Arvind S. Krishna, Aniruddha S. Gokhale , Douglas C. Schmidt
Visualization, analysis and mining of geo- spatial information in educational data sets using...
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Transcript of Visualization, analysis and mining of geo- spatial information in educational data sets using...
Visualization, analysis and mining of geo-spatial information in educational data sets using web-based tools
Aniruddha Desai |Winter 2013 PresentationCenter for Web and Data ScienceUniversity of Washington, Tacoma
Data Visualization GoalsLincoln County
No. of Participants Trained: XNo. of Responses Rcvd: X’
Population Density: YNumber of Trainings: Z
Go to Results
Data Analysis / Mining Goals
Can visualizations answer some of these questions?–Can we predict which area needs more professional development training next year?–Is the response rate on surveys and participant attendance rate co-related?–High volume / variety of data (some of it geo-spatial): survey responses / qualitative assessments / user zip codes / school locations / district boundaries – how do we extract useful information?
Data Analysis / Mining Goals
– Are demographic data (census), income levels, crime statistics, employment rates related to:the outcomes of intervention (for RTI)?the quality of professional development (for SNP)?
– Data collection, reporting and visualization is the first step – finding patterns potentially the next step.
– How do the visualizations scale up from state to national level?
Tools– Drupal CMS (already in-place)– Google Maps API – Gmap module to create an
interface to the Google Maps API within Drupalhttp://drupal.org/project/gmap
– D3.JS (Data Driven Documents) visualizations such as Heat Maps, Chloropleth Maps, Bar charts, Pie chartshttps://github.com/mbostock/d3/wiki/Gallery
– Open Street Maps APIhttp://www.openstreetmap.org/ (Drupal integration?)
Strategy
– Implement geographic map-based visualizations with appropriate amount of information at different zoom factors.
– At high level of granularity link data points on map to bar charts / reports for more detail.
– Analyze data visualizations for patterns.