The quality of temporal attributes and temporal relationship of features. The accuracy of attributes within features and their appropriate relationships. The accuracy of the position of features in relation to Earth. Many industries follow standards that are reflected in a geospatial data model as value domains, data formats, and topological consistency of how the data is being stored. Logical consistencyĪ degree of adherence to preestablished rules of a data model's structure, attribution, and relationships as defined by an organization or industry. A neighborhood with missing building footprint. The presence or absence of features, their attributes, and relationships in a data model. As defined by the International Organization for Standardization (ISO), these components include the following: GIS data has different components to its quality. Data quality elementsĭata quality elements describe a certain aspect required for a dataset to be used and accurate. The following diagram illustrates a variety of sources for quality requirements that may be applicable to your organization. Each organization defines quality differently and bases this definition on the intended purpose and use of the data. It is important to identify and understand the business requirements for your data before translating those into technical requirements that define good-quality data.Īn effective data quality control process is based on the understanding of how data and information products are used within and outside the organization. ![]() One of the challenges in implementing data quality control processes is the identification of technical data quality requirements for the organization.
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