News & Updates

Our analysis tool has expanded beyond Santa Clara County. San Mateo County data is now fully integrated - bringing the total to over 700 census tracts available for neighborhood-level risk analysis.

Since launching the Healthy Santa Clara risk tool, we have received consistent interest from community organizations and policymakers whose work crosses county lines. The Daly City-East Palo Alto corridor, for example, spans both Santa Clara and San Mateo counties - and until now, it was impossible to analyze as a single unit using our tool.

That changes today. San Mateo County CDC PLACES 2025 data and ACS 2019-2023 demographic estimates are now fully integrated into the tool, using the identical methodology as our Santa Clara analysis: the same Random Forest model, the same composite risk score (0-100), and the same five risk tiers from Very Low to Critical.

What this means for partners

Community organizations and policymakers can now look up any neighborhood in either county, compare risk scores across county lines, and identify shared high-burden corridors that a single-county view would have missed. The tool supports all existing features - including focus area customization, county average comparison, resource gap analysis, and funding source identification - for all San Mateo tracts.

What stays the same

The methodology is unchanged. Adding San Mateo County does not alter the Santa Clara findings - it extends them. Partners working exclusively in Santa Clara County will see no change to their existing outputs.

We are continuing to evaluate additional counties for future inclusion. If your organization operates in a county not yet covered, we would welcome the conversation.

Adding San Mateo County expands our dataset from 370 to over 700 census tracts - and opens up comparisons that a single-county analysis simply can't make.

When research is confined to a single county, it's hard to know whether the patterns you find are local anomalies or regional realities. Adding San Mateo County data to our analysis does more than extend the map - it strengthens the conclusions.

More tracts, more statistical confidence

Santa Clara County's 370 census tracts give us a solid foundation. San Mateo's additional tracts push our dataset past 700 - reducing the risk that any single finding is a quirk of local geography. Patterns that hold across both counties carry significantly more weight.

A natural comparison case

San Mateo and Santa Clara share similar economic profiles - high median incomes, significant tech employment, and stark internal inequality. That makes them ideal for comparison. When depression shows up as the #1 predictor of obesity in both counties, independently, the finding becomes harder to dismiss. When the east-west divide appears in both geographies, it stops being a San Jose story and starts being a regional one.

Richer targeting for partners

Community organizations and policymakers operating across county lines - which many do - can now use the tool to compare risk scores, identify shared high-burden corridors, and design interventions that don't stop at a county border. The Daly City-East Palo Alto corridor, for example, spans both counties and has never been analyzable as a single unit until now.

What stays the same

The methodology is identical: CDC PLACES 2025 data, ACS 2019-2023 demographics, the same Random Forest model and composite risk score. Adding San Mateo doesn't change the approach - it tests it.