Automated accuracy and quality assessment tools (AQAT = “a cat”) for generalized geospatial data Active
This project develops an open-source toolkit for the consistent, automated assessment of accuracy and cartographic quality of generalized geospatial data. The toolkit will aid USGS and other stakeholders with the development and use of multiscale data and with associated decision-making.
This project develops an open source toolkit called Generalization Quality Assessment Tools (GQAT) for the consistent, automated assessment of generalized geospatial data. The toolkit will implement four specific metrics to quantify key elements of accuracy and cartographic legibility: Hausdorff distance, average positional displacement, feature density difference and a legibility conflict score. The Python package will be computationally efficient, will read common spatial formats, and will produce overall summary scores, per-feature statistics and spatial summaries based on original and generalized data and target map scale. The tools will be developed with and tested on hydrographic and geologic feature data sets in coordination with the USGS Cartographic Applied Research Section (CARS) and the National Geologic Synthesis (NGS) projects and posted to an open source repository with full documentation and links to data that is available for testing.
This project develops an open-source toolkit for the consistent, automated assessment of accuracy and cartographic quality of generalized geospatial data. The toolkit will aid USGS and other stakeholders with the development and use of multiscale data and with associated decision-making.
This project develops an open source toolkit called Generalization Quality Assessment Tools (GQAT) for the consistent, automated assessment of generalized geospatial data. The toolkit will implement four specific metrics to quantify key elements of accuracy and cartographic legibility: Hausdorff distance, average positional displacement, feature density difference and a legibility conflict score. The Python package will be computationally efficient, will read common spatial formats, and will produce overall summary scores, per-feature statistics and spatial summaries based on original and generalized data and target map scale. The tools will be developed with and tested on hydrographic and geologic feature data sets in coordination with the USGS Cartographic Applied Research Section (CARS) and the National Geologic Synthesis (NGS) projects and posted to an open source repository with full documentation and links to data that is available for testing.