science

Georelational data model

Georelational data model 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 Georelational data model rather than just read about it. In short: A georelational data model is a geographic data model that represents geographic features as an interrelated set of spatial and attribute data. The georelational model was the dominant form of vector file format during the 1980s and 1990s, including the Esri coverage and Shapefile.

Key takeaways

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

Reference excerpt

A georelational data model is a geographic data model that represents geographic features as an interrelated set of spatial and attribute data. The georelational model was the dominant form of vector file format during the 1980s and 1990s, including the Esri coverage and Shapefile.

History The second era in the history of GIS, starting in the mid-1970s, was characterized by the rise of the first general-purpose GIS software programs (rather than the bespoke systems created in the 1960s and early 1970s). Each of these programs also created its own data file structures, primarily focused on finding innovative ways to store the spatial or geometric aspect of the data in the most efficient and error-free way. One example of this was the POLYVRT software and data structure (1973) from the Harvard Laboratory for Computer Graphics and Spatial Analysis, which inspired the Arc/INFO Coverage format. In experimental GIS software such as ODYSSEY, attribute data was only handled in a rudimentary way. Meanwhile, the relational database was quickly becoming the most promising software for managing non-spatial data, and several nascent GIS software companies chose to adopt it into their systems, especially Esri. Although there were exceptions such as the object-oriented data models in Smallworld GIS (1989) and Intergraph's experimental TIGRIS, georelational data dominated the GIS industry until the rise of spatial databases in the late 1990s. Most of them are obsolete, although the Shapefile is still in common (if decreasing) use.

Georelational formats In any vector data structure, the core unit is an object (either a geographic feature or a sample location for a field) that has a location in space (of 0, 1, 2, or 3 dimension) and a set of attributes. In the georelational model, these are stored as separate files: a geometry file that is usually custom-designed by a software developer for use in a particular program, and an attribute table that follows relational database principles; often, the latter is adopted directly from an existing relational database management system software. Examples of commonly used georelational data formats include:

ARC/INFO Coverage (Esri 1981-2005) The name ARC/INFO literally reflected the georelational design of the software and the coverage format. The ARC model or Coverage was the topological vector data structure developed by ESRI, based on earlier structures developed at Harvard such as POLYVRT. INFO was a relational database developed by Henco Software, Inc. (originally for financial management) that was licensed by ESRI. In the Coverage structure, each point, line, or polygon had an identification number, which could be joined to the row in the INFO table with the same primary key, as in a relational table join. In an ARC/INFO workspace (=directory/folder), all of the INFO tables were stored in a separate directory from the directories for the ARC data for each coverage. To process attribute data, the user had to leave the ARC program and start the INFO program. During the 1990s, Esri added support for other commercial RDBMS software for the attribute data. MGE (Intergraph 1989-2000) During the 1980s, Intergraph was an industry leader on workstation CAD with its IGDS software, including Microstation (developed by Bentley Systems). When it developed MGE (Modular GIS Environment), its first flagship GIS product, it directly incorporated the Microstation software as its interactive environment, and the Microstation Design File (.dgn, a non-topological vector graphics file format) for storing graphics. The associated attribute table could be stored in any RDBMS supported on Intergraph UNIX workstations, Informix being one of the most common. An ID attached to each object in the design file enabled a relational join to the rows in the attribute table. Shapefile (Esri 1992–present) As the GIS industry grew to incorporate more casual users, the inherent complexity of the coverage data structure became a concern. When Esri released ArcView GIS 2.0 in 1992, it introduced the new shapefile format for vector data. This was a much simpler data model, eliminating features such as topology, but was still a georelational design. A shape-"file" actually consisted of several files, including at the very least a .shp file to store the geometry, and a .dbf file for the attributes, the latter directly adopting the dBase format that was the dominant microcomputer database at the time (despite it being a proprietary trade secret, the .dbf format had been legally reverse-engineered by the xBase community and published). Rather than using a relational join to connect the two files, the shapefile merely uses file order: the first shape matches the first attribute row, and so on.

Worked examples

Example 1 — a first encounter with Georelational data model

Start with the simplest possible case. Write down what Georelational data model 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 Georelational data model 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 Georelational data model 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 Georelational data model

In research: Georelational data model 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 Georelational data model 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: Georelational data model is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data modeling, Geographic data and information, so understanding it makes those chapters shorter.
In everyday life: Look for Georelational data model 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 Georelational data model in 20 minutes

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

Frequently asked questions

What is Georelational data model in simple terms?

Why does Georelational data model 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 Georelational data model?

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 Georelational data model.