Evaluation of the Fitness for use of Oil Crops Spatial Datasets for Biodiversity Conservation
For information oriented organizations data quality is a main issue. The need to associate geographical data for quality specifications has become a particular evident for the last thirty years. The data is collected from the variety of sources and stored in a database. The different origins and quality of digital spatial data are usually integrated in GIS environments, through determining an indefinite level of global accuracy in such systems. However, in the information system, data quality problems can occur anywhere. Data Evaluation is a process used to determine inaccurate, incomplete or unreasonable data and then improving the quality through the correction of detected errors and omissions. The Ethiopian biodiversity institute was established in 1976, with the main objective of ensuring the appropriate conservation and utilization of the country’s Biodiversity. In this context, a dataset was created after the compilation of occurrence records (more than 81,500) of which 8,147 are oil crop species, obtained from the institute database. The present study aims to evaluate the quality of Oil Crop geospatial datasets and records that enable to access to basic and advanced functions to detect completeness and consistency issues as well as general errors in the existing or set of biodiversity conservation records. In assessing the fitness for use of Oil Crops spatial dataset, attribute query analyses were applied. Spatial dataset attribute query analysis approach was used to test the Significance of the differences mbetween expected and observed extent of the spatial data quality. To compare errors between the Positional and Attributem accuracy, attribute query analysis approach was used. Results showed that, 3357 records (41.2%) were considered as good quality and the rest 4,790 records (58.8%) dataset were erroneous due to various reasons. Generally five groups of causes of errors which could be resulted either at data collections or data encoding or at any other stages were found. Of all erroneous records, the 357 erroneous points were corrected using the Arc-GIS query analysis methods with support of Google Earth and Diva-GIS information and recommendations were given for future use.