Urban Heat Islands: How Geospatial Open Data Can Make Our Cities More Resilient

Cities account for more than two-thirds of Europe's population and consume around 80% of energy. In this context, climate change is having a particularly severe impact on urban environments, not only because of their density, but also because of their construction characteristics, their energy metabolism and the scarcity of vegetation in many consolidated areas. One of the most visible and worrying effects is the phenomenon known as urban heat island (UHI).

Heat islands occur when the temperature in urban areas is significantly higher than in nearby rural or peri-urban areas, especially at night. This thermal differential can easily exceed five degrees Celsius under certain conditions. The consequences of this phenomenon go beyond thermal discomfort: it directly affects health, air quality, energy consumption, urban biodiversity and social equity.

In recent years, the availability of open data—especially geospatial data—has made it possible to characterize, map, and analyze urban heat islands with unprecedented accuracy. This article explores how this data can be used to design urban solutions adapted to climate change, with heat island mitigation as its focus.

What are urban heat islands and why do they occur?

Figure 1. Illustrative element on heat islands.

 

To intervene effectively in heat islands, it is necessary to know where, when and how they occur. Unlike other natural hazards, the heat island effect is not visible to the naked eye, and its intensity varies depending on the time of day, time of year, and specific weather conditions. It therefore requires a solid and dynamic knowledge base, which can only be built through the integration of diverse, up-to-date and territorialized data.

At this point, open geospatial data is a critical tool. Through satellite images, urban maps, meteorological data, cadastral cartography and other publicly accessible sets, it is possible to build urban thermal models, identify critical areas, estimate differential exposures and evaluate the impact of the measures adopted.

The main categories of data that allow us to address the phenomenon of heat islands from a territorial and interdisciplinary perspective are detailed below.

Types of geoespatial data applicable to the study of the phenomenon

1. Earth observation satellite data

Thermal sensors on satellites  such as Landsat 8/9 (NASA/USGS) or Sentinel-3 (Copernicus) make it possible to generate urban surface temperature maps with resolutions ranging from 30 to 1,000 metres. Although these images have spatial and temporal limitations, they are sufficient to detect patterns and trends, especially if combined with time series.

This data, accessible through platforms such as the Copernicus Open Access Hub or the USGS EarthExplorer, is essential for comparative studies between cities or for observing the temporal evolution of the same area.

2. Urban weather data

The network of AEMET stations, together with other automatic stations managed by autonomous communities or city councils, allows the evolution of air temperatures in different urban points to be analysed. In some cases, there are also citizen sensors or networks of sensors distributed in the urban space that allow real-time heat maps to be generated with high resolution.

3. Urban mapping and digital terrain models

Digital surface models (DSM), digital terrain models (DTM) and mappings derived from LIDAR allow the study of urban morphology, building density, street orientation, terrain slope and other factors that affect natural ventilation and heat accumulation. In Spain, this data is accessible through the National Center for Geographic Information (CNIG).

4. Land cover and land use databases

Databases such as Corine Land Cover of the Copernicus Programme, or land use maps at the regional level make it possible to distinguish between urbanised areas, green areas, impermeable surfaces and bodies of water. This information is key to calculating the degree of artificialization of an area and its relationship with the heat balance.

5. Inventories of urban trees and green spaces

Some municipalities publish on their open data portals the detailed inventory of urban trees, parks and gardens. These georeferenced data make it possible to analyse the effect of vegetation on thermal comfort, as well as to plan new plantations or green corridors.

6. Socioeconomic and vulnerability data

Data from the National Institute of Statistics (INE), together with the social information systems of autonomous communities and city councils, make it possible to identify the most vulnerable neighbourhoods from a social and economic point of view. Its cross-referencing with thermal data allows a climate justice dimension to be incorporated into decision-making.

Practical applications: how open data is used to act

Once the relevant data has been gathered and integrated, multiple analysis strategies can be applied to support public policies and urban projects with sustainability and equity criteria. Some of the main applications are described below.

·        Heat zone mapping and vulnerability maps: Using thermal imagery, weather data, and urban layers together, heat island intensity maps can be generated at the neighborhood or block level. If these maps are combined with social, demographic and public health indicators, it is possible to build thermal vulnerability maps, which prioritize intervention in areas where high temperatures and high levels of social risk intersect. These maps allow, for example:

·        Identify priority neighborhoods for urban greening.

·        Plan evacuation routes or shaded areas during heat waves.

·        Determine the optimal location of climate refuges.

·        Assessing the impact of nature-based solutions: Open data also makes it possible to monitor the effects of certain urban actions. For example, using time series of satellite images or temperature sensors, it is possible to assess how the creation of a park or the planting of trees on a street has modified the surface temperature. This ex-post evaluation approach  allows justifying public investments, adjusting designs and scaling effective solutions to other areas with similar conditions.

·        Urban modelling and climate simulations: three-dimensional urban models, built from open LIDAR data or cadastral mapping, make it possible to simulate the thermal behaviour of a neighbourhood or city under different climatic and urban scenarios. These simulations, combined with tools such as ENVI-met or Urban Weather Generator, are essential to support decision-making in urban planning.

Existing studies and analysis on urban heat islands: what has been done and what we can learn

During the last decade, multiple studies have been carried out in Spain and Europe that show how open data, especially geospatial data, allow the phenomenon of urban heat islands to be characterised and analysed. These works are fundamental not only because of their specific results, but also because they illustrate replicable and scalable methodologies. Some of the most relevant are described below.

Polytechnic University of Madrid study on surface temperature in Madrid

A team from the Department of Topographic Engineering and Cartography of the UPM analysed the evolution of surface temperature in the municipality of Madrid using thermal images from the Landsat 8 satellite in the summer period. The study focused on detecting spatial changes in warmer areas and relating them to land use, urban vegetation and building density.

Figure 2. Illustrative image. Source: generated with AI

Methodology:

Remote sensing techniques were applied to extract the surface temperature from the TIRS thermal channel of the Landsat. Subsequently, a statistical analysis of correlation between thermal values and variables such as NDVI (vegetation index), type of land cover (CORINE data) and urban morphology was carried out.

Main results:

Areas with higher building density, such as the central and southern neighborhoods, showed higher surface temperatures. The presence of urban parks reduced the temperature of their immediate surroundings by 3 to 5 °C. It was confirmed that the heat island effect intensifies at night, especially during persistent heat waves.

This type of analysis is especially useful for designing urban greening strategies and for justifying interventions in vulnerable neighbourhoods.

Barcelona Climate Vulnerability Atlas

Barcelona City Council, in collaboration with experts in public health and urban geography, developed a Climate Vulnerability Atlas which includes detailed maps of heat exposure, population sensitivity, and adaptive capacity. The objective was to guide municipal policies against climate change, especially in the field of health and social services.

Figure 3. Image containing fence, exterior, buildings and grass. Source: generated with AI

Methodology:

The atlas was developed by combining open and administrative data at the census tract level. Three dimensions were analysed: exposure (air temperature and surface data), sensitivity (advanced age, density, morbidity) and adaptive capacity (access to green areas, quality of housing, facilities). The indicators were normalized and combined through multi-criteria spatial analysis to generate a climate vulnerability index. The result made it possible to locate the neighbourhoods most at risk from extreme heat and to guide municipal measures.

Main results:

Based on the atlas, the network of "climate shelters" was designed, which includes libraries, civic centers, schools and conditioned parks, activated during episodes of extreme heat. The selection of these spaces was based directly on the atlas data.

Multitemporal analysis of the heat island effect in Seville

Researchers from the University of Seville used satellite data from Sentinel-3 and Landsat 8 to study the evolution of the heat island phenomenon in the city between 2015 and 2022. The aim was to evaluate the effectiveness of certain urban actions – such as the "Green your neighbourhood" plan – and to anticipate the effects of climate change on the city.

Methodology:

Thermal imaging and NDVI data were used to calculate temperature differences between urban areas and surrounding rural areas. Supervised classification techniques were also applied to identify land uses and their evolution. Open data from tree inventories and urban shade maps were used to interpret the results.

Main results:

Specific renaturation actions have a very positive local impact, but their effect on the city as a whole is limited if they are not integrated into a metropolitan-scale strategy. The study concluded that a continuous network of vegetation and bodies of water is more effective than isolated actions.

European comparison of the Urban Heat Island Atlas (Copernicus) project

Although it is not a Spanish study, the viewer developed by Copernicus for the European Urban Atlas programme offers a comparative analysis between European cities.

Methodology:

The viewer integrates Sentinel-3 thermal imagery, land cover data, and urban mapping to assess the severity of the heat island effect.

Figure 4. Illustration: Infographic showing the main factors causing the urban heat island effect (UHI). Urban areas retain heat due to tall buildings, impermeable surfaces and heat-retaining materials, while green areas are cooler Source: Urban heat islands.

Main results:

This type of tool allows smaller cities to have a first approximation of the phenomenon without the need to develop their own models. As it is based on open and free data, the viewer allows direct consultations by technicians and citizens.

Current limitations and challenges

Despite progress in opening up data, there are still significant challenges:

• Territorial inequality: not all cities have the same quality and quantity of data.

• Irregular update: Some sets are released on a one-off basis and are not updated regularly.

• Low granularity: Data is often aggregated by districts or census tracts, making street-scale interventions difficult.

• Lack of technical capacities: Many local governments do not have staff specialized in geospatial analysis.

• Little connection with citizens: the knowledge generated from data does not always translate into visible or understandable actions for the population.

Conclusion: building climate resilience from geoespatial data

Urban heat islands are not a new phenomenon, but in the context of climate change they take on a critical dimension. Cities that do not plan based on data will be increasingly exposed to episodes of extreme heat, with unequal impacts among their populations.

Open data—and in particular geospatial data—offers a strategic opportunity to transform this threat into a lever for change. With them we can identify, anticipate, intervene and evaluate. But for this to happen, it is essential to:

·        Consolidate accessible, up-to-date and quality data infrastructures.

·        To promote collaboration between levels of government, research centres and citizens.

·        Train municipal technicians in the use of geospatial tools.

·        Promote a culture of evidence-based decision-making and climate sensitivity.

Data does not replace politics, but it allows it to be founded, improved and made more equitable. In a global warming scenario, having open geospatial data is a key tool to make our cities more livable and better prepared for the future.

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Content prepared by Mayte Toscano, Senior Consultant in Data Economy Technologies. The contents and points of view reflected in this publication are the sole responsibility of the author.