Generalization of GIS data as a tool supporting the understanding of the urban heat island effect. Case study of the city of Wrocław

Konrad Lewacki, Marta Smektała, Magdalena Baborska-Narożny

doi:10.37190/arc250212

Abstract

The article addresses the impact of individual urban factors on the urban heat island (UHI) effect and the correlation between these factors. The authors attempted to verify the possibility of using Geographic Information Systems (GIS) data to study the UHI effect and in further research to verify the nocturnal cooling potential in the city using Wrocław as an example. The study utilized Landsat 8 satellite images and spatial data concerning greenery and buildings to analyze the impact of these latter factors on radiant temperature in the city. The proposed method enables data simplification and calibration, allowing for comparison of different city areas with similar characteristics.

The conducted research demonstrated how individual factors affect ambient temperature. Building density and height significantly increase radiant temperature, while the presence of water and vegetation, especially tall vegetation, has a cooling effect. To assess differences in temperature distribution in urbanized space, four locations in Wrocław were compared, which differ in building intensity and greening level: Oławskie Suburb, Świdnickie Suburb, Grunwaldzki Square, and Olimpia Port. Results indicate that Oławskie Suburb, despite the presence of a river, has the highest radiant temperature due to dense buildings and little greenery. In contrast, Olimpia Port, with the lowest building density and the most greenery, is the coolest area.

The authors’ goal was to identify areas representative of urban fabric where further research stages could be conducted – local air temperature measurements. In subsequent project phases, additional factors such as airflow, impermeable surface, and building materials are planned to be included to more thoroughly examine the impact of urbanism on ambient temperature.

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Konrad Lewacki*, Marta Smektała**,

Magdalena Baborska-Narożny***

Generalization of GIS data as a tool supporting

the understanding of the urban heat island effect.

Case study of the city of Wrocław based on design experience

DOI: 10.37190/arc250212

Published in open access. CC BY NC ND license

Abstract

The article presents how various urban factors inuence the urban heat island (UHI) eect and explores the correlations between these factors.

The authors attempts to verify the potential of using data in Geographic Information System (GIS) analyses to study the urban heat island eect, and

in further research to analyze the assess to nighttime cooling potential in the city, using Wrocław as a case study. The study utilized Landsat 8 satellite

imagery and spatial data on greenery and built-up areas to analyze their impact on radiative temperature. The proposed method simplies and calibrates

data, enabling comparison of dierent areas. It also allows for the analysis of relationships between the values of various factors within a single grid

cell or study area. This approach helps identify areas across the entire city with similar factor combinations and compare their temperature distributions.

GIS analyses enable examination of neighboring areas and identication of other factors inuencing variations in radiative temperature.

The research presents how individual factors aect ambient temperature. Building density and height signicantly increase radiative temperature,

whereas the presence of water and greenery, especially in large amounts, has a cooling eect. To assess temperature distribution dierences in urban

spaces, four locations in Wrocław were compared based on building intensity and greenery levels: Przedmieście Oławskie, Przedmieście Świdnickie,

Plac Grunwaldzki, and Olimpia Port. Results indicate that despite the presence of a river, Przedmieście Oławskie has the highest radiative temperature

due to dense building and low greenery. In contrast, Olimpia Port, with the lowest building density and highest greenery, is the coolest area.

The aim of the research is to indicate the representative areas in urban tissue, where further research measuring the local air temperature can be

implemented. Future stages of the project plan to include additional factors such as air ow, impermeable surface area, and building materials to further

investigate the impact of urban planning on ambient temperature.

Key words: GIS analysis, urban heat island, Landsat analysis, radiation temperature, natural cooling potential

Introduction

The limited extent of urban green spaces and the high

degree of urbanization signicantly contribute to the inten-

sication of the urban heat island (UHI) eect – a climatic

phenomenon characterized by elevated temperatures with-

in urban areas compared to their rural surroundings (EPA

2025). The magnitude of the UHI eect is further exacerbat-

ed by the expansion of urbanized zones, commonly referred

to as urban sprawl (Gawuć at al. 2020; Błażejczyk, Błażej-

czyk 2023). The UHI phenomenon is typical ly analyzed

through three distinct dimensions: subsurface, surface, and

atmospheric, each assessed using dierent me thodological

approaches (Huang et al. 2020). Current re search explores

the variability of temperature within these dimensions, as

well as the interrelations among them, across diverse climat-

ic and urban congurations (Wonorahardjo et al. 2022; Dei-

lami, Kamruzzaman and Liu 2018). The present study con-

tributes to this research agenda by examining the potential of

* ORCID:

0009-0007-0886-2755. Faculty of Architecture, Wro-

cław University of Science and Technology, Poland.

** ORCID:

0000-0001-8533-8550. Faculty of Architecture, Wro-

cław University of Science and Technology, Poland, e-mail: marta.smek-

tala@pwr.edu.pl.

*** ORCID:

0000-0001-6860-5186. Faculty of Architecture, Wro-

cław University of Science and Technology, Poland.

124 Konrad Lewacki, Marta Smektała, Magdalena Baborska-Narożny

spatial surface temperature variation as a proxy for analyz-

ing air temperature dierentials in the vicinity of buildings.

Studies conducted in Poland’s regional capital cities be-

tween 1971 and 2020 have indicated a continuous increase

in the number of tropical nights – dened as nights during

which air temperature does not drop below 20°C. This

phenomenon is particularly pronounced in cities located

in western Poland (Mąkosza et al. 2024). It is important

to note, however, that meteorological data are typically

collected from stations intentionally situated away from

urbanized areas. Consequently, air temperatures in city

centers – where building materials accumulate heat more

intensely – may dier signicantly from those recorded at

monitoring stations located in suburbian areas (Eliasson,

Svensson 2003; Baborska‐Narożny, Kostka and Smektała

2024). As a result, the actual number of tropical nights

within densely built-up urban environments may be sub-

stantially higher. Tropical nights are known to impair sleep

quality and negatively impact cardiovascular and respi-

ratory function (Buguet et al. 2023). Daytime heat, com-

pounded by the reduced potential for nighttime cooling in

built-up areas, intensies thermal stress, adversely aect-

ing human health and contributing to increased mortality

rates (Peng et al. 2022; Błażejczyk et al. 2022; Runhaar et

al. 2012). Heatwaves may also exacerbate the detrimental

eects of air pollution and increase plants’ allergen pro-

duction (Pałczyński et al. 2018). It is estimated that during

the summer of 2022 alone, excessive heat caused 61,672

deaths across Europe (WHO 2024). Globally, the number

of people exposed to extreme temperatures is increasing ex-

ponentially as a result of climate change. Heat- related mor-

tality among individuals aged 65 and older rose by appro xi-

mately 85% between 2017–2021 compared to 2000–2004

(WHO 2024). The adverse impacts of thermal stress are

especially severe for the elderly, persons with disabilities,

and socially marginalized groups. Residents from econom-

ically disadvantaged neighborhoods often live farther from

green spaces and typically inhabit poorly insulated homes

lacking air conditioning (Jessel, Sawyer and Hernández

2019; Lundgren-Kownacki et al. 2018). While the use of

air conditioning can mitigate heat stress, it simultaneously

increases energy consumption – particularly from non-re-

newable sources – thereby contributing to further climate

warming. Moreover, it can elevate local outdoor tempera-

tures, especially in densely built urban canyon environ-

ments. Therefore, it is essential to explore alternative ur-

ban and architectural adaptation strategies. These include

deliberate design of urban geometry and building density,

integration of green infrastructure and water bodies, as well

as building-level measures such as thermal insulation and

solar gain control. Given the complexity of interventions

required to reshape urban systems, the implementation of

such changes may span several decades – amid ongoing

climate warming (Viguié et al. 2020).

Wrocław, along with Warsaw, Rzeszów, Łódź, and Kra-

ków, is one of the 100 European cities participating in the

so-called Mission for Climate-Neutral and Smart Cities

(Serwis Rzeczypospolitej Polskiej 2022). The authorities

of these cities have committed to developing agreements

that include a comprehensive plan for climate neutrality

across all sectors, such as energy, construction, waste man-

agement, and transport, along with associated investment

plans. The study presented in this article is part of the

project “Summer in the City (Without Air Conditioning)

– A Map of Night Cooling Potential and the Wrocław Ur-

ban Heat Island.” The goal of the project is to t the scope

of challenges set by cities aiming climate neutrality by ex-

amining whether appropriate urban planning, architectur-

al, and ventilation strategies can reduce the reliance on air

conditioning in Wrocław and take advantage of building

thermal mass night cooling potential to maintain indoor

thermal comfort (de Toldi, Craig, and Sushama 2022).

Results from previous studies indicate that despite

a warming climate, buildings located in Central and North-

ern Europe will be able to benet from night cooling poten-

tial at least for the coming decades (Artmann et al. 2008).

However, a publication concerning the UHI in Warsaw in-

dicated that the number of hot days requiring the use of

air conditioning may increase by more than 120% by the

end of the 21

century – from 32–34 to 76 days annually

(Błażejczyk et al. 2014). At present, merely 10% of house-

holds in Europe are equipped with air conditioning, where-

as in the United States this gure reaches as high as 90%

(IEA 2023). To prevent increasingly frequent and intense

heatwaves from causing a surge in air conditioning use

in Central and Northern European cities, it is essential to

explore the potential for reducing residential overheating

through passive solutions associated with appropriate ar-

chitectural and urban design. The eectiveness of passive

solutions, such as night cooling via natural ventilation, de-

pends, among other factors, on the outdoor air temperature.

Therefore it is closely linked to the local intensity of the

urban heat island (UHI). For this reason, understanding

the mechanisms responsible for urban overheating, which

inuence the UHI eect, is crucial for the eective imple-

mentation of passive cooling strategies.

In the article Klimat Wrocławia [The Climate of Wro -

cław], an instance of a particularly intense surface urban

heat island (SUHI) eect in Wrocław was described, oc

curring during the night of May 22–23, 2001. At that time,

under cloudless skies and low wind conditions, satellite

imagery capturing surface radiative temperature revealed

a dierence of over 8°C between the city center and its

outskirts (Dubicki, Dubicka and Szymanowski 2002).

This observation was made more than two decades ago,

and since then the city has undergone signicant develop

ment, likely expanding the area aected by elevated tem-

peratures. In 2023, the Institute of Meteorology and Water

Management (IMGW) named Wrocław the hottest city

in Poland, with the highest annual mean temperature of

11.3°C (IMGW 2024). These and other factors motivated

the authors to investigate the topic addressed in this study.

The aim is to explore the following research questions:

1. How can the relationship between the distribution of

surface radiative temperature and urban indicators describ-

ing built-up areas and greenery be visualized within a given

urban unit?

How can the proposed GIS-based analytical method be

applied to examine the inuence of these factors on the ur ban

heat island eect and the potential for nighttime cooling?

Generalization of GIS data as a tool supporting the understanding of the urban heat island eect. Case study of the city of Wrocław 125

Urban heat island and GIS analyses

– state of research

Despite increasing interest and growing knowledge about

the UHI phenomenon, accurately determining its spa tial in -

tensity remains a challenge due to insucient data or the

limited spatial distribution of available measurements (Szy -

manowski, Kryza 2009). A review of the literature indi-

cates that the topic is well understood from a theoretical

standpoint, across various spatial scales: macro (climate),

local (urban planning and land use), and micro (building-

level interventions and improving residents’ quality of life)

(Kleere koper, Van Esch and Salcedo 2012; Wonorahardjo

et al. 2022; Błażejczyk et al. 2014; Deilami, Kamruzzaman

and Liu 2018).

The literature distinguishes three types of UHI based on

the location of temperature measurement:

1. Canopy Urban Heat Island (CUHI) – refers to air

temperature measured at approximately 2 m above ground

level, typically within the tree canopy layer.

2. Surface Urban Heat Island (SUHI) – refers to sur-

face-level UHI, remotely sensed as radiative land surface

temperature (LST) (Li et al. 2023).

3. Subsurface UHI – refers to underground heat is-

lands, typically requiring temperature measurements from

boreholes or monitoring wells.

These three types of UHI dier in terms of their forma-

tion mechanisms and can be linked to energy exchange pro-

cesses between the surface, atmosphere, and soil (Huang

et al. 2020). Although air temperature (AT), which is typ-

ically measured in studies of Canopy Urban Heat Island

(CUHI), is more relevant from the perspective of thermal

comfort, the spatial resolution of such data is often limited

due to the number and distribution of ground-based sen-

sors. Surface temperature, or Land Surface Temperature

(LST), is frequently used in UHI research because it can

be derived from satellite imagery, oering broader spatial

coverage and representing surface radiative temperature

(Peng et al. 2022).

Radiative temperature (TR) diers from air temperature

(AT) in that it is calculated based on the amount of emit-

ted thermal (infrared) radiation. TR tends to align more

closely with AT during nighttime, when materials are not

reecting solar radiation. During the day, however, due to

incoming solar radiation, signicant dierences between

AT and RT can be observed – particularly in densely ur-

banized areas (Ferrini et al. 2020).

Urban areas exhibit substantial microclimatic variabil-

ity. Therefore, understanding the climatic characteristics

of a given location also requires attention to the temporal

framework of temperature measurement – whether instan-

taneous, hourly, or daily averages are used. The more tem-

perature values and spatial scales are averaged, the more

local variations in air temperature tend to be obscured

(Peng et al. 2022).

Within the European Union, strategic recommendations

have been established to counteract the development of

UHIs, including indicators and monitoring methodolo-

gies that help account for the impact of UHIs on cities and

their inhabitants. Pilot projects are also being conducted

in this area (Iodice et al. 2024). Researchers are attempt-

ing to measure the eectiveness of mitigation strategies,

such as urban greening and the implementation of green

roofs (Błażejczyk, Błażejczyk 2023). However, challeng-

es arise when attempting to generalize the results of pilot

studies to other urban contexts. Due to varying conditions

and the specic characteristics of each location, the UHI

eect must be studied individually in each city (Iodice et

al. 2024).

The literature identies several key factors contributing

to the UHI eect:

1. Absorption of shortwave solar radiation due to the

low albedo of urban materials, which reduces reectivity.

This eect is amplied by multiple reections between

building façades and street surfaces.

Air pollution in the urban atmosphere, which absorbs

and re-emits longwave radiation back into the urban envi-

ronment.

Urban street canyons (building geometry): the height-

to-width

ratio of buildings and streets inuences sky view

factor – reduced sky exposure lowers heat loss via long-

wave radiation. Reected heat is reabsorbed or re-radiated

back into the urban fabric.

4. Anthropogenic heat release, resulting from combus-

tion processes such as vehicular trac, space heating, and

industrial activity.

5. Increased heat storage by building materials with

high heat capacity. Additionally, urban areas have a greater

surface area than rural areas, allowing them to store more

heat.

6. Reduced evapotranspiration in cities due to the prev-

alence of impervious surfaces and lower vegetation cover

compared to rural areas. This leads to higher surface and

air temperatures and less atmospheric moisture.

7. Lower wind speeds in urban areas, which reduce the

transport of heat out of street canyons (Kleerekoper, Van

Esch and Salcedo 2012).

The collection of data on the above-mentioned factors

inuencing UHI and their development into urban indica-

tors may serve as a foundation for formulating planning

guidelines within a GIS system. GIS (Geographic Infor-

mation System) is a tool used for collecting, managing,

and analyzing spatial data, including data related to UHI.

It integrates various types of data, enabling the analysis of

spatial locations and the organization of information layers

that can be visualized through maps and three-dimensional

scenes. Point data (measurements), when processed using

dierent algorithms, are enriched with additional informa-

tion. This form of visualization facilitates data interpreta-

tion and supports planning decision-making (Ramasubra-

manian, Albrecht and De Leon Rojas 2024). In practice,

however, not all of the factors listed above may be subject

to GIS-based analysis due to the lack of local data or the

absence of clear parameterization methods based on exist-

ing datasets.

One of the commonly used tools for monitoring the UHI

eect is satellite imagery. These images allow for visualiz-

ing the phenomenon on a large spatial scale; however, they

also have certain limitations. Temperature is represented

in the form of a grid with cell sizes ranging from 30 m to

126 Konrad Lewacki, Marta Smektała, Magdalena Baborska-Narożny

1 km, depending on the satellite data source. For instance,

Landsat imagery provides a resolution of 30 m (Deilami,

Kamruzzaman and Liu 2018). The measurements capture

surface radiative temperature, which can be disrupted or

rendered unobservable due to atmospheric conditions such

as cloud cover. Depending on the satellite and its orbital

parameters, data is typically collected once every 1 to 16

days at a xed time of day (Deilami, Kamruzzaman and

Liu 2018). In the case of Wrocław, measurements are ac-

quired approximately every 7 days around 9:30 AM. As

a result, publicly available datasets rarely oer imagery

during nighttime hours, when the UHI eect is most pro-

nounced due to the slower cooling of urban areas and fast-

er cooling of rural surroundings. During daytime, the tem-

perature contrast between urban and non-urban surfaces is

signicantly lower (EPA 2025).

In the reviewed scientic studies on phenomena relat-

ed to UHI, satellite data were combined with urban factors

using various methods and for dierent purposes, such as

identifying relationships, determining the scale of impact,

or developing development scenarios that account for UHI.

Surface temperature data were typically presented in the

form of grid-based datasets. Land use data were also visua-

lized as grids, within which parameters were aggregated,

allowing for direct comparison with satellite-derived tem-

perature data. To examine the relationship between tempe -

rature and land use, grids of various resolutions were

em ployed

– for example, 2-meter grids were used in simu-

lations with the InVEST 3.8.7 Urban Cooling Model (Za-

wadz ka, Harris and Corstanje 2021). To investigate the in-

uence of specic urban factors on temperature (e.g., how

an increase in a given urban parameter aects the thermal

characteristics of an area, or how green areas contribute to

urban cooling), grid sizes of 30 m, 60 m, 90 m, 120 m,

150 m, 180 m, 210 m, and 240 m were applied (Dai, Guld-

mann and Hu 2018). In studies focusing on metropolitan

areas, a coarser 500 m × 500 m grid was used (Zardo et

al. 2017). The analyses revealed signicant correlations be-

tween urban factors and surface temperature, and proposed

counteracting scenarios for the UHI eect based on the

intensity of those factors. These scenarios addressed both

urban-scale actions on the periphery and building-scale in-

terventions in central areas where UHI eects are already

present (Wang et al. 2024). In the case of Wrocław, an UHI

Report was developed (IETU 2021), which identied zones

exposed to the occurrence of surface-level UHI. However,

the report did not examine the relationship between adja-

cent land use patterns and temperature distribution, nor did

it analyze the detailed urban factors discussed below.

Methods

Selection of areas for detailed analysis

The UHI Report for Wrocław (IETU 2021) was used to

identify contrasting locations – relatively cool/optimal and

hot zones – exhibiting varying degrees of exposure to the

UHI eect. Four locations were selected for analysis: two

situated in highly urbanized surroundings, characterized

by dense development and large impervious surface areas

(Przedmieście Oławskie, Przedmieście Świdnickie), and

two located in greener settings, in close proximity to the

river and with lower building density (Olimpia Port, Plac

Grunwaldzki) (see Fig. 1, Table 1). For the GIS analyses,

each selected location was delineated as a territory approx-

imately 1.2 km × 1.2 km in size.

Fig. 1. Location of residential areas analyzed using GIS (green frame), location of air temperature sensors (red frame),

and photographs of sample buildings where sensors were installed: 1 – Olimpia Port, 2 – Plac Grunwaldzki, 3 – Przedmieście Oławskie,

4 – Przedmieście Świdnickie (elaborated by M. Smektała)

Il. 1. Lokalizacja osiedli badanych z wykorzystaniem analizy GIS (zielona ramka), lokalizacja czujników temperatury powietrza (czerwona ramka)

oraz fotografie przykładowych budynków, na których zostały zamontowane czujniki: 1 – Olimpia Port, 2 – Plac Grunwaldzki,

3 – Przedmieście Oławskie, 4 – Przedmieście Świdnickie (oprac. M. Smektała)

Generalization of GIS data as a tool supporting the understanding of the urban heat island eect. Case study of the city of Wrocław 127

Range of radiative

temperatures [°C]

1 pixel = 30m × 30m

Type/name

of the study area

LST image with overlaid building footprints,

streets, low vegetation, trees, and water surfaces

(1.2 km × 1.2 km)

LST map with building outlines

(1.2 km × 1.2 km)

Cool study area

Osiedle Olimpia Port

Plac Grunwaldzki

Hot study area

Przedmieście Świdnickie

Przedmieście Oławskie

Table 1. Overview of selected study areas. Distribution of radiative temperature in the selected areas on July 8, 2023, at 9:44 AM

(source: USGS, https://earthexplorer.usgs.gov)

Tabela 1. Zestawienie wybranych obszarów do badań. Rozkład temperatury radiacyjnej na wybranych obszarach z 8 lipca 2023, godz. 9:44

(źródło: USGS, https://earthexplorer.usgs.gov)

128 Konrad Lewacki, Marta Smektała, Magdalena Baborska-Narożny

Data used in the analyses – radiative temperature

For each of the selected locations, maps depicting radi-

ative temperature were obtained from Landsat 8 satellite

imagery downloaded from the United States Geological

Survey (USGS 2024). These maps represent Land Surface

Temperature (LST), which primarily depends on material

albedo, vegetation cover, and soil moisture. From a climat

perspective, LST is a key variable for evaluating land–at-

mosphere exchange processes, constraining surface energy

budgets and model parameters, and providing observations

of surface temperature changes both globally and in criti-

cal regions (Global Climate Observing System 2025).

The data used refer to July 8, 2023, at 9:44 AM. Accord-

ing to measurements from the Wrocław-Starachowice sta-

tion (IMGW 2023), this was one of the hottest days of the

year, with a maximum air temperature of 30.40°C, an av-

erage temperature of 22.30°C, and a minimum temperature

recorded that day of approximately 11.20°C. At the time

the satellite images were taken, cloud cover was minimal,

ensuring complete data coverage of radiative temperature

for the entire study area.

The average radiative temperature from satellite imag-

ery is presented in a 30 × 30 m grid, with a color spectrum

indicating temperature intensity. For each study area, based

on zonal statistics, the following temperature metrics are

calculated: mean, median, minimum, and maximum.

Data used in the analyses – urban factors database

To identify the relationships inuencing the UHI eect,

radiative temperature from Landsat 8 imagery was com-

pared with a custom-developed database of urban factors

potentially aecting air temperature in the city. Based on

data provided by the Municipality of Wrocław Oce, in-

formation was collected to derive urban indicators includ-

ing: built-up area, building volume, and average building

height, as well as green area, green volume, and average

vegetation height. These indicators are commonly used by

researchers to calculate the so-called “roughness” coe-

cient. Roughness is linked to the assessment of urban venti-

lation potential and, consequently, the area’s natural capaci-

ty for cooling or heating (Wang et al. 2024).

To allow comparison of Landsat data with selected

urban indicators and to enable comparisons across study

areas on a city-wide scale, the indicators derived from

building and vegetation datasets were represented on a 30

× 30 m grid (Zawadzka, Harris and Corstanje 2021; Dai,

Guldmann and Hu 2018), calibrated to match the Landsat

8 grid with high spatial accuracy. Table 2 presents the types

of indicators, the method of their calculation, and the for-

mat of the resulting data.

The greenery dataset consists of raster data with a res-

olution of 3 × 3 m, derived from LiDAR data for the city

of Wrocław. These data represent the actual surface area

and height of vegetation across the entire urban area. The

dataset was processed using a zonal statistics algorithm,

which calculated the number of raster pixels represent-

ing vegetated surfaces of varying heights within each

grid cell. Based on this information, the vegetation cov-

er area, volume, and average height were computed for

each cell of the grid. The total green area was determined

by projecting vegetated objects onto the ground surface.

This approach accounts for both low vegetation such as

grass and the canopy projection of trees onto sidewalks.

The building dataset is a vector dataset containing various

attributes of buildings, including footprint area, height,

function, and year of construction. To facilitate further anal-

ysis, these data were transformed into a 30 × 30 m grid.

For each building segment within a grid cell, the footprint

area was calculated. Polygon data were converted into cen-

troids, which retained all associated building attributes (e.g.,

height, function). These centroid data were then aggregated

within each grid cell. As a result, three key indicators were

derived for each 30 × 30 m grid cell: total building footprint

area (sum of built-up area in [m

]), total building volume

(sum of built-up volume in [m

]), and average building

height (mean building height in [m]).

Results

The results of the conducted analyses can be catego-

rized into two types: general results comparing the study

areas with one another, and detailed results concerning the

impact of individual factors on LST. The general results

include indicators related to buildings, greenery, and radi-

ative temperature (Table 3). These indicators were used to

construct a prole for each area, based on various parame-

ters that allow for comparative analysis.

The highest average radiative temperature was recorded

in the Przedmieście Oławskie area, reaching 42.7°C. This

area also has the lowest green surface coverage. A slight-

ly lower average temperature of 41.2°C was observed in

Przedmieście Świdnickie, which features the highest build-

ing footprint and volume indicators. Compared to Przed-

mieście Oławskie, Przedmieście Świdnickie has a greater

extent of green areas. The lowest temperature, 29.2°C, was

recorded in the Plac Grunwaldzki neighborhood, specically

in the grid cells overlapping with the Odra River. The low-

est average temperature, 37.8°C, was noted in the Olimpia

Port neighborhood, where over 50% of the area is covered

by vege tation. However, the highest temperature overall

– 52.7°C

– was also observed in Olimpia Port, but in its

industrial zone, outside the residential area. As seen in pre-

vious examples, areas with high building indicators show

a noticeable cooling eect in the presence of water bodies

and vegetation.

Within the studied areas, the strongest correlation with

average temperature was observed for parameters related

to the surface area and volume of greenery, which at this

scale appears to play a signicant role in lowering the mean

temperature. A high correlation was also found between

indicators related to building footprint and volume and

temperature. There is a clear inverse correlation between

greenery-related indicators and both temperature and built-

up area.

To better understand these correlations and the character-

istics of specic city sections, it is necessary to examine the

spatial distribution of temperature on maps that provide the

contextual background of the analyzed factors (Tables 4, 5).

Generalization of GIS data as a tool supporting the understanding of the urban heat island eect. Case study of the city of Wrocław 129

No. Urban indicator Input data

Processing

method

Output data Area parameters

1 Build-up area

Vector data

aggregating

values into

grid cells

grid

30 × 30 m

building footprint area percentage, mean

size within the grid cell

2 Mean building height average building height

3 Building volume

building volume for the area,

mean size within the grid cell

4 Greenery area

grid

3 × 3 m

zonal

statistics

greenery area percentage,

mean size within the grid cell

5 Mean green height average greenery height

6 Greenery volume

geenery volume

for the area, mean size

within the grid cell

Table 2. Summary and specification of urban metrics (elaborated by K. Lewacki)

Tabela 2. Zestawienie i charakterystyka wskaźników urbanistycznych (oprac. K. Lewacki)

Urban and temperature Indicators Olimpia Port Plac Grunwaldzki

Przedmieście

Świdnickie

Przedmieście

Oławskie

Building footprint

Average building footprint area

in a grid of 900 m

]

85.85 185.93 215.63 205.98

Total building footprint area [m

] 137280.50 298053.44 345013.21 329563.24

Percentage of building footprint area [%] 9.53 20.70 23.96 22.89

Mean building volume area

per 900 m

plot]

749.93 2976.59 3976.20 2891.01

Total building volume [m

] 1199133 4771480 6361916 4625614

Average building height [m] 8.20 15.25 17.61 12.84

Greenery

Average greenery area

in a grid of 900 m

]

474.06 361.71 350.63 313.01

Total greenery area [m

] 758028 579825 561008 500819

Percentage of grenery area [%] 52.64 40.27 38.96 34.78

Average greenery volume

in a grid of m

]

2355.06 2140.45 2030.15 1437.99

Total greenery volume [m

] 3737479 3242787 3177192 2273458

Mean greenery height [m] 3.85 4.40 4.31 3.31

Radiative temperature [°C]

Average 37.80 38.89 41.15 42.72

Median 37.77 40.01 41.21 43.18

The lowest 31.56 29.17 35.47 34.27

The highest 52.70 48.93 46.19 46.95

Value range 21.14 19.76 10.73 12.67

Variance

(measure of dispersion around the mean)

11.97 16.07 3.55 4.97

Legend:

Value of the given indicator

related to temperature and built-up area

Low Mean High The highest

Value of the given indicator

related to greenery

Low Mean High The highest

Table 3. Urban and temperature indicators – results for the study areas (elaborated by K. Lewacki)

Tabela 3. Wskaźniki urbanistyczne i temperaturowe – wyniki dla badanych obszarów (oprac. K. Lewacki)

130 Konrad Lewacki, Marta Smektała, Magdalena Baborska-Narożny

Radiative temperature

[°C]

Buiding footprint area

percentage [%]

Building footprint area

]

Building height

[m]

Building volume

]

Legend

Cool area

Olimpia Port

Plac Grunwaldzki

Hot area

Przedmieście

Oławskie

Przedmieście

Świdnickie

Table 4. Summary of Land Surface Temperature (LST) with selected urban indicators describing the built environment

of the four analyzed areas measuring 1.2 × 1.2 km. Internal grid: 30 × 30 m (elaborated by K. Lewacki, M. Smektała)

Tabela 4. Zestawienie LST z wybranymi wskaźnikami urbanistycznymi opisującymi zabudowę czterech analizowanych obszarów

o wymiarach 1.2 × 1.2 km. Wewnętrzna siatka: 30 × 30 m (oprac. K. Lewacki, M. Smektała)

For example, the southern part of Przedmieście Świd-

nickie features more greenery combined with lower build-

ing density, which signicantly reduces radiative tempera-

ture compared to other parts of the neighborhood – by as

much as 7°C in some areas. Conversely, higher tempera-

ture values, exceeding 42°C, prevail in highly urbanized

zones of this neighborhood. Elevated temperatures are also

noticeable in closed urban blocks, where the dierence

compared to open urban layouts is approximately 3°C.

In Olimpia Port, characterized by a low average area

temperature, the highest temperature of 52.6°C was simul-

taneously recorded. This localized high temperature oc-

curs in industrial zones with a high percentage of built-up

area, while the building volume index remains at a moder-

ate level. In other parts of this area, where building volume

and coverage indicators are high but dispersed, no signi-

cant increase in radiative temperatures was observed. High

radiative temperatures are also exhibited by large commer-

cial buildings in Przedmieście Świdnickie and Plac Grun-

waldzki, despite their proximity to rivers or moats. Plac

Grunwaldzki is characterized by relatively dense develop-

ment; however, the presence of water, greenery, and wide

proles of main streets cools the area by 3°C to 8°C.

In Przedmieście Oławskie, it is noteworthy that the high-

est radiative temperature was recorded in railway areas with

very low built-up coverage and low building volume indices.

The results obtained from the indicator analysis sug-

gest that further research requires the collection of more

detailed data. Discrepancies were observed between the

relationship of average temperature and building-related

Generalization of GIS data as a tool supporting the understanding of the urban heat island eect. Case study of the city of Wrocław 131

indicators. Therefore, it is reasonable to expand the indi-

cator base, for example, by including data related to soil

permeability and construction materials.

Discussion

The literature studies on the UHI eect have addressed

various scales and aspects of the phenomenon, yet often

without integrating these perspectives or analyzing their

interrelationships. At the macro scale, Wrocław is located

in a climatic zone favorable for leveraging cooling through

ventilation, including so-called nighttime cooling (Art-

mann et al. 2008). However, the eectiveness of nighttime

cooling depends, among other factors, on the local intensity

of the atmospheric UHI eect, which is illustrated by the

variation in the number of tropical nights during the sum

mer of 2024, ranging from 2 to 31 across dierent parts of

the city (Baborska‐Narożny, Kostka and Smektała 2024).

In contrast, studies on surface-level UHI at smaller

scales have typically focused on the inuence of isolated

urban factors – such as greenery or buildings – on land

surface temperature (Wang et al. 2024; Zawadzka, Harris

and Corstanje 2021; Dai, Guldmann and Hu 2018). Two

major challenges hinder the development of studies link-

ing urban morphology parameters with LST: the lack of

suciently detailed data on the analyzed factors, and the

absence of established methodologies for assessing their

mutual interactions.

The results presented in this article indicate that identi-

fying relationships between these factors is crucial to miti-

Table 5. Summary of Land Surface Temperature (LST) with selected urban indicators describing greenery

within the four analyzed areas measuring 1.2 × 1.2 km. Internal grid: 30 × 30 m (elaborated by K. Lewacki, M. Smektała)

Tabela 5. Zestawienie LST z wybranymi wskaźnikami urbanistycznymi opisującymi zieleń w ramach czterech analizowanych obszarów

o wymiarach 1.2 × 1.2 km. Wewnętrzna siatka: 30 × 30 m (oprac. K. Lewacki, M. Smektała)

Radiative temperature [°C]

Percentage of grenery area [%]

Greenery height [m] Greenery volume [m

]

Legend

Cool area

Olimpia Port

Plac Grunwaldzki

Hot area

Przedmieście

Oławskie

Przedmieście

Świdnickie

132 Konrad Lewacki, Marta Smektała, Magdalena Baborska-Narożny

The GIS-based analytical method proposed in this ar-

ticle can support visualization of the relationship between

elevated radiative temperatures and the urban factors that

contribute to their increase. Further development of this

method could enable its application in determining optimal

locations for air temperature monitoring stations. Verifying

the inuence of urban form indicators in relation to air tem-

perature proles would make it possible to generalize data

from selected measurement points, allowing for extrapola-

tion to other urban areas with similar characteristics.

Conclusions

This article presents a GIS-based methodology for ana-

lyzing high-resolution urban morphology data provided by

the City of Wrocław. By integrating radiative tempe rature

measurements derived from Landsat 8 imagery captured

around 9:00 a.m. with a suite of urban indicators – name-

ly footprint area, built volume, and mean height for both

buildings and vegetation – mapped on a uniform 30 × 30 m

grid, we reveal interdependencies between these indicators

and the surface UHI eect. The study workow comprised

two analytical levels. First, general comparisons across

mul tiple study areas identied broad relationships between

urban-form metrics and radiative temperature. Second, de-

tailed within-area analyses at the same grid resolution deter-

mined the spatial scales at which particular factors exert the

most inuence. This dual approach allowed us to pinpoint

threshold values of environmental parameters that either ex-

acerbate or mitigate surface warming. The proposed meth-

od simplies complex vector and raster data into compara-

ble grid‐cell summaries, enabling the classication of city

blocks by their indicator proles. By matching areas with

similar urban-form signatures, we can test whether they ex-

hibit ana logous radiative temperature distributions. Where

discre pancies arise, further GIS analyses can explore addi-

tional variables – such as material emissivity, façade col-

or, or street-canyon geometry – to explain residual thermal

variation. Finally, we note that instantaneous radiative tem-

perature maps do not directly represent air temperature. For

a comprehensive assessment of nighttime cooling potential

during heatwaves, these surface data should be supplement-

ed with in-situ air temperature measurements.

Within the analyses, we quantied the inuence of in-

dividual indicators on land surface (radiative) temperature

and described these relationships at dierent spatial scales.

At the broader scale, the most signicant drivers of aver-

age temperature were the area of vegetation and the area

of built fabric. At the ner scale, building height – and the

spatial conguration of building footprint and volume – be-

came more important. In such contexts, clusters with high

values of these indicators corresponded to pronounced tem-

perature increases.

Our results show that high-canopy vegetation cover sub-

stantially reduces surface temperature. This cooling eect

also applies in built‐up zones, provided that built-up clusters

are not contiguous but interspersed with greenery. While veg-

etation volume and height have a lesser impact on absolute

temperature reduction, they modulate the spatial extent of the

cooling eect. We further assessed the reach of high‐canopy

gating the surface UHI eect and forming a basis for urban

planning guidelines. The method proposed here conrms

ndings available in the existing literature, yet by incor-

porating a greater number of urban variables, it enables

a more rened understanding of the issue – for example, by

allowing the cooling eect of green spaces to be estimated

in the context of surrounding urban morphology.

GIS data are increasingly being used in policymaking for

cities aiming to enhance resilience against heatwaves. These

cities focus primarily on identifying hotspots and directing

greening or cooling corridor interventions toward them

(Musco 2016; ClimateADAPT 2025). Researchers study-

ing

the UHI eect emphasize the relevance of surface per-

meability and vegetation height indicators in determining

the cooling potential of green areas, as these tend to exhibit

lower air temperatures (Kuchcik, Czarnecka and Błażejczyk

2024). It is examined that green spaces larger than 2 ha can

contribute to urban cooling, and the spatial reach of this

eect is linked to vegetation height. It is estimated that the

horizontal cooling impact of greenery extends up to ve

times the height of the trees (Zardo et al. 2017).

In the case of Wrocław, preliminary results for selected

areas show that building density and height can increase ra-

diative temperatures by up to 5°C, while proximity to water

and greenery can reduce them by about 7°C compared to

the hottest areas. Radiative temperatures in densely built-

up areas range from 42°C to 46°C, while similarly dense

zones adjacent to green spaces or rivers show temperatures

ranging from 35°C to 42°C. The cooling inuence of the

river can extend up to 350 m into the urban fabric.

Based on the analyses presented in this article, it is evi-

dent that the extent of the river’s cooling eect is also inu-

enced by other urban factors. The ndings suggest that this

eect is not uniform – there are observable relationships with

building-related indicators. Compared to the UHI Report

for Wrocław (IETU 2021), this study develops a database

incorporating a broader set of urban variables represented

as grid-based data. Identifying a larger number of interre-

lations enables a deeper understanding of the causes and

mitigation of the UHI eect, and supports the development

of typologies for dierent urban areas. Nonetheless, a more

comprehensive understanding of UHI still requires ex-

panding the diversity and granularity of the factors studied.

For cities, it is essential to develop future development

scenarios based on an understanding of the relationship be-

tween the local intensity of the UHI phenomenon – both

surface and atmospheric – and urban form indicators. The

literature provides examples where LST analyses have been

compared with air temperature measurements (Hartz et al.

2006). However, until now, the ability to verify and com-

pare surface and atmospheric UHI eects at the city scale

has been considered limited due to the insucient number

of air temperature measurement points (Gawuć et al. 2020).

Improved knowledge of the interconnections between dif-

ferent types of the UHI eect would enable more precise

formulation of guidelines for mitigating or preventing UHI.

Such guidelines could be applied at multiple scales – from

urban planning strategies (e.g., green space allocation) to

building-level interventions (e.g., typologies that support

natural ventilation or nightime cooling).

Generalization of GIS data as a tool supporting the understanding of the urban heat island eect. Case study of the city of Wrocław 133

cooling, nding it to be heterogeneous and dependent not

only on the area of tree cover but also on surrounding build-

ing parameters – especially vegetation volume and height.

Importantly, we identied sections of the Przedmieście

Oławskie rail yards where both vegetation and building in-

dicators are low, yet where the highest radiative tempera-

tures were recorded. We also observed that areas with the

lowest mean temperatures exhibited the greatest tempera-

ture ranges – that is, despite a low average, they experienced

both the lowest and highest extremes among all study sites.

Scaling up the GIS-based method presented here could

inform urban‐planning guidelines – such as prescribing

maximum building densities or minimum high‐canopy

green cover to mitigate the Urban Heat Island eect. For

a comprehensive depiction of urban form’s inuence on

ambient temperature, future studies should incorporate

additional indicators – street canyons and airow, imper-

vious surface fraction, building materials, wind direction

and speed – as well as surface-temperature data from times

other than morning overpasses and in situ air‐temperature

measurements at the building scale.

Translated by

Marta Smektała

Acknowledgements

The Authors acknowledge voluntary assistance of individuals and in-

stitutions (Wrocław Municipality, Faculty of Mathematics – Wrocław

TECH), KADRA Szkoła Policealna, Chilli Hostel) who provided ac-

cess to their buildings. This research was supported by Wrocław TECH

funding granted as part of Accademia Professorum Iuniorum Grant

Nr 50AP/0001/24.

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Streszczenie

Generalizacja danych GIS jako narzędzie wspomagające rozumienie efektu miejskiej wyspy ciepła. Studium przypadku Wrocławia

Tematem artykułu jest wpływ poszczególnych czynników urbanistycznych na efekt miejskiej wyspy ciepła (MWC) oraz korelacja między tymi

czynnikami. Autorzy podjęli próbę zwerykowania możliwości wykorzystania danych systemów informacji geogracznej (GIS) do zbadania efektu

MWC i w dalszych badaniach do zwerykowania potencjału chłodzenia nocnego w mieście na przykładzie Wrocławia. W badaniu wykorzystano

zdjęcia satelitarne Landsat 8 oraz dane przestrzenne dotyczące zieleni i zabudowy do przeanalizowania wpływu tych ostatnich czynników na tempe-

raturę radiacyjną w mieście. Zaproponowana metoda umożliwia uproszczenie i kalibrację danych, co pozwala na porównywanie różnych obszarów

miasta o podobnych charakterystykach. Umożliwia również sumowanie i badanie relacji wartości poszczególnych wskaźników urbanistycznych w ra-

mach jednego pola siatki lub badanego obszaru. Dzięki temu można wyznaczyć tereny, które mają podobne zestawienia wskaźników, i porównać ich

rozkłady temperatur. W przypadku różnic analizy GIS pozwalają na badanie sąsiedztwa i identykację innych czynników wpływających na różnice

w temperaturze radiacyjnej.

Dzięki przeprowadzonym badaniom wykazano, jak poszczególne czynniki wpływają na temperaturę otoczenia. Gęstość i wysokość zabudowy

znacząco podnoszą temperaturę radiacyjną, podczas gdy obecność wody i zieleni, zwłaszcza wysokiej, ma efekt chłodzący. Aby ocenić różnice w roz-

kładzie temperatury w przestrzeni zurbanizowanej porównano cztery lokalizacje we Wrocławiu, które różnią się intensywnością zabudowy i pozio-

mem zazielenienia: Przedmieście Oławskie, Przedmieście Świdnickie, Plac Grunwaldzki i Olimpia Port. Wyniki wskazują, że Przedmieście Oławskie,

pomimo obecności rzeki, ma najwyższą temperaturę radiacyjną z powodu gęstej zabudowy i małej ilości zieleni. W przeciwieństwie do niego, Olimpia

Port, z najmniejszą gęstością zabudowy i największą ilością zieleni, jest najchłodniejszym obszarem. Celem autorów było wyznaczenie reprezenta-

tywnych dla tkanki miejskiej obszarów, w których można przeprowadzić dalszy etap badań – pomiar lokalnej temperatury powietrza. W kolejnych

fazach projektu planowane jest uwzględnienie dodatkowych czynników, takich jak przepływ powietrza, powierzchnia nieprzepuszczalna i materiały

budowlane, aby bardziej szczegółowo zbadać wpływ urbanistyki na temperaturę otoczenia.

Słowa kluczowe: analizy GIS, miejska wyspa ciepła, Landsat, temperatura radiacyjna, naturalny potencjał chłodzenia