Exploratory Data Analysis for Spatio-Temporal Climate Data
In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods [link]. EDA for spatio-temporal data has additional challenges to traditional EDA, because of the spatial relations and temporal evolution of observations. The goal of this project is to provide EDA visualizations and summaries for large amounts of climate data (~ millions of observations per dataset).
The main task for this semester is to perform exploratory data analysis on large climatology datasets to discover correlations and patterns that can be used for further analysis.
- Visualizing spatio-temporal data using different dimensionality reduction algorithms
- Analyzing the main characteristics of large spatio-temporal climate datasets
- Programming experience in statistical computing (Python, R or MATLAB)