The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Andrew J Tatem - One of the best experts on this subject based on the ideXlab platform.
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high resolution Population Distribution maps for southeast asia in 2010 and 2015
PLOS ONE, 2013Co-Authors: Andrea E Gaughan, Forrest R Stevens, Catherine Linard, Andrew J TatemAbstract:Spatially accurate, contemporary data on human Population Distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and Population growth over the past decade, yet existing spatial Population Distribution datasets covering the region are based principally on Population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) Population Distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model Population Distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global Population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable Population Distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.
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Population Distribution settlement patterns and accessibility across africa in 2010
PLOS ONE, 2012Co-Authors: Catherine Linard, Andrew J Tatem, Marius Gilbert, Robert W Snow, Abdisalan M NoorAbstract:The spatial Distribution of Populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit Population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure Population concentration and accessibility. Here we outline approaches to developing a new high resolution Population Distribution dataset for Africa and analyse rural accessibility to Population centers. Contemporary Population count data were combined with detailed satellite-derived settlement extents to map Population Distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, Population concentration and spatial accessibility to major Population centres is exhibited across the continent. In Africa, 90% of the Population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural Populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.
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Assessing the use of global land cover data for guiding large area Population Distribution modelling
GeoJournal, 2011Co-Authors: Catherine Linard, Marius Gilbert, Andrew J TatemAbstract:Gridded Population Distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded Population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded Population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate Population Distribution data. The effectiveness of the Population Distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of Population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded Population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving Population Distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded Population datasets across large areas.
Haibo Wang - One of the best experts on this subject based on the ideXlab platform.
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evaluation of road traffic noise exposure based on high resolution Population Distribution and grid level noise data
Building and Environment, 2019Co-Authors: Zhiwei Zhang, Haibo WangAbstract:Abstract The primary objective of this study is to propose a method for assessing Population exposure to road traffic noise based on high-resolution Population and grid-level noise data. First, after meshing the study region and setting the receivers at the nodes, the noise values of all receivers are calculated by regarding roads as linear sound sources, and these values are rendered as the traffic noise Distribution. Next, a Population Distribution model is trained with point of interest (POI) sample data, Population sample data, and the random forests algorithm. Then, the statistics of the POIs in the grids are input to the trained model as the noise Distribution; the grid-level Population data are obtained and used to depict the Population Distribution. Then, to facilitate the assessment, the grid-level traffic noise Distribution data and Population Distribution data are combined according to the ID or location of the nodes of grids. Finally, evaluation indexes considering the traffic noise and Population Distribution are used to assess the Population exposure to traffic noise. The proposed method is applied to three types of regions in Guangzhou: a residential area, a commercial area and an industrial area. The noise pollution levels of all study regions are ‘Mild’, but the pollution of road traffic noise is most severe in the residential area with 12.00% of the Population and 7.60% of the area affected due to the denser road network and stricter standards. These results indicate that people in residential areas are more sensitive to road traffic noise.
Catherine Linard - One of the best experts on this subject based on the ideXlab platform.
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high resolution Population Distribution maps for southeast asia in 2010 and 2015
PLOS ONE, 2013Co-Authors: Andrea E Gaughan, Forrest R Stevens, Catherine Linard, Andrew J TatemAbstract:Spatially accurate, contemporary data on human Population Distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and Population growth over the past decade, yet existing spatial Population Distribution datasets covering the region are based principally on Population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) Population Distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model Population Distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global Population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable Population Distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.
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Population Distribution settlement patterns and accessibility across africa in 2010
PLOS ONE, 2012Co-Authors: Catherine Linard, Andrew J Tatem, Marius Gilbert, Robert W Snow, Abdisalan M NoorAbstract:The spatial Distribution of Populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit Population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure Population concentration and accessibility. Here we outline approaches to developing a new high resolution Population Distribution dataset for Africa and analyse rural accessibility to Population centers. Contemporary Population count data were combined with detailed satellite-derived settlement extents to map Population Distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, Population concentration and spatial accessibility to major Population centres is exhibited across the continent. In Africa, 90% of the Population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural Populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.
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Assessing the use of global land cover data for guiding large area Population Distribution modelling
GeoJournal, 2011Co-Authors: Catherine Linard, Marius Gilbert, Andrew J TatemAbstract:Gridded Population Distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded Population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded Population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate Population Distribution data. The effectiveness of the Population Distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of Population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded Population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving Population Distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded Population datasets across large areas.
Chun-hung Lin - One of the best experts on this subject based on the ideXlab platform.
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Multi-layer multi-class dasymetric mapping to estimate Population Distribution
Science of the Total Environment, 2010Co-Authors: Ming Dawa Su, Mei-chun Lin, Hsin-i Hsieh, Bor-wen Tsai, Chun-hung LinAbstract:The spatial patterns of Population Distribution are very important information for most regional planning and management decisions. But the socioeconomic data are usually published in areal aggregated format due to privacy concerns. Although choropleth maps are used extensively to display spatial Distributions of these areal aggregated data, patterns may be distorted due to assumptions of homogeneous Distributions and the modifiable areal unit problem. Most human activity, including Population Distribution, is spatially heterogeneous due to variations in topography and regional development. A multi-layer multi-class dasymetric (MLMCD) framework was proposed in this study to better redistribute the regionally aggregated Population statistics into smaller areal units and reveal more realistic spatial Population Distribution pattern. The Taipei metropolitan area in Taiwan was used as a case study area to demonstrate the disaggregation ability of the proposed framework and the improvements to the traditional binary or multi-class dasymetric method. Assorted data, including remote sensing images, land use zoning, topography, transportation and accessibility to facilities were introduced in different layers to improve the reDistribution of aggregated regional Population data. The concept of multi-layer multi-class dasymetric modeling is both useful and flexible. Different levels of accuracy in this Population reDistribution process can be achieved depending on data and budget availabilities and the needs for different data usage purposes. © 2010 Elsevier B.V.
Zhiwei Zhang - One of the best experts on this subject based on the ideXlab platform.
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evaluation of road traffic noise exposure based on high resolution Population Distribution and grid level noise data
Building and Environment, 2019Co-Authors: Zhiwei Zhang, Haibo WangAbstract:Abstract The primary objective of this study is to propose a method for assessing Population exposure to road traffic noise based on high-resolution Population and grid-level noise data. First, after meshing the study region and setting the receivers at the nodes, the noise values of all receivers are calculated by regarding roads as linear sound sources, and these values are rendered as the traffic noise Distribution. Next, a Population Distribution model is trained with point of interest (POI) sample data, Population sample data, and the random forests algorithm. Then, the statistics of the POIs in the grids are input to the trained model as the noise Distribution; the grid-level Population data are obtained and used to depict the Population Distribution. Then, to facilitate the assessment, the grid-level traffic noise Distribution data and Population Distribution data are combined according to the ID or location of the nodes of grids. Finally, evaluation indexes considering the traffic noise and Population Distribution are used to assess the Population exposure to traffic noise. The proposed method is applied to three types of regions in Guangzhou: a residential area, a commercial area and an industrial area. The noise pollution levels of all study regions are ‘Mild’, but the pollution of road traffic noise is most severe in the residential area with 12.00% of the Population and 7.60% of the area affected due to the denser road network and stricter standards. These results indicate that people in residential areas are more sensitive to road traffic noise.