Low fidelity data mining for planners, from Planning-ness 2014

HELLO, MY NAME IS IAN FITZPATRICK.
PLANNER
DATA SCIENTIST
DEVELOPER
PLANNING-NESS PDX 2014

HELLO, MY NAME IS @IANFITZPATRICK.
PLANNER
DATA SCIENTIST
DEVELOPER

MY JOB IS TO HELP ORGANIZATIONS BUILD
TOOLS AND SYSTEMS THAT ENABLE THEM
TO SEE THE WORLD THROUGH THE EYES
OF THEIR USERS.

LET’S GET EXCITED ABOUT WHAT WE CAN DO AND
MAKE WITH DATA BY STARTING WITH A FEW
THINGS THAT I’M GOING TO GIVE TO YOU.
PLANNING-NESS PDX 2014 | photo from Vintage Portland

GET EXCITED AND
MAKE
THINGS

MOST DATA DOESN’T EXIST YET — AT
LEAST NOT IN A FORM WE RECOGNIZE.

DO MOST RUNNERS CARRY A
SMARTPHONE WITH THEM WHEN THEY
? HEAD OUT FOR A RUN

WHAT DO WE KNOW ABOUT THE
RELATIONSHIPS BETWEEN RUNNERS AND
? THEIR PHONES

QUANTITATIVE VS. QUALITATIVE IS A FALSE
CHOICE. THEY ARE INTENDED TO FUEL
ONE ANOTHER.

MINES GIVE US ORE, NOT BARS OF IRON.

WE ARE LOOKING FOR THE TRUTH
WE ARE LOOKING FOR ANSWERS
WE ARE LOOKING FOR THE INTERESTING

THE INTERESTING?
!
THE UNANTICIPATED AT SCALE
THE MOST TYPICAL
THE LEAST TYPICAL
OUTSIDERS THAT LOOK LIKE INSIDERS

SO WE’VE GOT THIS BIG PILE OF
INFORMATION…WHERE DO WE GO TO FIND
? THE INTERESTING

CONVERT A RANGE OF RICH DATA AND
DATATYPES TO A COMMON STRUCTURE WE
CAN USE TO COMPARE IT. 1)

CONVERTING TO BINARY ALLOWS US TO
MEASURE IT DISPASSIONATELY AND
OBJECTIVELY.
!
THINK OF IT AS BEGINNER’S MIND.

AGE 13:
!
UNDER 15
OVER 12
OLD ENOUGH TO SEE A PG-13 MOVIE
OLD ENOUGH TO BE ON FACEBOOK
BORN IN THE 1990’S
NOT A DRIVER
DOESN’T UNDERSTAND BUFFY REFERENCES

ONCE THE DATA HAS A COMMON FORM, WE
CAN BEGIN TO DERIVE OUR OWN CASES AND
ASSOCIATIONS FROM IT. 2)

ALMOST ANY INFORMATION CAN BE
CONVERTED TO ONE OR MORE BINARY
DATA POINTS

ZIP CODE 97218 + VOLVO OWNER:
!
URBAN VEHICLE OWNER
URBAN EUROPEAN VEHICLE OWNER
URBAN UPSCALE VEHICLE OWNER?
GENTRIFER?

DERIVATION IS AN ART.
YOU WILL GET BETTER AT IT.
IT GETS MORE INTERESTING WHEN IT
BEGINS TO COMPOUND.

3)EQUATION.
INTRODUCE NEW, OUTSIDE DATA INTO THE

“I WONDER HOW __________ IS RELATED
TO __________”
? PLANNING-NESS PDX 2014 | photo from Vintage Portland

POPULATION DENSITY, AVERAGE
RAINFALL, BIRTH RATE, COST OF LIVING,
AIR QUALITY, WATER TABLES, SALES DATA,
AVERAGE HOME PRICE, BOOK SALES,
PRODUCT RECALLS AND GAS PRICES.

BUZZDATA
THE CENSUS BUREAU
DATA.GOV
DATA MARKET
FREEBASE
GOOGLE PUBLIC DATA
INFOCHIMPS
SOCRATA
[…]

“I WONDER HOW __________ IS RELATED TO __________”
!
CUSTOMER SATISFACTION w/ AN AUTOMOTIVE BRAND?
CUSTOMER SALES DATA FOR A SKATE BRAND?
A MEMBERSHIP SURVEY OF A LARGE URBAN LIBRARY?
TROOP SATISFACTION DATA ON US MILITARY INSTALLATIONS?
RENEWAL REGISTRATION DATA FOR MUSEUM MEMBERSHIPS?
CLICK-THROUGH ON A CAMPAIGN FOR A DIAPER BRAND?
MEMBERSHIP RENEWAL RATE FOR A WAREHOUSE CLUB?
RETENTION RATE FOR AN ONLINE UNIVERSITY?

THIS WILL CHANGE THE WAY YOU
CAPTURE DATA — AND THE DATA YOU
CAPTURE — FOREVER.

LOWERING THE COST OF ASKING A
QUESTION INCREASES THE LIKELYHOOD
THAT WE’LL ASK IT IN THE FIRST PLACE

$499.00 USER DATA FROM A FEW THOUSAND PEOPLE
+ $24.99 WUFOO SURVEY
+ $0.00 OPEN DATABASE
+ $0.00 JAVASCRIPT
+ $0.00 MATH, YO
!
$523.99

WHEN THAT COST APPROACHES ZERO,
YOUR CAPACITY TO WORK WITH DATA IS
LIMITED ONLY BY PROCESSING POWER
AND YOUR CURIOSITY.

4)RUN THE NUMBERS.

5)LOOK FOR THE UNANTICIPATED

DECIDING THAT WE’RE IN PURSUIT OF
QUESTIONS, NOT TRUTH, CREATES
OPPORTUNITY.

A PARTY FAVOR:

THANK YOU KINDLY.
@IANFITZPATRICK
BEALMIGHTY.COM
WINDING.CO

Low fidelity data mining for planners, from Planning-ness 2014

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Low fidelity data mining for planners, from Planning-ness 2014