[OC] I scraped 2.97 million home sales to rank the coziest cities in America. Bellingham, WA ranked #1. Anchorage ranked #7. Visualization

What This Visualization Shows

This data visualization displays "[OC] I scraped 2.97 million home sales to rank the coziest cities in America. Bellingham, WA ranked #1. Anchorage ranked #7." and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on Built a Python scraper that pulled Redfin MLS data across 15,245 zip codes to measure fireplace prevalence by actual home sales. Not surveys, not estimates.

The problem with raw data: Texas and Florida dominated because fireplaces are luxury amenities in warm markets. McAllen, TX had an 89.7% fireplace listing rate. So I applied two NOAA climate filters (150+ cloudy days/yr AND mean January temp under 50°F) which narrowed 217 metros down to 98 qualified cities.

Then scored each on 4 metrics:

* **Hearth (35%):** fireplace prevalence from MLS data * **Weather (30%):** cloudy days + rain days (NOAA 1991-2020) * **Coffee (20%):** shops per 100k residents * **Demand (15%):** Google Trends score for "fireplace"

Results surprised me. Bellingham, WA edged Seattle despite being a fraction of the size. Sioux Falls has the highest fireplace rate in the country at 39.4% and still ranks 12th because South Dakota winters arrive under clear skies. Pittsburgh ranks #1 in the US for coffee shops per capita which pushed it to #6.

Full dataset published on [data.world](http://data.world) and Zenodo (DOI: 10.5281/zenodo.20431525) under CC BY 4.0. Interactive map and full methodology at the link below.

[**bestburnfirewood.com/studies/coziest-cities-in-america/**](https://bestburnfirewood.com/studies/coziest-cities-in-america/), which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

Business data visualization transforms raw performance metrics into actionable insights that drive strategic decision-making and operational efficiency. Modern businesses generate enormous amounts of data from sales transactions, customer interactions, marketing campaigns, and operational processes that require sophisticated visualization techniques to extract meaningful patterns.

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Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.

The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.