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Urban Landscapes: Analyzing Building Dimensions, Elevation & Property UIDs in Thunder Bay

Unraveling the Complexity of Urban Landscapes: A Comprehensive Data-Driven Exploration of Building Dimensions, Elevation Patterns, and Unique Property Identifiers in the Architectural Tapestry of Thunder Bay


Data is a powerful tool that allows us to delve into diverse aspects of our world, offering new perspectives and insights. Today, we focus on the architectural landscape of Thunder Bay, examining building dimensions, elevation, and Property RSN (a unique identifier for each building property). We will utilize Python, pandas, and seaborn to manipulate and visualize our data using Thunder Bay's Open Data Portal.


Distribution of Property RSN (Building Property UID)

Property RSN, or the unique identifier for each building property, provides a means of distinguishing individual properties. A histogram of Property RSN reveals an approximately uniform distribution, with a slight skew towards higher values. This distribution indicates a wide range of unique identifiers for the building properties in Thunder Bay.


Relationship Between Elevation and Shape Area

The relationship between a building's elevation and its shape area can yield fascinating insights. From our scatter plot, there does not appear to be a clear correlation between the two. However, it does illustrate the range of building elevations and areas in Thunder Bay. Notably, the y-axis is on a logarithmic scale due to the wide range of shape area values, highlighting the diversity in building sizes.


Distribution of Building Elevations

The elevation of buildings offers insight into the geography of the region. In Thunder Bay, most buildings are situated at elevations between 190 and 200, suggesting a relatively flat terrain.


Relationship Between Building Shape Length and Area

The correlation between a building's shape length (approximating the perimeter) and its shape area is another compelling aspect of architectural data. Our analysis reveals a positive correlation, indicating that larger buildings generally have larger perimeters.


Distribution of Building Shape Lengths

The shape length of buildings, which approximates the perimeter, provides insights into building sizes and design characteristics. Upon visualizing the distribution of building shape lengths, we find that it is positively skewed, with a majority of buildings having relatively small shape lengths. This skewness indicates that smaller buildings are more prevalent in Thunder Bay. Interestingly, the distribution spans several orders of magnitude, suggesting a wide range of building sizes, as demonstrated by the logarithmic scale of the x-axis.


Conclusion


Through this exploration, we have gained valuable insights into the architectural and geographical characteristics of Thunder Bay. Our data-driven investigation revealed the distribution of unique building property identifiers, the relationship between building dimensions, and the distribution of building elevations. By shedding light on these aspects, we enhance our understanding of the city's urban landscape and the variety of its architectural features. As we continue to harness the power of data, we unlock new possibilities to understand and appreciate the complexity of our built environment.

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