Section 3 - Spatial Data Types

3.9 Designing a new attribute database

Whether you are using pre-existing attribute data or actually collecting new information yourself, you will need to think carefully about the design of your new attribute database. A great deal of literature exists on this complicated topic - it is quite literally a topic which has launched a thousand PhD research projects! Some good sources of basic information can be found in Batini et al. (1992), Date (1995), Ryan and Smith (1995), and Whittington (1988).

Archaeologists should also be aware of MIDAS, the Monument Inventory Data Standard (RCHME 1998). This standard is designed for those establishing a new attribute database in which to manage archaeological information or for those who have been working with archaeological attribute databases for a long time.

3.9.1 An introduction to the principal types of database structure

Database systems should be efficient tools for the storage, analysis and reporting of your data. As a result, the choice of database package and data structure used in a given project should be dictated by the requirements of each organisation. It is not within the scope of this guide to enter into a discussion of the merits and failings of software packages. Instead a short overview of the types of database data models is presented.

Data structures currently fall into four major types: flat file, hierarchical, relational, and object oriented. More detailed discussion of these can be found in Fundamentals of Spatial Information Systems (Laurini and Thompson 1996), especially pages 620-38 on object oriented databases.

3.9.1.1 Flat file data structures

In this simplest form of data structure, data are arranged in concurrent horizontal rows, with attributes stored in vertical columns. One row stores all attributes for a single entry (object) on the database. If many of the objects on the database have the same attributes they must be entered many times, leading to data redundancy and often to empty fields (resulting in wasted computer resources). A common example is the card index.

3.9.1.2 Hierarchical data structures

Hierarchical data structures have useful applications within archaeology as they arrange the objects in a database in a related tree of linked parent and child records. This can be used to model the breakdown of the historic environment into 'monuments within monuments' and allows flexible searching across the hierarchy. The most common applications for these database structures are in cultural resource management environments such as SMRs and national databases which contain large amounts of data and need efficient, speedy, searches.

3.9.1.3 Relational data structures

Currently the most common type of database used is based on the relational data model. If you imagine a series of tables similar to small flat file databases, with links or relationships between specific unique fields that allow complex queries of different data sets, you have the essence of the relational structure. One table could be of pottery types, another of contexts, a third of scientific dates and with easily structured queries it will be possible to construct chronologies based on pottery typology or scientific dating.

3.9.1.4 Object oriented data structures

The newest form of data structure, there are currently a limited number of GIS packages using the object oriented approach (e.g. Smallworld). While the relational data structure deals with an object's description by tearing it apart into single rows, and holding those rows in many discrete but linked tables of similarly grouped attributes, the object oriented approach to data structure allows the descriptive attributes of an object (e.g. a monument) to be encapsulated digitally in one place, allowing a more realistic model of the 'real world' to be assembled. The geographic location of the object is then just another characteristic of the object, just as function, date and period of existence are.