data quality

FC-27 - Thematic Accuracy Assessment

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.

FC-28 - Primary and secondary data sources
  • Explain the distinction between primary and secondary data sources in terms of census data, cartographic data, and remotely sensed data
  • Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use
FC-27 - Thematic accuracy
  • Explain the distinction between thematic accuracy, geometric accuracy, and topological fidelity
  • Outline the SDTS and ISO TC211 standards for thematic accuracy
  • Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy
  • Describe the component measures and the utility of a misclassification matrix
  • Describe the different measurement levels on which thematic accuracy is based
FC-27 - Thematic accuracy
  • Explain the distinction between thematic accuracy, geometric accuracy, and topological fidelity
  • Outline the SDTS and ISO TC211 standards for thematic accuracy
  • Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy
  • Describe the component measures and the utility of a misclassification matrix
  • Describe the different measurement levels on which thematic accuracy is based
FC-28 - Primary and secondary data sources
  • Explain the distinction between primary and secondary data sources in terms of census data, cartographic data, and remotely sensed data
  • Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use
FC-27 - Thematic accuracy
  • Explain the distinction between thematic accuracy, geometric accuracy, and topological fidelity
  • Outline the SDTS and ISO TC211 standards for thematic accuracy
  • Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy
  • Describe the component measures and the utility of a misclassification matrix
  • Describe the different measurement levels on which thematic accuracy is based
FC-28 - Primary and secondary data sources
  • Explain the distinction between primary and secondary data sources in terms of census data, cartographic data, and remotely sensed data
  • Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use
FC-28 - Primary and secondary data sources
  • Explain the distinction between primary and secondary data sources in terms of census data, cartographic data, and remotely sensed data
  • Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use
FC-27 - Thematic accuracy
  • Explain the distinction between thematic accuracy, geometric accuracy, and topological fidelity
  • Outline the SDTS and ISO TC211 standards for thematic accuracy
  • Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy
  • Describe the component measures and the utility of a misclassification matrix
  • Describe the different measurement levels on which thematic accuracy is based
FC-27 - Thematic Accuracy Assessment

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.

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