Terrain Suitability &
Road Routing Analysis

The goal of this analysis was to evaluate the feasibility of:

• • Identifying potential road alignments into the property

  • Locating any areas suitable for constructing up to two dwellings (one per parcel)

The property consists of undeveloped, mountainous forest terrain, making accessibility and buildability the primary challenges.

Based on prior on-site inspection, I expected the terrain to present significant constraints. The property includes steep ridgelines and limited visible flat areas, and only portions of the land had been physically explored.

My working hypothesis was that:

• Suitable flat areas for dwellings would be minimal or non-existent

• Identifying practical road paths would be difficult due to slope and terrain conditions

The project involved developing a raster-based site suitability model using surface analysis techniques. Key steps included:

• Acquiring and preparing spatial datasets (elevation, slope, hydrology)

• Generating derivative layers such as slope, hillshade, and flow accumulation

• Evaluating terrain constraints for both road construction and building suitability

• Interpreting results through map outputs and written analysis

• Presenting findings through an interim update and final report

The analysis confirmed the initial hypothesis. Flat areas across the property are extremely limited, and the terrain is dominated by steep slopes. There are no clearly suitable locations for residential construction without significant grading or engineering intervention.

Road development is also constrained. While not impossible, any viable route would likely require increased cost and effort due to slope and terrain challenges.

Hillshade analysis revealed a previously unknown road trace, which aligns with physical evidence observed during on-site visits. This path appears to follow a stream corridor for part of its length, suggesting it was likely created as a temporary or expedient access route rather than a sustainably planned road.

Flow accumulation analysis identified natural drainage paths across the property. These areas would require culverts if intersected by any future road construction, both to maintain natural water flow and to reduce the risk of erosion and storm-related washouts.

The final deliverables included a documented suitability model, supporting analysis, and a map-based presentation of results.

This project demonstrates my ability to apply GIS-based spatial analysis to real-world land use questions, interpret terrain constraints, and translate technical findings into practical insights for decision-making.

This analysis was built using a high-resolution Digital Elevation Model (DEM) at 3.125 ft (~1 m), which provides enough detail to capture micro-topography such as subtle slope breaks, small drainage paths, and remnants of prior land use (e.g., logging cuts). This resolution also aligns well with road-scale planning while remaining manageable from a processing standpoint.

To improve performance and maintain focus on the study area, the DEM was clipped to a 500 ft buffer surrounding the parcels.

Using this DEM, several key raster layers were derived to support the analysis:

• Slope (primary constraint for both roads and structures)

• Aspect (secondary terrain orientation insight)

• Flow Direction and Flow Accumulation (to model drainage patterns)

• Stream network (generated using conditional and stream extraction tools)

• Multiple Hillshade layers (to visually interpret terrain under varying illumination angles)

These layers formed the basis for both constraint identification and suitability modeling.

To evaluate potential road paths, selected criteria were reclassified and combined into a weighted cost surface. The model emphasized slope as the dominant constraint, supported by hydrology and boundary considerations.

Weighted cost model:

• Slope: 50%

• Parcel / boundary constraints: 20%

• Stream buffers: 20%

• Flow accumulation: 10%

This weighted surface was used as input for cost-distance analysis (Distance Accumulation) and least-cost path modeling.

The following factors were selected based on the realities of mountainous, forested terrain:

• Slope: The most critical constraint, directly impacting both road feasibility and construction effort

• Streams & Drainage Paths: Avoidance prioritized to reduce erosion and long-term maintenance; crossings considered only where culverts could be installed

• Stream Buffers: Added to further reduce risk from seasonal water flow

• Parcel Boundaries & Buffers: Ensured all development remains within property limits while avoiding edge constraints

• Elevation: Considered but ultimately excluded as a primary factor, as it did not meaningfully impact suitability beyond its influence on slope

For dwelling placement, the analysis prioritized ridgetop locations over valley floors to reduce water exposure and improve long-term livability.

While the cost surface and distance accumulation analysis were successfully generated, the least-cost path results were limited. The Optimal Path as Line tool produced only a minimal output, suggesting that the combined constraints may be too restrictive for meaningful path generation under current weighting and conditions.

Alternative approaches using legacy Cost Distance and Cost Path tools yielded similar results.

This outcome reinforces the broader conclusion of the study: terrain constraints significantly limit feasible development options and may require either adjusted modeling assumptions or more intensive engineering solutions.

Additional supporting layers were incorporated to provide context and validation, including:

• Start and end points for path modeling

• Existing road trace identified via hillshade analysis

• Parcel boundaries (merged)

• County-level parcel, road, and boundary datasets

• Orthoimagery for visual reference

Multiple intermediate layer variations were also generated during the analysis process to refine model inputs and validate assumptions.

Next steps will focus on refining the suitability model to improve path generation and overall decision support.

Key areas of refinement include:

• Revisiting classification and weighting: Adjusting thresholds and weights to better balance terrain constraints with model flexibility

• Testing alternative configurations: Exploring variations in cost modeling and path analysis parameters to evaluate sensitivity and identify more viable outputs

Incorporating additional datasets:

• Soil data to assess stability and construction feasibility

• Vegetation data to estimate clearing effort and environmental impact

• Enhanced LiDAR-derived features to identify obstacles such as large trees, boulders, and subtle surface irregularities

• Validation and review: Comparing model outputs against real-world observations and seeking expert feedback to strengthen assumptions and approach

This project also raises several important questions that will guide future analysis:

• How should cost thresholds be calibrated to reflect real-world feasibility without over-constraining the model?

• At what point do terrain conditions shift from “difficult” to “impractical” for road development?

• What combination of datasets provides the most meaningful improvement in decision-making for this type of terrain?

Data Sources Used

NC OneMap. (2019–2025). North Carolina Department of Information Technology, Center for Geographic Information and Analysis.
Datasets used: Elevation (DEM) – Graham County; Orthoimagery – Graham County (2023); Parcels – Graham County (July 12, 2025).
https://www.nconemap.gov/#directdatadownloads

U.S. Census Bureau. (2023). TIGER/Line Shapefiles: Roads – Graham County, NC.
https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2025&layergroup=Roads

Additional Data Reviewed (Not Used in Final Analysis)

NC OneMap. (n.d.). 1 ft Raster Contours (from 3 ft DEMs).
https://www.nconemap.gov/datasets/nconemap::1-ft-raster-contours-from-3-ft-dems/about

NOAA Fisheries. (2017). NCEM/NCDOT/USGS LiDAR: North Carolina Statewide Phase 5 (Huffington Creek / Buffalo Rd).
https://www.fisheries.noaa.gov/inport/item/66379

U.S. Department of Agriculture, Natural Resources Conservation Service (USDA NRCS). (n.d.). Gridded National Soil Survey Geographic Database (gNATSGO).
https://www.nrcs.usda.gov/resources/data-and-reports/gridded-national-soil-survey-geographic-database-gnatsgo

U.S. Geological Survey (USGS). (n.d.). National Hydrography Dataset (NHD).
https://www.usgs.gov/national-hydrography/national-hydrography-dataset

ArcGIS Pro ModelBuilder workflow used to automate raster processing, reclassification, and cost surface generation. This structure allowed for iterative testing of weighting and classification schemes.