Data Models

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 and output is less beautiful. Figure 1 shows vector and raster data representation of the real world phenomena.

Vector data model uses line segments or points represented by their explicit x, y coordinates to identify locations. Connecting set of line segments forms area objects. 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.

The vector model is extremely useful for describing discrete features, but less useful for describing continuously varying features such as soil type or accessibility costs for hospitals. The raster model has evolved to model such continuous features. A raster image comprises a collection of grid cells rather like a scanned map or picture. Both the vector and raster models for storing geographic data have unique advantages and disadvantages. Modern GIS packages are able to handle both models.

Figure 1: Vector and Raster data examples

Layers and Coverages

The common requirement to access data on the basis of one or more classes has resulted in several GIS employing organizational schemes in which all data of a particular level of classification, such as roads, rivers or vegetation types are grouped into so called layers or coverages. The concept of layers is to be found in both vector and raster models. The layers can be combined with each other in various ways to create new layers that are a function of the individual ones. The characteristic of each layer within a layer-based GIS is that all locations with each layer may be said to belong to a single Arial region or cell, whether it be a polygon bounded by lines in vector system, or a grid cell in a Raster system. But it is possible for each region to have multiple attributes. The Figure 2 shows layers and coverage concept in GIS.

Layers And Coverages
Figure 2: Layers and Coverage concept in GIS

Data Structures

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 software's 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 (Table 3). 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. Relation extracts the information, which are defined by query.

Table 3: Example of Relational Database

Settlement name

Settlement status

Settlement population

County name

















Hierarchical data structure (Fig. 3) 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 branches further out on the tree.

Figure 3: Hierarchical Data Structure

Network data structure (Fig. 4) 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 parents. The network data structure has the limitation that the pointers must be updated everytime a change is made to database causing considerable overhead.

Figure 4: Network Structure

Errors in GIS

Uncertainties and errors are intrinsic to spatial data and need to be addressed properly, not sweeping away the users by high quality colour outputs. Data accuracy is often grouped according to thematic accuracy, positional accuracy and temporal accuracy occurring at various stages in spatial data handling. Given below are some of them while creating the spatial database and analysis.

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

Age of data Map scale

Density of observation Relevance of data Data inaccuracy

Reliability decreases with age

Non-availability of data on a proper scale or Use of data at different scales

Sparsely distributed data set is less reliable

Use of surrogate data leads to errors

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

Rasteurization errors due to topological mismatch arising during approximation by grid

Spatial Integration errors - due to map integration resulting in spurious polygons

Attribute mismatch errors. Misuse of Logic

The errors are also added from the source of data. Care must be taken in creating spatial databases from the accurate and reliable sources of data. Realizing the importance the users may demand in future to provide them the desired data with a tag showing how much of the data is accurate, spatial data must be presented in quantitative terms.

Spatial Analysis

Whether it is effective utilization of natural resources or sustainable development or natural disaster management, selecting the best site for waste disposal, optimum route alignment or local problems have a geographical component; geoinformatics will give you power to create maps, integrate information, visualize scenarios, solve complicated problems, present powerful ideas, and develop effective solutions like never before. In brief it can be described as a supporting tool for decision-making process. Map making and geographic analysis are not new, but a GIS performs these tasks better and faster than do the old manual methods. Today, GIS is a multibillion-dollar industry employing hundreds of thousands of people worldwide.

Generalization errors due to aggregation process when features are abstracted to lower scale

GIS is used to perform a variety of Spatial analysis, including overlaying combinations of features and recording resultant conditions, analyzing flows or other characteristics of networks; proximity analysis (i.e. buffet zoning) and defining districts in terms of spatial criteria. GIS can interrogate geographic features and retrieve associated attribute information, called identification. It can generate new set of maps by query and analysis. It also evolves new information by spatial operations. Following are the analytical procedures applied with a GIS. GIS operational procedure and analytical tasks that are particularly useful for spatial analysis include:

• Single layer operations

• Multi layer operations/ Topological overlay

• Geometric modeling

Calculating the distance between geographic features Calculating area, length and perimeter Geometric buffers.

• Network analysis

• Surface analysis

• Raster/Grid analysis

There are many applications of Geoinformatics, viz. facility management, planning, environmental monitoring, population census analysis, insurance assessment, and health service provision, hazard mapping and many other applications. The following list shows few applications in natural resource management:

• Agricultural development

• Land evaluation analysis

• Change detection of vegetated areas

• Analysis of deforestation and associated environmental hazards

• Monitoring vegetation health

• Mapping percentage vegetation cover for the management of land

• Crop acreage and production estimation

• Wasteland mapping

• Soil resources mapping

• Groundwater potential mapping

• Geological and mineral exploration

• Snow-melt run-off forecasting

• Monitoring forest fire

• Monitoring ocean productivity etc.

• GIS application in Forestry

With the rise of World Wide Web, new Internet protocols such as the Hypertext Transfer Protocol (HTTP), as well as easy to use interfaces (browsers), tools and languages (HTML, XML, and Java), the Internet has become a hub for GIS functionalities from the client side without even any GIS software. The GIS field is still evolving and it will be the major force in various walks of life dealing with geographic information.


Geographic Information System (GIS) is used by multi-disciplines as tools for spatial data handling in a geographic environment. Basic elements of GIS consist of hardware, software, data and liveware. GIS is considered one of the important tool for decision making in problem solving environment dealing with geo-information.


Aronoff, S. 1989. Geographic Information Systems: A Management Perspective. Ottawa, Canada : WDC Publications.

Burrough, P.A. 1987. Principles of Geographical Information Systems for Land Resource Assessment. Oxford : Claredon Press.

Stillwell John and Clarke Graham (ed.) 1987. Applied GIS and Spatial Analysis. West Sussex : John Wiley and Sons, 2004. (Hongkong Baptist University)

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