1. Visualization, analysis and mining of geo- spatial information in educational data sets using web-based tools Aniruddha Desai |Winter 2013 Presentation Center for Web and Data Science University of Washington, Tacoma

2. Outline Motivation / Background Data sets for OSPI projects Goals: Visualization, Analysis, Mining Goals: Tools, Strategies

3. Motivation / Background Paper: “Top-k popular routes based on Foursquare check-ins” Paper: “Top-k optimal store locations based on Foursquare check-ins” Class Project: Using GPS traces created by a user to infer mode of transportation OSPI Project: Educational data Is the domain data-rich and tools poor? Does data have geo-spatial dimensions?

4. OSPI – RTI Reports The RTI (Response to Intervention Project) website collects data using a standard rubric with a “rating scale”. Goal: Measuring effectiveness of interventions at individual school sites across various metrics.

5. OSPI – SNP Reports SNP (State Needs Projects) data dashboard collects data using web based surveys. Goal: Measuring effectiveness of professional development / training efforts in special needs education.

6. Collect, Analyze, Share Data SurveysData Field Evidence Text Audio / Video Geographically distributed Managing participants Sharing data Analysis Multiple databases Variety of data types

7. Data Visualization Goals Chloropleth maps visualize no. of educators trained by region at state & national level (for SNP)

8. 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 Interactive regions on Chloropleth map that display more information

9. Data Visualization Goals Visualize data points at individual school sites by zooming in RTI data is spread across several districts / counties across the state.

10. Data Visualization Goals Tahoma School District ESD No. 17409 Go to Results Tahoma School District ESD No. 17409 Go to Results At a low zoom factor, mouse-overs display more information about data points and link to RTI results

11. 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?

12. 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?

13. Tools – Drupal CMS (already in-place) – Google Maps API – Gmap module to create an interface to the Google Maps API within Drupal http://drupal.org/project/gmap http://drupal.org/project/gmap – D3.JS (Data Driven Documents) visualizations such as Heat Maps, Chloropleth Maps, Bar charts, Pie charts https://github.com/mbostock/d3/wiki/Gallery https://github.com/mbostock/d3/wiki/Gallery – Open Street Maps API http://www.openstreetmap.org/ (Drupal integration?) http://www.openstreetmap.org/

14. 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.

15. Thank you! Q&A