth-party logistics is further discussed. The location distribution of key nodes can spread from the downtown district to suburban areas as the coverage of the distribution network is expanded. This research can help managers and decision makers address the last mile distribution for cross-border E-business in smart cities in emerging markets based on a vision of fourth-party logistics in response to COVID-19.
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Affiliation(s)
- Da Huo
- School of International Trade and Economics, Central University of Finance and Economics, Beijing, China
| | - Xiaotao Zhang
- School of International Trade and Economics, Central University of Finance and Economics, Beijing, China
| | - Yinghui Cai
- School of Law, Beijing Institute of Technology, Beijing, China
| | - Ken Hung
- A.R. Sanchez School of Business, Texas A&M International University, Laredo, TX, United States
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Garber K, Fox C, Abdalla M, Tatem A, Qirbi N, Lloyd-Braff L, Al-Shabi K, Ongwae K, Dyson M, Hassen K. Estimating access to health care in Yemen, a complex humanitarian emergency setting: a descriptive applied geospatial analysis. Lancet Glob Health 2020; 8:e1435-e1443. [PMID: 33069304 PMCID: PMC7561303 DOI: 10.1016/s2214-109x(20)30359-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 11/18/2022]
Abstract
BACKGROUND In conflict settings, data to guide humanitarian and development responses are often scarce. Although geospatial analyses have been used to estimate health-care access in many countries, such techniques have not been widely applied to inform real-time operations in protracted health emergencies. Doing so could provide a more robust approach for identifying and prioritising populations in need, targeting assistance, and assessing impact. We aimed to use geospatial analyses to overcome such data gaps in Yemen, the site of one of the world's worst ongoing humanitarian crises. METHODS We derived geospatial coordinates, functionality, and service availability data for Yemen health facilities from the Health Resources and Services Availability Monitoring System assessment done by WHO and the Yemen Ministry of Public Health and Population. We modelled population spatial distribution using high-resolution satellite imagery, UN population estimates, and census data. A road network grid was built from OpenStreetMap and satellite data and modified using UN Yemen Logistics Cluster data and other datasets to account for lines of conflict and road accessibility. Using this information, we created a geospatial network model to deduce the travel time of Yemeni people to their nearest health-care facilities. FINDINGS In 2018, we estimated that nearly 8·8 million (30·6%) of the total estimated Yemeni population of 28·7 million people lived more than 30-min travel time from the nearest fully or partially functional public primary health-care facility, and more than 12·1 million (42·4%) Yemeni people lived more than 1 h from the nearest fully or partially functional public hospital, assuming access to motorised transport. We found that access varied widely by district and type of health service, with almost 40% of the population living more than 2 h from comprehensive emergency obstetric and surgical care. We identified and ranked districts according to the number of people living beyond acceptable travel times to facilities and services. We found substantial variability in access and that many front-line districts were among those with the poorest access. INTERPRETATION These findings provide the most comprehensive estimates of geographical access to health care in Yemen since the outbreak of the current conflict, and they provide proof of concept for how geospatial techniques can be used to address data gaps and rigorously inform health programming. Such information is of crucial importance for humanitarian and development organisations seeking to improve effectiveness and accountability. FUNDING Global Financing Facility for Women, Children, and Adolescents Trust Fund; Development and Data Science grant; and the Yemen Emergency Health and Nutrition Project, a partnership between the World Bank, UNICEF, and WHO.
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Affiliation(s)
- Kent Garber
- Health, Nutrition, and Population Sector, Middle East and North Africa, World Bank, Washington, DC, USA.
| | - Charles Fox
- Department of Sustainable Development, World Bank, Washington, DC, USA
| | - Moustafa Abdalla
- Health, Nutrition, and Population Sector, Middle East and North Africa, World Bank, Washington, DC, USA
| | - Andrew Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | | | | | | | - Kennedy Ongwae
- Health and Nutrition Department for UNICEF Yemen, Amman, Jordan
| | - Meredith Dyson
- Health and Nutrition Department for UNICEF Yemen, Amman, Jordan
| | - Kebir Hassen
- Health and Nutrition Department for UNICEF Sudan, Khartoum, Sudan
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9
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Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data. REMOTE SENSING 2020. [DOI: 10.3390/rs12121910] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Remote sensing data have been widely used in research on population spatialization. Previous studies have generally divided study areas into several sub-areas with similar features by artificial or clustering algorithms and then developed models for these sub-areas separately using statistical methods. These approaches have drawbacks due to their subjectivity and uncertainty. In this paper, we present a study of population spatialization in Beijing City, China based on multisource remote sensing data and town-level population census data. Six predictive algorithms were compared for estimating population using the spatial variables derived from The National Polar-Orbiting Partnership/ Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) night-time light and other remote sensing data. Random forest achieved the highest accuracy and therefore was employed for population spatialization. Feature selection was performed to determine the optimal variable combinations for population modeling by random forest. Cross-validation results indicated that the developed model achieved a mean absolute error (MAE) of 2129.52 people/km2 and a R2 of 0.63. The gridded population density in Beijing at a spatial resolution of 500 m produced by the random forest model was also adjusted to be consistent with the census population at the town scale. By comparison with Google Earth high-resolution images, the remotely-sensed population was qualitatively validated at the intra-town scale. Validation results indicated that remotely sensed results can effectively depict the spatial distribution of population within town-level districts. This study provides a valuable reference for urban planning, public health and disaster prevention in Beijing, and a reference for population mapping in other cities.
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Qader SH, Lefebvre V, Tatem AJ, Pape U, Jochem W, Himelein K, Ninneman A, Wolburg P, Nunez-Chaim G, Bengtsson L, Bird T. Using gridded population and quadtree sampling units to support survey sample design in low-income settings. Int J Health Geogr 2020; 19:10. [PMID: 32216801 PMCID: PMC7099787 DOI: 10.1186/s12942-020-00205-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/16/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Household surveys are the main source of demographic, health and socio-economic data in low- and middle-income countries (LMICs). To conduct such a survey, census population information mapped into enumeration areas (EAs) typically serves a sampling frame from which to generate a random sample. However, the use of census information to generate this sample frame can be problematic as in many LMIC contexts, such data are often outdated or incomplete, potentially introducing coverage issues into the sample frame. Increasingly, where census data are outdated or unavailable, modelled population datasets in the gridded form are being used to create household survey sampling frames. METHODS Previously this process was done by either sampling from a set of the uniform grid cells (UGC) which are then manually subdivided to achieve the desired population size, or by sampling very small grid cells then aggregating cells into larger units to achieve a minimum population per survey cluster. The former approach is time and resource-intensive as well as results in substantial heterogeneity in the output sampling units, while the latter can complicate the calculation of unbiased sampling weights. Using the context of Somalia, which has not had a full census since 1987, we implemented a quadtree algorithm for the first time to create a population sampling frame. The approach uses gridded population estimates and it is based on the idea of a quadtree decomposition in which an area successively subdivided into four equal size quadrants, until the content of each quadrant is homogenous. RESULTS The quadtree approach used here produced much more homogeneous sampling units than the UGC (1 × 1 km and 3 × 3 km) approach. At the national and pre-war regional scale, the standard deviation and coefficient of variation, as indications of homogeneity, were calculated for the output sampling units using quadtree and UGC 1 × 1 km and 3 × 3 km approaches to create the sampling frame and the results showed outstanding performance for quadtree approach. CONCLUSION Our approach reduces the manual burden of manually subdividing UGC into highly populated areas, while allowing for correct calculation of sampling weights. The algorithm produces a relatively homogenous population counts within the sampling units, reducing the variation in the weights and improving the precision of the resulting estimates. Furthermore, a protocol of creating approximately equal-sized blocks and using tablets for randomized selection of a household in each block mitigated potential selection bias by enumerators. The approach shows labour, time and cost-saving and points to the potential use in wider contexts.
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Affiliation(s)
- Sarchil Hama Qader
- WorldPop, Geography and Environmental Science, University of Southampton, University Road, Southampton, UK.
- Natural Resources Department, College of Agricultural Engineering Sciences, University of Sulaimani, Sulaimani, Kurdistan Region, Iraq.
| | | | - Andrew J Tatem
- WorldPop, Geography and Environmental Science, University of Southampton, University Road, Southampton, UK
- Flowminder Foundation, Roslagsgatan 17, Stockholm, Sweden
| | | | - Warren Jochem
- WorldPop, Geography and Environmental Science, University of Southampton, University Road, Southampton, UK
| | | | - Amy Ninneman
- Flowminder Foundation, Roslagsgatan 17, Stockholm, Sweden
| | | | | | | | - Tomas Bird
- Flowminder Foundation, Roslagsgatan 17, Stockholm, Sweden
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11
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Nieves JJ, Sorichetta A, Linard C, Bondarenko M, Steele JE, Stevens FR, Gaughan AE, Carioli A, Clarke DJ, Esch T, Tatem AJ. Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night. COMPUTERS, ENVIRONMENT AND URBAN SYSTEMS 2020; 80:101444. [PMID: 32139952 PMCID: PMC7043396 DOI: 10.1016/j.compenvurbsys.2019.101444] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 11/11/2019] [Accepted: 11/11/2019] [Indexed: 05/15/2023]
Abstract
Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics.
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Affiliation(s)
- Jeremiah J. Nieves
- WorldPop Project, UK
- Department of Geography and Environment, University of Southampton, UK
| | - Alessandro Sorichetta
- WorldPop Project, UK
- Department of Geography and Environment, University of Southampton, UK
| | - Catherine Linard
- WorldPop Project, UK
- Department of Geography, Université de Namur, Belgium
| | - Maksym Bondarenko
- WorldPop Project, UK
- Department of Geography and Environment, University of Southampton, UK
| | - Jessica E. Steele
- WorldPop Project, UK
- Department of Geography and Environment, University of Southampton, UK
| | - Forrest R. Stevens
- WorldPop Project, UK
- Department of Geography and Geosciences, University of Louisville, KY, USA
| | - Andrea E. Gaughan
- WorldPop Project, UK
- Department of Geography and Geosciences, University of Louisville, KY, USA
| | - Alessandra Carioli
- WorldPop Project, UK
- Department of Geography and Environment, University of Southampton, UK
| | - Donna J. Clarke
- WorldPop Project, UK
- Department of Geography and Environment, University of Southampton, UK
| | - Thomas Esch
- German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, Germany
| | - Andrew J. Tatem
- WorldPop Project, UK
- Department of Geography and Environment, University of Southampton, UK
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Lloyd CT, Chamberlain H, Kerr D, Yetman G, Pistolesi L, Stevens FR, Gaughan AE, Nieves JJ, Hornby G, MacManus K, Sinha P, Bondarenko M, Sorichetta A, Tatem AJ. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. BIG EARTH DATA 2019; 3:108-139. [PMID: 31565697 PMCID: PMC6743742 DOI: 10.1080/20964471.2019.1625151] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 05/25/2019] [Indexed: 05/26/2023]
Abstract
Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics. The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.
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Affiliation(s)
- Christopher T. Lloyd
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Heather Chamberlain
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, Stockholm, Sweden
| | - David Kerr
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Greg Yetman
- Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, USA
| | - Linda Pistolesi
- Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, USA
| | - Forrest R. Stevens
- Department of Geography and Geosciences, University of Louisville, Louisville, KY, USA
| | - Andrea E. Gaughan
- Department of Geography and Geosciences, University of Louisville, Louisville, KY, USA
| | - Jeremiah J. Nieves
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Graeme Hornby
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- GeoData, University of Southampton, Southampton, UK
| | - Kytt MacManus
- Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, USA
| | - Parmanand Sinha
- Department of Geography and Geosciences, University of Louisville, Louisville, KY, USA
| | - Maksym Bondarenko
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Andrew J. Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- Flowminder Foundation, Stockholm, Sweden
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13
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Using Building Floor Space for Station Area Population and Employment Estimation. URBAN SCIENCE 2019. [DOI: 10.3390/urbansci3010012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Analyzing population and employment sizes at the local finer geographic scale of transit station areas offers valuable insights for cities in terms of developing better decision-making skills to support transit-oriented development. Commonly, the station area population and employment have been derived from census tract or even block data. Unfortunately, such detailed census data are hardly available and difficult to access in cities of developing countries. To address this problem, this paper explores an alternative technique in remote estimation of population and employment by using building floor space derived from an official administrative geographic information system (GIS) dataset. Based on the assumption that building floor space is a proxy to a number of residents and workers, we investigate to what extent they can be used for estimating the station area population and employment. To assess the model, we employ five station areas with heterogeneous environments in Tokyo as our empirical case study. The estimated population and employment are validated with the actual population and employment as reported in the census. The results indicate that building floor space, together with the city level aggregate information of building morphology, the density coefficient, demographic attributes, and real estate statistics, are able to generate a reasonable estimation.
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14
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Li L, Li J, Jiang Z, Zhao L, Zhao P. Methods of Population Spatialization Based on the Classification Information of Buildings from China's First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China. SENSORS 2018; 18:s18082558. [PMID: 30081569 PMCID: PMC6111606 DOI: 10.3390/s18082558] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 07/25/2018] [Accepted: 08/03/2018] [Indexed: 11/16/2022]
Abstract
Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.
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Affiliation(s)
- Linze Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
| | - Jiansong Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
| | - Zilong Jiang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
| | - Lingli Zhao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
- State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan 430072, China.
| | - Pengcheng Zhao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China.
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15
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Chew RF, Amer S, Jones K, Unangst J, Cajka J, Allpress J, Bruhn M. Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery. Int J Health Geogr 2018; 17:12. [PMID: 29743081 PMCID: PMC5944062 DOI: 10.1186/s12942-018-0132-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 05/03/2018] [Indexed: 11/10/2022] Open
Abstract
Background Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative sample. Geosampling is a probability-based, gridded population sampling method that addresses some of these issues by using geographic information system (GIS) tools to create logistically manageable area units for sampling. GIS grid cells are overlaid to partition a country’s existing administrative boundaries into area units that vary in size from 50 m × 50 m to 150 m × 150 m. To avoid sending interviewers to unoccupied areas, researchers manually classify grid cells as “residential” or “nonresidential” through visual inspection of aerial images. “Nonresidential” units are then excluded from sampling and data collection. This process of manually classifying sampling units has drawbacks since it is labor intensive, prone to human error, and creates the need for simplifying assumptions during calculation of design-based sampling weights. In this paper, we discuss the development of a deep learning classification model to predict whether aerial images are residential or nonresidential, thus reducing manual labor and eliminating the need for simplifying assumptions. Results On our test sets, the model performs comparable to a human-level baseline in both Nigeria (94.5% accuracy) and Guatemala (96.4% accuracy), and outperforms baseline machine learning models trained on crowdsourced or remote-sensed geospatial features. Additionally, our findings suggest that this approach can work well in new areas with relatively modest amounts of training data. Conclusions Gridded population sampling methods like geosampling are becoming increasingly popular in countries with outdated or inaccurate census data because of their timeliness, flexibility, and cost. Using deep learning models directly on satellite images, we provide a novel method for sample frame construction that identifies residential gridded aerial units. In cases where manual classification of satellite images is used to (1) correct for errors in gridded population data sets or (2) classify grids where population estimates are unavailable, this methodology can help reduce annotation burden with comparable quality to human analysts.
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Affiliation(s)
- Robert F Chew
- Center for Data Science, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, USA.
| | - Safaa Amer
- Division for Statistical and Data Sciences, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, USA
| | - Kasey Jones
- Center for Data Science, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, USA
| | - Jennifer Unangst
- Division for Statistical and Data Sciences, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, USA
| | - James Cajka
- Geospatial Science and Technology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, USA
| | - Justine Allpress
- Geospatial Science and Technology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, USA
| | - Mark Bruhn
- Geospatial Science and Technology Program, RTI International, 3040 East Cornwallis Road, Research Triangle Park, NC, USA
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16
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Accuracy Assessment of Multi-Source Gridded Population Distribution Datasets in China. SUSTAINABILITY 2018. [DOI: 10.3390/su10051363] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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17
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Spatially disaggregated population estimates in the absence of national population and housing census data. Proc Natl Acad Sci U S A 2018; 115:3529-3537. [PMID: 29555739 PMCID: PMC5889633 DOI: 10.1073/pnas.1715305115] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data.
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18
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Giorgi E, Osman AA, Hassan AH, Ali AA, Ibrahim F, Amran JGH, Noor AM, Snow RW. Using non-exceedance probabilities of policy-relevant malaria prevalence thresholds to identify areas of low transmission in Somalia. Malar J 2018; 17:88. [PMID: 29463264 PMCID: PMC5819647 DOI: 10.1186/s12936-018-2238-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 02/15/2018] [Indexed: 11/16/2022] Open
Abstract
Background Countries planning malaria elimination must adapt from sustaining universal control to targeted intervention and surveillance. Decisions to make this transition require interpretable information, including malaria parasite survey data. As transmission declines, observed parasite prevalence becomes highly heterogeneous with most communities reporting estimates close to zero. Absolute estimates of prevalence become hard to interpret as a measure of transmission intensity and suitable statistical methods are required to handle uncertainty of area-wide predictions that are programmatically relevant. Methods A spatio-temporal geostatistical binomial model for Plasmodium falciparum prevalence (PfPR) was developed using data from cross-sectional surveys conducted in Somalia in 2005, 2007–2011 and 2014. The fitted model was then used to generate maps of non-exceedance probabilities, i.e. the predictive probability that the region-wide population-weighted average PfPR for children between 2 and 10 years (PfPR2–10) lies below 1 and 5%. A comparison was carried out with the decision-making outcomes from those of standard approaches that ignore uncertainty in prevalence estimates. Results By 2010, most regions in Somalia were at least 70% likely to be below 5% PfPR2–10 and, by 2014, 17 regions were below 5% PfPR2–10 with a probability greater than 90%. Larger uncertainty is observed using a threshold of 1%. By 2011, only two regions were more than 90% likely of being < 1% PfPR2–10 and, by 2014, only three regions showed such low level of uncertainty. The use of non-exceedance probabilities indicated that there was weak evidence to classify 10 out of the 18 regions as < 1% in 2014, when a greater than 90% non-exceedance probability was required. Conclusion Unlike standard approaches, non-exceedance probabilities of spatially modelled PfPR2–10 allow to quantify uncertainty of prevalence estimates in relation to policy relevant intervention thresholds, providing programmatically relevant metrics to make decisions on transitioning from sustained malaria control to strategies that encompass methods of malaria elimination. Electronic supplementary material The online version of this article (10.1186/s12936-018-2238-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Emanuele Giorgi
- Lancaster Medical School, Lancaster University, Lancaster, UK.
| | | | | | | | | | | | - Abdisalan M Noor
- Population and Health Theme, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Robert W Snow
- Population and Health Theme, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
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19
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Thomson DR, Stevens FR, Ruktanonchai NW, Tatem AJ, Castro MC. GridSample: an R package to generate household survey primary sampling units (PSUs) from gridded population data. Int J Health Geogr 2017; 16:25. [PMID: 28724433 PMCID: PMC5518145 DOI: 10.1186/s12942-017-0098-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 07/04/2017] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Household survey data are collected by governments, international organizations, and companies to prioritize policies and allocate billions of dollars. Surveys are typically selected from recent census data; however, census data are often outdated or inaccurate. This paper describes how gridded population data might instead be used as a sample frame, and introduces the R GridSample algorithm for selecting primary sampling units (PSU) for complex household surveys with gridded population data. With a gridded population dataset and geographic boundary of the study area, GridSample allows a two-step process to sample "seed" cells with probability proportionate to estimated population size, then "grows" PSUs until a minimum population is achieved in each PSU. The algorithm permits stratification and oversampling of urban or rural areas. The approximately uniform size and shape of grid cells allows for spatial oversampling, not possible in typical surveys, possibly improving small area estimates with survey results. RESULTS We replicated the 2010 Rwanda Demographic and Health Survey (DHS) in GridSample by sampling the WorldPop 2010 UN-adjusted 100 m × 100 m gridded population dataset, stratifying by Rwanda's 30 districts, and oversampling in urban areas. The 2010 Rwanda DHS had 79 urban PSUs, 413 rural PSUs, with an average PSU population of 610 people. An equivalent sample in GridSample had 75 urban PSUs, 405 rural PSUs, and a median PSU population of 612 people. The number of PSUs differed because DHS added urban PSUs from specific districts while GridSample reallocated rural-to-urban PSUs across all districts. CONCLUSIONS Gridded population sampling is a promising alternative to typical census-based sampling when census data are moderately outdated or inaccurate. Four approaches to implementation have been tried: (1) using gridded PSU boundaries produced by GridSample, (2) manually segmenting gridded PSU using satellite imagery, (3) non-probability sampling (e.g. random-walk, "spin-the-pen"), and random sampling of households. Gridded population sampling is in its infancy, and further research is needed to assess the accuracy and feasibility of gridded population sampling. The GridSample R algorithm can be used to forward this research agenda.
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Affiliation(s)
- Dana R. Thomson
- Department of Social Statistics and Demography, University of Southampton, Building 58, Southampton, SO17 1BJ UK
- WorldPop, Department of Geography and Environment, University of Southampton, Building 44, Southampton, SO17 1BJ UK
- Flowminder Foundation, Roslagsgatan 17, 11355 Stockholm, Sweden
| | - Forrest R. Stevens
- Flowminder Foundation, Roslagsgatan 17, 11355 Stockholm, Sweden
- Department of Geography and Geosciences, University of Louisville, 200 E Shipp Ave, Louisville, KY 40208 USA
| | - Nick W. Ruktanonchai
- WorldPop, Department of Geography and Environment, University of Southampton, Building 44, Southampton, SO17 1BJ UK
- Flowminder Foundation, Roslagsgatan 17, 11355 Stockholm, Sweden
| | - Andrew J. Tatem
- WorldPop, Department of Geography and Environment, University of Southampton, Building 44, Southampton, SO17 1BJ UK
- Flowminder Foundation, Roslagsgatan 17, 11355 Stockholm, Sweden
| | - Marcia C. Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA 02115 USA
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Patel NN, Stevens FR, Huang Z, Gaughan AE, Elyazar I, Tatem AJ. Improving Large Area Population Mapping Using Geotweet Densities. TRANSACTIONS IN GIS : TG 2017; 21:317-331. [PMID: 28515661 PMCID: PMC5412862 DOI: 10.1111/tgis.12214] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo-located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo-located tweets in 1x1 km grid cells over a 2-month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests-based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media-derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.
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Affiliation(s)
- Nirav N. Patel
- Department of Geography and Geoinformation ScienceGeorge Mason UniversityFairfax
| | | | - Zhuojie Huang
- Department of GeographyGeoVISTA Center and Centre for Infectious Disease Dynamics, Pennsylvania State University
| | | | | | - Andrew J. Tatem
- WorldPop Project, Department of Geography and EnvironmentUniversity of Southampton
- Fogarty International CenterNational Institutes of Health
- Flowminder FoundationStockholm
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Sunyoto T, Potet J, Boelaert M. Visceral leishmaniasis in Somalia: A review of epidemiology and access to care. PLoS Negl Trop Dis 2017; 11:e0005231. [PMID: 28278151 PMCID: PMC5344316 DOI: 10.1371/journal.pntd.0005231] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Somalia, ravaged by conflict since 1991, has areas endemic for visceral leishmaniasis (VL), a deadly parasitic disease affecting the rural poor, internally displaced, and pastoralists. Very little is known about VL burden in Somalia, where the protracted crisis hampers access to health care. We reviewed evidence about VL epidemiology in Somalia and appraised control options within the context of this fragile state's health system. VL has been reported in Somalia since 1934 and has persisted ever since in foci in the southern parts of the country. The only feasible VL control option is early diagnosis and treatment, currently mostly provided by nonstate actors. The availability of VL care in Somalia is limited and insufficient at best, both in coverage and quality. Precarious security remains a major obstacle to reach VL patients in the endemic areas, and the true VL burden and its impact remain unknown. Locally adjusted, innovative approaches in VL care provision should be explored, without undermining ongoing health system development in Somalia. Ensuring VL care is accessible is a moral imperative, and the limitations of the current VL diagnostic and treatment tools in Somalia and other endemic settings affected by conflict should be overcome.
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Affiliation(s)
- Temmy Sunyoto
- Institute of Tropical Medicine, Antwerp, Belgium
- Médecins sans Frontières Campaign for Access to Medicines, Geneva, Switzerland
| | - Julien Potet
- Médecins sans Frontières Campaign for Access to Medicines, Geneva, Switzerland
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22
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High resolution global gridded data for use in population studies. Sci Data 2017; 4:170001. [PMID: 28140386 PMCID: PMC5283062 DOI: 10.1038/sdata.2017.1] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 01/06/2017] [Indexed: 12/04/2022] Open
Abstract
Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project website.
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Shields T, Pinchoff J, Lubinda J, Hamapumbu H, Searle K, Kobayashi T, Thuma PE, Moss WJ, Curriero FC. Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011. GEOSPATIAL HEALTH 2016; 11:410. [PMID: 27245798 PMCID: PMC4890610 DOI: 10.4081/gh.2016.410] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Revised: 01/07/2016] [Accepted: 01/10/2016] [Indexed: 06/05/2023]
Abstract
Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.
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Affiliation(s)
- Timothy Shields
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
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Wakefield J, Simpson D, Godwin J. Comment: Getting into Space with a Weight Problem. J Am Stat Assoc 2016; 111:1111-1118. [PMID: 28286352 DOI: 10.1080/01621459.2016.1200918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Jon Wakefield
- Department of Statistics, University of Washington; Department of Biostatistics, University of Washington
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Liu Y, Hu J, Snell-Feikema I, VanBemmel MS, Lamsal A, Wimberly MC. Software to Facilitate Remote Sensing Data Access for Disease Early Warning Systems. ENVIRONMENTAL MODELLING & SOFTWARE : WITH ENVIRONMENT DATA NEWS 2015; 74:247-257. [PMID: 26644779 PMCID: PMC4669966 DOI: 10.1016/j.envsoft.2015.07.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Satellite remote sensing produces an abundance of environmental data that can be used in the study of human health. To support the development of early warning systems for mosquito-borne diseases, we developed an open-source, client based software application to enable the Epidemiological Applications of Spatial Technologies (EASTWeb). Two major design decisions were full automation of the discovery, retrieval and processing of remote sensing data from multiple sources, and making the system easily modifiable in response to changes in data availability and user needs. Key innovations that helped to achieve these goals were the implementation of a software framework for data downloading and the design of a scheduler that tracks the complex dependencies among multiple data processing tasks and makes the system resilient to external errors. EASTWeb has been successfully applied to support forecasting of West Nile virus outbreaks in the United States and malaria epidemics in the Ethiopian highlands.
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Affiliation(s)
- Yi Liu
- Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA
| | - Jiameng Hu
- Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA
| | - Isaiah Snell-Feikema
- Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA
| | - Michael S. VanBemmel
- Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA
| | - Aashis Lamsal
- Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, USA
| | - Michael C. Wimberly
- Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, USA
- Corresponding Author:
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Tatem AJ, Campbell J, Guerra-Arias M, de Bernis L, Moran A, Matthews Z. Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births. Int J Health Geogr 2014; 13:2. [PMID: 24387010 PMCID: PMC3923551 DOI: 10.1186/1476-072x-13-2] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Accepted: 12/20/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The health and survival of women and their new-born babies in low income countries has been a key priority in public health since the 1990s. However, basic planning data, such as numbers of pregnancies and births, remain difficult to obtain and information is also lacking on geographic access to key services, such as facilities with skilled health workers. For maternal and newborn health and survival, planning for safer births and healthier newborns could be improved by more accurate estimations of the distributions of women of childbearing age. Moreover, subnational estimates of projected future numbers of pregnancies are needed for more effective strategies on human resources and infrastructure, while there is a need to link information on pregnancies to better information on health facilities in districts and regions so that coverage of services can be assessed. METHODS This paper outlines demographic mapping methods based on freely available data for the production of high resolution datasets depicting estimates of numbers of people, women of childbearing age, live births and pregnancies, and distribution of comprehensive EmONC facilities in four large high burden countries: Afghanistan, Bangladesh, Ethiopia and Tanzania. Satellite derived maps of settlements and land cover were constructed and used to redistribute areal census counts to produce detailed maps of the distributions of women of childbearing age. Household survey data, UN statistics and other sources on growth rates, age specific fertility rates, live births, stillbirths and abortions were then integrated to convert the population distribution datasets to gridded estimates of births and pregnancies. RESULTS AND CONCLUSIONS These estimates, which can be produced for current, past or future years based on standard demographic projections, can provide the basis for strategic intelligence, planning services, and provide denominators for subnational indicators to track progress. The datasets produced are part of national midwifery workforce assessments conducted in collaboration with the respective Ministries of Health and the United Nations Population Fund (UNFPA) to identify disparities between population needs, health infrastructure and workforce supply. The datasets are available to the respective Ministries as part of the UNFPA programme to inform midwifery workforce planning and also publicly available through the WorldPop population mapping project.
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Affiliation(s)
- Andrew J Tatem
- Department of Geography and Environment, University of Southampton, Highfield, Southampton, UK
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - James Campbell
- Instituto de Cooperación Social Integrare, Barcelona, Spain
| | | | | | - Allisyn Moran
- U.S. Agency for International Development, Washington DC, USA
| | - Zoë Matthews
- Department of Social Statistics and Demography, University of Southampton, Highfield, Southampton, UK
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Alegana VA, Atkinson PM, Wright JA, Kamwi R, Uusiku P, Katokele S, Snow RW, Noor AM. Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models. Spat Spatiotemporal Epidemiol 2013; 7:25-36. [PMID: 24238079 PMCID: PMC3839406 DOI: 10.1016/j.sste.2013.09.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2013] [Revised: 08/05/2013] [Accepted: 09/05/2013] [Indexed: 10/29/2022]
Abstract
As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination.
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Affiliation(s)
- Victor A Alegana
- Malaria Public Health Department, KEMRI-Wellcome Trust-University of Oxford Collaborative Programme, P.O. Box 43640, 00100 GPO Nairobi, Kenya; Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
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Millennium development health metrics: where do Africa's children and women of childbearing age live? Popul Health Metr 2013; 11:11. [PMID: 23875684 PMCID: PMC3724578 DOI: 10.1186/1478-7954-11-11] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Accepted: 07/11/2013] [Indexed: 11/22/2022] Open
Abstract
The Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress toward their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, and this has prompted the development of sophisticated cartographic techniques for mapping and modeling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition over large areas is lacking, prompting many influential studies that have rigorously accounted for health risk heterogeneities to overlook the substantial demographic variations that exist subnationally and merely apply national-level adjustments. Here we outline the development of high resolution age- and sex-structured spatial population datasets for Africa in 2000-2015 built from over a million measurements from more than 20,000 subnational units, increasing input data detail from previous studies by over 400-fold. We analyze the large spatial variations seen within countries and across the continent for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and find that substantial differences in health and development indicators can result through using only national level statistics, compared to accounting for subnational variation. Progress toward meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on derived metrics through accounting for spatial demographic heterogeneities that exist within nations across Africa.
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Childhood malaria admission rates to four hospitals in Malawi between 2000 and 2010. PLoS One 2013; 8:e62214. [PMID: 23638008 PMCID: PMC3637378 DOI: 10.1371/journal.pone.0062214] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Accepted: 03/17/2013] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION The last few years have witnessed rapid scaling-up of key malaria interventions in several African countries following increases in development assistance. However, there is only limited country-specific information on the health impact of expanded coverage of these interventions. METHODS Paediatric admission data were assembled from 4 hospitals in Malawi reflecting different malaria ecologies. Trends in monthly clinical malaria admissions between January 2000 and December 2010 were analysed using time-series models controlling for covariates related to climate and service use to establish whether changes in admissions can be related to expanded coverage of interventions aimed at reducing malaria infection. RESULTS In 3 of 4 sites there was an increase in clinical malaria admission rates. Results from time series models indicate a significant month-to-month increase in the mean clinical malaria admission rates at two hospitals (trend P<0.05). At these hospitals clinical malaria admissions had increased from 2000 by 41% to 100%. Comparison of changes in malaria risk and ITN coverage appear to correspond to a lack of disease declines over the period. Changes in intervention coverage within hospital catchments showed minimal increases in ITN coverage from <6% across all sites in 2000 to maximum of 33% at one hospital site by 2010. Additionally, malaria transmission intensity remained unchanged between 2000-2010 across all sites. DISCUSSION Despite modest increases in coverage of measures to reduce infection there has been minimal changes in paediatric clinical malaria cases in four hospitals in Malawi. Studies across Africa are increasingly showing a mixed set of impact results and it is important to assemble more data from more sites to understand the wider implications of malaria funding investment. We also caution that impact surveillance should continue in areas where intervention coverage is increasing with time, for example Malawi, as decline may become evident within a period when coverage reaches optimal levels.
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High resolution population distribution maps for Southeast Asia in 2010 and 2015. PLoS One 2013; 8:e55882. [PMID: 23418469 PMCID: PMC3572178 DOI: 10.1371/journal.pone.0055882] [Citation(s) in RCA: 114] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2012] [Accepted: 01/03/2013] [Indexed: 11/19/2022] Open
Abstract
Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.
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Checchi F, Stewart BT, Palmer JJ, Grundy C. Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations. Int J Health Geogr 2013; 12:4. [PMID: 23343099 PMCID: PMC3558435 DOI: 10.1186/1476-072x-12-4] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Accepted: 01/13/2013] [Indexed: 11/30/2022] Open
Abstract
Background Estimating the size of forcibly displaced populations is key to documenting their plight and allocating sufficient resources to their assistance, but is often not done, particularly during the acute phase of displacement, due to methodological challenges and inaccessibility. In this study, we explored the potential use of very high resolution satellite imagery to remotely estimate forcibly displaced populations. Methods Our method consisted of multiplying (i) manual counts of assumed residential structures on a satellite image and (ii) estimates of the mean number of people per structure (structure occupancy) obtained from publicly available reports. We computed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons’ camps and two urban neighbourhoods with a mixture of residents and displaced) ranging in population from 1,969 to 90,547, and compared these to “gold standard” reference population figures from census or other robust methods. Results Structure counts by independent analysts were reasonably consistent. Between one and 11 occupancy reports were available per site and most of these reported people per household rather than per structure. The imagery-based method had a precision relative to reference population figures of <10% in four sites and 10–30% in three sites, but severely over-estimated the population in an Ethiopian camp with implausible occupancy data and two post-earthquake Haiti sites featuring dense and complex residential layout. For each site, estimates were produced in 2–5 working person-days. Conclusions In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings. Based on these results, we discuss possible ways forward for the method’s development.
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Affiliation(s)
- Francesco Checchi
- London School of Hygiene and Tropical Medicine, London, United Kingdom
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Noor AM, Alegana VA, Patil AP, Moloney G, Borle M, Yusuf F, Amran J, Snow RW. Mapping the receptivity of malaria risk to plan the future of control in Somalia. BMJ Open 2012; 2:e001160. [PMID: 22855625 PMCID: PMC4400533 DOI: 10.1136/bmjopen-2012-001160] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2012] [Accepted: 06/18/2012] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES To measure the receptive risks of malaria in Somalia and compare decisions on intervention scale-up based on this map and the more widely used contemporary risk maps. DESIGN Cross-sectional community Plasmodium falciparum parasite rate (PfPR) data for the period 2007-2010 corrected to a standard age range of 2 to <10 years (PfPR(2-10)) and used within a Bayesian space-time geostatistical framework to predict the contemporary (2010) mean PfPR(2-10) and the maximum annual mean PfPR(2-10) (receptive) from the highest predicted PfPR(2-10) value over the study period as an estimate of receptivity. SETTING Randomly sampled communities in Somalia. PARTICIPANTS Randomly sampled individuals of all ages. MAIN OUTCOME MEASURE Cartographic descriptions of malaria receptivity and contemporary risks in Somalia at the district level. RESULTS The contemporary annual PfPR(2-10) map estimated that all districts (n=74) and population (n=8.4 million) in Somalia were under hypoendemic transmission (≤10% PfPR(2-10)). Of these, 23% of the districts, home to 13% of the population, were under transmission of <1% PfPR(2-10). About 58% of the districts and 55% of the population were in the risk class of 1% to <5% PfPR(2-10). In contrast, the receptivity map estimated 65% of the districts and 69% of the population were under mesoendemic transmission (>10%-50% PfPR(2-10)) and the rest as hypoendemic. CONCLUSION Compared with maps of receptive risks, contemporary maps of transmission mask disparities of malaria risk necessary to prioritise and sustain future control. As malaria risk declines across Africa, efforts must be invested in measuring receptivity for efficient control planning.
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Affiliation(s)
- Abdisalan Mohamed Noor
- Malaria Public Health and Epidemiology Group, Centre for Geographic
Medicine Research-Coast, Kenya Medical Research Institute/Wellcome Trust Research
Programme, Nairobi, Kenya
- Nuffield Department of Medicine, John Radcliffe Hospital, Centre for
Tropical Medicine, University of Oxford, Headington, Oxford, UK
| | - Victor Adagi Alegana
- Malaria Public Health and Epidemiology Group, Centre for Geographic
Medicine Research-Coast, Kenya Medical Research Institute/Wellcome Trust Research
Programme, Nairobi, Kenya
| | | | - Grainne Moloney
- Food Security and Nutrition Analysis Unit-Somalia, United Nations Food
and Agricultural Organization, Nairobi, Kenya
| | - Mohammed Borle
- Food Security and Nutrition Analysis Unit-Somalia, United Nations Food
and Agricultural Organization, Nairobi, Kenya
| | - Fahmi Yusuf
- World Health Organization, Malaria Control and Elimination, Somalia
Office, Nairobi, Kenya
| | - Jamal Amran
- World Health Organization, Malaria Control and Elimination, Somalia
Office, Nairobi, Kenya
| | - Robert William Snow
- Malaria Public Health and Epidemiology Group, Centre for Geographic
Medicine Research-Coast, Kenya Medical Research Institute/Wellcome Trust Research
Programme, Nairobi, Kenya
- Nuffield Department of Medicine, John Radcliffe Hospital, Centre for
Tropical Medicine, University of Oxford, Headington, Oxford, UK
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Linard C, Tatem AJ. Large-scale spatial population databases in infectious disease research. Int J Health Geogr 2012; 11:7. [PMID: 22433126 PMCID: PMC3331802 DOI: 10.1186/1476-072x-11-7] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2012] [Accepted: 03/20/2012] [Indexed: 01/26/2023] Open
Abstract
Modelling studies on the spatial distribution and spread of infectious diseases are becoming increasingly detailed and sophisticated, with global risk mapping and epidemic modelling studies now popular. Yet, in deriving populations at risk of disease estimates, these spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data. Moreover, a variety of different methods have been used to model population distribution at large spatial scales. In this review we describe the main global gridded population datasets that are freely available for health researchers and compare their construction methods, and highlight the uncertainties inherent in these population datasets. We review their application in past studies on disease risk and dynamics, and discuss how the choice of dataset can affect results. Moreover, we highlight how the lack of contemporary, detailed and reliable data on human population distribution in low income countries is proving a barrier to obtaining accurate large-scale estimates of population at risk and constructing reliable models of disease spread, and suggest research directions required to further reduce these barriers.
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Affiliation(s)
- Catherine Linard
- Biological Control and Spatial Ecology, Université Libre de Bruxelles, CP 160/12, Avenue FD Roosevelt 50, B-1050 Brussels, Belgium.
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Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ. Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS One 2012; 7:e31743. [PMID: 22363717 PMCID: PMC3283664 DOI: 10.1371/journal.pone.0031743] [Citation(s) in RCA: 244] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2011] [Accepted: 01/12/2012] [Indexed: 11/18/2022] Open
Abstract
The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and accessibility. Here we outline approaches to developing a new high resolution population distribution dataset for Africa and analyse rural accessibility to population centers. Contemporary population count data were combined with detailed satellite-derived settlement extents to map population distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, population concentration and spatial accessibility to major population centres is exhibited across the continent. In Africa, 90% of the population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.
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Affiliation(s)
- Catherine Linard
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium
| | - Marius Gilbert
- Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium
- Fonds National de la Recherche Scientifique, Brussels, Belgium
| | - Robert W. Snow
- Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI - University of Oxford - Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Abdisalan M. Noor
- Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI - University of Oxford - Wellcome Trust Research Programme, Nairobi, Kenya
| | - Andrew J. Tatem
- Department of Geography, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
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Vlahov D, Agarwal SR, Buckley RM, Caiaffa WT, Corvalan CF, Ezeh AC, Finkelstein R, Friel S, Harpham T, Hossain M, de Faria Leao B, Mboup G, Montgomery MR, Netherland JC, Ompad DC, Prasad A, Quinn AT, Rothman A, Satterthwaite DE, Stansfield S, Watson VJ. Roundtable on Urban Living Environment Research (RULER). J Urban Health 2011; 88:793-857. [PMID: 21910089 PMCID: PMC3191208 DOI: 10.1007/s11524-011-9613-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
For 18 months in 2009-2010, the Rockefeller Foundation provided support to establish the Roundtable on Urban Living Environment Research (RULER). Composed of leading experts in population health measurement from a variety of disciplines, sectors, and continents, RULER met for the purpose of reviewing existing methods of measurement for urban health in the context of recent reports from UN agencies on health inequities in urban settings. The audience for this report was identified as international, national, and local governing bodies; civil society; and donor agencies. The goal of the report was to identify gaps in measurement that must be filled in order to assess and evaluate population health in urban settings, especially in informal settlements (or slums) in low- and middle-income countries. Care must be taken to integrate recommendations with existing platforms (e.g., Health Metrics Network, the Institute for Health Metrics and Evaluation) that could incorporate, mature, and sustain efforts to address these gaps and promote effective data for healthy urban management. RULER noted that these existing platforms focus primarily on health outcomes and systems, mainly at the national level. Although substantial reviews of health outcomes and health service measures had been conducted elsewhere, such reviews covered these in an aggregate and perhaps misleading way. For example, some spatial aspects of health inequities, such as those pointed to in the 2008 report from the WHO's Commission on the Social Determinants of Health, received limited attention. If RULER were to focus on health inequities in the urban environment, access to disaggregated data was a priority. RULER observed that some urban health metrics were already available, if not always appreciated and utilized in ongoing efforts (e.g., census data with granular data on households, water, and sanitation but with little attention paid to the spatial dimensions of these data). Other less obvious elements had not exploited the gains realized in spatial measurement technology and techniques (e.g., defining geographic and social urban informal settlement boundaries, classification of population-based amenities and hazards, and innovative spatial measurement of local governance for health). In summary, the RULER team identified three major areas for enhancing measurement to motivate action for urban health-namely, disaggregation of geographic areas for intra-urban risk assessment and action, measures for both social environment and governance, and measures for a better understanding of the implications of the physical (e.g., climate) and built environment for health. The challenge of addressing these elements in resource-poor settings was acknowledged, as was the intensely political nature of urban health metrics. The RULER team went further to identify existing global health metrics structures that could serve as platforms for more granular metrics specific for urban settings.
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Affiliation(s)
- David Vlahov
- School of Nursing, University of California-San Francisco San Francisco, CA, USA,
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Fisher RP, Myers BA. Free and simple GIS as appropriate for health mapping in a low resource setting: a case study in eastern Indonesia. Int J Health Geogr 2011; 10:15. [PMID: 21352553 PMCID: PMC3051879 DOI: 10.1186/1476-072x-10-15] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2010] [Accepted: 02/25/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Despite the demonstrated utility of GIS for health applications, there are perceived problems in low resource settings: GIS software can be expensive and complex; input data are often of low quality. This study aimed to test the appropriateness of new, inexpensive and simple GIS tools in poorly resourced areas of a developing country. GIS applications were trialled in pilot studies based on mapping of health resources and health indicators at the clinic and district level in the predominantly rural province of Nusa Tenggara Timur in eastern Indonesia. The pilot applications were (i) rapid field collection of health infrastructure data using a GPS enabled PDA, (ii) mapping health indicator data using open source GIS software, and (iii) service availability mapping using a free modelling tool. RESULTS Through contextualised training, district and clinic staff acquired skills in spatial analysis and visualisation and, six months after the pilot studies, they were using these skills for advocacy in the planning process, to inform the allocation of some health resources, and to evaluate some public health initiatives. CONCLUSIONS We demonstrated that GIS can be a useful and inexpensive tool for the decentralisation of health data analysis to low resource settings through the use of free and simple software, locally relevant training materials and by providing data collection tools to ensure data reliability.
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Affiliation(s)
- Rohan P Fisher
- Charles Darwin University, Darwin, Northern Territory 0909, Australia.
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