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UC Berkeley School of Information MIMS 2014 Image Source: Breyer - PowerPoint PPT Presentation

UC Berkeley School of Information MIMS 2014 Image Source: Breyer Law O ff ices T H E T E A M Luis Aguilar Deb Linton Kate Rushton Raymon Sutedjo-The BACK-END ENGINEER RESEARCHER FRONT-END ENGINEER & MANAGER DESIGNER Coye Cheshire


  1. UC Berkeley School of Information MIMS 2014 Image Source: Breyer Law O ff ices

  2. T H E T E A M Luis Aguilar Deb Linton Kate Rushton Raymon Sutedjo-The BACK-END ENGINEER RESEARCHER FRONT-END ENGINEER & MANAGER DESIGNER Coye Cheshire ADVISOR

  3. C O N T E N T Overview Research & Insights Design Technology Challenges Demo

  4. O V E R V I E W Image Source: Baltimore You Are Marvelous

  5. In urban areas like San Francisco, “ ” more than a quarter of all trips are carried out on foot Source: Image Source: SFMTA Atlantic Cities

  6. O V E R V I E W Problem Existing navigation applications don’t take pedestrian safety into account

  7. O V E R V I E W Problem Many women don’t feel safe on the streets of their own city

  8. O V E R V I E W A web-based mobile mapping tool that helps pedestrians make more informed decisions about which route to take

  9. O V E R V I E W ... but we are not developing a “safety algorithm”

  10. R E S E A R C H & I N S I G H T Image Source: Desktop Wallpapers

  11. R E S E A R C H & I N S I G H T Safety, accessibility, and aesthetics. “ ” Each helps support walking. —Peter Lagerwey Regional O ff ice Director, Toole Design Group

  12. R E S E A R C H & I N S I G H T “How might a mobile application improve walking safety?” Pulling out a mobile I would not use a Since most property might give someone mobile application. crimes involve the " a reason to jump of a mobile device, me and steal it. using an app while walking can only make one a bigger I wouldn’t want my target. phone out at Getting mugged or night ... because I getting hit by a car wouldn't want to while looking at a get mugged. walking safety app Not sure. would really stink.

  13. R E S E A R C H & I N S I G H T “How might a mobile application improve walking safety?” The app could show I’d try an app that Visible crime stats the safest routes showed nearby over the map depending on the routes that were time of day you are well lighted or had walking around. lights at all. A list of nearby open businesses would make me feel safer. Heat maps with crime stats overlaid, highlight streets without adequate lighting, highlight streets where most businesses are closed.

  14. R E S E A R C H & I N S I G H T Safety-Oriented General Purpose Pedestrian Focus Directions-Oriented

  15. R E S E A R C H & I N S I G H T Gender-Sensitive Concerns + = Relevant Data Empowered Visualization Pedestrians + “Hands-Free” Directions Image Source: The Noun Project

  16. D E S I G N

  17. D E S I G N

  18. D E S I G N & D E V E LO P M E N T

  19. D E S I G N

  20. D E S I G N Key Feature Pedestrian-Relevant Data Visualization

  21. D E S I G N Key Feature Mnemonic Directions

  22. D E S I G N Key Feature Mnemonic Directions

  23. T E C H N O LO G Y

  24. T E C H N O LO G Y ? ? ? TSV CSV XLS ? ? Realtime API Access API Calls

  25. T E C H N O LO G Y User Input StreetSavvy Database Directions + Open Shops Map + Visualization Directions (Pictures and/or Text) + Mnemonic Option

  26. T E C H N O LO G Y HTML & CSS PostgreSQL Python Javascript/jQuery PostGIS NLTK

  27. T E C H N O LO G Y Mnemonic Text Generation (NLP) S INTERMEDIATE GRAMMAR STEPS NNP VBZ JJ NNS Le " Market Right Valencia Linda Makes Random Visualizations Proper Noun 3rd Person Adjective Plural Noun Singular Verb

  28. T E C H N O LO G Y Database streetsavvy_categories streetsavvy_artifact streetsavvy_hollaback streetsavvy_sfcrime streetsavvy_streetlights

  29. C H A L L E N G E S Image Source: The Indian Institute of Geographical Studies

  30. C H A L L E N G E S So Many Items, So Little Screen Open Shops User-Generated Report Crime Time Filter Directions Street Map Streetlights

  31. C H A L L E N G E S More Data, More Problems Raw crime data is exaggerated

  32. C H A L L E N G E S More Data, More Problems Visualizing crimes around 16th & Mission, San Francisco All Crimes Pedestrian-Relevant Crimes

  33. C H A L L E N G E S More Data, More Problems Which one of you am I going to “ ” RAPE first? “... [m]y girlfriend and I were walking through Dolores Park when...” —Hollaback! User

  34. C H A L L E N G E S More Data, More Problems Elusive streetlights data

  35. C H A L L E N G E S Image Source: Flickr (naggobot)

  36. D E M O

  37. D E M O Scenario Tina lives in the Hayes Valley neighborhood of San Francisco. It’s 11pm and she is about to head home from a networking event in the Tenderloin.

  38. D E M O Custom Directions Renderer

  39. D E M O Heatmap Components Marker Components

  40. Real data Real world problem Real user needs

  41. What makes people walk is what makes “ ” great places to live. — Harriet Tregoning Director of O ff ice of Economic Resilience, US Department of Housing & Urban Development Image Source: myurbanist

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