How to Utilize Spatineo Service Map to Your Advantage

Did you know that we provide a free tool for anyone to use, to see spatial web service availability all around Europe? That tool is called Spatineo Service Map. Service Map is an optimized tool for checking your country’s service availability in mere seconds. Being easy-to-use and free are also some benefits Spatineo Service Map offers, so why not to take advantage of it?

Using Spatineo Service Map to your advantage

We now see quite a wide adoption of the spatial web services provided by the public sector. Achieving widespread use requires not only good quality data and services, but also that the existence of these services are communicated and advertised to companies and private citizens. Our Service Map promotes openness, which should increase public curiosity and  scrutiny of the current service quality.

The more users know about your high quality services, the more impactful the quality of the service becomes. What do we mean by that? The more users your service has, the more it has potential to save time of all users combined. If one user saves one minute of their time, once your services have reached standards of high quality, think what kind of impact that quality would have on 10,000 or more users.

Once you have opened Spatineo Service Map, you’ll get a overall view of all services we have identified in Europe. At the bottom, you can see the timeline of the number of all known services over time split into the number of high-availability services (99% monthly availabily or more) and the rest of the services. This historic view of the availability data allows you to see Europe-wide and county specific trends in service number. You can also click on a particular month to see the availability statistics for that time on map.

Spatineo Service Map selection

On the right top side of the screen, there is a menu in which you can select which kind of information you see on the map and in the provider list. You have four themes to choose from:

  • Percentage of High Availability Services

  • Change in High Availability Services over the last three months

  • Total Number of Services

  • Change in Total Number of Services over the last three months

You can also dig deeper to region specific data. From this view you can see information on region level. In example Finland is divided into 17 regions and we can see how they compare to each other.  We have identified spatial web services in all but two regions in Finland. On the right side we list the most prominent data provider organisations located in the selected area. More detailed information for each service can be found in our advanced availability monitoring and usage analytics tool Spatineo Monitor, which is available for 14-day free trial.

How do we collect the data?

Spatineo harvests available spatial web services from service catalogues and search engines to keep its registry up to date. For the purposes of the map, services are broadly defined as any service endpoint that is described by a single service description document of a particular service type. For example, each WMS Capabilities document describes a single service. All services within our catalogue are continuously monitored. This monitoring procedure is compliant with the INSPIRE normalized testing procedure for availability and has provided us with data spanning back to 2012. To construct the map, availability results for each service are continuously tested month-by-month against the 99% availability threshold (not counting pre-announced maintenance windows) consistent with INSPIRE requirements.

Service Availability is vital for SDIs

The vision and goal of the INSPIRE legislation is to simultaneously open more data and increase its use. We at Spatineo believe it is crucial to show that organisations are working hard to fulfil their obligations. This transparency is necessary to inspire the private sector to discover and trust the spatial web services that can enable companies to both innovate and build new businesses that utilise the open spatial data.

For the actual service quality to improve, data providers should look for tools to monitor and analyse the quality of their services, tools such as Spatineo Monitor.

Simple Features make INSPIRE data more accessible

Simple Features INSPIRE DataThose of you involved with using and publishing spatial data probably have an idea of how complex Geography Markup Language (GML) documents can be sometimes. In principle XML and thus GML encodings are supposed to be readable to people as well as by computers. However, ingesting documents containing complex GML data can simply be too much for both us humans and GIS software to take. Even though there are valid reasons for using complex GML, the simple encoding alternatives including GML Simple Features profile and JSON are currently gaining support in many fields.

“Everything should be made as simple as possible, but not simpler”

The quote above is said to have originated from Albert Einstein, but it may actually be that an American composer Roger Sessions was only paraphrasing Einstein’s actual words in an article published in the New York Times on January 8th 1950. Regardless of its authenticity I really like this quote, as it nicely captures the evaluation criteria for the “right” level of conceptual modelling: If the representation of a real-world concept is too complex it’s difficult for the audience to understand, but if it’s too simplified, it no contains the essential information to make it useful for a particular use case. The world we live in is inherently complex, and thus it is very easy to overdo any conceptual model by adding to much detail or trying to generalize too far. At least for us humans the carefully designed simplification makes things and their relations easier to grasp, and thus improves our understanding and ability to make informed decisions.

GML is verbose – for a reason

Geography Markup Language (GML) is an international agreement for describing spatial features, or abstractions of location related real-world phenomena, in a way that makes reliable data exchange and storage possible between different organisations and computer systems. It’s standardized both by the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO) and widely used around the world. A countless number of domain specific GML-based data models called GML Application Schemas have been created during the years to describe features used in particular fields of applications such as traffic networks, buildings, weather phenomena etc. Notable examples are the GML Application Schemas defined for all the 34 environmental data themes of the INSPIRE Directive.

The GML data encoding (or any XML-based data encoding) is often seen as a extremely verbose way of delivering spatial information. GML files of several hundred megabytes or gigabytes in size are quite common, and the fraction of text containing the typically interesting actual data values may be just a few percent of the entire text within a GML file. For this reason, formats like JSON and various binary encodings are in some cases preferred over GML for spatial data delivery. The verboseness of GML is not just sloppy and inefficient design however. The structure of GML encoding is at least somewhat self-describing: the so called property-object-model of GML ensures that both the name and the type of each feature property is given within the GML file in addition to the property value. This makes easier to detect data encoding errors in GML files and adopting to small variations in the data structure, as the quite a lot of structural information is included with the data format itself. If the data structure description is separate from the data file, the data becomes completely unreadable if the structure information is lost.

Simpler alternatives for INSPIRE data

While GML certainly has its benefits, sometimes the GML Application Schemas just are too complex in structure to be useful for an average user. Many software libraries and applications have decided to support only a typically used subset of all the possible GML data structures and geometry types, as full GML support implementation would simply be too much work and complicated code to maintain. When users try to access complex GML feature data with this kind of software, the result varies from showing only part of the properties to refusing to show anything at all.

The GML complexity issue has been recognised in the INSPIRE community. The first strong arguments I personally heard for simplification of INSPIRE GML were given in the INSPIRE – What if..?” workshop of the OGC Technical Committee meeting in Delft on 23rd March 2017. The need for simplified data models and encodings was the key in presentation by Ine de Visser, Linda van den Brink and Thijs Brentjens of Geonovum as well as in the one by Paul van Genuchten from GeoCat. Since then, the issue has got into the Maintenance and Implementation Work Programme for 2016-2020: Action 2017.2 on alternative encodings will define ways to encode INSPIRE data that are more easily understood by current mainstream GIS software than the current INSPIRE GML Application Schemas, including GeoJSON and simple feature GML. This a great example of how the INSPIRE maintenance process works.

“Keep it simple stupid”

The KISS principle above is another popular quote related to design of both tangible and abstract things. It originates from the world of military aircraft design in 1960s. According to Wikipedia it was coined the lead engineer of Lockheed Skunk Works, Kelly Johnson. Design process following the KISS principle keep the simplicity of the system as a key design goal. The idea is not only to keep the systems understandable, but also to keep them running and fix them easily when something would break. Apart from the mechanical world of war machines, the KISS principle has been widely used in software and information design.

In the world of GIS data and software the KISS principle shows for example in how spatial features are modelled for storage, processing and visualisation: In many cases allowing all the complexity for features possible by the full GML specification is an overkill that leads too complicated, error-prone and inefficient data processing code. This issue has been noted and addressed by the OGC already in 1990s, and it lead to specifications for Simple Feature Access (SFA) including standardized geometry type restrictions and database storage solutions for GML features eventually adopted also by the as the ISO Standard 19125 in 2004.

Flat does not equal simple

The concept of restricted, “low adoption barrier” version of GML was taken further by the OGC GML Simple Features Profile for GML version 3.2 published in 2011. This specification defines complexity three levels of simple GML features starting from the simplest SF-0 and ending with the SF-2 corresponding to the aforementioned earlier OGC Simple Features Access Specification. At level SF-0 features may only contain simple property values like numbers, strings, dates, measures (with value and unit) and references to other features. Each property may also only appear zero or one time, and the selection of possible geometry types is limited. So a flat structure of the feature properties is required, but not enough for implementing the GML Simple Features Profile. Design and implementation of performant and reliable software limited to handling GML with these restrictions is considerably easier than supporting any kind of GML content. To make it easier for software applications to recognize data as Simple Feature GML the XML Schema definition of the data needs to explicitly declare conformance to one of the SF levels.

Weather and air quality data as Simple Features

I’ve written before about the history and importance of ISO/OGC Standard Observation and measurements (O&M) also known as ISO 19156. Standardized data models and encodings for observation and prediction data are really valuable for providing environmental information in reusable and widely understandable, open format. The probably most widely used data encoding for the O&M data model is the complex GML Application Schema defined in the OGC Observation and Measurements – XML Implementation Standard. For the reasons stated previously in this article, this GML encoding is however not easily accessible using many currently available generic GIS software libraries and applications. Having a standard data encoding for O&M which would be directly readable by common GIS software would make the offered data much easier to use in many use cases.

I’m involved in project for creating Simple Feature encodings for the O&M data model (OMSF). The project is common endeavour of the environmental measurement company Vaisala and the Finnish Meteorological Institute. The intention is to create a commonly agreed encoding for environmental observation and forecast data based on the O&M data model, that would be simple enough to be ingested by common, general-purpose GML-capable GIS software and common web mapping applications such as OpenLayers. The initial version of the GML Simple Feature schema for the OMSF is already available for comments in the OGC OMSF Github repository, and the goal is to define a parallel JSON encoding for these OM feature types as well. This project is well-connected with work currently underway both in the OGC (ISO 19156 revision, JSON encodings and the upcoming Web Feature Service 3 standard) as well as in INSPIRE (the Alternative encodings action mentioned before).

Want to stay ahead of the game, and get latest news from us? Subscribe now to our newsletter!

Spatial Data Infrastructures and Consulting – Cases where consulting has driven success

Spatial Data Infrastructures and services - consulting Spatineo

As most of our readers already know: Spatineo is the European quality assurance expert for spatial web services. We provide tools and solutions to organisations to enhance their spatial data infrastructures and services. Apart from our set of tools, we also give counseling, in the form of assisting our customers to make full use of our products and consulting.

Spatineo’s consulting varies from assessing the impact of your services and how to enhance it, to geospatial maturity of your organisation. In this blogpost we dissect couple of cases open, for you to have better understanding what kind of consultancy you could benefit from.

Case: Estimation of the economic value of spatially enabled services in Finland

Finland is in the middle of the government programme to unify the key spatial information from national, regional and municipal levels under one platform. National Geospatial Platform is the solutions that is in the making of. It will have a major impact on how spatial information and spatial web services will be available to even larger audience in the future.

Spatineo’s expertise was chosen to assess the potential economic value of spatially enabled services in Finland. This is the first time when a comprehensive understanding of the value is calculated. The impact of the National Geospatial Platform on the economic value is assessed as well. Impactfulness is something we have been focusing on lately. We have written about how key performance indicators reveal the impact of your services and Jaana Mäkelä made an exercise on how to calculate the impact of your services. The project will be ready at the end of August.

Finnish Meteorological Institute (FMI) – Modernizing forecasting tools & weather data delivery

Spatineo is helping the Finnish Meteorological Institute (FMI) in a software project for modernising forecasting tools and weather data delivery for aviation purposes. The project is related to a huge undertaking for improving the performance and safety of the European air traffic management systems (SESAR).  Common data models, reliable conversion tools and standardized data formats are essential for enabling data flows from aviation forecasters’ workstations to air traffic controllers and pilots responsible for millions of safety-critical day-to-day decisions in air traffic.

The project started in 2017 and it’s partly based on code created in previous FMI consulting projects dating back to 2014. The main role of Spatineo in this project has been in designing and developing software components for reliably handling and converting weather observation and forecast messages between different data models and formats. The code is published under the MIT Open Source license and is available for both commercial and non-commercial use.

Our expertise at your convenience – Spatial Web Service Consulting

So our consulting projects range from how organisations handle their spatial data, to what is the impactfulness of that data and how it can be enhanced. Spatineo’s team of experts would be able to adjust to needs of most spatial data related issues, so don’t be afraid to ask for quota or offer for consulting. Every situation is worth evaluation and discussion.

Want to stay ahead of the game, and get latest news from us? Subscribe now to our newsletter!