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Zhuang H, Liu X, Li B, Wu C, Yan Y, Zeng L, Zheng C. Mapping high-resolution global gridded population distribution from 1870 to 2100. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 955:176867. [PMID: 39414039 DOI: 10.1016/j.scitotenv.2024.176867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 10/01/2024] [Accepted: 10/09/2024] [Indexed: 10/18/2024]
Abstract
Long-term global gridded population data is crucial in deepening our understanding of spatiotemporal population dynamics and essential in disaster exposure assessment studies. Several gridded population datasets exist but only cover a single period of observational, historical, or future. Here, based on a unified data and method framework, we created a coherent and consistent gridded population dataset at 1 km resolution with a 10-year interval spanning from 1870 to 2100. Using the observed population maps (2000-2020), historical population hindcast (1870-2000), and future population projection (2020-2100) under the Shared Socioeconomic Pathways (SSPs) were modeled. The validation shows that the constructed dataset achieves a high quantitative agreement with existing datasets and can better distribute the population within the built-up area, resulting in a more reasonable allocation. The constructed gridded population dataset can clearly show the evolution of population distribution over a long period in a spatially explicit way and exhibit high temporal consistency. From 1870 to 2100 (SSPs), the global population showed an S-shaped growth pattern, increasing by about 4.17 to 8.49 times, which has exerted substantial pressure on global sustainable development. At the local scale, the consistent, long-term, high-resolution gridded population data reveals diverse spatial (cluster, linear, and ring) and temporal (emergence, increase, stable, decrease) dynamics of population patterns across distinct regions, periods, and scenarios. Applying the long-term gridded population data, we revealed a substantial increase in the proportion of the global population exposed to floods, rising from 10.61 % in 1870 to 11.98 %-13.93 % in 2100 (SSPs), highlighting a rapid population expansion within flood-prone areas. In general, this study provides a consistent global gridded population dataset spanning over 200 years, which can provide insights into the whole life cycle of the global population spatiotemporal dynamics and holds great application value in various fields.
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Affiliation(s)
- Haoming Zhuang
- School of Geography and Tourism, Jiaying University, Meizhou, China; Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
| | - Xiaoping Liu
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China.
| | - Bingjie Li
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
| | - Changjiang Wu
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
| | - Yuchao Yan
- Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
| | - Li Zeng
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, China
| | - Chunyan Zheng
- School of Geography and Tourism, Jiaying University, Meizhou, China
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2
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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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3
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Xu W, Zhou Y, Taubenböck H, Stokes EC, Zhu Z, Lai F, Li X, Zhao X. Spatially explicit downscaling and projection of population in mainland China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 941:173623. [PMID: 38815823 DOI: 10.1016/j.scitotenv.2024.173623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/09/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024]
Abstract
Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.
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Affiliation(s)
- Wenru Xu
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Yuyu Zhou
- Department of Geography, The University of Hong Kong, Hong Kong.
| | - Hannes Taubenböck
- German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, 82234 Weßling, Germany
| | | | - Zhengyuan Zhu
- Department of Statistics, Iowa State University50011, Ames, IA, USA
| | - Feilin Lai
- Department of Geography and Planning, St. Cloud State University, MN 56301, USA
| | - Xuecao Li
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China
| | - Xia Zhao
- Institute of Land and Urban-Rural Development, Zhejiang University of Finance & Economics, Hangzhou 310018, China
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Pranzini N, Maiorano L, Cosentino F, Thuiller W, Santini L. The role of species interactions in shaping the geographic pattern of ungulate abundance across African savannah. Sci Rep 2024; 14:19647. [PMID: 39179790 PMCID: PMC11344126 DOI: 10.1038/s41598-024-70668-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 08/20/2024] [Indexed: 08/26/2024] Open
Abstract
Macroecologists traditionally emphasized the role of environmental variables for predicting species distribution and abundance at large scale. While biotic factors have been increasingly recognized as important at macroecological scales, producing valuable biotic variables remains challenging and rarely tested. Capitalizing on the wealth of population density estimates available for African savannah ungulates, here we modeled species average population density at 100 × 100 km as a function of both environmental variables and proxies of biotic interactions (competition and predation) and estimated their relative contribution. We fitted a linear mixed effect model on 1043 population density estimates for 63 species of ungulates using Bayesian inference and estimated the percentage of total variance explained by environmental, anthropogenic, and biotic interactions variables. Environmental and anthropogenic variables were the main drivers of ungulate population density, with NDVI, Distance to permanent water bodies and Human population density showing the highest contribution to the variance. Nonetheless, biotic interactions altogether contributed to a quarter of the variance explained, with predation and competition having a negative effect on species density. Despite the limitations of modelling biotic interactions in macroecological studies, proxies of biotic interactions can enhance our understanding of biological patterns at broad spatial scales, uncovering novel predictors as well as enhancing the predictive power of large-scale models.
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Affiliation(s)
- N Pranzini
- Department of Biology and Biotechnologies ''Charles Darwin,'' ''Sapienza,'', University of Rome, 00185, Roma, Italy.
| | - L Maiorano
- Department of Biology and Biotechnologies ''Charles Darwin,'' ''Sapienza,'', University of Rome, 00185, Roma, Italy
| | - F Cosentino
- Department of Biology and Biotechnologies ''Charles Darwin,'' ''Sapienza,'', University of Rome, 00185, Roma, Italy
| | - W Thuiller
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, 38000, Grenoble, France
| | - L Santini
- Department of Biology and Biotechnologies ''Charles Darwin,'' ''Sapienza,'', University of Rome, 00185, Roma, Italy.
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Villena OC, Arab A, Lippi CA, Ryan SJ, Johnson LR. Influence of environmental, geographic, socio-demographic, and epidemiological factors on presence of malaria at the community level in two continents. Sci Rep 2024; 14:16734. [PMID: 39030306 PMCID: PMC11271557 DOI: 10.1038/s41598-024-67452-5] [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: 05/10/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024] Open
Abstract
The interactions of environmental, geographic, socio-demographic, and epidemiological factors in shaping mosquito-borne disease transmission dynamics are complex and changeable, influencing the abundance and distribution of vectors and the pathogens they transmit. In this study, 27 years of cross-sectional malaria survey data (1990-2017) were used to examine the effects of these factors on Plasmodium falciparum and Plasmodium vivax malaria presence at the community level in Africa and Asia. Monthly long-term, open-source data for each factor were compiled and analyzed using generalized linear models and classification and regression trees. Both temperature and precipitation exhibited unimodal relationships with malaria, with a positive effect up to a point after which a negative effect was observed as temperature and precipitation increased. Overall decline in malaria from 2000 to 2012 was well captured by the models, as was the resurgence after that. The models also indicated higher malaria in regions with lower economic and development indicators. Malaria is driven by a combination of environmental, geographic, socioeconomic, and epidemiological factors, and in this study, we demonstrated two approaches to capturing this complexity of drivers within models. Identifying these key drivers, and describing their associations with malaria, provides key information to inform planning and prevention strategies and interventions to reduce malaria burden.
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Affiliation(s)
- Oswaldo C Villena
- The Earth Commons Institute, Georgetown University, Washington, DC, 20057, USA.
| | - Ali Arab
- Department of Mathematics and Statistics, Georgetown University, Washington, DC, 20057, USA
| | - Catherine A Lippi
- Department of Geography, University of Florida, Gainesville, FL, 32611, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Sadie J Ryan
- Department of Geography, University of Florida, Gainesville, FL, 32611, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
- School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Leah R Johnson
- Department of Statistics, Virginia Tech, Blacksburg, VA, 24061, USA
- Computational Modeling and Data Analytics, Virginia Tech, Blacksburg, VA, 24061, USA
- Department of Biology, Virginia Tech, Blacksburg, VA, 24061, USA
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6
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Li W, Zhang Y, Li M, Long Y. Rethinking the country-level percentage of population residing in urban area with a global harmonized urban definition. iScience 2024; 27:110125. [PMID: 38904069 PMCID: PMC11186970 DOI: 10.1016/j.isci.2024.110125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/15/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024] Open
Abstract
The UN (United Nations) collects global data on the country-level Percentage of Population Residing in Urban Area (PPRUA). However, variations in urban definitions make these data incomparable across countries. This study assesses national defined PPRUA within UN statistics against estimates we derived using global comparable definitions. Refer to the UN's Degree of Urbanization framework, we propose 90 global harmonized methods for estimating PPRUA by combining different configurations of three global population datasets, six urban total population thresholds, and five urban population density thresholds. This approach demonstrated significant variations in country-level PPRUA estimations, with wide 95% confidence intervals using the Z score method. Most national defined PPRUA fall between the upper 95% CI and the median of the estimations, underscoring the need for globally harmonious PPRUA estimates. This study advocates for a reassessment of datasets and thresholds in the future and for investigating urbanization on a scale beyond the country level.
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Affiliation(s)
- Wenyue Li
- School of Architecture, Tsinghua University, Beijing 100084, China
- School of Architecture, Harbin Institute of Technology, Shenzhen 518055, China
| | - Yecheng Zhang
- School of Architecture, Tsinghua University, Beijing 100084, China
| | - Mengxing Li
- Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Ying Long
- School of Architecture, Tsinghua University, Beijing 100084, China
- Hang Lung Center for Real Estate, Key Laboratory of Ecological Planning & Green Building, Ministry of Education, Tsinghua University, Beijing 100084, China
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7
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Liu L, Cao X, Li S, Jie N. A 31-year (1990-2020) global gridded population dataset generated by cluster analysis and statistical learning. Sci Data 2024; 11:124. [PMID: 38267476 PMCID: PMC10808219 DOI: 10.1038/s41597-024-02913-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Continuously monitoring global population spatial dynamics is crucial for implementing effective policies related to sustainable development, including epidemiology, urban planning, and global inequality. However, existing global gridded population data products lack consistent population estimates, making them unsuitable for time-series analysis. To address this issue, this study designed a data fusion framework based on cluster analysis and statistical learning approaches, which led to the generation of a continuous global gridded population dataset (GlobPOP). The GlobPOP dataset was evaluated through two-tier spatial and temporal validation to demonstrate its accuracy and applicability. The spatial validation results show that the GlobPOP dataset is highly accurate. The temporal validation results also reveal that the GlobPOP dataset performs consistently well across eight representative countries and cities despite their unique population dynamics. With the availability of GlobPOP datasets in both population count and population density formats, researchers and policymakers can leverage the new dataset to conduct time-series analysis of the population and explore the spatial patterns of population development at global, national, and city levels.
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Affiliation(s)
- Luling Liu
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Xin Cao
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
- Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Shijie Li
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Na Jie
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
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8
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Kiana OM, Mundia CN, Gachari MK, Kimwatu DM. Spatio-temporal modeling of rangeland degradation in response to changing environment in the Upper Ewaso Ngiro River Basin, Kenya. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1311. [PMID: 37831413 DOI: 10.1007/s10661-023-11898-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/26/2023] [Indexed: 10/14/2023]
Abstract
Rangelands primarily provide forage for grazing and browsing animals, yet their ecosystems are degraded due to natural causes and anthropogenic activities such as pastoralism, tourism, and ranching. Increased rangeland detrimental effects led the present research to model the severity of rangeland degradation in the Upper Ewaso Ngiro River Basin (UENRB) in Kenya between 1986 and 2021 and predict the future scenario for 2031. The severity of rangeland degradation was analysed using the multi-criteria analytic hierarchical process and principal component analysis, while the cellular automata Markov chain-analysis model was used for prediction. The models utilized datasets including land-use land cover, surface albedo, bareness index, vegetation health index, soil moisture index, topographic wetness index, reconnaissance drought index, k-factor, slope, and population density. The findings indicated that rangeland degradation varied sporadically, with the reconnaissance drought index being the significant influencing parameter, contributing to about 19.2% of the total degradation. In average, between the years under study, non-rangeland zones covered 10.4%, while low, moderate, high, and very high degradability severity covered 15.3%, 49.1%, 25.2%, and 0%, respectively. Prediction results for the year 2031 revealed that non-rangeland zones will cover 5.3%, whereas low, moderate, high and very high will cover 18.1%, 39.2%, 37.4%, and 0%, respectively. The hybrid model proved to be effective in modeling rangeland degradation. The study recommends the county and national governments to propose and adopt by-laws on legislation to regulate the exploitation of natural resources in the study area in order to restore the rangelands.
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Affiliation(s)
- Obed Mogare Kiana
- Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, P.O Box Private Bag-10143 Dedan Kimathi, Nyeri, Kenya.
| | - Charles Ndegwa Mundia
- Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, P.O Box Private Bag-10143 Dedan Kimathi, Nyeri, Kenya
| | - Moses Karoki Gachari
- Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, P.O Box Private Bag-10143 Dedan Kimathi, Nyeri, Kenya
| | - Duncan Maina Kimwatu
- Institute of Geomatics, GIS and Remote Sensing, Dedan Kimathi University of Technology, P.O Box Private Bag-10143 Dedan Kimathi, Nyeri, Kenya
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Uhl JH, Leyk S. Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2023; 123:103469. [PMID: 37975073 PMCID: PMC10653213 DOI: 10.1016/j.jag.2023.103469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.
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Affiliation(s)
- Johannes H. Uhl
- University of Colorado Boulder, Institute of Behavioral Science, 483 UCB, Boulder, CO 80309, USA
- University of Colorado Boulder, Cooperative Institute for Research in Environmental Sciences (CIRES), 216 UCB, Boulder, CO 80309, USA
| | - Stefan Leyk
- University of Colorado Boulder, Institute of Behavioral Science, 483 UCB, Boulder, CO 80309, USA
- University of Colorado Boulder, Department of Geography, 260 UCB, Boulder, CO 80309, USA
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10
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McKeen T, Bondarenko M, Kerr D, Esch T, Marconcini M, Palacios-Lopez D, Zeidler J, Valle RC, Juran S, Tatem AJ, Sorichetta A. High-resolution gridded population datasets for Latin America and the Caribbean using official statistics. Sci Data 2023; 10:436. [PMID: 37419895 PMCID: PMC10328919 DOI: 10.1038/s41597-023-02305-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023] Open
Abstract
"Leaving no one behind" is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the 'top-down' approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.
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Affiliation(s)
- Tom McKeen
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
| | - Maksym Bondarenko
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - David Kerr
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Thomas Esch
- German Aerospace Centre (DLR), Wessling, Germany
| | | | | | | | - R Catalina Valle
- United Nations Population Fund (UNFPA), Regional Office for Latin America and the Caribbean, Panama, Panama
| | - Sabrina Juran
- United Nations Population Fund (UNFPA), Regional Office for Latin America and the Caribbean, Panama, Panama
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Alessandro Sorichetta
- Dipartimento di Scienze della Terra "A. Desio", Università degli Studi di Milano, Milano, Italy
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11
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Jayaprakasam M, Chatterjee N, Chanda MM, Shahabuddin SM, Singhai M, Tiwari S, Panda S. Human anthrax in India in recent times: A systematic review & risk mapping. One Health 2023; 16:100564. [PMID: 37363236 PMCID: PMC10288098 DOI: 10.1016/j.onehlt.2023.100564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/28/2023] Open
Abstract
The disease anthrax occurs generally in herbivores and the causative organism (Bacillus anthracis) infects humans who come in contact with infected animals or their products. The persistence of anthrax spores for decades and its lethality contribute to its biowarfare potential. We conducted this systematic review along with risk mapping to investigate the spatio-temporal distribution, clinico-epidemiological, socio-behavioural and programmatic issues pertaining to anthrax in India over the last two decades. Peer reviewed quantitative and qualitative studies and grey literature comprising weekly reports of the 'Integrated Disease Surveillance Program' (IDSP), were accessed for extracting data. IDSP data were used for geo-referencing of the villages of anthrax cases; Pseudo-absence was generated to fit a Bayesian Additive Regression Trees (BART) model to develop anthrax risk map. The case fatality rate of cutaneous anthrax ranged from 2% to 38%, while the gastrointestinal and inhalational types were 100% fatal. Our synthesis revealed that human anthrax outbreaks in India were clustered around the eastern coastal regions. The states of Odisha, West Bengal, Andhra Pradesh and Jharkhand reported maximum number of outbreaks. Odisha reported a maximum number of 439 human anthrax cases since 2009, of which Koraput district contributed to 200 cases (46%). While handling or consumption of infected animal product were proximal drivers of these events, poverty, lack of awareness, traditional beliefs and local practices served as facilitatory factors. Other structural determinants were wild life-livestock interface, historical forest loss, soil pH, soil-water balance, organic carbon content, temperature, rainfall and humidity. The programmatic issues identified through this review were lack of active surveillance, non-availability of diagnostic facility at the periphery, delayed reporting, absence of routine livestock vaccination and lack of adequate veterinary services. Interventions based on One-health approach in the country merit immediate policy and program attention; high risk zones for anthrax identified during present investigation, should be prioritized.
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Affiliation(s)
| | - Nabendu Chatterjee
- Division of Basic Medical Sciences, Indian Council of Medical Research, New Delhi, India
| | - Mohammed Mudassar Chanda
- ICAR - National Institute of Veterinary Epidemiology and Disease Informatics (NIVEDI), Bangalore, India
| | | | - Monil Singhai
- Center for Arboviral and Zoonotic Diseases (CAZD), National Center for Disease Control, New Delhi, India
| | - Simmi Tiwari
- Division of Zoonotic Diseases Program, National Centre for Disease Control, New Delhi, India
| | - Samiran Panda
- Indian Council of Medical Research, New Delhi, India
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12
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Pezzulo C, Tejedor-Garavito N, Chan HMT, Dreoni I, Kerr D, Ghosh S, Bonnie A, Bondarenko M, Salasibew M, Tatem AJ. A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India. Sci Data 2023; 10:86. [PMID: 36765058 PMCID: PMC9918481 DOI: 10.1038/s41597-023-01961-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/11/2023] [Indexed: 02/12/2023] Open
Abstract
Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015-16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.
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Affiliation(s)
- Carla Pezzulo
- WorldPop, School of Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK.
| | - Natalia Tejedor-Garavito
- WorldPop, School of Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
| | - Ho Man Theophilus Chan
- WorldPop, School of Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
- School of Mathematical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Ilda Dreoni
- WorldPop, School of Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
- Social Statistics & Demography, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
| | - David Kerr
- WorldPop, School of Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
| | - Samik Ghosh
- Children's Investment Fund Foundation (CIFF), London, UK
| | - Amy Bonnie
- WorldPop, School of Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
| | - Maksym Bondarenko
- WorldPop, School of Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
| | | | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Highfield Campus, Southampton, SO17 1BJ, UK
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13
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Mshelbwala PP, J. Soares Magalhães R, Weese JS, Ahmed NO, Rupprecht CE, Clark NJ. Modelling modifiable factors associated with the probability of human rabies deaths among self-reported victims of dog bites in Abuja, Nigeria. PLoS Negl Trop Dis 2023; 17:e0011147. [PMID: 36809362 PMCID: PMC9983858 DOI: 10.1371/journal.pntd.0011147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 03/03/2023] [Accepted: 02/07/2023] [Indexed: 02/23/2023] Open
Abstract
Canine-mediated rabies kills tens of thousands of people annually in lesser-developed communities of Asia, Africa, and the Americas, primarily through bites from infected dogs. Multiple rabies outbreaks have been associated with human deaths in Nigeria. However, the lack of quality data on human rabies hinders advocacy and resource allocation for effective prevention and control. We obtained 20 years of dog bite surveillance data across 19 major hospitals in Abuja, incorporating modifiable and environmental covariates. To overcome the challenge of missing information, we used a Bayesian approach with expert-solicited prior information to jointly model missing covariate data and the additive effects of the covariates on the predicted probability of human death after rabies virus exposure. Only 1155 cases of dog bites were recorded throughout the study period, out of which 4.2% (N = 49) died of rabies. The odds for risk of human death were predicted to decrease among individuals who were bitten by owned dogs compared to those bitten by free-roaming dogs. Similarly, there was a predicted decrease in the probability of human death among victims bitten by vaccinated dogs compared to those bitten by unvaccinated dogs. The odds for the risk of human death after bitten individuals received rabies prophylaxis were predicted to decrease compared to no prophylaxis. We demonstrate the practical application of a regularised Bayesian approach to model sparse dog bite surveillance data to uncover risk factors for human rabies, with broader applications in other endemic rabies settings with similar profiles. The low reporting observed in this study underscores the need for community engagement and investment in surveillance to increase data availability. Better data on bite cases will help to estimate the burden of rabies in Nigeria and would be important to plan effective prevention and control of this disease.
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Affiliation(s)
- Philip P. Mshelbwala
- School of Veterinary Science, The University of Queensland, Gatton, Australia
- Department of Veterinary Medicine, Faculty of Veterinary Medicine, University of Abuja, Abuja, Nigeria
- * E-mail: ,
| | - Ricardo J. Soares Magalhães
- School of Veterinary Science, The University of Queensland, Gatton, Australia
- Children’s Health and Environment Program, UQ Children’s Health Research Centre, The University of Queensland, South Brisbane, Australia
| | - J. Scott Weese
- Department of Pathobiology, Ontario Veterinary College, Guelph, Canada
| | | | - Charles E. Rupprecht
- LYSSA LLC, Atlanta, Georgia, United States of America
- Auburn University, Auburn, Alabama, United States of America
| | - Nicholas J. Clark
- School of Veterinary Science, The University of Queensland, Gatton, Australia
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14
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Yan D, Zhang X, Qin T, Li C, Zhang J, Wang H, Weng B, Wang K, Liu S, Li X, Yang Y, Li W, Lv Z, Wang J, Li M, He S, Liu F, Bi W, Xu T, Shi X, Man Z, Sun C, Liu M, Wang M, Huang Y, Long H, Niu Y, Dorjsuren B, Gedefaw M, Li Y, Tian Z, Mu S, Wang W, Zhou X. A data set of distributed global population and water withdrawal from 1960 to 2020. Sci Data 2022; 9:640. [PMID: 36271026 PMCID: PMC9587213 DOI: 10.1038/s41597-022-01760-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
Population and water withdrawal data sets are currently faced with difficulties in collecting, processing and verifying multi-source time series, and the spatial distribution characteristics of long series are also relatively lacking. Time series is the basic guarantee for the accuracy of data sets, and the production of long series spatial distribution is a realistic requirement to expand the application scope of data sets. Through the time-consuming and laborious basic processing work, this research focuses on the population and water intake time series, and interpolates and extends them to specific land uses to ensure the accuracy of the time series and the demand of spatially distributed data sets. This research provides a set of population density and water intensity products from 1960 to 2020 distributed to the administrative units or the corresponding regions. The data set fills the gaps in the multi-year data set for the accuracy of population density and the intensity of water withdrawal. Measurement(s) | distributed global population and water withdrawal | Technology Type(s) | mathematical statistics and analysis |
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Affiliation(s)
- Denghua Yan
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Xin Zhang
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Tianling Qin
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China.
| | - Chenhao Li
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China.
| | - Jianyun Zhang
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, China
| | - Hao Wang
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Baisha Weng
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Kun Wang
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Shanshan Liu
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Xiangnan Li
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Yuheng Yang
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Weizhi Li
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Zhenyu Lv
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Jianwei Wang
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Meng Li
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Shan He
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Fang Liu
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Wuxia Bi
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Ting Xu
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Xiaoqing Shi
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Zihao Man
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Congwu Sun
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Meiyu Liu
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Mengke Wang
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Yinghou Huang
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Haoyu Long
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Yongzhen Niu
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Batsuren Dorjsuren
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Mohammed Gedefaw
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Yizhe Li
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Zihao Tian
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Shizhou Mu
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Wenyu Wang
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
| | - Xiaoxiang Zhou
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, No. 1 Fuxing Road, Haidian District, Beijing, 100038, China
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15
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Swanwick RH, Read QD, Guinn SM, Williamson MA, Hondula KL, Elmore AJ. Dasymetric population mapping based on US census data and 30-m gridded estimates of impervious surface. Sci Data 2022; 9:523. [PMID: 36030258 PMCID: PMC9422266 DOI: 10.1038/s41597-022-01603-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/01/2022] [Indexed: 11/21/2022] Open
Abstract
Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. In the United States, Census data is the most common source for information on population. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. The dataset, known as the U.G.L.I (updatable gridded lightweight impervious) population dataset, compares favorably against other population data sources, and provides a useful balance between resolution and complexity. Measurement(s) | Population Density | Technology Type(s) | satellite imaging | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Environment | populated place | Sample Characteristic - Location | contiguous United States of America |
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Affiliation(s)
- Rachel H Swanwick
- National Socio-Environmental Synthesis Center, Annapolis, MD, 21401, USA. .,Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, 05405, USA.
| | - Quentin D Read
- National Socio-Environmental Synthesis Center, Annapolis, MD, 21401, USA.,Agricultural Research Service, United States Department of Agriculture, Raleigh, NC, 27606, USA
| | - Steven M Guinn
- Integration and Application Network, University of Maryland Center for Environmental Science, Annapolis, MD, 21403, USA.,Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, 21532, USA
| | | | - Kelly L Hondula
- National Socio-Environmental Synthesis Center, Annapolis, MD, 21401, USA.,Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ, 85287, USA
| | - Andrew J Elmore
- National Socio-Environmental Synthesis Center, Annapolis, MD, 21401, USA. .,Integration and Application Network, University of Maryland Center for Environmental Science, Annapolis, MD, 21403, USA. .,Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, 21532, USA.
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16
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Villena OC, Ryan SJ, Murdock CC, Johnson LR. Temperature impacts the environmental suitability for malaria transmission by Anopheles gambiae and Anopheles stephensi. Ecology 2022; 103:e3685. [PMID: 35315521 PMCID: PMC9357211 DOI: 10.1002/ecy.3685] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 10/13/2021] [Accepted: 11/30/2021] [Indexed: 11/06/2022]
Abstract
Extrinsic environmental factors influence the spatiotemporal dynamics of many organisms, including insects that transmit the pathogens responsible for vector-borne diseases (VBDs). Temperature is an especially important constraint on the fitness of a wide variety of ectothermic insects. A mechanistic understanding of how temperature impacts traits of ectotherms, and thus the distribution of ectotherms and vector-borne infections, is key to predicting the consequences of climate change on transmission of VBDs like malaria. However, the response of transmission to temperature and other drivers is complex, as thermal traits of ectotherms are typically nonlinear, and they interact to determine transmission constraints. In this study, we assess and compare the effect of temperature on the transmission of two malaria parasites, Plasmodium falciparum and Plasmodium vivax, by two malaria vector species, Anopheles gambiae and Anopheles stephensi. We model the nonlinear responses of temperature dependent mosquito and parasite traits (mosquito development rate, bite rate, fecundity, proportion of eggs surviving to adulthood, vector competence, mortality rate, and parasite development rate) and incorporate these traits into a suitability metric based on a model for the basic reproductive number across temperatures. Our model predicts that the optimum temperature for transmission suitability is similar for the four mosquito-parasite combinations assessed in this study, but may differ at the thermal limits. More specifically, we found significant differences in the upper thermal limit between parasites spread by the same mosquito (A. stephensi) and between mosquitoes carrying P. falciparum. In contrast, at the lower thermal limit the significant differences were primarily between the mosquito species that both carried the same pathogen (e.g., A. stephensi and A. gambiae both with P. falciparum). Using prevalence data, we show that the transmission suitability metric S T $$ S(T) $$ calculated from our mechanistic model is consistent with observed P. falciparum prevalence in Africa and Asia but is equivocal for P. vivax prevalence in Asia, and inconsistent with P. vivax prevalence in Africa. We mapped risk to illustrate the number of months various areas in Africa and Asia predicted to be suitable for malaria transmission based on this suitability metric. This mapping provides spatially explicit predictions for suitability and transmission risk.
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Affiliation(s)
| | - Sadie J. Ryan
- Department of GeographyUniversity of FloridaGainesvilleFloridaUSA
- Emerging Pathogens InstituteUniversity of FloridaGainesvilleFloridaUSA
- School of Life SciencesUniversity of KwaZulu‐NatalDurbanSouth Africa
| | - Courtney C. Murdock
- Odum School of EcologyUniversity of GeorgiaAthensGeorgiaUSA
- Center for the Ecology of Infectious DiseasesUniversity of GeorgiaAthensGeorgiaUSA
- Center for Vaccines and ImmunologyCollege of Veterinary Medicine, University of GeorgiaAthensGeorgiaUSA
- Riverbasin CenterUniversity of GeorgiaAthensGeorgiaUSA
- Department of EntomologyCollege of Agriculture and Life Sciences, Cornell UniversityIthacaNew YorkUSA
| | - Leah R. Johnson
- Department of StatisticsVirginia TechBlacksburgVirginiaUSA
- Computational Modeling and Data AnalyticsVirginia TechBlacksburgVirginiaUSA
- Department of BiologyVirginia TechBlacksburgVirginiaUSA
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17
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The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2022. [DOI: 10.3390/ijgi11070403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess its capacity to capture the distribution of population counts in several urban areas, spread across the major world regions. This analysis was performed to assess its suitability for global population modelling. We acquired the most detailed local population data available for several cities and compared this with the GHS-POP layer. Results showed diverse error rates and degrees depending on the geographic context. In general, cities in High-Income (HIC) and Upper-Middle-Income Countries (UMIC) had fewer model errors as compared to cities in Low- and Middle-Income Countries (LMIC). On a global average, 75% of all urban spaces were wrongly estimated. Generally, in central mixed or non-residential areas, the population was overestimated, while in high-density residential areas (e.g., informal areas and high-rise areas), the population was underestimated. Moreover, high model uncertainties were found in low-density or sparsely populated outskirts of cities. These geographic patterns of errors should be well understood when using population models as an input for urban growth models, as they introduce geographic biases.
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18
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On the Exploitation of Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions. REMOTE SENSING 2022. [DOI: 10.3390/rs14102384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Remote sensing technologies are extensively applied to prevent, monitor, and forecast hazardous risk conditions in the present-day global climate change era. This paper presents an overview of the current stage of remote sensing approaches employed to study coastal and delta river regions. The advantages and limitations of Earth Observation technology in characterizing the effects of climate variations on coastal environments are also presented. The role of the constellations of satellite sensors for Earth Observation, collecting helpful information on the Earth’s system and its temporal changes, is emphasized. For some key technologies, the principal characteristics of the processing chains adopted to obtain from the collected raw data added-value products are summarized. Emphasis is put on studying various disaster risks that affect coastal and megacity areas, where heterogeneous and interlinked hazard conditions can severely affect the population.
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19
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Hohl A, Tang W, Casas I, Shi X, Delmelle E. Detecting space-time patterns of disease risk under dynamic background population. JOURNAL OF GEOGRAPHICAL SYSTEMS 2022; 24:389-417. [PMID: 35463848 PMCID: PMC9018970 DOI: 10.1007/s10109-022-00377-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
We are able to collect vast quantities of spatiotemporal data due to recent technological advances. Exploratory space-time data analysis approaches can facilitate the detection of patterns and formation of hypotheses about their driving processes. However, geographic patterns of social phenomena like crime or disease are driven by the underlying population. This research aims for incorporating temporal population dynamics into spatial analysis, a key omission of previous methods. As population data are becoming available at finer spatial and temporal granularity, we are increasingly able to capture the dynamic patterns of human activity. In this paper, we modify the space-time kernel density estimation method by accounting for spatially and temporally dynamic background populations (ST-DB), assess the benefits of considering the temporal dimension and finally, compare ST-DB to its purely spatial counterpart. We delineate clusters and compare them, as well as their significance, across multiple parameter configurations. We apply ST-DB to an outbreak of dengue fever in Cali, Colombia during 2010-2011. Our results show that incorporating the temporal dimension improves our ability to delineate significant clusters. This study addresses an urgent need in the spatiotemporal analysis literature by using population data at high spatial and temporal resolutions.
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Affiliation(s)
- Alexander Hohl
- Department of Geography, University of Utah, Salt Lake City, UT 84112 USA
| | - Wenwu Tang
- Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC 28223 USA
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223 USA
| | - Irene Casas
- School of History and Social Sciences, Louisiana Tech University, Ruston, LA 71272 USA
| | - Xun Shi
- Department of Geography, Dartmouth College, Hanover, NH 03755 USA
| | - Eric Delmelle
- Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC 28223 USA
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223 USA
- Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu Campus, P.O. Box 111, FI-80101 Finland
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20
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A Population Spatialization Model at the Building Scale Using Random Forest. REMOTE SENSING 2022. [DOI: 10.3390/rs14081811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas.
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21
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Gladson LA, Cromar KR, Ghazipura M, Knowland KE, Keller CA, Duncan B. Communicating respiratory health risk among children using a global air quality index. ENVIRONMENT INTERNATIONAL 2022; 159:107023. [PMID: 34920275 DOI: 10.1016/j.envint.2021.107023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/29/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
Air pollution poses a serious threat to children's respiratory health around the world. Satellite remote-sensing technology and air quality models can provide pollution data on a global scale, necessary for risk communication efforts in regions without ground-based monitoring networks. Several large centers, including NASA, produce global pollution forecasts that may be used alongside air quality indices to communicate local, daily risk information to the public. Here we present a health-based, globally applicable air quality index developed specifically to reflect the respiratory health risks among children exposed to elevated outdoor air pollution. Additive, excess-risk air quality indices were developed using 51 different coefficients derived from time-series health studies evaluating the impacts of ambient fine particulate matter, nitrogen dioxide, and ozone on children's respiratory morbidity outcomes. A total of four indices were created which varied based on whether or not the underlying studies controlled for co-pollutants and in the adjustment of excess risks of individual pollutants. Combined with historical estimates of air pollution provided globally at a 25 × 25 km2 spatial resolution from the NASA's Goddard Earth Observing System composition forecast (GEOS-CF) model, each of these indices were examined in a global sample of 664 small and 140 large cities for study year 2017. Adjusted indices presented the most normal distributions of locally-scaled index values, which has been shown to improve associations with health risks, while indices based on coefficients controlling for co-pollutants had little effect on index performance. We provide the steps and resources need to apply our final adjusted index at the local level using freely-available forecasting data from the GEOS-CF model, which can provide risk communication information for cities around the world to better inform individual behavior modification to best protect children's respiratory health.
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Affiliation(s)
- Laura A Gladson
- Marron Institute of Urban Management, New York University, New York, USA; New York University Grossman School of Medicine, New York, NY, USA
| | - Kevin R Cromar
- Marron Institute of Urban Management, New York University, New York, USA; New York University Grossman School of Medicine, New York, NY, USA.
| | - Marya Ghazipura
- Marron Institute of Urban Management, New York University, New York, USA; New York University Grossman School of Medicine, New York, NY, USA
| | - K Emma Knowland
- Universities Space Research Association, Columbia, MD, USA; NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Christoph A Keller
- Universities Space Research Association, Columbia, MD, USA; NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Bryan Duncan
- NASA Goddard Space Flight Center, Greenbelt, MD, USA
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Mally R, Ward SF, Trombik J, Buszko J, Medzihorský V, Liebhold AM. Non-native plant drives the spatial dynamics of its herbivores: the case of black locust (Robinia pseudoacacia) in Europe. NEOBIOTA 2021. [DOI: 10.3897/neobiota.69.71949] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Non-native plants typically benefit from enemy release following their naturalization in non-native habitats. However, over time, herbivorous insects specializing on such plants may invade from the native range and thereby diminish the benefits of enemy release that these plants may experience. In this study, we compare rates of invasion spread across Europe of three North American insect folivores: the Lepidoptera leaf miners Macrosaccus robiniella and Parectopa robiniella, and the gall midge Obolodiplosis robiniae, that specialize on Robinia pseudoacacia. This tree species is one of the most widespread non-native trees in Europe. We find that spread rates vary among the three species and that some of this variation can be explained by differences in their life history traits. We also report that geographical variation in spread rates are influenced by distribution of Robinia pseudoacacia, human population and temperature, though Robinia pseudoacacia occurrence had the greatest influence. The importance of host tree occurrence on invasion speed can be explained by the general importance of hosts on the population growth and spread of invading species.
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23
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Hinkel J, Feyen L, Hemer M, Le Cozannet G, Lincke D, Marcos M, Mentaschi L, Merkens JL, de Moel H, Muis S, Nicholls RJ, Vafeidis AT, van de Wal RSW, Vousdoukas MI, Wahl T, Ward PJ, Wolff C. Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk. EARTH'S FUTURE 2021; 9:e2020EF001882. [PMID: 34435072 PMCID: PMC8365640 DOI: 10.1029/2020ef001882] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 05/12/2021] [Accepted: 06/01/2021] [Indexed: 05/21/2023]
Abstract
This study provides a literature-based comparative assessment of uncertainties and biases in global to world-regional scale assessments of current and future coastal flood risks, considering mean and extreme sea-level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea-level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio-economic development (factors 2.3-5.8), digital elevation data (factors 1.2-3.8), ice sheet models (factor 1.6-3.8) and greenhouse gas emissions (factors 1.6-2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth-damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea-levels, storm surges, tides and waves. Advancing the state-of-the-art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth-damage functions would be best reduced through open community-based efforts, in which many scholars work together in collecting and validating these data.
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Affiliation(s)
- J. Hinkel
- Global Climate Forum (GCF)BerlinGermany
- Division of Resource EconomicsAlbrecht Daniel Thaer‐Institute and Berlin Workshop in Institutional Analysis of Social‐Ecological Systems (WINS)Humboldt‐UniversityBerlinGermany
| | - L. Feyen
- European CommissionJoint Research Centre (JRC)IspraItaly
| | - M. Hemer
- CSIRO Oceans and AtmosphereHobart TASAustralia
| | | | - D. Lincke
- Global Climate Forum (GCF)BerlinGermany
| | - M. Marcos
- Mediterranean Institute for Advanced Studies (IMEDEA)PalmaSpain
- Department of PhysicsUniversity of the Balearic IslandsPalmaSpain
| | - L. Mentaschi
- European CommissionJoint Research Centre (JRC)IspraItaly
- Department of Physics and Astronomy Augusto RighiUniversity of BolognaBolognaItaly
| | - J. L. Merkens
- Institute of GeographyChristian‐Albrechts University KielKielGermany
| | - H. de Moel
- Institute for Environmental Studies (IVM)Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - S. Muis
- Institute for Environmental Studies (IVM)Vrije Universiteit AmsterdamAmsterdamNetherlands
- DeltaresDelftNetherlands
| | - R. J. Nicholls
- Tyndall Centre for Climate Change ResearchUniversity of East AngliaNorwichUK
| | - A. T. Vafeidis
- Institute of GeographyChristian‐Albrechts University KielKielGermany
| | - R. S. W. van de Wal
- Institute for Marine and Atmospheric Research Utrecht and Department of Physical GeographyUtrecht UniversityUtrechtNetherlands
| | | | - T. Wahl
- Department of Civil, Environmental and Construction EngineeringNational Center for Integrated Coastal ResearchUniversity of Central FloridaOrlandoFLUSA
| | - P. J. Ward
- Institute for Environmental Studies (IVM)Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - C. Wolff
- Institute of GeographyChristian‐Albrechts University KielKielGermany
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24
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Schug F, Frantz D, van der Linden S, Hostert P. Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLoS One 2021; 16:e0249044. [PMID: 33770133 PMCID: PMC7996978 DOI: 10.1371/journal.pone.0249044] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/09/2021] [Indexed: 11/18/2022] Open
Abstract
Gridded population data is widely used to map fine scale population patterns and dynamics to understand associated human-environmental processes for global change research, disaster risk assessment and other domains. This study mapped gridded population across Germany using weighting layers from building density, building height (both from previous studies) and building type datasets, all created from freely available, temporally and globally consistent Copernicus Sentinel-1 and Sentinel-2 data. We first produced and validated a nation-wide dataset of predominant residential and non-residential building types. We then examined the impact of different weighting layers from density, type and height on top-down dasymetric mapping quality across scales. We finally performed a nation-wide bottom-up population estimate based on the three datasets. We found that integrating building types into dasymetric mapping is helpful at fine scale, as population is not redistributed to non-residential areas. Building density improved the overall quality of population estimates at all scales compared to using a binary building layer. Most importantly, we found that the combined use of density and height, i.e. volume, considerably increased mapping quality in general and with regard to regional discrepancy by largely eliminating systematic underestimation in dense agglomerations and overestimation in rural areas. We also found that building density, type and volume, together with living floor area per capita, are suitable to produce accurate large-area bottom-up population estimates.
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Affiliation(s)
- Franz Schug
- Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany
- Integrated Research Institute on Transformations of Human-Environment Systems, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Frantz
- Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Patrick Hostert
- Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany
- Integrated Research Institute on Transformations of Human-Environment Systems, Humboldt-Universität zu Berlin, Berlin, Germany
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25
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Kimwatu DM, Mundia CN, Makokha GO. Developing a new socio-economic drought index for monitoring drought proliferation: a case study of Upper Ewaso Ngiro River Basin in Kenya. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 193:213. [PMID: 33759015 DOI: 10.1007/s10661-021-08989-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 03/02/2021] [Indexed: 06/12/2023]
Abstract
The study focused on developing a novel socio-economic drought index (SeDI) for monitoring the severity of drought in a dry basin ecosystem dominated by nomadic pastoralists. The study utilized the domestic water deficit index, bareness index, normalized difference vegetation index, and water accessibility index as the input variables. An ensembled stochastic framework that coupled the 3D Euclidean feature space algorithm, least-squares adjustment, and iteration was used to derive the new SeDI. This approach minimized the uncertainties propagated by the stochastic nature of the input variables that has been a major bottleneck exhibited by the existing models. The regression analyses between the simulated SeDI and the observed ground river discharge registered a correlation coefficient (r) of -0.84 and a p-value of 0.02, while the correlation between the Hull's score-derived SeDI and ground river discharge registered a correlation coefficient (r) of -0.75 and a p-value of 0.05. The assessment revealed that the newly derived SeDI was more sensitive to the river discharge than the Hull's score-derived SeDI. The SeDI's classification results for the period between 1986 and 2018 revealed that only January 2009 manifested a significant slight severity level covering about 12.4% of the basin. Additionally, the results indicated that the basin exhibited a moderate severity level ranging between 85 and 96%, a severe level ranging between 2.2 and 13.3%, and an extreme level ranging between 0.73 and 1.17%. The derived SeDI would serve as an early warning tool necessary for increasing the resilience to climate-related risks and offer support in reducing the loss of life and livelihood.
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Affiliation(s)
- Duncan Maina Kimwatu
- Geospatial Information Systems and Remote Sensing, Institute of Geomatics, Dedan Kimathi University of Technology, Nyeri, Kenya.
| | - Charles Ndegwa Mundia
- Geospatial Information Systems and Remote Sensing, Institute of Geomatics, Dedan Kimathi University of Technology, Nyeri, Kenya
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26
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High-Resolution Gridded Population Datasets: Exploring the Capabilities of the World Settlement Footprint 2019 Imperviousness Layer for the African Continent. REMOTE SENSING 2021. [DOI: 10.3390/rs13061142] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.
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27
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Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13030373] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Urban functional zones are important space carriers for urban economic and social function. The accurate and rapid identification of urban functional zones is of great significance to urban planning and resource allocation. However, the factors considered in the existing functional zone identification methods are not comprehensive enough, and the recognition of functional zones stops at their categories. This paper proposes a framework that combines multisource heterogeneous data to identify the categories of functional zones and draw the portraits of functional zones. The framework comprehensively describes the features of functional zones from four aspects: building-level metrics, landscape metrics, semantic metrics, and human activity metrics, and uses a combination of ensemble learning and active learning to balance the identification accuracy of functional zones and the labeling cost during large-scale generalization. Furthermore, sentiment analysis, word cloud analysis, and land cover proportion maps are added to the portraits of typical functional zones to make the image of functional zones vivid. The experiment carried out within the Fifth Ring Road, Haidian District, Beijing, shows that the overall accuracy of the method reached 82.37% and the portraits of the four typical functional zones are clear. The method in this paper has good repeatability and generalization, which is helpful to carry out quantitative and objective research on urban functional zones.
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28
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Nieves JJ, Bondarenko M, Kerr D, Ves N, Yetman G, Sinha P, Clarke DJ, Sorichetta A, Stevens FR, Gaughan AE, Tatem AJ. Measuring the contribution of built-settlement data to global population mapping. SOCIAL SCIENCES & HUMANITIES OPEN 2021; 3:100102. [PMID: 33889839 PMCID: PMC8041065 DOI: 10.1016/j.ssaho.2020.100102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 12/11/2020] [Accepted: 12/20/2020] [Indexed: 11/24/2022]
Abstract
Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.
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Affiliation(s)
- Jeremiah J. Nieves
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Maksym Bondarenko
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - David Kerr
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Nikolas Ves
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Greg Yetman
- Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, USA
| | - Parmanand Sinha
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
- Department of Geography and Geosciences, University of Louisville, Kentucky, USA
| | - Donna J. Clarke
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
| | - Forrest R. Stevens
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
- Department of Geography and Geosciences, University of Louisville, Kentucky, USA
| | - Andrea E. Gaughan
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
- Department of Geography and Geosciences, University of Louisville, Kentucky, USA
| | - Andrew J. Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, UK
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29
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A spatial population downscaling model for integrated human-environment analysis in the United States. DEMOGRAPHIC RESEARCH 2020. [DOI: 10.4054/demres.2020.43.54] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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30
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Mapping Changing Population Distribution on the Qinghai–Tibet Plateau since 2000 with Multi-Temporal Remote Sensing and Point-of-Interest Data. REMOTE SENSING 2020. [DOI: 10.3390/rs12244059] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Advanced developments have been achieved in urban human population estimation, however, there is still a considerable research gap for the mapping of remote rural populations. In this study, based on demographic data at the town-level, multi-temporal high-resolution remote sensing data, and local population-sensitive point-of-interest (POI) data, we tailored a random forest-based dasymetric approach to map population distribution on the Qinghai–Tibet Plateau (QTP) for 2000, 2010, and 2016 with a spatial resolution of 1000 m. We then analyzed the temporal and spatial change of this distribution. The results showed that the QTP has a sparse population distribution overall; in large areas of the northern QTP, the population density is zero, accounting for about 14% of the total area of the QTP. About half of the QTP showed a rapid increase in population density between 2000 and 2016, mainly located in the eastern and southern parts of Qinghai Province and the central-eastern parts of the Tibet Autonomous Region. Regarding the relative importance of variables in explaining population density, the variables “Distance to Temples” is the most important, followed by “Density of Villages” and “Elevation”. Furthermore, our new products exhibited higher accuracy compared with five recently released gridded population density datasets, namely WorldPop, Gridded Population of the World version 4, and three national gridded population datasets for China. Both the root-mean-square error (RMSE) and mean absolute error (MAE) for our products were about half of those of the compared products except for WorldPop. This study provides a reference for using fine-scale demographic count and local population-sensitive POIs to model changing population distribution in remote rural areas.
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Abstract
The assessment of populations affected by urban flooding is crucial for flood prevention and mitigation but is highly influenced by the accuracy of population datasets. The population distribution is related to buildings during the urban floods, so assessing the population at the building scale is more rational for the urban floods, which is possible due to the abundance of multi-source data and advances in GIS technology. Therefore, this study assesses the populations affected by urban floods through population mapping at the building scale using highly correlated point of interest (POI) data. The population distribution is first mapped by downscaling the grid-based WorldPop population data to the building scale. Then, the population affected by urban floods is estimated by superimposing the population data sets onto flood areas, with flooding simulated by the LISFLOOD-FP hydrodynamic model. Finally, the proposed method is applied to Lishui City in southeast China. The results show that the population affected by urban floods is significantly reduced for different rainstorm scenarios when using the building-scale population instead of WorldPop. In certain areas, populations not captured by WorldPop can be identified using the building-scale population. This study provides a new method for estimating populations affected by urban flooding.
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32
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Liang L, Gong P. Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. Sci Rep 2020; 10:18618. [PMID: 33122678 PMCID: PMC7596069 DOI: 10.1038/s41598-020-74524-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/24/2020] [Indexed: 01/15/2023] Open
Abstract
Most air pollution research has focused on assessing the urban landscape effects of pollutants in megacities, little is known about their associations in small- to mid-sized cities. Considering that the biggest urban growth is projected to occur in these smaller-scale cities, this empirical study identifies the key urban form determinants of decadal-long fine particulate matter (PM2.5) trends in all 626 Chinese cities at the county level and above. As the first study of its kind, this study comprehensively examines the urban form effects on air quality in cities of different population sizes, at different development levels, and in different spatial-autocorrelation positions. Results demonstrate that the urban form evolution has long-term effects on PM2.5 level, but the dominant factors shift over the urbanization stages: area metrics play a role in PM2.5 trends of small-sized cities at the early urban development stage, whereas aggregation metrics determine such trends mostly in mid-sized cities. For large cities exhibiting a higher degree of urbanization, the spatial connectedness of urban patches is positively associated with long-term PM2.5 level increases. We suggest that, depending on the city's developmental stage, different aspects of the urban form should be emphasized to achieve long-term clean air goals.
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Affiliation(s)
- Lu Liang
- Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX, 76203, USA.
| | - Peng Gong
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
- Tsinghua Urban Institute, Tsinghua University, Beijing, 100084, China
- Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing, 100084, China
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33
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Identification of Polycentric Cities in China Based on NPP-VIIRS Nighttime Light Data. REMOTE SENSING 2020. [DOI: 10.3390/rs12193248] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Nighttime light data play an important role in the research on cities, while the urban centers over a large spatial scale are still far from clearly understood. Aiming at the current challenges in monitoring the spatial structure of cities using nighttime light data, this paper proposes a new method for identifying urban centers for massive cities at the large spatial scale based on the brightness information captured by the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor. Based on the method for extracting the peak point based on digital elevation model (DEM) data in terrain analysis, the maximum neighborhood and difference algorithms were applied to the NPP-VIIRS data to extract the pixels with the peak nighttime light intensity to identify the potential locations of urban centers. The results show 7239 urban centers in 2200 cities in China in 2017, with an average of 3.3 urban centers per city. Approximately 68% of the cities had significant polycentric structures. The developed method in this paper is useful for identifying the urban centers and can provide the reference to the city planning and construction.
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34
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Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data. REMOTE SENSING 2020. [DOI: 10.3390/rs12193235] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Where urban dwellers live at a fine scale is essential for the planning of services and response to city emergencies. Currently, most existing population mapping approaches considered census data as observational data for specifying models. However, census data usually have low spatial resolution and low frequency. Here, we presented a framework for mapping populations in residential neighborhoods with 30 m spatial resolution with little dependency upon census data. The framework integrated remote sensing and crowdsourcing data. The observational populations and number of households at residential neighborhood scale were obtained from real-time crowdsourcing data instead of census data. We tested our framework in Beijing. We found that (1) the number of households from a real estate trade platform could be a good proxy for accurate observational population. (2) The accuracy of the mapping population in residential neighborhoods was reasonable. The mean absolute percentage error was 47.26% and the R2 was 0.78. (3) Our framework shows great potential in mapping the population in real time. Our findings expand the knowledge in estimating urban population. In addition, the proposed framework and approach provide an effective means to quantify population distribution data for cities, which is particularly important for many of the cities worldwide lacking census data at the residential neighborhood scale.
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35
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Mousa A, Al-Taiar A, Anstey NM, Badaut C, Barber BE, Bassat Q, Challenger JD, Cunnington AJ, Datta D, Drakeley C, Ghani AC, Gordeuk VR, Grigg MJ, Hugo P, John CC, Mayor A, Migot-Nabias F, Opoka RO, Pasvol G, Rees C, Reyburn H, Riley EM, Shah BN, Sitoe A, Sutherland CJ, Thuma PE, Unger SA, Viwami F, Walther M, Whitty CJM, William T, Okell LC. The impact of delayed treatment of uncomplicated P. falciparum malaria on progression to severe malaria: A systematic review and a pooled multicentre individual-patient meta-analysis. PLoS Med 2020; 17:e1003359. [PMID: 33075101 PMCID: PMC7571702 DOI: 10.1371/journal.pmed.1003359] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Delay in receiving treatment for uncomplicated malaria (UM) is often reported to increase the risk of developing severe malaria (SM), but access to treatment remains low in most high-burden areas. Understanding the contribution of treatment delay on progression to severe disease is critical to determine how quickly patients need to receive treatment and to quantify the impact of widely implemented treatment interventions, such as 'test-and-treat' policies administered by community health workers (CHWs). We conducted a pooled individual-participant meta-analysis to estimate the association between treatment delay and presenting with SM. METHODS AND FINDINGS A search using Ovid MEDLINE and Embase was initially conducted to identify studies on severe Plasmodium falciparum malaria that included information on treatment delay, such as fever duration (inception to 22nd September 2017). Studies identified included 5 case-control and 8 other observational clinical studies of SM and UM cases. Risk of bias was assessed using the Newcastle-Ottawa scale, and all studies were ranked as 'Good', scoring ≥7/10. Individual-patient data (IPD) were pooled from 13 studies of 3,989 (94.1% aged <15 years) SM patients and 5,780 (79.6% aged <15 years) UM cases in Benin, Malaysia, Mozambique, Tanzania, The Gambia, Uganda, Yemen, and Zambia. Definitions of SM were standardised across studies to compare treatment delay in patients with UM and different SM phenotypes using age-adjusted mixed-effects regression. The odds of any SM phenotype were significantly higher in children with longer delays between initial symptoms and arrival at the health facility (odds ratio [OR] = 1.33, 95% CI: 1.07-1.64 for a delay of >24 hours versus ≤24 hours; p = 0.009). Reported illness duration was a strong predictor of presenting with severe malarial anaemia (SMA) in children, with an OR of 2.79 (95% CI:1.92-4.06; p < 0.001) for a delay of 2-3 days and 5.46 (95% CI: 3.49-8.53; p < 0.001) for a delay of >7 days, compared with receiving treatment within 24 hours from symptom onset. We estimate that 42.8% of childhood SMA cases and 48.5% of adult SMA cases in the study areas would have been averted if all individuals were able to access treatment within the first day of symptom onset, if the association is fully causal. In studies specifically recording onset of nonsevere symptoms, long treatment delay was moderately associated with other SM phenotypes (OR [95% CI] >3 to ≤4 days versus ≤24 hours: cerebral malaria [CM] = 2.42 [1.24-4.72], p = 0.01; respiratory distress syndrome [RDS] = 4.09 [1.70-9.82], p = 0.002). In addition to unmeasured confounding, which is commonly present in observational studies, a key limitation is that many severe cases and deaths occur outside healthcare facilities in endemic countries, where the effect of delayed or no treatment is difficult to quantify. CONCLUSIONS Our results quantify the relationship between rapid access to treatment and reduced risk of severe disease, which was particularly strong for SMA. There was some evidence to suggest that progression to other severe phenotypes may also be prevented by prompt treatment, though the association was not as strong, which may be explained by potential selection bias, sample size issues, or a difference in underlying pathology. These findings may help assess the impact of interventions that improve access to treatment.
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Affiliation(s)
- Andria Mousa
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
- * E-mail:
| | - Abdullah Al-Taiar
- School of Community & Environmental Health, College of Health Sciences, Old Dominion University, Norfolk, Virginia, United States of America
| | - Nicholas M. Anstey
- Global Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia
- Division of Medicine, Royal Darwin Hospital, Darwin, Northern Territory, Australia
| | - Cyril Badaut
- Unité de Biothérapie Infectieuse et Immunité, Institut de Recherche Biomédicale des Armées, Brétigny-sur-Orge, France
- Unité des Virus Emergents (UVE: Aix-Marseille Univ—IRD 190—Inserm 1207—IHU Méditerranée Infection), Marseille, France
| | - Bridget E. Barber
- Global Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Quique Bassat
- ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
- ICREA, Barcelona, Spain
- Pediatric Infectious Diseases Unit, Pediatrics Department, Hospital Sant Joan de Déu (University of Barcelona), Barcelona, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Joseph D. Challenger
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Aubrey J. Cunnington
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, United Kingdom
| | - Dibyadyuti Datta
- Ryan White Center for Pediatric Infectious Disease and Global Health, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Chris Drakeley
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Azra C. Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Victor R. Gordeuk
- Sickle Cell Center, Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Matthew J. Grigg
- Global Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia
| | - Pierre Hugo
- Medicines for Malaria Venture, Geneva, Switzerland
| | - Chandy C. John
- Ryan White Center for Pediatric Infectious Disease and Global Health, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Alfredo Mayor
- ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Robert O. Opoka
- Department of Paediatrics and Child Health, Makerere University School of Medicine, Kampala, Uganda
| | - Geoffrey Pasvol
- Imperial College London, Department of Life Sciences, London, United Kingdom
| | - Claire Rees
- Centre for Global Public Health, Institute of Population Health Sciences, Barts & The London School of Medicine & Dentistry, London, United Kingdom
| | - Hugh Reyburn
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Eleanor M. Riley
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Institute of Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Binal N. Shah
- Sickle Cell Center, Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Antonio Sitoe
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
| | - Colin J. Sutherland
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | | | - Stefan A. Unger
- Department of Child Life and Health, University of Edinburgh, United Kingdom
- Department of Respiratory Medicine, Royal Hospital for Sick Children, Edinburgh, United Kingdom
| | - Firmine Viwami
- Institut de Recherche Clinique du Bénin (IRCB), Cotonou, Benin
| | - Michael Walther
- Medical Research Council Unit, Fajara, The Gambia at the London School of Hygiene and Tropical Medicine, Fajara, The Gambia
| | - Christopher J. M. Whitty
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Timothy William
- Infectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit, Kota Kinabalu, Sabah, Malaysia
- Gleneagles Hospital, Kota Kinabalu, Sabah, Malaysia
| | - Lucy C. Okell
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
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Wahl B, Knoll MD, Shet A, Gupta M, Kumar R, Liu L, Chu Y, Sauer M, O'Brien KL, Santosham M, Black RE, Campbell H, Nair H, McAllister DA. National, regional, and state-level pneumonia and severe pneumonia morbidity in children in India: modelled estimates for 2000 and 2015. THE LANCET. CHILD & ADOLESCENT HEALTH 2020; 4:678-687. [PMID: 32827490 PMCID: PMC7457699 DOI: 10.1016/s2352-4642(20)30129-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/12/2020] [Accepted: 04/09/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND The absolute number of pneumonia deaths in India has declined substantially since 2000. However, pneumonia remains a major cause of morbidity in children in the country. We used a risk factor-based model to estimate pneumonia and severe pneumonia morbidity in Indian states in 2000 and 2015. METHODS In this modelling study, we estimated the burden of pneumonia and severe pneumonia in children younger than 5 years using a risk factor-based model. We did a systematic literature review to identify published data on the incidence of pneumonia from community-based longitudinal studies and calculated summary estimates. We estimated state-specific incidence rates for WHO-defined clinical pneumonia between 2000 and 2015 using Poisson regression and the prevalence of risk factors in each state was obtained from National Family Health Surveys. From clinical pneumonia studies, we identified studies reporting the proportion of clinical pneumonia cases with lower chest wall indrawing to estimate WHO-defined severe pneumonia cases. We used the estimate of the proportion of cases with lower chest wall indrawing to estimate WHO-defined severe pneumonia cases for each state. FINDINGS Between 2000 and 2015, the estimated number of pneumonia cases in Indian HIV-uninfected children younger than 5 years decreased from 83·8 million cases (95% uncertainty interval [UI] 14·0-300·8) to 49·8 million cases (9·1-174·2), representing a 41% reduction in pneumonia cases. The incidence of pneumonia in children younger than 5 years in India was 657 cases per 1000 children (95% UI 110-2357) in 2000 and 403 cases per 1000 children (74-1408) in 2015. The estimated national pneumonia case fatality rate in 2015 was 0·38% (95% UI 0·11-2·10). In 2015, the estimated number of severe pneumonia cases was 8·4 million (95% UI 1·2-31·7), with an incidence of 68 cases per 1000 children (9-257) and a case fatality ratio of 2·26% (0·60-16·30). In 2015, the estimated number of pneumonia cases in HIV-uninfected children was highest in Uttar Pradesh (12·4 million [95% UI 2·1-45·0]), Bihar (7·3 million [1·3-26·1]), and Madhya Pradesh (4·6 million [0·7-17·0]). Between 2000 and 2015, the greatest reduction in pneumonia cases was observed in Kerala (82% reduction). In 2015, pneumonia incidence was greater than 500 cases per 1000 children in two states: Uttar Pradesh (565 cases per 1000 children [95% UI 94-2047]) and Madhya Pradesh (563 cases per 1000 children [88-2084]). INTERPRETATION The estimated number of pneumonia and severe pneumonia cases among children younger than 5 years in India decreased between 2000 and 2015. Improvements in socioeconomic indicators and specific government initiatives are likely to have contributed to declines in the prevalence of pneumonia risk factors in many states. However, pneumonia incidence in many states remains high. The introduction of new vaccines that target pneumonia pathogens and reduce risk factors will help further reduce the burden of pneumonia in the country. FUNDING Bill & Melinda Gates Foundation.
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Affiliation(s)
- Brian Wahl
- International Vaccine Access Center, Baltimore, MD, USA.
| | | | - Anita Shet
- International Vaccine Access Center, Baltimore, MD, USA
| | - Madhu Gupta
- Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Rajesh Kumar
- Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Li Liu
- Institute for International Programs, Baltimore, MD, USA; Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Yue Chu
- Department of Sociology, Institute for Population Research, Ohio State University, Columbus, OH, USA
| | - Molly Sauer
- International Vaccine Access Center, Baltimore, MD, USA
| | - Katherine L O'Brien
- International Vaccine Access Center, Baltimore, MD, USA; World Health Organization, Geneva, Switzerland
| | | | - Robert E Black
- Institute for International Programs, Baltimore, MD, USA
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Harish Nair
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK; Public Health Foundation of India, New Delhi, India
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Deka MA. Mapping the Geographic Distribution of Tungiasis in Sub-Saharan Africa. Trop Med Infect Dis 2020; 5:E122. [PMID: 32722011 PMCID: PMC7558156 DOI: 10.3390/tropicalmed5030122] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 07/14/2020] [Accepted: 07/19/2020] [Indexed: 12/30/2022] Open
Abstract
The geographic distribution of tungiasis is poorly understood, despite the frequent occurrence of the disease in marginalized populations of low socioeconomic status. To date, little work is available to define the geography of this neglected tropical disease (NTD). This exploratory study incorporated geostatistical modeling to map the suitability for tungiasis transmission in sub-Saharan Africa (SSA). In SSA, environmental suitability is predicted in 44 countries, including Angola, Nigeria, Ghana, Cameroon, Cote de Ivoire, Mali, Ethiopia, the Democratic Republic of the Congo, Kenya, Gabon, Uganda, Rwanda, Tanzania, Zambia, Zimbabwe, Madagascar, and South Africa. In total, an estimated 668 million people live in suitable areas, 46% (304 million) of which reside in East Africa. These evidence-based maps provide vital evidence of the potential geographic extent of SSA. They will help to guide disease control programs, inform policymakers, and raise awareness at the global level. Likewise, these results will hopefully provide decisionmakers with the pertinent information necessary to lessen morbidity and mortality in communities located in environmentally suitable areas.
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Affiliation(s)
- Mark A Deka
- Department of Geography, Texas State University; 601 University Drive, San Marcos, TX 78666, USA
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38
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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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Alegana VA, Okiro EA, Snow RW. Routine data for malaria morbidity estimation in Africa: challenges and prospects. BMC Med 2020; 18:121. [PMID: 32487080 PMCID: PMC7268363 DOI: 10.1186/s12916-020-01593-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 04/14/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The burden of malaria in sub-Saharan Africa remains challenging to measure relying on epidemiological modelling to evaluate the impact of investments and providing an in-depth analysis of progress and trends in malaria response globally. In malaria-endemic countries of Africa, there is increasing use of routine surveillance data to define national strategic targets, estimate malaria case burdens and measure control progress to identify financing priorities. Existing research focuses mainly on the strengths of these data with less emphasis on existing challenges and opportunities presented. CONCLUSION Here we define the current imperfections common to routine malaria morbidity data at national levels and offer prospects into their future use to reflect changing disease burdens.
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Affiliation(s)
- Victor A Alegana
- Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box 43640, Nairobi, 00100, Kenya.
- Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
- Faculty of Science and Technology, Lancaster University, Lancaster, LAI 4YW, UK.
| | - Emelda A Okiro
- Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box 43640, Nairobi, 00100, Kenya
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme, P.O. Box 43640, Nairobi, 00100, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7LJ, UK
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Accurate Suitability Evaluation of Large-Scale Roof Greening Based on RS and GIS Methods. SUSTAINABILITY 2020. [DOI: 10.3390/su12114375] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Under increasingly low urban land resources, carrying out roof greening to exploit new green space is a good strategy for sustainable development. Therefore, it is necessary to evaluate the suitability of roof greening for buildings in cities. However, most current evaluation methods are based on qualitative and conceptual research. In this paper, a methodological framework for roof greening suitability evaluation is proposed based on the basic units of building roofs extracted via deep learning technologies. The building, environmental and social criteria related to roof greening are extracted using technologies such as deep learning, machine learning, remote sensing (RS) methods and geographic information system (GIS) methods. The technique for order preference by similarity to an ideal solution (TOPSIS) method is applied to quantify the suitability of each roof, and Sobol sensitivity analysis of the score results is conducted. The experiment on Xiamen Island shows that the final evaluation results are highly sensitive to the changes in weight of the green space distance, population density and the air pollution level. This framework is helpful for the quantitative and objective development of roof greening suitability evaluation.
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Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China. REMOTE SENSING 2020. [DOI: 10.3390/rs12101618] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density.
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Exploring the Socioeconomic Co-benefits of Global Environment Facility Projects in Uganda Using a Quasi-Experimental Geospatial Interpolation (QGI) Approach. SUSTAINABILITY 2020. [DOI: 10.3390/su12083225] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Since 1992, the Global Environment Facility (GEF) has mobilized over $131 billion in funds to enable developing and transitioning countries to meet the objectives of international environmental conventions and agreements. While multiple studies and reports have sought to examine the environmental impact of these funds, relatively little work has examined the potential for socioeconomic co-benefits. Leveraging a novel database on the geographic location of GEF project interventions in Uganda, this paper explores the impact of GEF projects on household assets in Uganda. It employs a new methodological approach, Quasi-experimental Geospatial Interpolation (QGI), which seeks to overcome many of the core biases and limitations of previous implementations of causal matching studies leveraging geospatial information. Findings suggest that Sustainable Forest Management (SFM) GEF projects with initial implementation dates prior to 2009 in Uganda had a positive, statistically significant impact of approximately $184.81 on the change in total household assets between 2009 and 2011. Leveraging QGI, we identify that (1) this effect was statistically significant at distances between 2 and 7 km away from GEF projects, (2) the effect was positive but not statistically significant at distances less than 2 km, and (3) there was insufficient evidence to establish the impact of projects beyond a distance of approximately 7 km.
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Abstract
The Global Human Settlement Population Grid (GHS-POP) the latest released global gridded population dataset based on remotely sensed data and developed by the EU Joint Research Centre, depicts the distribution and density of the total population as the number of people per grid cell. This study aims to assess the GHS-POP data accuracy based on root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) and the correlation coefficient. The study was conducted for Poland and Portugal, countries characterized by different population distribution as well as two spatial resolutions of 250 m and 1 km on the GHS-POP. The main findings show that as the size of administrative zones decreases (from NUTS (Nomenclature of Territorial Units for Statistics) to LAU (local administrative unit)) and the size of the GHS-POP increases, the difference between the population counts reported by the European Statistical Office and estimated by the GHS-POP algorithm becomes larger. At the national level, MAPE ranges from 1.8% to 4.5% for the 250 m and 1 km resolutions of GHS-POP data in Portugal and 1.5% to 1.6%, respectively in Poland. At the local level, however, the error rates range from 4.5% to 5.8% in Poland, for 250 m and 1 km, and 5.7% to 11.6% in Portugal, respectively. Moreover, the results show that for densely populated regions the GHS-POP underestimates the population number, while for thinly populated regions it overestimates. The conclusions of this study are expected to serve as a quality reference for potential users and producers of population density datasets.
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Premature mortality related to United States cross-state air pollution. Nature 2020; 578:261-265. [PMID: 32051602 DOI: 10.1038/s41586-020-1983-8] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 11/01/2019] [Indexed: 01/31/2023]
Abstract
Outdoor air pollution adversely affects human health and is estimated to be responsible for five to ten per cent of the total annual premature mortality in the contiguous United States1-3. Combustion emissions from a variety of sources, such as power generation or road traffic, make a large contribution to harmful air pollutants such as ozone and fine particulate matter (PM2.5)4. Efforts to mitigate air pollution have focused mainly on the relationship between local emission sources and local air quality2. Air quality can also be affected by distant emission sources, however, including emissions from neighbouring federal states5,6. This cross-state exchange of pollution poses additional regulatory challenges. Here we quantify the exchange of air pollution among the contiguous United States, and assess its impact on premature mortality that is linked to increased human exposure to PM2.5 and ozone from seven emission sectors for 2005 to 2018. On average, we find that 41 to 53 per cent of air-quality-related premature mortality resulting from a state's emissions occurs outside that state. We also find variations in the cross-state contributions of different emission sectors and chemical species to premature mortality, and changes in these variations over time. Emissions from electric power generation have the greatest cross-state impacts as a fraction of their total impacts, whereas commercial/residential emissions have the smallest. However, reductions in emissions from electric power generation since 2005 have meant that, by 2018, cross-state premature mortality associated with the commercial/residential sector was twice that associated with power generation. In terms of the chemical species emitted, nitrogen oxides and sulfur dioxide emissions caused the most cross-state premature deaths in 2005, but by 2018 primary PM2.5 emissions led to cross-state premature deaths equal to three times those associated with sulfur dioxide emissions. These reported shifts in emission sectors and emission species that contribute to premature mortality may help to guide improvements to air quality in the contiguous United States.
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Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000. SUSTAINABILITY 2020. [DOI: 10.3390/su12031231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This study established a random forest regression model (RFRM) using terrain factors, climatic and river factors, distances to the capitals of provinces, prefectures (Fu, in Chinese Pinyin), and counties as independent variables to predict the population density. Then, using the RFRM, we explicitly reconstructed the spatial distribution of the population density of Gansu Province, China, in 1820 and 2000, at a resolution of 10 by 10 km. By comparing the explicit reconstruction with census data at the township level from 2000, we found that the RFRM-based approach mostly reproduced the spatial variability in the population density, with a determination coefficient (R2) of 0.82, a positive reduction of error (RE, 0.72) and a coefficient of efficiency (CE) of 0.65. The RFRM-based reconstructions show that the population of Gansu Province in 1820 was mostly distributed in the Lanzhou, Gongchang, Pingliang, Qinzhou, Qingyang, and Ningxia prefecture. The macro-spatial pattern of the population density in 2000 kept approximately similar with that in 1820. However, fine differences could be found. The 79.92% of the population growth of Gansu Province from 1820 to 2000 occurred in areas lower than 2500 m. As a result, the population weighting in the areas above 2500 m was ~9% in 1820 while it was greater than 14% in 2000. Moreover, in comparison to 1820, the population density intensified in Lanzhou, Xining, Yinchuan, Baiyin, Linxia, and Tianshui, while it weakened in Gongchang, Qingyang, Ganzhou, and Suzhou.
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Shapiro JT, Sovie AR, Faller CR, Monadjem A, Fletcher RJ, McCleery RA. Ebola spillover correlates with bat diversity. EUR J WILDLIFE RES 2020. [DOI: 10.1007/s10344-019-1346-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Leyk S, Balk D, Jones B, Montgomery MR, Engin H. The heterogeneity and change in the urban structure of metropolitan areas in the United States, 1990-2010. Sci Data 2019; 6:321. [PMID: 31844062 PMCID: PMC6915769 DOI: 10.1038/s41597-019-0329-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 11/21/2019] [Indexed: 11/09/2022] Open
Abstract
While the population of the United States has been predominantly urban for nearly 100 years, periodic transformations of the concepts and measures that define urban places and population have taken place, complicating over-time comparisons. We compare and combine data series of officially-designated urban areas, 1990-2010, at the census block-level within Metropolitan Statistical Areas (MSAs) with a satellite-derived consistent series on built-up area from the Global Human Settlement Layer to create urban classes that characterize urban structure and provide estimates of land and population. We find considerable heterogeneity in urban form across MSAs, even among those of similar population size, indicating the inherent difficulties in urban definitions. Over time, we observe slightly declining population densities and increasing land and population in areas captured only by census definitions or low built-up densities, constrained by the geography of place. Nevertheless, deriving urban proxies from satellite-derived built-up areas is promising for future efforts to create spatio-temporally consistent measures for urban land to guide urban demographic change analysis.
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Affiliation(s)
- Stefan Leyk
- Department of Geography, University of Colorado, Boulder, USA.
| | - Deborah Balk
- CUNY Institute for Demographic Research and Baruch College, Marxe School of International and Public Affairs, City University of New York, New York, USA.
| | - Bryan Jones
- CUNY Institute for Demographic Research and Baruch College, Marxe School of International and Public Affairs, City University of New York, New York, USA
| | | | - Hasim Engin
- CUNY Institute for Demographic Research, New York, USA
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Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China. SUSTAINABILITY 2019. [DOI: 10.3390/su11247033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The fine-grained population distributions of different age groups are crucial for urban planning applications. With the development of information and communication technology (ICT), detailed population data retrieved from various big data sources, especially on a fine scale, have been extensively used for urban planning. However, studies estimating the detailed population distributions of different age groups are still lacking. This study constructs a framework to generate fine-grained population data for different age groups and explores the influence of various factors on the distributions of different age groups. The population is divided into the following four age groups: (1) early adulthood people: 18 ≤ age ≤ 24, (2) young people: 25 ≤ age ≤ 39, (3) middle-aged people: 40 ≤ age ≤ 59, and (4) elderly people: 60 ≤ age. The results indicate that education and accommodation factors have a major influence on the distributions of early adulthood and elderly people, respectively. Business, restaurant, and accommodation factors are the main factors influencing the population distributions of young and middle-aged people. The accommodation factor plays a major controlling role at night, and its explanatory power gradually decreases during the day, while the explanatory powers of the business and restaurant factors increase and become leading factors during the day. Specifically, the hospital factor has a greater effect on the distribution of elderly people. The entertainment factor has very little explanatory power for the population distributions of the different age groups.
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New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products. SUSTAINABILITY 2019. [DOI: 10.3390/su11216056] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the production of gridded population maps, remotely sensed, human settlement datasets rank among the most important geographical factors to estimate population densities and distributions at regional and global scales. Within this context, the German Aerospace Centre (DLR) has developed a new suite of global layers, which accurately describe the built-up environment and its characteristics at high spatial resolution: (i) the World Settlement Footprint 2015 layer (WSF-2015), a binary settlement mask; and (ii) the experimental World Settlement Footprint Density 2015 layer (WSF-2015-Density), representing the percentage of impervious surface. This research systematically compares the effectiveness of both layers for producing population distribution maps through a dasymetric mapping approach in nine low-, middle-, and highly urbanised countries. Results indicate that the WSF-2015-Density layer can produce population distribution maps with higher qualitative and quantitative accuracies in comparison to the already established binary approach, especially in those countries where a good percentage of building structures have been identified within the rural areas. Moreover, our results suggest that population distribution accuracies could substantially improve through the dynamic preselection of the input layers and the correct parameterisation of the Settlement Size Complexity (SSC) index.
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Hall O, Bustos MFA, Olén NB, Niedomysl T. Population centroids of the world administrative units from nighttime lights 1992-2013. Sci Data 2019; 6:235. [PMID: 31659159 PMCID: PMC6817843 DOI: 10.1038/s41597-019-0250-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 09/18/2019] [Indexed: 11/09/2022] Open
Abstract
Knowledge about the past, current and future distribution of the human population is fundamental for tackling many global challenges. Censuses are used to collect information about population within a specified spatial unit. The spatial units are usually arbitrarily defined and their numbers, size and shape tend to change over time. These issues make comparisons between areas and countries difficult. We have in related work proposed that the shape of the lit area derived from nighttime lights, weighted by its intensity can be used to analyse characteristics of the population distribution, such as the mean centre of population. We have processed global nighttime lights data for the period 1992–2013 and derived centroids for administrative levels 0–2 of the Database of Global Administrative Areas, corresponding to nations and two levels of sub-divisions, that can be used to analyse patterns of global or local population changes. The consistency of the produced dataset was investigated and distance between true population centres and derived centres are compared using Swedish census data as a benchmark. Measurement(s) | light • Population Density | Technology Type(s) | digital curation • computational modeling technique | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | Earth (planet) |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9939494
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Affiliation(s)
- Ola Hall
- Department of Human and Economic Geography, Lund University, Sölvegatan 10, S-223 00, Lund, Sweden.
| | | | - Niklas Boke Olén
- Centre for Environmental and Climate Research, Lund University, Sölvegatan 37, S-223 62, Lund, Sweden
| | - Thomas Niedomysl
- Department of Human and Economic Geography, Lund University, Sölvegatan 10, S-223 00, Lund, Sweden.,Department of Analysis, Region Halland, Södra vägen 9, P.O. Box 517, S-301 80, Halmstad, Sweden
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