Issuse in GIS
ISSUES in GIS
‘I want to combine several maps into one and the data won't line up. Objects in drawings using the same projection do not align well’. This is a very common refrain among GIS data users and analysts. Spatial data industry and users are looking forward to the opening up of GIS data (thematic maps) through the National Spatial Data Infrastructure. Are we well prepared and geared up to put the data to use straight away for efficient decision making and management of our Natural Resources? Or will we be entering into the whirlpool of matching scales, registration, compatibility, resolution, projections and searching for data / metadata?
Although the use of geographic information technologies is pervasive throughout business, government, industry and the scientific community everywhere; it has its own intrinsic problems and issues. Conflicts are arising on a daily basis for those using geographic information systems and their affiliated databases, for those implementing such systems, and for those designing the next generations of spatial information technologies. Balancing among competing interests and resolving conflicts involved in the use of these technologies are growing problems for numerous parties within society. Among the problem domains of greatest concern in use of these technologies are those involving personal information privacy, intellectual property rights in geographic information, liability in the use of geographic data sets, public access to government geographic data sets, public goods aspects of geographic information in libraries, and sales of geographic information by government agencies.
Issues related to usage of Data from Different Sources
You would have to be very lucky to have found data for your entire final project; that was at the same scale, in the same projection, with the same datum, collected at the same time and in the same manner. What is worse is that unless people have been very diligent in their data documentation, you will not know all of the parameters.
As soon as data from different sources, times, scales, etc., is mixed it is subject to errors. If we use this data to derive new data, the errors propagate into the new analysis. An easy example is we overlay data on top of each other, the areas where the data do not agree forms a myriad of little slivers, if it is polygon data and over/under shoots if it is line data. The correct choice is to generalize data to the same scale so that you do not get a sense of false precision.
Projections as you know cause distortions in at least one aspect of the data. There is no meaning in calculating area on a data set that has not preserved it, or similarly, measuring distances in a dataset that distorts distance is also wrong. Extracting data from a larger dataset can cause problems with combining data as edge effects might be present in one dataset and not another. UTM projected data for example are prone to edge effects. Raster data from aerial sources also have distortions that should be corrected by the orthorectification process, but sometimes they are not.
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