science

Spatial ETL

Spatial ETL is a science 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 ETL rather than just read about it. In short: Spatial extract, transform, load (spatial ETL), also known as geospatial transformation and load (GTL), is a process for managing and manipulating geospatial data, for example map data. It is a type of extract, transform, load (ETL) process, with software tools and libraries specialised for geographical information.

Key takeaways

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

Reference excerpt

Spatial extract, transform, load (spatial ETL), also known as geospatial transformation and load (GTL), is a process for managing and manipulating geospatial data, for example map data. It is a type of extract, transform, load (ETL) process, with software tools and libraries specialised for geographical information. A common use of spatial ETL is to convert geographical information from a data source into another format that can be more easily used, for example by importing it into GIS software. A tool may translate data directly from one format to another, or via an intermediate format. Intermediate formats are often used when data transformation must be carried out.

Origins and history Although ETL tools for processing non-spatial data have existed for some time, ETL tools that can manage the unique characteristics of spatial data only emerged in the early 1990s. Spatial ETL tools emerged in the GIS industry to enable interoperability (or the exchange of information) between the industry's diverse array of mapping applications and associated proprietary formats. However, spatial ETL tools are also becoming increasingly important in the realm of management information systems as a tool to help organizations integrate spatial data with their existing non-spatial databases, and also to leverage their spatial data assets to develop more competitive business strategies. Traditionally, GIS applications have had the ability to read or import a limited number of spatial data formats, but with few specialist ETL transformation tools; the concept being to import data then carry out step-by-step transformation or analysis within the GIS application itself. Conversely, spatial ETL does not require the user to import or view the data, and generally carries out its tasks in a single predefined process. With the push to achieve greater interoperability within the GIS industry, many existing GIS applications are now incorporating spatial ETL tools within their products; the ArcGIS Data Interoperability Extension being an example of this.

Transformation The transformation phase of a spatial ETL process allows a variety of functions; some of these are similar to standard ETL, but some are unique to spatial data. Spatial data commonly consists of a geographic element and related attribute data; therefore spatial ETL transformations are often described as being either geometric transformations – transformation of the geographic element – or attribute transformations – transformations of the related attribute data.

Common geospatial transformations Reprojection: the ability to convert spatial data between one coordinate system and another. Spatial transformations: the ability to model spatial interactions and calculate spatial predicates Topological transformations: the ability to create topological relationships between disparate datasets Resymbolisation: the ability to change the cartographic characteristics of a feature, such as colour or line-style Geocoding: the ability to convert attributes of tabular data into spatial data

Additional features Desirable features of a spatial ETL application are:

Data comparison: Ability to carry out change detection and perform incremental updates Conflict management: Ability to manage conflicts between multiple users of the same data Data dissemination: Ability to publish data via the internet or deliver by email regardless of source format Semantic processing: Ability to understand the rules of different data formats to minimize user input whilst preserving meaning

Uses Spatial ETL has a number of distinct uses:

Data cleansing: The removal of errors within a dataset Data merging: The bringing together of multiple datasets into a common framework – conflation is a good example of this Data verification: The comparison of multiple datasets for verification and quality assurance purposes Data conversion: Conversion between different data formats.

Examples of spatial ETL tools

FME (Feature Manipulation Engine) GDAL (Geospatial Data Abstraction Library) Wherobots (Open Source Spatial Library)

See also Business intelligence Object–relational database Spatial database

References

Worked examples

Example 1 — a first encounter with Spatial ETL

Start with the simplest possible case. Write down what Spatial ETL claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, 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 ETL 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 ETL 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 ETL

In research: Spatial ETL appears in science 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 ETL 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 ETL is common in secondary-school and first-year university syllabi. It links to neighbouring topics Extract, transform, load tools, Geographic information systems, so understanding it makes those chapters shorter.
In everyday life: Look for Spatial ETL 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.

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How to study Spatial ETL in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Spatial ETL 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 ETL out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Spatial ETL in simple terms?

Why does Spatial ETL matter?

Because it connects several science 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 ETL?

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 ETL.