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.

A README file is included into the dataset.Abstract of the corresponding article:Biodiversity conservation measures designed to ensure ecological connectivity depend on the reliable modeling of species movements. Least cost path modeling makes it possible to identify the most likely dispersal paths within a landscape and provide two items of ecological relevance: (i) the spatial location of these least cost paths (LCPs) and (ii) the accumulated cost along them ('cost distance', CD). This spatial analysis requires that cost values be assigned to every type of land cover. The sensitivity of both LCPs and CDs to the cost scenarios has not been comprehensively assessed across realistic landscapes and diverging cost scenarios. We therefore assessed it in diverse landscapes sampled over metropolitan France and with widely diverging cost scenarios. The spatial overlap of the LCPs was more sensitive to the cost scenario than the CD values were. Besides, highly correlated CD matrices could derive from very diff?erent cost scenarios. Although the range of the cost values and the properties of each cost scenario signi?ficantly in influenced the outputs of LCP modeling, landscape composition and con?guration variables also explained their variations. Accordingly we provide guidelines for the use of LCP modeling in ecological studies and conservation planning.

This layer was created as part of Esri’s Green Infrastructure Initiative and is one of five newly generated companion datasets that can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. If used together, these layers should have corresponding date-based suffixes (YYYYMMDD). The corresponding layer names are: Intact Habitat Cores, Habitat Connectors, Habitat Fragments, Habitat Cost Surface, and Intact Habitat Cores by Betweeness. These Esri derived data, and additional data central to GI planning from other authoritative sources, are also available as Map Packages for each U.S. State and can be downloaded from the Green Infrastructure Data Gallery.This layer represents the modeled Least Cost Paths (LCPs) among neighboring Intact Habitat Cores. Least cost paths reflect the route of least resistance between neighboring habitat core edges, and by extension, represent possible paths of wildlife movement. Esri generated this comprehensive network of LCPs using the Cost Connectivity tool which was introduced in ArcGIS 10.4 and ArcGISPro in 1.3. The Habitat Cost Surface layer was used as the input computational surface. The resulting network was also utilized to compute Betweenness Centrality attribution for the Intact Habitat Cores by Betweenness layer, denoting a measure of the Core’s connectivity importance compared to all others in the network.The PathCost field represents the non-directional cumulative cost of this route. Cost is not accrued for movement within habitat cores, thus the portion of each path that falls within a core’s boundary should be considered schematic only. These paths can be used to create a network dataset for use in additional analysis. If a network dataset is created, it should be cost-based, rather than length-based due to the schematic and costless nature of traveling within a core. The PathCost, LowCoreValue, and HighCoreValue fields were used to generate a network graph.While least cost paths are useful for illuminating the discrete path of least resistance from one location to another, they should not be interpreted as least cost corridors. Least cost corridors expand least cost paths to encompass functionally larger areas that may facilitate species movement.

Input and Output Data of LCP implementation for OWFs Transmission lines and O&M cost assessment and paths' delineation.

More details about this layer are available in the combined Southeast Blueprint 2023 Hubs & Corridors map.

This repository contains the data necessary for the realization of a least cost network for camel transport during antiquity (Ptolemaic and Roman period) in the Egyptian eastern desert. The details of the network construction and data processing can be found in the the associated paper and datapaper.

Paper
Manière, L., Crépy, M., Redon, B. (2020) Modelling the Hellenistic and Roman Road Networks of the Eastern desert of Egypt, a Semi-Empirical Approach Based on Modern Travelers’ Itineraries. Submitted for publication

Datapaper
Manière, L., Crépy, M., Redon, B. (2020) Geospatial data from the “Modelling the Hellenistic and Roman Road Networks of the Eastern desert of Egypt, a Semi-Empirical Approach Based on Modern Travelers’ Itineraries” paper. Submitted for publication

This packaged data collection contains all of the outputs from our primary model, including the following data layers: Habitat Cores (vector polygons) Least-cost Paths (vector lines) Least-cost Corridors (raster) Least-cost Corridors (vector polygon interpretation) Modeling Extent (vector polygon) Please refer to the embedded spatial 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 shapefile (.shp) or raster GeoTIFF (.tif) formats.

The friction (cost allocation/effort) surface was assembled using three primary input datasets on land surface characteristics that help or hinder travel speeds: land cover, roads and topography. Landcover data were from the ESA CCI Landcover map for Africa 2016, roads data were merged from Open-Street Map (OSM) and the MapwithAi project and topography was taken from the SRTM Digital Elevation Model. The costs for travel consider walking/pedestrian travel in this data, but the software is supplied with an easy to change set of travel speeds so they can be adapted easily to consider travel speeds reflecting motorised transportation use. We have reduced the walking speeds to reflect the fact that adults walking with children move approximately 22% slower. There are two friction surfaces provided, the first defines open water as a barrier to travel and so the speed allocated to this landcover is NA. The second defines open water with an associated speed (1 km/hr). To create a walking speed array, first the road walking speeds were used and then missing values were filled with landcover walking speed values. This walking speed array was multiplied by the slope impact grid. The speed for each cell was converted from kilometers per hour to meters per second. Finally, the time (in seconds) to walk across each cell was calculated. The outputs are 20-m spatial resolution geotiffs indicating the time to walk across each cell. They are subsequently used in the least cost path analysis to estimate travel time to the nearest health facilities. However,these friction surfaces can be used by others to estimate travel speed to other destinations in a GIS.

ABSTRACT As a result of the fragmentation and degradation of forests, the connectivity of natural habitats has been decreasing. Thus, problems in gene flow in wildlife have begun to arise. The connection of landscape patches with corridors is now an important subject of landscape planning. Central Anatolia has been affected by forest fragmentation due to its fragile ecologies. The purpose of this study was i) to identify the spatial location of landscape corridors in order to create ecological networks among the natural landscape reserves in the Northern Central Anatolia Region and ii) to develop a guideline that can be applied for landscape connectivity in fragmentation areas. Landscape resistances were determined according to the target species (Lynx lynx) and a resistance map was formed. Corridors were determined by using Least-cost path (LCP) approach with GIS. As a result, six corridors and major barriers were identified among the core areas and north forests. The methodology and results of this study has promising potential, which can be considered by experts, planners, and researchers in Turkey and others regions of the world as references for identifying and planning optimal patches for habitat sustainability.

One of the defining features of the Anthropocene is eroding ecosystem services as a function of decreases in biodiversity and overall reductions in the abundance of once-common organisms, including many insects that play innumerable roles in natural communities and agricultural systems that support human society. It is now clear that the preservation of insects cannot rely solely on the legal protection of natural areas far removed from the densest areas of human habitation. Instead, a critical challenge moving forward is to intelligently manage areas that include intensively farmed landscapes, such as the Central Valley of California. Here we attempt to meet this challenge with a tool for modeling landscape connectivity for insects (with pollinators in particular in mind) that builds on available information including lethality of pesticides and expert opinion on insect movement. Despite the massive fragmentation of the Central Valley, we find that connectivity is possible, especially ..., We used publicly-available land cover data and reported pesticide application rates to develop resistance-to-movement scenarios for pollinating insects in the Central Valley of California, U.S.A. The primary land cover datasets were LandIQ, NOAA C-CAP, and the USGS National Land Cover dataset. Additional minor datasets included Normalized Difference Vegetation Index (NDVI) from National Agriculture Imagery Program 2016 and the California Protected Areas Database. These datasets were combined to create a single static land cover map covering the entire Central Valley at 30-meter resolution. Reported pesticide application rates were obtained from the California Department of Pesticide Report Pesticide Use Reporting (PUR) for 2104, 2015, and 2016. Pesticide application rates were converted to LD50s (lethal doses) and were further converted to resistance-to-movement to create maps of resistance-to-movement under low, medium, and high pesticide application rates. We further tested three agri..., Shapefiles and GeoTIFF files can be opened and viewed in a wide range of Geographic Information System software including ArcGIS, QGIS, R, GRASS, ArcGIS Online, etc. Vector geographic data is in shapefile format. The following file types are associated with shapefiles: cpg, dbf, prj, sbn, sbx, shp, shp.xml, shx. These files should not be separated from one another. If moved, they may cause the data to be unable to be displayed. Raster geographic data is in geotiff format. The following file types are associated with geotiff: tfw, tif, tif.aux.xml, tif.ovr, tif.vat.cpg, tif.vat.dbf, tif.xml. These files should not be separated from one another. If moved, they may cause the data to be unable to be displayed. The coordinate system for all geographic data is NAD 1983 UTM Zone 10N.

A global analysis of accessibility to high-density urban centres at a resolution of 1×1 kilometre for 2015, as measured by travel time.

To model the time required for individuals to reach their most accessible city, we first quantified the speed at which humans move through the landscape. The principle underlying this work was that all areas on Earth, represented as pixels within a 2D grid, had a cost (that is, time) associated with moving through them that we quantified as a movement speed within a cost or ‘friction’ surface. We then applied a least-cost-path algorithm to the friction surface in relation to a set of high-density urban points. The algorithm calculated pixel-level travel times for the optimal path between each pixel and its nearest city (that is, with the shortest journey time). From this work we ultimately produced two products: (a) an accessibility map showing travel time to urban centres, as cities are proxies for access to many goods and services that affect human wellbeing; and (b) a friction surface that underpins the accessibility map and enables the creation of custom accessibility maps from other point datasets of interest. The map products are in GeoTIFF format in EPSG:4326 (WGS84) project with a spatial resolution of 30 arcsecs. The accessibility map pixel values represent travel time in minutes. The friction surface map pixels represent the time, in minutes required to travel one metre. This DANS data record contains these two map products.

Data used to model Colonel William Leake's journey in the southern Mani Peninsula, Greece, in the year 1805. Leake's journey is described in the book, Travels in the Morea: Volume I (Leake 1830, pp. 233-321). The data may be used to calculate least-cost paths between the places where Leake stopped, taking into consideration the contemporary path network and calculating cost in time based on Tobler's hiking function and the Modified Tobler function. A paper interpreting these data, 'Reconstructing Historical Journeys with Least-Cost Analysis: Colonel William Leake in the Mani Peninsula, Greece,' is published in Journal of Archaeological Science: Reports and can be accessed here: https://doi.org/10.1016/j.jasrep.2019.01.014. The article pre-print can be accessed here: https://works.bepress.com/rebecca-seifried/11/. Dr. Rebecca M. Seifried mapped the pre-modern paths as part of a PhD dissertation completed in 2016 through the Department of Anthropology at the University of Illinois at Chicago, entitled 'Community Organization and Imperial Expansion in a Rural Landscape: The Mani Peninsula, Greece (AD 1000-1821)' (https://hdl.handle.net/10027/21274). Fieldwork was conducted in 2014 and 2016 under the auspices of the 5th Ephorate of Byzantine Antiquities in Sparta and in collaboration with the Diros Project, an archaeological survey and excavation co-directed by Dr. Giorgos Papathanassopoulos and Dr. Anastasia Papathanasiou through the Ephorate of Palaeoanthropology & Speleology of Southern Greece. The remaining datasets were created in collaboration with Dr. Chelsea A.M. Gardner as part of the 'CART-ography Project: Cataloguing Ancient Routes and Travels in the Mani Peninsula,' whose goal is to catalogue the historic accounts of travelers to Mani and to model their routes throughout the peninsula. This research was funded by the National Science Foundation (BCS-1346694), Marie Sklodowska-Curie Actions (H2020-MSCA-IF-2016 750843), the DigitalGlobe Foundation, the National Cadastre and Mapping Agency, SA (Ktimatologio), ArchaeoLandscapes Europe, the University of Illinois at Chicago, the Society of Women Geographers, the Archaeological Institute of America, and Mount Allison University.

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025Dataset OverviewThis dataset contains comprehensive spatial and analytical data supporting the research on balancing biodiversity conservation with renewable energy infrastructure development in Queensland, Australia. The materials include energy system modeling results, conservation priority analyses using Zonation software, species and ecological community data. The code to analyse this data can be found here: https://github.com/amrogers/Biodiversity_and_energy_system_planning_QLD_2025Study Area: Queensland, AustraliaTemporal Scope: 2030, 2040, 2050 projection yearsData Volume: ~7.8 GB totalCoordinate System: GDA2020 / MGA Zone 56 (EPSG:7856)Dataset ContentsEnergy System Analysis DataQLD_v202412_eplus_tx1.gdb.zip (1.0 GB): Geodatabase containing renewable energy infrastructure scenarios under transmission development option 1. Includes solar photovoltaic, onshore wind, and offshore wind potential development areas under different biodiversity protection thresholds (0%, 10%, 30%, 50%, 70%, 90%).QLD_v202412_eplus_tx2.gdb.zip (2.7 GB): Geodatabase for transmission development option 2, containing the same renewable energy technologies and protection scenarios as tx1 but under alternative transmission infrastructure assumptions.cost_increase_results.csv: Economic analysis results showing percentage cost increases for residential and industrial energy consumers under different High Biodiversity Value Area (HBVA) exclusion scenarios.eplus_Domestic_NPV_2025.xlsx: Net Present Value calculations for domestic renewable energy projects across different protection thresholds and projection years (2030, 2040, 2050).Conservation Priority AnalysisZonation_output/250m_SNES_ECNES_red_zones_weighted_QLD/: Complete Zonation conservation prioritization analysis results at 250m resolution, including:feature_curves.csv (17.7 MB): Performance curves for 524+ conservation features showing coverage across priority ranksfeature_coverage_summary_with_CI.csv: Summary statistics with confidence intervals for feature coverage at different protection thresholdsrankmap.tif (47.5 MB): Spatial priority ranking mapMNES_2019_prioritisation_QLD.tif (47.5 MB): Matters of National Environmental Significance prioritization layerConfiguration files, analysis logs, and metadataBiodiversity DataSpecies_files_weights_table.xlsx: Weighting schemes applied to individual species in conservation planning, including rationale for differential weighting based on threat status and endemism.Table 8_The 524 species and their associated threat status.xls: Comprehensive list of fauna species included in the analysis with IUCN Red List categories, national conservation status, and state-level classifications.Table 9_The 22 ecological communities and their threat status.xlsx: Threatened ecological communities included in conservation planning with threat classifications and distribution information.Spatial ConstraintsSupplementary table_other spatial exclusions.xlsx: Non-biodiversity spatial exclusion layers used in energy system modeling, including urban areas, protected areas, infrastructure corridors, and other development constraints.Analysis ScriptsComplete set of R scripts for reproducing all analyses:percent cost increase_line plot.R: Creates visualizations of energy cost impacts under different conservation scenariosZonation curves.R: Generates conservation performance curves and coverage statisticsNPV_bar_plot.R: Produces economic analysis plots with Net Present Value breakdownsdomestic_export_map_iterations.R: Creates spatial maps of renewable energy infrastructure for domestic and export scenariosTechnical SpecificationsData FormatsSpatial Data: ESRI Geodatabase (.gdb), Shapefile (.shp), GeoTIFF (.tif)Tabular Data: CSV, Microsoft Excel (.xlsx, .xls)Analysis Code: R scripts (.R)Software RequirementsR (≥4.0.0) with packages: sf, dplyr, ggplot2, readr, readxl, tidyr, furrr, ozmaps, ggpatternESRI ArcGIS or QGIS for geodatabase access and spatial analysisZonation conservation planning software (for methodology understanding)Hardware RecommendationsRAM: 16GB minimum (32GB recommended for full spatial analysis)Storage: 15GB free space for data extraction and processingCPU: Multi-core processor recommended for parallel processing scriptsDetailed Description of the VRE Siting and Cost-Minimization ModelThis section provides an in-depth description of the Variable Renewable Energy (VRE) siting model, including the software, the core algorithm, and the optimisation process used to determine the least-cost, spatially constrained development trajectory for VRE infrastructure in Queensland, Australia.Software and Spatial ResolutionThe VRE siting model is implemented using Python and relies heavily on ArcGIS for comprehensive spatial data handling and analysis.Spatial Resolution: The analysis uses a working spatial resolution of 250-meter grid cells to generate Candidate Project Areas (CPAs).Core Tool: CPAs are generated using a custom fork of the source code (released with this Article) supplied by the Multi-criteria Analysis for Planning Renewable Energy (MapRE) initiative.2. Model Inputs and Exclusion CriteriaThe overall methodology is based on a prior economy-wide energy system modeling framework, which we modified to incorporate detailed spatial land-use data.A. Static Exclusion LayersThe model begins by applying a common set of static and predetermined land-use norms over the entire 40-year transition period. These permanent exclusions prevent VRE development in specific areas based on economic, technical, and environmental factors:Existing Development: Built-up or remote communities, defence areas.Infrastructure: Transport infrastructure, existing energy infrastructure.Economic/Technical: Active mines, irrigated areas, areas with low VRE resources.Topography: Slope.Offshore: Offshore shipping lanes.B. Biodiversity and Natural Capital CasesIn addition to the static exclusions, the analysis considers increasing biodiversity protection that apply different levels of exclusion thresholds for natural capital layers. These biodiversity exclusions are combined with the common exclusion criteria to generate aggregate exclusion maps for each VRE resource type (solar PV, onshore wind, and offshore wind).3. VRE Siting Algorithm and OptimizationThe VRE siting model uses a cost-minimization optimization approach to select the most cost-efficient project sites to meet a projected energy mix target.A. Least-Cost, Sequential OptimizationThe model simulates a realistic development trajectory by selecting projects in sequential five-year periods from 2025 to 2050.Demand Projection: At the beginning of each time step, the model determines the required VRE capacity for each technology based on projected energy demand. This aligns with the domestic energy generation scenarios considered, involving nearly full electrification by 2050.Site Identification: For that time step, the model identifies and maps candidate projects with the lowest Levelized Cost of Energy (LCOE) that are required to meet the capacity target within a given Queensland region.Capacity Allocation: If sufficient suitable sites are unavailable within the target region due to land-use constraints, the remaining required capacity is automatically allocated to the next nearest region with available resources.Land-Use Tracking: Once a site is selected, it is removed from the candidate pool until its projected end-of-life, ensuring no double-counting of the land used.B. Project Cost CalculationProject selection is driven by minimizing costs, specifically balancing generation and transmission costs.Generator Costs: Capital cost projections incorporate significant reductions by 2050. Costs are sourced from the 2021 Australian Energy Market Operator (AEMO) Integrated System Plan (ISP) and the CSIRO GenCost Report.Transmission Costs: Transmission assets and their costs are based on AEMO’s transmission cost database.Cost Prorating: Costs for new transmission infrastructure are prorated based on the VRE project's capacity and the assumption that lines will serve multiple users, allocating only a portion of the bulk transmission costs to the specific VRE project.Financial Basis: All costs are shown in 2025 Australian dollars. Capital costs are annualised using a weighted average cost of capital. The total Net Present Value (NPV) of costs is cumulative since 2020 and discounted using a social discount rate of 2.7%.4. Transmission Line RoutingThe transmission routing model is integrated into the VRE siting process to ensure selection minimises the combined cost of generation and transport. The method identifies the least-cost path between VRE projects and load centres or between two loads.Least-Cost Path: Transmission projects connected to CPAs are sited using established least-cost path methods. The specific tool is the Cost Path as a Polyline tool from ArcGIS Pro.Routing Surface: Routing is guided by a cost surface adjusted by multipliers that reflect the significance of obstacles (social, environmental, technical) or easements (e.g., preference for existing easements).Corridor Preference: The model prioritises augmentation of transmission in existing easements. Note that this approach prioritises existing right-of-way corridors without fully accounting for potential secondary impacts on surrounding natural capital.Conservation PlanningSystematic conservation prioritisation was conducted using Zonation software with 524 vertebrate species and 22 threatened ecological communities. Analysis incorporated species threat status, range size, and habitat specificity through differential weighting schemes.Economic AnalysisCost-benefit analysis quantified the economic implications of biodiversity protection on energy system development, including infrastructure costs, consumer price impacts, and Net Present Value calculations for different scenarios.Data

This data set contains 3 shapefiles and associated files that map linkages, which are least-cost paths between adjacent habitat cores for three wildlife species in the Southeastern U.S. The species are: the American black bear (Ursus americanus), Rafinesque's big-eared bat (Corynorhinus rafinesquii), and Timber rattlesnake (Crotalus horridus). We mapped habitat cores based on c. 2006 land cover, then used LinkageMapper software to identify least-cost paths between them, and buffered the least-cost paths by 2.5 km using ArcGIS, for a total width of 5 km. The buffered least-cost paths are the linkages provided here. The attribute tables for these shapefiles contain fields that describe the importance of each linkage to the overall habitat connectivity network, contemporary and future average modeled habitat suitability within the linkage, change in average proportion suitable, percent of urban land within the linkage, percent of linkage that is protected for conservation, and categorical values for climate threat, whether the linkage was designated as highly important, protection status, and future urbanization threat.

Methods We mapped habitat cores based on c. 2006 land cover, then used LinkageMapper software to identify least-cost paths between them, and buffered the least-cost paths by 2.5 km using ArcGIS, for a total width of 5 km. The buffered least-cost paths are the linkages provided here. One input data set for defining habitat cores and mapping least cost paths was an ensemble habitat suitability model for each species. Those data are posted here: https://doi.org/10.5061/dryad.r7sqv9s8v

For more information about the purpose of the data and methods used to create them, see: Costanza et al. Preserving connectivity under climate and land-use change: no one-size-fits-all approach for focal species in similar habitats, Biological Conservation.

This layer was created as part of Esri’s Green Infrastructure Initiative and is one of five newly generated companion datasets that can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. If used together, these layers should have corresponding date-based suffixes (YYYYMMDD). The corresponding layer names are: Intact Habitat Cores, Habitat Connectors, Habitat Fragments, Habitat Cost Surface, and Intact Habitat Cores by Betweeness. These Esri derived data, and additional data central to GI planning from other authoritative sources, are also available as Map Packages for each U.S. State and can be downloaded from the Green Infrastructure Data Gallery.This layer represents a cost surface for use in landscape connectivity modeling. It reflects the relative ease of movement for terrestrial species taking into account several factors including: NLCD landcover classes, slope, proximity to water, and habitat core score. Generally speaking, natural land cover classes and areas proximal to water are parametrized to exhibit lower costs to species movement while developed areas and areas proximal to built infrastructure are parameterized to exhibit higher costs to species movement. Esri created this layer by following a methodology outlined by the Green Infrastructure Center, Inc. The cost surface was generated using a raster overlay process to create a composite comprised of several landscape variables. Characteristics within each variable were scored based on their perceived impact on species movement whereas reduced movement is reflected as high cost. Landscape variables were categorized into three themed classes based on their expected influence on the cost surface. The first class, impedance, represents the expected cost of species movement as it relates to land cover. The second class, bonuses, represents reductions in cost resulting from being within an existing core, fragment, or proximal to surface water; these conditions are assumed to enhance movement. The third class, penalties, represents increases in cost resulting from steeply sloping terrain and road infrastructure; these conditions are assumed to discourage movement.

This cost surface was used to generate a comprehensive network of Least Cost Paths (LCPs) using the Cost Connectivity tool which was introduced in ArcGIS 10.4 and ArcGISPro in 1.3. The resulting network was also utilized to compute Betweenness Centrality attribution for the Intact Habitat Cores by Betweenness layer, denoting a measure of the Core’s connectivity importance compared to all others in the network. This cost surface may be applicable for use in additional structurally focused landscape connectivity assessments and the generation of landscape corridors.Data Coordinate System: NAD_1983_Albers

The Provincial Digital Elevation Model (PDEM) is a general-purpose dataset designed to represent true ground elevation where possible and is based on best-available data across the province. This dataset has not been conditioned for any specific application. Please see the User Guide below for more information. Zoom in on the map and click your area of interest to determine which package(s) you require for download. Now also available through a web service which exposes the data for visualization and geoprocessing. The service is best accessed through the ArcGIS REST API, either directly or by setting up an ArcGIS server connection using the REST endpoint URL. The service draws using the Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca. Service Endpointshttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_Provincial_DEM/ImageServer https://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_Provincial_DEM/ImageServer (Government of Ontario Internal Users)Additional DocumentationProvincial Digital Elevation Model - User Guide (Word) Provincial Digital Elevation Model - Methods and Processes (Word) Updating Provincial Elevation Data Using Least Cost Path Analysis (Word) Provincial Digital Elevation Model - Boundary in shape file format (Shapefile) OBM Photo Block Index (Zip file) PDEM Spatial Metadata Index (Elevation Source) - August 11th, 2025 (Zip file) Product PackagesProvincial Digital Elevation Model -North (CGVD28) Provincial Digital Elevation Model - South (CGVD28) Provincial Digital Elevation Model - North (CGVD2013)Provincial Digital Elevation Model - South (CGVD2013)StatusOn going: Data is continually being updated Maintenance and Update Frequency As needed: Data is updated as deemed necessary RSS FeedFollow our feed to get the latest announcements and developments concerning our PDEM product. Visit our feed at the bottom of our ArcGIS Online PDEM page. Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

The models, data, and information provided here were created as part of the Fault Displacement Hazard Initiative. We provide the Electronic Supplement for Chiou et al., 2023, CDF Fortran subroutines; the ArcGIS least-cost path (LCP) model and implementation guide, LCP MATLAB and Python scripts, and the LCP for 75 events in a shapefile and KMZ format for Thomas et al., 2023; and the Fortran code for Chiou et al. in review for Earthquake Spectra.

This packaged data collection contains all of the outputs from our secondary model, which replaced habitat cores with the boundaries of the four existing coastal marten populations (known as Extant Population Areas, as defined in the USFWS Species Status Assessment v2.0) and used Linkage Mapper to produce corridors connecting these populations. This package includes the following data layers: Coastal Marten Extant Population Areas (EPAs) Least-cost Paths Connecting EPAs Least-cost Corridors Connecting EPAs 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 shapefile (.shp) or raster GeoTIFF (.tif) formats.

The National Census of Ferry Operators (NCFO) Routes dataset was collected through December 31, 2022 and compiled on October 27, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Ferry Routes dataset represents all ferry routes from operators that provided responses to the 2022 National Census of Ferry Operators. Areas covered by the dataset include the 50 states as well as the territories of Puerto Rico, the U.S. Virgin Islands, and American Samoa. Each segment in the dataset connects two terminals from the Ferry Terminals dataset, and the route ferry vessels travel between terminals are present here. Route geometries were determined using GPS points from Automatic Identification System data, as well existing government datasets from the Census Bureau, the US Geological Survey, the National Oceanic and Atmospheric Association, and the US Army Corps of Engineers. Other routes were determined using least-cost analysis or were determined manually. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529042

We performed a factorial least-cost paths experiment to determine potential pollinator connectivity for the Central Valley of California, USA. We crossed three levels of landscape resistance with three agricultural margin scenarios (current conditions, agricultural margins as natural, agricultural margins as crop). We calculated least-cost paths across the Central Valley using each of these nine scenarios.