4,499 research outputs found
Tanzania Road Data for Travel Time Maps
No full textA polyline data set of roads used for estimating travel time in Tanzania. The roads have been merged from two source datatsets.TZ_OSM_road_merge_complete.shp - road shapefile containing merged OSM roads and Facebook MapwithAI road
Zimbabwe Road Data for Travel Time Maps
No full textA polyline shapefile of roads in Zimbabwe used for estimating travel times. It was merged from two source datasets (1) Open street map roads and (2) MapwithAI roads.OSM_rds_merg.shp - Polyline shapefile merged road data containing OSM roads and MapwithAi roads
Uganda Road Data for Travel Time Maps
No full textThis .shp file is a polyline shapefile containing the roads data in Uganda that was used to create the Uganda cost allocation/friction surfaces. The data are a combination (merged) of Open Street Map roads data and MapwithAi project roads data.AllRoads.shp - polyline shapefile containing the roads that have been merged from two source data sets (1) Open street map roads and (2) facebook map with AI project roads
Mozambique Land Cover Data for Cost Surface estimation
No full textThe ESA CCI Landcover20-m Map for Africa Geotiff file containing 9 land cover types. This was used to create the cost allocation surfaces for the CPAS project. This data set was downloaded from the ESA data portal and then clipped to Mozambique. It is not available elsewhere at the Mozambique level.Sentinel2_LULC.tif - A geotiff land cover map of land cover in Mozambique. Containing 9 land cover types: Trees
Shrubs
Grassland
Cropland
Often Flooded
Sparse Vegetation
Bare Areas
Built Up
Open Water
No Dat
Mozambique Road Data for Travel Time Maps
No full textA polyline shapefile dataset containing roads merged from two source datasets (1) Open street map and (2) MapwithAI project. Used in the creation of travel time estimates in mozambiqueall_roads.shp - polyline shapefile of merged OSM roads and Facebook MapwithAI roads datasets
Exploring the spatial associations between census based socioeconomic conditions and remotely sensed environmental metrics in Assam northeast India
No full textThis thesis explores and quantifies the associations between socioeconomic variables and environmental metrics. Remotely sensed satellite data is often used to monitor environmental conditions. However, it is less frequently used for socioeconomic purposes. Several studies have attempted to use remotely sensed data to monitor socioeconomic conditions in urban areas. Non-causal associations between poverty and development and environmental conditions are frequently found in the scientific literature for rural areas of developing countries. This research uses environmental metrics derived from remotely sensed imagery from an Earth observation satellite to explore if associations, similar to those in the literature, can be found for extensive spatial areas. If non-causal associations can be found between census-based socioeconomic variables and remotely sensed environmental metrics it may be possible to use remotely sensed imagery as a limited, but valuable source of information regarding socioeconomic conditions of rural communities. Socioeconomic data is collected in national census datasets at the household level. However, this fine spatial resolution means that it is an expensive process and is typically only conducted once every 10 years. This coarse temporal resolution limits the relevance of census data for planning resource allocation by governments and targeting development assistance, especially in rapidly changing economies. Therefore, the increased temporal resolution that remotely sensed imagery offers over the traditional ground survey methods may provide a way of increasing the understanding of information available to policy makers for monitoring socioeconomic conditions.An extensive area of Assam in northeast India was used as a case study to explore the associations between socioeconomic variables derived from the Indian national census and remotely sensed environmental metrics derived from Landsat Enhanced Thematic Mapper Plus (ETM+) data. Field work first identified; (i) two socioeconomic variables that appeared to be associated with poverty which were female literacy and participation in economic alternatives to agricultural work, and; (ii) a series of land cover types that appeared to be associated with broad level socioeconomic conditions. Cloud and transparent cloud cover were removed from satellite data prior to an object-based land cover classification which defined nine land cover types identified as having potential associations with poverty in the literature and a field work study. Socioeconomic and environmental data were integrated at the village level prior to statistical analysis. No village boundary information was available and therefore, research aimed to identify the most appropriate method of approximating the village boundary using Thiessen polygons and several radial buffer zones. Statistical analyses were conducted to explore; (i) the associations between female literacy and economic alternatives to agricultural work and several environmental metrics, and; (ii) which village boundary approximation provided the lowest AIC model fit statistic. Logistic regression and generalised autoregressive error models explored the associations between socioeconomic conditions and environmental metrics on a global level. Geographically weighted logistic regression was also used to explore the spatial variation in the associations. Findings indicated that significant associations exist between female literacy and economic alternatives to agricultural work and remotely sensed environmental metrics. Many of the associations identified could be interpreted meaningfully in relation to both the understanding gained from field observations and in relation to generally accepted associations in the literature. Thus, the quantitative findings of the research were in keeping with expectations and research hypotheses, lending credibility to the associations observed by other researchers. The methods used here could be developed further and the increased temporal resolution that remotely sensed imagery offers over the traditional ground survey methods may, in the future, increase the relevance and understanding of information available to policy makers for monitoring socioeconomic conditions
Zimbabwe Travel Time to the nearest health clinic Map Output
No full textTravel_time_rural_health_centres.jpg (311.6Kb) - jpeg image of the geotiff file to indicate how the map should look once loaded.
service_area_Zim_clinics.tif (11.56Gb) - geotiff with 20 m spatial resolution for whole of Zimbabwe that shows the the travel speed in seconds from every pixel to the nearest health clinic.
data was created using WGS84 geographic projection which might need specifying when opening for the first time
Childrens travel time to nearest Level III health centres in Uganda
No full textA 20 m spatial resolution geotiff of travel time to level III health facilities in Uganda. Level III facilities typically have Qualified nurses, Nurse aids and Clinical officers (physicians assistants) present withing them. The services that Level III facilities typically provide are; Basic laboratory services, Maternity care, Inpatient care. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds and has been reduced by 22% to reflect the fact that children do not walk as fast adults. A full description will be available in a paper that has been submitted for review.To produce this dataset required the following input data: ESA Sentinel-2 CCI Landcover Prototype Land Cover 20 m Map of Africa 2016 http://2016africalandcover20m.esrin.esa.int/ Open Street Map Vector Polyline Shapefile of Uganda Roads https://data.maptiler.com/downloads/africa/uganda/ Shuttle Radar Topography Mission (SRTM) Digitial Elevation Model (DEM) – 30 m Mosaic available from RCMRD Geoportal http://geoportal.rcmrd.org/layers/?limit=100&offset=0&title__icontains=Uganda%20SRTM%2030%20Meters MapwithAI Uganda Roads https://github.com/facebookmicrosites/Open-Mapping-At-Facebook/wiki/Available-Countries Health Facility locations from Maina et al. 2019 doi: 10.1038/s41597-019-0142
These data are also available here on Edinburgh Data Share as per the requirements for Scientific Data paper submission
Childrens travel time to health facilities in Uganda
No full textA 20 m spatial resolution geotiff of travel time to health facilities in Uganda. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds and has been reduced by 22% to reflect the fact that children do not walk as fast adults. A full description will be available in a paper that has been submitted for review.To produce this dataset required the following input data:
ESA Sentinel-2 CCI Landcover Prototype Land Cover 20 m Map of Africa 2016 http://2016africalandcover20m.esrin.esa.int/
Open Street Map Vector Polyline Shapefile of Uganda Roads https://data.maptiler.com/downloads/africa/uganda/
Shuttle Radar Topography Mission (SRTM) Digitial Elevation Model (DEM) – 30 m Mosaic available from RCMRD Geoportal http://geoportal.rcmrd.org/layers/?limit=100&offset=0&title__icontains=Uganda%20SRTM%2030%20Meters
MapwithAI Uganda Roads https://github.com/facebookmicrosites/Open-Mapping-At-Facebook/wiki/Available-Countries
Health Facility locations from Maina et al. 2019 doi: 10.1038/s41597-019-0142
These data are also available here on Edinburgh Data Share as per the requirements for Scientific Data paper submission
Zimbabwe Travel Time to any health facility Map Output
No full textA 20 m spatial resolution geotiff of travel time to the nearest of any tyoe of health facility in Zimbabwe. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds and has been reduced by 22% to reflect the fact that children do not walk as fast adults. A full description will be available in a paper that has been submitted for review
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