Melbourne is usually described as a liveable city, but heat does not affect every suburb in the same way. Some places carry a heavier burden because heat exposure, limited shade and social vulnerability overlap.
This article uses the Victorian Government’s Cooling and Greening Melbourne datasets from 2018 to explore that pattern. The focus is not simply which places are hot, but which places appear less protected when heat, vegetation cover and care/support need are viewed together.
Source: Victorian Government Cooling and Greening Melbourne data, 2018. Note: map areas are coloured by heat vulnerability index and include hover information for local population density and care/support need.
The map shows that heat vulnerability is not randomly scattered across Melbourne. Higher vulnerability appears in clusters, especially in parts of the north, west and south-east. Hovering over the map shows the local heat vulnerability index, population density and care/support need for each area.
Source: Victorian Government Cooling and Greening Melbourne data, 2018. Note: values show average heat vulnerability index by SA2 area.
The ranking makes the map more direct by naming the places with the highest average heat vulnerability scores. St Albans, Kings Park, Meadow Heights, Lalor, Broadmeadows and parts of Melton appear strongly in the upper end of the distribution.
This matters because heat risk is not only a physical climate issue. In a city, it is also shaped by where people live, the built environment around them and how much local cooling protection is available.
Source: Victorian Government Cooling and Greening Melbourne data, 2018. Note: vegetation values are area-weighted from mesh block records and joined to SA2 heat vulnerability averages.
A clearer pattern appears when heat vulnerability is compared with vegetation cover. The red points show areas where higher heat vulnerability overlaps with lower vegetation cover. These are the places where the “shade gap” becomes visible in the data.
Not every low-shade area has high vulnerability, and not every vulnerable area has the same vegetation profile. But the cluster of red points shows that some suburbs face both pressures at once.
Source: Victorian Government Cooling and Greening Melbourne data, 2018. Note: percentage and index measures are shown together for comparison.
Compared with other areas, shade-gap suburbs have higher heat vulnerability and higher urban heat values. At the same time, they have noticeably lower vegetation cover and tree cover.
This comparison is important because it turns the story from a simple heat map into an equity question. The problem is not just where Melbourne gets hotter. It is where heat combines with fewer natural cooling protections.
Source: Victorian Government Cooling and Greening Melbourne data, 2018. Note: score combines heat vulnerability, urban heat, care/support need, low vegetation and low tree cover.
The final chart combines five signals into a simple cooling priority score: heat vulnerability, urban heat, care/support need, low vegetation and low tree cover. This score is not a final policy ranking, but it helps show where multiple pressures overlap most strongly.
Areas such as Delahey, St Albans, Kings Park, Campbellfield–Coolaroo, Lalor and Melton stand out because they are not high on just one measure. They combine several forms of heat risk at the same time.
This is the main message of the shade gap: greening policy should not only ask where trees can be planted, but where shade would most protect people facing the strongest combined heat burden.
The generative AI tool used in this assessment was ChatGPT by OpenAI. I used it mainly as a learning and study-support tool to understand some complex parts of the assignment, such as working with shapefiles, using Leaflet and Plotly, creating interactive charts, summarising spatial data, and improving the overall Five Charts workflow. I then applied these ideas myself in RStudio and checked the final code, charts, interactions and interpretations against my own RStudio outputs, the knitted HTML file, the official Victorian Government datasets, and the assignment requirements before including them in the final submission.
Victorian Government. (2018). Metropolitan Melbourne Heat
Vulnerability Index 2018 [Data set]. Data Vic.
https://discover.data.vic.gov.au/dataset/metropolitan-melbourne-heat-vulnerability-index-2018
Victorian Government. (2018). Metropolitan Melbourne Urban Heat
Islands and Urban Vegetation 2018 [Data set]. Data Vic.
https://discover.data.vic.gov.au/dataset/metropolitan-melbourne-urban-heat-islands-and-urban-vegetation-2018
OpenAI. (2026). ChatGPT (GPT-5.5 Thinking) [Large language
model]. OpenAI.
https://chatgpt.com/