This document discusses using online recipe data to analyze spatiotemporal dietary patterns. It proposes analyzing whether food preferences are more similar between geographically close regions than distant ones, and how weekday and season impact users' diets. The data comes from the server logs of Austria's most popular online recipe platform from 2012-2013, containing around 180,000 recipes and 1,700 regions. Next steps discussed include obtaining more recipe data, estimating nutritional values, and modeling the dynamics and spreading of food preference popularity over time, regions, weekdays and seasons.

1. Spatio and Temporal Dietary Patterns
Claudia Wagner
GESIS – Leibniz Institut für die Sozialwissenschaften

2. Motivation
• Who eats what, when and how?
• Health
– Relation between deseases and nutrition
• Sociology
– Social Groups differentiate from each
other by
• What they eat
• How they prepare the food
• When they eat
• Which things they eat together
Eva Barlösius, Soziologie des Essens, Kaptitel 6, S. 146, Juventa Verlag 1999
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3. Motivation
• Diet monitoring is expensive
– Consumer panels
– NEMONIT
• One monitoring per year
– NVS
• Laste one: November
2005 - Januar 2007
• 20k people
• 24h Recall
• Diet History Interviews
• Weight protocolls
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4. Idea
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5. Data
• Most popular online recipe
platform in Austria
• Recipes provided on the
platform
• Server logs (Region – click
– timestamp - page)
• ~180.000 Rezepte, ~1.700
Regionen
• August 2012 – November
2013
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6. Are the food preferences of
geographically close regions
more similar than those of distant
regions?
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7. 0.15
0.2
0.20
0.25
0.3
0.30
0.35
0.4
0.40
0.5
0.45
0.50
0.6
Geographic proximity
and food preferences
distant
closed
Ingredients
distant
closed
Recipes
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8. To what extent does the weekday
/ season impact users' diet?
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9. Temporal patterns
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10. Normalized Access
Volume per Weekday
Meat
Carbohydrates
Fish
Vegetable
Alcohol
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11. Change Rate
per Weekday
Meat
Carbohydrates
Fish
Vegetable
Alcohol
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12. Seasonal Nutrients
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13. Seasonal Nutrients
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14. Seasonal Nutrients
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15. Next Steps
• More data: chefkoch.de and maybe kochbar.de
• Make data available via GESIS’ secure data center?
• Algorithm for estimating nutritional values of online
recipes
• What drives the evolution of online food preferences?
– Can we simulate the popularity dynamics?
– How does the popularity of ingredients spread across different
regions?
– Weekday-specific or season-specific spreading?
– How do social factors of regions impact the spreading?
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