In order to avoid the consequences of using low-quality data in decision-support, good spatial data quality approaches are of essence to be considered.
This learning path will first and foremost look at Data Quality which brings our attention to what is spatial Data Quality in order to understand its essence in the Geo-environment as spatial data is being used for "decision-support".
In the surveying and mapping profession has a long tradition in determining and minimizing errors as all measurements made in surveying and photogrammetric instruments are subject to errors. This therefore, makes it relevant to look at sources of errors likely to associate with data collected spatially. These errors include: 1. Human errors; 2. Instrumental errors ; and 3. Random errors, which will be considered in detail in this learning path.
Uncertainty will consist part of the discussion in this learning path which draws our attention to the fact that what we measure or model errors occur.
In this learning path you will be introduced to the differences between two separate concepts - "Precision" and "Accuracy" which are used interchangeably most times in the world of measurement. We will first look at "Precision".
Subsequently in this learning path we now look at "Accuracy" to distinguish it from "Precision". It is important to note that Accuracy is divided into three parts, namely: 1. Positional Accuracy; 2. Attribute Accuracy; and 3. Temporal Accuracy.
Here we will start first to learn about Positional Accuracy which is part of Accuracy.
The next division of Accuracy will focus on here is Attribute Accuracy which relates to two types of data, thus: 1. Nominal or categorical data --- The accuracy of labelling; and 2. Numerical data --- numerical accuracy.
This takes us the third division of Accuracy called the Temporal Accuracy, which includes not only the accuracy and precision of time measurements but also the temporal consistency of different data sets.
For the description of the history of a data set, we need to know "Lineage" as a concept in this learning path.
Logical consistency is the next concept that this learning path will be introducing you to.
In order to know whether there are data lacking in a database compared to what exists in the real world, we need to learn about "Completeness" as our next concept in this learning path.