The LobsterHauls project explores how Maine’s lobster hauls have changed over time, using open data from the Maine Department of Marine Resources (DMR). The data—covering annual landings, revenue, and fishing zones—was cleaned and standardized in Python using Pandas to ensure consistent column types, remove anomalies, and aggregate results by year and zone.

From Data to Insight

After preprocessing, I used Streamlit, Plotly, and PyDeck to visualize the patterns across zones. The dashboard allows users to:

• Explore year-over-year trends in total pounds and revenue.
• View zone-level choropleth maps showing shifts in landings along the coast.
• Compare regional changes and identify where catches are increasing or declining.

What the Visualizations Show

The visualizations reveal a distinct northward shift in lobster hauls—supporting the hypothesis that warming ocean temperatures are influencing lobster distribution. By combining environmental data and fisheries data, the app paints a clear picture of how climate change is reshaping Maine’s most iconic fishery.

Tools and Technologies

• Data Source: Maine DMR Landings Dataset (CSV)
• Languages: Python
• Libraries: Pandas, Plotly, PyDeck, Streamlit
• Features: Data validation, filtering by year and region, interactive maps, and KPIs showing total pounds landed and revenue.