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DC-42 - Changes in Geospatial Data Capture Over Time: Part 2, Implications and Case Studies

Advances in technological approaches and tools to capture geospatial data have contributed to a vast collection of applications and enabled capacity for new programs, functions, products, workflows, and whole national-level spatial data infrastructure. In this entry, such outcomes and implications are described, focusing on developmental changes in specific application areas such as land use & land cover inventory, land parcel administration, and business, as well as examples from federal agencies, including the US Geological Survey, the Census Bureau, US Fish and Wildlife Service, and the US Department of Agriculture. These examples illustrate the diverse ways that the dramatic changes in geospatial data capture methods and approaches have affected workflows within agencies and have spatially empowered millions of users and the general public. For additional information on specific technical changes, see Part 1: 

DC-29 - Volunteered Geographic Information

Volunteered geographic information (VGI) refers to geo-referenced data created by citizen volunteers. VGI has proliferated in recent years due to the advancement of technologies that enable the public to contribute geographic data. VGI is not only an innovative mechanism for geographic data production and sharing, but also may greatly influence GIScience and geography and its relationship to society. Despite the advantages of VGI, VGI data quality is under constant scrutiny as quality assessment is the basis for users to evaluate its fitness for using it in applications. Several general approaches have been proposed to assure VGI data quality but only a few methods have been developed to tackle VGI biases. Analytical methods that can accommodate the imperfect representativeness and biases in VGI are much needed for inferential use where the underlying phenomena of interest are inferred from a sample of VGI observations. VGI use for inference and modeling adds much value to VGI. Therefore, addressing the issue of representativeness and VGI biases is important to fulfill VGI’s potential. Privacy and security are also important issues. Although VGI has been used in many domains, more research is desirable to address the fundamental intellectual and scholarly needs that persist in the field.