Geo-Relational Data Model: All spatial data files will be geo-referenced. Geo-referencing refers to the location of a layer or coverage in space defined by the coordinate referencing system. The geo relational approach involves abstracting geographic information into a series of independent layers or coverages, each representing a selected set of closely associated geographic features (e.g., roads, land use, river, settlement, etc). Each layer has the theme of a geographic feature and the database is organized in the thematic layers.
With this approach users can combine simple feature sets representing complex relationships in the real world. This approach borrows heavily on the concepts of relational DBMS, and it is typically closely integrated with such systems. This is fundamental to database organization in GIS.
Topological Data Structure: Topology is the spatial relationship between connecting and adjacent coverage features (e.g., arc, nodes, polygons, and points). For instance, the topology of an arc includes from and to nodes (beginning of the arc and ending of the arc representing direction) and its left and right polygon. Topological relationships are built from simple elements into complex elements: points (simplest elements), arcs (sets of connected points), and areas (sets of connected arcs). Topological data structure, in fact, adds intelligence to the GIS database.
Attribute Data Management: All Data within a GIS (spatial data as well as attribute data) are stored within databases. A database is a collection of information about things and their relationships to each other. For example, you can have an engineering geological database, containing information about soil and rock types, field observations and measurements, and laboratory results. This is interesting data, but not very useful if the laboratory data, for example, cannot be related to soil and rock types. The objective of collecting and maintaining information in a database is to relate facts and situations that were previously separate.
With this approach users can combine simple feature sets representing complex relationships in the real world. This approach borrows heavily on the concepts of relational DBMS, and it is typically closely integrated with such systems. This is fundamental to database organization in GIS.
Topological Data Structure: Topology is the spatial relationship between connecting and adjacent coverage features (e.g., arc, nodes, polygons, and points). For instance, the topology of an arc includes from and to nodes (beginning of the arc and ending of the arc representing direction) and its left and right polygon. Topological relationships are built from simple elements into complex elements: points (simplest elements), arcs (sets of connected points), and areas (sets of connected arcs). Topological data structure, in fact, adds intelligence to the GIS database.
Attribute Data Management: All Data within a GIS (spatial data as well as attribute data) are stored within databases. A database is a collection of information about things and their relationships to each other. For example, you can have an engineering geological database, containing information about soil and rock types, field observations and measurements, and laboratory results. This is interesting data, but not very useful if the laboratory data, for example, cannot be related to soil and rock types. The objective of collecting and maintaining information in a database is to relate facts and situations that were previously separate.
The principle characteristics of a DBMS are:
- Centralized control over the database is possible, allowing for better quality management and operator-defined access to parts of the database;
- Data can be shared effectively by different applications;
- The access to the data is much easier, due to the use of a user-interface and the user-views (especially designed formula for entering and consulting the database);
- Data redundancy (storage of the same data in more than one place in the database) can be avoided as much as possible; redundancy or unnecessary duplication of data are an annoyance, since this makes updating the database much more difficult; one can easily overlook changing redundant information whenever it occurs; and
- The creation of new applications is much easier with DBMS.
The disadvantages relate to the higher cost of purchasing the software, the increased complexity of management, and the higher risk, as data are centrally managed.
The Core of Your Mapping / GIS Project: When most people begin a GIS project, their immediate concern is with purchasing computer hardware and software. They enter into lengthy discussions with vendors about the merits of various components and carefully budget for acquisitions. Yet they often give little thought to the core of the system, the data that goes inside it. They fail to recognize that the choice of an initial data set has a tremendous influence on the ultimate success of their GIS project.
The Core of Your Mapping / GIS Project: When most people begin a GIS project, their immediate concern is with purchasing computer hardware and software. They enter into lengthy discussions with vendors about the merits of various components and carefully budget for acquisitions. Yet they often give little thought to the core of the system, the data that goes inside it. They fail to recognize that the choice of an initial data set has a tremendous influence on the ultimate success of their GIS project.
Data, the core of any GIS project, must be accurate - but accuracy is not enough. Having the appropriate level of accuracy is vital. Since an increase in data accuracy increases acquisition and maintenance costs, data that is too detailed for your needs can hurt a project just as surely as inaccurate data can. All any GIS project needs is data accurate enough to accomplish its objectives and no more. For example, you would not purchase an engineering workstation to run a simple word-processing application. Similarly, you would not need third-order survey accuracy for a GIS-based population study whose smallest unit of measurement is a county. Purchasing such data would be too costly and inappropriate for the project at hand. Even more critically, collecting overly complex data could be so time-consuming that the GIS project might lose support within the organization.
Even so, many people argue that, since GIS data can far outlast the hardware and software on which it runs, no expense should be spared in its creation. Perfection, however, is relative. Projects and data requirements evolve. Rather than overinvest in data, invest reasonably in a well-documented, well-understood data foundation that meets today's needs and provides a path for future enhancements. This approach is a key to successful GIS project implementation.
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