Urban Data Science VII: 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: 4/4/2019
Start / End Time: 1:10 PM / 2:50 PM (Eastern Standard Time)
Room: Congressional A, Omni, West
Organizers: Levi Wolf, Wei Kang, Taylor Oshan
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 Micah L Brachman*, University of Maryland, Russell Provost, Montgomery Planning, David Anspacher, Montgomery Planning, Solving the first mile problem with low-stress bicycling routes 20 1:10 PM
Presenter Natalie Rose*, University of Liverpool, Les Dolega, University of Liverpool, Using machine learning to explore the impact of weather on high street retail in the UK 20 1:30 PM
Presenter Susie Philp*, University of Liverpool, Les Dolega, University of Liverpool, Mark Green, University of Liverpool, Alex Singleton, University of Liverpool, Sensing Dynamic Retail Environments 20 1:50 PM
Presenter Paul Longley*, University College London, Tim Rains, University College London, Consumer data measures of hardship in London and the rest of the United Kingdom 20 2:10 PM
Presenter Krasen Samardzhiev*, University of Liverpool, Topological data analysis of urban telecommunication activity patterns 20 2:30 PM
Discussant Levi Wolf University of Bristol 5 2:50 PM

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