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Main Authors: Ullah, Isaac, Barton, C Michael
Format: Recurso digital
Language:English
Published: Zenodo 2025
Subjects:
geomorphometry
GIS
Archaeology
Online Access:https://doi.org/10.5281/zenodo.17162104
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author Ullah, Isaac
Barton, C Michael
author_facet Ullah, Isaac
Barton, C Michael
contents <p><strong>Overview</strong><br>The <strong>Wadi Hasa Sample Dataset — GRASS GIS Location</strong> packages a complete, ready-to-use spatial workspace for archaeological geomorphometry and landscape analysis in the Wadi Hasa region (central Jordan). It accompanies a teaching chapter and supports reproducible exercises in terrain modeling, hydrology, viewshed/visibility, movement and least-cost analysis, site catchments/territories, and predictive modeling (both deductive and inductive/ML).</p> <p>The archive is a standard GRASS GIS <strong>Location</strong> with multiple <strong>Mapsets</strong> and an included <code>Workspace.gxw</code> project file so users can open, inspect, and run workflows immediately. The dataset targets graduate and advanced undergraduate instruction in Archaeological GIS and is suitable for method demonstrations and small research replications.</p> <p><strong>Contents (summary)</strong></p> <ul> <li> <p><strong>Data model</strong>: GRASS Location with SQLite attribute storage; rasters/vectors organized by thematic Mapsets.</p> </li> <li> <p><strong>Layer inventory</strong>: <strong>230 raster</strong> maps and <strong>34 vector</strong> maps spanning 10 m, ~30 m, ~208–232 m, and 1 km resolutions.</p> </li> <li> <p><strong>Mapsets</strong> (thematic subprojects):<br><code>Terrain_Analysis</code>, <code>Visibility_Analysis</code>, <code>Least_Cost_Analysis</code>, <code>Territory_Modeling</code>, <code>Site_Buffer_Analysis</code>,<br><code>Boolean_Predictive_Modeling</code>, <code>Machine_Learning_Predictive_Modeling</code>,<br><code>Landsat_Imagery</code>, <code>Sentinel2_Imagery</code>,<br><code>ISRIC_soilgrid</code>, <code>Trace21k_Paleoclimate_Downscale_Example</code>,<br><code>Landscape_Evolution_Modeling</code>, <code>PERMANENT</code></p> </li> <li> <p><strong>Codebooks & data dictionaries (CSV at archive root)</strong>: <code>Landform_Codes.csv</code>, <code>Site_Type_Codes.csv</code>, <code>Temporal_periods.csv</code>.</p> </li> <li> <p><strong>Representative content</strong>:<br>• <strong>Terrain & hydrology</strong>: SRTM-derived DEM and derivatives (slope/aspect/curvature, geomorphons/landforms, flow accumulation, basins), insolation/topo-climatology.<br>• <strong>Archaeological vectors</strong>: Wadi Hasa Survey (WHS) and Wadi Hasa North Bank Survey (WHNBS) points; Nabataean and Roman towers; derived path/stream/catchment features.<br>• <strong>Movement</strong>: isotropic/anisotropic cost surfaces, time-to-travel rasters, and least-cost paths.<br>• <strong>Visibility</strong>: single and cumulative viewsheds for selected features.<br>• <strong>Predictive modeling</strong>: Boolean/deductive surfaces and ML-based workflows with training/test partitions.<br>• <strong>Environmental context</strong>: ISRIC SoilGrids (soil classes and properties), CHELSA TraCE21k paleoclimate examples (MAT/AP time slices), Landsat and Sentinel-2 products (e.g., NIR/RED/NDVI; RGB composites).</p> </li> </ul> <p><strong>Spatial reference</strong></p> <ul> <li> <p><strong>CRS</strong>: WGS 84 / UTM Zone 36N (EPSG:32636); horizontal/vertical units in meters.</p> </li> <li> <p><strong>Default computational region</strong>: set within the Location; reproducibility commands are provided in the included <code>README.txt</code>.</p> </li> </ul> <p><strong>Methods (brief)</strong><br>Workflows rely on core GRASS GIS 8.x modules for terrain analysis (<code>r.slope.aspect</code>, <code>r.param.scale</code>, <code>r.geomorphon</code>, <code>r.watershed</code>, <code>r.sun</code>), cost/paths (<code>r.walk</code>, <code>r.cost</code>, <code>r.path</code>, <code>r.drain</code>), visibility (<code>r.viewshed</code>), and attribute/overlay tools (<code>v.what.rast</code>, <code>v.rast.stats</code>, <code>v.db.*</code>). Example ML paths reference GRASS’s <code>r.ml.*</code> interface; soil and paleoclimate products are resampled to harmonize with 30 m terrain derivatives for teaching purposes (with scale caveats noted in the README).</p> <p><strong>Provenance & related resources</strong></p> <ul> <li> <p><strong>Primary survey data</strong>: Wadi Hasa project legacy datasets curated by the author (original release on Figshare: <em>Wadi Hasa Ancient Pastoralism Project</em>, <a href="https://figshare.com/articles/dataset/Wadi_Hasa_Ancient_Pastoralism_Project/1404216" target="_new" rel="noopener">https://figshare.com/articles/dataset/Wadi_Hasa_Ancient_Pastoralism_Project/1404216</a>).</p> </li> <li> <p><strong>Base elevation</strong>: SRTM 30 m (USGS/NASA).</p> </li> <li> <p><strong>Soils</strong>: ISRIC SoilGrids.</p> </li> <li> <p><strong>Paleoclimate</strong>: CHELSA TraCE21k (selected time slices).</p> </li> <li> <p><strong>Satellite imagery</strong>: Landsat and Sentinel-2 derivatives.</p> </li> <li> <p><strong>Forthcoming book context</strong>: This dataset accompanies the chapter “Geomorphometry for archaeology,” prepared for the second edition of <em>Geomorphometry: Concepts, Software, Applications</em> (eds. H. I. Reuter, C. H. Grohmann, V. Lecours; Elsevier; expected 2026). Publisher listing (metadata subject to change): <a href="https://www.google.com/books/edition/Geomorphometry/EYoI0QEACAAJ?hl=en&utm_source=chatgpt.com" target="_new" rel="noopener">https://www.google.com/books/edition/Geomorphometry/EYoI0QEACAAJ?hl=en</a></p> </li> </ul> <p><strong>Intended use</strong><br>Designed for classroom labs, methods demonstrations, and reproducible examples in archaeological landscape analysis. Users can adopt the Mapsets piecemeal, or open the provided GRASS workspace to reproduce figures/analyses from the teaching chapter.</p> <p><strong>How to cite</strong><br>Ullah, I. I. and Barton, C.M. (2025). <em>Wadi Hasa Sample Dataset (GRASS GIS Location)</em>. Zenodo. <a target="_new" rel="noopener">https://doi.org/10.5281/zenodo.17162040</a></p> <p><strong>License</strong><br><strong>Creative Commons Attribution 4.0 International (CC BY 4.0).</strong> Third-party data retain their original attributions; see <code>README.txt</code> for details.</p> <p><strong>Contact</strong><br><strong>Isaac I. Ullah</strong> — San Diego State University — <a href="mailto:iullah@sdsu.edu" rel="noopener">iullah@sdsu.edu</a></p> <p><strong>Keywords</strong><br>Archaeology; Archaeological GIS; Geomorphometry; Landscape archaeology; Wadi Hasa; Jordan; SRTM; SoilGrids; CHELSA; Landsat; Sentinel-2; Viewshed; Least-cost path; Predictive modeling; GRASS GIS; Reproducible research; Teaching dataset</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_17162104
institution Zenodo
language eng
publishDate 2025
publisher Zenodo
record_format zenodo
spellingShingle Wadi Hasa Sample Dataset — GRASS GIS Location
Ullah, Isaac
Barton, C Michael
geomorphometry
GIS
Archaeology
<p><strong>Overview</strong><br>The <strong>Wadi Hasa Sample Dataset — GRASS GIS Location</strong> packages a complete, ready-to-use spatial workspace for archaeological geomorphometry and landscape analysis in the Wadi Hasa region (central Jordan). It accompanies a teaching chapter and supports reproducible exercises in terrain modeling, hydrology, viewshed/visibility, movement and least-cost analysis, site catchments/territories, and predictive modeling (both deductive and inductive/ML).</p> <p>The archive is a standard GRASS GIS <strong>Location</strong> with multiple <strong>Mapsets</strong> and an included <code>Workspace.gxw</code> project file so users can open, inspect, and run workflows immediately. The dataset targets graduate and advanced undergraduate instruction in Archaeological GIS and is suitable for method demonstrations and small research replications.</p> <p><strong>Contents (summary)</strong></p> <ul> <li> <p><strong>Data model</strong>: GRASS Location with SQLite attribute storage; rasters/vectors organized by thematic Mapsets.</p> </li> <li> <p><strong>Layer inventory</strong>: <strong>230 raster</strong> maps and <strong>34 vector</strong> maps spanning 10 m, ~30 m, ~208–232 m, and 1 km resolutions.</p> </li> <li> <p><strong>Mapsets</strong> (thematic subprojects):<br><code>Terrain_Analysis</code>, <code>Visibility_Analysis</code>, <code>Least_Cost_Analysis</code>, <code>Territory_Modeling</code>, <code>Site_Buffer_Analysis</code>,<br><code>Boolean_Predictive_Modeling</code>, <code>Machine_Learning_Predictive_Modeling</code>,<br><code>Landsat_Imagery</code>, <code>Sentinel2_Imagery</code>,<br><code>ISRIC_soilgrid</code>, <code>Trace21k_Paleoclimate_Downscale_Example</code>,<br><code>Landscape_Evolution_Modeling</code>, <code>PERMANENT</code></p> </li> <li> <p><strong>Codebooks & data dictionaries (CSV at archive root)</strong>: <code>Landform_Codes.csv</code>, <code>Site_Type_Codes.csv</code>, <code>Temporal_periods.csv</code>.</p> </li> <li> <p><strong>Representative content</strong>:<br>• <strong>Terrain & hydrology</strong>: SRTM-derived DEM and derivatives (slope/aspect/curvature, geomorphons/landforms, flow accumulation, basins), insolation/topo-climatology.<br>• <strong>Archaeological vectors</strong>: Wadi Hasa Survey (WHS) and Wadi Hasa North Bank Survey (WHNBS) points; Nabataean and Roman towers; derived path/stream/catchment features.<br>• <strong>Movement</strong>: isotropic/anisotropic cost surfaces, time-to-travel rasters, and least-cost paths.<br>• <strong>Visibility</strong>: single and cumulative viewsheds for selected features.<br>• <strong>Predictive modeling</strong>: Boolean/deductive surfaces and ML-based workflows with training/test partitions.<br>• <strong>Environmental context</strong>: ISRIC SoilGrids (soil classes and properties), CHELSA TraCE21k paleoclimate examples (MAT/AP time slices), Landsat and Sentinel-2 products (e.g., NIR/RED/NDVI; RGB composites).</p> </li> </ul> <p><strong>Spatial reference</strong></p> <ul> <li> <p><strong>CRS</strong>: WGS 84 / UTM Zone 36N (EPSG:32636); horizontal/vertical units in meters.</p> </li> <li> <p><strong>Default computational region</strong>: set within the Location; reproducibility commands are provided in the included <code>README.txt</code>.</p> </li> </ul> <p><strong>Methods (brief)</strong><br>Workflows rely on core GRASS GIS 8.x modules for terrain analysis (<code>r.slope.aspect</code>, <code>r.param.scale</code>, <code>r.geomorphon</code>, <code>r.watershed</code>, <code>r.sun</code>), cost/paths (<code>r.walk</code>, <code>r.cost</code>, <code>r.path</code>, <code>r.drain</code>), visibility (<code>r.viewshed</code>), and attribute/overlay tools (<code>v.what.rast</code>, <code>v.rast.stats</code>, <code>v.db.*</code>). Example ML paths reference GRASS’s <code>r.ml.*</code> interface; soil and paleoclimate products are resampled to harmonize with 30 m terrain derivatives for teaching purposes (with scale caveats noted in the README).</p> <p><strong>Provenance & related resources</strong></p> <ul> <li> <p><strong>Primary survey data</strong>: Wadi Hasa project legacy datasets curated by the author (original release on Figshare: <em>Wadi Hasa Ancient Pastoralism Project</em>, <a href="https://figshare.com/articles/dataset/Wadi_Hasa_Ancient_Pastoralism_Project/1404216" target="_new" rel="noopener">https://figshare.com/articles/dataset/Wadi_Hasa_Ancient_Pastoralism_Project/1404216</a>).</p> </li> <li> <p><strong>Base elevation</strong>: SRTM 30 m (USGS/NASA).</p> </li> <li> <p><strong>Soils</strong>: ISRIC SoilGrids.</p> </li> <li> <p><strong>Paleoclimate</strong>: CHELSA TraCE21k (selected time slices).</p> </li> <li> <p><strong>Satellite imagery</strong>: Landsat and Sentinel-2 derivatives.</p> </li> <li> <p><strong>Forthcoming book context</strong>: This dataset accompanies the chapter “Geomorphometry for archaeology,” prepared for the second edition of <em>Geomorphometry: Concepts, Software, Applications</em> (eds. H. I. Reuter, C. H. Grohmann, V. Lecours; Elsevier; expected 2026). Publisher listing (metadata subject to change): <a href="https://www.google.com/books/edition/Geomorphometry/EYoI0QEACAAJ?hl=en&utm_source=chatgpt.com" target="_new" rel="noopener">https://www.google.com/books/edition/Geomorphometry/EYoI0QEACAAJ?hl=en</a></p> </li> </ul> <p><strong>Intended use</strong><br>Designed for classroom labs, methods demonstrations, and reproducible examples in archaeological landscape analysis. Users can adopt the Mapsets piecemeal, or open the provided GRASS workspace to reproduce figures/analyses from the teaching chapter.</p> <p><strong>How to cite</strong><br>Ullah, I. I. and Barton, C.M. (2025). <em>Wadi Hasa Sample Dataset (GRASS GIS Location)</em>. Zenodo. <a target="_new" rel="noopener">https://doi.org/10.5281/zenodo.17162040</a></p> <p><strong>License</strong><br><strong>Creative Commons Attribution 4.0 International (CC BY 4.0).</strong> Third-party data retain their original attributions; see <code>README.txt</code> for details.</p> <p><strong>Contact</strong><br><strong>Isaac I. Ullah</strong> — San Diego State University — <a href="mailto:iullah@sdsu.edu" rel="noopener">iullah@sdsu.edu</a></p> <p><strong>Keywords</strong><br>Archaeology; Archaeological GIS; Geomorphometry; Landscape archaeology; Wadi Hasa; Jordan; SRTM; SoilGrids; CHELSA; Landsat; Sentinel-2; Viewshed; Least-cost path; Predictive modeling; GRASS GIS; Reproducible research; Teaching dataset</p>
title Wadi Hasa Sample Dataset — GRASS GIS Location
topic geomorphometry
GIS
Archaeology
url https://doi.org/10.5281/zenodo.17162104

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