GIS For Dummies

TINs are topological data models, just like DIME and TIGER models presented earlier in this chapter, but they’re specifically designed to incorporate the Z value component, whether it’s topography or economic data. Like in other topological data models, the TIN data model contains explicit information about which features are related spatially to others.

The TIN model, shown in Figure 5-11, is pretty close to the model you see in Figure 5-7. Each model has a table of point values, and each has a set of

polygons and links. In the case of the TIN model, the polygons are always triangles, and each triangle’s vertices define it. The Triangle File table in the TIN model is much like the polygon table in Figure 5-7. Specifically, the Triangle File table shows

The vertices that make up each triangular facet (the first three columns)

The neighboring polygons (the second three columns)

The Point File table is different in the TIN topological model, as compared to the chain table in Figures 5-8 and 5-9, because it also contains the elevation

(Z) value for each.

The TIN model has some definite positive features:

It’s incorporated in many vector GIS packages, so it can be used immediately for surface representation.

You can easily convert it to matrix models, such as the digital elevation model (DEM).

It allows for interpolation of missing Z values by using a wide variety of algorithms.

You can make cross-sectional profiles of terrain and perform many advanced functions related to topography.

Knowing the Simple Systems for Tracking Descriptions

There are many kinds of software for dealing with different forms of graphical data like house plans, satellite images, and maps. In fact, development of these innovative programs took place simultaneously and for different purposes, as follows:

For analyzing satellite images: People needed ways to manipulate electronic images gathered remotely from satellites orbiting the Earth. They began developing raster manipulation (image processing) software to analyze those digital images.

For basic mapping of Earth features: Other folks were interested in mapping Earth’s features, so they developed raster and vector mapping software.

For drafting and design work: And people involved in drafting complex buildings, machinery, and so on created computer-aided design (CAD) and drafting software.

You can still find each type of software in the preceding list available today in one form or another. Each software type has its specific application, and various developers are collaborating across applications to the mutual benefit

of all the different types of users. Because of the convergence of the different tasks, the properties of CAD programs have changed from their original form to a more GIS-like software. The descriptions in the following sections are somewhat simplistic, reflecting the original purpose and design of each type of software. When you consider your options, pay attention to the original conceptual design, as well as the capabilities that the software possesses in its current state.

Understanding computer-assisted cartography

Like GIS, computer-assisted cartography (CAC) was created to store, edit, and output maps of the Earth and — crazy as this sounds — other planets. CAC doesn’t have the analysis capability that’s at the heart of GIS, however. CAC is a straightforward process that moves from input to storage, retrieval, editing, and finally, output (see Figure 6-1). A CAC system just needs to store the attributes, or descriptive data, and have labels attached to the attributes so that those descriptive labels can be applied during output.

CAD is capable of performing visual overlay because of its inherent layered data model — unlike CAC, CAD is structured to store multiple data descriptions that apply to the same entity. Many times, the graphical overlay operations produce graphics that help you visualize the data, but they don’t create new data from the various combinations of layers. GIS is designed to do so, however, which allows you to compare one map pattern to another.

Another major difference between CAC and CAD is that, generally, CAD doesn’t include much capability to change projection or coordinate systems. You can’t convert from one form of map projection to another or change from one coordinate system to another, for example.

One nice feature of CAD is that a standardized exportable version of the typical .dwg file (a common CAD drawing file), called the drawing exchange format (.dxf), is often easily translated from CAD to both CAC and GIS software. In fact, many people use CAD software for input because they find the user interface for the input task easier to use than the GIS interface.

Exploring raster systems

The basic raster system method of handling attribute data is to store them as a set of ASCII characters (meaning letters and numbers), usually numbers. Each grid cell has a single numeric code associated with it. In most raster systems, the numbers are whole numbers. The code, in turn, is linked to a legend description (the names of the categories) and to a symbol set (the colors or shades used to represent the categories) for final display.

Storing attribute data in a raster system

Storing attribute data in a raster (grid) system requires that you store the value of each individual square based on its location in the sequence of grid cells. So if you are using a grid system that starts in the upper left and moves to the right, the first value you store would be for upper-leftmost grid cell. You assign the numbers (codes) based on how you define the attributes from the start. While the values may be actual measurements for things like elevation or slope, you may also assign numbers to represent categories, such as 0 for water, and 6 for agriculture, and you make these decisions before you start putting the numbers in the database.

Suppose that you want to use the number 6 to represent agriculture. To do so, you place the value (code number) 6 in the first column, first row. It turns out the next cell in sequence is also agriculture, so you put the number 6 in the second column, first row. If you know that the third grid cell over (the third column) in the first row is water and you want to use the code of 0 for that, you just enter 0 in that grid cell (third column, first row).

Because each grid cell contains attribute data, you can easily retrieve the data and compare it to its respective grid cell in another map layer. You use this property when you compare one map to another (especially when you use the Map Analysis Package, or MAP, data model, which I cover in Chapter 5). In Chapter 17, I show you how this characteristic was used to create a complete GIS modeling language called map algebra.

Because raster grid cells are uniform in size and identically placed from one map layer to another, you may find them especially useful when you must perform map overlay operations for map analysis. Also, because the raster model is so simple, it makes map overlay faster than in vector systems, which usually have more complicated data structures (see the section “Managing data in Vector GIS” for more about these structures).

Working with Tables and Database Management Systems

Database management systems (DBMS) were created to manage data. As glib (and completely obvious) as that may sound, the idea is significant because DBMS weren’t originally designed to manage all types of data, and specifically, not spatial data. The original database management system was a separate technology that had a different clientele in mind. In the early development of GIS, the DBMS stored attribute data, and the graphic data were stored elsewhere.

Structuring simple relational data

At the heart of these systems is the database management system itself. These systems are called relational database management systems (RDBMS) because of their ability to form associations (relations) among the various data they contain. The data you search are contained in rows called tuples (pronounced tooples) and are grouped with corresponding rows called relations. These groupings of rows are called relations because the information they contain is related. Specifically, each piece of data (column) in a single row is related to that column’s data in the other rows because each row contains identical categories that can be compared to one another. Each column represents an attribute — such as a parcel ID or house number for an address book — that’s common to each tuple in the entire relation.

100 Part II: Geography Goes Digital

A good rule for database management systems is to make your tables very simple so that you know exactly what you’re going to search, what your primary key is, and what you expect to find. Focus on one or two queries that you want to make for each table. Ask yourself, “What questions do I want to be able to ask about the data in this table?”

Getting more complex with relational joins