Mapping Roadkill Hotspots With GIS
ISEF Category: Animal Sciences
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Subcategory: Ecology and Agriculture · Difficulty: Intermediate · Setup: Home Setup · Time: 1 to 2 Months
The Hook
Roadkill clusters are not random. They often line up with roads that cut through connected habitat, just like spills collect where a floor slopes downhill. If you map those patterns, you can turn public reports into a real wildlife-risk study.
What Is It?
This project asks where wildlife collisions pile up and why. You start with crowdsourced roadkill points, then compare them with traffic volume and habitat-connectivity layers. Habitat connectivity means how easily animals can move through the landscape without running into barriers like wide roads or dense development.
Think of the road network like a net stretched across animal movement routes. Where the net gets tighter, more animals may meet cars. GIS, or geographic information systems, lets you stack these layers and measure which places share the same pattern.
Why This Is a Good Topic
This is a strong science fair topic because you can test real spatial patterns without a lab bench. The question connects wildlife safety, road planning, and habitat loss, so your result has clear real-world meaning. You can learn GIS, hotspot mapping, and model comparison with data that many students can access for free.
Research Questions
- How does traffic volume affect the density of roadkill reports along road segments?
- What is the effect of habitat-connectivity score on roadkill hotspot strength?
- Does adding road type improve hotspot prediction beyond traffic volume alone?
- To what extent do land-cover classes around a road segment predict collision counts?
- Which hotspot method, grid-based or road-segment-based, finds the most stable clusters?
- How does season or month change the location of the strongest hotspots?
Basic Materials
- Laptop with internet access.
- Free GIS software such as QGIS.
- Spreadsheet software or Google Sheets.
- Crowdsourced roadkill dataset from a local reporting portal.
- Traffic volume data from a state DOT open-data portal.
- Land-cover or habitat-connectivity layers from USGS or NASA.
- Notebook for map notes and model comparisons.
Advanced Materials
- Laptop or workstation with enough RAM for raster processing.
- QGIS or ArcGIS Pro with spatial analysis tools.
- R with sf, tmap, and spatstat packages.
- High-resolution traffic count files from a transportation agency.
- Wildlife-vehicle collision database with segment IDs or coordinates.
- Detailed land-cover rasters and connectivity metrics.
- GPS unit or field survey access for validation points.
Software & Tools
- QGIS: Maps roadkill points, traffic layers, and habitat connectivity in one project.
- R: Runs hotspot tests, regression models, and spatial autocorrelation checks.
- Python: Cleans source files and matches points to roads.
- Google Earth Pro: Helps you inspect whether mapped collisions line up with roads, intersections, or habitat edges.
Experiment Steps
- Define whether you will measure roadkill as counts per road segment, grid cell, or buffer zone.
- Choose one geographic scale and one time window so every layer lines up the same way.
- Match your roadkill points with traffic, road type, and habitat-connectivity layers.
- Pick a hotspot method and a baseline model so you can compare simple clustering with predictor-based mapping.
- Plan controls for duplicate reports, coordinate errors, and missing traffic data.
- Set up a validation test that checks whether your pattern still appears in held-out areas or a second year of data.
Common Pitfalls
- Mixing latitude-longitude points with projected GIS layers, which shifts hotspots away from the real roads.
- Counting duplicate crowd reports as separate collisions, which inflates hotspot strength.
- Using traffic counts that do not match the same road segment length, which makes busy highways look hotter than they are.
- Comparing habitat layers at a different resolution than the roadkill data, which hides the connectivity signal.
- Skipping spatial autocorrelation checks, which makes the model look stronger than it really is.
What Makes This Competitive
A stronger version of this project goes beyond a map of red dots. You compare more than one hotspot method, test whether habitat connectivity adds signal after traffic is already in the model, and check the result on data you did not use to fit it. If you can show the pattern holds across road types, seasons, or species groups, your project starts to look like real wildlife-risk research.
Project Variations
- Use amphibian crossing reports instead of all wildlife collisions to see whether wetland edges create tighter hotspots.
- Compare daytime and nighttime traffic layers to test whether nocturnal movement changes hotspot maps.
- Swap habitat connectivity for road curvature, stream crossings, or urban edge distance to see which landscape factor matters most.
Learn More
- QGIS Documentation: Free guides for mapping, joins, and spatial analysis tools. Find it on the official QGIS website.
- USGS National Map: Roads, elevation, hydrography, and land-cover basemaps. Find it through the USGS data portal.
- NOAA National Centers for Environmental Information: Weather and climate layers if you want to test rain or temperature effects. Search the NOAA data site.
- PubMed: Review articles on wildlife-vehicle collisions, road ecology, and spatial analysis. Search for terms like roadkill, wildlife crossings, and hotspot mapping.
- NASA Earthdata: Satellite land-cover products for building habitat layers. Find it in NASA's Earthdata portal.
- State DOT Open Data Portals: Traffic counts and road attributes for many states. Search your state transportation department's open-data site.