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Building Spatial Databases – Theory / Defining topology using relational tables

Learners guide

Summary

In this unit a relational data structure will be introduced, which describes and preserves the topology.

Requirement

To make the self-test successfully

Define topology using relational tables

Figure 31. Relational tables with the names points, contours, polygons and countries [Rigaux at al]

In order to implement a vector data model, it is natural to use relational data structure. In the case of the spaghetti model, we have experienced that polygon structures can be well defined by using a relational data structure. Now, let us examine how to refine the relational structure to achieve a topologically correct structure automatically.

Let us specify the above mentioned example by defining countries in Europe as additional data (attributes). As described in Figure 31, basic geometries are stored in the "pontok" table, the "konturok" table containing contours is referencing to the "pontok" table, the "poligonok" table containing polygons is referencing to the "konturok" table. The "konturok" table contains as many linestrings as many borderlines (neighbour polygons) each polygon (surface) has, and the "Orszagok" table references to the "poligonok" table as geometrical data. Other additional data (like name, capital, population) are stored inside the table structure. Notice that in the case of modifying a node (e.g. shifting) it does not ruin the topological structure at all. An efficient structure like this ensures the topological properties, and despite its topological structure, we are still calling it a relational model.

For example, let us look at an SQL command to select the contour lines of France:

Source-code
SELECT poligons.id-contour 
  FROM countries, poligons, contours, points WHERE name=`France`
  AND countries.id-boundary=poligons.id-boundary
  AND poligons.id-contour=contours.id-contour
  AND contours.id-point=points.id-point
  ORDER BY poligons.id-contour, point-num

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