Data Visualization · 2025

Where the Birds Are

A data portrait of birding across Newfoundland and Labrador, mapping diversity, hotspots, and habitat composition from the 2025 eBird field season.

27,000+Observations
280+Unique Species
1,400+Localities

The Project

Produced for the Johnson Geo Centre in St. John's, this infographic transforms thousands of citizen-science birding records into a single visual narrative. It shows where birders are watching, which regions have the highest diversity, and how species composition varies across Important Bird Areas.

The project includes a print-ready 36x24 landscape poster built in Python and Matplotlib, plus an interactive Dash dashboard for exploratory analysis.

Where the Birds Are infographic poster

36 x 24 inch landscape poster · 200 DPI · Python / Matplotlib

Three Visualizations

Visualization 1 - Geographic hotspots map Click to view

Geographic Hotspots

Scatter mapping of NL localities where marker size shows observations and color intensity shows species diversity.


                    
Visualization 2 - County species richness bar chart Click to view

County Species Richness

Horizontal ranking of all counties by unique species count with tiers and labels for quick comparison.


                    
Visualization 3 - IBA species composition chart Click to view

IBA Species Composition

Stacked bars for top Important Bird Areas showing relative species-group composition across habitats.


                    

Key Findings

~65%

Of all observations come from the Avalon Peninsula, reflecting access and birder density.

11 Counties

Species richness varies significantly across the province, with Avalon leading.

8 IBAs

Distinct ecological profiles appear across coastal and inland Important Bird Areas.

Songbirds

Songbirds dominate most IBAs while seabirds lead in key coastal birding zones.

Process & Tech Stack

  • Data source: eBird Basic Dataset (2025), filtered to Newfoundland and Labrador.
  • Core tooling: Python, Matplotlib, Pandas, NumPy, GeoPandas, PIL, Plotly Dash, Jupyter Notebook.
  • Output: print-ready poster and companion interactive dashboard for deeper exploration.
Python Matplotlib Pandas GeoPandas Plotly Dash Jupyter