The concept of quality is something we all think we understand, but often fail to define exactly. The same thing happens with spatial data. For this reason, from Geograma we leave you this article in which we clarify it, as well as we emphasize the main aspects involved when generating geographic information with high quality standards and its relevance in the current context.
What is spatial data quality?
We understand the quality of spatial data as their ability to meet defined requirements and specifications that are intended to satisfy the needs of their users and producers.
To be more specific, we can refer to the definition provided by ISO 19101 – Geographic information. Reference model. According to which quality is:
“The totality of the characteristics of a product that influence its ability to satisfy stated and implied needs”.
Within spatial data quality, we must pay special attention to the errors in the data. It must be borne in mind that all geographic data always have an error associated with them, so we cannot eliminate them completely. Therefore, a quality geographic data is one that establishes and declares a margin of error in a correct way and that makes the data fit the purposes for which it is used.
Why do we need quality spatial data in GIS?
Data is the basis from which other data is generated by processing it in Geographic Information Systems (GIS). This makes us aware that, if our starting point is wrong, we will never reach our desired destination. All this despite having invested a lot of valuable resources to do so.
This becomes even more important at a time when the growth in the amount of spatial data is palpable. This information is increasingly taken into account when making key management decisions, both in private companies and in Public Administrations.
We are not only talking about the volume of geographic data, but also about the variety of data. Here we are talking about satellite imagery, sensor imagery, mobile devices, mobile mapping technology and many other sources.
How to generate quality spatial data for GIS
Take into account attributes of spatial data quality
First of all, in order to shape quality geographic data, we need to know what the 7 components that define quality are, which are:
- Positional accuracy: accuracy of the geographical reference associated with each datum. That is to say, its coordinates.
- Attribute accuracy: refers to the correctness of the values associated with an element at a given position. For example, the population density of an area delimited by a cell on a map.
- Logical consistency: the existing relationships between the different spatial data must be captured.
- Topological consistency: this means that the values must have a value within the expected values and in the expected units.
- Completeness: there should be clear criteria explaining why certain data are not collected. For example, because they are not useful or for reasons of scale.
- Temporal quality: the environment is changeable, so the quality of a spatial data may decline if, due to the passage of time, the reality is different from what was reflected at the time it was captured.
- Origin of the data: it is necessary to know the origin of the data to determine its quality, as well as the processes that have originated it, in case it comes from other primary data.
Identifying errors and measuring their relevance
As we have already discussed above, the basis of spatial data quality lies in the error associated with the spatial data, its nature, magnitude and the fact that it is suitable for the intended use of the geographic information.
As far as errors are concerned, we must know their type and magnitude and manage them appropriately. Among the types of errors associated with a spatial data, we have:
- Concept and model error: this is associated with the technique used to represent the information, such as raster and vector. We will have to choose the one whose errors are more acceptable in order to achieve our objective.
- Data source errors: these occur, for example, when processing primary data that are not correct. Here we can speak of an erroneous map or a measurement from a poorly calibrated sensor.
- Map creation errors: these can arise, among other origins, when transferring label data from a physical map to a digital map by an operator or when transforming data from vector to raster format and vice versa.
- Analysis errors: these arise when the data comes from the analysis of another original data, which was not carried out correctly.
Taking all these parameters into account, as well as following a set of specific technical instructions for each case, we will obtain the geographic data we need with the right quality. A key process to work correctly with GIS and provide stakeholders with the conclusions that will help them to improve the management of resources in their respective projects.
That is why at Geograma we assure you that we will provide you with spatial data with the ideal quality to satisfy your needs or those of your clients. To find out more about how we do it, you can contact one of our professionals without any kind of obligation. Shall we get to it?