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Urban heat island - FUAs

<p align="justify">This dataset provides an indicator of the urban heat island intensity in FUAs. Cities often record higher temperatures than their surrounding areas due to the urban heat island effect. This phenomenon results from high building density, heat generated by human activities, building materials and limited vegetation.</p>

<h3>Data sources and methodology</h3> <p align="justify">The indicator is defined as the difference between the mean temperature for urban lands and the mean temperature for non-urban lands. Following the International Geoshpere-Biosphere Programme (IGBP) classification, urban lands correspond to the "urban and built-up lands" class from the <a href="https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD12Q1">MODIS yearly land cover data</a>, and non-urban lands correspond to the remaining classes, except "water bodies". Land surface temperature is derived from <a href="https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1">MODIS Terra</a> and <a href="https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1">Aqua</a> land surface temperature daily datasets. The mean temperature is computed for both urban and non-urban lands for the whole year and for summer only (1 June to 31 August for the Northern Hemisphere and 1 December to 28 February for the Southern hemisphere). More details can be found in <a href="https://www.oecd.org/en/publications/oecd-regions-and-cities-at-a-glance-2022_14108660-en/full-report/component-36.html#section-d1e18979">Regions and Cities at a Glance 2022</a>. MODIS is used because it offers globally consistent, high frequency land surface temperature observations for both daytime and nighttime at medium-high spatial resolution (1 km), enabling the production of harmonised and comparable subnational indicators across countries.</p>

<p align="justify"> These estimates may differ from official subnational climate statistics due to differences in methodological approaches, such as the use of top-down satellite measurement versus in situ observations, along with variations in input data sources, spatial resolution, and the models and algorithms used to generate land surface temperature estimates </p>

<h3>Defining FUAs and cities</h3> <p align="justify">The OECD, in cooperation with the EU, has developed a harmonised <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">definition of functional urban areas</a> (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns <a href=https://doi.org/10.1787/9789264174108-en>(OECD, 2012)</a>. FUAs consist of: <ol> <li><b>A city</b> – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city.</li> <li><b>A commuting zone</b> – including all local areas where at least 15% of employed residents work in the city.</li> </ol> The delineation process includes: <ul> <li>Assigning municipalities surrounded by a single FUA to that FUA.</li> <li>Excluding non-contiguous municipalities.</li> </ul> The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries.</p> <h3>Cite this dataset</h3> <p>OECD Regions, cities and local areas database (<a href="http://data-explorer.oecd.org/s/1dh">Urban heat island - FUAs</a>), <a href=http://oe.cd/geostats>http://oe.cd/geostats</a></p>

<h3>Further information</h3> <ul> <li> <a href=https://localdataportal.oecd.org/>OECD Local Data Portal </a> </li> <li> <a href=https://www.oecd.org/en/publications/oecd-regions-and-cities-at-a-glance-2024_f42db3bf-en.html/>OECD Regions and Cities at a Glance </a> </li> </ul> <p align="justify">For questions and/or comments, please email <a href="mailto:CitiesStat@oecd.org">CitiesStat@oecd.org</a>