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GS-15 - Feminist Critiques of GIS

Feminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.

At the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.

DM-70 - Problems of Large Spatial Databases

Large spatial databases often labeled as geospatial big data exceed the capacity of commonly used computing systems as a result of data volume, variety, velocity, and veracity. Additional problems also labeled with V’s are cited, but the four primary ones are the most problematic and focus of this chapter (Li et al., 2016, Panimalar et al., 2017).  Sources include satellites, aircraft and drone platforms, vehicles, geosocial networking services, mobile devices, and cameras. The problems in processing these data to extract useful information include query, analysis, and visualization. Data mining techniques and machine learning algorithms, such as deep convolutional neural networks, often are used with geospatial big data. The obvious problem is handling the large data volumes, particularly for input and output operations, requiring parallel read and write of the data, as well as high speed computers, disk services, and network transfer speeds. Additional problems of large spatial databases include the variety and heterogeneity of data requiring advanced algorithms to handle different data types and characteristics, and integration with other data. The velocity at which the data are acquired is a challenge, especially using today’s advanced sensors and the Internet of Things that includes millions of devices creating data on short temporal scales of micro seconds to minutes. Finally, the veracity, or truthfulness of large spatial databases is difficult to establish and validate, particularly for all data elements in the database.