Vector And Raster Data In Gis Computer Research Essay

A Geographical Information System (GIS) is a way of spatially holding, analysing, manipulating, taking care of and displaying physical data. GIS data represents real items such as roads, rivers, urban areas, place labels, railway, tourist destinations, town labels etc. with digital data deciding the combination. A geodatabase is a databases that is in some way referenced to locations on earth. Customarily, there are two wide methods used to store data in a GIS; raster images and vector. Ordnance Review 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, lines (or arc) and point data. Vector is a way for saving spatial data affecting assigning coordinates for every entity; an X, Y, Z for a point, a set of such things for a range and a series of such lines for a polygon. This technique is very helpful for modeling discrete physical features.

Different physical features are indicated by different kinds of geometry

Points

A point is a zero-dimensional abstraction of any object symbolized by a single X, Y co-ordinate. It is normally used to symbolize a geographic feature too small to be exhibited as a range or a location (e. g. location of any building on a little range map or, for example, cities over a map of the world might be symbolized by tips not polygons). No measurements are possible with point features.

Figure 1- Vector representation

Source: http://www. geom. unimelb. edu. au/gisweb/GISModule/GIST_Vector. html

Lines or polylines

A group of co-ordinates that signify the form of geographic features that are too narrow to be exhibited as a location, such as, region boundary lines or contours. At small scales geographic features may have no area, e. g. streams or streets and may be displayed as linear features somewhat than as a polygon. Series features can evaluate distance.

Polygons

Polygons are used to signify areas. Such as for example lakes, park boundaries or land uses etc. Polygons convey the most amount of information of the document types and can assess perimeter and area.

Rigaux et al. (2002:p. 38) areas, 'A point is represented by its couple of coordinates, whereas more complex linear and surfacic things are represented by buildings (lists, models, arrays) on the idea representation. ' These geometries can be linked to a row in a repository that represents their attributes. For instance, a database that details lakes may include a lake's depth, drinking water quality, pollution level. Different geometries can even be likened and the GIS could be used, for example, to identify all wells (point geometry) that are within one kilometre of any lake (polygon geometry) that has a higher level of pollution. Vector data can be displayed at any level and individual tiers (e. g. highways, complexes, etc) can be exhibited or omitted (see Appendix A).

Raster

Ellis states 'that raster is a method for the safe-keeping, 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 skin cells, with each cell holding a single value. Raster data can be images containing specific dots with coloring values, called cells (or pixels), assemble in a rectangular equally spaced array.

'Each cell must be rectangular in shape, but not necessarily square' (Ellis 2001). Each cell within this matrix includes location co-ordinates as well as an attribute value. The spatial location of every cell is implicitly comprised within the purchasing of the matrix, unlike a vector composition which stores topology explicitly. Areas filled with the same feature value are accepted as such, however, raster constructions cannot identify the limitations of areas such as polygons.

Raster data can be 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. Using the raster data model, spatial data is not continuous but split into discrete products. Ellis areas that 'this makes raster data specifically suitable for certain types of spatial operation, for example overlays or area calculations. '

Raster structures may lead to increased storage using situations, since they store each cell in the matrix regardless of whether it is an attribute or simply 'clear' space. Additional ideals recorded for each and every cell may be considered a discrete value, such as land use, a continuing value, such as heat, or a null value if no data is available. While a raster cell stores a single value, it could be long by using raster bands to represent RGB (red, renewable, blue) colours, coloring maps (a mapping between a thematic code and RGB value), or an extended attribute desk with one row for each unique cell value. The image resolution of the raster data place is its cell width in surface units.

Anyone who's familiar with portrait digital photography will acknowledge the Raster graphics pixel as the tiniest individual grid product building block of a graphic, usually not immediately recognized as an artifact shape until a graphic is produced on a very large range (see Appendix B). A blend of the pixels making up an image coloring formation program will compose information on an image, as is unique from the frequently used items, lines, and polygon area location icons of vector graphics. Aerial photographs and satellite television images are examples of raster images found in mapping.

Figure 2 - Aerial Photo Digitally scanned and ortho-rectified raster shade picture taking. The ortho-rectification process takes away distortions caused by camera tilt and topographical features to produce a scale appropriate image.

Source: OSI

Raster data is stored in a variety of formats; from a standard file-based composition of TIF, JPEG, etc. to binary large subject data stored directly in a relational databases management system.

Raster v Vector

There are some important benefits and drawbacks to utilizing a raster or vector data model to symbolize reality

Vector images are usually more aesthetically pleasing. Raster data will appear as an image that may have a blocky appearance for object boundaries (depending on the image resolution of the raster document).

Vector data is simpler to update and keep maintaining, whereas a raster image will have to be completely reproduced (e. g. a fresh road is added).

Vector data allows a lot more analysis capability, especially for "networks" such as highways, rail, telecommunications etc. Distances and areas can be calculated automatically.

With raster data it is difficult to effectively symbolize linear features depending on cell image resolution. Therefore, network linkages are difficult to establish.

Vector data require less disk space for storage than raster data.

Raster data allows easy implementation of overlay operations, which are more challenging with vector data.

Raster data structure allows simple spatial evaluation procedures

An put together of the use of vector and raster data by OSI in Ireland is included in Appendix C.

Non-spatial data

Relating the spatial aspect along with the non-spatial features of the existing data e. g. census information (see Appendix D) enhances the user's understanding and gives new insights in to the patterns and romantic relationships in the data that otherwise wouldn't normally be found.

Non-spatial data can be stored along with the spatial data symbolized by the coordinates of vector geometry or the position of a raster cell. In vector data, the excess data contains capabilities of the feature. In raster data the cell value can store attribute information, but it can be used as an identifier that can relate to documents in another table.

Software is currently being developed to aid the solutions to spatial problems being integrated with answers to non-spatial problems. This can cause non experts using GIS to integrate spatial and non spatial standards to view solutions to complex problems also to assist in decision-making.

Data capture

The functions of data collection are also variously referred to as data take, data

automation, data alteration, data copy, data translation, and digitizing.

The two main types of data get are

Primary data sources e. g. those accumulated in digital format designed for used in a GIS project.

Secondary options, digital and analog datasets that were collected for a different purpose and need to be converted into the right digital format for use in a GIS job.

For vector data get both main branches are floor surveying and Gps navigation. Study data can be directly joined into a GIS from digital data collection systems on study tools. Positions from a worldwide Navigation Satellite System like Global Placement System (GPS), another study tool, can even be directly inserted into a GIS. New solutions allow creating maps as well as analysis straight in the field and consequently projects are more efficient and mapping is more accurate.

Remotely sensed data also plays an important role in data collection and involves detectors (e. g. cams, digital scanners) mounted on a platform which often consist of aircraft and satellites.

The most digital data presently comes from image interpretation of aerial images. Workstations are used to digitize features straight from stereo pairs of digital photographs. These systems allow data to be captured in two and three sizes, with elevations assessed immediately from a stereo pair using rules of photogrammetry. Photos are gathered by analog or optical camcorders before being joined into a soft backup system, but as high quality digital cameras become cheaper this step will be eradicated.

Satellite remote sensing provides another important source of spatial data. Remote sensing collects raster data that can be further processed to recognize objects and classes of interest, such as forested areas. The negatives are that the quality is often too course or sensors are constrained by cloud cover.

Entering data into GIS usually requires editing and enhancing, to remove problems, or further handling. For vector data it must be made "topologically accurate" before it could be used for some advanced analysis. For instance, in a highway network, lines must connect with nodes at an intersection. For scanned maps, blemishes on the source map may need to be taken off the causing raster. To ensure that the data is specific and reliable and this represents as directly as you can the spatial world we reside in, it requires a quality insurance process to manage completeness, validity, logical consistency, physical steadiness, referential integrity and positional reliability of data.

Raster-to-vector translation

Vectorisation is the procedure of converting raster data into vector data. For example, a GIS may be used to convert a satellite image map to a vector framework by producing lines around all cells with the same classification, while deciding the cell spatial relationships.

One of the biggest issues with data obtained from external sources is that they can be encoded in a variety of formats. Many tools have been developed to go data between systems also to reuse data through wide open application coding interfaces. Therefore, a GIS must have the ability to convert geographic data from one structure to some other.

CONCLUSION

When data is captured, the user should think about if the data should be captured with the relative reliability or absolute accuracy and reliability, as this could not only influence how information will be interpreted but also the cost of data catch.

Vector data can be manipulated, tiers can be turned on and off, data can be edited or removed and additional data can be added in. Raster data is usually used as a qualifications map. Raster is not as wise as Vector, Rigaux et al. (2002: p. 39) declares the structure is regrettably not powerful enough to ensure the correctness of the representation. It is more useful as a display map for brochures, internet and electricity point presentations.

Oosterom Van, P. J. (1993:p. vii) suggests the increasing availabilitiy of hardware such as digitizers, scanners workstations, visual displays, printers and plotters for the suggestions, processing, and productivity of geographic data only partly talks about the growing desire for GISs. GIS allows us to view, understand, question, interpret, and visualise data in many ways that reveal relationships, patterns, and movements in the form of maps, globes, reviews, and charts. GIS helps one answer questions and solve problems by looking at data in a manner that is quickly realized and easily distributed.

Figure 3 - GIS continues to evolve

Source: Cummens 2010 ERSI

Many makes are converging changing how we work and increasing efficiency and decision making (see Fig. 3 above). GIS Is now Mainstream Technology going beyond focused applications (Cummens 2010). GIS is helping people, business and Administration by increasing planning, management, marketing communications and decision making.

REFERENCES

Cummens, Patricia (2010) Geographic Information Enabling a Smarter Federal and Market at the SCS Discussion 2010. ESRI.

Ellis, F. (2001) Benefits to GIS. Melbourne: School of Melbourne.

Oosterom Truck, P. J. (1993) Reactive Data Constructions for Geographic Information Systems. NY: Oxford University or college Press.

Rigaux, P. , Scholl, M. , Voisard, A (2002) Spatial Databases with Applications to GIS. San Fransisco: Morgan Kaufmann Web 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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