• Although GIS provides a tremendous capability for data processing, one should be aware of situations where things may go wrong. For this one should know various sources of errors which may limit the applicability of results.
• Data quality is an important issue in GIS and indicates how good data are (Heywood et. al, 2000). It describes the overall fitness or suitability of data for specific purpose. It also describes that the data are free from error and other problems. Various ways in which this can be assessed is by understanding terms like error, accuracy, precision, and bias. Further, the resolution and generalization of source data and the data model can also influence the final results. The data also need to be complete, compatible and consistent, and applicable for the analysis.

• Some aspects of data quality can be understood in terms of the following
  • Accuracy
  • Precision
  • Bias
  • Resolution
  • Generalization
  • Completeness
  • Compatibility
  • Consistency
  • Applicability

• Definition of Error
  • Difference between a measured quantity and its true (unknown) value
    e = M - T;  where e = error, M = measured value, T = true value

Caused by imperfections in instrument construction or adjustment.

Caused by changing conditions in the surrounding environment.

Caused by limitations in human senses and manual dexterity.

• Accuracy : Indicates nearness of measured value (M) to true value (T).
  • True value is always unknown. Therefore, accuracy is indeterminate in practice and we resort to other methods of assessing the reliability of measurement to decide which of the measurements is the best (provided we have decided what is the best).
  • An accurate GIS database indicates a true representation of reality.
• Precision allows us to make such judgments and refers to the degree of consistency between measurements and is based on the size of the discrepancies in a data set. It is helpful in two ways:
  • It is an indication of the spread of the measured values of the quantity. Several values grouped closely together are said to be more precise set of measurements than other with a broader spread of values. The precision is inversely proportional to variance.
  • Denotes something to a higher manufacturing standard.
• Bias : It indicates a systematic variation of data from reality. It is a consistent error throughout the dataset.

Figure 40.1 gives a comparison between accuracy and precision. It shows that in surveying measurements:

• Pacing less precise
• Taping more precise
• EDM most precise

The figure also shows the following interpretations

• Resolution : It describes the smallest feature in the data set that can be displayed or mapped. In raster GIS, it is indicated by the raster cell size. For example, a square cell of 20 m will indicate that only those features will be mapped which are above 20 m x 20 m. In vector GIS, resolution is determined by scale of the original map and the size of feature represented on that.
• Generalization : It is the process of simplifying the complexities of the real world to produce maps and models.
• Completeness : It refers to the fact that complete set of spatial and temporal data is available.
• Compatibility and consistency : It refers to the fact that different data sets used in creating GIS database should be developed using similar methods of data capture, storage, manipulation and editing. For example, the maps used should be at very different scales.
• Applicability: It describes the appropriateness or suitability of data for a set of commands, operations or analyses.