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Healthy Cities: Tracking Population Health from Grocery Bags and Smart Watches
Published on 2019-07-1927 Views
We will see how to aggregate both readings from consumer wearable devices and records of food purchases to track people’s well-being at scale. From 11,600 Nokia Health wearables, we collected readings
Presentation
The data and life of great future cities00:00
Design for urban beauty22:22
Design for urban beauty - 215:11:38
Design for urban beauty - 324:48:27
The Shortest Path to Happiness 41:13:18
The Shortest Path to Happiness - 244:31:13
The Shortest Path to Happiness - 360:49:40
Chatty maps: constructing sound maps of urban areas from social media data140:26:22
First urban sound dictionary147:22:00
First urban sound dictionary - 2179:45:18
Chatty maps184:49:57
Chatty maps - 2191:50:11
#nature pics -> calm208:31:13
Good city life245:54:55
Smell walks Amsterdam, Pamplona, Glasgow, Edinburgh, Newport, Paris, New York.252:26:07
Match collected words to social media297:16:31
What are you up to today332:57:13
Good city life353:32:28
Hearts and politics357:53:08
Hearts and Politics: Metrics for tracking biorhythm changes during Brexit and Trump361:12:53
Aggregate avg volume of heart rate374:34:46
3 Metrics386:31:32
Volume392:45:47
Volume: steps393:14:17
Volume: sleep400:05:16
Volume: avg heart rate411:50:02
Volume: avg heart rate - 2414:22:03
Ruling out confounding factors422:31:53
Ruling out confounding factors - 2439:51:29
Rhythms454:36:21
Rhythm disruption455:34:05
Rhythm disruption - results464:30:43
Rhythm disruption - results - 2476:48:06
Rhythm disruption - results - 3482:55:01
Synchronicity485:15:20
Synchronicity disruption 487:52:00
Synchronicity disruption - results500:38:44
Synchronicity disruption - results - 2515:06:08
Good city life518:28:12
Your life in a grocery bag523:15:13
Essential part of our life524:38:51
Lifestyle diseases535:28:49
Food consuption547:06:08
Penetration562:28:13
The “average” food product566:21:28
Dataset coming soon571:45:47
Prescription data575:29:35
Map of nutrient diversity594:51:57
You can predict diabetes from nutrient diversity & calories, for example607:08:47
What can we learn from billions of food purchases derived from fidelity cards?627:48:27
Large-scale and high-resolution analysis of food purchases and health outcomes631:15:04
Self-representation688:37:20
Food consumption vs. online representation: soup697:32:32
Food consumption vs. online representation: alcohol703:30:20
Help me716:32:29
[ACM cscw’14] Aesthetic Capital: What Makes London Look Beautiful, Quiet, and Happy? 745:31:15
Satellite data751:00:30
NLP Dreams: continuity hypothesis759:00:59
Goodcitylife.org778:35:27