Secure Management/Use of Location to Improve User Experience

Shoji Morikawa
LINE LINE Planning Team2 Product Manager
https://linedevday.linecorp.com/2020/ja/sessions/4644
https://linedevday.linecorp.com/2020/en/sessions/4644

Transcript

2. Life on LINE LINE offers a variety of local services
3. Life on LINE with Location How location relates to everyday events 1. 2. 3. 4. 5. 6. Check news News about home area Commute Avoid congested times Eat Book a restaurant nearby Chat Share location with friends Search Search nearby local info Shop Get coupons for your usual stores
4. Bring the local closer to you
5. Scope of Location Platform Role Data Analysis Understand users and geography Governance Security Secure data management Delivery Search Deliver content at the right moment
6. Security How location data is securely managed in LINE User Agreement Platform Environment Operations / Policy
7. User Agreement Options for user Agreement / Opt-in, Opt-out Data removal option
8. Platform Environment User data anonymization Location Process Location Deletion Process Analysis / Applications Over 1 billion reports / day Refresh every day (small data size) Hadoop Hadoop Report Latitude, Longitude Location Data (with Anonymized ID) Anonymized ID – User ID Mapping Location Deletion Request Delete User Mapping
9. Subtitle Operation / Policy Service Implementation Request Access Grant Access • Check use case • Approve access • Grant permission via authentication server • Check data deletion process • Check opt-out user data process
10. Analysis Location Stats User Profiling Case Study
11. Location Stats – Countrywide Distribution Statistics for Japan LINE Account Hokkaido 4.0% Tohoku 6.0% Kanto 35.1% Chubu 17.0% Kinki 18.5% Chugoku 5.4% Shikoku 2.7% Kyushu/ Okinawa 11.3% Source: LINE Location DB, Oct 2020
12. Location Stats - Numbers Statistics for Japan LINE Account DAU 36M Daily Log 600M MAU 45M Source: LINE Location DB, Oct 2020
13. User Profiling - Feature Statistics for Japan LINE Account 41M home 12M / 17M commuting rail- line / station 40M work Domestic: 53M Oversea: 2M travel Source: LINE Location DB, Oct 2020
14. User Profiling – Feature Expand volume using IP 41M home 39M work 62M 58M IP Address:Location User's IP User's Location Location Data
15. Estimation of Commuting Rail Line Quiz: Here are the location logs for two users. Which rail lines do they use? ? ?
16. Estimation of Commuting Rail Line Quiz: Here are the location logs for two users. Which rail lines do they use? Sobu Line Mita Line
17. Shaping Estimation of Commuting Rail Line Estimate commuting rail line by analyzing user's location Estimation Rail Line Shape User Location 11,001 Stations & 616 Lines Inter-Station Graph Connecting stations User Commuting Rail Line A Mita Line B Yamanote Line C Odakyu Line D Keio Line E Oedo Line
18. Case Study – Population Change over Time Population density in the city center changes drastically depending on the time of day The population density in the city center is high in the daytime, and the population density in the suburbs is high at night.
19. Search How location data is securely managed in LINE Filtering x Find Users API Geo Fencing Case Study
20. Area Filtering Subtitle Master Code Geo Hash
21. Area Filtering Subtitle User Home Geo Hash (Level 7) User Profile (Home / Work) Polygon Circle
22. Time / Last Location Filtering Subtitle 17:00 15:00 13:00 16:00 14:00 12:00 Time between 13:00 ~ 15:00 Only the last reported location
23. Find Users API Extract users with multiple filtering options in real-time MASTER CODE CIRCLE POLYGON INTERSECTION GEO HASH (Level 8) / User Profiling (Home / Work) GEO HASH (Level 7) CIRCLE POLYGON UNION CIRCLE HOLE
24. Geo Fencing Detect fence cross events in real-time ENTER EXIT STAY 2. Set Fence LINE Service Server 1. Fence Registration 3. Callback
25. Case Study – Disaster Notification Area targeted yet mass scale message delivery Provide content to whom related to the area based on user location
26. Disaster Notification Delay Challenge As many as 20M people to notify as quickly as possible Delivery in under 1min done using only existing infra Elapsed Seconds Users Count User / Sec Area Categories 22.5 sec 14M 612K Tokyo, Kanagawa, Chiba, Saitama, Shizuoka Home 40 sec 18M 437K Last 59 sec 22M 371K Last, Home, Work
27. Case Study – LINE Ad Geo Targeting Advertiser can set a target audience who live in or visited some place by administrative area or radius. CMS for Performance Display Ads 100M~150M daily ad impressions using geo targeting in JP - 15~20% of total ad impressions More advertisers using geo targeting since the release of radius targeting
28. Roadmap of Location Platform POI data and Geodemography Analysis Geodemography Discover regional clusters Data POI POI location and contents
29. POI – User x POI What are POIs? Category Retail store Name Lawson Shiba Koen 4 Cho-me Address Tokyo, Minato-ku, Shiba Koen 4-X-X Tel 03-XXXX-YYYY Coordinates Latitude, Longitude Convenience Store Category Tourist facilities Name Tokyo Tower Address Tokyo, Minato-ku, Shiba Koen 4-X-X Tel 03-XXXX-YYYY Coordinates Latitude, Longitude Tokyo Tower POI (Point of Interest) For example, landmarks and shops that exists in coordinates are converted into data in order to give meaning to specific coordinates on the map. POI DB (6 Million POIs) Example POI Data
30. POI – User x POI Integrated POI DB / Combination of POI and User location Restaurant POI Retail POI Merchant POI POI POI Contents User Location Coupon Flyer Review Demography Service managed POI are integrated into LINE POI DB Example use cases • Deliver nearby store content • Estimate how frequently POI are visited Service managed content is also integrated into LINE POI DB
31. POI Traffic Provide traffic stats to avoid congestion at POI 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 Hourly average for the past few weeks Current real-time number of users Compare traffic by time to avoid busy hours Compare traffic by past average traffic and current traffic
32. Geodemography Study of user based on where they live Demography Behavior Geography Gender Age Income Occupation Nationality Urbanicity Average household income Language Climate Culture Location of home, work Book / Review restaurant Pay at store Online action
33. Geodemography Study of people based on where they live
34. Thank you