Boston Building Emissions

Introduction

We built a web application to help tackle climate change in the city of Boston. The app specifically addresses buildings which make up three-fourths of Boston’s greenhouse gas emissions.  In 2013 Boston enacted the Building Energy Reporting and Disclosure Ordinance (BERDO) that requires large buildings to report their annual energy and water use to the City. 

 

Our objective was to create a tool to better analyze and understand the energy consumption and greenhouse gas emissions across the buildings covered by Boston’s BERDO ordinance.

Demo Application

The Team

Our team consisted of designers and web developers and a data scientist. I was part of UX design team and was responsible for the research and design phases of the project.

Process

Research 

The primary users of the app would be city officials, building owners and residents. We looked at Previous year BERDO reports to see what type of analyses had been done. We also looked at other emissions dashboards for inspiration. 

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Two main types of analysis we identified were

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City-Wide Trends and Aggregate Analysis for a city official

  • Are there energy use patterns in different building segments?

  • Which buildings segments are most energy-efficient? Which are least? What variables most strongly correlated with this performance?

  • Which buildings are anomalies?

  • How is the building performing against peers?

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Building-Level Reporting Assessment and Analysis for a building owner/resident

  • How is the building performing?

  • Did the building report data? Over what period?

  • How is the building performing against the 15% reduction target?

  • How is the building performing against peers?

  • Which buildings are performing most strongly against the 15% reduction target? Which could use additional support?

Iterative Ideation

We explored ideas individually and then came together as a group to discuss them. Our ideation was loosely structured around the key questions and what visualizations would provide insight.

For each question we were trying to answer, we explored different types of ways to visualize the data. We built off of each other's ideas, evaluated the positives, and negatives of each data visualization and sometimes we even combined visualizations to come up with something new. 

The images below show how our concepts changed as we iterated through each of the key questions.

Mockups

The mockups below were created using Sketch.

Outcome Prediction

Takeaways

  • Remember the why

  • Iterate, iterate, iterate

  • Collaborate with developers

  • Don’t underestimate the power of small changes

lizageorge92@gmail.com​

 Tel: 404-820-1599