UTHECA_USE: a multi-source dataset on human thermal perception and urban environmental factors in Seville

Academic Editors: Vladimir Sreckovic and Zoran Mijic Received: 6 August 2025 Revised: 8 September 2025 Accepted: 11 September 2025 Published: 16 September 2025 Citation: Hernández-Barba, N.; Rodríguez-Gallego, J.-A.; Rivera-Gómez, C.; Galán-Marín, C. UTHECA_USE: A Multi-Source Dataset on Human Thermal Perception and Urban Environmental Factors in Seville. Data 2025,10, 146. https://doi.org/10.3390/ data10090146 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article UTHECA_USE: A Multi-Source Dataset on Human Thermal Perception and Urban Environmental Factors in Seville Noelia Hernández-Barba 1,* , José-Antonio Rodríguez-Gallego 2, Carlos Rivera-Gómez 1 and Carmen Galán-Marín 1,* 1 Departamento de Construcciones Arquitectónicas I, Escuela Técnica Superior de Arquitectura, 9 Universidad de Sevilla, Avda. Reina Mercedes 2, 41012 Seville, Spain; [email protected] 2 Departamento de Matemática Aplicada I, Escuela Técnica Superior de Ingeniería Informática, 11 Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012 Seville, Spain; [email protected] *Correspondence: [email protected] (N.H.-B.); [email protected] (C.G.-M.) Abstract This paper introduces UTHECA_USE, a dataset of 989 observations collected in Seville, Spain (2023–2025), integrating microclimatic, personal, and urban morphological data. It comprises 55 variables, including in situ measurements of air and globe temperatures, humidity, wind speed, derived indices such as the Universal Thermal Climate Index (UTCI), demographic and physiological participant data, subjective thermal perception, and detailed urban form characteristics. The surface temperature data of urban materials are included in a subset. The dataset is openly accessible under a permissive license, and this data descriptor documents the collection methods, calibration, survey design, and data processing to ensure reproducibility and transparency. The UTHECA project aims to develop a more accurate and adaptive outdoor thermal comfort (OTC) assessment model to guide effective, inclusive urban strategies to improve human thermal perception and climate resilience. UTHECA_USE facilitates research on outdoor thermal comfort and urban microclimates, supporting diverse analyses linking human perception, environmental conditions, and urban morphology. Keywords: urban microclimate; human thermal perception; urban morphology; outdoor environment; environmental monitoring; UTCI 1. Introduction 1.1. Background and Motivation Rising urban temperatures and the growing frequency of heatwaves have positioned outdoor thermal comfort as a crucial area of research [ 1 , 2 ]. To assess thermal environments, indices like the Universal Thermal Climate Index (UTCI) are often employed, integrating microclimatic variables such as the air temperature, humidity, wind speed, and radiant temperature [ 3 , 4 ]. While these indices offer an objective assessment, they fail to incorporate additional influential elements shaping individual thermal perception, such as personal attributes (e.g., age, gender) and environmental factors like vegetation density or ambient noise [ 5 , 6 ]. This restriction hinders their capacity to completely encompass human thermal sensations within intricate urban environments. Moreover, recent advances in machine learning have created promising opportunities to model and predict subjective thermal comfort more precisely by integrating these diverse influences [ 7 , 8 ]. While outdoor thermal comfort is recognized as a multifaceted phenomenon [ 9 ], there remains a scarcity Data 2025,10, 146 https://doi.org/10.3390/data10090146 Data 2025,10, 146 2 of 22 of datasets that simultaneously combine microclimatic data, morphological factors, and perception-based information from outdoor settings. Specifically, data collections are still lacking adequate representation from Southern European cities, which are particularly susceptible to intense heat stress [ 10 , 11 ]. Recent datasets, notably those of Tiago et al. [12] and Silva et al. [ 13 ], have offered significant insights through itinerant and roving methodologies, delivering additional urban thermal comfort data across various seasons and spatial dimensions. The urban heat island (UHI) phenomenon, resulting from increased surface temperatures in urban areas, significantly impacts pedestrian thermal comfort, underscoring the importance of the thermal properties of urban surfaces [ 14 , 15 ]. Recent studies, such as that by Middel et al. [16], have employed panoramic infrared thermography to evaluate human thermal exposure, demonstrating that high-resolution 3D surface temperature data are critical in accurately capturing the complex longwave radiation from various materials and enhancing the precision of comfort indices like the UTCI. Likewise, Lamberti et al. [ 17 ] integrated infrared thermography with 3D urban modeling to measure directional radiation on specific body parts, advancing the understanding of pedestrian heat exposure and highlighting the importance of detailed surface temperature measurements in urban microclimate studies. These contributions emphasize that combining surface temperature information from urban materials with microclimatic and morphological variables considerably enhances evaluations of thermal comfort and aids in developing mitigation strategies. Urban planning initiatives like the use of reflective pavements, the extension of green infrastructure, and the selection of adaptive materials have demonstrated quantifiable potential to lessen heat stress [18,19]. To obtain reliable datasets, it is crucial to adhere to well-defined measurement protocols involving calibrated sensors and standardized field techniques, along with quota sampling. This method is vital in accurately capturing subjective thermal sensations, ensuring that environmental and human factors are consistently incorporated into thermal comfort research [20–22]. 1.2. Related Work The examination of outdoor thermal comfort and its interaction with urban microclimates has gained more attention due to the acceleration of urbanization and the challenges posed by climate change. Significant research underscores the importance of blending objective measurements of microclimates with personal thermal perceptions to effectively assess and enhance urban thermal comfort [ 20 , 21 ]. These comprehensive methods emphasize that outdoor thermal comfort is shaped by intricate interactions among the climate, urban form, and human physiological and psychological adjustments [ 9 , 23 ]. Recently, expansive datasets that incorporate environmental factors with subjective feedback and urban structural data have improved the understanding of the multifaceted elements influencing outdoor comfort and aided in planning for resilient urban climates [ 10 , 12 , 13 , 24 ]. Even with these advancements, there persists a significant demand for freely available and thoroughly annotated datasets that facilitate reproducibility and allow for robust cross-comparisons across various urban settings and climatic conditions. The dataset introduced in this study expands upon the groundwork set by previous initiatives such as the RUROS project [ 20 ] and modern machine learning research [ 25 , 26 ] by integrating detailed microclimate data, demographic details, subjective thermal comfort responses, and urban morphological characteristics for an entire city. Such expansive datasets are essential in creating sophisticated thermal comfort models that account for both macroand microclimate factors, as well as human diversity [ 7 , 23 , 27 ]. Furthermore, the application of deep learning and convolutional neural networks has recently improved Data 2025,10, 146 3 of 22 the spatial mapping and predictive precision of thermal comfort metrics, providing urban planners and designers with significant data-driven insights [8,28]. 1.3. Study Objectives and Dataset Contribution This paper introduces an innovative open database gathered from diverse public urban areas in Seville, Spain, over several warm seasons between 2023 and 2025. It integrates onsite microclimatic data collection and in-depth characterizations of the urban morphology, such as street layouts, vegetation, and building densities, along with comprehensive surveys of personal thermal comfort that provide subjective experiences and demographic information. The dataset reflects spatial and temporal variations in residential, tourist, and mixed-use areas, offering a comprehensive foundation for a multifactorial analysis of the factors influencing outdoor thermal comfort. Aimed at promoting the development and validation of sophisticated thermal comfort evaluation tools, this dataset acknowledges the diversity in human physiological and psychological responses, the intricacy of urban structures, and environmental fluctuations. It seeks to support evidence-informed urban planning, climate adaptation plans, and public health strategies by enabling comprehensive multivariate and machine learning evaluations of outdoor thermal comfort adaptability and responses [25,26,29]. 2. Study Area and Experimental Context 2.1. Case Study: Seville, Spain Located in the southern region of Spain, Seville exemplifies a Mediterranean urban area facing heightened thermal stress driven by both climate change and urbanization. With its hot-summer Mediterranean climate (codified as Csa in Köppen’s classification [ 30 ]), the city endures extended warm periods and recurrent heatwaves that profoundly impact outdoor thermal comfort. The varied urban layout of Seville, which spans from densely packed historical districts to expansive green areas, renders it an ideal subject for the examination of microclimatic differences and human thermal experiences in intricate urban environments. 2.2. Urban Environment and Climate Overview The research area encompasses diverse urban layouts with varying characteristics, including the building density, street orientation, vegetation presence, and surface materials. These elements significantly influence local microclimates and subsequently impact the thermal comfort of outdoor environments. The Mediterranean climate intensifies thermal stress during the warmer months from April to October, highlighting the need for an in-depth analysis of these factors to guide urban planning and climate adaptation strategies in Seville. 2.3. Site Selection and Morphological Diversity Observation sites were strategically selected to include a broad range of morphological types, such as residential zones, commercial areas, and public squares. Figure 1shows the map of Seville with the different measurement locations included in this study. This was intended to examine how the characteristics of the built environment—like the street canyon geometry, the presence of vegetation, and the thermal properties of materials—affect outdoor thermal comfort [ 6 , 31 ]. Studies show that the height-to-width (H/W) ratio of street canyons and the presence of trees and water features can greatly influence thermal sensations and comfort in urban outdoor environments [ 23 , 32 ]. By sampling these diverse areas, we compiled a comprehensive dataset that reflects the varied thermal environments experienced by Seville’s residents, facilitating advanced modeling and machine learning efforts to accurately predict human thermal perception [ 10 , 25 ]. Table 1shows the different Data 2025,10, 146 4 of 22 combinations of morphological characteristics present at the measurement sites where data were collected. Figure 1. Map of Seville showing the measurement sites used in this study. Data 2025,10, 146 5 of 22 Table 1. Urban morphological characteristics of the measurement sites. Longitude Latitude Height Width HW_Ratio Pavement Façades Nature Shadow Water −6.000 37.399 10.000 50.000 0.200 Asphalt–black pavement White paint 20–29 20–29 Abundant −5.998 37.401 10.000 50.000 0.200 Ochre pavement Ochre brick 20–29 70 or more Abundant −5.998 37.401 14.000 20.000 0.700 Ochre pavement Ochre brick 20–29 40–49 Abundant −5.997 37.402 10.000 50.000 0.200 Ochre pavement Ochre brick 20–29 70 or more Abundant −5.995 37.404 15.000 14.000 1.071 Ochre pavement White paint 10–19 Less than 10 Abundant −5.993 37.404 9.750 31.125 0.313 Ochre pavement White paint 40–49 50–59 None −5.992 37.403 8.000 15.000 0.533 Ochre pavement White paint Less than 10 50–59 None −5.990 37.403 16.000 37.500 0.427 Ochre pavement Ochre brick Less than 10 40–49 None −5.989 37.403 16.000 38.333 0.417 Ochre pavement Ochre brick Less than 10 40–49 None −5.989 37.403 16.000 40.000 0.400 Ochre pavement Ochre brick 20–29 40–49 None −5.988 37.403 16.000 40.000 0.400 Ochre pavement White paint Less than 10 20–29 None −5.987 37.401 12.000 7.000 1.714 Ochre pavement Ochre brick Less than 10 70 or more None −5.988 37.401 12.000 27.000 0.444 Ochre pavement Ochre brick 10–19 20–29 None −5.999 37.385 12.000 24.000 0.500 Ochre pavement Concrete–red brick Less than 10 Less than 10 Abundant −5.996 37.383 11.000 10.000 1.100 Concrete–red pavement Concrete–red brick Less than 10 30–39 Abundant −5.996 37.385 3.000 12.000 0.250 Asphalt–black pavement White paint Less than 10 10–19 Occasional −5.997 37.386 10.000 50.000 0.200 Concrete–red pavement White paint 70 or more 70 or more None −5.999 37.388 20.000 68.000 0.294 Asphalt–black pavement White paint Less than 10 50–59 None −5.997 37.388 12.000 30.000 0.400 Asphalt–black pavement Concrete–red brick Less than 10 40–49 None −5.995 37.387 9.000 5.250 1.714 Concrete–red pavement White paint 30–39 40–49 None −5.994 37.385 8.000 15.000 0.533 Concrete–red pavement White paint 10–19 10–19 None −5.992 37.386 16.000 40.000 0.400 Concrete–red pavement White paint 10–19 10–19 Abundant −5.993 37.387 9.000 6.000 1.500 Ochre pavement White paint 30–39 50–59 None −5.985 37.380 12.000 30.000 0.400 Asphalt–black pavement Vegetation 30–39 Less than 10 None −5.985 37.382 15.000 35.000 0.429 Concrete–red pavement Concrete–red brick Less than 10 20–29 None −5.984 37.383 17.000 3.000 5.667 Asphalt–black pavement Concrete–red brick 30–39 10–19 Occasional −5.986 37.383 20.000 26.000 0.769 Asphalt–black pavement White paint 10–19 20–29 None −5.987 37.383 14.000 15.000 0.933 Asphalt–black pavement White paint 10–19 10–19 Occasional Data 2025,10, 146 6 of 22 3. Materials and Methods 3.1. Data Collection Protocols Data on microclimatic conditions were gathered by architecture graduates utilizing precise instruments like the ones shown in Figure 2. These measurements adhered to the protocols recommended in urban outdoor comfort studies. Following ASHRAE Standard 55 [ 33 ], all probes were placed at a height of approximately 1.1 m above the ground to represent the pedestrian level. The thermohygrometer and the anemometer were kept in an unobstructed orientation to ensure accurate readings. During each measurement, the instruments were held stable at the specified height in accordance with standard thermal comfort analysis practices. Researchers collected morphological data directly from the site, as illustrated in Figure 3, adhering to methods described in studies pertaining to the urban form and thermal comfort. The methodology for personal surveys utilized a quota sampling technique segmented by gender and age, conducted via intercept surveys in urban public areas. The complete questionnaire used for the surveys can be found in Appendix A. These surveys predominantly utilized questionnaires modified from the works of [ 20 ] This approach, which involved randomly encountering participants, is a standard practice in surveys examining thermal comfort. Of the individuals approached, roughly 20% opted to participate, with most completing the surveys completely while on location. In total, 989 surveys were completed. This participation rate is consistent with those found in other studies of outdoor comfort. The basic descriptive statistics of the respondents showed a balanced gender distribution (53.4% female, 46.0% male, and 0.6% preferred not to say). The survey aimed to cover all age groups, with the largest representation in the 30–39 age group. In addition, the respondents represented 32 different nationalities. (a) (b) (c) Figure 2. Measurement equipment used in the study: (a) TROTEC TC100 thermohygrometer (Trotec GmbH, Heinsberg, Germany), (b) TROTEC TA300 anemometer (Trotec GmbH, Heinsberg, Germany), and (c) Testo 905-T2 surface thermometer (Testo SE & Co. KGaA, Lenzkirch, Germany). 3.2. Fixed Outdoor Monitoring In Seville, Spain, data collection occurred during the warmer months, from April to October, over the years 2023 to 2025. As outlined in Section 2.2, the observation locations were carefully selected across public urban areas to represent various urban morphologies, including residential zones, tourist areas, plazas, street canyons, and parks. The criteria for selecting these sites were designed to ensure diversity in vegetation, building density, and surface materials, providing comprehensive coverage of Seville’s diverse urban en- Data 2025,10, 146 7 of 22 vironment, in line with methodologies for the study of urban microclimates and thermal comfort. The surface temperature measurements were taken on up to three different materials located near the observation point. These included a wide variety of urban surfaces, such as land, metal, playgrounds, and multiple types of pavement (e.g., black asphalt, cobblestone, grass, grey concrete, light stone, ochre, red concrete, white, dark stone, green concrete), as well as façades (e.g., ochre brick, red brick–concrete, vegetation, white paint). Surveys were administered at each site multiple times, creating groups of responses within identical microclimatic and morphological settings. Architecture graduates carried out all data collection tasks, a practice recommended in biometeorological studies to maintain high data quality and accurate contextual understanding. Moreover, they documented the specific morphological features of each location, capturing variations in urban form that are essential in evaluating thermal comfort. Figure 3. Data gathering: personal surveys using a standardized questionnaire and obtainment of microclimatic and morphological data. 3.3. Mobile Transects Mobile survey campaigns, such as those conducted on foot or by bike, follow established protocols to ensure the simultaneous gathering of both subjective and objective data, as outlined in recent machine learning studies on urban thermal comfort. Routes were chosen to ensure the broad spatial representation of various urban environments, while portable microclimatic devices and survey tools allowed for on-the-go data collection. The alignment of microclimatic measurements with thermal sensation surveys from participants adheres to the recommended practices for the accurate capture of dynamic outdoor comfort levels. 3.4. Measured Parameters and Equipment Fixed Stations and Manual Measurements Environmental data were collected on-site using portable, calibrated instruments, selected for their accuracy and reliability in outdoor urban conditions. The key measurement devices included the following: • Thermohygrometer: Employed to measure the air temperature, globe temperature, and relative humidity. The black globe measuring head has a diameter of approximately 5 cm, while international standards recommend 15 cm globes. The smaller size alters the balance between radiation and convection, increases the sensitivity to Data 2025,10, 146 8 of 22 changes in solar radiation and wind, and reduces thermal inertia. Under direct sunlight, it may overestimate shortwave radiation, while, at higher wind speeds, it tends to underestimate the radiant effect. These limitations prevent direct comparison with measurements taken using standardized globes, especially in comfort or heat stress studies. The instrument offers accuracy of ±0.6 °C for air temperature measurements, ±0.6 °C for globe temperature measurements in the 20–50 °C range and ±1 °C outside this range, and ±5% for relative humidity. The air temperature can be measured from 0 to 50 °C, the globe temperature from 0 to 80 °C, and relative humidity from 0 to 99.9%, ensuring precision in capturing microclimatic conditions relevant to thermal comfort assessment. • Anemometer: Utilized to capture wind speed data, with accuracy of ±0.2 m/s, allowing the detailed characterization of airflow variability in urban canyons and open spaces. • Surface Thermometer: Used to measure the surface temperature in a range of −50 to 350 °C, with accuracy of ±(1 °C + 1% of the measured value). Its probe allows measurements to be taken on various surfaces. Morphological parameters, critical in contextualizing the microclimatic observations, were documented through systematic manual surveys conducted by trained researchers at each measurement site. These included the average building height, street width, and estimated vegetation cover (naturalization, measured on a 0–100% scale in 10% increments, with each increment corresponding to the estimated green cover at the site). 4. Dataset Description 4.1. Dataset Organization and File Formats The dataset is provided in both .xlsx and .csv formats to ensure accessibility and usability for a wide range of users. The file UTHECA_USE.xlsx contains two spreadsheets. The first, named data, includes the complete main dataset. The second, named supplementary, contains additional variables for a subset of records. These correspond to the .csv files UTHECA_USE.csv and UTHECA_USE_additional.csv, respectively. 4.2. Variable Definitions and Metadata The main dataset contains 989 observations and 55 variables. Each row represents an individual surveyed at a specific time and place, with multiple surveys possibly sharing the same microclimatic and morphological context when conducted at the same locations. The first column (id) uniquely identifies each record, followed by variables detailing temporal information (Date, Hour), geographic coordinates (Longitudinal.coord,Latitudinal.coord), and specific location descriptors (Location). The remaining variables fall into three categories: • Microclimatic: Objective measurements including Air.temperature, Globe.temperature, Relative.humidity, Wind.speed, Radiant.temperature, UTCI, and UTCI.classification. • Personal survey: Demographic and physical characteristics (Height.cm, Weight.kg, BMI), clothing insulation (Clothing.cover.clo), and subjective thermal sensation and well-being metrics (Subjective.thermal.sensation, Subjective.well.being.sensation), as well as perception-related variables such as Vegetation.likeable. • Morphological: Urban environmental descriptors such as Average.height, Street.width, Naturalization, and Noise.level, among others. The supplementary dataset contains 820 observations and 3 variables, providing surface temperature data for various urban materials (pavements, façades, urban furniture). Entries link to the main dataset via the id variable. Material types are classi- Data 2025,10, 146 9 of 22 fied under Surface.temperature.material, with corresponding temperature measurements in Surface.temperature. In Tables 2–5, we show all the main variables included in the UTHECA_USE dataset. Table 2. Identification and temporal variables. Name Type Description id Integer Identification number (1–989) Hour Numeric Time of interview/measurement Date Date Data collection date (yyyy-mm-dd) Longitudinal.coord Numeric Site longitude Latitudinal.coord Numeric Site latitude Location Categorical Qualitative location name (e.g., Avenida De La Constitución, Calle Torneo, etc.) Table 3. Microclimatic variables. Name Type Description Air.temperature Numeric Air temperature (°C), Trotec TC100 (±0.6 °C) Globe.temperature Numeric Globe temperature (°C), Trotec TC100 (±0.6 °C) Relative.humidity Numeric Relative humidity (%), Trotec TC100 (±5%) Wind.speed Numeric Wind speed (m/s), Trotec TA300 (±0.2 m/s) Radiant.temperature Numeric Mean radiant temperature (°C), UNE-EN ISO 7726:2002 [34] UTCI Numeric Universal Thermal Climate Index value UTCI.classification Categorical UTCI thermal stress class (e.g., no thermal stress, moderate heat stress, etc.) Table 4. Personal survey variables. Name Type Description Height.cm Numeric Height in centimeters Weight.kg Numeric Weight in kilograms BMI Numeric Body mass index: weight/(height/100)2 Clothing.cover.clo Numeric Clothing insulation (clo units, 0.2–1.2) Activity.met Numeric Metabolic activity (met units, 0.8–8) Visit.frequency Numeric Visit frequency (1 = rarely to 5 = always) Mood Numeric Mood (1–5) Gender Categorical Female, male, prefer not to say Age.range Categorical Age (e.g., 20–29, 30–39, etc.) Nationality Categorical Respondent’s nationality Living.in.Seville Categorical Duration living in Seville Accompanied.by Categorical Accompanied during interview (Yes/No) You.answered Categorical Posture (sitting, standing, walking) Shaded.during.survey Categorical Sun/shade exposure during survey Time.here Categorical Time at location before survey Previously.in Categorical Previous environment (e.g., air-conditioned interior) Reason.visit Categorical Reason for presence (family, tourism, work, etc.) Would.change.Temperature Logical Wants to change temperature Would.change.Humidity Logical Wants to change humidity Would.change.Wind Logical Wants to change wind Would.change.Radiation Logical Wants to change radiation Would.change.Nothing Logical Would not change anything Vegetation.likeable Numeric Vegetation: unpleasant (−1), neutral (0), pleasant (1) Materiality.likeable Numeric Materiality: unpleasant (−1), neutral (0), pleasant (1) Spatiality.likeable Numeric Spatiality: unpleasant (−1), neutral (0), pleasant (1) Noise.likeable Numeric Noise: unpleasant (−1), neutral (0), pleasant (1) Number.of.people.likeable Numeric Number of people: unpleasant (−1), neutral (0), pleasant (1) Shade.likeable Numeric Shade: unpleasant (−1), neutral (0), pleasant (1) Subjective.thermal.sensation Categorical Perceived thermal sensation (e.g., cold, neutral, hot) Subjective.well.being.sensation Categorical Perceived comfort (very uncomfortable–very comfortable) Data 2025,10, 146 16 of 22 6. Discussion 6.1. Dataset Value and Key Insights This dataset represents the robust integration of microclimatic data, urban morphological characteristics, and personal survey insights, enabling a comprehensive analysis of the multifaceted factors influencing outdoor thermal perception. By incorporating both environmental and personal elements, it assists researchers and urban planners in pinpointing key determinants of extreme thermal sensations and discomfort, crucial for effective climate adaptation strategies [ 21 , 42 ]. Additionally, this dataset provides a solid basis for the creation and validation of predictive models, including advanced machine learning tools such as random forest, XGBoost, and neural networks [ 43 – 45 ], to forecast subjective thermal responses using objective environmental and personal data, increasing the accuracy over traditional indices [ 27 , 46 ]. Incorporating detailed urban morphology variables, such as the widths of streets, average heights of buildings, and types of surface materials, enhances the ability to assess their impacts on thermal comfort and aids in creating urban areas resilient to heat [ 4 , 32 ]. Moreover, additional data that correlate the surface temperatures of different urban elements—like pavements, building façades, and street furniture—with local microclimatic conditions offer valuable insights into the thermal properties of these materials in real-world outdoor settings, supporting evidence-based choices in material selection to alleviate urban heat [ 47 , 48 ]. This extensive dataset is crucial in advancing scientific knowledge and practical solutions in urban thermal comfort and sustainable urban planning. 6.2. Comparison with Previous Studies The UTHECA_USE dataset focuses on the city of Seville, Spain, during the warm months—a period of particular interest due to the pronounced urban heat island effect characterizing the city in summer. This temporal and geographical scope was deliberately chosen to accurately capture the critical microclimatic conditions affecting urban thermal comfort, rather than aiming for year-round representativeness. With 989 records, UTHECA_USE offers comprehensive sampling for a single urban environment. By comparison, the RUROS dataset [ 20 ]—collected between 2001 and 2002—comprises approximately 10,000 observations across five countries and fourteen sites, only two of which exceeded the number of interviews collected in the present study. UTHECA_USE’s questionnaire design was informed by RUROS, incorporating core variables such as age group, sex, shading availability, and reasons for visiting, while optimizing the survey length and relevance. Notably, UTHECA_USE enhances the thermal perception detail by using ten levels for thermal sensation and five levels for comfort, compared to RUROS’s simpler scales, thereby capturing wider variability and minimizing neutral response bias. Additional personal variables, such as height, weight, and mood, which the recent literature indicates may influence thermal perception, were also incorporated. The microclimatic data collected included all parameters required to compute the UTCI, complemented by detailed morphological variables for each measurement point—such as the dominant pavement and façade materials, vegetation, water presence, average building height, and street width. A unique feature of UTHECA_USE is the inclusion of surface temperature readings for various urban materials, allowing integrated studies of materials’ responses to environmental conditions. In comparison, the Lisbon thermal comfort dataset from Tiago et al. [ 12 ] presents data obtained using itinerant meteorological measurements combined with surveys administered during walking routes across multiple urban morphological settings throughout all seasons. Their meteorological data are highly detailed, including the air temperature, relative humidity, wind speed/direction, dew point, black globe temperature, and both shortwave and longwave radiation components, allowing the calculation of the UTCI and mean Data 2025,10, 146 17 of 22 radiant temperature at a high temporal resolution (every 5 to 10 s). Participants provided pre-walk sociodemographic and clothing insulation data, alongside thermal sensations, with more comprehensive thermal pleasantness and preference surveys conducted during the walks. This mobile and multi-seasonal approach contrasts with UTHECA_USE’s fixed stations focused on warm seasons, but both datasets offer critical complementary insights into pedestrian thermal comfort and the microclimate across different urban contexts. The study by Silva et al. (2024) [ 13 ] similarly used mobile roving missions conducted throughout all seasons and during the day and night, explicitly incorporating local climate zone (LCZ) spatial scales to characterize pedestrian thermophysiological comfort. Their approach enabled the identification of typologies of urban areas with varying comfort levels, finding significant thermal stress differences temporally and spatially—such as a 44% increased UTCI at night and specific discomfort in compact, low-rise areas. The higher spatial and temporal resolutions of their dataset complement the UTHECA_USE dataset’s fixed monitoring and detailed morphological data, together enriching the understanding of urban thermal environments. Both the Lisbon and Seville studies shared a common objective: to improve the understanding of outdoor thermal comfort in urban environments in order to support more sustainable planning and enhance citizens’ quality of life. In both cases, the Universal Thermal Climate Index (UTCI) was employed as a fundamental objective metric, and microclimatic variables at the pedestrian level were measured using portable equipment. This shared framework ensured comparability while allowing each study to address different aspects of the problem. The methodological approaches, however, diverge. The Lisbon study was structured around mobile transects across the city, using meteorological stations mounted on carts to capture high-resolution spatial variability in thermal conditions. This approach facilitated the identification of patterns at the scale of local climate zones (LCZs) and neighborhoods, producing a climate-oriented diagnosis of the city throughout the year. By contrast, the Seville study focused on strategically selected fixed points that represented diverse urban morphologies, where both environmental measurements and on-site surveys of passersby were conducted. This design allowed the direct coupling of physical conditions with individual perceptions, generating insights into the interplay between objective and subjective dimensions of thermal comfort. The datasets also differ in scope and depth. While both include core microclimatic variables such as the air temperature, humidity, and wind, the Lisbon dataset emphasizes spatial coverage and classification according to LCZs, whereas the Seville dataset incorporates a broader set of descriptors, including detailed morphological features for each location and individual-level data such as demographics, clothing, activity, and thermal sensations. As a result, the Lisbon study offers a comprehensive climatological mapping of urban comfort, while the Seville study provides a more integrated view that combines environmental, morphological, and personal dimensions. Together, these complementary approaches contribute valuable perspectives to advance the study of outdoor thermal comfort in urban contexts. The integrated detail in UTHECA_USE, consisting of morphological and surface temperature data combined with refined subjective thermal sensation scales, provides a valuable contribution, complementing these earlier datasets and enabling advanced urban thermal comfort modeling focused on hot Mediterranean climates. 6.3. Limitations and Future Work For variables such as height, weight, and age range, when the surveyed person had to leave prematurely, before completing the questionnaire, values were estimated by the interviewer when there was a reasonable degree of visual certainty; otherwise, the response Data 2025,10, 146 18 of 22 was left blank. This methodological choice, while necessary to preserve anonymity, resulted in some missing values that were subsequently imputed. It was also necessary to impute certain microclimatic variables due to occasional interruptions in measurements, caused by technical failures in the sensors or unstable environmental conditions, which prevented the collection of consistent data in some observations. 7. Conclusions The UTHECA_USE dataset comprises 989 instances collected in Seville, Spain, during the years 2023 to 2025, specifically in the warmer months of April through October. It includes 55 variables organized into three primary categories: microclimatic information (like air and globe temperatures, relative humidity, wind speed, mean radiant temperature, and UTCI) and personal survey data (covering demographic details, clothing insulation, metabolic rates, and subjective thermal comfort assessments), as well as urban morphological characteristics (including building heights, street widths, naturalization, pavement and façade types, and noise levels). Some cases also documented the surface temperatures of urban materials. Our study exposed significant differences between the objective Universal Thermal Climate Index (UTCI) categorizations and the subjective thermal perceptions of participants. Notably, in the UTCI’s ’no thermal stress’ classification, 62% of respondents indicated a neutral feeling, while about 20% felt warm, and a few described sensations such as ’heat’ or ’very hot’. In the ’moderate heat stress’ category, some participants felt neutral or even cold, indicating that traditional biometeorological indices might not always accurately reflect true human thermal comfort in specific situations (Figure 4). Moreover, individual factors like gender and age played a substantial role in shaping thermal perception. Women reported extreme heat sensations more frequently (72.5% noting ’heat’ or ’very hot’) compared to men (53.0%), with a statistically significant link (chisquared test: p= 0.037; Figure 5). Age showed even more distinct variations; younger individuals (under 40 years) experienced a wider array of warmer sensations, while older participants predominantly reported moderate sensations (chi-squared: p<0.0001 ; Figure 6 ). Morphologically, measurements of the mean radiant temperature (MRT) revealed evident differences based on the surrounding façade material. Areas with vegetationcovered surfaces showed median MRTs of around 30.5 °C, which was notably lower than for façades composed of red or ochre brick, which had temperatures exceeding 46 °C, demonstrating vegetation’s role in reducing thermal exposure (see Figure 7). Correlation analyses indicated strong positive relationships between meteorological variables like the air temperature, globe temperature, radiant temperature, and UTCI, with correlation coefficients over 0.9, whereas relative humidity showed strong negative correlations. Personal characteristics (such as height, weight, and BMI) displayed moderate correlations, and subjective attributes like mood and frequency of visit were linked to clothing insulation, revealing the intricate interactions that affect thermal perception (refer to Figure 8). Overall, these results confirm that the UTHECA_USE dataset effectively captures both the objective and subjective dimensions of urban thermal comfort, enabling complex modeling that considers a range of human and environmental factors. Our comprehensive and diverse methodology, which merges standardized weather data, detailed morphological descriptions, and thorough personal survey information, offers a solid basis for upcoming investigations. The extensive scope and size of the dataset enable comparative research and sophisticated analyses, such as statistical modeling and machine learning, to more effectively guide urban planning and public health strategies targeting heat stress reduction. Data 2025,10, 146 19 of 22 In essence, UTHECA_USE showcases how cohesive datasets encompassing both empirical environmental data and personal human responses can reveal the subtleties of outdoor thermal comfort, particularly in Mediterranean urban areas confronting escalating heat dangers as a result of climate change. Author Contributions: Conceptualization, C.R.-G. and C.G.-M.; methodology, N.H.-B. and J.-A.R.-G. ; software, N.H.-B. and J.-A.R.-G.; validation, J.-A.R.-G.; formal analysis, N.H.-B. and J.-A.R.-G.; investigation, N.H.-B.; data curation, N.H.-B. and J.-A.R.-G.; writing—original draft preparation, N.H.-B.; writing—review and editing, N.H.-B., J.-A.R.-G., C.R.-G. and C.G.-M.; visualization, N.H.-B.; supervision, C.R.-G. and C.G.-M.; project administration, C.R.-G. and C.G.-M.; funding acquisition, C.R.-G. and C.G.-M. All authors have read and agreed to the published version of the manuscript. Funding: This research has been supported by the project TED2021-129347B–C21 funded by MICIU/AEI/10.13039/501100011033 and by the European Union’s ’NextGenerationEU/PRTR’. Institutional Review Board Statement: Ethical review and approval were not required for this study, as it was conducted using fully anonymous data that cannot be linked to any individual. Therefore, the provisions of data protection regulations, including the GDPR and Spanish LOPDGDD, do not apply. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are openly available in the institutional repository idUS at DOI https://doi.org/10.12795/11441/174124, with the direct handle link https: //hdl.handle.net/11441/174124 (last accessed on 8 September 2025). Conflicts of Interest: The authors declare no conflicts of interest. Appendix A Data 2025,10, 146 20 of 22 References 1. 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