Description
Cities, infrastructures and the environment today generate a constant flow of data from sensors, transport networks, weather stations and Internet of Things (IoT) platforms, understood as networks of physical devices (digital traffic lights, air quality sensors, etc.) capable of measuring and transmitting information through digital systems. This growing volume of information makes it possible to improve the provision of public services, anticipate emergencies, plan the territory and respond to challenges associated with climate, mobility or resource management.
The increase in connected sources has transformed the nature of geospatial data. In contrast to traditional sets – updated periodically and oriented towards reference cartography or administrative inventories – dynamic data incorporate the temporal dimension as a structural component. An observation of air quality, a level of traffic occupancy or a hydrological measurement not only describes a phenomenon, but also places it at a specific time. The combination of space and time makes these observations fundamental elements for operating systems, predictive models and analyses based on time series.
In the field of open data, this type of information poses both opportunities and specific requirements. Opportunities include the possibility of building reusable digital services, facilitating near-real-time monitoring of urban and environmental phenomena, and fostering a reuse ecosystem based on continuous flows of interoperable data. The availability of up-to-date data also increases the capacity for evaluation and auditing of public policies, by allowing decisions to be contrasted with recent observations.
However, the opening of geospatial data in real time requires solving problems derived from technological heterogeneity. Sensor networks use different protocols, data models, and formats; the sources generate high volumes of observations with high frequency; and the absence of common semantic structures makes it difficult to cross-reference data between domains such as mobility, environment, energy or hydrology. In order for this data to be published and reused consistently, an interoperability framework is needed that standardizes the description of observed phenomena, the structure of time series, and access interfaces.
The open standards of the Open Geospatial Consortium (OGC) provide that framework. They define how to represent observations, dynamic entities, multitemporal coverages or sensor systems; establish APIs based on web principles that facilitate the consultation of open data; and allow different platforms to exchange information without the need for specific integrations. Its adoption reduces technological fragmentation, improves coherence between sources and favours the creation of public services based on up-to-date data.
Interoperability: The basic requirement for opening dynamic data
Public administrations today manage data generated by sensors of different types, heterogeneous platforms, different suppliers and systems that evolve independently. The publication of geospatial data in real time requires interoperability that allows information from multiple sources to be integrated, processed and reused. This diversity causes inconsistencies in formats, structures, vocabularies and protocols, which makes it difficult to open the data and reuse it by third parties. Let's see which aspects of interoperability are affected:
- Technical interoperability: refers to the ability of systems to exchange data using compatible interfaces, formats and models. In real-time data, this exchange requires mechanisms that allow for fast queries, frequent updates, and stable data structures. Without these elements, each flow would rely on ad hoc integrations, increasing complexity and reducing reusability.
- The Semantic interoperability: Dynamic data describe phenomena that change over short periods – traffic levels, weather parameters, flows, atmospheric emissions – and must be interpreted consistently. This implies having observation models, Vocabularies and common definitions that allow different applications to understand the meaning of each measurement and its units, capture conditions or constraints. Without this semantic layer, the opening of data in real time generates ambiguity and limits its integration with data from other domains.
- Structural interoperability: Real-time data streams tend to be continuous and voluminous, making it necessary to represent them as time series or sets of observations with consistent attributes. The absence of standardized structures complicates the publication of complete data, fragments information and prevents efficient queries. To provide open access to these data, it is necessary to adopt models that adequately represent the relationship between observed phenomenon, time of observation, associated geometry and measurement conditions.
- Interoperability in access via API: it is an essential condition for open data. APIs must be stable, documented, and based on public specifications that allow for reproducible queries. In the case of dynamic data, this layer guarantees that the flows can be consumed by external applications, analysis platforms, mapping tools or monitoring systems that operate in contexts other than the one that generates the data. Without interoperable APIs, real-time data is limited to internal uses.
Together, these levels of interoperability determine whether dynamic geospatial data can be published as open data without creating technical barriers.
OGC Standards for Publishing Real-Time Geospatial Data
The publication of georeferenced data in real time requires mechanisms that allow any user – administration, company, citizens or research community – to access them easily, with open formats and through stable interfaces. The Open Geospatial Consortium (OGC) develops a set of standards that enable exactly this: to describe, organize and expose spatial data in an interoperable and accessible way, which contributes to the openness of dynamic data.
What is OGC and why are its standards relevant?
The OGC is an international organization that defines common rules so that different systems can understand, exchange and use geospatial data without depending on specific technologies. These rules are published as open standards, which means that any person or institution can use them. In the realm of real-time data, these standards make it possible to:
- Represent what a sensor measures (e.g., temperature or traffic).
- Indicate where and when the observation was made.
- Structure time series.
- Expose data through open APIs.
- Connect IoT devices and networks with public platforms.
Together, this ecosystem of standards allows geospatial data – including data generated in real time – to be published and reused following a consistent framework. Each standard covers a specific part of the data cycle: from the definition of observations and sensors, to the way data is exposed using open APIs or web services. This modular organization makes it easier for administrations and organizations to select the components they need, avoiding technological dependencies and ensuring that data can be integrated between different platforms.
The OGC API family: Modern APIs for accessing open data
Within OGC, the newest line is family OGC API, a set of modern web interfaces designed to facilitate access to geospatial data using URLs and formats such as JSON or GeoJSON, common in the open data ecosystem.
Estas API permiten:
- Get only the part of the data that matters.
- Perform spatial searches ("give me only what's in this area").
- Access up-to-date data without the need for specialized software.
- Easily integrate them into web or mobile applications.
In this report: "How to use OGC APIs to boost geospatial data interoperability", we already told you about some of the most popular OGP APIs. While the report focuses on how to use OGC APIs for practical interoperability, this post expands the focus by explaining the underlying OGC data models—such as O&M, SensorML, or Moving Features—that underpin that interoperability.
On this basis, this post focuses on the standards that make this fluid exchange of information possible, especially in open data and real-time contexts. The most important standards in the context of real-time open data are:
| OGC Standard | What it allows you to do | Primary use in open data |
| OGC API – Features It is an open web interface that allows access to datasets made up of "entities" with geometry, such as sensors, vehicles, stations or incidents. It uses simple formats such as JSON and GeoJSON and allows spatial and temporal queries. It is useful for publishing data that is frequently updated, such as urban mobility or dynamic inventories. |
Query features with geometry; filter by time or space; get data in JSON/GeoJSON. |
Open publication of dynamic mobility data, urban inventories, static sensors. |
| OGC API – Environmental Data Retrieval (EDR) It provides a simple method for retrieving environmental and meteorological observations. It allows data to be requested at a point, an area or a time range, and is particularly suitable for weather stations, air quality or climate models. Facilitates open access to time series and predictions. |
Request environmental observations at a point, zone or time interval. |
Open data on meteorology, climate, air quality or hydrology. |
| OGC SensorThings API It is the most widely used standard for open IoT data. It defines a uniform model for sensors, what they measure and the observations they produce. It is designed to handle large volumes of data in real time and offers a clear way to publish time series, pollution, noise, hydrology, energy or lighting data. |
Manage sensors and their time series; transmit large volumes of IoT data. |
Publication of urban sensors (air, noise, water, energy) in real time. |
| OGC API – Connected Systems It allows sensor systems to be described in an open and structured way: what devices exist, how they are connected to each other, in what infrastructure they are integrated and what kind of measurements they generate. It complements the SensorThings API in that it does not focus on observations, but on the physical and logical network of sensors. |
Describe networks of sensors, devices and associated infrastructures. |
Document the structure of municipal IoT systems as open data. |
|
OGC Moving Features |
Represent moving objects using space-time trajectories. | Open mobility data (vehicles, transport, boats). |
| WMS-T Extension of the classic WMS standard that adds the time dimension. It allows you to view maps that change over time, for example, hourly weather, flood levels or regularly updated images. |
View maps that change over time. | Publication of multi-temporal weather or environmental maps. |
Table 1. OGC Standards Relevant to Real-Time Geospatial Data
Models that structure observations and dynamic data
In addition to APIs, OGC defines several conceptual data models that allow you to consistently describe observations, sensors, and phenomena that change over time:
- O&M (Observations & Measurements): A model that defines the essential elements of an observation—measured phenomenon, instant, unity, and result—and serves as the semantic basis for sensor and time series data.
- SensorML: Language that describes the technical and operational characteristics of a sensor, including its location, calibration, and observation process.
- Moving Features: A model that allows mobile objects to be represented by means of space-time trajectories (such as vehicles, boats or fauna).
These models make it easy for different data sources to be interpreted uniformly and combined in analytics and applications.
The value of these standards for open data
Using OGC standards makes it easier to open dynamic data because:
- It provides common models that reduce heterogeneity between sources.
- It facilitates integration between domains (mobility, climate, hydrology).
- Avoid dependencies on proprietary technology.
- It allows the data to be reused in analytics, applications, or public services.
- Improves transparency by documenting sensors, methods, and frequencies.
- It ensures that data can be consumed directly by common tools.
Together, they form a conceptual and technical infrastructure that allows real-time geospatial data to be published as open data, without the need to develop system-specific solutions.
Real-time open geospatial data use cases
Real-time georeferenced data is already published as open data in different sectoral areas. These examples show how different administrations and bodies apply open standards and APIs to make dynamic data related to mobility, environment, hydrology and meteorology available to the public.
Below are several domains where Public Administrations already publish dynamic geospatial data using OGC standards.
Mobility and transport
Mobility systems generate data continuously: availability of shared vehicles, positions in near real-time, sensors for crossing in cycle lanes, traffic gauging or traffic light intersection status. These observations rely on distributed sensors and require data models capable of representing rapid variations in space and time.
OGC standards play a central role in this area. In particular, the OGC SensorThings API allows you to structure and publish observations from urban sensors using a uniform model – including devices, measurements, time series and relationships between them – accessible through an open API. This makes it easier for different operators and municipalities to publish mobility data in an interoperable way, reducing fragmentation between platforms.
The use of OGC standards in mobility not only guarantees technical compatibility, but also makes it possible for this data to be reused together with environmental, cartographic or climate information, generating multi-thematic analyses for urban planning, sustainability or operational transport management.
Example:
The open service of Toronto Bike Share, which publishes in SensorThings API format the status of its bike stations and vehicle availability.
Here each station is a sensor and each observation indicates the number of bicycles available at a specific time. This approach allows analysts, developers or researchers to integrate this data directly into urban mobility models, demand prediction systems or citizen dashboards without the need for specific adaptations.
Air quality, noise and urban sensors
Networks for monitoring air quality, noise or urban environmental conditions depend on automatic sensors that record measurements every few minutes. In order for this data to be integrated into analytics systems and published as open data, consistent models and APIs need to be available.
In this context, services based on OGC standards make it possible to publish data from fixed stations or distributed sensors in an interoperable way. Although many administrations use traditional interfaces such as OGC WMS to serve this data, the underlying structure is usually supported by observation models derived from the Observations & Measurements (O&M) family, which defines how to represent a measured phenomenon, its unit and the moment of observation.
Example:
The service Defra UK-AIR Sensor Observation Service provides access to near-real-time air quality measurement data from on-site stations in the UK.
The combination of O&M for data structure and open APIs for publication makes it easier for these urban sensors to be part of broader ecosystems that integrate mobility, meteorology or energy, enabling advanced urban analyses or environmental dashboards in near real-time.
Water cycle, hydrology and risk management
Hydrological systems generate crucial data for risk management: river levels and flows, rainfall, soil moisture or information from hydrometeorological stations. Interoperability is especially important in this domain, as this data is combined with hydraulic models, weather forecasting, and flood zone mapping.
To facilitate open access to time series and hydrological observations, several agencies use OGC API – Environmental Data Retrieval (EDR), an API designed to retrieve environmental data using simple queries at points, areas, or time intervals.
Example:
The USGS (United States Geological Survey), which documents the use of OGC API – EDR to access precipitation, temperature, or hydrological variable series.
This case shows how EDR allows you to request specific observations by location or date, returning only the values needed for analysis. While the USGS's specific hydrology data is served through its proprietary API, this case demonstrates how EDR fits into the hydrometeorological data structure and how it is applied in real operational flows.
The use of OGC standards in this area allows dynamic hydrological data to be integrated with flood zones, orthoimages or climate models, creating a solid basis for early warning systems, hydraulic planning and risk assessment.
Weather observation and forecasting
Meteorology is one of the domains with the highest production of dynamic data: automatic stations, radars, numerical prediction models, satellite observations and high-frequency atmospheric products. To publish this information as open data, the OGC API family is becoming a key element, especially through OGC API – EDR, which allows observations or predictions to be retrieved in specific locations and at different time levels.
Example:
The service NOAA OGC API – EDR, which provides access to weather data and atmospheric variables from the National Weather Service (United States).
This API allows data to be consulted at points, areas or trajectories, facilitating the integration of meteorological observations into external applications, models or services based on open data.
The use of OGC API in meteorology allows data from sensors, models, and satellites to be consumed through a unified interface, making it easy to reuse for forecasting, atmospheric analysis, decision support systems, and climate applications.
Best Practices for Publishing Open Geospatial Data in Real-Time
The publication of dynamic geospatial data requires adopting practices that ensure its accessibility, interoperability, and sustainability. Unlike static data, real-time streams have additional requirements related to the quality of observations, API stability, and documentation of the update process. Here are some best practices for governments and organizations that manage this type of data.
- Stable open formats and APIs: The use of OGC standards – such as OGC API, SensorThings API or EDR – makes it easy for data to be consumed from multiple tools without the need for specific adaptations. APIs must be stable over time, offer well-defined versions, and avoid dependencies on proprietary technologies. For raster data or dynamic models, OGC services such as WMS, WMTS, or WCS are still suitable for visualization and programmatic access.
- DCAT-AP and OGC Models Compliant Metadata: Catalog interoperability requires describing datasets using profiles such as DCAT-AP, supplemented by O&M-based geospatial and observational metadata or SensorML. This metadata should document the nature of the sensor, the unit of measurement, the sampling rate, and possible limitations of the data.
- Quality, update frequency and traceability policies: dynamic datasets must explicitly indicate their update frequency, the origin of the observations, the validation mechanisms applied and the conditions under which they were generated. Traceability is essential for third parties to correctly interpret data, reproduce analyses and integrate observations from different sources.
- Documentation, usage limits, and service sustainability: Documentation should include usage examples, query parameters, response structure, and recommendations for managing data volume. It is important to set reasonable query limits to ensure the stability of the service and ensure that management can maintain the API over the long term.
- Licensing aspects for dynamic data: The license must be explicit and compatible with reuse, such as CC BY 4.0 or CC0. This allows dynamic data to be integrated into third-party services, mobile applications, predictive models or services of public interest without unnecessary restrictions. Consistency in the license also facilitates the cross-referencing of data from different sources.
These practices allow dynamic data to be published in a way that is reliable, accessible, and useful to the entire reuse community.
Dynamic geospatial data has become a structural piece for understanding urban, environmental and climatic phenomena. Its publication through open standards allows this information to be integrated into public services, technical analyses and reusable applications without the need for additional development. The convergence of observation models, OGC APIs, and best practices in metadata and licensing provides a stable framework for administrations and reusers to work with sensor data reliably. Consolidating this approach will allow progress towards a more coherent, connected public data ecosystem that is prepared for increasingly demanding uses in mobility, energy, risk management and territorial planning.
Content created by Mayte Toscano, Senior Consultant in Technologies related to the data economy. The content and views expressed in this publication are the sole responsibility of the author.
