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Geo Information System


    Conversion of real world geographical variation into discrete objects is done through data models. It represents the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

    Data models are of two types: Raster and Vector. In raster type of representation of the geographical data, a set of cells located by coordinate is used; each cell is independently addressed with the value of an attribute. Each cell contains a single value and every location corresponds to a cell. One set of cell and associated value is a LAYER. Raster models are simple with which spatial analysis is easier and faster. Raster data models require a huge volume of data to be stored, fitness of data is limited by cell size & output is less beautiful.

    Vector data model uses line segments or points represented by their explicit x, y coordinates to identify locations. Discrete objects are formed by connecting line segments which area is defined by set of line segments. Vector data models require less storage space, outputs are appreciable, Estimation of area / perimeter is accurate and editing is faster and convenient. Spatial analysis is difficult with respect to writing the software program.


    Figure 3. Vector and raster data examples


    There are number of different ways to organize the data inside the information system. The choice of data structure affects both; Data storage volume and processing efficiency. Many GIS have specialized capabilities for storing and manipulating attribute data in addition to spatial information. Three basic data structures are – Relational, Hierarchical and Network.

    Relational data structure organizes the data in terms of two-dimensional tables where each table is a separate file. Each row in the table is a record and each record has a set of attributes. Each column in the table is an attribute. Different tables are related through the use of a common identifier called KEY. The information is extracted by relations who are defined by query.

    Hierarchical data structure stores the data in a way that a hierarchy is maintained among the data items. Each node can be divided into one or more additional node. Stored data gets more and more detailed as one branch further out on the tree.

    Network data structure is similar to hierarchy structure with the exception that in this structure a node may have more than one parent. Each node can be divided into one or more additional nodes. Nodes can have many parent. The network data structure has the limitation that the pointers must be updated every time a change is made to database causing considerable overhead.


    (i) Errors in GIS environment can be classified into following major groups

    Age of data - Reliability decreases with age
    Map scale - Non-availability of data are proper scale or Use of data at different scales
    Density of observation - Sparsely dense data set is less reliable
    Relevance of data - Use of surrogate data leads to errors
    Data inaccuracy - Positional, elevation, minimum mapable unit etc.
    Inaccuracy of contents - Attributes are erroneously attached

    (ii) Errors associated with processing

    Map digitization errors - due to boundary location problems on maps and Errors associated with digital representation of features
    Rasterisation errors - due to topological mismatch arising during approximation by grid
    Spatial Integration errors - due to map integration resulting in spurious polygons
    Generalization errors - due to aggregation process when features are abstracted to lower scale
    Attribute mismatch errors
    Misuse of logic