Research Focus Objectives: The Data Analysis and Intelligent Systems (DAIS) Lab  aims at the development of data analysis, data mining, GIS and artificial intelligence techniques to solve critical problems of our society.  Applications: education emotion and happiness mapping crime analysis urban computing flood prediction and flood risk assessment community resilience http://www2.cs.uh.edu/~ceick/ceick.html

UH-DAIS Research Projects 6/18-5/19 Interestingness Hotspot Discovery Spatial and Spatio-temporal Data Analysis Frameworks and their Application to Emotion Mapping, Crime Analysis and Flood Risk Assessment St^2—Tools for Spatio-temporal Data Storytelling Development of Scalable Data Mining Algorithms Data-Driven Flood Prediction Models Using AI for Route Planning after Disasters Educational Data Mining

Project: 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

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 People: Chong Wang, Arjun SV, Qian Qiu

Section 4: see other Slide Show 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

Investigated Research Topics EMOA&St2 Spatio-Temporal Data Analysis Frameworks that Operate on Continuous Functions, such as Density Functions and Interpolation Functions. Spatial and Spatio-temporal Clustering and Hotspot Discovery Algorithms Emotion and Emotion Change Mapping Design of Animations that Summarize the Change of Emotions of for a Region of Interest over a Period of Time St^2: Tools for Spatio-temporal Storytelling Also: The long term goal is to create a Website that makes our emotion analysis and animation services available to the public. Not so much: We mostly rely on tools, designed by other, for the assessment of emotions expressed in text documents, such as tweets; that is, this subject is not the main focus of our research.

Tweet Emotion Analysis http://users.humboldt.edu/mstephens/hate/hate_map.html

Tweet Analysis

http://www.fearofcrime.com/

Display Tools1 for St^2: Bubble Charts Gapminder: https://www.gapminder.org/tools/#_data_/_lastModified:1521732308820;&chart-type=bubbles   Video: https://www.ted.com/talks/hans_rosling_asia_s_rise_how_and_when Bubble Chart r Data Analysis and Intelligent Systems Lab

Display Tools2 for St^2: Density Polygon Trees proposed by Y. Zhang & Ch. Eick at ACM SIGSPATIAL 2017 Conference r Data Analysis and Intelligent Systems Lab

Tweet Barometer and ST-Animation Research Given a set of tweets with the location (longitude and latitude), time they were posted and their emotional assessment in [-1,+1] (+1:=very positive emotions, 0:=no emotions, -1: very negative emotions) Research Goals: Identify spatial regions 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 an observation period; e.g. a year. Identify spatial regions of high discrepancies in emotions (variance of the emotional assessment values is high for the tweets in the region) for an observation period. Identify dominant topics in the tweets identified in steps 1 and 2. Capture temporal patterns of the evolution of the regions identified in step 1 and 2 including: Continuity over time Disappearance / Appearance of new regions Growth / Shrinkage Seasonality Drastic Variations in Emotions in Short Periods of Time … Convert results found in steps 1, 2, 4 into movies/animations that capture the spatio-temporal evolution of emotions in an observation region (e.g. Texas, US,…) over a period of time (e.g. 5 years, 1 year, 1 month): kind of facilitating (spatio-temporal) data storytelling; similar to: https://www.ted.com/talks/hans_rosling_asia_s_rise_how_and_when