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Curated Track: AAG's GeoEthics Initiative and Related Effort

Description:

Social inequality and segregation have far-reaching effects worldwide. These effects have manifested themselves in all sectors of the geospatial problems, e.g., segregation due to different spatial distributions of population groups across their activity spaces, as well as unequal accessibilities to different services, opportunities, and resources (e.g., workplaces, food access, transportation, education, health care and medical facilities, recreation and entertainment). Better understanding and effectively measuring the nature of social inequality, segregation, and their correlation are of great importance for urban planning and policymaking.
With the advancement of mobile sensing technologies, mobile devices have become a game changing data acquisition platform. Various emerging geospatial big data can be collected from mobile devices (e.g., social media, mobile device apps and records, remote sensing systems, vehicles, VGI platforms, business and transaction records). These individual-level data sources effectively capture fine-grained activities and mobility patterns from a large number of users with little or zero extra cost, and therefore pave a new way for researchers to re-assess social inequality and segregation by incorporating individuals’ activities, accessibility, and mobility patterns, into the analytical frameworks.
However, there is not enough empirical evidence that explains social inequality and segregation systematically and quantitatively using geospatial big data. In this session, we have a strong interest in empirical studies (but not exclusively) that capture and measure social inequality and segregation among different groups (e.g. intergenerational, income, gender, education, race-ethnicity) across activity space, as well as the (spatial) correlation between social inequality and segregation. We are also interested in papers that use novel methods (but not exclusively, such as GeoAI, machine learning, or deep learning) to overcome data problems, in developing more unified frameworks for quantifying social inequality/segregation, as well as in geo-computation and geo-visualization tools and platforms for social inequality/segregation analytics and measurement. We encourage authors to emphasize informed policy-making conclusions from their human dynamics analysis that will assist in devising policy instruments that mitigate social inequality/segregation.
To present your work in this session, you will register and submit your abstract to the AAG annual meeting website, and email your presenter identification number (PIN) and the abstract to Meiliu Wu (mwu233@wisc.edu) by Nov 11, 2022 along with your preference for an in-person or virtual presentation. Should you have any questions, please don't hesitate to reach out to the session organizers.

Non-Presenting Participants