Quality on the forefront

Quality is our responsibility; everyone performs appropriately, on time, first time, each time Continuous refinement of internal processes and methodologies are encouraged.

A formal corporate quality policy with total commitment from every individual in our organization is the backbone of our quality initiative. It is the policy at Geovision to produce products in timely manners that satisfy client requirements. Standards, dedicated groups for process definition, improvement and Quality Assurance, formal training on process and standards, are just some of the steps that we take to ensure Top Quality. Intensive training and highly skilled specialists for every operation ensure quality and productivity well above industry norms. Our commitment to providing excellent products and services is the foundation of management's vision, goals, and plans.

  • Planning for Quality.
  • Finding Errors Fixing Errors.
  • Ensuring a high confidence level that all errors are found.
  • Creating a feedback loop and Continuously improving theproduction process.

    GIS databases evolve constantly. From paper maps through the digital conversion process to data maintained in a database, GIS data are being constantly transformed. Maintaining the integrity and accuracy of these data through a well-designed quality assurance (QA) plan that integrates the data conversion and maintenance phases is key in implementing a successful GIS project.

Categories of Quality Assurance

The key to developing and implementing a successful GIS project is a well-designed Quality Assurance (QA) plan that is integrated with both the data conversion and maintenance phases of the GIS project. The fundamentals of Quality Assurance never change; completeness, validity, logical consistency, physical consistency, referential integrity and positional accuracy are the cornerstones of the QA plan. To maximise the quality of GIS databases there should exist a well-designed Quality Assurance plan that is strategically integrated with all facets of the GIS project.

  • Completenses : Completeness refers to a lack of errors of omission in a database. It is assessed relative to the database specification, which defines the desired degree of generalization and abstraction (selective omission). "Data completeness" is a measurable error of omission observed between the database and the specification. Even highly generalized databases can be "data complete" if they contain all of the objects described in the specification. A database is "model complete" if its specification is appropriate for a given application.
  • Validity : Validity is a measure of the attribute accuracy of the database. Each attribute must have a defined domain and range. Database validation is the process of determining if database values are reasonably accurate, complete, and logically consistent wrt. the intended use of the data. Validation will often consist of several steps, including logical checks, accuracy assessments, and error analysis.
  • Logical Consistency : Logical consistency covers on the one hand topological aspects and on the other hand the validity ranges of values occurring in the data set and can occur in spatial, thematic, and temporal parameters. For a Measure of topological consistency one can investigate for example the correctness of polygons.
    Physical consistency : measures 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 is one that describes a physically consistent spatial requirement.
  • Referential integrity : measures the associativity of related tables based upon their primary and foreign key relationships. Primary and foreign keys must exist and must be associated sets of data in the tables given predefined rules for each table.
  • Positional Accuracy : measures how well each spatial object's position in the database matches reality. Positional error can be introduced in many ways. Incorrect cartographic interpretation, through insufficient densification of vertices in line segments, or digital storage precision inadequacies are just a couple sources of positional inaccuracies. These errors can be random, systematic, and/or cumulative in nature. Positional accuracy must always be qualified because the map is a representation of reality.
    Automated QA : Quality Assurance plans can broadly be classified into two categories, viz; Visual QA and Automated QA and discussed below.

    Visual QA Visual QA is meant to detect not only random error such as a misspelled piece of text, but also systematic error such as an overall shift in the data caused by an unusually high RMS value. Existence and absence of data as well as positional accuracy can only be checked with a visual inspection. The hard copy plotting of data is the best method for checking for missing features, misplaced features and registration to the original source. On-screen views are an excellent way to verify that edits to the database were made correctly. Visual inspection should occur during initial data capture, at feature attribution, and then at final data delivery. At initial data capture the data should be inspected for missing or misplaced features, as well as alignment problems that could point to a systematic error. In either case 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.

    Automated QA 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 QA must occur in conjunction with visual inspection. The goal of the automated quality assurance is to quickly inspect very large amounts of data and report inconsistencies in the database that may not appear in the visual inspection process. Both random and systematic errors are detected using automated QA procedures. Once again the feedback loop has to be short in order to correct any flawed data conversion processes.

 

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