Historical data on transport infrastructures are raising increasing interest due to their power to stimulate economic and social change. Bearing this in mind, this GIS database provides six files organized in two Shapefile formats, with three for the Spanish railway lines (wide, narrow and high-speed) and another three for railway stations, between 1848 and 2023.

The 1:1M Geological Map of Spain covers the Spanish part of the Iberian Peninsula, the Balearic Islands, the Canary Islands, and Ceuta and Melilla (African territories).Enlace al navegador de información geocientífica del IGME (INFOIGME), el cual permite la visualización y consulta de la información de los elementos de cada capa.http://info.igme.es/visorweb/

Smart agriculture refers to tools that collect, store and analyze digital data along the agricultural value chain. Geographic Information System (GIS) system software is one of those tools used in the agricultural sector. The GIS System market in Spain had a value of over ** million dollars in 2019.

The 1:1M Quaternary Map of Spain covers the Spanish part of the Iberian Peninsula, the Balearic Islands, the Canary Islands, and Ceuta and Melilla (African territories).Enlace al navegador de información geocientífica del IGME (INFOIGME), el cual permite la visualización y consulta de la información de los elementos de cada capa.http://info.igme.es/visorweb/

Transport Network of the National Cartographic System (www.scne.es). Transport Network is a 3D linear network of Spain defined and published in accordance with the INSPIRE Directive. It is made up of five modes of transport: road network (which includes the road network, urban network and roads), rail transport network, inland waterway transport network, air transport network and cable transport network, in addition to their respective intermodal connections and associated infrastructures. It has been carried out from the integration of product data from the General State Administration (IGN, ADIF...) and from the Administrations of certain Autonomous Communities

The history of Spain has always been linked to navigation and its related infrastructure. This GIS database provides three files that reconstruct the ports, lighthouses and inland waterways that were in operation during the Modern Age and the Liberal Period. La historia de España siempre ha estado vinculada a la navegación y sus infraestructuras asociadas. En esta base de datos SIG se aportan tres ficheros que reconstruyen los puertos, faros y vías navegables interiores que estuvieron en operación durante la Edad Moderna y el Período Liberal.

The Spain Location-Based Services (LBS) market is experiencing robust growth, projected to reach a market size of €880 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 13.75% from 2019 to 2033. This expansion is driven by several key factors. Firstly, the increasing smartphone penetration and widespread adoption of mobile internet in Spain fuels demand for location-aware applications across various sectors. Secondly, advancements in technologies such as GPS, GIS, and IoT are enhancing the accuracy and capabilities of LBS, creating new opportunities in areas like smart city initiatives, logistics optimization, and personalized advertising. The tourism sector in Spain also acts as a significant catalyst, with tourists heavily reliant on navigation apps, location-based recommendations, and other LBS features. Furthermore, government initiatives promoting digital transformation and smart infrastructure development contribute positively to market growth. However, challenges exist. Data privacy concerns and regulations surrounding the collection and utilization of location data impose constraints. Competition among established players and new entrants in the LBS market intensifies, potentially impacting profit margins. Also, the effective implementation of LBS requires reliable infrastructure and consistent internet connectivity, which may pose challenges in certain areas of Spain. Despite these restraints, the market's strong growth trajectory is expected to continue, fueled by the increasing integration of LBS into various aspects of daily life and the continuous innovation within the sector. The market segmentation by component (hardware, software, services), location (indoor, outdoor), application (mapping, analytics, advertising, etc.), and end-user (transportation, IT, healthcare, etc.) reveals diverse opportunities for market players to specialize and capitalize on specific segments' unique demands and growth potential within the Spanish context. Recent developments include: February 2023: Mercedes-Benz and Google unveiled an extensive and visionary partnership aimed at revolutionizing the automotive industry and elevating the digital luxury car experience to new heights. In an industry-first move, Mercedes-Benz is set to develop its distinct navigation system, harnessing the advanced capabilities of the Google Maps Platform to craft an unparalleled driving experience. This groundbreaking collaboration will grant Mercedes-Benz exclusive access to Google's cutting-edge geospatial technologies, providing users with an array of exceptional features. These include comprehensive location data, automatic route optimization, up-to-the-minute traffic updates, and even predictive traffic insights, among other remarkable functionalities., January 2023: Mapbox, the leading platform for mapping and location services, joined forces with Toyota Motor Europe to introduce Cloud Navigation powered by Mapbox Dash. This transformative partnership brings an unprecedented level of real-time information to Toyota's Yaris, Yaris Cross, and Aygo X models, enhancing the driving experience in terms of efficiency, convenience, and safety. Alongside precision lane-level navigation, drivers can access a wealth of features such as live parking availability, speed limit alerts, and warnings for speed cameras. Furthermore, an upcoming pilot program will enable Toyota drivers to conveniently handle parking and fuel payments directly through their infotainment systems, further streamlining the driving experience.. Key drivers for this market are: Growing Demand for Geo-based Marketing, Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Potential restraints include: Growing Demand for Geo-based Marketing, Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Notable trends are: Indoor Location Segment is Expected to Hold Significant Share of the Market.

The historical settlement data compilation for Spain (HISDAC-ES) is a geospatial dataset consisting of over 240 gridded surfaces measuring the physical, functional, age-related, and evolutionary characteristics of the Spanish building stock. We scraped, harmonized, and aggregated cadastral building footprint data for Spain, covering over 12,000,000 building footprints including construction year attributes, to create a multi-faceted series of gridded surfaces (GeoTIFF format), describing the evolution of human settlements in Spain from 1900 to 2020, at 100m spatial and 5 years temporal resolution. Also, the dataset contains aggregated characteristics and completeness statistics at the municipality level, in CSV and GeoPackage format.!!! UPDATE 08-2023 !!!: We provide a new, improved version of HISDAC-ES. Specifically, we fixed two bugs in the production code that caused an incorrect rasterization of the multitemporal BUFA layers and of the PHYS layers (BUFA, BIA, DWEL, BUNITS sum and mean). Moreover, we added decadal raster datasets measuring residential building footprint and building indoor area (1900-2020), and provide a country-wide, harmonized building footprint centroid dataset in GeoPackage vector data format.File descriptions:Datasets are available in three spatial reference systems:HISDAC-ES_All_LAEA.zip: Raster data in Lambert Azimuthal Equal Area (LAEA) covering all Spanish territory.HISDAC-ES_IbericPeninsula_UTM30.zip: Raster data in UTM Zone 30N covering all the Iberic Peninsula + Céuta and Melilla.HISDAC-ES_CanaryIslands_REGCAN.zip: Raster data in REGCAN-95, covering the Canary Islands only.HISDAC-ES_MunicipAggregates.zip: Municipality-level aggregates and completeness statistics (CSV, GeoPackage), in LAEA projection.ES_building_centroids_merged_spatjoin.gpkg: 7,000,000+ building footprint centroids in GeoPackage format, harmonized from the different cadastral systems, representing the input data for HISDAC-ES. These data can be used for sanity checks or for the creation of further, user-defined gridded surfaces.Source data:HISDAC-ES is derived from cadastral building footprint data, available from different authorities in Spain:Araba province: https://geo.araba.eus/WFS_Katastroa?SERVICE=WFS&VERSION=1.1.0&REQUEST=GetCapabilitiesBizkaia province: https://web.bizkaia.eus/es/inspirebizkaiaGipuzkoa province: https://b5m.gipuzkoa.eus/web5000/es/utilidades/inspire/edificios/Navarra region: https://inspire.navarra.es/services/BU/wfsOther regions: http://www.catastro.minhap.es/INSPIRE/buildings/ES.SDGC.bu.atom.xmlData source of municipality polygons: Centro Nacional de Información Geográfica (https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)Technical notes:Gridded dataFile nomenclature:./region_projection_theme/hisdac_es_theme_variable_version_resolution[m][_year].tifRegions:all: complete territory of Spaincan: Canarian Islands onlyibe: Iberic peninsula + Céuta + MelillaProjections:laea: Lambert azimuthal equal area (EPSG:3035)regcan: REGCAN95 / UTM zone 28N (EPSG:4083)utm: ETRS89 / UTM zone 30N (EPSG:25830)Themes:evolution / evol: multi-temporal physical measurementslanduse: multi-temporal building counts per land use (i.e., building function) classphysical / phys: physical building characteristics in 2020temporal / temp: temporal characteristics (construction year statistics)Variables: evolutionbudens: building density (count per grid cell area)bufa: building footprint areadeva: developed area (any grid cell containing at least one building)resbufa: residential building footprint arearesbia: residential building indoor areaVariables: physicalbia: building indoor areabufa: building footprint areabunits: number of building unitsdwel: number of dwellingsVariables: temporalmincoy: minimum construction year per grid cellmaxcoy: minimum construction year per grid cellmeancoy: mean construction year per grid cellmedcoy: median construction year per grid cellmodecoy: mode (most frequent) construction year per grid cellvarcoy: variety of construction years per grid cellVariable: landuseCounts of buildings per grid cell and land use type.Municipality-level datahisdac_es_municipality_stats_multitemporal_longform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in long form. Note that a value of 0 for the year attribute denotes the statistics for records without construction year information.hisdac_es_municipality_stats_multitemporal_wideform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in wide form. Note that a value of 0 for the year suffix denotes the statistics for records without construction year information.hisdac_es_municipality_stats_completeness_v1.csv: This CSV file contains the missingness rates (in %) of the building attribute per municipality, ranging from 0.0 (attribute exists for all buildings) to 100.0 (attribute exists for none of the buildings) in a given municipality.Column names for the completeness statistics tables:NATCODE: National municipality identifier*num_total: number of buildings per municperc_bymiss: Percentage of buildings with missing built year (construction year)perc_lumiss: Percentage of buildings with missing landuse attributeperc_luother: Percentage of buildings with landuse type "other"perc_num_floors_miss: Percentage of buildings without valid number of floors attributeperc_num_dwel_miss: Percentage of buildings without valid number of dwellings attributeperc_num_bunits_miss: Percentage of buildings without valid number of building units attributeperc_offi_area_miss: Percentage of buildings without valid official area (building indoor area, BIA) attributeperc_num_dwel_and_num_bunits_miss: Percentage of buildings missing both number of dwellings and number of building units attributeThe same statistics are available as geopackage file including municipality polygons in Lambert azimuthal equal area (EPSG:3035).*From the NATCODE, other regional identifiers can be derived as follows:NATCODE: 34 01 04 04001Country: 34Comunidad autónoma (CA_CODE): 01Province (PROV_CODE): 04LAU code: 04001 (province + municipality code)

The Itinerarium Orbis Christiani, edited by Hogenberg between 1579 and 1580, is little known, despite being considered the first European road atlas. The Hispania map offers us a network of transport routes the implementation of which was believed to be later and representative of innovative advances in terms of the visual differentiation of these routes. This article contextualizes this map and analyzes it in detail, dating its creation and identifying its sources. It then studies the Spanish transport network during the sixteenth century and, specifically, the characteristics of the three types of routes shown on the map. This is done by comparing it with several itineraries and maps of the time. To confirm the reliability of the Hogenberg map and to ensure the actual existence of the other types of routes shown on the map, proximity algorithms were applied. The research results question the traditional view of the origin and motivations of the Spanish transport network, tracing its unplanned birth at least as far back as the sixteenth century.

Digital Surface Model (DSM) has three layers. Two layers come from the rasterisation of the building and vegetation classes among all the points of the LiDAR file .las; and the third layer is the hydrography of the Geographical Reference Information. By applying a suitable colour for each layer, the final product is visualised. ECW file format. ETRS89 reference geodetic system (in the Canary Islands REGCAN95, compatible with ETRS89) and EPSG projection: 3857 throughout the national territory

The Spanish Cultural Sites & Facilities dataset provides geospatial and descriptive information about cultural facilities across Spain, including theaters, museums, libraries, cultural centers, and other cultural venues. The dataset consists of 24,577 records and 28 attributes, capturing key details about each facility, such as its name, classification, location, contact information, and associated institutions.

This dataset is valuable for spatial analysis, urban planning, cultural heritage management, and public policy research. It enables researchers, policymakers, and cultural organizations to analyze the distribution, accessibility, and characteristics of cultural facilities across Spain.

The dataset is stored in GeoPackage (.gpkg) format, ensuring efficient geospatial data storage and compatibility with GIS software such as QGIS, ArcGIS, and other geospatial tools.

This dataset has been developed by Econcult, the Research Group on Cultural Economics at the University of Valencia, through the collection, cleaning, and processing of data from multiple sources of information.

A geographical name is a proper noun applied to a natural, man-made or cultural feature on Earth.

A feature can have different names in one or several languages and the names may be provided, together with appropriate information on the feature, in different products like maps and gazetteers as well as respective services.

An endonym is a name for a geographical feature in an official or well-established language occurring in that area where the feature is situated. An exonym is a name used in a specific language for a geographical feature situated outside the area where that language is widely spoken, and differing in form from the respective endonym(s) in the area where the geographical feature is situated. (UNGEGN, 2007).

In some cases names can be applied as attributes of appropriately modeled spatial objects. However, often the definition, classification, geometry and other attributes of these objects do not correspond with the respective named features. Besides, commonly named features such as elevations, islands, natural shoreline features and stretches of water bodies are seldom modeled as objects in spatial data sets.

A geographical name serves as a means to identify a location. Gazetteers and gazetteer services associate the names with corresponding features – or locations – by means of co-ordinates, feature types and/or other necessary information. A multi-lingual gazetteer (service) shall most probably be established as a part of INSPIRE.

This topographic vector tile layer is designed to be used as a basemap and a reference map. The map has been compiled by Esri and the ArcGIS user community from a variety of best available sources. The layer is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the World Topographic Map.Esta capa de baldosas vectoriales topográficas está diseñada para ser utilizada como mapa base y de referencia. El mapa ha sido compilado por Esri y la comunidad de usuarios de ArcGIS a partir de una variedad de las mejores fuentes disponibles. La capa está pensada para apoyar la galería de mapas base de ArcGIS Online. Para obtener más detalles sobre el mapa, visite el Mapa Topográfico Mundial.

This map displays the predominant age groups throughout Spain at the Census Section scale. The pop-up is configured to show the following information:Predominant age group and percentageTotal populationTotal householdsBreakdown of the number of people in each age groupTotal female and male populationData Note: Certain regions are historical areas belonging to more than one municipality, and are considered deserted. These areas have no official names or data associated with them. Due to this, these areas will appear on the map as "No Data".The source of this data is AIS, Instituto Nacional de Estadística (INE), and Esri Spain. The vintage of the data is 2013.

A on-line gis showing the geology of spain at 1:50 000 scale and 1:1000000 scale. The map is queryable and attribtuted with rock type and geological age. Both soild and drift geology is shown

Website: http://info.igme.es/visorweb/

The application shows building footprints being extruded into 3D models. The location being shown is in and around El Ejido in the City of Malaga. This process was accomplished in ESRI CityEngine.

Spain Geospatial Analytics Market size was valued at USD 1.83 Billion in 2024 and is projected to reach USD 4.43 Billion by 2032 growing at a CAGR of 9.57% from 2026 to 2032. The Spain Geospatial Analytics market is driven by increasing government investments in smart city projects and infrastructure modernization. These initiatives demand advanced spatial data analysis for urban planning, transportation, and environmental monitoring, boosting adoption of geospatial technologies. Additionally, the rise of location-based services across sectors like agriculture, logistics, and defense is fueling market growth. Integration of AI and big data with GIS tools further enhances decision-making capabilities, accelerating demand in both public and private sectors.

Hydrogeological Map of Spain. Layer names in English.Enlace al navegador de información geocientífica del IGME (INFOIGME), el cual permite la visualización y consulta de la información de los elementos de cada capa.http://info.igme.es/visorweb/

Cartography of benthic communities to promoting adequate strategies for the use, management and conservation of littoral areas depending on the ecological value of the different benthic communities established and on the local geographical distribution.

The database contains 95 selected walnut plantations monitored with the financings of the H2020 WOODnat project in Italy and Spain and georeferenced in WGS84 reference system (EPSG 4326). For each plantation, stationary, cultural and climatic data are available; on a sample of 30 trees for each plot data of growth, wood quality and sanitary conditions are available. These data can be exploited to assess potential wood volume obtainable and quality of raw material, and to identify the weaknesses and errors, strengths and opportunities of the experiences conducted to plan future plantings with greater awareness.