Symposium on Scale in Spatial Analytics and Modeling: Scaling and Analysis on Urban Big Data
Type: Virtual Paper
Day: 2/28/2022
Start Time: 8:00 AM
End Time: 9:20 AM
Theme:
Sponsor Group(s):
Spatial Analysis and Modeling Specialty Group
, Geographic Information Science and Systems Specialty Group
, Cyberinfrastructure Specialty Group
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Organizer(s):
Qunshan Zhao
, Xiang Ye
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Chairs(s):
Ding Ma, Research Institute for Smart Cities, Shenzhen University
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Description:
Cities provide fertile ground for the development of new theories, methods, and models across many problem domains that span sociology, economics, political science, epidemiology, urban planning, public policy, and geography. Further, the development of a new “city science” is emerging from these fields, co-opting both theory and methods for a new inquiry. Urban space, where a majority of human activities take place on the Earth’s surface, is with a great heterogeneity both geometrically and statistically. However, the current urban analysis may lack the ability to reveal such an underlying heterogeneity. The arrival of big data, e.g., location-based social media (LBSM), opens a new horizon for urban analytics. The fine-grained spatio-temporal data sets tremendously help us investigate the fractal urban forms and nonlinear urban processes (e.g., Ma et al. 2021). As part of the Symposium on Scale in Spatial Analytics and Modeling, this session specifically calls for research on geospatial analysis including fractal geometry, scaling hierarchy, and heavy-tailed distribution statistics, for better understanding urban forms, functions, as well as their dynamics in a context of novel geographic data science for dynamic urban processes.
Presentation(s), if applicable
Qunshan Zhao, University of Glasgow; Identifying locations for new bike-sharing stations in Glasgow: an analysis of spatial equity and demand factors |
Zhe Wang, University of Idaho; Examining the socio-economic determinants of urban forests structure in a desert city using multi-scale geographically weighted regression |
Mingkang Wang, ; Identifying Tree Preservation Order (TPO) by Deep Learning in Greater London Area |
Rene Ingersoll, ; A Probabilistic Approach to Assessing Flood Vulnerability in Nebraska |
Michael Sinclair, ; Advancing home location detection and exploring socio-demographic representativeness of new forms of mobile phone data |
Non-Presenting Participants Agenda
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Symposium on Scale in Spatial Analytics and Modeling: Scaling and Analysis on Urban Big Data
Description
Virtual Paper
Contact the Primary Organizer
Xiang Ye - xiangye@buffalo.edu
Session Sponsored By
