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Tool Reference › Corridor Designer Tools › Corridor Analysis Data Report › Example report

A complete Corridor Analysis Data Report

Corridor Designer Tools · example output · by Jeff Jenness

The full text of one report from the Corridor Analysis Data Report window, copied out with Copy as text. The dataset described is the cumulative surface at the end of the Land Facet Tutorial, the count of nineteen corridor strands built from two feature classes. After the dataset's own section, the report gives the entire history of each of its inputs: the interspersion corridor back through the diversity surface, and the facet corridors back through the density raster, both to the clustering run that defined the eighteen land facets from the elevation, slope and insolation rasters and the topographic position classes.

Every path, parameter and count below came from the metadata the tools wrote as they ran; nothing was typed in.

Corridor Analysis Data Report
=============================
Dataset: Diversity_Cumulative_Corridors
Location: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Test_Geometry.gdb\Diversity_Cumulative_Corridors


Cumulative surface of 2 layers  (this dataset)
----------------------------------------------
Created: 2026-09-14 20:49 UTC
A raster whose cell value counts how many of the input layers occur on that cell -- an estimate of the relative importance (or number of species served) of preserving that ground. Each LAYER counts at most once per cell: a polygon layer where any of its (selected) polygons covers the cell center, a raster where its value is neither NoData nor 0. A modernized port of the CorridorDesigner Evaluation Tools' Cumulative Surface Tool.
Legend label: "Land Facets" (classes read "3 Land Facets" and so on)
   (Cells where NO layer occurs were written as NoData rather than 0.)
Cell size: 79.77
Layers combined:
Layer 1: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Test_Geometry.gdb\Facet_Diversity_Termini_Corridors
   (Polygon input; record kind: corridor.)
   (1 of 11 polygons were selected and used.)
Layer 2: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Facet_Corridors
   (Polygon input; record kind: corridor.)
   (18 of 209 polygons were selected and used.)



Background of Source 1 — Facet_Diversity_Termini_Corridors
----------------------------------------------------------
   (The full analysis history of this union input, as its own report would read.)

Step 1 — Land Facet Clustering
------------------------------
Read from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Land_Facet_Clusters
Created: 2026-09-14 00:49 UTC
The landscape was divided into land facets by fuzzy c-means clustering of continuous variables within each class of a categorical raster (D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Land_Facet_Tutorial.gdb\Topographic_Position).
Clustering variables, in the order used (this order also governs the later Mahalanobis statistics):
  * Solar Insolation   [D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Land_Facet_Tutorial.gdb\Solar_Insolation_WHm2]
  * Slope   [D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Land_Facet_Tutorial.gdb\Slope_Degrees]
  * Elevation   [D:\ArcGIS_stuff\consultation\az_linkages\tutorial\tutorial\Corridor_Tutorial_Data\Corridor_Tutorial_Data.gdb\dem_m]
Settings: fuzziness 1.5; confusion-index threshold 0.6 (cells with a less decisive cluster assignment became NoData); outliers screened by Gaussian kernel density (Scott bandwidth).
Per-category clustering:
  * Canyon bottom: 6 clusters, 10% flagged as outliers, from 358,673 cells
  * Flat-gentle slope: 5 clusters, 10% flagged as outliers, from 647,238 cells
  * Steep slope: 3 clusters, 10% flagged as outliers, from 676,078 cells
  * Ridgetop: 4 clusters, 10% flagged as outliers, from 339,759 cells
The land facets (raster value — facet):
  * 1 — Canyon bottom, Cluster 1   (37,907 cells)
  * 2 — Canyon bottom, Cluster 2   (74,730 cells)
  * 3 — Canyon bottom, Cluster 3   (49,176 cells)
  * 4 — Canyon bottom, Cluster 4   (217,386 cells)
  * 5 — Canyon bottom, Cluster 5   (90,142 cells)
  * 6 — Canyon bottom, Cluster 6   (79,093 cells)
  * 7 — Flat-gentle slope, Cluster 1   (353,443 cells)
  * 8 — Flat-gentle slope, Cluster 2   (16,578 cells)
  * 9 — Flat-gentle slope, Cluster 3   (1,122,451 cells)
  * 10 — Flat-gentle slope, Cluster 4   (895,797 cells)
  * 11 — Flat-gentle slope, Cluster 5   (348,083 cells)
  * 12 — Steep slope, Cluster 1   (237,017 cells)
  * 13 — Steep slope, Cluster 2   (944,138 cells)
  * 14 — Steep slope, Cluster 3   (246,193 cells)
  * 15 — Ridgetop, Cluster 1   (105,499 cells)
  * 16 — Ridgetop, Cluster 2   (224,477 cells)
  * 17 — Ridgetop, Cluster 3   (130,372 cells)
  * 18 — Ridgetop, Cluster 4   (69,674 cells)


Step 3 (alternative) — Diversity Indices (facet interspersion)
--------------------------------------------------------------
Read from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Facet_Diversity
Created: 2026-09-14 01:49 UTC
Neighborhood diversity of the categorical raster: the Shannon's index (H, natural log) within a circle radius 5 cells. High values mark ground where many classes are present in balanced amounts.
In the Land Facet workflow this surface, inverted (Invert Raster), becomes the cost surface for the corridor of maximum facet interspersion.
Computed from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Land_Facet_Clusters


Step 5 — Identify Termini Polygons (corridor endpoints)
-------------------------------------------------------
Read from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Facet_Diversity_Termini
Created: 2026-09-14 16:11 UTC
The corridor termini: the concentrations of the analysis surface inside each Wildland Block (D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Test_Geometry.gdb\Tumacacori_SantaRita_Merged, 2 blocks) that the least-cost corridors run between. Cells above the threshold were aggregated into regions (edge- or corner-touching cells connect), each region clipped to its block.
Threshold rule: each Wildland Block's own MEDIAN, computed from its own cells — the manual's rule for diversity surfaces ("the half of all cells inside each Wildland Block with the highest H'"). Each polygon's Threshold field holds the median its block used.
Within each block (and each category), only regions at least 50% of the size of the block's largest region were kept (geodesic areas).
Termini per category and Wildland Block (the output's Category and Block fields):
  * Facet_Diversity — 2 terminus polygons (Block 1: 1; Block 2: 1)
Cut from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Facet_Diversity


Step 6 — Least-Cost Corridor
----------------------------
Read from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Test_Geometry.gdb\Facet_Diversity_Termini_Corridors
Created: 2026-09-14 16:19 UTC
The least-cost corridor polygons between pairs of Wildland Blocks, combining the accumulated cost distance from each block (computed without Spatial Analyst) into a surface that is zero along the literal least-cost path and rises away from it, cut at percentile-width thresholds (0.1%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%).
Corridors in this output (the Category, Block_A and Block_B fields):
  * Facet_Diversity — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Inv_Facet_Diversity (inverted raster)
Termini used: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Facet_Diversity_Termini



Background of Source 2 — Facet_Corridors
----------------------------------------
   (The full analysis history of this union input, as its own report would read.)

Step 1 — Land Facet Clustering
------------------------------
Read from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Land_Facet_Clusters
Created: 2026-09-14 00:49 UTC
The landscape was divided into land facets by fuzzy c-means clustering of continuous variables within each class of a categorical raster (D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Land_Facet_Tutorial.gdb\Topographic_Position).
Clustering variables, in the order used (this order also governs the later Mahalanobis statistics):
  * Solar Insolation   [D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Land_Facet_Tutorial.gdb\Solar_Insolation_WHm2]
  * Slope   [D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Land_Facet_Tutorial.gdb\Slope_Degrees]
  * Elevation   [D:\ArcGIS_stuff\consultation\az_linkages\tutorial\tutorial\Corridor_Tutorial_Data\Corridor_Tutorial_Data.gdb\dem_m]
Settings: fuzziness 1.5; confusion-index threshold 0.6 (cells with a less decisive cluster assignment became NoData); outliers screened by Gaussian kernel density (Scott bandwidth).
Per-category clustering:
  * Canyon bottom: 6 clusters, 10% flagged as outliers, from 358,673 cells
  * Flat-gentle slope: 5 clusters, 10% flagged as outliers, from 647,238 cells
  * Steep slope: 3 clusters, 10% flagged as outliers, from 676,078 cells
  * Ridgetop: 4 clusters, 10% flagged as outliers, from 339,759 cells
The land facets (raster value — facet):
  * 1 — Canyon bottom, Cluster 1   (37,907 cells)
  * 2 — Canyon bottom, Cluster 2   (74,730 cells)
  * 3 — Canyon bottom, Cluster 3   (49,176 cells)
  * 4 — Canyon bottom, Cluster 4   (217,386 cells)
  * 5 — Canyon bottom, Cluster 5   (90,142 cells)
  * 6 — Canyon bottom, Cluster 6   (79,093 cells)
  * 7 — Flat-gentle slope, Cluster 1   (353,443 cells)
  * 8 — Flat-gentle slope, Cluster 2   (16,578 cells)
  * 9 — Flat-gentle slope, Cluster 3   (1,122,451 cells)
  * 10 — Flat-gentle slope, Cluster 4   (895,797 cells)
  * 11 — Flat-gentle slope, Cluster 5   (348,083 cells)
  * 12 — Steep slope, Cluster 1   (237,017 cells)
  * 13 — Steep slope, Cluster 2   (944,138 cells)
  * 14 — Steep slope, Cluster 3   (246,193 cells)
  * 15 — Ridgetop, Cluster 1   (105,499 cells)
  * 16 — Ridgetop, Cluster 2   (224,477 cells)
  * 17 — Ridgetop, Cluster 3   (130,372 cells)
  * 18 — Ridgetop, Cluster 4   (69,674 cells)


Step 2 — Land Facet Density
---------------------------
Read from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Land_Facet_Cluster_Density
Created: 2026-09-14 01:06 UTC
For every land facet, the proportion of the surrounding neighborhood composed of that facet (0–1), one band per facet, over a circle radius 3 cells.
The denominator counted every neighborhood cell inside the raster (the original ArcMap rule): NoData cells dilute the proportion, and windows at the raster edge are renormalized to their in-raster part.
Values are stored as integers scaled by 10,000; divide by 10,000 for the 0–1 proportion. Later tools do this automatically, and treat 100% (not the mean) as density's ideal value in Mahalanobis analyses.
Band — facet assignments:
  * Band 1 — Canyon bottom, Cluster 1
  * Band 2 — Canyon bottom, Cluster 2
  * Band 3 — Canyon bottom, Cluster 3
  * Band 4 — Canyon bottom, Cluster 4
  * Band 5 — Canyon bottom, Cluster 5
  * Band 6 — Canyon bottom, Cluster 6
  * Band 7 — Flat-gentle slope, Cluster 1
  * Band 8 — Flat-gentle slope, Cluster 2
  * Band 9 — Flat-gentle slope, Cluster 3
  * Band 10 — Flat-gentle slope, Cluster 4
  * Band 11 — Flat-gentle slope, Cluster 5
  * Band 12 — Steep slope, Cluster 1
  * Band 13 — Steep slope, Cluster 2
  * Band 14 — Steep slope, Cluster 3
  * Band 15 — Ridgetop, Cluster 1
  * Band 16 — Ridgetop, Cluster 2
  * Band 17 — Ridgetop, Cluster 3
  * Band 18 — Ridgetop, Cluster 4
Source land facet raster: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Land_Facet_Clusters


Step 5 — Identify Termini Polygons (corridor endpoints)
-------------------------------------------------------
Read from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Land_Facet_Cluster_Density_Termini
Created: 2026-09-14 01:22 UTC
The corridor termini: the concentrations of the analysis surface inside each Wildland Block (D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Pro_Test_Project\Test_Geometry.gdb\Tumacacori_SantaRita_Merged, 2 blocks) that the least-cost corridors run between. Cells above the threshold were aggregated into regions (edge- or corner-touching cells connect), each region clipped to its block.
Threshold rule: cells strictly greater than 0 — any density at all, the manual's recommendation for density surfaces.
Within each block (and each category), only regions at least 50% of the size of the block's largest region were kept (geodesic areas).
Termini per category and Wildland Block (the output's Category and Block fields):
  * Canyon bottom, Cluster 1 — 6 terminus polygons (Block 1: 3; Block 2: 3)
  * Canyon bottom, Cluster 2 — 4 terminus polygons (Block 1: 3; Block 2: 1)
  * Canyon bottom, Cluster 3 — 8 terminus polygons (Block 1: 4; Block 2: 4)
  * Canyon bottom, Cluster 4 — 7 terminus polygons (Block 1: 3; Block 2: 4)
  * Canyon bottom, Cluster 5 — 8 terminus polygons (Block 1: 1; Block 2: 7)
  * Canyon bottom, Cluster 6 — 8 terminus polygons (Block 1: 3; Block 2: 5)
  * Flat-gentle slope, Cluster 1 — 2 terminus polygons (Block 1: 1; Block 2: 1)
  * Flat-gentle slope, Cluster 2 — 2 terminus polygons (Block 1: 1; Block 2: 1)
  * Flat-gentle slope, Cluster 3 — 3 terminus polygons (Block 1: 1; Block 2: 2)
  * Flat-gentle slope, Cluster 4 — 2 terminus polygons (Block 1: 1; Block 2: 1)
  * Flat-gentle slope, Cluster 5 — 2 terminus polygons (Block 1: 1; Block 2: 1)
  * Steep slope, Cluster 1 — 7 terminus polygons (Block 1: 1; Block 2: 6)
  * Steep slope, Cluster 2 — 3 terminus polygons (Block 1: 1; Block 2: 2)
  * Steep slope, Cluster 3 — 3 terminus polygons (Block 1: 2; Block 2: 1)
  * Ridgetop, Cluster 1 — 6 terminus polygons (Block 1: 2; Block 2: 4)
  * Ridgetop, Cluster 2 — 6 terminus polygons (Block 1: 5; Block 2: 1)
  * Ridgetop, Cluster 3 — 10 terminus polygons (Block 1: 4; Block 2: 6)
  * Ridgetop, Cluster 4 — 14 terminus polygons (Block 1: 3; Block 2: 11)
Cut from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Land_Facet_Cluster_Density


Step 6 — Least-Cost Corridor
----------------------------
Read from: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Facet_Corridors
Created: 2026-09-14 01:35 UTC
The least-cost corridor polygons between pairs of Wildland Blocks, combining the accumulated cost distance from each block (computed without Spatial Analyst) into a surface that is zero along the literal least-cost path and rises away from it, cut at percentile-width thresholds (0.1%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%).
Corridors in this output (the Category, Block_A and Block_B fields):
  * Canyon bottom, Cluster 1 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Canyon_bottom_Cluster_1 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Canyon bottom, Cluster 2 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Canyon_bottom_Cluster_2 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Canyon bottom, Cluster 3 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Canyon_bottom_Cluster_3 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Canyon bottom, Cluster 4 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Canyon_bottom_Cluster_4 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Canyon bottom, Cluster 5 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Canyon_bottom_Cluster_5 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Canyon bottom, Cluster 6 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Canyon_bottom_Cluster_6 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Flat-gentle slope, Cluster 1 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Flat_gentle_slope_Cluster_1 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Flat-gentle slope, Cluster 2 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Flat_gentle_slope_Cluster_2 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Flat-gentle slope, Cluster 3 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Flat_gentle_slope_Cluster_3 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Flat-gentle slope, Cluster 4 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Flat_gentle_slope_Cluster_4 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Flat-gentle slope, Cluster 5 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Flat_gentle_slope_Cluster_5 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Steep slope, Cluster 1 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Steep_slope_Cluster_1 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Steep slope, Cluster 2 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Steep_slope_Cluster_2 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Steep slope, Cluster 3 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Steep_slope_Cluster_3 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Ridgetop, Cluster 1 — Wildland Block 1 ↔ 2 (13 percentile polygon pieces) — cost surface: Mahal_Ridgetop_Cluster_1 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Ridgetop, Cluster 2 — Wildland Block 1 ↔ 2 (13 percentile polygon pieces) — cost surface: Mahal_Ridgetop_Cluster_2 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Ridgetop, Cluster 3 — Wildland Block 1 ↔ 2 (11 percentile polygon pieces) — cost surface: Mahal_Ridgetop_Cluster_3 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
  * Ridgetop, Cluster 4 — Wildland Block 1 ↔ 2 (18 percentile polygon pieces) — cost surface: Mahal_Ridgetop_Cluster_4 (Mahalanobis distance, Squared distance (D2): Traditional for ecological modeling)
Termini used: D:\ArcGIS_stuff\general_tools\Pro_Wildlife\Test_Data.gdb\Land_Facet_Cluster_Density_Termini


This dataset created: 2026-09-14 20:49 UTC
Analysis identifier: d2625f7a-09b0-4b87-96bf-0b271fec4d6c