Unfolding the Urban Landscape of Thunder Bay: A Comprehensive Data-Driven Analysis of Street Sign and Address Distributions Across Various Street Types
Heatmaps are powerful data visualization tools that can represent complex datasets in an intuitive, easy-to-understand format. In this post, we utilize heatmaps to analyze the distribution of street signs and addresses across different types of streets in the city of Thunder Bay.
Data Overview
We examine three datasets: the "Street Network" dataset, which provides information about different types of streets; the "Street Signs" dataset, detailing various types of street signs; and the "Municipal Address" dataset, containing information about municipal addresses. These datasets were obtained from the City of Thunder Bay's Open Data Portal.
Heatmap of Sign Types per Street Type
Our first heatmap illustrates the distribution of the top 10 most frequent sign types across different types of streets. Each cell's colour signifies the number of occurrences of a particular sign type on a certain street type, with darker colours indicating higher frequencies.
Observing the heatmap, we notice that certain sign types are more prevalent on particular types of streets, providing insight into how different sign types are distributed throughout the city's streets.
Heatmap of Address Distribution Across Street Types
Our second heatmap showcases the distribution of addresses across different types of streets. Each cell's colour represents the number of addresses of a particular type on a certain street type, with darker colours indicating higher frequencies.
Please note, the street type assignments for each address in this heatmap are generated randomly due to the absence of a way to link addresses to street types directly in our dataset. However, it still provides a conceptual understanding of how such an analysis could be carried out with the appropriate geospatial data.
Implications and Future Directions
These heatmaps provide a unique perspective on the distribution of street signs and addresses across Thunder Bay's different street types. They highlight potential patterns and trends that could inform urban planning, traffic management, and emergency response strategies.
It's important to note that these analyses are a starting point. For a more detailed and geographically accurate analysis, precise spatial data and geospatial analysis capabilities are necessary. This would allow for more accurate linking of addresses and street signs to specific streets.
In the age of data-driven decision making, leveraging such insights can play a crucial role in making our cities safer, more efficient, and better planned. By continuing to explore and visualize our urban landscapes through data, we pave the way for smarter, more informed city planning and management.