Link to Session Platform

Chairs(s):

Yi Qiang, 

; ,

Description:

Multi-scale analysis forms a longstanding challenge in GIScience and many other disciplines. Ideally, geospatial data should be analyzed at the scales where spatio-temporal processes become evident or where spatio-temporal dependency is maximized. However, the scale of analysis that is suited to a given problem is not always immediately evident. Geospatial data are traditionally represented as flat layers at pre-determined resolutions, while spatial analysis tools usually only operate at a single scale or a few selected scales. Analysis and modeling conducted at a pre-defined scale may miss critical relationships/processes concealed at other scales. Meanwhile, the increasing diversity of geospatial data collected at different resolutions (e.g. satellite, UAV, and field-survey data) complicates analytical and modeling tasks that involve multi-scale data. These challenges raise a compelling need for exploring solutions to address issues in multi-scale data fusion, pattern/relation detection, visualization and model coupling. As part of the Symposium of Scale in Spatial Analytics and Modeling, this paper session specifically calls for research that develops or utilizes novel data models, analytical tools and modeling frameworks to gain insights from multi-scale spatial and/or temporal data.

Non-Presenting Participants Agenda