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GeoAI and Deep Learning Symposium: Spatial-Temporal Modeling and Data Mining II

Type: Paper
Theme:
Sponsor Groups: Geographic Information Science and Systems Specialty Group, Cyberinfrastructure Specialty Group, Spatial Analysis and Modeling Specialty Group
Organizers: Daniel Runfola, Diep Dao
Chairs: Daniel Runfola

Description

Geographic information science has been experiencing an unprecedented increase in data accessibility and computational capability. This enables us to apply and enhance various data mining techniques such as rule learning, pattern recognition, classification, similarity and distances, and clustering, for spatial and spatio-temporal data analytics and knowledge discovery. However, unique characteristics of spatial data, including spatial and spatio-temporal relations, correlations, heterogeneity, hierarchy, interactions, and mix of data at different formats and resolutions, prevent a direct deployment of traditional data mining approaches in spatial and spatio-temporal mining problems. Spatial and spatio-temporal data mining on big datasets, in addition, requires implementation under high performance computing environment with unique challenges for data preparation, data manipulation, data simulation, large-scale and multi-dimensional vector analysis, and memory allocation, to name a few.

This session welcomes contributions exploring theoretical and practical issues of data mining approaches for spatial or spatio-temporal datasets. Applications within a broad range of GIScience applied domains in support of society and/or environmental challenges are welcomed. We hope to create an opportunity for researchers to exchange ideas around the use of data mining approach as an inductive approach toward big geographical data analysis.

Specific topics include (but are not limited to):
- Applications of data mining approaches for GIScience big data
- Handling spatial and spatio-temporal dimensions in data mining
- Developments of novel spatial or spatio-temporal data mining techniques
- Understand text-based mined results for evaluation through visualization
- From data mining to knowledge discovery: start the validation process with support from domain knowledge
- Applications of High Performance Computing (HPC) tools
- Development of tools or approaches to implementing spatial data mining in HPC environments
- Computational challenges with large vector data
- Computational challenges with global-scope, high resolution analyses of imagery data


Agenda

Type Details Minutes Start Time
Presenter Daniel Runfola*, The College of William and Mary | AidData, Challenges and Opportunities in Analyzing Multi-band Satellite Imagery with Convolutional Neural Networks 15 12:00 AM
Presenter Diep Dao*, University of Colorado - Colorado Springs, Craig Ravesloot, Rural, The Rural Institute, University of Montana, Lillie Greiman, The Rural Institute, University of Montana, Tannis Hargrove, The Rural Institute, University of Montana, Extracting spatio-temporal sequential patterns of daily activities, mobility, and health responses using GEMA Data 15 12:00 AM
Presenter Zachary Cleland*, , Diep Dao, University of Colorado Colorado Springs, Towards a Better Understanding of Human Caused Wildfire in Colorado With Data Mining 15 12:00 AM
Presenter Yihong Yuan*, , Exploring Urban Mobility from Taxi Trajectories: A Time Series Analysis 15 12:00 AM
Presenter Seth Goodman*, William & Mary, Daniel Runfola, William & Mary, Ariel BenYishay, William & Mary, Using Machine Learning to Predict Non Permissive Environments in Nigeria 15 12:00 AM

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