This dataset contains 2018 Urban Heat Island (UHI) and urban vegetation features represented by polygons. Each polygon is based on 2016 ABS Mesh Blocks. It's part of a collection of data from Plan Melbourne Action 91 initiative also referred to as Cooling & Greening or Vegetation and Urban heat mapping. https://mapshare.vic.gov.au/coolinggreening/

The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC�s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC�s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).

Heat Vulnerability Index (HVI) including heat exposure, sensitivity, and adaptive capability indicators were created for whole Australia.The dataset supports the development of a national heat vulnerability assessment toolkit for Australia, designed to identify areas and populations most susceptible to heat-related risks. The project addresses the growing need for understanding the relationship between urbanization, land surface temperature (LST), and the urban heat island effect, particularly for vulnerable communities. Integrating satellite-derived environmental data (LST, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI)) with socioeconomic data, this toolkit provides a comprehensive resource for building heat-resilient cities and suburbs. This dataset includes both raw environmental data for the 2020-2021 Australian summer (November to March) and a calculated Heat Vulnerability Index (HVI) aggregated to the Australian Bureau of Statistics (ABS) Statistical Area Level 1 (SA1) polygon dataset. The HVI, based on the IPCC's vulnerability conceptual framework, is a composite index comprised of three core components: heat exposure (derived from LST), sensitivity to heat (influenced by socioeconomic factors), and adaptive capability. Each SA1 is assigned a vulnerability rating ranging from 0 to 5, with 0 indicating no population and 5 representing high vulnerability, based on the aggregated indicator scores and quartile distribution. The methodology employs Google Earth Engine (GEE) to derive LST, NDBI, and NDVI. The HVI, along with its components, allows for spatial analysis and facilitates understanding of the complex relationships between heat, environmental factors, and socioeconomic conditions, enabling targeted policy and decision-making at local levels. This work aims to support dynamic and interactive vulnerability assessment, enabling users to update and construct their own indicators and indices for diverse applications. Detailed methodology for HVI generation can be found in this paper. Additional resources are available on the project's GitHub repository, the web application, and the toolkit.

The 2022 Heat Vulnerability Index (HVI) for Greater Sydney aims to combine information on urban heat, built form and population demographics to provide a fine-grained understanding of the spatial distribution of heat vulnerable populations. The Index combines indicators of heat exposure, sensitivity to heat, and adaptive capacity to produce the composite vulnerability index. The 2022 HVI dataset is built upon the methodology established in the creation of the 2016 Sydney HVI dataset (Sun et al 2018), integrating land cover, urban heat, and demographic data, aggregated to Statistical Area Level 1 (SA1) of the Australian Statistical Geography Standard (ASGS) produced by the Australian Bureau of Statistics (ABS). Broad comparisons can be made between the 2022 and 2016 HVI datasets, however there are multiple factors that may limit direct comparability over time. This includes variations in underlying datasets, the relative nature of the HVI, and the change in size of the study area between 2016 and 2022. When undertaking comparison it is recommended to examine the changes in the underlying datasets and the absolute values of the heat exposure, sensitivity and adaptive capacity indicators. This approach helps to explain the variations in HVI and informs effective heat mitigation strategies. The 2022 HVI is most useful at the SA1 scale. It is not recommended to aggregate the HVI dataset to larger scales (i.e. average HVI for a suburb or LGA). Aggregating spatially specific and individual data to geographic areas smooths out local variation, losing locational specificity and population variation. In cases where individual human exposure is of concern, this may either increase or decrease the representation of the actual exposure of a given individual, causing the neighbourhood effect averaging problem (NEAP) (Kwan 2018). Please refer to the methodology report for more information.