aplicaciones

Opening up public data is just the first step on a much more ambitious path. The true success of open data policies is not measured in the number of datasets published or in the volume of gigabytes downloaded, but in the real impact that this data generates on society, the economy and innovation. That is, in its reuse to generate value-added services, support strategic decision-making, etc.

However, due to the anonymity that usually prevails in downloading data, open data initiatives are often unaware of who is using the information and for what. Implementing an active methodology for capturing use cases is essential to break this barrier and know the value of data.

Next, we’ll examine why this practice is crucial, what criteria to follow when selecting cases to consider, and what key information we should gather.

Why is it important to capture and publish examples of reuse?

The capture and analysis of use cases is one of the mechanisms that open data publishers have to measure the impact of their open data initiatives. In this area, we understand a use case as  any business model, application, platform, service, analytics, etc. developed by an entity (whether a company, startup, NGO or the citizens themselves) that generates tangible value through the reuse of public data. In other words, we focus on processes that transform abstract data into practical solutions that solve a real problem, improve decision-making or create a new business opportunity in the market. Open data platforms usually have a section where they publish localized use cases, either through catalogs or repositories where companies with business models based on open data, applications, services or success stories are collected through specific articles or reports. It is a showcase that benefits all actors in the data ecosystem:

• For reuse companies: it works as a free high-visibility institutional showcase. Appearing on official portals, whether international, national, regional or local, endorses its reputation, its technological capacity and its business model in the eyes of potential customers and investors.

• For society: it acts as an inspirational element that can trigger a "pull effect". Showing real and tangible solutions fosters a culture of data and stimulates entrepreneurs, researchers and developers to create new services.

  For the Public Administration: it allows us to know which datasets are the most in demand and what aspects they have in common (quality, formats, frequency of updating, etc.), which gives clues as to which issues should be promoted or improved in the publication exercise. In addition, knowledge about the use of data is very useful to justify the investment of resources in opening data and demonstrate the social return on investment (SROI).

  

Figure 1. Benefits of compiling open data use cases. Source: own elaboration - datos.gob.es.

Three ways to nurture the repository continuously

Locating companies with open data-driven business models and specific use cases may seem like a complicated task at first, but the secret lies in combining automation with presence in the right forums. To keep the catalog constantly updated, it is recommended to activate three complementary ways:

• Proactive listening: it consists of constantly monitoring social networks, the technological media, the lists of companies from associations in the sector (such as ASEDIE), as well as the winners of hackathons and innovation awards.
• Reactive channels: in parallel to the proactive search, it is necessary to maintain a permanent and visible communication channel on the web portal. It is usual to have a simple form so that the companies themselves can apply autonomously. Disseminating this communication channel through the various means of the initiative (such as social networks, periodic newsletters, etc.) is essential to guarantee the growth of the catalog of use cases.
• Ecosystem alliances: another good option is to collaborate closely with business associations, universities, startup incubators  and technology parks, which are usually the main dynamizers and birthplaces of these reuse companies.

 How to choose the companies and cases to categorize?

In order for the collection of use cases to be a reference tool and maintain a high standard of quality, it is necessary to apply objective filtering criteria. It is recommended to prioritize projects under the following premises:

• Significant use of public data: the business model or solution must be based totally or partially on the reuse of datasets of public origin (local, regional, national or European), with a positive emphasis on the hybridization of different data sources (data mashup).
• Social or economic impact and relevance: priority will be given to those companies and solutions that solve real problems of citizens or the productive sectors (for example, optimisation of urban mobility, health diagnostic tools, energy efficiency or financial transparency).
• Maturity and viability: companies that offer applications, platforms or services that are already operational in the market or, at least, that have a tested and functional Minimum Viable Product (MVP) should be considered. It is advisable to avoid ideas or projects in a purely conceptual phase. These initial solutions can be showcased in the data competitions organised by various bodies, such as the Junta de Castilla y León or the Cabildo de Tenerife, among others.
• Quality and functionality: technological solutions must have a correct design and technical operation, with an optimal user experience. The goal is to ensure that the reuse of the data translates into a truly efficient and robust service for your target audience.
• Sectoral diversity: it is important to seek a thematic balance to demonstrate that open data is transversal. The repository or catalogue should reflect cases in sectors as diverse as agriculture, tourism, culture or education.

What information should be included about each use case?

In order for the use case sheets to be homogeneous, comparable and useful for portal users, the collection of information must be structured in a homogeneous way. Some of the basic pillars to include are:

1. Reuser profile: name of the company, organization or person that has implemented the business model or developed the solution. In the case of companies, you can include their year of foundation, size, sector of activity, link to their corporate website, etc.
2. Description of the business model/solution: name of the products or services, problems it solves, description of its functionality, target audience to which it is directed, etc.
3. Open data sources used: explicit detail of the datasets consumed, including their source of origin (e.g. "Meteorological data from the State Meteorological Agency - AEMET"). This directly helps connect supply with demand for data.
4. Impact obtained: Quantitative or qualitative indicators of the benefit generated for both the company and the reuser (time savings, emission reduction, turnover, jobs created, etc.).

Examples of use case catalogs

To inspire the design of your own repository or to understand how these methodologies are reflected in the real environment, it is useful to analyse how different public administrations are implementing them.

In the case of datos.gob.es, we have two different sections, one for companies and the other for applications. Both sections allow you to filter by sector of activity or tags, and also include a free text search engine, so that users can more easily find the use cases that correspond to their needs.

At  the regional and local level, there are also many bodies that have decided to include a specific section on their platforms that shows the potential use of the published datasets. This is the case of the Junta de Andalucía, the Basque Government or the Madrid City Council.

If we look at Europe, our neighbours also have this functionality in their open data platforms. National initiatives such as those of France or Lithuania, which occupy the top positions in open data maturity according to Open Data Maturity 2025, also have this type of showcase.

Conclusion: moving from published data to shared value

Measuring the impact of open data is critical to ensuring the long-term sustainability of open data initiatives. Without a clear methodology for capturing and structuring success stories, portals risk becoming mere warehouses of inert digital files.

By making real examples of the usefulness of open data available to the public, the Administration not only justifies public investment in this area, but also gives back to society the knowledge necessary to continue innovating.

“Save on Electricity” is a simple, user-friendly app that displays the price per kilowatt-hour under the PVPC (Voluntary Price for Small Consumers) rate. This is Spain’s regulated electricity rate, with prices that vary hourly based on the wholesale market and intended for households and small businesses with a contracted power capacity of up to 10 kW.

With this free information, users can plan ahead and manage their use of the most energy-intensive appliances to save on their next bill. Therefore, in addition to providing electricity price information, it helps raise awareness among users about responsible energy use.

The app has been available for over 12 years and has been downloaded by nearly 2 million users in Spain. It is compatible with Android devices.

Madriwa is an interactive web application designed to help people find the most suitable neighbourhood to live in Madrid by analysing urban data, nearby services and personal preferences.

The platform collects and processes information from more than 100 data sources, many of them from the Madrid City Council's open data, and updates this information regularly through automatic data analysis processes. With this data, it generates personalized profiles and recommendations on different areas of the city.

Through the application, the user can enter their interests or profile (for example, student, family or couple) and the tool analyzes which neighborhoods best fit those preferences. It also allows you to indicate important places such as work, university or the gym, to calculate distances and travel times.

Madriwa's key features include:

• Classification of neighborhoods in Madrid according to user preferences.
• Display on an interactive map with information on nearby services and facilities.
• Analysis of travel and isochronous times from different locations.
• Consultation of urban indicators such as average income, safety, population density or presence of facilities (schools, hospitals, public transport, etc.).

The app uses data analytics technologies, geographic information systems, and mapping visualization to transform large volumes of urban information into understandable recommendations for those looking for housing in the city.

Embarriados Atlas of the new urban and social vulnerability in Spain is a research project by the company 300,000 km/s and the COTEC Foundation that seeks to provide new knowledge about the relationship between city, mobility and urban inequality to support the formulation of public policies and avoid the potential risks of social segregation resulting from decarbonization. 

Embarriados is based on the idea that the new urban models of low mobility promote health and social well-being, but at the same time entail a risk of greater segregation of the population due to the new spatial dynamics that reduce the interaction between different social groups. Its objective is to provide evidence to design more inclusive cities and anticipate segregation risks in the transition to decarbonized urban models. 

The research is based on the analysis of massive data that allows describing the socioeconomic level of the population, urban fabrics and their uses, and daily movements from mobile telephony, integrating these layers to build territorial metrics and indicators. The project publishes replicable methodology and the data model for reuse. 

The portal includes a section with the final data model that includes the indicators and the clustering is shared so that it can be used. 

The application is a tourist mobility analytics platform that integrates overnight stay data from the INE's experimental statistics with its own spatial estimation algorithms, capable of identifying the attraction of tourists at the 100-metre grid level. This approach allows working with a high granularity, facilitating a precise understanding of how tourist flows are distributed and concentrated in the territory.

On this basis, the solution incorporates advanced models for the processing of mobility matrices, aimed at detecting patterns, recurrences and relevant dynamics in the movements of tourists. Based on this data, an automated system of insights generation  is activated  that identifies high-value events, that is, significant findings that emerge from large volumes of complex, redundant or apparently obvious information, and that are transformed into useful and actionable knowledge.

All this analytical capacity is presented through a conversational interface based on generative artificial intelligence (GenAI/LLM), which allows non-technical users to interact with the system using natural language. In this way, the exploration, understanding and interpretation of data is facilitated, reducing technical complexity and favoring informed decision-making in areas such as tourism planning, destination management and the optimization of strategies within the sector.

IGN Eclipses is the official app for viewing and planning the three major eclipses that will be visible in Spain in 2026, 2027, and 2028. The mobile app uses official data from the National Geographic Institute (IGN), ensuring accuracy, reliability, and scientific rigor in all the information it provides.

Designed for all types of users, from astronomy enthusiasts to those who simply want to enjoy these spectacular natural phenomena, IGN Eclipses provides detailed information on how each eclipse will appear from any location in Spain.

The app includes comprehensive ephemeris data for each event, including exact times, phases, magnitudes, duration, and path. Thanks to its interactive map and location tools, you can immediately check whether the eclipse will be visible at your location or anywhere else in Spain.

Key features:

• Official data from the National Geographic Institute (IGN).

• Detailed ephemerides for the solar eclipses of 2026, 2027, and 2028.

• Visibility by location: see how it will be observed from anywhere in Spain.

• Interactive map showing the path and optimal viewing areas.

• Planning tools to prepare your trip and choose the best location.

• Weather forecast provided by AEMET.

• Clear and accessible information, designed for all audiences.

IGN Eclipses accompanies you during this exceptional period of celestial phenomena.

Before performing a data visualization, it is important to understand two issues. On the one hand, what exactly you have in your hands, that is, the type of data, its format and other relevant characteristics; and, on the other hand, what is to be visualized, the objective of the graphic representation that is going to be made.

In the specific case of geographical data , enormous narrative possibilities open up because visualizations allow territorial distributions to be shown, spatial patterns to be identified, regions to be compared, or the evolution of a phenomenon to be traced in time and space. To take advantage of these possibilities, it is important to keep in mind that the archive can:

• Contain coordinates in different reference systems.
• Represent phenomena that require very specific types of maps.

Taking a few minutes to understand those features before choosing a tool is actually the shortest path to a useful and rigorous result. In this post we review, step by step, how geographic data should be worked on and what tools exist to represent it graphically.

Before drawing any map: format, scale and projection

The first pitfall when working with geospatial data is often format. Georeferenced data comes in a wide variety of presentations: from a simple CSV with latitude and longitude columns, to more specialized formats such as GeoJSON (ideal for exchanging geometries in web environments), Shapefile (SHP, the historical standard for geographic information systems), or scientific formats such as NetCDF and GRIB (designed for climate and meteorological data in grids). Knowing what format the data is in and which one is most suitable for each tool saves a lot of time and avoids import errors.

The second critical aspect is the coordinate reference system (CRS). Not all coordinates speak the same language. The WGS84 system is the one used by GPS and most web map services; UTM, on the other hand, works in meters and is more accurate for distance or area calculations. Mixing data into different systems without reprojecting them (i.e., without converting coordinates from one reference system to another) produces displacements and geometries that do not fit.

The third element to consider before choosing a tool is the type of representation that best communicates the data. It is not the same to show points of interest as it is to trace trajectories, make a choropleth map (with areas colored according to a statistical value), or build digital elevation models or 3D visualizations. Each type of data and each analytical question has its most appropriate cartographic representation.

With these three clear factors (format, projection and type of map) it is time to choose the tool.

Basic Tools: Exploration Without Installation

For those who are new to geographic data visualization, or for those who need to explore a dataset quickly without going into complex configurations, there are accessible options that work directly from the browser or with minimal installation. They are ideal for a first contact with data and for communicating results to non-technical audiences.

Kepler.gl is probably the best option for those who want to get quality interactive maps without writing a single line of code. It is a free and open-source web tool that allows you to drag and drop files in formats such as CSV, GeoJSON or Shapefile and get visualizations immediately.

• What it's used for: Visual exploration of large volumes of mobility data, spatial distribution, and geographic patterns.
• Supported formats: CSV, GeoJSON, Shapefile, and JSON.
• Strength: it offers multiple types of layers – points, arcs, hexbinning, contours – with an intuitive visual interface and visually very careful results, without the need to install anything.

Google Earth is another accessible option for initial exploration. It's free but not open-source, and uploaded data can be processed by Google. Its web version allows you to import KML/KMZ files and is useful for contextualizing information on satellite imagery.

• What it's used for: Contextualization of data on satellite imagery and visual geographic exploration.
• Supported formats: KML and KMZ.
• Strength: the quality and updating of its satellite imagery database makes it a reference tool for placing data in its real territorial context. For rigorous analysis or institutional publication, it is advisable to evaluate more open alternatives.

Intermediate level: Python libraries for analysis and publishing

When the initial exploration gives way to analysis and the need to reproduce, automate, or integrate maps into broader workflows, there are Python libraries that can be a good option. Their use requires basic programming knowledge, but in return they allow much greater control over every aspect of the visualization and facilitate integration with other data analysis tools.

Cartopy is a library that integrates with Matplotlib and is oriented towards the representation of scientific and climate data. Its great strength is the management of cartographic projections, with support for dozens of reference systems.

• What it is used for: Generation of publication maps with scientific data, especially climate and atmospheric data in grid format.
• Supported formats: NetCDF, GRIB, and any source compatible with Matplotlib.
• Strength: fine control over projections and cartographic elements, ideal when the deformation introduced by the projection has a direct impact on the interpretation of the data.

Folium occupies a different niche: it generates interactive web maps based on Leaflet.js directly from Python code, without the need for JavaScript knowledge. It's especially convenient for producing visualizations that are integrated into  Jupyter notebooks or web pages.

• What it's used for: Creating interactive maps for web publishing or notebook presentation, with markers, layers, and pop-ups.
• Supported formats: GeoJSON, CSV, and data sources from pandas and GeoPandas.
• Strength: It combines the convenience of Python with the interactivity of Leaflet.js, allowing you to generate complete web visualizations with very few lines of code. Its main limitation is performance with very large datasets.

Advanced level: web maps with full control

If the goal is to build cartographic applications integrated into their own web environments, with the capacity to handle large volumes of data and offer a fluid user experience, it is necessary to go a step further. Tools at this level require web development skills, but offer virtually unlimited control over the behavior and appearance of the map.

OpenStreetMap (OSM)  is not exactly a visualization tool, but the world's largest collaborative geographic database, with an open license (ODbL). Its ecosystem includes tools like Overpass Turbo for querying and extracting data, and its cartographic tiles are the foundation on which many web maps are built.

• What it's used for: Obtaining open geographic data and using it as a basemap in web projects.
• Supported formats: OSM XML, PBF and GeoJSON via export.
• Strength: It is the most comprehensive and up-to-date source of open geographic data in the world. For projects committed to open data, using OSM as a foundation is the most consistent option with those principles.

MapLibre GL JS is an open-source JavaScript library that allows you to build high-performance interactive web maps using vector tiles.

• What it's used for: Web mapping app development with full style customization, dynamic data layers, and interactive filters.
• Supported formats: Vector tiles (MVT), GeoJSON, and raster tile fonts.
• Strength: performance far superior to libraries based on SVG or classic canvas, with the ability to handle large geometries smoothly and almost unlimited visual customization.

Professional level: geographic information systems

When spatial analysis goes beyond visualization and requires complex operations on data such as reprojections, network analysis, interpolations, geometrie editing, or precision mapping production, a desktop geographic information system (GIS) is the right tool. This type of software is specifically designed for rigorous work with geospatial data and offers capabilities that no web solution can match.

QGIS is the go-to desktop GIS in the open source world. Free, cross-platform and with a very active community, it covers practically any need for analysis and cartographic production.

• What it's used for: Complex spatial analysis, layer editing, reprojections, generating quality maps for print or digital publishing, and automating geospatial workflows.
• Supported formats: Shapefile, GeoJSON, GeoTIFF, PostGIS, WMS, WFS, and dozens more.
• Strength: The combination of analytical power, flexibility, and zero licensing cost makes it the go-to choice for agencies that regularly work with geospatial data. The learning curve is real, but the investment pays for itself quickly.

ArcGIS, developed by Esri, is the most widely used commercial GIS platform in professional and institutional settings. It offers advanced map analysis, editing, and publishing capabilities, and its cloud ecosystem makes it easy to collaborate and manage geographic data portals.

• What it is used for: advanced spatial analysis, management of geospatial data infrastructures and publication of institutional cartographic portals.
• Supported formats: All industry standards, with native integration with Esri services.
• Strength: very mature ecosystem with professional technical support and wide implementation in the public sector. Its licensing model comes at a high cost that puts it out of reach for many teams. It is mentioned here because of its relevance in the sector, with QGIS being the open alternative that covers most needs without license cost.

Figure 1. Displays open spatial data. Source: own creation – datos.gob.es

None of these tools is better than the others in absolute terms: each one responds well to a type of task, a user profile and a context of use. However, in this post we select some of the most used according to the level of technical knowledge of each professional profile:

• For fast exploration and data communication: Kepler.gl
• For accessible geographic visualization and 3D exploration of the territory: Google Earth
• For scientific analysis reproducible in Python: Cartopy and Folium
• For web development with advanced mapping: MapLibre GL JS
• For open base mapping and projects that require free and editable data: OpenStreetMap
• And for spatial analysis and mapping production: QGIS

In all cases, the starting point is always the same: know the data, understand its structure, and make sure that the map to be built is the one that best communicates what that data has to say. The tool, in the end, is only the last step in a process that begins much earlier.

Description: The Housing Viewer of the Institute of Statistics and Cartography of Andalusia (IECA) is an interactive web application that allows you to explore and analyse the spatial distribution of housing in Andalusia using detailed maps and geographical data.

The tool is part of the statistical project "Characterization and distribution of built space in Andalusia", whose objective is to offer exhaustive information on the housing stock of the autonomous community. The data come mainly from the Real Estate Cadastre and are organised into a statistical grid of cells of 250 × 250 metres, which allows the location and characteristics of the dwellings to be studied with great territorial precision. 

Through the viewer, the user can:

• To visualize the geographical distribution of homes in the Andalusian territory.
• Consult statistical information associated with each area of the map.
• Search and navigate the map using zoom and cell selection tools.
• Access alphanumeric data and cartographic layers that show different characteristics of the residential stock. 

The application is designed for citizens, researchers, technicians and public administrations, as it facilitates the territorial analysis of housing and supports urban planning, the study of the real estate market or research on the development of the territory. In addition, the data can be downloaded and used in other geographic information systems.

Fuelconomy is a free fuel price comparison platform, covering 52,000+ petrol stations across France, Spain, Italy, United Kingdom, and Portugal. It aggregates official government price data (including datos.gob.es for Spain) into a single live database, updated twice daily. 

Users can compare fuel prices on an interactive map, find the cheapest station nearby, track 30-day price history, set price alerts, and plan fuel-efficient routes. 

The app is available in 5 languages (English, French, Spanish, Italian, Portuguese).

AL TEU COSTAT is an application that aims to improve the quality of life of people with disabilities and their environment through the strategic use of technology and data generated by the community itself. It is a platform that centralises information, resources, activities and support channels in a single accessible, intuitive and secure space.

This solution allows you to consult activities and news, access a specialized directory and, for those who register, publish needs, offer support and share proposals for improvement. This interaction generates real-time data on demands, concerns and opportunities for improvement in the field of disability.

The project, which arose from a citizen proposal in participatory budgets, is also an example of collaborative public innovation. The alliance with IThinkUPC and the Universitat Politècnica de Catalunya guarantees a development based on technical rigour, accessibility, digital security and long-term vision, avoiding commercial dependencies and reinforcing the public technology model at the service of the common good.

In short, AL TEU COSTAT is not only an informative app, but a community digital infrastructure that transforms citizen data into public action, strengthening inclusion, participation and quality of life in Rubí.