1. 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

2. UH-DAIS Research Projects 6/18-5/19
Interestingness Hotspot Discovery
Spatial and Spatio-temporal Data Analysis Frameworks and Algorithms 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

3. 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

4. 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: &
Inspired by:
Great Dismal Swamp, Virginia
Data Analysis and Intelligent Systems Lab

5. 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.

6. Tweet Emotion Analysis

7. Tweet Analysis

10. Display Tools1 for St^2: Bubble Charts
Gapminder:  
Video:
Bubble Chart r
Data Analysis and Intelligent Systems Lab

11. 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

12. 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: