What two characteristics comprise data quality in GIS?

Data quality in GIS is fundamentally defined by characteristics that ensure the data is useful and accurate for analysis. Completeness and consistency are particularly vital to maintaining data integrity.

Completeness refers to whether all required data is present and adequately represents the phenomenon being studied. For example, in a dataset representing a city's infrastructure, completeness would ensure that all roads, intersections, and relevant features are included. If any part of this information is missing, it could lead to flawed analyses and conclusions.

Consistency is related to how uniformly the data is maintained across different datasets. This includes not only the formatting of data but also the logical coherence of the information. For instance, if one dataset uses metric units while another uses imperial units, this inconsistency can create confusion and inaccuracies in analysis. Ensuring data is consistent helps in making reliable comparisons and integrations between different sources of geospatial information.

While the other characteristics mentioned in the choices, such as relevance, accuracy, timeliness, and reliability, do play important roles in data quality, completeness and consistency are the foundational attributes that directly impact the capacity for reliable analysis in GIS. These characteristics work together to form a robust framework for evaluating and ensuring high-quality geospatial data.