The GIS stores information about the world in a database. This accounts for up to 75 of the time and effort involved in developing a GIS.
It is important to view GIS databases as more than simple stores of information. They allow us to abstract very specific sorts of information about reality and organize it in various ways. They should be viewed as a representation or model of the world developed for specific application.
A raster based system displays, locates, and stores graphical data by using a matrix or grid of cells. A unique reference coordinate represents each pixel either at a corner or the centroid. In turn each cell or pixel has discrete attribute data assigned to it. Raster data resolution is dependent on the pixel or grid size and may vary from sub-meter to many kilometers.
Because these data are two-dimensional, GISs store various information such as forest cover, soil type, land use, wetland habitat, or other data in different layers. Layers are functionally related map features.
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A vector based system displays graphical data as points, lines or curves, or areas with attributes. Cartesian coordinates (i.e., x and y) and computational algorithms of the coordinates define points in a vector system. Lines or arcs are a series of ordered points. Areas or polygons are also stored as ordered lists of points, but by making the beginning and end points the same node the shape is closed and defined. Vector systems are capable of very high resolution and graphical output is similar to hand-drawn maps. This system works well with bearings, distances, and points, but it requires complex data structures but is less compatible with remote sensing data.
Comparison of Raster and Vector Systems
It is important to stress that any given real world situation can be represented in both raster and vector modes, the choice is up to the user.
Collection of contiguous blocks
Series of connected straight lines that join together to form a boundary.
Series of touching blocks
Series of touching blocks
Series of Single lines
(River & Island)
Banks of river and island represented by a series of touching blocks
Banks of river and island represented by a series of single lines
Raster data requires less processing than vector data, but uses more computer storage space.
Related vector data are always organized by themes, which are also referred to as layers:
for themes covering a very large geographic area, the data are always divided into tiles so that they can be managed more easily
a tile is the digital equivalent of an individual map in a map series and is uniquely identified by a file name
Data captured by imaging devices in remote sensing and digital cartography (such as multi-spectral scanners, digital cameras and image scanners) are made up of a matrix of picture elements (pixels) of very fine resolution. This data follows the raster format.
Geographic features in such form of data can be visually recognized but not individually identified in the same way that geographic features are identified in the vector method they are recognizable by differentiating their spectral or radiometric characteristics from pixels of adjacent features
e.g. a lake can be visually recognized on a satellite image because the pixels forming it are darker than those of the surrounding features; but the pixels forming the lake are not identified as a single discrete geographic entity, i.e. they remain individual pixels
e.g. a highway can be visually recognized on the same satellite image because of its particular shape; but the pixels forming the highway do not constitute a single discrete geographic entity as in the case of vector data
In the past, the vector and raster methods represented two distinct approaches to information systems
they were based on different concepts of information organization and data structure
they used different technologies for data input and output
Recent advances in computer technologies allow these two types of data to be used in the same applications
Always on Time
Marked to Standard
computers are now capable of converting data from the vector format to the raster format (rasterization) and vice versa (vectorization)
computers are now able to display and overlay vector and raster models simultaneously
vector and raster data are largely seen as complimentary to, rather than competing against, one another in geographic data processing
Each of these systems of representation has its advantages and disadvantages:
Simple data structure
Compatible with remotely sensed or scanned data
Simple spatial analysis procedures
Requires greater storage space on computer
Depending on pixel size, graphical output may be less pleasing
Projection transformations are more difficult
More difficult to represent topological relationships
Requires less disk storage space
Topological relationships are readily maintained
Graphical output more closely resembles hand-drawn maps
More complex data structure
Not as compatible with remotely sensed data
Software and hardware are often more expensive
Some spatial analysis procedures
may be more difficult
Overlaying multiple vector maps is often time consuming
Organizing Attribute Data
GIS use raster and vector representations to model location, but how they must also record information about the real-world phenomena positioned at each location and the attributes of these phenomena. That is, the GIS must provide a linkage between spatial and non-spatial data. These linkages make the GIS "intelligent" insofar as the user can store and examine information about where things are and what they are like. The relationship can be diagrammed as a linkage between:
Location <<< >>> What Is There
Spatial Data <<< >>> Non-Spatial Data
Geographic Features <<< >>> Attributes
In a raster system, this symbol is a grid cell location in a matrix. In a vector system, the locational symbol may be a one-dimensional point; a two-dimensional line, curve, boundary, or vector; or a three- dimensional area, region, or polygon.
The linkage between symbol and meaning is established by giving every geographic feature at least one unique means of identification, a name or number usually just called its ID. Non-spatial attributes of the feature are then stored, usually in one or more separate files, under this ID number. In other words, locational information is linked to specific information in a database
It is important to realize that this non-spatial data can be filed away in several different forms depending on how it needs to be used and accessed. Perhaps the simplist method is the flat file or spreadsheet, where each geographic feature is matched to one row of data.
Flat Files and Spreadsheets
A flat file or spreadsheet is a simple method for storing data. All records in this data base have the same number of "fields". Individual records have different data in each field with one field serving as a key to locate a particular record. For example, your social security number may be the key field in a record of your name, address, phone number, sex, ethnicity, place of birth, date of birth, and so on. For an person, or a tract of land there could be hundreds of fields associated with the record. When the number of fields becomes lengthy a flat file is cumbersome to search. Also the key field is usually determined by the programmer and searching by other determinants may be difficult for the user. Although this type of database is simple in its structure, expanding the number of fields usually entails reprogramming. Additionally, adding new records is time consuming, particularly when there are numerous fields. Other methods offer more flexibility and responsiveness in GIS.
Hierarchical files store data in more than one type of record. This method is usually described as a "parent-child, one-to-many" relationship. One field is key to all records, but data in one record does not have to be repeated in another. This system allows records with similar attributes to be associated together. The records are linked to each other by a key field in a hierarchy of files. Each record, except for the master record, has a higher level record file linked by a key field "pointer". In other words, one record may lead to another and so on in a relatively descending pattern. An advantage is that when the relationship is clearly defined, and queries follow a standard routine, a very efficient data structure results. The database is arranged according to its use and needs. Access to different records is readily available, or easy to deny to a user by not furnishing that particular file of the database. One of the disadvantages is one must access the master record, with the key field determinant, in order to link "downward" to other records.
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Relational files connect different files or tables (relations) without using internal pointers or keys. Instead a common link of data is used to join or associate records. The link is not hierchical. A "matrices of tables" is used to store the information. As long as the tables have a common link they may be combined by the user to form new inquires and data output. This is the most flexible system and is particularly suited to SQL (Structured Query Language). Queries are not limited by a hierarchy of files, but instead are based on relationships from one type of record to another that the user establishes. Because of its flexibility this system is the most popular database model for GIS.
Flat, Hierarchical, and Relational Files Compared
Fast data retrieval
Simple structure and easy to program
Difficult to process multiple values of a data item
Adding new data categories requires reprogramming
Slow data retrieval without the key
Adding and deleting records is easy
Fast data retrieval through higher level records
Multiple associations with like records in different files
Pointer path restricts access
Each association requires repetitive data in other records
Pointers require large amount of computer storage
Easy access and minimal technical training for users
Flexibility for unforeseen inquiries
Easy modification and addition of new relationships, data and records
Physical storage of data can change without affecting relationships between records
New relations can require considerable processing
Sequential access is slow
Method of storage an disks impacts processing time
Easy to make logical mistakes due to flexibility of relationships between records
GIS have the power to record more than location and simple attribute information. In some situations, we will want to examine spatial relationships based upon location, as well as functional and logical relationships among geographic features.
Absolute and relative location
Proximity of features
Distance between features
Features in the "neigborhood" of other features
Direction and movement from place to place
Boolean relationships of "and," "or," etc
Functional Relationships among Geographic Features and Their Attributes.
This includes information about how features are connected and interact in real-life terms. A road network might be classified functionally from the largest superhighway down to the most isolated rural road or suburban cul-de-sac based upon their role in the overall transportation system. Minor roads and suburban streets "feed" major highways, but are not directly connected to them. As another example in assessing wildlife habitats, various environmental conditions function together to define the optimal living environments for certain species. Within cities, ownership is a functional classification of great importance as is landuse and zoning classification.
Logical Relationships among Geographic Features and Their Attributes.
Logical relationships involve "if-then" and "and-or" conditions that must exist among features stored in the dataset. For example, no land may be permitted to be zoned for residential use if it lies within a rivers five-year flood plain. Development may disallowed in the habitat of some
Databases can be designed to represent, model, and store information about these relationships as needed for particular applications.
Topology is one of the most useful relationships maintained in many spatial databases. It is defined as the mathematics of connectivity or adjacency of points or lines that determines spatial relationships in a GIS. The topological data structure logically determines exactly how and where points and lines connect on a map by means of nodes (topological junctions). The order of connectivity defines the shape of an arc or polygon. The computer stores this information in various tables of the database structure. By storing information in a logical and ordered relationship missing information, e.g., a line segment of a polygon, is readily apparent. A GIS manipulates, analyzes, and uses topological data in determining data relationships.
Network analysis uses topological modeling for determining shortest paths and alternate routes. For example, a GIS for emergency service dispatch may use topological models to quickly ascertain optional routes for emergency vehicles. Automobile commuters perform a similar mental task by altering their route to avoid accidents and traffic congestion. Likewise an electrical utility GIS could rapidly determine different circuit paths to route electricity when service is interrupted by equipment damage.
To see how topology is represented or modeled, it is useful to consider an example to see how connections are coded into a database. This involves recording more than use the absolute location of points, lines, and regions.
The first step is to record the location of all "nodes," that is endpoints and intersections of lines and boundaries.
Based upon these nodes, "arcs" are defined. These arcs have endpoints, but they are also assigned a direction indicated by the arrowheads. The starting point of the vector is referred to as the "from node" and the destination the "to node." The orientation of a given vector can be assigned in either direction, as long as this direction is recorded and stored in the database.
By keeping track of the orientation of arcs, it is possible to use this information to establish routes from node to node or place to place. Thus, if one wants to move from node 3 to node 1, we can locate the necessary connections in the database.
Now, "polygons" are defined by arcs. To define a given polygon, trace around its area in a clockwise direction recording the component arcs and their orientations. If an arc has to be followed in its reverse orientation to make the tracing, it is assigned a negative sign in the database.
A, D, G
C, D, E
B, E, G, -F
Finally, for each arc, you must record which polygon lies to the left and right side of its direction of orientation. If an arc is on the edge of the study area, it is bounded by the "universe."
What polygons adjoin polygon A?
To find the solution, we first look to see what arcs define polygon A, then we check to see what other polygons are defined by these arcs in their negative orientation.
What is the shortest route from node 3 to node 2?
Trace all arc paths that lead from node 3 to node 2, sum their lengths by calculating distances from node list. Choose path with shortest total length.
What polygon is directly across from polygon B along arc D?
Search for the polygon that is defined by the inverse (negative) of arc D.
Arc-node topology, as this is called, was developed several decades ago as a convenient way of store information of this sort.
The methods of file organization discussed above depend upon the careful description of real-world phenomena in terms of their attributes, such as height, weight, or age. It is these attributes that are stored in the database and together they provide a sort of abstracted depiction of the real-world feature. Much recent attention has focused on how to organize this information in ways that more readily represent the way users gather and use information about the world around them. That is, humans recognize "objects" immediately in terms of their totality or "wholeness." Houses and skyscrapers are recognized immediately by form and function. The differences can be described in terms of the underlying attributes, but people recognize these from experience.
The idea of "object-oriented" database is to organize information (that is group attributes) into the sorts of "wholes" that people recognize. Instead of "decomposing" each feature a distinctive list of attributes, emphasis is placed on "grouping" the attributes of a given object into a unit or template that can be stored or retrieved by its natural name.
Consider the following situation involving two ways of organizing information about buildings zoned for different uses. This information can be broken down into attributes, as follows:
( ft )
Size ( ft2 )
of Dwelling Units
To organize this information differently, let us first define some "templates" that reflect the different "objects" we wish to include in the database.
SF Single Family
Token 1=Large Lot
Token 2=Low Density
Token 5=High Density
LO Limited Office
Must Specify Predominate Use
Maximum Height=40 ft Minimum Lot Size=5,750 ft2
GO General Office
Must Specify Predominate Use
Maximum Height=60 feet Minimum Lot Size=5,750 ft2
Once these are created, information can be added to our database by referring to the template. The template maintains in one place all attributes held in common by a certain class of object. It may be the case that slight differences exist between objects of a given category. These differences can be stored as "tokens" or additional attributes.
Although templates and tokens may be stored in two different files, it is easy to see how this method of organization changes the database. It is not merely a process of simplication. By using templates, users can enter and retrieve data in terms of "real" items. A query might ask for all "Single Family Houses."
Object-oriented databases thus have the advantage of organizing information in ways that users often find easier to use. The database has as an intuitive feel because it employs that categories that users employ naturally in day-to-day life. For this reason, object-oriented databases are gaining increased attention in GIS.
The Idea of the Expert Systems
If a database has been designed to store information about spatial, functional, and logical relationships, the user can pose more complex questions of the data. That is, the user can program the system to consider a variety of spatial, functional, and logical conditions during query or analysis. Such efforts result in what are termed expert systems or, if carried further, artificially intelligent systems. At there simplest, expert systems allow the user to set "rules" that must be followed as data is analyzed. These rules are written to mirror the way an experienced user would compare or judge data. As more and more rules are written, the system becomes more adept or "expert" at finding solutions with less directed guidance by users.
The point of expert systems is to build sets of rules that reflect the sorts of comparisons and judgments that experienced users would make. By programming these rules into the system, more and more of the work of decision making can be passed on to the computer system--including complex comparisons that may be difficult or time consuming for even experienced users to undertake.
Such systems are of interest to GIS practioners in many fields including urban planning and resource analysis. Complex issues involving zoning and land use can often be written in terms of rules that need to be followed.
At the same time, following rules is only a step toward "intelligence." The difference between expert systems and artificial intelligence is much in debate. But to be truly "intelligent" a system must be able to "learn," "think," or "reason," perhaps really to write its own rules from experience. The definition of artificial intelligence is, in fact, still a contentious issue. So far, it has been very difficult to program computer systems to provide a semblance of human thought processes. Yet, the potential of such systems makes the effort irresistible. The idea that computer systems might one day be able to reason about real- world environmental and geographical problems and issues is a reason why GIS theorists maintain an interest in developments in the area of artificial intelligence.