Turning Commute Frustration into Data-Driven Housing Decisions
When I heard about our company’s plans to relocate to a brand new headquarters, my analytical mind immediately kicked into high gear. As someone who values both sleep and work-life balance, I wasn’t thrilled about potentially extending my daily commute. Could data help me find the sweet spot between affordable housing and a reasonable walk to work?
Rather than browsing endless rental listings or asking around for opinions, I decided to approach this challenge like any good analyst would—by gathering concrete data and visualizing it. This side project would not only help me make a more informed personal decision but also sharpen my geospatial analysis skills.
Building My Geospatial Analysis Toolkit
I’d never worked with Google Maps API before, but it seemed perfect for calculating walking distances across the city grid. Google’s excellent documentation and Python library made the learning curve surprisingly gentle. Within a weekend, I was pulling walking time estimates between coordinates and transforming them into beautiful heatmaps.
The project gave me a chance to:
- Learn a powerful new API for geospatial analysis
- Practice data collection automation
- Create intuitive visualizations of complex spatial data
- Make what was initially a personal question into a reusable analysis framework
Surprising Insights from the Walking Map
My first visualization revealed something unexpected—the new office location was surrounded almost exclusively by public buildings, universities, hospitals, and parks:

Walking time heatmap around the new office location
The data told a clear story: even with a generous 20-minute walking radius, there were virtually no residential areas within reach. This immediately shifted my perspective—I needed to expand my analysis to include public transportation options.
Expanding the Analysis to Public Transit
Next, I incorporated every mode of public transport available—subway lines, buses, trolleys, trams, and commuter trains—to create a 30-minute transit accessibility map:

30-minute public transit accessibility map
The visualization revealed fascinating patterns that wouldn’t be obvious from simply looking at a standard map:
- The Moscow River created a natural barrier that dramatically altered transit accessibility
- “Islands” of high accessibility formed around major subway stations (marked with arrows)
- Some areas that seemed close as the crow flies were actually transit deserts
From Analysis to Action
Armed with this data-driven understanding of the area, I now had a targeted approach to my apartment hunt. I could focus exclusively on the high-accessibility “islands” that fit my commute-time budget, rather than wasting time on listings in areas that looked deceptively convenient.
Using a popular real estate platform, I cross-referenced my accessibility map with rental prices to identify optimal neighborhoods balancing affordability and convenience. What started as a personal problem-solving exercise became a perfect example of how data analysis can transform everyday decisions.
Key Takeaways
This weekend project reinforced my belief that data skills can enhance decision-making in both professional and personal contexts. Beyond finding an ideal apartment, I gained:
- Hands-on experience with geospatial APIs and visualization techniques
- A reusable methodology for analyzing location-based decisions
- A concrete example of translating analytical insights into practical action
Sometimes the most valuable data projects aren’t the ones with massive datasets or complex algorithms, but those that bring clarity to decisions that impact our daily lives.