Metadata and quality

In this lesson you will

Be introduced to the definition, components of, acquiring, importing, editing and exporting of Metadata.
Learn how to quantify the positional accuracy of data and evaluate its quality.
Learn how to manage the errors in order to produce quality results.

Learning Objectives

You will learn about documenting data about data or metadata. You have to create metadata for the data you’ve created. You have to acquire metadata if you acquired data from someone else. You will learn about elements of metadata, metadata standards and tools used to create metadata.
Positional accuracy is discussed from the perspective of metadata documenting and quality assessment if you acquired the data from a data provider or you created the data. You will learn that accurate data (accuracy information from the metadata) does not mean the data is precisely measured. Moreover, precise measurements (precision information from the metadata) do not necessarily indicate accurate data. In addition, you will learn that these are scale dependent and depending on the accuracy requirements of your particular project. No single universal accuracy standard is correct for every application GIS Project.
Attribute accuracy is discussed from the perspective of metadata documenting and quality assessment if you acquired the data from a data provider or you created the data. It is recommended to test the attribute accuracy of the dataset based on enough sample points to validate the accuracy before using the dataset, if you acquired it or before distributing the dataset if you created it. You will learn a quantitative method to calculate the sample size for testing and a method for testing the attribute accuracy. The results would be documented in the metadata.
You will learn how to live with errors because we cannot eliminate all the errors in GIS. However, the error can be managed; a checklist for data quality is provided. Moreover, the concepts of acceptable and unacceptable known positions are provided to validate precision and accuracy of your data. In addition, error propagation and map overlay errors are discussed to control data quality throughout the analysis process. You will also learn sensitivity analyses to control the quality of data and results.