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GENERIC STEP-BY-STEP APPROACH STEP TWO

Accurate spatial information is essential for conducting

accessibility studies

You should quality control all your spatial datasets before undertaking the accessibility study. The aim
is ultimately to identify spatial features and attributes that are incorrect so that they can be corrected
where possible. The quality control also provides an understanding of the level of accuracy associated
with datasets that will be used in the accessibility studies. When errors in the data cannot be corrected,
the quality control exercise will indicate the levels of inaccuracy that may be associated with the results of
the accessibility study.

The quality control exercise should follow standardised approaches that are internationally accepted and
it needs to include aspects such as completeness, validity, logical consistency, physical consistency,
referential integrity and positional accuracy. For a detailed description of the methods that you can use
to do quality control refer to Annexure 1 of this Guideline.

The South African Spatial Data Infrastructure provides

policies and standards for spatial information

The South African Spatial Data Infrastructure (SASDI) provides policies and standards for spatial
information which are applicable to all public sector organisations. Ideally your department should
have policies on the capture and maintenance of spatial information, the use of data standards and
the management of data, which are aligned to SASDI.

Furthermore, national standards of the South African Bureau of Standards and international
ISO standards on geographic information, especially ISO 19111 on spatial referencing, ISO 19114 on
quality evaluation procedures and ISO 19115 on metadata provide guidance on the collection of the
spatial information and its metadata.

The South African Statistical Quality Assessment Framework which has been developed by
StatsSA provides the framework for the quality improvement of all official statistics produced by organs
of state.

Figure 15 provides a template in which you can capture the results of the quality control exercise.
The percentage column indicates how accurate (high percentages) or inaccurate (low percentages)
particular factors associated with the datasets are. The factors with low percentages should be focused
on in improving the quality of the data. Where errors in the data cannot be corrected, the inaccurate
percentages should be noted.

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