This dataset contains the least-cost corridors produced by our habitat connectivity model for the coastal marten, as described below in greater detail: After developing the coastal marten landscape resistance surface (i.e. movement model) and mapping habitat cores, we used Linkage Mapper to identify Least-Cost Paths (LCPs) between cores and to map broader mosaicked corridors around these single-pixel width paths. Linkage Mapper proceeds through several steps to complete these tasks (McRae and Kavanagh 2016). First, it identifies and lists which habitat cores are nearest neighbors using both the Euclidean distance (the “as-the-crow-flies” straight-line distance between nearest points on the edges of a pair of cores regardless of the character of the intervening landscape) and the cost-weighted distance (CWD). Second, it creates a “stick map” using straight-line linkages to connect core area pairs that are candidates for corridor mapping. Third, it locates the LCPs through the resistance surface between these pairs of cores and calculates their cost-weighted distance. This LCP is a single 30m pixel wide. A single pixel’s cost-weighted distance is the cell’s resistance value multiplied by the size of the cell, and the cost-weighted distance of an LCP is the sum of the cost-weighted distances of the pixels it runs through. This allows CWD to be reported in units that are directly comparable to the LCP length and the Euclidean distance between habitat cores (i.e. normalized to meters or kilometers), and all three of these metrics are included in the Linkage Mapper output for each linked pair of cores. Finally, Linkage Mapper creates least-cost corridors, which are wider swathes surrounding the LCPs that have only slightly higher movement costs and are more biologically realistic for conservation planning. It does this by calculating for each pixel on the landscape how much more costly a pathway passing through it between two cores would be than the LCP. Pixels closest to the LCP tend to be relatively close to it in CWD value, with the potential contribution of pixels to connectivity tending to decrease further away from the LCP. Linkage Mapper then creates a composite linkage map by assigning each pixel its minimum value relative to the nearest LCP (WHCWG 2010). Thus, the final map is a mosaic of normalized least-cost corridors around the LCPs. These corridors will vary in width depending on the resistance values surrounding the LCP. The creation of mosaicked corridors is the most fundamentally important function of Linkage Mapper in providing an informative depiction of habitat connectivity on the landscape, and is a significant advance over the simple LCP estimation that is a basic function available in ArcMap. The output of this least-cost corridor mosaic is a continuous surface that can be interpreted in various ways depending on how the relative values of pixels on the landscape are rendered in the final mapping process (WHCWG 2010). After some experimentation, we chose to depict corridors using a color ramp scaled from 0 – 4885 cost-weighted meters (i.e. the sum of the resistance values of the cells multiplied by their size to normalize them for comparison to meters). The specific value of the upper end of this scale was based on five times the radius of an average female coastal marten home range (977m X 5 = 4885m). This is an abbreviated and incomplete description of the dataset. Please refer to the spatial metadata for a more thorough description of the methods used to produce this dataset, and a discussion of any assumptions or caveats that should be taken into consideration.

A flow chart indicating overlay process and data layers for cost–distance analysis.

Yearly citation counts for the publication titled "Least cost path analysis as an objective and automatic method to define the main fault trace for probabilistic fault displacement hazard analyses".

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.

Purpose: The dataset is the one used the manuscript "An open source GIS-based decision support system for integrated forest management" (Journal of Maps, T&F) refers to. Specific content: Vector and raster input dataset include administrative boundaries, land uses, forest types, road network, forest management plan and morphological data computed using a digital elevation model, necessary to run the GIS-based DSS model SOFIA described in the manuscript. The dataset including filename, description and data source is detailed in the Readme.txt file.

1. One of the major planning tools to respond to urban landscape fragmentation is the development of ecological corridors, i.e. interconnected networks of urban green and blue spaces. Least-cost paths (LCP) appear to be an easy and appropriate resistance-based modeling method to respond to urban planners’ needs. However, the ecological validation of urban corridors using LCP is rarely performed and needs to be generalized to different species, habitats and cities.

2. We developed an experimental design to test the efficiency of LCP predictions to detect highly connecting landscape contexts that facilitate individual movements compared to movements in less connecting landscape contexts. We deliberately assigned LCP analysis parameters based on the scientific literature and expert knowledge to test a method potentially easy to use for urban stakeholders. To extend the validation, we applied our LCP model to two biological taxa with different habitat requirements: grassland-dwelling moths and forest-dwelling passerines, and to two medium-sized cities.

3. We used mark-release-recapture (MRR) methods for moths and playback recall protocols for passerines to compare the patterns of individual movement between two contrasted connectivity contexts determined by the presence and absence of modelled LCPs. MRR protocol estimated movement rates between herbaceous patches and the two contrasted connectivity contexts. Playback recall protocol consisted in attracting individuals from wooded patches to the two contrasted connectivity contexts. A movement was considered facilitated, when displacement was rapidly engaged and individuals moved a long distance from their wooded patch.

4. Moth and passerine movement patterns differed between the two connectivity contexts: moth recapture rates were higher in highly connecting contexts than in less connecting contexts. For passerine birds, responses to playback recalls were faster and movement distance longer in highly connecting contexts. All results support the hypothesis that both taxa were more prone to move in corridors modeled by LCP.

5. The convergence of the results for different biological models and across cities strengthens the relevance of LCP analysis for planning urban greenways and provides guidelines for landscape planners in the development of these corridors to favor the movement and survival of multiple urban species.

Methods We used mark‐release‐recapture (MRR) methods for moths and playback recall protocols for passerines to compare the patterns of individual movement between two contrasted connectivity contexts determined by the presence and absence of modelled LCPs.

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.

In this study, Spatial Multi-Criteria Evaluation and the least cost path analysis were applied to find the optimal by-pass road alignment in the Tlokweng Planning Area in Botswana. One-At-a-Time sensitivity analysis and the statistical test for zero proportion were used to investigate the robustness of the entire model. Four alternative by-pass roads were produced stressing economic, environmental, and social suitability as well as trade-offs between the groups. The results showed that the social alternative performs best. Sensitivity analysis and statistical test for zero proportion revealed four criteria as sensitive.

Abstract

This study evaluates multiple methodologies and their variants for Least Cost Path (LCP) modelling, applied in combination with different Digital Elevation Models (DEMs), to explore the broader applicability of the Movecost package, using the Mycenaean road networks of the Peloponnese (Greece) as case studies. Using a geographic information system (GIS) and the R programming environment, this paper employs the Movecost package for the R statistical package to simulate ancient routes based on existing road segments. By integrating a variety of functions and parameters, this study evaluates their effectiveness across different DEMs, including both Shuttle Radar Topography Mission DEM (SRTM-DEM) and Copernicus DEM (COP-DEM) at 30 m spatial resolution. The study also examines how varying these parameters can lead to different modelling outcomes, underscoring the necessity of calibrating least-cost analysis to specific regional contexts. The road segments around Nichoria (Messenia), Ayios Ioannis Kazarma (Argolis), and in the Berbati Valley (Argolis), provide a historical canvas against which these methodological innovations are tested with the ultimate aim of exploring the capabilities of the Movecost package and how different combinations of DEM, function, parameter, and path points can effectively model the route through the existing road remains, highlighting the variability and context-specific nature of LCP modelling. The results suggest that the 'Wheeled-vehicle critical cost function' (WCS) was effective in modelling the roads through the extant remains based on start and endpoints suggested by previous research and posited by this paper. These results further suggest that Mycenaean roads likely served as key infrastructure links between major centres and ports or harbours, underscoring their role in facilitating regional trade and communication. However, this outcome represents one of several possible results, as the appropriateness of functions and the parameters tested depend on the specific landscape and archaeological context. This underscores the importance of careful parameter selection, providing insights into the economic and social landscapes of Mycenaean Greece, while also highlighting the potential of integrating spatial data with robust computational tools to enhance our understanding of ancient infrastructure.

This dataset contains the data used for the below-mentioned publication of our transport network and least-cost path model. It analyzes 16th- and 19th-century transport networks between Istanbul and Sofia. The historical settlement data is based on work by the UrbanOccupationsOETR (https://urbanoccupations.ku.edu.tr/) and POPGEO_BG projects.

If you would like to use the dataset for further publication, please use the credentials specified below:

Kabadayi, M Erdem, Piet Gerrits, and Grigor Boykov. “Geospatial Mapping of a 16th Century Transport Corridor for Southeast Europe.” Digital Scholarship in the Humanities 37, no. 3 (2022): 788–812. https://doi.org/10.1093/llc/fqab084.

Least Cost Path Analysis between granite sources and study sites.

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.

This tool is used to determine paths from roads to destination points, and specify which land parcels are crossed in the process. To use this tool, input layers for land parcels, roads, and destination points. If advanced path-finding is chosen, a cost raster is required and an expression can be added to avoid certain parcels.Parcels: This is the input feature layer with polygon parcel data. It is used to avoid certain administrative types of land in drawing routes, as well as for providing output data showing which lots are crossed by each route.Roads: This is the input feature layer with polyline road data. All routes will be generated from this feature to destination points.Destinations: This is the input feature layer with point destination data. These are the points to which access routes will be generated from the roads layer. This layer can have as many features as needed, but each point will generate a separate crossed-parcels file in the output geodatabase, so an excessive number of points should be avoided.Output Geodatabase: This is the output geodatabase where the resulting files will be saved. The title of the GDB will be used as a name radical for the resulting files as well. For example, if the geodatabase is called "MyData.gdb", the routes feature class will be called "MyData_Routes".Path-finding: This is the method used to generate routes from roads to destinations. If "Basic" is chosen, the tool will run a euclidean distance calculation to create shortest path routes. If "Advanced" is chosen, the tool will run a least-cost path analysis to create least-cost routes. This option will also require an input cost raster, and allow the user to specify certain parcels to be avoided by route generation.Cost Raster: This is the input raster layer to be used in least-cost path analysis for generating the routes.Avoid Parcels by Attribute: This is an SQL expression representing parcels for generated routes to avoid. If no expression is provided, all routes are treated as equally acceptable. If the tool is unable to generate a route respecting this exclusion, a route will still be generated that minimizes crossings of unwanted parcels, and the tool will display a warning that the exclusion was violated.

This dataset contains an alternative representation of the least-cost corridors produced by our habitat connectivity model for the coastal marten, where a Maximum Euclidean Corridor Distance parameter of 45km has been set (i.e. corridors with a Euclidean distance greater than 45km have been excluded). This single parameter change was the only difference between the output from this model run, and the output from our Primary Model. This dataset was used in our report to make maps depicting corridors that are well-connected (≤15km), moderately-connected (≤45km), or poorly connected (>45km; i.e. all remaining corridors in the Primary Model that didn't fall into the first two categories) based on Euclidean distance. To construct these figures, you need two or three of the aforementioned datasets layered on top of one another. See Figures A6.1 and A6.2 in the report for symbology examples. After developing the coastal marten landscape resistance surface (i.e. movement model) and mapping habitat cores, we used Linkage Mapper to identify Least-Cost Paths (LCPs) between cores and to map broader mosaicked corridors around these single-pixel width paths. Linkage Mapper proceeds through several steps to complete these tasks (McRae and Kavanagh 2016). First, it identifies and lists which habitat cores are nearest neighbors using both the Euclidean distance (the “as-the-crow-flies” straight-line distance between nearest points on the edges of a pair of cores regardless of the character of the intervening landscape) and the cost-weighted distance (CWD). Second, it creates a “stick map” using straight-line linkages to connect core area pairs that are candidates for corridor mapping. Third, it locates the LCPs through the resistance surface between these pairs of cores and calculates their cost-weighted distance. This LCP is a single 30m pixel wide. A single pixel’s cost-weighted distance is the cell’s resistance value multiplied by the size of the cell, and the cost-weighted distance of an LCP is the sum of the cost-weighted distances of the pixels it runs through. Finally, Linkage Mapper creates least-cost corridors, which are wider swathes surrounding the LCPs that have only slightly higher movement costs and are more biologically realistic for conservation planning. It does this by calculating for each pixel on the landscape how much more costly a pathway passing through it between two cores would be than the LCP. Pixels closest to the LCP tend to be relatively close to it in CWD value, with the potential contribution of pixels to connectivity tending to decrease further away from the LCP. Linkage Mapper then creates a composite linkage map by assigning each pixel its minimum value relative to the nearest LCP (WHCWG 2010). Thus, the final map is a mosaic of normalized least-cost corridors around the LCPs. These corridors will vary in width depending on the resistance values surrounding the LCP. We opted not to set a maximum Euclidean or cost-weighted distance for least-cost corridors between habitat cores in our primary model, but we did impose a 45km Maximum Euclidean Corridor Distance parameter for this alternative model run. This is an abbreviated and incomplete description of the dataset. Please refer to the spatial metadata for a more thorough description of the methods used to produce this dataset, and a discussion of any assumptions or caveats that should be taken into consideration.

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.

This dataset contains an alternative representation of the least-cost corridors produced by our habitat connectivity model for the coastal marten, where a Maximum Cost-Weighted Corridor Distance parameter of 15km has been set (i.e. corridors with a cost-weighted distance greater than 15km have been excluded). This single parameter change was the only difference between the output from this model run, and the output from our Primary Model. This dataset was used in our report to make maps depicting corridors that are well-connected (≤15km), moderately-connected (≤45km), or poorly connected (>45km; i.e. all remaining corridors in the Primary Model that didn't fall into the first two categories) based on cost-weighted distance. To construct these figures, you need two or three of the aforementioned datasets layered on top of one another. See Figures 14, A6.3, and A6.4 in the report for symbology examples. After developing the coastal marten landscape resistance surface (i.e. movement model) and mapping habitat cores, we used Linkage Mapper to identify Least-Cost Paths (LCPs) between cores and to map broader mosaicked corridors around these single-pixel width paths. Linkage Mapper proceeds through several steps to complete these tasks (McRae and Kavanagh 2016). First, it identifies and lists which habitat cores are nearest neighbors using both the Euclidean distance (the “as-the-crow-flies” straight-line distance between nearest points on the edges of a pair of cores regardless of the character of the intervening landscape) and the cost-weighted distance (CWD). Second, it creates a “stick map” using straight-line linkages to connect core area pairs that are candidates for corridor mapping. Third, it locates the LCPs through the resistance surface between these pairs of cores and calculates their cost-weighted distance. This LCP is a single 30m pixel wide. A single pixel’s cost-weighted distance is the cell’s resistance value multiplied by the size of the cell, and the cost-weighted distance of an LCP is the sum of the cost-weighted distances of the pixels it runs through. Finally, Linkage Mapper creates least-cost corridors, which are wider swathes surrounding the LCPs that have only slightly higher movement costs and are more biologically realistic for conservation planning. It does this by calculating for each pixel on the landscape how much more costly a pathway passing through it between two cores would be than the LCP. Pixels closest to the LCP tend to be relatively close to it in CWD value, with the potential contribution of pixels to connectivity tending to decrease further away from the LCP. Linkage Mapper then creates a composite linkage map by assigning each pixel its minimum value relative to the nearest LCP (WHCWG 2010). Thus, the final map is a mosaic of normalized least-cost corridors around the LCPs. These corridors will vary in width depending on the resistance values surrounding the LCP. We opted not to set a maximum Euclidean or cost-weighted distance for least-cost corridors between habitat cores in our primary model, but we did impose a 15km Maximum Cost-Weighted Corridor Distance parameter for this alternative model run. This is an abbreviated and incomplete description of the dataset. Please refer to the spatial metadata for a more thorough description of the methods used to produce this dataset, and a discussion of any assumptions or caveats that should be taken into consideration.

Network of 44 papers and 59 citation links related to "Least cost path analysis as an objective and automatic method to define the main fault trace for probabilistic fault displacement hazard analyses".

coastal-marten connectivity-modeling general-biology-at-risk-biota-te-species general-biology-species-mammals general-landscapes-landscape-dynamics general-landscapes-landscape-ecology general-management-habitat-management-habitat-models general-management-landscape-management-landscape-connectivity gnarly-landscape-utilities humboldt-coastal-marten humboldt-marten least-cost-path-analysis linkage-mapper martes-caurina-humboldtensis pacific-marten

A table showing classes for assessing site potential for representation.