Data quality control in a GIS project
Standards
There are three main phases in a GIS project: a) compilation of data: In this stage the
company needs to review the data that will be collected, based on the type of analysis and
results that will be needed. Standards will play an important role in this phase to
determine if the data will be useful; b) analysis of the data in order to produce
information for planning and decision-making. The results produced will mostly depend
on the quality of the data collected. Once the first results are in, the company should
review it to see if any standards need to be redefined, and apply the new rules as soon as
possible. Generally, the compilation is the longest phase and will continue for nearly the
entire duration of the project; c) monitoring: for example companies will want to
periodically inspect the facilities of an electric company.
When a company decides to invest in a project that involves the acquisition of new data,
it should determine if standards exist inside the company and if these comply with the
scope of the project.
In a GIS project, data standards should be considered at different levels (Chrisman 1991);
they need to be based on the following: a) positional accuracy, b) attribute accuracy, c)
logical consistency, d) timeframe, e) representation, f) abstraction, g)
selection/completeness as well as h) integration and i) presentation of the information.
Databases have played a growing role in the homogenization and integration of data in
GIS applications. They provide (Healey 1991)*: a) a good way to store data; b) a
standardized and consistent way to input and update data; c) a secure environment in
which restrictions can be applied for viewing, modifying data; and d) a method for
managing various users.
For a project to be successful, two more considerations are necessary: a) workflow: It is
important to standardize the various workflows required to acquire data. These need to be
clear at every level, inside and outside of the organization (e.g. contractors) and should be
included as part of the specifications for designing, evaluating and testing, making sure
that the data will meet project requirements. A pilot project will help validate these
methods for reviewing, correcting and rejecting data. These will also give an idea of the
types of errors that can easily be corrected through automated operations and if it would
be more expensive to reject them. Exceptions should be documented as well as how to
handle them; and b) the human factor: the interpretation and participation of the operator
is critical. Workshops should be held periodically in order to promote the exchange of
experience and knowledge among project participants, to answer questions and to make
sure that the methodology is correct and that the objectives are clear. Based on operator
experience, the methods and standards can be tuned or changed at the different stages of
the project. Even though this may appear to be costly, it will help prevent
misinterpretations as well as delays caused by having to repeat or re-do work. It is just as
important to invest in human resources as in technology.
Summary
Standards for good data need to exist or be defined according to the company’s rules and
practices; and should take into account the various sources and types of data, integration
with other systems, the available technology and the experience and knowledge of the
human resources involved, all toward achieving the primary goal: providing integrated
and useful information at every level of the enterprise to facilitate planning and rto allow
better decision-making.
References
- Chrisman N.R. (1991). “The error component in spatial data”. Geographical Information
Systems. Volume 1: Principles, edited by Maguire D.J., Goodchild M.F, and Rhind D.W.
pp. 165 – 174.
- Flowerdew R. (1991). “Spatial data integration”. Geographical Information Systems.
Volume 1: Principles, edited by Maguire D.J., Goodchild M.F, and Rhind D.W. pp. 375 –
387.
- Healey R.G. (1991). “Database management systems”. Geographical Information
Systems. Volume 1: Principles, edited by Maguire D.J., Goodchild M.F, and Rhind D.W.
pp. 251- 265.
- Navarro Maria D.C., Legorreta Gabriel.(1998) “Sistemas de Informacion Geografica.
Teoria introductoria y ejercicios con AutoCad e Idrisi”. pp. 55 –58.
- Shepherd I.D.H. (1991). “ Information Integration and GIS”. Geographical Information
Systems. Volume 1: Principles, edited by Maguire D.J., Goodchild M.F, and Rhind D.W.
