What was built Two wildlife distribution maps and one regional tracking pattern analysis integrating camera trap records and scat observations for bobcat and puma across 520 square miles of Marin County terrain — enabling habitat corridor identification, monitoring coverage assessment, and conservation planning decisions.
Who it was for The Felidae Conservation Fund research team — biologists working to identify where bobcat and puma populations concentrate, where movement corridors connect habitat patches, and where monitoring gaps might be obscuring actual wildlife presence.
Why it mattered The spatial layer makes a critical management distinction visible: whether absence of detections reflects a monitoring gap or genuine absence of animals. That distinction determines how conservation resources get allocated — and was invisible in the raw field data.

The Challenge

Wildlife conservation decisions require spatial context that field data in spreadsheets cannot provide. The Felidae Conservation Fund had accumulated years of camera trap records across 520 square miles of Marin County terrain — Muir Woods, Mount Tamalpais, Tennessee Valley, Rodeo Valley. The data existed. The spatial picture didn't. Without a map, the team couldn't see where observations clustered, whether detection gaps reflected animal absence or monitoring gaps, or where habitat corridors might connect isolated landscape patches.

These maps were built to give the research team that spatial decision-support layer. The first plotted camera trap locations and scat observations for bobcat and puma across the full study area — showing where monitoring had occurred and where animals had been detected. The second mapped regional tracking patterns across the North Bay, revealing wildlife activity distribution at a landscape scale.

The analytical value was in making existing monitoring data spatially legible: where observation clusters form, where movement corridors likely connect habitat patches between landscape features, and where absence of detections reflects a monitoring gap rather than absence of animals. That spatial distinction determines how conservation resources get prioritized.

These outputs provided the research team with a decision-support spatial layer they could use to direct future monitoring effort, identify priority habitat areas for conservation action, and communicate findings to stakeholders and funders.

"The data had been sitting in spreadsheets for years. The maps turned it into something the team could actually see — where the animals were, where the gaps were, where to look next."

Process

  1. 1
    Research Reviewed monitoring data structure; identified camera trap locations, scat observations, and tracking data spanning 2012–2018.
  2. 2
    Data Prep Organized spatial point features; verified geographic coordinates against known habitat areas; structured attribute data for both map outputs.
  3. 3
    GIS Work Mapped approximately 100 camera trap locations and scat observations; designed symbology distinguishing species and detection type; produced regional tracking map for North Bay.
  4. 4
    Output 2 maps reviewed with research team; supported conservation planning decisions, monitoring program prioritization, and internal spatial interpretation of long-term field data.
Principle 02
The map is for a person making a decision

Whether the audience is a conservation biologist or a policymaker, the question is the same: what spatial information does this person not have, and what decision could they make differently if they had it? That question drives every design choice.