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Abstract
Accuracy is very important aspect of GIS. This is one of the main factors governing the reliability of information and hence the decision making. To assess the magnitude of errors at different stages of computerisation of information from the source data, it is necessary to consider various factors likely to affect the accuracy. Five indicators of accuracy given in US Spatial Standards (Clarke, 1992) are Lineage, Positional Accuracy, Attribute Accuracy, Logical Consistency and Completeness. These have been considered in the present study.
The error analysis was carried out by various tests on the data available. Above factors were taken into account. In view of the results obtained, it appears that achieving reasonable accuracy is possible with the present data conversion in most of the cases, however, within respective limitations. Although, some times such accuracy is difficult to achieve due to various deviations, inherited with the data or introduced due to methods of conversion / techniques adopted, even then to a greater extent it is found to be possible.
It has been attempted in this study to incorporate as many data conversion from different sources as possible and the results obtained were found to be related to aspects of quality control in a given situation.
Introduction
Reliability of a Geographical Information System (GIS) mainly depends upon its accuracy with which the data is arranged and the way it is integrated and displayed for the purpose of extracting information for decision making. Since decision depends on the information contents, the accuracy of such information is hardly required to be over emphasised. In order to be sure about the accuracy, it is necessary that the data conversion is tested and compared against the data sources, considering prescribed accuracy standards.
The quality standards are such that some of the aspects are related with the figures and ratios whereas some others emphasize on relative quality. However, there are five quality indicators of accuracy given in the US Spatial Data Standards (Clarke, 1992) to consider a good quality GIS.
It is necessary that the data conversion from the data source to the computer database is tested taking these factors into account. The same has been attempted in the present study to present a case for developing a Quality Control method.
Testing of data and errors
The vector data was tested for accuracy when compared with the source data. The quality indicator given in terms of the scales were considered for testing, specially in grading into numbers, ranging from 0 to 9 scale. This provides an opportunity to get an idea about the relative accuracy of the data. In this case a vector data (the source was considered to be the accurate one).
Above factors were tried in a sequence. Some of the basic examinations were necessary to be carried out on the data, which are as follows:
- Lineage was the basic factor considered for giving quality indicators.
- The vector data was tested for accuracy for all the well defined details and found to be 90% accurate within an error of 0.5 mm and 100% within an error of 1.00 mm.
- Attribute accuracy was tested based on polygon overlay, and it was found that 95% of the polygons tested are accurate.
- Positional accuracy and completeness were tested by super-imposing the raster and vector data in the digitisation stage itself. And found to be satisfactory.
- The attribute accuracy was also taken care while giving quality indicator.
- Logical consistency was checked on the database generated by performing topological tests that were possible in the MGE environment. Topologically clean data extraction was one of the main aims of the study. This was achieved by ensuring that all the chains intersect at nodes and remains consistent around the polygons. Also, inner rings embed consistently in closing the polygon. This is a rigorous testing and took considerable time to ensure that the data is topologically clean even in small polygons.
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