Habitat 2100

Predicting habitat loss in a changing climate

Overview

In my first Harvard MDE studio project, my partner and I created an interactive data visualization tool to examine how climate change impacts species across regions by modeling future habitats based on climate data. This interactive approach enables users to explore areas where species are most at risk and better understand the long-term effects of environmental change on biodiversity.

Toolkit

Miro, Figma, Maxent, Python, Three.js, After Effects

My roles

Research, data analyst, data visualization, wireframing, prototype, user testing, interface design

Team

Developer, designer (team of 2)

Duration

Sep - Oct 2024 (2 months)

Problem

According to the World Count Report, by the year 2100, half of all species will be at critical risk of extinction due to climate change. Once a species disappears, that loss is irreversible.

Our solution

We modeled habitat loss over the next 75 years, from present to year 2100 to examine how individual species will be affected by the changing climate in the context of ecosystem.

Research focus

To begin with, we focused our study on the Cascades mountain range in the Pacific Northwest, with Mount Rainier as a focal point. Centering on Pika, a small mountain-dwelling mammal that lives in the Cascades as they are the climate change indicator species.

But species don’t exist in a vacuum. We studied habitat loss within the context of ecosystems. This gave us a unique understanding of how the habitat loss of one species can threaten the existence of an entire ecosystem.

Gathering insights

We reached out to key individuals and organizations to gather insights, ensuring a well-rounded approach aligned with real-world conservation needs. Their feedback refined our focus areas and highlighted critical gaps, directing our efforts where they’re needed most.

Problem statement

How can we identify areas where species are most at risk from the climate change?

The big idea - predicting future habitat

To identify areas where species are most at risk from climate change, we modeled habitat loss for each species in the Cascades. By combining species location data with historical climate data and applying machine learning, we built a predictive model to define each species' “suitable habitat” under changing climate conditions.

Ideas development

We brought our ideas to life through sketching, prototyping, and multiple iterations, refining each design with insights gained from user testing and feedback.

Initial idea sketches

Gathering and cleaning data

Heatmap generation

Prototype, test, and iterate

Data transforming process

  • We sourced over 300GB of climate and species data from several sources such as GBIF, WorldClim, and NOAA.

  • The processed this data in Python and fed it into the Maximum Entropy machine learning model.

  • From the prediction scores, we generated heatmap images, which we overlaid on a map rendering in Three.js.

  • Lastly, we overlaid our 3D rendering with more data visualizations and UI components in Figma.

Visual elements

For the visual design, we used a dark navy background paired with bright colors to highlight important areas.

We kept everything as clean and simple as possible, to make sure that the focus stays on the map.

Final design

Timeline exploration

Scroll through a 20-year timeline, observing pika habitat shrinking due to climate change

Habitat interplay

Layer multiple species' habitats to better understand their interrelationships

Best-worst scenarios

Provide different future habitat scenarios based on the predicted climate models

Expanded timeline

Explore past and predicted climate data across various scenarios

Comparison mode

Allow users to compare scenarios on one screen and zoom in for more details

Full demo video

Key takeaways

Pika habitat shrinks

Pikas are forced to higher elevations as temperatures rise

Pikas & foxes diverge

Habitats barely overlap in the worst case scenario

Grasses flourish

Grasses species expand to both higher and lower elevations

Project's impact

Understand

how habitats will transform over time

Highlight

areas that will become unsuitable in the future

Guide

conservation efforts to areas that are most at risk

What I learned

Heatmap generation

Creating heatmaps with climate models and coding for the first time, thanks to support from my project partner!

Turning raw data to visualization

Gaining experience turning complex data into clear and impactful visualizations while preserving data integrity.

Adapting & iterating

Learning the importance of flexibility in design, continuously improving based on feedback and new insights.

Copyright © Sirinda Limsong 2023

Copyright © Sirinda Limsong 2023

Copyright © Sirinda Limsong 2023