Portions of the world's interior, such as central Asia are extremely secluded from the ocean and are more than 2,000 km from the nearest coast. Distance to coast can be used in asset management and modeling project costs. Phenomenon Mapped: Distance to coastUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance to Coast layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

One important reason for performing GIS analysis is to determine proximity. Often, this type of analysis is done using vector data and possibly the Buffer or Near tools. In this course, you will learn how to calculate distance using raster datasets as inputs in order to assign cells a value based on distance to the nearest source (e.g., city, campground). You will also learn how to allocate cells to a particular source and to determine the compass direction from a cell in a raster to a source.What if you don't want to just measure the straight line from one place to another? What if you need to determine the best route to a destination, taking speed limits, slope, terrain, and road conditions into consideration? In cases like this, you could use the cost distance tools in order to assign a cost (such as time) to each raster cell based on factors like slope and speed limit. From these calculations, you could create a least-cost path from one place to another. Because these tools account for variables that could affect travel, they can help you determine that the shortest path may not always be the best path.After completing this course, you will be able to:Create straight-line distance, direction, and allocation surfaces.Determine when to use Euclidean and weighted distance tools.Perform a least-cost path analysis.

This dataset was created in ArcGIS and provides the direct distance from all grid cell points of the Australian Exclusive Economic Zone (adjacent to the mainland) to the Australian coastline. The distance measured is in decimal degrees and meters.

The distance to the coastline is likely to reflect some degree of exposure to a wave/current regime. The proximity to land is also likely to reflect the potential influence of river discharge (sediment and fresh water), wind blown dust, and anthropogenic pollutants.

Portions of the world's oceans are extremely remote including areas in the South Pacific that are more the 2,500 km from the nearest land. Distance from shore can be used in asset management, modeling project costs, and as an index of human influence. Phenomenon Mapped: Distance from shoreUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance from Shore layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

The arrangement of water in the landscape affects the distribution of many species including the distribution of humans. This layer provides a landscape-scale estimate of the distance from large water bodies. This layer provides access to a 250m cell-sized raster of distance to surface water. To facilitate mapping, the values are in units of pixels. To convert this value to meters multiply by 250. The layer was created by extracting surface water values from the World Lithology and World Land Cover layers to produce a surface water layer. The distance from water was calculated using the ArcGIS Euclidian Distance Tool. The layer was created by Esri in 2014. Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis.” Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis. Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. “An individual-based quantitative approach for delineating core areas of animal space use.” Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas for the Bi-State management area.

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.

The nine-banded Armadillo (Dasypus novemcinctus) is the only species of Armadillo in the United States and alters ecosystems by excavating extensive burrows used by many other wildlife species. Relatively little is known about its habitat use or population densities, particularly in developed areas, which may be key to facilitating its range expansion. We evaluated Armadillo occupancy and density in relation to anthropogenic and landcover variables in the Ozark Mountains of Arkansas along an urban to rural gradient. Armadillo detection probability was best predicted by temperature (positively) and precipitation (negatively). Contrary to expectations, occupancy probability of Armadillos was best predicted by slope (negatively) and elevation (positively) rather than any landcover or anthropogenic variables. Armadillo density varied considerably between sites (ranging from a mean of 4.88 – 46.20 Armadillos per km2) but was not associated with any environmental or anthropogenic variables. Methods Site Selection Our study took place in Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville, Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan, Utah, USA) over the course of two winter seasons, December 2020-March 2021, and November 2021-March 2022. We sampled 10 study sites in year one, and 12 study sites in year two. All study sites were located in the Ozark Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory dominated hardwood forests at similar elevation (213.6 – 541 m). Devils Eyebrow and ONSC are public natural areas managed by the Arkansas Natural heritage Commission (ANHC). Devil’s Den and Hobbs are managed by the Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust (Ninestone) and Forbes, are all privately owned, though Markham has a publicly accessible trail system throughout the property. Lake Sequoyah, Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps Mountain are all city parks and managed by the city of Fayetteville. Lastly, both Weddington and White Rock are natural areas within Ozark National Forest and managed by the U.S. Forest Service. We sampled 5 sites in both years of the study including Devils Eyebrow, Markham Hill, Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain. We chose our study sites to represent a gradient of human development, based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt and Fristrup 2016). We chose open spaces that were large enough to accommodate camera trap research, as well as representing an array of anthropogenic noise values. Since anthropogenic noise is able to permeate into natural areas within the urban interface, introducing human disturbance that may not be detected by other layers such as impervious surface and housing unit density (Buxton et al. 2017), we used dB values for each site as an indicator of the level of urbanization. Camera Placement We sampled ten study sites in the first winter of the study. At each of the 10 study sites, we deployed anywhere between 5 and 15 cameras. Larger study areas received more cameras than smaller sites because all cameras were deployed a minimum of 150m between one another. We avoided placing cameras on roads, trails, and water sources to artificially bias wildlife detections. We also avoided placing cameras within 15m of trails to avoid detecting humans. At each of the 12 study areas we surveyed in the second winter season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then created a 150m buffer on each side of the trail. We then created random points within these buffered areas to decide where to deploy cameras. Each random point had to occur within the buffered areas and be a minimum of 150m from the next nearest camera point, thus the number of cameras at each site varied based upon site size. We placed all cameras within 50m of the random points to ensure that cameras were deployed on safe topography and with a clear field of view, though cameras were not set in locations that would have increased animal detections (game trails, water sources, burrows etc.). Cameras were rotated between sites after 5 or 10 week intervals to allow us to maximize camera locations with a limited number of trail cameras available to us. Sites with more than 25 cameras were active for 5 consecutive weeks while sites with fewer than 25 cameras were active for 10 consecutive weeks. We placed all cameras on trees or tripods 50cm above ground and at least 15m from trails and roads. We set cameras to take a burst of three photos when triggered. We used Timelapse 2.0 software (Greenberg et al. 2019) to extract metadata (date and time) associated with all animal detections. We manually identified all species occurring in photographs and counted the number of individuals present. Because density estimation requires the calculation of detection rates (number of Armadillo detections divided by the total sampling period), we wanted to reduce double counting individuals. Therefore, we grouped photographs of Armadillos into “episodes” of 5 minutes in length to reduce double counting individuals that repeatedly triggered cameras (DeGregorio et al. 2021, Meek et al. 2014). A 5 min threshold is relatively conservative with evidence that even 1-minute episodes adequately reduces double counting (Meek et al. 2014). Landcover Covariates To evaluate occupancy and density of Armadillos based on environmental and anthropogenic variables, we used ArcGIS Pro to extract variables from 500m buffers placed around each camera (Table 2). This spatial scale has been shown to hold biological meaning for Armadillos and similarly sized species (DeGregorio et al. 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro map. We extracted maximum housing unit density (HUD) using the SILVIS housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic noise from the layer created by Mennitt and Fristrup (2016, Buxton et al. 2017, Table 2) and used the “L50” anthropogenic sound level estimate, which was calculated by taking the difference between predicted environmental noise and the calculated noise level. Therefore, we assume that higher levels of L50 sound corresponded to higher human presence and activity (i.e. voices, vehicles, and other sources of anthropogenic noise; Mennitt and Fristrup 2016). We derived the area of developed open landcover, forest area, and distance to forest edge from the 2019 National Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover refers to open spaces with less than 20% impervious surface such as residential lawns, cemeteries, golf courses, and parks and has been shown to be important for medium-sized mammals (Gallo et al. 2017, Poessel et al. 2012). Forest area was calculated by combing all forest types within the NLCD layer (deciduous forest, mixed forest, coniferous forest), and summarizing the total area (km2) within the 500m buffer. Distance to forest edge was derived by creating a 30m buffer on each side of all forest boundaries and calculating the distance from each camera to the nearest forest edge. We calculated distance to water by combining the waterbody and flowline features in the National Hydrogeography Dataset (U.S. Geological Survey) for the state of Arkansas to capture both permanent and ephemeral water sources that may be important to wildlife. We measured the distance to water and distance to forest edge using the geoprocessing tool “near” in ArcGIS Pro which calculates the Euclidean distance between a point and the nearest feature. We extracted Average Daily Traffic (ADT) from the Arkansas Department of Transportation database (Arkansas GIS Office). The maximum value for ADT was calculated using the Summarize Within tool in ArcGIS Pro. We tested for correlation between all covariates using a Spearman correlation matrix and removed any variable with correlation greater than 0.6. Pairwise comparisons between distance to roads and HUD and between distance to forest edge and forest area were both correlated above 0.6; therefore, we dropped distance to roads and distance to forest edge from analyses as we predicted that HUD and forest area would have larger biological impacts on our focal species (Kretser et al. 2008). Occupancy Analysis In order to better understand habitat associations while accounting for imperfect detection of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used a single-species, single-season occupancy model (Mackenzie et al. 2002) even though we had two years of survey data at 5 of the study sites. We chose to do this rather than using a multi-season dynamic occupancy model because most sites were not sampled during both years of the study. Even for sites that were sampled in both years, cameras were not placed in the same locations each year. We therefore combined all sampling into one single-season model and created unique site by year combinations as our sampling locations and we used year as a covariate for analysis to explore changes in occupancy associated with the year of study. For each sampling location, we created a detection history with 7 day sampling periods, allowing presence/absence data to be recorded at each site for each week of the study. This allowed for 16 survey periods between 01 December 2020, and 11 March 2021 and 22 survey periods between 01 November 2021 and 24 March 2022. We treated each camera as a unique survey site, resulting in a total of 352 sites. Because not all cameras were deployed at the same time and for the same length of time, we used a staggered entry approach. We used a multi-stage fitting approach in which we

African swine fever (ASF) is a highly contagious, lethal and economically devastating haemorrhagic disease of domestic pigs. Insights into the dynamics and scale of virus transmission can be obtained from estimates of the basic reproduction number (R0). We estimate R0 for ASF virus in small holder, free-range pig production system in Gulu, Uganda. The estimation was based on data collected from outbreaks that affected 43 villages (out of the 289 villages with an overall pig population of 26,570) between April 2010 and November 2011. A total of 211 outbreaks met the criteria for inclusion in the study. Three methods were used, specifically; (i) GIS- based identification of the nearest infectious neighbour based on the Euclidean distance between outbreaks, (ii) epidemic doubling time, and (iii) a compartmental susceptible-infectious (SI) model. For implementation of the SI model, three approaches were used namely; curve fitting (CF), a linear regression model (LRM) and the SI/N proportion. The R0 estimates from the nearest infectious neighbour and epidemic doubling time methods were 3.24 and 1.63 respectively. Estimates from the SI-based method were 1.58 for the CF approach, 1.90 for the LRM, and 1.77 for the SI/N proportion. Since all these values were above one, they predict the observed persistence of the virus in the population. We hypothesize that the observed variation in the estimates is a consequence of the data used. Higher resolution and temporally better defined data would likely reduce this variation. This is the first estimate of R0 for ASFV in a free range smallholder pig keeping system in sub-Saharan Africa and highlights the requirement for more efficient application of available disease control measures.

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.

Dataset Summary This dataset focuses on the spatiotemporal evolution and driving mechanisms of cultivated land resources in Nanzhang County, Hubei Province, from 2000 to 2023. It includes multi-source geospatial data and attribute data, aiming to provide data support for cultivated land protection, territorial spatial planning, and sustainable development in mountain-plain transition zones. Dataset Content: 1. Land use data: Six phases (2000, 2005, 2010, 2015, 2020, 2023) of land use raster data (30-meter resolution), covering 6 major land types such as cultivated land, forest land, and construction land, sourced from the Resource and Environment Science Data Platform (http://www.resdc.cn). 2. Driving factor data: 10 key indicators, including natural elements (elevation, slope, annual precipitation, etc.) and socioeconomic elements (population density, GDP, distance to highways, etc.), obtained from public data sources such as the National Geographical Information Resource Directory Service System, World Soil Database, and Geospatial Data Cloud Platform. 3. Auxiliary data: Vector boundaries of administrative divisions, township-level dynamic degree of cultivated land change, grid-cell data on cultivated land increase and decrease, etc. Data Values and Structure: The data are mainly in raster (.tif) and vector (.shp) formats. After preprocessing such as coordinate unification (WGS84), resolution standardization (30 meters), and Euclidean distance analysis, they can be directly used for spatial overlay, PLUS model simulation, and other analyses.Reuse Potential: It is applicable to fields such as land use change simulation, analysis of cultivated land driving mechanisms, and research on coordination between ecological protection and food security, providing reference data for land management in similar mountain-plain transition zones.Legal and Ethical Considerations: All data are obtained from public platforms, comply with map usage specifications such as GS (2016) 1600, have no copyright restrictions, do not involve personal privacy or sensitive information, and can be reused under the CC BY 4.0 license. Methods Section 1. Data Collection: - Land use data and administrative division data were obtained from the Resource and Environment Science Data Platform, with secondary land types merged into 6 major dominant types. - Natural driving factors (e.g., DEM, slope) were sourced from the Geospatial Data Cloud Platform; climate data (temperature, precipitation) were obtained from the National Tibetan Plateau Data Center; socioeconomic factors (population, GDP) and infrastructure data (highways, government residences) were derived from the National Geographical Information Resource Directory Service System. 2. Data Processing: - Geographic coordinate systems and projection parameters were unified in ArcGIS 10.8, and raster data were standardized to 30-meter resolution through resampling. - For vector data such as transportation networks and water bodies, Euclidean distance algorithms were used to generate continuous distance factor raster layers. - Multiple rounds of verification were conducted on the consistency of spatiotemporal resolution and coordinate unity to ensure data comparability and model adaptability (meeting the simulation requirements of the PLUS model).

The populations of many species of large mammals occur in small isolated and fragmented habitat patches in the human-dominated landscape. Maintenance of habitat connectivity in fragmented landscapes is important for maintaining a healthy population of large mammals. This study evaluated the landscape patches and their linkages on two carnivores (leopard and Himalayan black bear) and seven prey species (northern red muntjac, chital, sambar, wild pig, Himalayan goral, rhesus macaque, and langur) between Chitwan National Park (CNP) and Annapurna Conservation Area (ACA) by using the least-cost path approach and the Linkage Mapper tool in ArcGIS. A total of 15 habitat patches (average area 26.67 ± 12.70 km2) were identified that had more than 50% of the total studied mammals. A weak relation among the habitat patches was found for chital and sambar (Cost-weighted distance CWD: Euclidean distance EucD >100), showed poor connectivity between the habitat patches, while the ratio of CWD and EucD was low (i.e., low least-cost path) between the majority of the patches for muntjac, wild pig and leopard hence had potential functional connectivity along the landscape. Similarly, a low least cost path between the habitat patches located in the mid-hills was observed for Himalayan goral and Himalayan black bears. Furthermore, the multi-species connectivity analysis identified the potential structural connectivity between the isolated populations and habitat patches. Therefore, these sites need to be considered connectivity hotspots and be prioritized for the conservation of large mammals in the landscape. Methods Occurrence points collection The study blocks were divided into four distinct blocks, namely A, B, C, and D considering landscape features, the main courses of rivers, and topographical features (Figure 2C). Block A covers the BCF, part of CNP, and surrounding areas of BCF (Kabilas, Jugedi, Kerabari, Chaukidanda, Simaldhap) up to the Mahabharat range (it runs closely parallel to the Chure range and separates the Terai with the Hill region, i.e., mid-hill) of Chitwan district. Block B covers human-dominated mid-hill landscapes such as Devghat, Bandipur, Abu Khairani Rural Municipalities, and Vyas Municipality of Tanahun district. It follows the Seti and Trishuli River basins along with mid-hills. Block C covers the Bhimad Municipality, parts of Rishing Rural Municipality, Ghiring Rural Municipality, Magde Rural Municipality and Shuklagandaki Municipality of Tanahun District, and Rupa Rural Municipality of Kaski District along the Seti River basin. Block D covers Bharatpokhari, Nirmalpokhari, Pumdibhumdi, Panchase, Lumle, Ghandruk, Landruk, Deurali, and the Australian Camp area. This block harbors four types of forests: national forest, community forest, protected forest (Panchase), and conservation area (Annapurna). Transects were laid for the collection of presence points of selected species (nine species of mammals) in the landscape (Figure 1). The presence points of ungulates and primates were collected on the basis of direct sighting whereas, the presence points of the carnivores were collected on the basis of signs left by them (e.g., scats, scrapes, pugmarks, scent spray, etc.). Transect size and length were determined based on forest patch size. After identifying forest patches using a topo-base map (Esri, 2017), transects were overlaid, with patches selected based on diameter; patches less than 2 km in diameter were excluded. Transects (150 out of 164) were systematically laid out according to patch size and accessibility in four blocks (31 in A, 35 in B, 38 in C, and 46 in D). Inaccessible areas (14 transects) due to deep river gorges, steep mountains, and swampy lands were excluded. Transect lengths ranged from 1.18 to 7.84 km, with a minimum 500 m separation in regular forest patches, varying in scattered habitats like Mid Hills (Figure 2C, Table S1). We also collected the presence of those mammals opportunistically from other possible sites of the study area (e.g., croplands, river banks). These presence coordinates were recorded by using the Global Positioning System (GPS- Garmin eTrex 10). The collected occurrence data were spatially filtered in 30 m by using the Spatially Rarify Occurrence Data tools of SDMtoolbox 2.0.0 in ArcGIS (Brown, 2020; Kaboodvandpour et al., 2021). The filtered data were converted into .CSV format for Maxent modelling (Table 1). The large mammals whose presence locations were less than 25, were removed from further analysis. Environmental variables To minimize the risk of over-fitting the model and develop the most parsimonious model, the environmental variables were selected based on field knowledge, experts’ suggestions, and an extensive literature review of studied large mammals (Dickman & Marker, 2005; Mishra, 1982; Rather et al., 2020; Watts et al., 2019). The slope and terrain ruggedness index (TRI) were extracted by using the digital elevation model (DEM) in ArcGIS 10.8 (ESRI, 2019). The classified image from Landsat (acquisition date 2020-03-17) (Landsat 8, Operational Land Imager (OLI)) was used for calculating the Euclidian distances to the nearest forest, grassland, water sources, developed area or human settlements, and cropland. We classified the images into eight different classes (Water sources, barren areas, grassland, riverine forest, Sal-dominated forest, mixed forest, cropland, and developed area) by using supervised classification based on the ground-truthing points (Adhikari et al., 2022). Among these classified eight classes, we merged riverine forest, Sal-dominated forest, and mixed forest as a single forest layer. We extracted water sources, grassland, forest, cropland, and developed areas from the available data and calculated Euclidian distances in ArcGIS 10.8 to be used as environmental variables for modeling. The Normalized Difference Vegetation Index (NDVI) is the most popular and used to quantify the greenness of the vegetation, and vegetation density and detect the changes in plant health using red and near infra-red bands of a remotely sensed image (Pettorelli et al., 2011; USGS, 2022; Yengoh et al., 2015), hence we selected NDVI as one environmental layer for mammals. Additionally, the modified Normalized Difference Water Index (MNDWI) is calculated by using the green and Short-wave Infrared (SWIR) bands and it enhances the features of open water. MNDWI also minimizes the features of developed areas that are correlated with open water in other indices (Xu & Guo, 2014; Xu, 2006). Furthermore, the Normalized Difference Built-up Index (NDBI) is a ratio that minimizes the effects of terrain brightness differences and atmospheric effects (Zha et al., 2003). Two spectral bands NIR and SWIR are used to enhance the build-up or developed area, thus differentiating built-up over the natural area. The values of each environmental variable were extracted at presence locations (Table 1). For the layer of prey richness of leopard, the suitability map of preys was calibrated as 0 for absent and 1 for the present of the species based on mean equal test sensitivity and specificity logistic threshold. Then, these layers were combined as a single layer. A total of 13 environmental variables were used for the modelling (Table 2). The variables were differed on the basis of nature of the mammals (Table 2). The selected variable layers were converted into ASCII format with the same resolution, extent and projection system. The spatial resolution of 30 m and UTM 45 N projected coordinate system was used for the modelling. Habitat suitability models Maxent develops a model based on a series of features (environmental variables) (Phillips et al., 2006). Two types of data (occurrence data and environmental layers) were used for processing in the Maxent program (Phillips et al., 2006). The CSV file of the occurrence points in the samples menu and all selected variables layers in ASCII format in the environmental layers’ menu bar were loaded for analysis. The replicates and replicated run type were fixed 25 and subsample respectively. The Maxent model ran with 25 iterations and 1000 background points with 70 % of the points used as training data and 30 % points used as validation of the model. The output of the model was logistic. The performance of the model was evaluated based on AUC values of the receiver operator characteristic (ROC) plot analysis (Phillips, 2008; Phillips et al., 2006; Phillips & Dudík, 2008). The value of the predicted suitability ranges from 0 to 1. The logistic probability of suitability was further regrouped as 0 – 0.2 = unsuitable, 0.2 – 0.4 = moderately suitable, 0.4 – 0.6 = suitable, and 0.6 – 1 = highly suitable (Ansari & Ghoddousi, 2018; Kogo et al., 2019). All the spatial analysis and classification were performed in ArcGIS 10.8 (ESRI, 2019). We used these results of habitat suitability to identify the habitat patches of the species and preparation of resistance layer. Landscape resistance The resistance or cost map was prepared using a raster habitat suitability map (Figure S1). Every cell on the map has a numeric value that indicates the cost that should be paid to pass through each cell (Bagli et al., 2011; Morovati et al., 2020). The cost map was developed by inverting the value of habitat suitability using the following formula (Almasieh et al., 2019; Morovati et al., 2020). Cost = 100 × (1 - habitat suitability)
The lower cost is assigned to highly suitable areas whereas the highest cost is for the habitats with low suitability (Almasieh et al., 2019; Morovati et al., 2020). Identification of habitat patch The continuous probability of occurrence was converted to binary predictions of presence and absence based on average equal sensitivity and specificity threshold. The predicted maps of all species were combined to identify the species richness of an

This shapefile represents proposed management categories (Core, Priority, General, and Non-Habitat) derived from the intersection of habitat suitability categories and lek space use. Habitat suitability categories were derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California formed from the multiplicative product of the spring, summer, and winter HSI surfaces. Summary of steps to create Management Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons. Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and season using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. For each season, subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell. The three seasonal HSI rasters were then multiplied to create a composite annual HSI. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). SPACE USE INDEX CALCULATION: Updated lek coordinates and associated trend count data were obtained from the 2015 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/20/2015). Leks count data from the California side of the Buffalo-Skedaddle and Modoc PMU's that contributed to the overall space-use model were obtained from the Western Association of Fish and Wildlife Agencies (WAFWA), and included count data up to 2014. We used NDOW data for border leks (n = 12), and WAFWA data for those fully in California and not consistently surveyed by NDOW. We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years (through the 2014 breeding season). Pending leks comprised leks without consistent breeding activity during the prior 3 - 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’, or newly discovered leks with at least 2 males. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2011 - 2015) for the number of male grouse (or NDOW classified 'pseudo-males' if males were not clearly identified but likely) attending each lek was calculated. Compared to the 2014 input lek dataset, 36 leks switched from pending to inactive, and 74 new leks were added for 2015 (which included pending ‘new’ leks with one year of counts. A total of 917 leks were used for space use index calculation in 2015 compared to 878 leks in 2014. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2011 - 2015) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was re-scaled between zero and one by dividing by the maximum pixel value. The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 - 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was re-scaled between zero and one by dividing by the maximum cell value. A Spatial Use Index (SUI) was calculated by taking the average of the lek utilization distribution and non-linear distance-to-lek rasters in ArcGIS, and re-scaled between zero and one by dividing by the maximum cell value. The volume of the SUI at cumulative at specific isopleths was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the Nevada state boundary. MANAGEMENT CATEGORIES: The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was categorized into 4 classes: High, Moderate, Low, and Non-Habitat as described above, and intersected with the space use index to form the following management categories . 1) Core habitat: Defined as the intersection between all suitable habitat (High, Moderate, and Low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality habitat

Sage-Grouse habitat areas divided into proposed management categories within Nevada and California project study boundaries.MANAGEMENT CATEGORY DETERMINATION The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was determined from a statewide resource selection function model and first categorized into 4 classes: high, moderate, low, and non-habitat. The standard deviations (SD) from a normal distribution of RSF values created from a set of validation points (10% of the entire telemetry dataset) were used to categorize habitat ‘quality’ classes. High quality habitat comprised pixels with RSF values < 0.5 SD, Moderate > 0.5 and < 1.0 SD, Low < 1.0 and > 1.5, Non-Habitat > 1.5 SD. Proposed Habitat Management Categories were then defined and calculated as follows.1) Core habitat: Defined as the intersection between all suitable habitat (high, moderate, and low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality falling outside the 85% SUI and all non-habitat falling within the 85% SUI. 3) General habitat: Defined as moderate and low quality habitat falling outside the 85% SUI. 4) Non habitat. Defined as non-habitat falling outside the 85% SUI. SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., ... Visit https://dataone.org/datasets/559fada2-3b0d-47b7-81d3-09b1b1b32220 for complete metadata about this dataset.

Urban fabrics for the Helsinki region 2016, 2030 and 2050 GIS dataset represents modelled urban fabric areas (walking, transit, and automobile urban fabrics) in the Helsinki city region (14 municipalities) in Finland. The data is associated with the regional MAL 2019 (land use, housing, and transport) work and was developed at the Finnish Environment Institute (Syke). The method used to produce the data has also been applied to other city regions in Finland (Helminen et al., 2020) and is an application of Newman et al.'s (2016) theory of three urban fabrics. The method is based on the overlay analysis of three variables: population and job density, accessibility to local services, and public transportation supply, with threshold values set for each variable. The definition of threshold values is based on previous applications of urban fabrics (Ristimäki et al., 2017) and a workshop conducted for urban planning and transportation professionals in the Helsinki metropolitan area. All accessibility measures used in creating the data were calculated as Euclidean distances.

The data was created using ArcMap Advanced software (version 10.6) and includes shapefiles for each modeling year's urban structures (UF_2016, UF_2030, and UF_2050) as well as description styles (UF_Fi.qml and UF_En.qml) in Finnish and English for the QGIS software. The names of the structures are in the fields 'Kudos' (in Finnish) and 'UrbFab' (in English). The coordinate system of the data is EPSG:3067. Detailed descriptions of the data and the method can be found in the report 'Helsingin seudun kaupunkikudokset 2016, 2030 a 2050' (Tiitu et al., 2018, in Finnish) and in the downloadable ReadMe files below (both in Finnish and English).

Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 -paikkatietoaineisto

Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 -paikkatietoaineisto kuvaa mallinnettuja kaupunkikudosten alueita (jalankulku-, joukkoliikenne- ja autokaupunki) Helsingin seudun (14 kuntaa) alueelta Suomesta. Aineisto liittyy seudun MAL 2019 -työhön, ja se on kehitetty Suomen ympäristökeskuksessa (Syke). Menetelmää, jolla aineisto on tuotettu, on sovellettu myös muille Suomen kaupunkiseuduille (Helminen ym. 2020), ja se on sovellutus Newmanin ym. (2016) kolmen kaupunkikudoksen teoriasta. Menetelmä perustuu päällekkäisanalyysiin kolmesta muuttujasta: asukas- ja työpaikkatiheys, lähikaupan saavutettavuus ja joukkoliikenteen tarjonta, sekä muuttujille asetettuihin kynnysarvoihin. Kynnysarvojen määrittely perustui kaupunkikudosten aiempiin sovellutuksiin (Ristimäki ym. 2017) sekä Helsingin seudun maankäytön ja liikenteen suunnittelijoille suunnattuun työpajaan. Kaikki aineiston muodostamiseen käytetyt saavutettavuudet on laskettu linnuntie-etäisyyksinä.

Aineisto on muodostettu ArcMap Advanced -ohjelmistolla (versio 10.6.) ja se sisältää shp-tiedostot kunkin mallinnusvuoden kaupunkikudoksille (UF_2016, UF_2030 ja UF_2050) sekä kuvaustekniikan (UF_Fi.qml ja UF_En.qml) suomeksi ja englanniksi QGIS-ohjelmistolle. Kudosten nimet ovat sarakkeissa Kudos (suomeksi) ja UrbFab (englanniksi). Aineiston koordinaattijärjestelmä on EPSG:3067. Aineiston ja menetelmän tarkka kuvaus on luettavissa raportista Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 (Tiitu ym. 2018) sekä alla ladattavista ReadMe-tiedostoista.

References

Helminen V., Tiitu M., Kosonen, L. & Ristimäki, M. (2020). Identifying the areas of walking, transit and automobile urban fabrics in Finnish intermediate cities. Transportation Research Interdisciplinary Perspectives 8, 100257. https://doi.org/10.1016/j.trip.2020.100257

Newman, L. Kosonen & J. Kenworthy (2016). Theory of urban fabrics; planning the walking, transit/public transport and automobile/motor car cities for reduced car dependency. Town planning Review 87 (4): 429–458. http://hdl.handle.net/20.500.11937/11247

Ristimäki M., Tiitu M., Helminen V., Nieminen H., Rosengren K., Vihanninjoki V., Rehunen A., Strandell A., Kotilainen A., Kosonen L., Kalenoja H., Nieminen J., Niskanen S. & Söderström P. (2017). Yhdyskuntarakenteen tulevaisuus kaupunkiseuduilla – Kaupunkikudokset ja vyöhykkeet. Suomen ympäristökeskuksen raportteja 4/2017. Suomen ympäristökeskus, Helsinki. http://hdl.handle.net/10138/176782

Tiitu M., Helminen V., Nurmio K. & Ristimäki M. (2018). Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050. MAL 2019 publication. https://www.hsl.fi/sites/default/files/uploads/helsingin_seudun_kaupunkikudokset_loppuraportti_27082018_0.pdf

License / Lisenssi

Syke applies Creative Commons By 4.0 International license for open datasets.
This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. The source references for credits can be found in the metadata of each data product.

Suomen ympäristökeskuksen (Syke) avointen aineistojen käyttölupa on Creative Commons Nimeä 4.0 Kansainvälinen.
Lisenssin kohteena olevaa dataa voi vapaasti käyttää kaikin mahdollisin tavoin edellyttäen, että datan lähde mainitaan: Lisenssinantajan nimi ja aineiston nimi.

Credits / Lähdemerkintä

Urban Fabrics for the Helsinki Region / Source: Finnish Environment Institute Syke 2018.
Where applicable, please also cite the references listed above.

Helsingin seudun kaupunkikudokset / Lähde: Syke 2018.
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README for: Integrated County-level Dataset for Spatial Coupling Analysis of Cultural Heritage in Southwest China================================================================================1. OverviewThis dataset contains integrated county-level data that supports all the key analyses in the associated manuscript, including the Grey Relational Analysis, Bivariate Local Spatial Autocorrelation (LISA), and Geodetector model. It covers the entire Southwest China region, encompassing the provinces of Sichuan, Yunnan, Guizhou, the municipality of Chongqing, and the Xizang Autonomous Region.2. File Description- File Name: Southwest_China_Heritage_Coupling_Data.csv- Format: Comma-Separated Values- Rows: 511 (Each row represents one county-level administrative unit).- Columns: 163. Variable Description| Column Name | Description (English) | Description (中文) | Unit/Values | Data Source & Notes || ID | Sequential identifier | 序号 | Integer | Generated for this dataset. || County_Name | Name of the county-level unit | 县名称 | Text (in Chinese) | Official administrative divisions. || Prefecture | Name of the prefecture-level city or autonomous prefecture | 地州 | Text (in Chinese) | Official administrative divisions. || Province | Name of the province, municipality, or autonomous region | 省份 | Text (in Chinese). Values: 四川, 云南, 贵州, 重庆, 西藏自治区 | Official administrative divisions. || ICH_Count | Number of national-level Intangible Cultural Heritage items | 国家级非物质文化遗产数量 | Count (Integer) | Source: Ministry of Culture and Tourism (as of 2023). || CHCUs_Count| Number of national-level Cultural Heritage Conservation Units | 国家级文物保护单位数量 | Count (Integer) | Source: National Cultural Heritage Administration (as of 2022). || LISA_I | Dependent Variable: Local bivariate Moran's I index value | 双变量局部莫兰指数 | Continuous (Float) | Calculated in this study. Measures the spatial coupling intensity between ICH and CHCUs distribution densities. This is the core dependent variable for the Geodetector analysis. || Elevation | Average elevation | 平均海拔 | Meters (m) | Source: Derived from 30m DEM (Geospatial Data Cloud, CAS). || Slope | Average slope | 平均坡度 | Degrees (°) | Source: Derived from 30m DEM. || Temperature | Annual mean temperature | 年平均气温 | Degrees Celsius (°C) | Source: National Tibetan Plateau / Third Pole Environment Data Center (2020). || NDVI | Normalized Difference Vegetation Index (mean) | 归一化植被指数 | Index (Range: -1 to 1) | Source: Derived from Landsat 8 OLI images (2020 growing season). || GDP | Gross Domestic Product | 地区生产总值 | Yuan | Source: Provincial Statistical Yearbooks (2024 edition, reporting 2023 data). || Population_Density | Population density | 人口密度 | Persons per square kilometer (persons/km²) | Source: Provincial Statistical Yearbooks. || Urbanization_Rate | Urbanization rate | 城镇化率 | Percentage (%) | Source: Provincial Statistical Yearbooks. || Road_Density | Highway density | 公路密度 | Kilometers per square kilometer (km/km²) | Source: Calculated from total highway length (Statistical Yearbooks) and county area. || Ethnic_Minority_Ratio | Minority population ratio | 少数民族人口占比 | Percentage (%) / or the ordinal scale you used | Source: Provincial Statistical Yearbooks. [0=None, 1=With ethnic villages, 2=In autonomous prefecture, 3=Autonomous county|| Distance_to_Routes | Euclidean distance to historical trade routes | 到历史贸易路线的欧氏距离 | Kilometers (km) | Source: Digitized from historical atlases and academic publications. || Number_of_Inheritors | Number of nationally-recognized ICH representative inheritors | 国家级非遗代表性传承人数量 | Count (Integer) | Source: Ministry of Culture and Tourism (as of 2023). |4. Usage Notes- Core Purpose: This dataset is the direct input for the Geodetector model reported in the manuscript. The variable LISA_I is the dependent variable, and all other variables (from Elevation to Number_of_Inheritors) are the independent factors.- Geodetector Analysis: For replicating the Geodetector results, all continuous independent variables should be discretized using the Natural Breaks (Jenks) method into 5 categories within the Geodetector software itself, as described in the manuscript.- Missing Data: Any missing values are represented as blank cells in the CSV file.5. LicensingThis dataset is made available under the CC BY 4.0 license. Please cite both the dataset (using its DOI) and the associated manuscript if you use this data.6. AcknowledgmentsThe compilation of this dataset was supported by the National Natural Science Foundation of China (Grant No. 52168011). We acknowledge the data providers listed in the "Data Source" column.

Median travel time to reach health facilities by travel scenario.

Percentage of villages in each travel time catchment by travel scenario.

Spider lines depict Euclidean distance-based routes that connect each pair of points. They are a useful tool for visualization. In landscape genetics this represents the isolation-by-distance hypothesis. Previously there were other 3rd party tools that achieved this on the Arcscripts website. Currently it appears that this functionality is only available with a Business Analyst license of ArcGIS. This tool make a few assumptions: 1. You wish to connect all pairs of points.2. You have a point shapefile.3. The point shapefile is in a projected coordinate system.4. The point shapefile has fields called "Easting" and "Northing" that represent the X and Y coordinates respectively. If these fields are not named this excatly then the tool will fail.5. You have a field that describes the name of the pairs of the site (point). Please do not use a geodatabase feature class.

Brief Methods: In version 2 of the Sierra Nevada Multi-source Meadow Polygons Compilation, polygon boundaries from the original layer (SNMMPC_v1 - https://meadows.ucdavis.edu/data/4) were updated using ‘heads-up’ digitization from high-resolution (1m) NAIP imagery. In version 1, only polygons larger than one acre were retained in the published layer. In version 2, existing polygon boundaries were split, reduced in size, or merged, and additional polygons not captured in the original layer were digitized. If split, original IDs from version 1 were retained for one half and a new ID was created for the other half. In instances where adjacent meadows were merged together, only one ID was retained and the unused ID was “decommissioned”. If digitized, a new sequential ID was assigned. AcknowledgementsTim Lindemann, Dave Weixelman, Carol Clark, Stacey Mikulovsky, Qiqi Jiang, Joel Grapentine, Kirk Evans - USDA Forest Service, Pacific Southwest Region Wes Kitlasten - U.S. Geological Survey Sarah Yarnell, Ryan Peek, Nick Santos - UC Davis, Center for Watershed Sciences Anna Fryjoff-Hung - UC Merced Meadow Polygon Attributes Field DescriptionAREA_ACRE Meadow area in acresSTATE State in which the meadow is located (CA or NV)ID* Unique meadow identifier UCDSNMxxxxxx*Note: IDs are non-sequential* HUC12 Unique identifier for the Hydrologic Unit Code (HUC), level 12, in which the meadow is locatedOWNERSHIP Land ownership status (multiple sources)EDGE_COMPLEXITY Gives an indication of the meadow's exposure to external conditions EDGE COMPLEXITY = (MEADOWperimeter/EAC perimeter) [EAC = Equal Area Circle]DOM_ROCKTYPE Dominant rock type on which the meadow is located based on the USGS layerVEG_MAJORITY Vegetation majority based on the LANDFIRE layer (GROUPVEG attribute)SOIL_SURVEY Soil survey from which SOIL_COKEY, MAPUNIT_Kf, MAPUNIT_ClayTot_r, SOIL_MUKEY, and SOIL_COMP_NAME were assigned to each meadow (SSURGO or STATSGO depending on layer coverage)SOIL_MUKEY Mapunit Key: Unique identifier for the Mapunit in which the meadow is locatedSOIL_COKEY Component Key: Unique identifier for the major component of the mapunit in which the meadow is located SOIL_COMP_NAME Component Name: Name of the soil component with the highest representative value in the mapunit in which the meadow is located MAPUNIT_Kf K factor: A soil erodibility factor that quantifies the susceptibility of soil particles to detachment by water. Low: 0.05-0.2 Moderate: 0.25-0.4, High: >0.4MAPUNIT_ClayTot_r Representative value (%)of total clayCATCHMENT_AREA The approximate area of the upstream catchment exiting through the meadow(sq. m)ELEV_MEAN Mean elevation (m)ELEV_RANGE Elevation range (m) across each meadowED_MIN_FStopo_ROADS Minimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Roads ED_MIN_FStopo_TRAILS Minimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Trails ED_MIN_LAKE Minimum Euclidean Distance (m) to lake edges ED_MIN_FLOW Minimum Euclidean Distance (m) to NHD Streams/Rivers ED_MIN_SEEP Minimum Euclidean Distance (m) to NHD Seeps/Springs MDW_DEM_SLOPE Median DEM based slope (in degrees)STRM_SLOPE_GRADE Length-weighted average slope of all NHD flowline segments in each meadow. Given for meadows with flowlines. Meadows without flowlines are null for this attribute.POUR_POINT_LAT Latitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) POUR_POINT_LON Longitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) HGM_Type Dominant meadow hydrogeomorphic (HGM) type LAT_DD Latitude of polygon centroid in decimal degreesLONG_DD Longitude of polygon centroid in decimal degreesShape_Length Meadow perimeter in metersShape_Area Meadow area in sq. meters Detailed Attribute Descriptions:GeologyField: DOM_ROCKTYPEData Source: USGS - https://pubs.usgs.gov/of/2005/1305/Dominant rock type was attributed to the meadow polygons based on available state geology layers. Using Zonal Statisitics in ArcGIS, the most abundant lithology in the map unit (ROCKTYPE1) was identified for each meadow. VegetationField: VEG_MAJORITYData Source: LANDFIRE - https://www.landfire.gov/version_comparison.php?mosaic=YUsing Zonal Statisitics in ArcGIS, the 2014 LANDFIRE dataset was used to attribute generalized vegetation (GROUPVEG) to the meadow polygons. SoilsFields: SOIL_SURVEY, SOIL_MUKEY, SOIL_COKEY, SOIL_COMP_NAME, MAPUNIT_Kf, MAPUNIT_ClayTot_rData Source: USDA, Natural Resources Conservation ServiceSSURGO: https://gdg.sc.egov.usda.gov/STATSGO: https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htmSSURGO (1:24,000 scale) datasets were compiled for the entirety of the study area. Gaps were filled with compiled STATSGO data (1:250,000 scale). Components were assigned based on the soil component with the highest representative value in the map unit in which the meadow was located. For each component, the clay and Kf values from the top-most horizon were assigned to each meadow polygon using Zonal Statistics. Note: MAPUNIT_Kf may be null if the mapunit dominant condition is a miscellaneous area component such as Rock outcrop. Also, forested components with organic litter surface horizons will also return a null K-factor when the surface horizon K-factor is used.STATSGO does not have the detail for approximation of soil properties in the mountain meadows. The polygons are so big (Order 4) that they do not recognize the soils in the meadows as unique components, so there are no data for the meadows anywhere in those map units. As for the K and clay values for CA790 (Yosemite NP), because it is a new survey, O horizons were populated for those components. There may be a similar issue with the Tahoe Basin. NRCS does not populate the K factor for O horizons. And, at least at the time, NRCS is not populating any mineral material in the O horizons. Many NRCS national interpretations have been edited to look at the first mineral horizon and exclude the O. There is also a lot of Rock Outcrop and no horizon data are populated for those components.Slope Field: MDW_DEM_SLOPE Data Source: USGS 10m DEMThe median Digital elevation model (DEM) based slope (in degrees) was assigned via Zonal Statistics to each meadow.All meadows have a value for this attribute. Field: STREAM_SLOPE_GRADEData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlA length-weighted average slope of all NHD flowline segments was calculated within each meadow polygon. Meadows with no NHD flowline will have a NULL value for this attribute. Catchment AreaField: MDW_CATCHMENT_AREA (sq meters)Data Source: USGS NHDPlus V2, NHDPlusHydrodem- http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.phpScript Source: USGS, Wes Kitlasten; USFS, Kirk Evans, Carol ClarkUsing python scripting and the Watershed tool in ArcGIS, the area of the upstream catchment exiting through the meadow was obtained using a flow direction raster created from the NHDPlusHydrodem.Euclidean Distance Fields: ED_MIN_SEEP, ED_MIN_LAKE, ED_MIN_FLOW, ED_MIN_FSTopo_ROADS, ED_MIN_FSTopo_TRAILSData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlFSTopo - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FSTopoUsing the Euclidean Distance (Spatial Analyst) tool in ArcGIS, the minimum distance to each meadow was calculated for NHD Springs/Seeps, NHD Streams/Rivers (flow), NHD Waterbodies (lakes), and FS Topographic Transportation Trails and Roads. HGM Type During the mapping process, the dominant Hydrogeomorphic (HGM) type (Weixelman et al 2011) was estimated for each meadow larger than one acre. Visual inspection of NAIP 1-m resolution imagery was used in this process. DEM layers were used to estimate the landform position. The USGS hydrographic layer was used to determine locations of flowlines. Google Earth imagery was used to estimate greenness during the summer months. Meadows are often composed of more than one HGM type. In this effort, the dominant type was estimated. HGM types have not yet been estimated for Yosemite and Sequoia Kings Canyon National Parks. Types were mapped according to the following visual interpretation. 1. Meadows adjacent to lakes or reservoirs and at nearly the same elevation as the Water bodyLacustrine Fringe (LF)1’. Not as above22. Meadow sites located in an obvious topographic depression. 32’. Not as above43. Sites with obvious standing water after mid-summer or vegetation remaining dark green after mid-summer. Depressional Perennial (DEPP)3’. Not as above. Sites with no standing water after mid-summer or apparently not remaining dark green after mid-summer.Depressional Seasonal (DEPS)4. Meadows with a flow line (using the USGS hydrographic layer) entering from above the meadow and exiting below the meadow, or meadows located in a swale or drainway ………………………………Riparian (RIP)4’. Not as above55. Meadows fed by a spring or seep. No flowline entering from above the meadow. Typically occurring on hillslopes or toeslopes. In addition, the USGS DEM layer was used to look for the text label “Springs” and/or a symbol indicating a spring. Discharge Slope (DS)5’. Dry meadows without a visible flowline entering from above the meadow, vegetation greenness disappears by mid-summer. No apparent groundwater inputs from springs or seeps. May occur in a swale, drainageway, gentle hillslope, or crest. Dry (Dry)OwnershipField: OWNERSHIPData Sources by priority:1. USDA Forest Service Basic Ownership (OWNERCLASSIFICATION) - https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundaries1. National Parks Service (UNIT_NAME) - https://irma.nps.gov/DataStore/1. California Protected Areas Database – CPAD (LAYER) - http://www.calands.org/1. Protected Area Database-US (CBI Edition) Version 2.1 (OWN_NAME) -