How quick can we be? (data visualization techniques for engineers)
summary
Aggregated 1,300+ registration rows and manually labeled gender to measure the 20 percent goal across Random Hacks of Kindness sites and find surprising pockets — Chicago hits 31% with the winning hack 80% female, Zambia near 30%, Colombia a one-person anomaly. Tried OpenHeatMap, Google Fusion Tables, Google Charts, Socrata, Google/Yahoo/Bing Maps APIs, markercluster and fusion table layers. End result: a working map of circles whose radius scales to attendance and whose color encodes percentage women, because nonprogrammatic tools can’t show both metrics together.
AI-written summary · about
chapters (18)
- 0:00Start
- 4:30Raw Data
- 6:46Open Heat Map
- 10:56Limitations
- 11:26Create a Map Directly from Data in a Google Spreadsheet
- 12:56Programmatic Tools
- 13:22Google Fusion Tables
- 15:15Intensity Map
- 17:42Custom Markers
- 21:39Google Maps Api
- 21:55Google Map Api
- 23:23Marker Cluster
- 25:18Drill Down
- 27:48Creating Circles
- 29:51Yahoo Maps Web Services
- 31:47Google Charts Tools
- 33:03Conclusion
- 34:23Conclusions
related talks
26mMapping for impact: Scaling social good with DataKind and Mapbox
12mVisualizing Disparity
47mData, Design, Code
46mDiscovering 40 Geo-APIs in 40 Minutes
49mSaving the world with JavaScript: A Data Visualization story
28mDelicious Democracy: snagging and mapping citizen data
05mThe Art of Data Visualization
01h 55mUse your data to tell stories