"How we used Python to try and save lives"
summary
Claims concrete outcomes: Python back ends scrape and geocode messy feeds, an App Engine app finds people and cars in days not months, and aggregated Honda vehicle data marks recently passable roads while a polar orbiter GI1 satellite gives roughly one useful pass per day. Code lands in hours, sometimes KML and Flash renderings are pragmatic stopgaps, and crisis maps embed on news sites at high QPS. Expect legal traps, copyright limits, and real burnout from repeated emergency launches.
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chapters (16)
- 0:00Introduction
- 3:18Legal advice necessity
- 6:10Python in crisis response
- 8:39Timeline of disasters
- 10:26Black Saturday fire mapping
- 14:21Haiti missing persons tool
- 15:43Christ Church car tracking
- 19:33Street View time machine
- 20:56Burnout cost in crisis work
- 21:17Japan tsunami road data
- 22:41Crisis Map platform
- 24:25Map data format issues
- 25:49Satellite imagery challenges
- 27:11Disaster mapping problems
- 28:36Code availability
- 29:21Q&A
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