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Computational spatial science approaches to building smarter and healthier cities I

Type: Paper
Theme:
Sponsor Groups: Spatial Analysis and Modeling Specialty Group, Transportation Geography Specialty Group, Geographic Information Science and Systems Specialty Group
Organizers: Avipsa Roy, Ziqi Li
Chairs: Avipsa Roy

Description

The advances in sensor technologies and availability of connected devices have enabled the generation of large volumes of disparate, dynamic and geolocated data by both scientific communities and citizens. Such technological innovations have given birth to the concept of smart and connected cities. A smart city is forward-looking and progressive and has the potential to provide a higher-quality of life while meeting the daily needs of its citizens. The growing awareness among state authorities and local agencies regarding health, environmental, and economic benefits of smarter cities have motivated development of new methods in order to plan and design smarter cities.
Resilience is of utmost importance to design smarter cities. Therefore, the need for computational methods that can combine online and real-time knowledge discovery from dynamic geospatial data streams in conjunction with socio-economic data has risen enormously. These methods are designed to help practitioners and researchers with improved policymaking. The recent developments in data science and artificial intelligence have enabled integration of online (social-media) and static data sets (census/demographics/socio-economic/land-use) to develop robust spatial analytics approaches. The issue of modeling urban designs to build a smart city which is also resilient needs to be well understood to maximize the benefits of connected technologies - smartphones, GPS devices, health tracking devices etc. Visualization techniques are also important to allow public participation not only in data collection, but also in analytics and decision-making efforts to reduce error and uncertainties. Incorporating good quality and bias-corrected crowdsourced data with traditional data sources is another approach to improve data quality for connected applications and ease the decision-making process by policymakers.


Agenda

Type Details Minutes Start Time
Presenter Bandana Kar*, Oak Ridge National Laboratory, Exploring Mobility Pattern Using Trajectory Data For Emergency Management 15 12:00 AM
Presenter Suman Mitra*, University of Arkansas, Deconstructing Gender Differences: A Cluster Analysis of Mobility Patterns of Elderly in the United States 15 12:00 AM
Presenter Geoffrey Fairchild*, Los Alamos National Laboratory, Designing a Disease Surveillance System fit for a Smart City 15 12:00 AM
Presenter Avipsa Roy*, Arizona State University, Trisalyn Nelson, Arizona State University, Understanding mobility patterns from bias-corrected crowdsourced data to plan safer and smarter cities 15 12:00 AM

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