Input Data Sets In Gis example essay topic

emote sensing and geographic information systems can be defined as follows: "Remote sensing is any method of obtaining and recording information from a distance. The most common sensor is the camera; cameras are used in aircraft, satellites, and space probes to collect information and transmit it back to Earth (often by radio). The resulting photographs provide a variety of information, including archeological evidence and weather data. The images are also used in map-making. Microwave sensors use radar signals that penetrate cloud. Infrared sensors measure temperature differences over an area.

Computers process data from sensors". ('remote sensing' World Encyclopedia. Philip's, 2005. Oxford Reference Online. Oxford University Press. Charles Darwin University.

12 September 2005). "Geographic (Geographical) Information Systems (GIS), GIS are integrated, spatial, data-handling programmes which will collect, store, and retrieve spatial data from the real world. They are powerful tools in decision-making, as they can incorporate co-ordinated data. It should be noted, however, that GIS only contain selected data; solely the properties which investigators have considered relevant, so that many variables will not be fed into the systems". 'Geographic (Geographical) Information Systems' A Dictionary of Geography. Susan Mayhew.

(Oxford University Press, 2004. Oxford Reference Online. Oxford University Press. 12 September 2005) The relationship between remote sensing and GIS is that remotely sensed data is one of the input data sets in GIS. GIS can have other input data sets such as population data, species data, climate data etc. "GIS has the ability to analyse these input data sets in a spatial context and produce a conclusion which the operator of the system can interpret and use to help develop a solution to the problem being investigated". (web poster) The differences in remote sensing and GIS can be discovered in the data storage format and information processing focus of the data.

Remote sensing will first be discussed first. The data collected in remote sensing is in the form of a raster image. The data in the images are implicit in geo referencing. This means that the spatial addresses of individual elements are determined by the ordering of the element in the data structure. The data does not have an inherent topology. This is where the grouping of elements into structural units is not inherently done.

The data structure is simplistic - regular Cartesian grid. GIS data on the other hand is often, but not always, stored in vector format. The data collected in GIS is explicit geo referencing. This means that the spatial position of elements is a make up of part of the definition. The topology of the data is specified with each element. The data structures are more complex than raster format data.

Remote sensing and raster processing are analysis oriented. Continuous spatial overlap, proximity etc. and 'landscape' wide statistics are easily calculated. Spatial analysis is simpler in raster form. GIS and vector processing are usually database management oriented. Analysis of network inter-relationships, that involve topological elements, is more natural in vector form. Element length, area, perimeter etc. are explicitly defined (this is hard to do in raster systems!) In a modern GIS workflow, data moves from its original sources to government agencies, inspectors, design firms, environmental consulting firms, construction firms, facility managers, and so forth.

At each step, the data may be converted from one format to another and is often printed, handed off, and subsequently entirely re-entered. Data conversion tools introduce problems as data moves from one system to another, from geometry to vertices, and from double to single precision. For example, a user may convert double-precision Autodesk Map TM files into a double-precision GIS tool, but a proprietary spatial database (ESRI's Spatial Database Engine or SDE, for example) supports only integer data storage. When the data is extracted as a double-precision CAD layer and sent to an engineering firm, precision and accuracy are often lost. Further increasing the chance for errors, engineers may assume that the double precision is still available since the source file was a design drawing. Newer integrated software has overcome these limitations of traditional GIS by managing both CAD and GIS features in a seamless and precise database environment. (web Critical Tools. pdf.) Despite these powerful applications, GIS does have some current limitations.

GIS software companies are focusing a lot of attention on these limitations, because if they can be overcome, GIS will enter a new era that increases its power -- and market -- many fold. For instance, it is currently impossible to account for temporal changes in GIS. A data set may change over time, but GIS is not able to incorporate those changes for analysis purposes. Although there are ways to 'fake out' the GIS to simulate temporal analysis, real time-based analysis is still in the developmental phase. Temporal analysis could be used to track urban growth, monitor changes in water quality over time, and many other powerful applications. It is one of the highest priorities in the field right now.

Another priority in the GIS field is the support of three dimensional analysis in GIS. Three-dimensional GIS would allow analysts to see how vertical layers interact with each other. This could include layers of air, soil, rock, or water. As with temporal analysis, some programs are able to simulate a 'pseudo-3-D' mode, but no real 3-D applications are available. This would have tremendous applications for geology, air and weather, which, in turn, would affect groundwater and air pollution control, to name just a few. (web / d sw / gas //1 intro / intro 2. html).