Scale is a fundamental topic in geography and carries various meanings when mentioned in different contexts. We conduct 'large scale' studies to understand general patterns and relations, and also use 'local scale' indicators and models to analyze spatial variations of the patterns and relations. The scale of mapping, spatial analysis and modeling to a large extent determines the insights that can be gained from geographical phenomena. The importance of scale in spatial analysis has been epitomized in the well-known Modifiable Areal Unit Problem (MAUP) and its temporal equivalent.
With the advent of the Big Data era, geospatial data are collected in novel and ever diversifying ways at unprecedented speeds. These changes provide everyone new opportunities to study geographic phenomena at new spatial and temporal scales. Meanwhile, the revolutions in Artificial Intelligence (AI) and computing techniques have created advanced modeling capacities for spatial pattern/object detection, data assimilation, multi-scale analysis and modeling. With technological advances, various spatial metrics and modeling frameworks have been developed to detect and quantify scales of spatial processes and explore interplays among spatial processes at multiple scales.
Note: all times are US Eastern Time. The meeting links will be released before the conference. You need to log in to your AAG account to access the meeting links.
Yi Qiang, University of South Florida
Taylor Matthew Oshan, University of Maryland
Peter Kedron, Arizona State University
Amy Frazier, Arizona State University
Mehak Sachdeva, Arizona State University
Levi Wolf, University of Bristol
Xiang Ye, Shenzhen University
Somayeh Dodge, University of California, Santa Barbara
Eun-Hye Yoo, University of Buffalo
Lei Zou, Texas A&M University
Heng Cai, Texas A&M University
Ziqi Li, University of Glasgow
Yongze Song, Curtin University
Ding Ma, Shenzhen University
Qunshan Zhao, University of Glasgow