When I joined the Citadel Midwest Datathon, I didn’t expect our project to end up making a policy argument. But that’s the power of data: when analyzed rigorously, it doesn’t just reveal what is; it hints at what could be. Citadel Midwest Datathon The Spark The Spark Cities spend billions each year trying to become greener, healthier, and more resilient to climate change. Yet decisions about where to invest, such as which neighborhoods should get a park or which waterfronts should be protected, are often based more on intuition than on data. That question, "Can we use public datasets to make urban policy decisions more evidence-driven?" became the motivation behind our winning entry - where our goal was to quantify how greenspaces (parks and vegetation) and bluespaces (rivers, lakes, and coastlines) interact to affect local microclimates and public sentiment. " Can we use public datasets to make urban policy decisions more evidence-driven?" Can we use public datasets to make urban policy decisions more evidence-driven?" winning entry Visualizing the Urban Fabric Visualizing the Urban Fabric We used open data to study how nature affects city life in New York. The CDC PM2.5 dataset gave air quality levels, and the Heat Health Census Tracts dataset provided summer temperature data. The NYC Census Data added population and income details, while NYC TIGER/Line Shapefiles helped map city areas and measure how much land and water each tract had. Finally, the Twitter Climate Change Sentiment dataset included millions of climate-related tweets that showed how people felt about their environment. CDC PM2.5 dataset Heat Health Census Tracts dataset NYC Census Data NYC TIGER/Line Shapefiles Twitter Climate Change Sentiment dataset The first step was to simply visualize the raw geography and see how natural features are distributed across New York City. These maps make an immediate point: nature is not evenly distributed. The city’s waterfronts and parklands cluster in specific areas, leaving many districts nearly devoid of natural cooling effects. Turning Data into Models Turning Data into Models To move beyond visualizations, we developed an gaussian distance metric that captured how the influence of nearby green or blue spaces spreads across census tracts. This was followed by K-means clustering, which segmented NYC into four environmental archetypes: High Green / High BlueHigh Green / Low BlueLow Green / High BlueLow Green / Low Blue High Green / High Blue High Green / Low Blue Low Green / High Blue Low Green / Low Blue This grouping helped us compare the effect of natural infrastructure under controlled, data-driven conditions. We modeled how these categories impacted temperature, air quality, and even human sentiment using 13 years of geotagged climate-related tweets. The result was consistent: both greenspaces and bluespaces lowered temperatures and improved air quality, but their combined effect was less than additive. In other words, they act as substitutes rather than complements and provide diminishing returns when placed in the same area. less than additive substitutes From Correlations to Policy Since creating new water bodies is often impractical, we concentrated on expanding tree cover and identifying where adding new greenery would have the biggest impact. Using temperature data and our models, we mapped the areas most likely to benefit from additional greenspaces. The results reveal that neighborhoods with low tree and low water coverage stand to gain the most. These are the city’s heat-dense, concrete-heavy regions, where targeted green infrastructure could deliver the greatest cooling effect and improve overall livability. Why It Matters: A Call to Data-Driven Action Why It Matters: A Call to Data-Driven Action This project reinforced something I now firmly believe: open data can drive better policy when combined with robust statistical modeling. Our findings show that cities can, using only public data and careful analysis, identify where each new park or tree would have the highest return on investment in terms of cooling and livability. That principle extends beyond climate resilience. Education, healthcare access, and transportation equity can all benefit from similar open-data analytics pipelines. open data can drive better policy when combined with robust statistical modeling If hackathons and datathons can reveal actionable policy insights in a single weekend, imagine what sustained collaboration between data scientists and city planners could achieve. The next time you browse a government open-data portal, think of it not as “raw data” but as policy waiting to happen, one regression, one visualization, one insight at a time.

How Public Data and Statistical Modeling Can Help Shape Smarter Urban Policy

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

The Lottery Ticket Hypothesis: Why Pruned Models Can Sometimes Learn Just as Well as Full Networks

The New Anatomy of Customer Experience - Part 1

How to Make Docker Builds Faster with Layer Caching

I Shaved 1MB Off My Next.js App – The Fix Was Hiding in Plain Sight

A Step By Step Guide To Data Visualization With Power BI

An A-Z Guide to Decision Trees

The Lottery Ticket Hypothesis: Why Pruned Models Can Sometimes Learn Just as Well as Full Networks

The New Anatomy of Customer Experience - Part 1

How to Make Docker Builds Faster with Layer Caching

I Shaved 1MB Off My Next.js App – The Fix Was Hiding in Plain Sight

A Step By Step Guide To Data Visualization With Power BI

An A-Z Guide to Decision Trees

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps