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A Geographical Information System (GIS) is a method of spatially storing, analysing, manipulating, managing and displaying geographical data. GIS data represents real objects such as roads, rivers, urban areas, place names, railway, places of interest, town names etc. with digital data determining the mix. A geodatabase is a database that is in some way referenced to locations on earth. Traditionally, there are two broad methods used to store data in a GIS; raster images and vector. Ordnance Survey Ireland (OSI) data is supplied in both Vector and Raster format. In both cases the data is geo-referenced.
VECTOR AND RASTER DATA
Vector data is split into three types; polygon, line (or arc) and point data. Vector is a method for storing spatial data involving assigning coordinates for each entity; an X,Y, Z for a point, a pair of such points for a line and a series of such lines for a polygon. This method is very useful for modeling discrete physical features.
Different geographical features are expressed by different types of geometry:
A point is a zero-dimensional abstraction of an object represented by a single X, Y co-ordinate. It is normally used to represent a geographic feature too small to be displayed as a line or an area (e.g. location of a building on a small scale map or, for example, cities on a map of the world might be represented by points not polygons). No measurements are possible with point features.
Figure 1- Vector representation
Lines or polylines
A set of co-ordinates that represent the shape of geographic features that are too narrow to be displayed as an area, such as, county boundary lines or contours. At small scales geographic features may have no area, e.g. streams or streets and may be represented as linear features rather than as a polygon. Line features can measure distance.
Polygons are used to represent areas. Such as lakes, park boundaries or land uses etc. Polygons convey the most amount of information of the file types and can measure perimeter and area.
Rigaux et al. (2002:p.38) states, ‘A point is represented by its pair of coordinates, whereas more complex linear and surfacic objects are represented by structures (lists, sets, arrays) on the point representation.’ These geometries can be linked to a row in a database that describes their attributes. For example, a database that describes lakes may contain a lake’s depth, water quality, pollution level. Different geometries can also be compared and the GIS could be used, for example, to identify all wells (point geometry) that are within one kilometre of a lake (polygon geometry) that has a high level of pollution. Vector data can be displayed at any scale and individual layers (e.g. roads, buildings, etc) can be displayed or omitted (see Appendix A).
Ellis states ‘that raster is a method for the storage, processing and display of spatial data.’ There are three types of raster datasets; thematic data, spectral data and pictures. Raster data consists of rows and columns of cells, with each cell storing a single value. Raster data can be images containing individual dots with colour values, called cells (or pixels), arranged in a rectangular evenly spaced array.
‘Each cell must be rectangular in shape, but not necessarily square’ (Ellis 2001). 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 recognised as such, however, raster structures cannot identify the boundaries of areas such 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. Ellis states that ‘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. Additional values recorded for each cell may be a discrete value, such as land use, a continuous value, such as temperature, or a null value if no data is available. While a raster cell stores a single value, it can be extended by using raster bands to represent RGB (red, green, blue) colours, colour maps (a mapping between a thematic code and RGB value), or an extended attribute table with one row for each unique cell value. The resolution of the raster data set is its cell width in ground units.
Anyone who is familiar with digital photography will recognize the Raster graphics pixel as the smallest individual grid unit building block of an image, usually not readily identified as an artifact shape until an image is produced on a very large scale (see Appendix B). A combination of the pixels making up an image colour formation scheme will compose details of an image, as is distinct from the commonly used points, lines, and polygon area location symbols of vector graphics. Aerial photographs and satellite images are examples of raster images used in mapping.
Figure 2 – Aerial Photo Digitally scanned and ortho-rectified raster colour photography. The ortho-rectification process removes distortions caused by camera tilt and topographical features to produce a scale accurate image.
Raster data is stored in various formats; from a standard file-based structure of TIF, JPEG, etc. to binary large object data stored directly in a relational database management system.
Raster v Vector
There are some important advantages and disadvantages to using a raster or vector data model to represent reality:
Vector graphics are usually more aesthetically pleasing. Raster data will appear as an image that may have a blocky appearance for object boundaries (depending on the resolution of the raster file).
Vector data is simpler to update and maintain, whereas a raster image will have to be completely reproduced (e.g. a new road is added).
Vector data allows much more analysis capability, especially for “networks” such as roads, rail, telecommunications etc. Distances and areas can be calculated automatically.
With raster data it is difficult to adequately represent linear features depending on the cell resolution. Therefore, network linkages are difficult to establish.
Vector files require less disk storage space than raster data.
Raster data allows easy implementation of overlay operations, which are more difficult with vector data.
Raster data structure allows simple spatial analysis procedures
An outline of the application of vector and raster data by OSI in Ireland is included in Appendix C.
Relating the spatial component along with the non-spatial attributes of the existing data e.g. census figures (see Appendix D) enhances the user’s understanding and gives new insights into the patterns and relationships in the data that otherwise would not be found.
Non-spatial data can be stored along with the spatial data represented by the coordinates of vector geometry or the position of a raster cell. In vector data, the additional data contains attributes of the feature. In raster data the cell value can store attribute information, but it can also be used as an identifier that can relate to records in another table.
Software is currently being developed to support the solutions to spatial problems being integrated with solutions to non-spatial problems. This will result in non experts using GIS to integrate spatial and non spatial criteria to view solutions to complex problems and to assist in decision-making.
The processes of data collection are also variously referred to as data capture, data
automation, data conversion, data transfer, data translation, and digitizing.
The two main types of data capture are:
Primary data sources e.g. those collected in digital format specifically for use in a GIS project.
Secondary sources, digital and analog datasets that were collected for a different purpose and need to be converted into a suitable digital format for use in a GIS project.
For vector data capture the two main branches are ground surveying and GPS. Survey data can be directly entered into a GIS from digital data collection systems on survey instruments. Positions from a Global Navigation Satellite System like Global Positioning System (GPS), another survey tool, can also be directly entered into a GIS. New technologies allow creating maps as well as analysis directly in the field and as a result projects are more efficient and mapping is more accurate.
Remotely sensed data also plays an important role in data collection and consists of sensors (e.g. cameras, digital scanners) attached to a platform which usually consist of aircraft and satellites.
The majority of digital data currently comes from photo interpretation of aerial photographs. Workstations are used to digitize features directly from stereo pairs of digital photographs. These systems allow data to be captured in two and three dimensions, with elevations measured directly from a stereo pair using principles of photogrammetry. Photographs are collected by analog or optical cameras before being entered into a soft copy system, but as high quality digital cameras become cheaper this step will be eliminated.
Satellite remote sensing provides another important source of spatial data. Remote sensing collects raster data that can be further processed to identify objects and classes of interest, such as forested areas. The disadvantages are that the resolution is often too course or sensors are restricted by cloud cover.
Entering data into GIS usually requires editing, to remove errors, or further processing. For vector data it must be made “topologically correct” before it can be used for some advanced analysis. For example, in a road network, lines must connect with nodes at an intersection. For scanned maps, blemishes on the source map may need to be removed from the resulting raster. To ensure that the data is specific and reliable and that represents as closely as possible the spatial world we live in, it requires a quality insurance process to manage completeness, validity, logical consistency, physical consistency, referential integrity and positional accuracy of data.
Vectorisation is the process of converting raster data into vector data. For example, a GIS may be used to convert a satellite image map to a vector structure by generating lines around all cells with the same classification, while determining the cell spatial relationships.
One of the biggest problems with data obtained from external sources is that they can be encoded in many different formats. Many tools have been developed to move data between systems and to reuse data through open application programming interfaces. Therefore, a GIS must be able to convert geographic data from one structure to another.
When data is captured, the user should consider if the data should be captured with either a relative accuracy or absolute accuracy, as this could not only influence how information will be interpreted but also the cost of data capture.
Vector data can be manipulated, layers can be turned on and off, data can be edited or deleted and additional data can be added in. Raster data is usually used as a background map. Raster is not as intelligent as Vector, Rigaux et al. (2002: p.39) states the structure is unfortunately not powerful enough to ensure the correctness of the representation. It is more useful as a display map for brochures, internet and power point presentations.
Oosterom Van, P.J. (1993:p.vii) states the ever increasing availabilitiy of hardware such as digitizers, scanners workstations, graphic displays, printers and plotters for the input, processing, and output of geographic data only partly explains the growing interest in GISs. GIS allows us to view, understand, question, interpret, and visualise data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts. GIS helps one answer questions and solve problems by looking at data in a way that is quickly understood and easily shared.
Figure 3 – GIS continues to evolve
Source: Cummens 2010 ERSI
Many forces are converging transforming how we work and improving efficiency and decision making (see Fig. 3 above). GIS Is becoming Mainstream Technology going beyond focused applications (Cummens 2010). GIS is helping citizens, business and Government by improving planning, management, communications and decision making.
Cummens, Patricia (2010) Geographic Information Enabling a Smarter Government and Economy at the SCS Conference 2010. ESRI.
Ellis, F. (2001) Introduction to GIS. Melbourne: University of Melbourne.
Oosterom Van, P.J. (1993) Reactive Data Structures for Geographic Information Systems. New York: Oxford University Press.
Rigaux, P., Scholl, M., Voisard, A (2002) Spatial Databases with Applications to GIS. San Fransisco: Morgan Kaufmann Publishers.
http://www.osi.ie/en/academic/third-level-and-academic.aspx?article=4bf958eb-bf0b-4b28-a0d9-24586fadbaab Accessed 27/10/2010
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