Big Data for Trails

CREATING, ENJOYING, AND MAINTAINING TRAILS:

“WHAT’S DATA GOT TO DO WITH IT?” Linda G. George, Ph.D. Photo: Mt. Tallac above S. Lake Tahoe

OVERVIEW

•  “Big Data” – Basic deJinitions – Examples – Steps – Skills – & about trails…

GOALS FOR THE SESSION

•  Understand more about this global phenomenon

•  Spark new ideas for your use of data, whether you’re in a small, medium, or large organization

•  Give you pointers to helpful resources

WHAT IS “BIG DATA”?

(WARNING…)


•  An explosion in the amount of data available

•  Inexpensive ways to store it

•  Sheer quantity changes what we can do

“The deluge”


Graphic: Diya Soubra. 3Vs: Gartner, 2001

WHAT IS BIG DATA?

The 0’s •  Megabyte 1,000,000 •  Gigabyte 1,000,000,000 •  Terabyte 1,000,000,000,000 = 1k GB •  Petabyte 1,000,000,000,000,000 •  Exabyte 1,000,000,000,000,000,000

Data generated in one minute on the Internet, ca. 2011

WHAT IS “BIG DATA”? •  Structured data – traditional, has a set format) •  Unstructured data -‐ forum posts, blogs, ratings, websites, environmental sensors, books, videos, … –  Breakthroughs in analyzing unstructured data

Source: mediabistro.com


•  “Big Data” is not completely about the data: it reJlects a paradigm shift.

•  Data has new prominence in the decision making process of individuals and organizations.

•  New technologies have emerged through companies like Google and Yahoo!

•  These technologies can be useful to other organizations, large and small.

ISSUES AND CONCERNS

•  Assumption: Data + Technology = “Actionable Insights, Magic Ponies, and Superpowers”

ISSUES AND CONCERNS

•  Privacy •  Bias •  Risk: Jinding patterns and connections where none exist

Source: hCp://m.xkcd.com/552/

WHERE IS IT?

BIG PLAYERS

•  Google, Facebook, NetJlix, Etsy, eBay, Yahoo, Yelp, LinkedIn, Orbitz, Twitter, … Walmart, Zions Bancorp., the medical research world, …

“THE CLOUD”

•  Where is all of this data gathered, stored, and analyzed?

•  Amazon Web Services – Large, Jlexible storage and computing power – A place to store large quantities of data; an alternative to in-‐house storage

IN-‐HOUSE STORAGE •  Heating/cooling system, Google -‐ Oregon

IN-‐HOUSE STORAGE •  Google servers in Georgia

EXAMPLES

THE “BIG GUYS”

Facebook: 950,000,000 users, generating 500+ TB of new data daily: visiting a page, uploading a photo, reading an update via link.

THE “BIG GUYS”

Thomas Guides -‐>

Google Maps

HEALTH CARE

•  Genomic research

HEALTH CARE

•  Research on drug side effects and interactions

EMERGENCY RESPONSE

•  10TB of data assisted the FBI in investigating the Boston Marathon tragedy: call logs, city cameras, local businesses, gas stations, media outlets, and spectators – videos and photos

TRAILS -‐ ?

TRAILS…

•  NPS Visitor Centers “The technology should help people have an enhanced, deeper, more meaningful connection with the real thing” (J. Washburn, NPS)

TRAILS…

•  2/14/13: Outdoor Industry Association released a state-‐by-‐state reports on the economic beneJits of recreation.

http://www.outdoorindustry.org/advocacy/recreation/economy.html

TRAILS…

•  Economic beneJits – quantify in new ways? – Tourism – Events – Property value – Health care savings –  Jobs and investment – Consumer spending (equipment, horses, bikes)

(from americantrails.org)

TRAILS…

•  Florida DEP, OfJice of Greenways & Trails – The state’s trail corridor data was updated through online comments from individuals and organizations, who were later able to view data interactively online.

– “In less than twelve months, the trail opportunity corridor data for the entire state was updated.”

(5 years ago)

“INTERNET OF THINGS”

THE “INTERNET OF THINGS”

•  The “quantiJied self”: blood pressure, sleep, body mass, exercise, etc. Data from a person’s daily actions and behavior.

Busterbenson.com

QuanJfiedself.com

GLASSES, WRISTWATCHES…

GLASSES, WRISTWATCHES…

•  http://www.google.com/glass/start/how-‐it-‐feels/

TOOLS: FIRST STEPS

TOOLS

Google: website analytics •  How many people look at your site? •  How do people Jind it? •  What are they looking at? •  What do we want them to do on the site, and are they doing those things?

A/B TESTING

xkcd.com/773 ‎

Facebook data

FACEBOOK INSIGHTS

Implications for advocacy, programs and fundraising: •  How many people on facebook know about your organization and care about it, and how deeply?

•  What do they care about the most? •  What communications reach the most people? •  What do you know about your facebook fans?

source: socialbright.org

TWITTER

HOW?

Photo: The New Yorker

HOW? General guidelines: •  Start small, build on successes – iterative •  Consider “medium data”: you don’t need lots of data to do something new

•  Leave room for experiments, failures: explore – hypothesize – test – repeat

•  Celebrate successes!

1. DEFINE YOUR GOAL

Key result areas: – Increase volunteer hours a speciJic amount? – Achieve a new fundraising goal? – Create a compelling argument for a trail proposal? – Understand more about park or trail use: access to entrance, weekly/seasonal patterns, …?

2. COLLECT – Transaction information: memberships, event registrations, certiJications, etc. – Social data: website analytics, social media sharing – Sensor data, GPS data, census data Can various types of data – from inside and outside of the organization -‐ be pulled together in a new way?

3. ANALYZE

•  What can it tell you? – Spend more time learning from your data than gathering it. – “Insights require reJlection, not just counting

4. ACT •  Use insights to enhance, revise and innovate programs and services. For example…

•  Tailor your use of social media for your audience

•  Create online communities, encourage interaction and dialog to meet identiJied issues or needs

•  Help tell the story about how you’re making a difference in your community

www.socialbrite.org

GETTING STARTED

SKILLS

Source: DrewConway.com

VISUALIZATION…

•  Here is Marvin the Martian. Caption “The Jirst image has now been received from Curiosity on Mars”

http://www.facebookstories.com/stories/2200/data-‐visualization-‐photo-‐sharing-‐explosions

VISUALIZATION: HOW MARVIN THE MARTIAN WENT VIRAL

STAFFING

•  If you don’t have any “data geeks” on staff – how about volunteers?

“CANOPY” PROJECT FOR NYC PARKS

CONTESTS •  Bike sharing program in Boston: “Hubway Data Visualization” challenge

•  User engagement + results •  Example: russellgoldenberg.com/hubway/

RESOURCES – BRIEF OVERVIEW

“OPEN DATA” Google.com/trends

DATA SOURCES •  Data.gov United States: raw data, geo data, and tools)

•  U.S. Census http://www.census.gov •  Universities, such as http://www.icpsr.umich.edu •  Open portals to scientiJic literature, e.g. nature.com

•  The Guardian www.guardian.co.uk/data (data sets, ideas, tools)

•  Sites that gather links to data sets, such as datahub.io, Infochimps, Factual

DATA SOURCES •  Less obvious: – Social network proJiles – Social commentary: user forums, twitter, facebook “likes” – Activity-‐generated data: mobile device log Jiles, sensor data, application logs, … – “Scraping” websites – Commercial data providers

READING

•  http://measurenetworkednonproJit.org •  Fundraising Analytics: Using Data to Guide Strategy

•  Head First Data Analysis: A Learner’s Guide to Big Numbers, Statistics, and Good Decisions

•  Building Data Science Teams

TECHNICAL SKILLS

•  Tools vary, depending on your questions – Excel – Python* – R statistical software* – Database software such as MySQL* * Open source: free

TECHNICAL SKILL DEVELOPMENT

•  Coursera, EdX, Udacity, Khan academy, … “MOOCs”

•  “Hackathons” in local communities •  Meetups •  Kaggle.com •  College courses and certiJicate programs

COMMENTS?

•  Discussion: – New ideas, things to try with data in your organization? – Any particular challenges you’d like to address?

GOALS

•  Understand more about “big data” •  Spark ideas for using data in new ways, whether you’re in a small, medium, or large organization

•  Give you pointers to helpful resources

THANKS!

TYPES OF ANALYSES •  Analytics

–  Google Analytics for your website –  Data mining* –  Sentiment analysis –  Sensor data (& phones/devices, etc.) –  Biostatistics –  Machine learning: train computers to Jind patterns* –  Data science* –  Natural language processing –  Signal processing –  Business analytics –  Econometrics * Large Volume, Variety, and Velocity of data

Big Data for Trails

Technology

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