This packaged data collection contains two sets of two additional model runs that used the same inputs and parameters as our primary model, with the exception being we implemented a "maximum corridor length" constraint that allowed us to identify and visualize the corridors as being well-connected (≤15km) or moderately connected (≤45km). This is based on an assumption that corridors longer than 45km are too long to sufficiently accommodate dispersal. One of these sets is based on a maximum corridor length that uses Euclidean (straight-line) distance, while the other set is based on a maximum corridor length that uses cost-weighted distance. These two sets of corridors can be compared against the full set of corridors from our primary model to identify the remaining corridors, which could be considered poorly connected. This package includes the following data layers: Corridors classified as well connected (≤15km) based on Cost-weighted Distance Corridors classified as moderately connected (≤45km) based on Cost-weighted Distance Corridors classified as well connected (≤15km) based on Euclidean Distance Corridors classified as moderately connected (≤45km) based on Euclidean Distance Please refer to the embedded metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in raster GeoTIFF (.tif) format.

distance to water data 5 arc minThis dataset describes the euclidean distance (i.e. straight line; in meters) of each given raster cell to the closest freshwater feature (lake or river). Resolution 5 arc min.distance2water_5arcmin.tifdistance to water data 30 arc secThis dataset describes the euclidean distance (i.e. straight line; in meters) of each given raster cell to the closest freshwater feature (lake or river). Resolution 30 arc seconds.distance2water_30arcsec.tif

The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The values of the raster are the distance (in kilometres) from the cell centre to the nearest feature

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

This dataset consist of inputs and intermediate results from the coastal scenario modelling. It is an analysis of the bio-physical factors that best explain the changes in QLUMP land use change between 1999 and 2009 along the Queensland coastal region for the classifications used in the future coastal modelling.

Methods:

The input layers (variables etc) were produced using a range of sources as shown in Table 1. Source datasets were edited to produce raster dataset at 50m resolution and reclassified to suit the needs for the analysis.

The analysis was made using the IDRISI Land Use Change Modeler using multi-layer perceptron neural network with explanatory power of bio-physical variables. In this process a range of bio-physical layers such as slope, rainfall, distance to roads etc (see full list in Table 1) are used as potential explanatory variables for the changes in the land use. The neutral network is trained on a subset of the data then tested against the remaining data, thereby giving an estimate of the accuracy of the prediction. This analysis produces suitability maps for each of the transitions between different land use classifications, along with a ranking of the important bio-physical factors for explaining the changes.

The 1999 - 2009 Land use change was analysed with of which 4 were found to be the strongest predictors of the change for various transitions between one land use and another. This dataset includes the rasters of the 4 best predictors along with a sample of the highest accuracy transition probability maps.

Format:

Table 1 (Table 1 NERP 9_4 e-atlas dataset) This table contains the list of names, short descriptions, data source and data manipulation for the input rasters for the land use change model

All GIS files are in GDA 94 Albers Australia coordinate system.

1999.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 1999 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

2009.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 2009 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes (with addition of tourism land use) then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

Rainfall.rst This layer shows the average annual rainfall (in mm) sourced from the Average Yearly Rainfall Isohyets Queensland dataset (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The data was re-classified and resampled at 50m resolution.

Slope.rst This layer shows the slope (in degrees) value at 50m pixel resolution (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The slope was derived from the Australian Digital Elevation Model in ArcGIS (using the Slope tool of the 3D analyst Tools) at a 200m resolution. The data was resampled at 50m resolution.

SeaDist.rst This layer shows the distance (in m) to the nearest coastline (including estuaries) at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the “Mainland coastline” feature in the GBR features dataset available from GBRMPA.

UrbanDist.rst This layer shows the distance (in m) to the nearest pixel of urban land use at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the QLUMP 2009 dataset on the selected urban polygons.

Transition_potential_Other_to_DryHorticulture.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Horticulture. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 92% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_horticulture.docx This shows the results of the analysis of change from land use Others to rain-fed horticulture between 1999 and 2009 using four variables: Distance to existing horticulture, Rainfall, Soil type and Slope.

Transition_potential_Other_to_Drysugar.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Sugar cane. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 84% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_sugar.docx This shows the results of the analysis of change from land use Others to rain-fed sugar between 1999 and 2009 using three variables: Rainfall, Soil type and Slope.

Transition_potential_Other_to_Forestry.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Forestry. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 73% was calculated during testing.

Land Change Modeler MLP Model Results_Forestry.docx This shows the results of the analysis of change from land use Others to Forestry between 1999 and 2009 using three variables: Rainfall, Soil type and Proximity to existing forestry.

Transition_potential_Other_to_Urban.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Urban. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 75% was calculated during testing.

Land Change Modeler MLP Model Results_Urban.docx This shows the results of the analysis of change from land use Others to Urban between 1999 and 2009 using two variables: Slope and Proximity to existing urban areas.

Sampling design used in the production of the global maps of grassland dynamics 2000–2022 at 30 m spatial resolution in the scope of the Global Pasture Wath initiative. The sampling desing was based in Feature Space Coverage Sampling and resulted in 10,000 sample tiles (1x1 km) distributed across the World, which were visual interpreted in Very-High Resolution imagery thorugh the QGIS plugin QGIS Fast Grid Inspection.

FSCS steps include:

• Short vegetation mask that includes all pixels mapped as mosaic, shrubland, grassland, and sparse vegetation in at least one year from 1993 to 2021 according to ESA/CCI global land cover (gpw_short.veg.mask_esacci.lc_p_1km_s_19920101_20201231_go_epsg.3857_v1.tif),
• 87 input raster layers (including vegetation indices, terrain, land temperature, climate and water variable),
• Principal Components Analysis (PCA) using all input layers,
• Selection of the 10 first components (explaining 75% of variance),
• K-Means with 10,000 clusters (targeted number of samples -
  gpw_grassland_fscs.kmeans.cluster_c_1km_20000101_20221231_go_epsg.3857_v1.tif)
• Calculation of euclidean distance (in the principal component space) of all 1-km pixels to the centre of each cluster,
• Selection of the pixel with the shortest distance for each cluster,
• Conversion of the selected pixels into sample tiles ()

The file gpw_grassland_fscs_tile.samples_1km_20000101_20221231_go_epsg.3857_v1.gpkg provides the sample tiles and include the follow collumns:

• X: Latitude in Web Mercator projection (EPSG:3857),
• Y: Longitude in Web Mercator projection (EPSG:3857),
• cluster_id: K-Means output ranging from 0—9999,
• cluster_distance: Distance from the selected sample to the centre of the cluster,
• cluster_size: Number o 1-km pixels inside the K-Means cluster, estimated using Web Mercator projection (EPSG:3857)
• cluster_size_equal_area: Number o 1-km pixels inside the K-Means cluster, estimated using Goode Homolosine Land projection (ESRI:54052)
• cluster_size_corr: Correction factor to adjust the area distortion due to Web Mercator projection, estimated by the difference in normalized propotional values of cluster_size and cluster_size_equal_area.
• rf_n_pred: Number of pixels predicted by a RF model trained to estimate probability to select the pixel closer to the centre of the KMeans cluster. The RF models were trained individually per each cluster using the 10 first components derived by PCA (gpw_comps_fscs.pca_m_1km_20000101_20221231_go_epsg.3857_v1.tar.gz).
• rf_samp_prob: Sampling probability based on RF model (rf_n_pred / cluster_size)
• rf_samp_wei: Sampling weight estimated in Web Mercator projection.
• rf_samp_wei_coor: Corrected sampling weight estimated in Goode Homolosine Land projection.

Related resources

• Maps of dominant grassland:
  2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2022
• Probability maps of cultivated grassland:
  2000-2022 (All URLs)
• Probability maps of natural/semi-natural grassland:
  2000-2022 (All URLs)
• Grassland reference samples based on VHR imagery (2000–2022):
  GeoPackage files
• Global machine learning models (Random Forest):
  Parquet and joblib python files
• Reference sampling design derived by FSCV:
  GeoPackage and raster files
• Harmonized reference samples based on existing LULC dataset:
  GeoPackage and raster files
• Source code for reproducibility:
  GitHub release
  
• Mapping feedback tool:
  GeoWiki
• Data catalogues:
  OpenLandMap STAC Google Earth Engine

Support

For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watch

Digital surfaces and thicknesses of selected hydrogeologic units of the Floridan aquifer system were developed to define an updated hydrogeologic framework as part of the U.S. Geological Survey Groundwater Resources Program. This feature class contains a gridded surface depicting the transmissivity of the Upper Floridan aquifer in feet squared per day.The well site data, from the TransmissivityWells.shp shapefile, containing 1487 transmissivity estimates, in units of feet squared per day, were used to produce this raster grid. The Geostatistical analyst inverse-distance weighting interpolation tool was used on the log10T field with weights from the weight field. Transmissivity estimates using test type, single- and multi-well aquifer tests (APT) were given a weight of 1.0 and specific capacity (SPC), a weight of 0.5. The map shows the estimated transmissivity based on interpolation of aquifer tests at the 1487 locations in units of log base 10 square feet per day.

Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using presence-absence and presence-only data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models’ predictions were associated to spatial estimates of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies. Methods Presence absence (PA) data PA data for each species were obtained from Coillte based on surveys performed in a fraction of the 6,000 properties they manage (Table 1) by asking property managers (who visit the forests they manage on a regular basis) whether deer were present and, if so, what species. Properties range in size from less than one to around 2,900 ha, and to assign the PA value to a specific location, we calculated the centroid of each property using the function st_centroid() from the package sf in R (Pebesma 2018). The survey was mainly performed in 2010 and 2013, in addition to further data collected between 2014 and 2016. Some properties were surveyed only once in the period 2010–2016, but for those that were surveyed more than once, the value for that location was considered “absence” if deer had never been detected in the property in any of the surveys, and “presence” in all other cases. In addition to these surveys, Coillte commissioned density surveys based on faecal pellet sampling in a subset of their properties between the years 2007 and 2020. Any non-zero densities in these data were considered “presences”, and all zeros were considered “absences”. These data were also summarised across years when a property had been repeatedly sampled and counted as presence if deer had been detected in any of the sampling years. PA data for NI were obtained from a survey carried out by the British Deer Society in 2016. The survey divided the British territory into 100 km2 grid cells, and deer presence or absence was assessed based on public contributions, which were then reviewed and collated by BDS experts. Since 100 km2 grid cells are quite large, we did not, as with the Coillte properties, calculate the centroid of each cell and assign the PA value of the cell to it. Instead, we randomly simulated positions within each cell and assigned the presence or absence value of the cell to each of them. We performed a sensitivity analysis to calculate an optimal number of positions that would capture the environmental variability within each cell, which was set to 5 random positions per grid cell. After processing, we obtained a total of 920 PA data across NI. 2.2.2 Presence-only (PO) data PO data were collected from various sources, mainly (but not only) from citizen science initiatives. The National Biodiversity Data Centre (NBDC) is an Irish initiative that collates biodiversity data coming from different sources, from published studies to citizen contributions. From this repository, we obtained all contributions on the three species, a total of 1,430 records. To this, we added the 164 records of deer in Ireland downloaded from the iNaturalist site, another citizen-contributed database that collects the same type of data. From the resulting dataset, we (1) removed all observations with a spatial resolution lower than 1 km2; (2) did a visual inspection of the data and comments and removed all observations that were obviously incorrect (i.e. at sea or that the comment specified it was a different species); (3) filtered out all the fallow deer reported in Dublin’s enclosed city park (Phoenix Park) since the population there was introduced and is artificially maintained and disconnected from the rest of populations in Ireland; and (4) filtered duplicate observations by retaining only one observation per user, location, and day. The Centre for Environmental Data and Recording (CEDaR) is a data repository for Northern Ireland (NI) that operates in the same way as the NBDC. They provided 872 records of deer in NI, coming from different survey, scientific, and citizen science initiatives, from which we removed all records provided with a spatial resolution lower than 1 km2. The location and species of 469 deer culled between 2019 and 2021 in NI were obtained from the British Agri-Food and Biosciences Institute. For the observations that did not have specific coordinates, we derived them from the location name or postcode if provided. As part of a nationally funded initiative to improve deer monitoring in Ireland (SMARTDEER), we developed a bespoke online tool to facilitate the reporting of deer observations by the general public and all relevant stakeholders e.g. hunters, farmers, or foresters. Observations were reported in 2021 and 2022 by clicking on a map to indicate a 1 km2 area where deer have been observed. For each user and session, we calculated the area of the surface covered in squares, simulated a number of positions proportional to the size of the polygon, and distributed them within it to generate a number of exact positions equivalent to the area where the user had indicated an observation. In total, the SMARTDEER tool allowed us to collect 4,078 presences across Ireland and NI. 2.3.2 Covariate selection Raster environmental covariates used in the models were obtained from the Copernicus Land Monitoring Service (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency EEA), whereas the vector layers (roads, paths) were obtained from the Open Street Map service (OpenStreetMap contributors, 2017. Planet dump [Data file from January 2022]. https://planet.openstreetmap.org). Vector layers were transformed into distance layers (distance to roads, distance to paths) using the distance() function from the package raster, and into density layers (density of roads, paths) using the rasterize() function of the same package (Hijmans 2021). All raster layers were resampled to the lowest resolution available in the used covariates, resulting in a 1 km2 resolution. A full description of the process of covariate selection (including screening for collinearity) can be found in the supplementary material. The covariates eventually used in the model were elevation (m), slope (degrees), tree cover (%), small woody feature density (%), distances to forest edge (m, positive distances indicate a location outside a forest, negative distances indicate a location within a forest), and human footprint index (Venter et al. 2016, 2018). All covariates were scaled by subtracting the mean and dividing by the standard deviation before entering the model (function scale() from the raster package).

The Kernel Density tool calculates the density of features in a neighborhood around those features.Kernel Density calculates the density of point features around each output raster cell. Conceptually, a smoothly curved surface is fitted over each point. The surface value is highest at the location of the point and diminishes with increasing distance from the point, reaching zero at the Search radius distance from the point. Only a circular neighborhood is possible. The volume under the surface equals the Population field value for the point, or 1 if NONE is specified. The density at each output raster cell is calculated by adding the values of all the kernel surfaces where they overlay the raster cell center. This layer is included in a storymap about the Panama City crayfish, a species listed as Threatened under the Endangered Species Act in 2022. Storymap link: https://fws.maps.arcgis.com/home/item.html?id=a791906fe3f8433eabadda5898184372

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.

Crop Storage Final Location: Aggregated Production, consists of a 0.01 decimal degree grid produced under the scope of the Covid-19 sub-Saharan African Corridor project pilot case, using a Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) methodology for the identification and definition of mobile storage locations (movable warehouses). The Top Score Locations were computed using the Location Score raster and adding as criteria, access to finance (distance to bank agency) and linear distance from major roads. This output step filters the location score grid (aggregated production) into a top location score raster. Applying the following functions: • Buffering: o Banks - 10km (0.09 degree) buffer radius; o Major roads - 2km (0.18 degree) buffer radius; • Intersection - extracts the overlapping portions of Banks_Buffer and Roads_Buffer. • Dissolve - Takes the intersection vector layer and combines the features into a new feature, a single polygon; • Clip Raster by Mask Layer - Extracts the raster-production aggregate location score using the polygon obtained by the dissolved intersection of the banks and roads buffers; • The raster pixel cell in the final areas are classified into the final location map.