Wind Farm Distribution and Population Density Analysis

The objective of this project was to analyze and visualize the relationship between wind farm distribution and population density in Southern California, with a focused view of the San Gorgonio Pass.

Specifically, the map was designed to:

• Compare population density with wind farm locations

• Incorporate wind density as a supporting environmental factor

• Communicate spatial relationships clearly to a non-expert audience

This project emphasized cartographic design over deep analytical modeling. Research requirements were intentionally limited to data acquisition and preparation sufficient to support map creation.

Key workflow steps included:

• • Acquiring and preparing datasets (population, wind farms, wind density raster)

  • Applying geoprocessing techniques (clipping, joining, dissolving, merging)

  • Integrating raster and vector data within a single layout

  • Designing a multi-scale map layout (regional + focused inset maps)

  • Refining labels and layout using annotation tools

The final output was designed as a large-format (34" x 44") map intended for poster-style presentation.

The map integrates three primary variables:

• • Population Density (people per square mile)

  • Wind Farm Energy Output (megawatts)

  • Wind Density (raster surface for environmental context)

Design decisions focused on balancing readability with information density, particularly given the geographic scale and clustering of features.

A multivariate cartographic approach was used to visualize relationships between datasets:

• Graduated color symbology was applied to population density and wind farm output

• A semi-transparent raster layer was used to show wind density

• Wind farm locations were symbolized using custom icons

• Reference cities were added to provide geographic context

To address scale challenges, the layout includes:

• • A primary map centered on San Gorgonio Pass

  • Two inset maps showing broader regional patterns

Key design considerations included:

• Visual hierarchy: Ensuring the primary map remains the focal point

• Clarity vs. detail: Balancing multiple datasets without overwhelming the viewer

• Audience accessibility: Designing for interpretation by non-experts

• Geographic scale: Managing dense data across a large region

The map reveals several clear spatial patterns:

• Wind farms are generally located away from areas of high population density

• Larger wind farm installations tend to align with areas of higher wind density

• Smaller installations appear in areas with moderate wind density

• Population density is concentrated in coastal and urban regions, with minimal overlap with wind farm locations

These patterns suggest a strong relationship between environmental suitability (wind) and land use constraints (population density).

Several challenges emerged during the project:

• Data density: Difficulty representing multiple datasets clearly at a regional scale

• Layout complexity: Managing three maps within a single layout composition

• Legend clarity: Combining multiple variables into a single legend reduced readability

• Labeling constraints: High feature density required manual adjustment using annotation

These challenges highlight the trade-offs between information richness and visual clarity in cartographic design.

The final deliverable was a multivariate thematic map designed for presentation to a general audience.

This project demonstrates:

• Strong understanding of cartographic design principles

• Ability to integrate multiple datasets into a unified visualization

• Practical experience with ArcGIS Pro tools and workflows

• Awareness of the challenges involved in balancing complexity and clarity

Future improvements could focus on simplifying and refining the map design:

• Reducing visual clutter by limiting variables or separating themes

• Improving legend clarity by simplifying symbology

• Exploring alternative layout strategies (e.g., separate maps instead of a combined layout)

• Refining label density and placement for improved readability

Global Wind Atlas. (n.d.). Global Wind Atlas v4.0 – Wind Density Raster (TIF).
https://globalwindatlas.info

U.S. Geological Survey (USGS). (n.d.). U.S. Wind Turbine Database (USWTDB) – Wind Turbine Locations (Shapefile).
https://energy.usgs.gov/uswtdb/data

U.S. Census Bureau. (2022). TIGER/Line Shapefiles: California Census Tracts.
https://catalog.data.gov/dataset/tiger-line-shapefile-2022-state-california-ca-census-tract

Esri. (2020). USA Census Population Characteristics – Place and Tribal Geographies (ArcGIS Living Atlas).

• Place Geographies - https://www.arcgis.com/home/item.html?id=9c84c24c55a04c3b8317f37e536e6a8a

• Tribal Geographies - https://www.arcgis.com/home/item.html?id=6c2d9d1a814841229556704b019e5fa0