Urban Data Science I: Methods & Models for our Changing Cities

Type: Paper
Theme:
Sponsor Groups: Spatial Analysis and Modeling Specialty Group, Geographic Information Science and Systems Specialty Group
Poster #:
Day: 4/3/2019
Start / End Time: 9:55 AM / 11:35 AM (Eastern Standard Time)
Room: Harding, Marriott, Mezzanine Level
Organizers: Levi Wolf, Taylor Oshan, Wei Kang
Chairs: Levi Wolf

Call for Submissions

The city is the darling of geographical data science. Population density often begets data density, so urban data science provides fertile ground for the development of new methods and models across many problem domains that span sociology, economics, political science, epidemiology, and geography. Specifically, urban data science is experiencing a significant bout of high-profile attention as exciting new dynamics are captured with increasing detail. This moment provides an immense opportunity for cutting-edge quantitative geographical research, with recent books, high-profile papers, and new research institutes & environments springing up at multiple institutions around the emerging domain of geographic data science. Thus, we aim to help define this new research frontier by fostering a wide-ranging series of sessions showcasing novel geographic data science for dynamic urban processes. We are seeking all folks interested in many of the different core topics in urban geographic data science, including:

Analysis, modelling, and prediction of movement through, across, and between cities
Modeling and analysis of social media in urban spaces, including text & image sentiment
New methods or applications for geo-social or spatial interaction models
Agent-based models & cellular automata for urban processes
Urban spatial counterfactuals, counterfactual demographies, & stochastic simulation
New methods for geodemographic segmentation and analysis
Applications of geodemographic classifications in urban data science
Place detection, regionalization, clustering, or boundary analysis
Spatial, temporal, or spatio-temporal neighborhood dynamics
Estimation and identification of neighborhood effects
Critical analysis of neighborhood effects, including validity & stationarity assumptions
Local models of neighborhood effects
Urban econometrics, causal inference, and urban program evaluation
Extreme event risk and crisis analysis for complex urban systems
Streetview image sentiment and analysis

Please submit your abstracts and registration PIN to levi.john.wolf@bristol.ac.uk, weikang@ucr.edu, & toshan@umd.edu by October 31, 2018. Please note the conference abstract submission deadline is earlier, on October 25th 2018. This symposium is hosted in conjunction with the University of Bristol Quantitative Spatial Sciences Research Group, the University of Maryland Center for Geospatial Information Science, and the University of California, Riverside Center for Geospatial Sciences.


Description

The city is the darling of geographical data science. Population density often begets data density, so urban data science provides fertile ground for the development of new methods and models across many problem domains that span sociology, economics, political science, epidemiology, and geography. Specifically, urban data science is experiencing a significant bout of high-profile attention as exciting new dynamics are captured with increasing detail. This moment provides an immense opportunity for cutting-edge quantitative geographical research, with recent books, high-profile papers, and new research institutes & environments springing up at multiple institutions around the emerging domain of geographic data science. Thus, we aim to help define this new research frontier by fostering a wide-ranging series of sessions showcasing novel geographic data science for dynamic urban processes. We are seeking all folks interested in many of the different core topics in urban geographic data science.


Agenda

Type Details Minutes Start Time
Presenter Danlin Yu*, Montclair State University, Yaojun Zhang, Renmin University of China, An eigenfunction based spatial filtering analysis of how socioeconomic and natural condition factors impact on different groups of people’s migration patterns in China 20 9:55 AM
Presenter Byong-Woon Jun*, Kyungpook National University, A Geographically Weighted Poisson Regression Approach to Satellite Imagery Based Population Estimation 20 10:15 AM
Presenter Ziqi Li*, Arizona State University, FastGWR: Computational Improvements to Geographically Weighted Regression Models 20 10:35 AM
Presenter Yuanyuan Tian*, Arizona State University, Wenwen Li, Arizona State University, Shaohua Wang, Arizona State University, Uncovering Multi-scale Processes of Bike Sharing Context in the Megacity 20 10:55 AM

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