The quality of data sources for GIS processing is always of great concern among GIS application specialists and End users. We have seen in the “Executive Introduction to Geographic Information System (GIS)” that there are variety of data input sources exsits for the GIS system. The most important consideration regarding the accuracy of the data which is not known to the end users is that, the accuracy of geospatial data decreases as spatial resolution becomes more coarse. With the increase in GIS software on the commercial market and the accelerating application of GIS technology to problem solving and decision making roles, the quality and reliability of GIS products is under lot of discussion and concern. We will look at the Geographic Data Accuracy,Quality and Error for GIS in the respective article.

Much concern has been raised as to the relative error that may be inherent in GIS processing methodologies. While research is ongoing, and no finite standards have yet been adopted in the commercial GIS marketplace, several practical recommendations have been identified which help to locate possible error sources, and define the quality of data. Also the inclusion of Crowd Source mapping for the online web based GIS , have also raised the quality and accuracy of geographic data a lot. We have focused on the following geographic data quality focuses on three distinct components, data accuracy, quality, and error.

                                                                                     GIS Accuracy and Cost

Accuracy

The most basic issue with respect to geographic data is accuracy. Accuracy is the acceptable  approach of results of observations to the true values or values accepted as being true. This means that observations of most spatial phenomena are usually only considered to estimates of the true value. The difference between observed and true (or accepted as being true) values indicates the accuracy of the observations.
There are 02 two types of accuracy exist :

• Positional accuracy  
• Attribute Accuracy   

Positional accuracy 

It is the expected deviance in the geographic location of an object from its true ground position. This is what we commonly think of when the term accuracy is discussed.

There are two components to positional accuracy. These are relative and absolute accuracy. Absolute accuracy concerns the accuracy of data elements with respect to a coordinate scheme, e.g. UTM. Relative accuracy concerns the positioning of map features relative to one another. Often relative accuracy is of greater concern than absolute accuracy. For example, most GIS users can live with the fact that their survey township coordinates do not coincide exactly with the survey fabric, however, the absence of one or two parcels from a tax map can have immediate and costly consequences.  

Attribute Accuracy

it is equally as important as positional accuracy. It also reflects estimates of the truth. Interpreting and depicting boundaries and characteristics for forest stands or soil polygons can be exceedingly difficult and subjective. Most resource specialists will attest to this fact. Accordingly, the degree of homogeneity found within such mapped boundaries is not nearly as high in reality as it would appear to be on most maps.  

Quality

Quality can simply be defined as the fitness for use for a specific data set. Data that is appropriate for use with one application may not be fit for use with another. It is fully dependant on the scale, accuracy, and extent of the data set, as well as the quality of other data sets to be used. The recent U.S. Spatial Data Transfer Standard (SDTS) identifies five components to data quality definitions.

1. Lineage
2. Positional Accuracy
3. Attribute Accuracy
4. Logical Consistency
5. Completeness  

1. Lineage

The lineage of data is concerned with historical and compilation aspects of the data such as the:
1. Source of the data;
2. Content of the data;
3. Data capture specifications;
4. Geographic coverage of the data;
5. Compilation method of the data, e.g. digitizing versus scanned;
6. Transformation methods applied to the data;
7. The use of an pertinent algorithms during compilation, e.g. linear simplification, feature             generalization.

2. Positional Accuracy

The identification of positional accuracy is important. This includes consideration of inherent error (source error) and operational error (introduced error). A more detailed review isprovided in the next section.  

3. Attribute Accuracy

Consideration of the accuracy of attributes also helps to define the quality of the data. This quality component concerns the identification of the reliability, or level of purity (homogeneity), in a data set.  

4. Logical Consistency

This component is concerned with determining the faithfulness of the  at a structure for a data set. This typically involves spatial data inconsistencies such as incorrect line intersections, duplicate lines or boundaries, or gaps in lines. These are referred to as spatial or topological errors.

5. Completeness

The final quality component involves a statement about the completeness of the data set. This
includes consideration of holes in the data, unclassified areas, and any compilation procedures
that may have caused data to be eliminated.  

Error

Commonly there are two(02) sources of error, inherent and operational, contribute to the reduction in quality of the products that are generated by geographic information systems. Inherent error is the error present in source documents and data. Operational error is the amount of error produced through the data capture and manipulation functions of a GIS. Possible sources of operational errors include:

1. Mis-labelling of areas on thematic maps;
2. misplacement of horizontal (positional) boundaries;
3. human error in digitizing
4. classification error;.
5. GIS algorithm inaccuracies; and
6. human bias.

While error will always exist in any scientific process, the aim within GIS processing should be to identify existing error in data sources and minimize the amount of error added during processing. Because of cost constraints it is often more appropriate to manage error than attempt to eliminate it. There is a trade-off between reducing the level of error in a data base and the cost to create and maintain the database.  

One of the major problems currently existing within GIS is the aura of accuracy surrounding digital geographic data. Often hardcopy map sources include a map reliability rating or confidence rating in the map legend. This rating helps the user in determining the fitness for use for the map. However, rarely is this information encoded in the digital conversion process.