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Visualization, analysis and mining of geo-spatial information in educational data sets using web-based toolsAniruddha Desai |Winter 2013 PresentationCenter for Web and Data ScienceUniversity of Washington, Tacoma
Collect, Analyze, Share Data BIG DATA IN SPECIAL ED
Data Visualization Goals Lincoln County No. of Participants Trained: X No. of Responses Rcvd: X’ Population Density: Y Number of Trainings: Z Go to Results
Data Visualization Goals Tahoma School District ESD No.17409 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.
Thank you! Q&A