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Intelligent Crowdsourcing for Disaster Informatics

Enhancing crowdsourcing with AI techniques for disaster response. Psychological and social aspects exploration. Research based on user emotions in time and space. Using educational data mining and scalable algorithms. Development of AI route planning.

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Intelligent Crowdsourcing for Disaster Informatics

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  1. Christoph F. Eick

  2. UH-DAIS Research Projects 6/18-5/19 • Flood Forecasting and Flood Risk Assessment • Understanding US Emotions in Time and Space • Spatial and Spatio-temporal Data Analysis Frameworks • St^2—Tools for Spatio-temporal Data Storytelling • Scalable Algorithms for Interestingness Hotspot Discovery • Educational Data Mining • Using AI for Route Planning • Disaster Informatics • Intelligent Crowdsourcing Christoph F. Eick

  3. Data Driven Flood Forecasting Frameworks

  4. Polygon Analysis for Better Flood Risk Mapping Knowledge of Flooded Areas from Past Floods FEMA Flood Risk Zones Find Correspondence Find Agreement, Combine, Validate, Evaluate Hand Value Multi-Contour Maps DEM (Digital Elevation Maps) HEC-RAS Generated Polygons Austin Fire First Response Vehicle Flood Risk Map UH-DAIS Christoph F. Eick

  5. Intelligent Crowdsourcing Motivation: Crowdsourcing has become quite popular: • Companies use it to create datasets for machine learning and to outsource tasks to the crowd. • OpenStreetMap and Waze are examples of crowdsourcing success stories. Research Goals: • Investigate AI Techniques to Enhance Crowdsourcing • Develop Intelligent Crowdsourcing Apps for Disaster Informatics • Investigate the psychological and social aspects of intelligent crowdsourcing

  6. Christoph F. Eick UH-DAIS Section 4:see other Slide Show Tweet Emotion Mapping: Understanding US Emotions in Time and Space Related to: http://worldhappiness.report/ed/2018/ & http://hedonometer.org Inspired by: https://www.ted.com/talks/hans_rosling_asia_s_rise_how_and_when Great Dismal Swamp, Virginia Data Analysis and Intelligent Systems Lab

  7. K2: Happiness and Opinion Mapping (from Tweets & …) Everyday in every way my life gets better & better I wanna scream, I wanna shout as I’m not okay Amazing company values wow! Why do I hate myself so much You make me sick Happy Monday! Good start Another lovely day We lost ourselves ❤️ I hate you Emotion scores I love you Emotion scores

  8. K2 Project Goals • Given a set of tweets (or questionnaires soliciting preferences and opinions) with the location (longitude and latitude), time they were posted and their emotional assessment in [-1,+1] (+1:=very positive emotions, 0:=no or even mix of emotions, -1: very negative emotions) • Research Steps and Goals: • Subdivide the dataset into batches, corresponding to different time intervals • Identify spatial clusters of highly positive emotions (e.g. average emotional assessment >0.4) and regions of highly negative emotions (e.g. average emotion assessment < -0.4) for each batch. • Capture patterns of change and evolution of the regions identified in 2. • Based on a selected story type and user preferences, convert results found in steps 2 and 3 into a narrative and animations that tells the story of spatio-temporal evolution of emotions in an region (e.g. Texas, US,…) over a period of time (e.g. 5 years, 1 year, 1 month), similar to: https://www.ted.com/talks/hans_rosling_asia_s_rise_how_and_when

  9. Educational Data Mining (EDM) UH-DMML

  10. Fast Interestingness Hotspot Discovery • Objective: Find interesting contiguous regions in spatial data sets based on the domain expert’s notion of interestingness which is captured in an interestingness function • Methodology: • Transform Dataset Into Graphs • Identify hotspot seeds • Grow seeds by adding neighboring objects • Remove redundant hotspots using a graph-based approach • Find Scope of hotspots (polygonal boundary detection) • Data sets: Gridded, polygonal, point-based data sets

  11. Helping Scientists to Make Sense Out of their Data Figure 1: Co-location regions involving deep and shallow ice on Mars Figure 2: Interestingness hotspots where both income and CTR are high. Figure 3: Maryland Crime Hotspots UH-DAIS

  12. Recent Contributors to UH-DAIS Research PhD Students: Yongli Zhang, Romita Banerjee, Chong Wang and KarimaElgarroussi. Master Students: Yue Cao, AnushaNemilidinne, AnjanaKumari, Priyal Kulkarni, Qian Qiu, Arjun SV and AkhilTalari. Undergraduate Students: DenizBurduroglu, Duong Nguyen, Victor Zeng, Yilei Tian, Pallovi Romero, Israel Perez, Jackson Murrell Visiting/Exchange Students: Khadija Khaldi Contributing Alumni: Sujing Wang and Paul Amalaman. UH-DAIS

  13. Some UH-DAIS Graduates 1 Christopher T. Ryu, Professor, Department of Computer Science, California State University, Fullerton Dr. Wei Ding, Associate Professor, Department of Computer Science, University of Massachusetts, Boston Sharon M. Tuttle, Professor, Department of Computer Science, Humboldt State University, Arcata, California Sujing Wang, Assistant Professor, Department of Computer Science, Lamar University, Beaumont, Texas Christoph F. Eick

  14. Some UH-DAIS Graduates 2 Chun-sheng Chen, PhD Amazon Chong Wang, MS Apple Justin Thomas MS Section Supervisor at Johns Hopkins University Applied Physics Laboratory Mei-kang Wu MSMicrosoft, Bellevue, Washington Jing Wang MS AOL, California RachsudaJiamthapthaksin PhD Faculty, Assumption University, Bangkok, Thailand Christoph F. Eick

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