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

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Jan 01, 2023 466 likes •890 views

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

SLIDE 1 UC Berkeley School of Information MIMS 2014 Image Source: Breyer Law Offices

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

SLIDE 3 C O N T E N T

Overview Research & Insights Design Technology Challenges Demo

SLIDE 4

O V E R V I E W

Image Source: Baltimore You Are Marvelous

SLIDE 5

In urban areas like San Francisco, more than a quarter of all trips are carried out on foot

Source: SFMTA

“ ”

Image Source: Atlantic Cities

SLIDE 6 O V E R V I E W

Existing navigation applications don’t take pedestrian safety into account

Problem

SLIDE 7 O V E R V I E W

Many women don’t feel safe on the streets of their own city

Problem

SLIDE 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

SLIDE 9 O V E R V I E W

... but we are not developing a “safety algorithm”

SLIDE 10

R E S E A R C H & I N S I G H T

Image Source: Desktop Wallpapers

SLIDE 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 Office Director, Toole Design Group

SLIDE 12 R E S E A R C H & I N S I G H T

“How might a mobile application improve walking safety?”

I wouldn’t want my phone out at night ... because I wouldn't want to get mugged. Since most property crimes involve the"

f a mobile device,

using an app while walking can only make one a bigger target. Pulling out a mobile might give someone a reason to jump me and steal it. I would not use a mobile application. Not sure. Getting mugged or getting hit by a car while looking at a walking safety app would really stink.

SLIDE 13 R E S E A R C H & I N S I G H T

“How might a mobile application improve walking safety?”

I’d try an app that showed nearby routes that were well lighted or had lights at all. Heat maps with crime stats overlaid, highlight streets without adequate lighting, highlight streets where most businesses are closed. The app could show the safest routes depending on the time of day you are walking around. Visible crime stats

ver the map

A list of nearby open businesses would make me feel safer.

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

SLIDE 15 R E S E A R C H & I N S I G H T

Relevant Data Visualization Gender-Sensitive Concerns “Hands-Free” Directions Image Source: The Noun Project

Empowered Pedestrians +

SLIDE 16

D E S I G N

SLIDE 17 D E S I G N

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

SLIDE 19 D E S I G N

SLIDE 20 D E S I G N

Key Feature Pedestrian-Relevant Data Visualization

SLIDE 21 D E S I G N

Key Feature Mnemonic Directions

SLIDE 22 D E S I G N

Key Feature Mnemonic Directions

SLIDE 23

T E C H N O LO G Y

SLIDE 24 T E C H N O LO G Y

?

TSV

?

API Access

?

Realtime API Calls

?

CSV

?

XLS

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

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

SLIDE 27 Proper Noun 3rd Person Singular Verb Adjective Plural Noun T E C H N O LO G Y

Mnemonic Text Generation (NLP)

NNP VBZ JJ NNS Le" Market Right Valencia S Linda Makes Random Visualizations INTERMEDIATE GRAMMAR STEPS

SLIDE 28 T E C H N O LO G Y

Database

streetsavvy_artifact

streetsavvy_categories streetsavvy_hollaback streetsavvy_sfcrime streetsavvy_streetlights

SLIDE 29

C H A L L E N G E S

Image Source: The Indian Institute of Geographical Studies

SLIDE 30 C H A L L E N G E S

So Many Items, So Little Screen

Crime Open Shops User-Generated Report Streetlights Directions Street Map Time Filter

SLIDE 31 C H A L L E N G E S

More Data, More Problems

Raw crime data is exaggerated

SLIDE 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

SLIDE 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

SLIDE 34 C H A L L E N G E S

More Data, More Problems

Elusive streetlights data

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

SLIDE 36

D E M O

SLIDE 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.

SLIDE 38 D E M O Custom Directions Renderer

SLIDE 39 D E M O Heatmap Components Marker Components

SLIDE 40

Real data Real world problem Real user needs

SLIDE 41

What makes people walk is what makes great places to live.

— Harriet Tregoning Director of Office of Economic Resilience, US Department of Housing & Urban Development Image Source: myurbanist