On a hot day, tree-lined streets can provide shade that exposed roads and built-up areas lack. But how evenly is tree cover distributed across Melbourne?
The Shade Divide explores vegetation cover across different land-use classifications using Victoria’s 2018 vegetation dataset. Five interactive visualisations examine tree cover, total vegetation cover and differences between individual spatial records.
The analysis describes vegetation patterns that may help guide further urban greening research. It does not directly measure temperatures, pedestrian shade or social vulnerability.
Key insight: Tree cover varies substantially between land-use categories. Other, Infrastructure and Parkland have relatively high calculated tree cover, while Commercial, Transport and Industrial have considerably lower values.
Key insight: Total vegetation cover differs across land uses, with Other and Education recording relatively high values. These results show the broader distribution of vegetation, which should be distinguished from tree canopy when considering potential shade.
Key insight: Comparing both measures reveals that total vegetation and tree cover are not interchangeable. Their differences help show why overall vegetation alone may not adequately describe the extent of tree canopy in each land-use category.
Key insight: The distribution is positively skewed, with many spatial records concentrated at lower tree cover percentages and fewer records at higher levels. This variation indicates that an overall average may conceal differences between individual locations.
Key insight: The boxplots reveal variation in tree cover within each land-use category. Wide distributions and high-value outliers indicate that individual records can differ substantially, even when they belong to the same land-use classification.
The five visualizations indicate the difference in vegetation cover among different types of land use as well as individual spatial records. Tree cover versus total vegetation gives a better understanding of the amount of greenery, and the histogram and boxplots give an indication of differences that are hidden by the percentage values only.
These differences can provide a way to focus on places of interest in planning urban greening. However, the dataset from 2018 does not include information regarding urban temperatures or social vulnerability; thus, more information is needed about the heat and demography to determine which neighborhoods are at risk.