This document discusses editing and quality control procedures for geographic data. It describes common data errors like locational, topological, and attribute errors. It also outlines quality parameters to evaluate data such as completeness, validity, logical consistency, and accuracy. Finally, it discusses quality control procedures including visual quality checking, edge matching, conflict resolution, and automated validation.

1. Editing and Data Quality
Brighton
Chamunorwa

2. Data editing
• Data editing aims to remove the errors that arise
during encoding of geographic data.
• The errors include locational, topological and
attribute data errors.
• Location errors are positional inaccuracies of
digitised features including:
– Locational displacement of features
– Doubling digitising
– Omission error (missing features)
– Commission errors – features erroneously included in the
data when they should have been excluded.

3. Topological errors
• Topological errors are those that violate
topology rules defined by either the GIS
software or user.
• For example:
– only one point may exist at a given location.
– Lines must intersect at a node.
– Overlapping polygons do not exist.

4. Pseudo Nodes
• These are unintentional nodes that occur at along a line or a
polygon.
• They can be due to misplaced point or to push wrong button during
digitising
• To correct pseudo notes, first determine whether they are in deed
errors
– This can be achieved by comparing with the data sources
• Incorrect pseudo nodes can be selected manually and deleting
them
• GIS software can also do it automatically

5. Dangling Nodes
• The dangling node are defined as a single node
connected to a single line entity.
• They result from three possible mistakes:
• failure to close a polygon (unclosed polygon)
• failure to connect the node to the object it was supposed to be
connected to (undershoot)
• going beyond the entity you were supposed to connect to
(overshoot)

6. Correcting dangling nodes
Dangling nodes errors are identified by a graphic symbol different from the
one used for pseudo nodes and actual nodes

7. Correcting dangling nodes
• If the dangling node is an open polygon, the software alerts you by providing
the number of complete polygons in the dataset.
• If the number is different from expected the errors can then be corrected
manually
• In the case of an open polygon, you merely move one of the nodes to connect
with the other.
• For undershoots, the node is identified and is moved or 'snapped' to the
object to which it should have been connected
• Overshoot errors are corrected by identifying the intended line intersection
point and 'clipping' the line so that it connects where it is supposed to.

8. Attribute Errors and editing
• Attribute data errors are more difficult to identify
than locational errors as they are not apparent until
later on in the data processing analysis.
• Simple data entry errors such as missing or
duplicate data records may become evident when
linking spatial and attribute data.
• Attribute data errors may include:
– Incorrect assignment of features unique identifiers.
– Missing data records or too many records.
– Missing attribute
– Incorrect attribute value

9. Attribute Errors and editing
• Attribute data error may result from:
– Observation or measurement errors.
– Data entry errors
– Out dated data
• Attribute data editing in GIS main includes the
following on attributes associated with features
and their values:
– Adding
– Deleting
– Updating

10. Quality parameters - Completeness
• It is assessed according to the database
• All data adhere to the database design.
• All data must conform to a known standard for
topology, table structure, precision, projection,
and other data model specific requirements

11. Quality parameters - Validity
• Measures the attribute accuracy of the database.
• Each attribute must have a defined domain and
range.
– The domain is the set of all legal values for the
attribute.
– The range is the set of values within which the data
must fall.

12. Quality parameters - Logical
consistency
• Measures the interaction between the values of two or more
functionally related attributes.
• If the value of one attribute changes, the values of functionally
related attributes must also change.
• For example, in a database in which the attribute SLOPE and the
attribute LANDUSE are related, if LANDUSE value is "water," then
SLOPE must be 0, as any other value for SLOPE would be illogical

13. Quality parameters - Physical
consistency
• The topological correctness and geographic
extent of the database.
• For example, the requirement that all electrical
transformers in an electrical distribution
database's GIS have annotation denoting phasing
placed within 15 feet of the transformer object.

14. Quality parameters - Accuracy
• Accuracy is the degree to which information on a map or in a
digital database matches Actual/ True or Accepted values.
• Spatial Accuracy.
– the accuracy of the spatial component of the database. The metrics
used depend on the dimensionality of the entities under
consideration.
• Temporal accuracy.
– the conformity between the encoded and the actual temporal
coordinates of an entity.
• Thematic Accuracy.
– Thematic GIS information is generated by collecting and assigning
the properties of spatial data to stored objects or areas, that may
lead to errors, that can be due to a misclassification error in the
first place, or in the second, that originates from the number of
different data classes occurring in the same spatial object

15. Quality parameters - Resolution
• Resolution (or precision) refers to the amount of details that can be
discerned in space, time or theme.
• Resolution is an aspect of the database specification that
determines how useful a given database may be for a particular
application.
• Resolution is linked to accuracy, since the level of resolution affects
the database specification against which accuracy is assessed.

16. Quality Control procedures - Visual
QC
• Performed whilst performing database construction stages
• Checking for
– Systematic errors
– Random errors
• Early identification of important and potentially costly errors represents real
savings
• Each error type needs to be evaluated along with the process that created the
data in order to determine the appropriate root cause and solution.

17. Quality Control procedures - Edge Matching
• Requires that all features that cross or are near the map edge
be reviewed with respect to logical and physical consistency
requirements as well as noted for positional accuracy and
duplication.
• The temporal factor must be considered. If adjacent maps
differ greatly in age there are bound to be edge matching
errors between these maps.
• Cultural features are especially prone to this problem

18. Quality Control procedures - Conflict
Resolution
• Conflicts resulting when the same data coming from two or more
sources differ must be worked out.
• Map series must be reviewed together to identify duplicated
features and resolve conflicting positional locations and conflicting
feature attributes

19. Quality Control procedures -
Automated QC and Validation
• Visual inspection of GIS data is reinforced by automated QA methods.
• GIS databases can be automatically checked for adherence to database design,
attribute accuracy, logical consistency, and referential integrity.
• Automated QC must occur in conjunction with visual inspection.
• Automated quality Check allows quick inspection of large amounts of data.
• It will report inconsistencies in the database that may not appear during the visual
inspection process.
• Both random and systematic errors are detected using automated QC procedures