INSPIRE Knowledge Base
ddsdssd

Data Quality

Go to training module: 
Data Quality
Abstract: 

Geospatial data quality elements describes the inherent characteristics of geographical data that should allow the user to evaluate its fitness for use. Usually, geospatial data quality is often specified by intangible properties such as completeness, accuracy and consistency. The quality of a geospatial dataset shall be specified by a standardised quality element as part of the metadata. The module describes the basic concepts of geospatial data quality and how the concepts are applied for specifying geospatial data quality according to international standards. The module also provides hand-on experience on procedures for the estimation of data quality elements and how they are to be specified using metadata standards. The module is provided as a self-learning module, or, if feedback on exercises are requested, also as an instructor led module (optional).

Structure: 

The module consists of three lectures (power point files with recorded voice), reading instructions for each lecture, two exercises using Microsoft Excel and a self-test.
 

  • Part 1: Basic concepts of geospatial data quality
  • Part 2: Estimation of quality elements
  • Part 3: Specification of data quality
Learning outcomes: 

On completion of this module, the learner is expected to be able to define and describe the basic concepts of geospatial data quality. The learner is also expected to apply these concepts according to international standards.

Intended Audience: 

Professionals without experience but with interest in the basic concepts of the quality of geospatial information.

Pre-requisites: 

When entering this training module, we assume basic knowledge in GI processing.

Training format: 

PDF documents, presentations with voice, reading instructions of open resources, exercises and a self-test. The module is provided as a self-learning module, or, if feedback on exercises are requested, also as an instructor led module (optional).

Expected Workload: 
12 hours
Language: 
EN
Source: 

An earlier versions of this training module has been developed within the GI-INDEED project (http://www.gisig.it/gi-indeed/).

Ownership: 

Author: Anders Östman, Novogit AB. The material is provided under Creative Commons Attribution Share-Alike License (http://creativecommons.org/licenses/by-sa/3.0/

Training Category: 
organisational
technical
Level: 
1. Context knowledge for INSPIRE
Data Specifications
Metadata