engineering

Spatial data infrastructure

Spatial data infrastructure is a engineering topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Spatial data infrastructure rather than just read about it. In short: A spatial data infrastructure (SDI), also called geospatial data infrastructure, is a data infrastructure implementing a framework of geographic data, metadata, users and tools that are interactively connected in order to use spatial data in an efficient and flexible way. Another definition is "the technology, policies, standards, human resources, and related activities necessary to acquire, process, distribute, use…

Key takeaways

Spatial data infrastructure belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Spatial data infrastructure to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Spatial data infrastructure from memory before moving on to harder problems.

Reference excerpt

A spatial data infrastructure (SDI), also called geospatial data infrastructure, is a data infrastructure implementing a framework of geographic data, metadata, users and tools that are interactively connected in order to use spatial data in an efficient and flexible way. Another definition is "the technology, policies, standards, human resources, and related activities necessary to acquire, process, distribute, use, maintain, and preserve spatial data". Most commonly, institutions with large repositories of geographic data (especially government agencies) create SDIs to facilitate the sharing of their data with a broader audience. A further definition is given in Kuhn (2005): "An SDI is a coordinated series of agreements on technology standards, institutional arrangements, and policies that enable the discovery and use of geospatial information by users and for purposes other than those it was created for."

General Some of the main principles are that data and metadata should not be managed centrally, but by the data originator and/or owner, and that tools and services connect via computer networks to the various sources. A GIS is often the platform for deploying an individual node within an SDI. To achieve these objectives, good coordination between all the actors is necessary and the definition of standards is very important. The original example of an SDI is the United States National Spatial Data Infrastructure (NSDI), first mandated in the OMB Circular A-16 in 1996. In Europe since 2007, INSPIRE is a European Commission initiative to build a European SDI beyond national boundaries; the United Nations Spatial Data Infrastructure (UNSDI) plans to do the same for over 30 UN Funds, Programs, Specialized Agencies and member countries.

Software components An SDI should enable the discovery and delivery of spatial data from a data repository, via a spatial service provider, to a user. As mentioned earlier it is often wished that the data provider is able to update spatial data stored in a repository. Hence, the basic software components of an SDI are:

Software client - to display, query, and analyse spatial data (this could be a browser or a desktop GIS) Catalogue service - for the discovery, browsing, and querying of metadata or spatial services, spatial datasets and other resources Spatial data service - allowing the delivery of the data via the Internet Processing services - such as datum and projection transformations, or the transformation of cadastral survey observations and owner requests into Cadastral documentation (Spatial) data repository - to store data, e.g., a spatial database GIS software (client or desktop) - to create and update spatial data Besides these software components, a range of (international) technical standards are necessary that allow interaction between the different software components. Among those are geospatial standards defined by the Open Geospatial Consortium (e.g., OGC WMS, WFS, GML, etc.) and ISO (e.g., ISO 19115) for the delivery of maps, vector and raster data, but also data format and internet transfer standards by W3C consortium.

National spatial data infrastructures

List by country or administrative zone. It is not complete, is a sample of National Spatial Data Infrastructure (NSDI) official websites.

See also GeoSUR GEOSS GMES INSPIRE UNSDI GIS file formats GIS software International Cartographic Association (ICA) ArcGIS Geographic information system (GIS)

References

External links

The INSPIRE Directive: a brief description (JRC Audiovisuals) GSDI 11 World Conference: The Geo-Spatial event of 2009, Rotterdam The Netherlands Global Spatial Data Infrastructure (GSDI) Association Links to SDI initiatives from the GSDI Association website The Netherlands Coordination Office of UNSDI (UNSDI-NCO) The GeoNetwork portal of UNSDI-NCO (with over 17.800 metadata sets) Laboratory of Geo-Information Science and Remote Sensing SNIG - Portuguese National System for Geographic Information Journals International Journal of Spatial Data Infrastructure Research Books The SDI Cookbook from the Global Spatial Data Infrastructure Organisation (GSDI) Research and Theory in Advancing Spatial Data Infrastructure Concepts GIS Worlds: Creating Spatial Data Infrastructures Building European Spatial Data Infrastructures Software geOrchestra is a free, modular and interoperable Spatial Data Infrastructure software that includes other software like GeoNetwork, GeoServer, GeoWebCache,..., GeoNetwork is a free and open source (FOSS) cataloging application for spatially referenced resources, GeoNode is a web-based application and platform for developing geospatial information systems (GIS) and for deploying spatial data infrastructures (SDI), OpenSDI includes Open Source components like GeoServer and GeoNetwork, easySDI is a complete web-based platform for deploying any geoportal. Geoportal Server is an open source solution for building SDI models where a central SDI node is populated with content from distributed nodes, as well as SDI models where each node participates equally in a federated mode.

Worked examples

Example 1 — a first encounter with Spatial data infrastructure

Start with the simplest possible case. Write down what Spatial data infrastructure claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Spatial data infrastructure before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Spatial data infrastructure ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Spatial data infrastructure

In research: Spatial data infrastructure appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Spatial data infrastructure in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Spatial data infrastructure is common in secondary-school and first-year university syllabi. It links to neighbouring topics Geographic data and information regulation, IT infrastructure, Spatial analysis, so understanding it makes those chapters shorter.
In everyday life: Look for Spatial data infrastructure outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Spatial data infrastructure in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Spatial data infrastructure means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Spatial data infrastructure out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Spatial data infrastructure in simple terms?

Why does Spatial data infrastructure matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Spatial data infrastructure?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Spatial data infrastructure.