The structured query language (SQL) for database interrogation is presented and illustrated with a few examples using attribute tables one might find in a common GIS database.  A short background is presented on the history and goals that the creators of the SQL language hoped to achieve, followed by a review of SQL utility for data query, editing, and definition.  While the SQL language is rich in content and breadth, this article attempts to build on a simple SQL and then iteratively add additional complexity to highlight the power that SQL affords to the GIS professional who has limited programming capabilities.  The reader is asked to consider how minor modifications to SQL syntax can add complexity and even create more dynamic mathematical models with simple English-like command statements.  Finally, the reader is challenged to consider how terse SQL statements may be used to replace relatively long and laborious command sequences required by a GIS GUI approach.

Geographic Information System (GIS) applications often involve various analytical techniques and geographic data to produce thematic maps for gaining a better understanding of geospatial situations to support spatial decisions. Accuracy assessment of a thematic map is necessary for evaluating the quality of the research results and ensuring appropriate use of the geographic data. Thematic accuracy deals with evaluating the accuracy of the attributes or labels of mapped features by comparing them to a reference that is assumed to be true. The fundamental practice presents the remote sensing approach to thematic accuracy assessment as a good guidance. For instance, the accuracy of a remote sensing image can be represented as an error matrix when the map and reference classification are conducted based on categories. This entry introduces basic concepts and techniques used in conducting thematic accuracy with an emphasis on land cover classification based on remote sensing images. The entry first introduces concepts of spatial uncertainty and spatial data quality standards and further gives an example of how spatial data quality affects thematic accuracy. Additionally, the entry illustrates the techniques that can be used to access thematic accuracy as well as using spatial autocorrelation in thematic accuracy sampling design.