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 The term Geographic Information System (GIS) is hard to define. It represents the integration of many subjects. A broadly accepted definition of GIS is the one provided by the National Centre of Geographic Information and Analysis: a GIS is a system of hardware, software and procedures to facilitate the management, manipulation, analysis, modelling, representation and display of georeferenced data to solve complex problems regarding planning and management of resources.

 Geographic information systems have emerged in the last decade as an essential tool for urban and resource planning and management. Their capacity to store, retrieve, analyze, model and map large areas with huge volumes of spatial data has led to an extraordinary proliferation of applications. 

Geographic information systems are now used for land use planning, utilities management, ecosystems modelling, landscape assessment and planning, transportation and infrastructure planning, market analysis, visual impact analysis, facilities management, tax assessment, real estate analysis and many other applications

Functions of GIS include: data entry, data display, data management, information retrieval and analysis. A more comprehensive and easy way to define GIS is the one that looks at the disposition, in layers of its data sets. “Group of maps of the same portion of the territory, where a given location has the same coordinates in all the maps included in the system”. This way, it is possible to analyse its thematic and spatial characteristics to obtain a better knowledge of this zone.

GIS applications

Finding distances: GIS can be used to find out what’s occurring within a set distance of a feature. 

Mapping and monitoring change: GIS can be used to map the change in an area to anticipate future conditions, decide on a course of action, or to evaluate the results of an action or policy. 

Mapping quantities: People map quantities, like where the most and least are, to find places that meet their criteria and take action, or to see the relationships between places. This gives an additional level of information beyond simply mapping the locations of features.

Mapping locations: GIS can be used to map locations. GIS allows the creation of maps through automated mapping, data capture, and surveying analysis tools. 

 Mapping densities: While you can see concentrations by simply mapping the locations of features, in areas with many features it may be difficult to see which areas have a higher concentration than others. A density map lets you measure the number of features using a uniform areal unit, such as acres or square miles, so you can clearly see the distribution.

Geospatial data

Geospatial data has both spatial and thematic components. 

Geographic data can be broken up in two elements: observation or entity and attribute or variable. GIS have to be able to manage both elements.

 Spatial component: The observations have two aspects in its localization: absolute localization based in a coordinates system and topological relationship referred to other observations.

Thematic component: The variables or attributes can be studied considering the thematic aspect (statistics), the locational aspect (spatial analysis) or both (GIS).

Data for GIS applications

Data for GIS applications includes: 

  • databases 
  • remote sensing and aerial photography
  • GPS field sampling of attributes  
  • digitized and scanned data

Vector based GIS

 Vector is a data structure, used to store spatial data. Vector data is comprised of lines or arcs, defined by beginning and end points, which meet at nodes. The locations of these nodes and the topological structure are usually stored explicitly. Features are defined by their boundaries only and curved lines are represented as a series of connecting arcs. 

Vector storage involves the storage of explicit topology, which raises overheads, however it only stores those points which define a feature and all space outside these features is ‘non-existent’. A vector based GIS is defined by the vectorial representation of its geographic data. 

According with the characteristics of this data model, geographic objects are explicitly represented and, within the spatial characteristics, the thematic aspects are associated. There are different ways of organizing this double data base (spatial and thematic). 

Vectorial systems are composed of two components: the one that manages spatial data and the one that manages thematic data. This is the named hybrid organization system, as it links a relational data base for the attributes with a topological one for the spatial data. A key element in these kind of systems is the identifier of every object. This identifier is unique and different for each object and allows the system to connect both data bases.

Vector representation of data

 Vector data, the basic units of spatial information are points, lines (arcs) and polygons. Each of these units is composed simply as a series of one or more co-ordinate points, for example, a line is a collection of related points, and a polygon is a collection of related lines.


Pairs of numbers expressing horizontal distances along orthogonal axes, or triplets of numbers measuring horizontal and vertical distances, or n-numbers along n-axes expressing a precise location in n-dimensional space. Co-ordinates generally represent locations on the earth’s surface relative to other locations.


A zero-dimensional abstraction of an object represented by a single X,Y co-ordinate. A point normally represents a geographic feature too small to be displayed as a line or area; for example, the location of a building location on a small-scale map, or the location of a service cover on a medium scale map.


A set of ordered co-ordinates that represent the shape of geographic features too narrow to be displayed as an area at the given scale (contours, street centrelines, or streams), or linear features with no area (county boundary lines). A lines is synonymous with an arc.


 An ARC/INFO term that is used synonymously with line.


A feature used to represent areas. A polygon is defined by the lines that make up its boundary and a point inside its boundary for identification. Polygons have attributes that describe the geographic feature they represent.


Raster based GIS

Raster representation of data Raster is a method for the storage, processing and display of spatial data. Each area is divided into rows and columns, which form a regular grid structure. Each cell must be rectangular in shape, but not necessarily square. Each cell within this matrix contains location co-ordinates as well as an attribute value.

 The spatial location of each cell is implicitly contained within the ordering of the matrix, unlike a vector structure which stores topology explicitly. Areas containing the same attribute value are recognized as such, however, raster structures cannot identify the boundaries of such areas as polygons. 

Raster data is an abstraction of the real world where spatial data is expressed as a matrix of cells or pixels, with spatial position implicit in the ordering of the pixels. With the raster data model, spatial data is not continuous but divided into discrete units. This makes raster data particularly suitable for certain types of spatial operation, for example overlays or area calculations. 

Raster structures may lead to increased storage in certain situations, since they store each cell in the matrix regardless of whether it is a feature or simply ’empty’ space.

Grid size and resolution

 A pixel is the contraction of the words picture element. Commonly used in remote sensing to describe each unit in an image. In raster GIS the pixel equivalent is usually referred to as a cell element or grid cell. Pixel/cell refers to the smallest unit of information available in an image or raster map. This is the smallest element of a display device that can be independently assigned attributes such as color. Pixel size and number of rows and columns: “The size of the pixel must be half of the smallest distance to be represented”.


Advantages and Disadvantages of raster and vector data models

Raster model

It is a simple data structure

Overlay operations are easily and efficiently implemented

High spatial variability is efficiently represented

It is required for more efficient enhancement and manipulation of digital images.

Vector model

It provides more compact data than the raster model

It provides efficient encoding of topology and as a result more efficient application of operations such as network analysis.

Better suited to supporting graphics that closely approximate  hand drawing maps.

Data capture

 Data capture for raster datasets can include:

 Automatic digitization 


Manual digitization:  

  • Points 
  • Lines 
  • Polygons 

Remote Sensing

Rasterisation of vector data

The process of converting vector data, which is a series of points, lines and polygons, into raster data, which is a series of cells each with a discrete value. This process is essentially easier than the reverse process, which is converting data from raster format to vector format.

Raster to vector conversion

The process of converting an image made up of raster cells into one described by vector data. This may or may not involve the encoding of topology.