Basic Raster Analysis

Basic Raster Analysis

Kendrick Wittmer

Date: 3/11/2025

    This portfolio includes raster-based spatial analyses performed in ArcGIS Pro. The lab was divided into two major projects: (1) improving and analyzing elevation models using hillshading, filtering, and mosaicking; and (2) building a cost surface model for road construction in Duluth, MN using slope and distance to roads. These exercises introduced key raster operations such as reclassification, map algebra, filtering, and cost surface modeling.

Relief of Mount Shasta

    This map shows the elevation and terrain around Mount Shasta using a colorized elevation raster with a transparent hillshade overlay. The hillshade was created from a 30-meter resolution DEM and enhanced using standard deviation stretching for visual clarity. Insets highlight local relief patterns. This map serves as a foundation for understanding terrain representation with raster data.

Shasta DEM Noise and Filtering Comparison

Several maps were created to evaluate and improve the quality of a digital elevation model (DEM) for the Shasta region:

• Shasta DEM Noise shows artifacts such as data spikes and pits that distort the surface model.

  
  
  

• Filtered vs. Unfiltered Hillshade compares a low-pass filtered version of the DEM to the original, revealing cleaner but less detailed surfaces.

  
  
  

• Hillshade Cell Size Comparison demonstrates how different raster resolutions (3m vs 9m) affect clarity and detail.

  
  
  

Raster calculator functions such as Con() and IsNull() were used to smooth the DEM while preserving important topographic features.

Duluth, MN Road Construction Cost Model

This project used raster modeling to estimate the cost of building roads based on terrain slope and distance to existing roads. Four maps were created in a single layout:

• Distance Cost: Calculated using Euclidean distance from roads and multiplied by a rate of $25 per meter.

• Slope Cost: Derived by converting slope into an exponential cost factor, capped at a maximum of $300, then scaled by $1000.

• Mask: A binary raster showing areas with total costs under $25,000.

• Final Cost: The combined cost surface, masked to include only affordable areas.

    Each step used map algebra and reclassification tools. Symbology was adjusted using standard deviation stretching and custom color ramps for consistent interpretation.

  

  This lab demonstrated the power of raster analysis for spatial modeling and decision-making. Through map algebra and cost surface modeling, complex geographic questions like optimal road siting and terrain correction were effectively addressed using real-world data.