Social Media and Big Data for Disasters I

Type: Paper
Theme: Geographies of Human Rights: The Right to Benefit from Scientific Progress
Sponsor Groups: Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group, Hazards, Risks, and Disasters Specialty Group
Poster #:
Day: 4/5/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Forum Room, Omni, West
Organizers: Bandana Kar, Edwin Chow, ZhiQiang Chen
Chairs: Edwin Chow

Call for Submissions

The growth of information and communication technologies (ICT) has enabled citizen participation in scientific investigation (a.k.a. citizen science), and sharing of data and information via social media (e.g. Twitter) and social networking sites (e.g. Facebook). The advancements in Internet of Things (IoTs) and connected devices including drones and aerial robotics have enabled the use of social media generated big data to understand human dynamics, and their interaction with the built environments. Significant advancements have been made to collect and analyze these data for emergency response, risk communication, mobility studies among others.

The big data derived from citizen sensors tend to suffer from a myriad of uncertainties in terms of positional accuracy, context ambiguity, credibility, reliability, representativeness and completeness. Moreover, there are also serious concerns about data provenance and privacy. While there is no shortage in big data applications, the quality issue of these data remains an intellectual and practical challenge. A lack of data provenance for these data combined with unavailability of high-quality reference data appropriate to its enormous volume, heterogeneous structure in near real-time make it difficult to evaluate the quality of these data. Moreover, the notion of “ground truth” in social science research is subjected to the discourse of space-place dichotomy, the spatial and contextual randomness in human behaviors. The heterogenous nature of these data in terms of data structure and content requires tremendous amount of processing at various stages of analytics before the data could be integrated with other geospatial datasets for decision-making purposes. Privacy awareness is of increasing importance to data management, dissemination and distribution in many research projects. Although aggregation, permutation or masking techniques can be used to protect data privacy without compromising the overall quality of data, its effectiveness depends on the degree of distribution heterogeneity of the geographic phenomenon. This session welcomes basic and empirical research that advances existing understanding and techniques to address the issue of big data quality and its impact on applications pertaining to human dynamics, built environments and hazards. Possible topics may include but are not limited to:

* Quality issues in big data
* Calibration and validation techniques/approaches in big data
* Data integration of multi-source and/or heterogeneous datasets
* Big data analytics in hazards and built-environment
* Big data analytics in human movements and behaviors
* Big data quality and its impact in decision making
* Challenges in collecting, processing and analyzing big data for real-time applications
* Geo-visualization techniques to analyze and visualize social media data
* Privacy and big data management

Organizers
Bandana Kar, Oak Ridge National Laboratory, karb@ornl.gov
T. Edwin Chow, Texas State University, chow@txstate.edu
ZhiQiang Chen, University of Missouri-Kansas City, chenzhiq@umkc.edu

If you would like to participate, please send us your abstract PIN and your abstract (250 words max) by November 8th, 2018.

Where/when: Association of American Geographers Annual Meeting, April 3-7, 2019, Washington DC. Additional information regarding the conference could be found at: http://annualmeeting.aag.org/.


Description

The growth of information and communication technologies (ICT) has enabled citizen participation in scientific investigation (a.k.a. citizen science), and sharing of data and information via social media (e.g. Twitter) and social networking sites (e.g. Facebook). The advancements in Internet of Things (IoTs) and connected devices including drones and aerial robotics have enabled the use of social media generated big data to understand human dynamics, and their interaction with the built environments. Significant advancements have been made to collect and analyze these data for emergency response, risk communication, mobility studies among others.

The big data derived from citizen sensors tend to suffer from a myriad of uncertainties in terms of positional accuracy, context ambiguity, credibility, reliability, representativeness and completeness. Moreover, there are also serious concerns about data provenance and privacy. While there is no shortage in big data applications, the quality issue of these data remains an intellectual and practical challenge. A lack of data provenance for these data combined with unavailability of high-quality reference data appropriate to its enormous volume, heterogeneous structure in near real-time make it difficult to evaluate the quality of these data. Moreover, the notion of “ground truth” in social science research is subjected to the discourse of space-place dichotomy, the spatial and contextual randomness in human behaviors. The heterogenous nature of these data in terms of data structure and content requires tremendous amount of processing at various stages of analytics before the data could be integrated with other geospatial datasets for decision-making purposes. Privacy awareness is of increasing importance to data management, dissemination and distribution in many research projects. Although aggregation, permutation or masking techniques can be used to protect data privacy without compromising the overall quality of data, its effectiveness depends on the degree of distribution heterogeneity of the geographic phenomenon. This session welcomes basic and empirical research that advances existing understanding and techniques to address the issue of big data quality and its impact on applications pertaining to human dynamics, built environments and hazards. Possible topics may include but are not limited to:

* Quality issues in big data
* Calibration and validation techniques/approaches in big data
* Data integration of multi-source and/or heterogeneous datasets
* Big data analytics in hazards and built-environment
* Big data analytics in human movements and behaviors
* Big data quality and its impact in decision making
* Challenges in collecting, processing and analyzing big data for real-time applications
* Geo-visualization techniques to analyze and visualize social media data
* Privacy and big data management

Organizers
Bandana Kar, Oak Ridge National Laboratory, karb@ornl.gov
T. Edwin Chow, Texas State University, chow@txstate.edu
ZhiQiang Chen, University of Missouri-Kansas City, chenzhiq@umkc.edu

If you would like to participate, please send us your abstract PIN and your abstract (250 words max) by November 8th, 2018.

Where/when: Association of American Geographers Annual Meeting, April 3-7, 2019, Washington DC. Additional information regarding the conference could be found at: http://annualmeeting.aag.org/.


Agenda

Type Details Minutes Start Time
Presenter Antonio Vallejo*, , Smart Cities and Quality of Life 20 9:55 AM
Presenter Victor Santoni*, University of Cergy-Pontoise, Citizen outreach and Social Media for Emergency Management (SMEM) 20 10:15 AM
Presenter Angela D. Ambrose*, University of Calgary, Intersections of Digital Colonialism and Digital Humanitarianism: An Analysis 20 10:35 AM
Presenter Bing She*, University of Michigan, Tom Murphy, University of Michigan, Harshakumar Ummerpillai, University of Michigan, Enabling Spatial Search and Geovisualization in Archonnex 20 10:55 AM
Presenter Manzhu Yu*, George Mason University, Chaowei Yang, George Mason University, Dan Duffy, NASA Center for Climate Simulation, GeoEvent Detection and Spatiotemporal Analytics - using Tropical Cyclone Hot Tower as an example 20 11:15 AM

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