Cadastral plots dataset: Availability of passive climatization strategies in Barcelona’s residential buildings

Abstract

This dataset contains information on 61.781 cadastral plots in Barcelona (Spain). The information was derived from alphanumeric and vector public open databases through operations in Microsoft Excel and in the open-source software Quantum GIS. The dataset describes typological and constructive features of the residential buildings contained in the plots, useful to assess the availability of passive climatization strategies to face extreme heat conditions. To this end, residential plots are classified according to three characteristics: the cross-ventilation potential of housing units within the buildings, the compactness of the urban fabric surrounding the plot, and the presence of thermal insulation in the buildings’ envelope. Additionally, plots are also categorized according to their proximity to public parks, libraries or sports centres within the local network of climate havens. This dataset, combined with the cadastral plots vector dataset available at the Spanish Cadastre’s website, is useful to quantify, locate and visualize the conditions of adaptive capacity to extreme heat of residential buildings in Barcelona. It can also be combined with other social, economic or climatic indicators to assess the overall urban heat vulnerability of the city. This dataset can be used by city planners, researchers and citizens to increase awareness on climate adaptation of cities, and to develop more comfort-oriented building retrofitting strategies. Moreover, the method used to generate the dataset can be transferred to any other city in Spain and adapted to other European contexts.

Specifications Table

Subject	Architecture
Specific subject area	Adaptive capacity to extreme heat of the built environment
Type of data	Processed alphanumeric data
Data collection	Alphanumeric and vector data were downloaded from the Spanish Cadastre’s website and from the Open Data Barcelona website. They were later processed though pivot tables in Microsoft Excel and geospatial operations in Quantum GIS software. Data is provided at cadastral plot level. The dataset does not include any data from questionnaires.
Data source location	Sede Electrónica del Catastro: https://www.sedecatastro.gob.es/ Open Data Barcelona: https://opendata-ajuntament.barcelona.cat/
Data accessibility	Repository name: CORA.RDR Data identification number: DOI: 10.34810/data2342 Direct URL to data: https://doi.org/10.34810/data2342
Related research article	[1] A. Moccia, C. Alonso Montolìo, H. Coch, 2025. Assessing Adaptive Capacity to Extreme Heat in European Cities: An Open Data-Based Methodology. Available at SSRN: http://dx.doi.org/10.2139/ssrn.5316898

1. Value of the Data

• •The dataset [2] leverages open-access data to depict the conditions of adaptive capacity to extreme heat in Barcelona’s residential buildings. This information complements vulnerability indices provided by the Spanish Urban Vulnerability atlas, which only focus on social, economic or hygienic variables.
• •Data can be linked back to cadastral vector datasets using the cadastral reference number of each plot to map and visualize the information.
• •The dataset can be cross-referenced with information available at the Open Data Barcelona website to further inquire into conditions of climate vulnerability within the city.
• •Data can help researchers and planners to deepen their knowledge of the thermal conditions of Barcelona and plan actions to increase its adaptive capacity to extreme heat.
• •The methodology developed to create this dataset can be used to assess adaptive capacity to extreme heat in any other Spanish city.
• •The results can be grouped by cadastral plots and cadastral units, offering insights on the effect of housing density (units per plot) on the climatic conditions of buildings.

2. Background

As urban population grows and climate change advances, the mitigation of urban vulnerability is a priority for most city administrators. Urban vulnerability is commonly assessed through indices focused on social or economic variables, with a limited scope concerning the built environment [[3], [4], [5]]. For example, the Urban Vulnerability Atlas [6], which represents the main tool to access information on urban vulnerability in Spain, only addresses minimal hygiene standards of housing buildings [7,8]. However, the characteristics of urban fabric and buildings have relevant effects on thermal conditions of dwellers and influence vulnerability to extreme heat [[9], [10], [11]]. Through the elaboration of open public data, this dataset complements the Urban Vulnerability Atlas and provides a climate-oriented insight on urban vulnerability studies in Barcelona. The dataset provides information on three characteristics of the built environment of Barcelona influencing availability of passive climatization strategies in dwellings. The results can be used to evaluate the levels of adaptive capacity to extreme heat of different areas of the city, and can help planners to identify and address disparities in heat vulnerability conditions. This data article better details the methodology section of the related original research article, enhancing the scalability of the methodology to the whole Spanish territory.

3. Data Description

This dataset consists of a single file in .csv format. The file contains 61.782 rows, corresponding to the cadastral plots in Barcelona, and 24 columns describing their typological and constructive characteristics (Table 1). The first row contains the names of the columns.

Table 1.

Fields contained in the dataset.

Assigned name	Description	Type	Measurement units or value labels
_RefPlot	Plot cadastral reference	Text	-
_B_Housing	Boolean parameter indicating if the plot contains housing units	Boolean	-
_YearConRen	Construction year of the building or year of complete renovation of the cadastral plot	Integer	Years
_B_Single	Boolean parameter indicating if the plot corresponds to a single-family house	Boolean	-
_UnitsxPlot	Total number of housing units in the cadastral plot	Integer	Housing units/plot
_M_UnitsxFloor	Mean number of property units per floor of a cadastral plot	Integer	Property units/floor
_M_SurfUnit	Mean surface of the housing units in the cadastral plot	Decimal	Square meters
_FloorsxPar	Approximated number of floors in the cadastral plot	Integer	Floors
_Happrox	Approximated height of the buildings in the plot (average height of 3 m per floor)	Integer	Meters
_WSApprox	Approximated width of the narrowest street adjacent to the cadastral plot	Integer	Meters
_Wfacade	Width of the largest continuous segment facing a street in a plot’s perimeter	Integer	Meters
_FacadesxPlot	Number of continuous segments that face a street within a plot’s perimeter	Integer	Facades/plot
_Perimeter	Perimeter of a cadastral plot	Decimal	Meters
_B_BlockPat	Boolean parameter indicating if the plot has access to a block courtyard	Boolean	-
_B_VentPat	Boolean parameter indicating if the plot has access to a ventilation shaft	Boolean	-
_B_OppFacades	Boolean feature indicating if the plot has 2+ different facades facing 2+ different streets	Boolean	-
_C_VenType	Class of predisposition to cross-ventilation assigned to the plot	Text	“C0”, C1”, “C2”, “C3”, “0″, “N/D”
_H/Wapprox	Ratio between building height and width of its narrowest neighboring street	Decimal	Dimensionless (ratio)
_P_Ref	Walking time form public parks, libraries or sports centers	Integer	Minutes
_C_Vent	Field indicating whether the plot allows optimal cross-ventilation or not	Text	“VENT”, “N_VENT”, “0″, “N/D”
_C_Comp	Field indicating whether the plot is located in a compact urban fabric or not	Text	“COMP”, “N_COMP”, “0″, “N/D”
_C_Ins	Field indicating whether the envelope of buildings in the plot is thermally insulated or not	Text	“INS”, “N_INS”, “0″, “N/D”
_C_Comb	Combination of the three climatic variables	Text	“VENT_N_COMP_N_INS”, “N_VENT_COMP_N_INS”, “N_VENT_N_COMP_INS”, “N_VENT_COMP_INS”, “VENT_COMP_N_INS”, “VENT_N_COMP_INS”, “N_VENT_N_COMP_N_INS”, “VENT_COMP_INS”, “0_0_0″, “N/D_N/D_N/D”
_L_Adaptive	Level of adaptive capacity to extreme heat	Integer	“1″, “2″, “3″, “4″, “0″, “N/D”

The first column, named “_Refplot”, contains the cadastral reference number of the plots. This is a unique numerical code identifying each cadastral plot in Spain. It is present among the features of any item representing cadastral plots in alphanumeric and vectorial databases from the Spanish cadastre. The cadastral reference number can be used as an identifier to link each item in the proposed dataset to its geometric representation in vector cadastral data, and visualize the information on a map. In the open-source programme Quantum GIS, these would be the steps to perform the linking: import the dataset and the vector layer in the programme, go to the properties of the vector layer, go to “manage joins to other layer”, click on “+”, select the dataset in “join layer”, select “_Refplot” as “join field”, select “REFCAT” as “target field”, click on “OK”.

Column 2, named “_B_Housing”, corresponds to a Boolean parameter indicating if the cadastral plot contains residential units (value 1) or doesn’t (value 0). Columns 3 to 18 contain information used to classify the plots according to three climatic variables: the cross-ventilation potential of housing units, the compactness of the urban fabric surrounding the plot, and the presence of thermal insulation in the buildings’ envelope. Among these fields, column 5 “_UnitsxPlot” and column 7 “_M_SurfUnit” provide information on the number of housing units contained in each cadastral plot and their mean surface area. These fields enable to analyse the dataset by both housing plots and housing units. Column 18, named “_C_VentType”, categorizes housing plots according to their cross-ventilation typology, as follows:

• •Type 0: Single family buildings.
• •Type 1: Buildings allowing natural ventilation between two opposite facades.
• •Type 2: Buildings allowing natural ventilation between a facade and a ventilation patio.
• •Type 3: Buildings allowing natural ventilation though one facade only.

Column 19, named “_P_Haven”, categorizes residential plots according to four ranges of walking time to climate havens, considering a 2 km/h walking speed: 0–5 min, 5–10 min, 10–15 min, >15 min. Columns 20 to 22 contain Boolean fields that categorize residential plots according to the three climatic variables: high or low cross ventilation potential (column 20 “_C_Vent”), high or moderate compactness of the urban fabric (column 21 “_C_Comp”), presence or absence of thermal insulation in the buildings’ envelope (column 22 “_C_Ins”). Column 23, named “_C_Comb”, combines the values of columns 20, 21 and 22. Finally, column 24 “_L_Adaptive”, describes the level of adaptive capacity to extreme heat of the residential plots according to their cross-ventilation potential and, in the case of low cross-ventilation potential, their proximity to climate havens. It considers four possible levels, form high (level 1) to very low adaptive capacity (level 4).

Non-residential plots have value “0″ in the fields “_C_VentType”, “_P_Haven”, “_C_Vent”, “_C_Comp”, “_C_Ins”, “_C_Comb”, “_L_Adaptive”. Unclassified plots have value “N/D” in the fields “_WFacade”, “_FacadesxPlot”, “_B_OppFacades”, “_C_VentType”, “_C_Vent”, “_C_Comp”, “_C_Ins”, “_C_Comb”.

The following Fig. 1, Fig. 2 exemplify two possible statistical and graphical outputs of the database. The first represents a map of the housing plots in Barcelona, classified according to the proposed cross-ventilation typologies. The second shows the number of housing plots and housing units in the city according to the same classification.

Fig. 1.

Map of Barcelona showing cross-ventilation typologies of housing plots.

Fig. 2.

Number of housing plots and units in Barcelona by cross-ventilation typology.

4. Experimental Design, Materials and Methods

After downloading the public open databases, the methodology used to generate the dataset follows 5 steps: the creation of a property units database (step 1), a floors database (step 2), and a first, partial cadastral plots database from alphanumeric cadastral data (step 3), the extraction of data from vectorial databases from the national cadastre and the city municipality (step 4), and the classification of the plots according to the climatic variables (step 5). The following Fig. 3 portrays the workflow of the methodology.

Fig. 3.

Workflow of the methodology.

Cadastral alphanumeric data is distributed by the Spanish Cadaster in a Catalog file format (‘.CAT’ extension). The methodology uses data from three out of the eight different record types available in the Catalog file: Type 13 (Construction unit record), Type 14 (Construction record) and Type 15 (Property record). The fields extracted from the three record types and used in the methodology are named: T13_RefPlot, T14_RefPlot, T14_RefUnit, T14_Block, T14_Stair, T14_Floor, T14_Door, T14_CodeUse, T14_CodeRen, T14_YearRen, T14_YearCon, T14_SurfUnit, T14_CodeCon, T15_RefPlot, T15_RefUnit, T15_Block, T15_Stair, T15_Floor, T15_CodeUse. The information contained in these fields is processed and partially merged to simplify the dataset, leading to the creation of new custom fields, identified by the symbol “_” at the beginning of their names. This process generates the property units database (Table 2). Then, information is partially aggregated to create the floors database (Table 3). Finally, data is further aggregated to create a partial version of the final dataset (Table 4). The Spanish cadastre provides vector data at different scales, from building blocks to urban sub-lots contained in cadastral plots. Files are distributed in ESRI Shapefile format (‘.shp’ extension). The methodology uses three cadastral vector datasets: MASAS (building blocks), PARCELAS (cadastral plots) and CONSTRU (urban sub-lots). The first file is used to detect the width of the narrowest street surrounding the cadastral plots. The second file is manipulated to detect the number, position and width of the facades of the plot, its perimeter and its access to building block patios. The third file is used to detect the presence of ventilation patios within cadastral parcels. This information is then embedded in the partial cadastral plots database.

Table 2.

Fields contained in the property units database.

Assigned name	Description	Type	Measurement units or value labels
_RefPlot	Cadastral reference of the plot	Text	-
_RefUnit	Cadastral reference of the unit	Text	-
_B_Unbuilt	Boolean parameter indicating if the plot is unbuilt land	Boolean	-
_RefFloor	Reference code for each floor of each cadastral plot	Text	-
T15_CodeUse	Use group code according to Dirección General del Catastro	Text	-
T14_CodeUse	Unit’s use code according to Dirección General del Catastro	Text	-
T14_CodeRen	Unit’s renovation type code	Text	-
T14_YearRen	Year of the unit’s renovation	Integer	Years
T14_YearCon	Year of inscription in the registry	Integer	Years
T14_SurfUnit	Cadastral surface of the unit	Decimal	Square meters
T14_CodeCon	Building type code according to Norma Técnica de Valoración	Text	-
_BlockStair	Building block and staircase reference	Boolean	-
T14_Door	Door reference	Text	-
_B_Basement	Boolean parameter indicating if the unit is at a basement floor	Boolean	-

Table 3.

Fields contained in the floors database.

Assigned name	Description	Type	Measurement units or value labels
_RefPlot	Cadastral reference of the plot	Text	-
_RefFloor	Reference code for each floor of each cadastral plot	Text	-
_UnitsxFloor	Number of property units on each floor of a building	Integer	Property units/floor

Table 4.

Fields contained in the partial cadastral plots database.

Assigned name	Description	Type	Measurement units or value labels
_RefPlot	Plot cadastral reference	Text	-
_B_Housing	Boolean parameter indicating if the plot contains housing units	Boolean	-
_YearConRen	Construction year of the building or year of complete renovation of the cadastral plot	Integer	Years
_B_Single	Boolean parameter indicating if the plot corresponds to a single-family house	Boolean	-
_UnitsxPlot	Total number of housing units in the cadastral plot	Integer	Housing units/plot
_M_UnitsxFloor	Mean number of property units per floor of a cadastral plot	Integer	Property units/floor
_M_SurfUnit	Mean surface of the housing units in the cadastral plot	Decimal	Square meters
_FloorsxPar	Approximated number of floors in the cadastral plot	Integer	Floors
_HApprox	Approximated height of the buildings in the plot (average height of 3 m per floor)	Integer	Meters

Among the files available for downloading at the Open Data Barcelona website, the methodology relies on the following three: BCN GrafVial Trams (‘.shp’ format), 2017 equip refugi (‘.gpkg’ format), and pev_iso2km4km_od (‘.gpkg’ format). The first file contains the road network of Barcelona, the second one represents public libraries and sports centres forming the city’s network of climate shelters, and the last file shows areas of walking time form parks larger than 0,5 ha considering a walking speed of 2 km/h. After computing walking time areas from climate shelters using the first two files, the methodology merges the result with the third file to classify plots by their proximity to climate shelters and parks. The result is also embedded in the partial cadastral plots database (Table 5).

Table 5.

Fields added to the partial cadastral plots database from vector data.

Assigned name	Description	Type	Measurement units or value labels
_WSApprox	Approximated width of the narrowest street adjacent to the cadastral plot	Integer	Meters
_WFacade	Width of the largest continuous segment facing a street in a plot’s perimeter	Integer	Meters
_FacadesxPlot	Number of continuous segments that face a street within a plot’s perimeter	Integer	Facades/plot
_Perimeter	Perimeter of a cadastral plot	Decimal	Meters
_B_BlockPat	Boolean parameter indicating if the plot has access to a block courtyard	Boolean	-
_B_VentPat	Boolean parameter indicating if the plot has access to a ventilation shaft	Boolean	-
_B_OppFacades	Boolean feature indicating if the plot has 2+ different facades facing 2+ different streets	Boolean	-
_P_Ref	Walking time form public parks, libraries or sports centers	Integer	Minutes

Finally, cadastral plots are classified according to the three characteristics and to their overall adaptive capacity (Table 6). The classification relies on the following assumptions:

• •Housing units with openings on two opposite facades have higher ventilation potential than housing units with openings on one facade only [12].
• •An H/W threshold of 1,5 effectively separates ancient and modern urban fabric in Barcelona.
• •Since the first national regulation in Spain was adopted in 1979, buildings built after this date are likely to be thermally insulated, to comply with minimal energy standards.

Table 6.

Fields added to the partial cadastral plots database for classification.

Assigned name	Description	Type	Measurement units or value labels
_C_Vent	Field indicating whether the plot allows optimal cross-ventilation or not	Text	“VENT”, “N_VENT”, “0″, “N/D”
_C_Comp	Field indicating whether the plot is located in a compact urban fabric or not	Text	“COMP”, “N_COMP”, “0″, “N/D”
_C_Ins	Field indicating whether the envelope of buildings in the plot is thermally insulated or not	Text	“INS”, “N_INS”, “0″, “N/D”
_C_Comb	Combination of the three climatic variables	Text	“VENT_N_COMP_N_INS”, “N_VENT_COMP_N_INS”, “N_VENT_N_COMP_INS”, “N_VENT_COMP_INS”, “VENT_COMP_N_INS”, “VENT_N_COMP_INS”, “N_VENT_N_COMP_N_INS”, “VENT_COMP_INS”, “0_0_0″, “N/D_N/D_N/D”
_L_Adaptive	Level of adaptive capacity to extreme heat	Integer	“1″, “2″, “3″, “4″, “0″, “N/D”

Plots in cross ventilation typologies 0 and 1 are assigns “VENT” value in the field “_C_Vent”. Plots with a value of “_H/Wapprox” greater than 1,5 are assigned “COMP” in the field “_C_Comp”. Plots with a value of “_YearConRen” greater than 1979 are assignes “INS” in the field “_C_Ins”. The other plots are assignes “N_VENT”, “N_COMP”, and “N_INS” respectively. Levels of adaptive capacity are assigned as follows:

• •Level 1: Plots with “_C_Vent” value equal to “VENT”.
• •Level 2: Plots with “_C_VentType” value equal to “C2” and “_P_Ref” value equal to “5″, “10″, or “15”.
• •Level 3: Plots with “_C_VentType” value equal to “C2” and “_P_Ref” value equal to “20″; plots with “_C_VentType” value equal to “C3” and “_P_Ref” value equal to “5”.
• •Level 4: Plots with “_C_VentType” value equal to “C3” and “_P_Ref” value equal to “10″, “15″ or “20”.

The following Fig. 4 exemplifies the elaboration of cadastral data to include a cadastral plot in the final dataset. The plot’s reference number is “1025720DF3812E” and it is located in Carrer de Trafalgar, right outside the ancient city centre of Barcelona. From the property units database, we see that the plot hosts 29 property units, 2 of which are at the basement floor. It was built in 1861, and has not been renovated. From the floors database, we see the building has 7 floors above ground, hosting an average of 4 units each. From the elaboration of vector data, we can infer that the plot is located in a building block provided with a central patio, it has a single, 12 m wide facade overlooking a 25 m wide street, and it is provided with a ventilation patio. Therefore, its cross-ventilation typology is T3 (facade-patio), its H/W value is 0,8 and it is not thermally insulated. It is located at a 15 min walking distance from a climate haven, and has moderate adaptive capacity to extreme heat.

Fig. 4.

Example of data elaboration for a cadastral plot.

Limitations

The dataset is subject to some limitations due to simplifications applied in the methodology. These were made necessary by the complexity and the lack of detail characterizing the cadastral databases. The methodology is intended to be applied at large scales, where its limitations are less significant to the final results. Limitations apply to the following fields:

• •“_B_Housing”: some non-residential plots may contain small residential units and are therefore classified as residential by the methodology. These items were corrected manually.
• •“_H_Approx”: since cadastral data does not provide information on buildings’ height, the methodology assumes an average floor height of 3 m.
• •“_WSApprox”: due to simplifications in the MASAS layer, some street width may not be computed.
• •“_WFacade” and “FacadesxPlot”: these fields describe the plot’s boundary, which corresponds to the actual shape of buildings only when they occupy the whole plot area.
• •“_B_BlockPat”: the field considers a minimum building depth of 12 m for the whole city to detect the presence of building blocks.
• •“_B_VentPat”: the presence of ventilation patios within plots is detected by analysing the built height of items within the CONSTRU cadastral vector layer. Therefore, some small buildings could be classified as patios.

Ethics Statement

The authors confirm that they have read and adhered to the ethical requirements for publication in Data in Brief.

CRediT Author Statement

Aldo Moccia: Data curation, Formal analysis, Methodology, Validation, Visualization, Writing - original draft. Carlos Alonso Montolío: Conceptualization, Methodology, Writing - review & editing. Helena Coch: Conceptualization, Methodology, Resources, Writing – review & editing.

Data Availability

• CORA - Repositori de Dades de recercaElaboration of Spanish public open data to assess climatic variables of the built environment (Original data).