Community Impact & Investment Index

Problem Statement

Environmental pollution data existed primarily at city or county levels, masking significant disparities at the neighborhood level. This coarse granularity made it impossible to identify pollution hotspots or target interventions where they were most needed. Vulnerable communities often bore the brunt of poor air quality, but lacked the data to prove it or demand accountability.

Traditional monitoring stations were sparse—sometimes just one or two per entire city—and typically located away from pollution sources. This meant regulators made decisions based on data that didn't reflect actual exposure patterns. Communities living near freeways, industrial sites, or ports experienced pollution levels dramatically different from city averages, but had no way to quantify or communicate that difference.

The absence of granular data wasn't just a technical limitation—it was a political barrier to environmental justice. Without block-by-block evidence, it was difficult to demonstrate disparate impacts, hold polluters accountable, or prioritize resources for the most affected communities.

Technical Approach

I developed the Community Impact and Investment Index, scaling pollution monitoring from sparse, sporadic measurements to continuous block-by-block data coverage—a 25x increase in granularity. The work combined mobile sensor technology, data infrastructure, and analytical frameworks to create unprecedented environmental transparency.

Data Collection & Infrastructure

• Mobile Sensing Network: Deployed sensor-equipped vehicles that continuously measured air quality while driving regular routes, creating dense spatial coverage
• Multi-Pollutant Monitoring: Tracked NO₂, CO₂, CO, particulate matter, and other pollutants with research-grade accuracy
• Temporal Resolution: Captured pollution patterns across different times of day, days of week, and seasons
• Data Pipeline: Built infrastructure to process, validate, and aggregate millions of measurements into actionable insights

Analytical Framework

Created the Community Impact & Investment Index to translate raw pollution data into meaningful metrics for policy and advocacy:

• Block-level pollution exposure scores
• Comparison of neighborhood exposure to city and regional averages
• Identification of pollution hotspots and their sources
• Correlation with demographic and socioeconomic data to reveal environmental justice patterns
• Before/after analysis to measure impact of interventions

Impact

The project revealed previously hidden pollution hotspots in vulnerable communities and enabled targeted policy interventions:

Data Transparency

• 25x increase in pollution monitoring granularity—from city-wide to block-by-block
• Public dashboards making hyperlocal air quality data accessible to communities
• Datasets used by environmental justice advocates to support policy campaigns

Policy Influence

• Data informed California Air Resources Board regulatory decisions
• Evidence used in environmental justice litigation and advocacy
• Influenced infrastructure planning to mitigate pollution impacts
• Supported community organizing around specific pollution sources

Environmental Justice

• Quantified disparate pollution exposure in low-income communities and communities of color
• Provided evidence that city-wide averages masked severe localized impacts
• Enabled data-driven conversations about pollution burden and accountability

What I Learned

Measurement changes power dynamics. When communities have data about their own exposure, they can demand accountability in ways that weren't possible with sparse, distant monitoring. Granular data democratizes environmental discourse.

Averages hide injustice. City-wide air quality metrics can look acceptable while specific neighborhoods experience harmful pollution levels. Aggregation washes out the disparities that matter most. Justice requires disaggregated data.

Technology deployment isn't neutral. Who has access to data, how it's presented, and whether communities can act on it determines whether technology serves justice or just serves data. We had to think carefully about data access, visualization, and community partnership—not just technical accuracy.

Scale enables pattern recognition. Individual pollution measurements are noisy. Block-by-block coverage over time reveals systemic patterns—proximity to freeways, industrial zoning decisions, enforcement gaps. Scale transforms data from observation to evidence.

Field deployment requires iteration. Sensors fail. Routes need optimization. Data pipelines have edge cases. Building systems that work reliably in the real world requires continuous refinement based on operational experience, not just initial design.

Technical-Social Bridge

This project exemplifies bridging technical capability and social impact. It wasn't enough to build accurate sensors or sophisticated analytics—we had to ensure the data reached communities who needed it, in forms they could use, at times when it mattered for advocacy or policy decisions.

Working with community organizations taught me to present technical findings accessibly without sacrificing accuracy. Engaging with regulators showed me how to frame data in ways that supported policy action. Collaborating with environmental justice advocates revealed how data alone isn't enough—it must be paired with organizing and political will.

The lessons from this work—about data granularity, systemic bias made visible through measurement, and the importance of field-tested systems—carried through to my subsequent work on humanitarian data at the UN and autonomous systems at Intramotev.