Dictionary, Census of Population, 2021
Spatial data quality elements

Definition

Spatial data quality elements provide information on the fitness for use of a spatial database by describing why, when and how the data are created, and how accurate the data are. The elements include an overview describing the purpose and usage, as well as specific quality elements reporting on lineage, positional accuracy, attribute accuracy, logical consistency and completeness. This information is provided to users for all spatial data products disseminated for the census.

Reported in

2021, 2016, 2011, 2006, 2001, 1996 and 1991

Remarks

Current technology makes it possible for a growing number of spatial data producers and users to access geospatial data. Digital datasets can now be obtained through geospatial warehouses by users with diverse backgrounds. Furthermore, data producers can now more easily add new features, attributes and relationships to those already in the database. Therefore, any given dataset may be the result of the contributions of a number of data producers. Since perfect, complete and correct spatial data rarely exist, the assumptions and limitations affecting the creation or modification of data must be fully documented. Consequently, the need to communicate information about datasets to this ever-increasing pool of users becomes critical.

Data quality concepts provide an important framework for both data producers and users. Proper documentation provides spatial data producers with better knowledge of their holdings and allows them to more effectively manage data production, storage, updates and reuse. Data users can use this information to determine the appropriateness of a dataset for a given application and lessen the possibility of misuse. Elements of spatial data quality are highlighted below.

Overview elements

1. Purpose statement—The purpose statement describes the rationale for creating a dataset and contains information about its intended use.
2. Usage statement—Describes the applications for which a dataset is used by the data producer or by data users.

Specific elements

1. Lineage—Describes the history of the spatial data, including descriptions of the source material from which the data were derived, and the methods of derivation. It also contains the dates of the source material, and all transformations involved in producing the final digital files or map products.
2. Positional accuracy—Refers to the absolute and relative accuracy of the positions of geographic features. Absolute accuracy is the closeness of the coordinate values in a dataset to true values or values accepted as true. 'Relative accuracy' is the closeness of the relative positions of features to their respective relative positions accepted as or being true. Descriptions of positional accuracy include the quality of the final file or product after all transformations.
3. Attribute accuracy—Refers to the accuracy of the quantitative and qualitative information attached to each feature (such as population for a population centre, a street name, or a census subdivision name and code).
4. Logical consistency: The logical consistency describes the dependability of relationships encoded in the data structure of the digital spatial data.
5. Completeness: The completeness refers to the degree to which geographic features, their attributes and their relationships are included or omitted in a dataset. It also includes information on selection criteria, definitions used and other relevant mapping rules.

These elements are reported in the reference guides that accompany the spatial files and products.

Changes prior to the current census

Prior to 1991, the data quality elements were not described in the supporting documentation for spatial data products.