Data visualization new york 20130701
Transcript of Data visualization new york 20130701
![Page 1: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/1.jpg)
Data discovery
with
Ben Jones Tableau Public product marketing manager
Twitter: @DataRemixed
July 1st, 2013
![Page 2: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/2.jpg)
question gathering
data
structuring
data
exploring
data
communicating
data
The Data Discovery Horse Track
![Page 3: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/3.jpg)
Tableau is like Secretariat
because visualizing data can be the fastest way around the track
![Page 4: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/4.jpg)
I acquire
I have the “I
don’t know”
feeling, so
data
an experience
verbal
responses
I have the “I
know” feeling
Is it
enough
?
which I analyze and
interpret
Wonder Seek Discover Mature
No
and learn through
inference or
deduction
Do I
have
the right
info?
Yes
Yes
No
![Page 5: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/5.jpg)
I acquire
I have the “I
don’t know”
feeling, so
data
an experience
verbal
responses
I have the “I
know” feeling
Is it
enough
?
which I analyze and
interpret
Wonder Seek Discover Mature
No
and learn through
inference or
deduction
Do I
have
the right
info?
Yes
Yes
No
“Data Discovery”
![Page 6: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/6.jpg)
data
an experience
verbal
responses
tacit
explicit
Best types of questions
Who? What? When? Where?
How many?
Why?
How?
Types of information
Types of knowledge
![Page 7: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/7.jpg)
Explicit vs. Tacit Knowledge
(sometimes you just have to try for yourself)
or
![Page 8: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/8.jpg)
know
what where when how many
And yet there is great value in explicit data
![Page 9: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/9.jpg)
New York City is a place of great abundance,
and data is no exception
https://data.cityofnewyork.us/
![Page 10: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/10.jpg)
know what
correlations with Tableau
![Page 11: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/11.jpg)
“When you leave New York, you are
astonished at how clean the rest of the
world is. Clean is not enough.”
-- Fran Lebowitz
![Page 12: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/12.jpg)
![Page 13: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/13.jpg)
know where
geospatial visualization with Tableau
![Page 14: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/14.jpg)
Where are the
bridges in NY/NJ that
are in the worst
condition?
![Page 15: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/15.jpg)
The Bridges of NY & NJ
![Page 16: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/16.jpg)
Zip code only
(no lat/long in data)
[Bonus] Electric Consumption - 2010
![Page 17: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/17.jpg)
know when
longitudinal visualization with Tableau
![Page 18: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/18.jpg)
When am I most
likely to spot a rat
in New York?
![Page 19: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/19.jpg)
The best time of the year to spot a
rat in New York is summertime
![Page 20: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/20.jpg)
![Page 21: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/21.jpg)
Graffiti artist “Zeb” is caught in Queens on Nov. 4th, 2010. His email is
searched and photos of his work are recovered
![Page 22: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/22.jpg)
know how many
using Tableau when you only have categorical data
![Page 23: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/23.jpg)
![Page 24: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/24.jpg)
question gathering
data
structuring
data
exploring
data
communicating
data
The Data Discovery Horse Track
![Page 25: Data visualization new york 20130701](https://reader033.fdocuments.us/reader033/viewer/2022052823/55509bb2b4c90595208b4903/html5/thumbnails/25.jpg)
thank you
(contact me with comments or questions)
Ben Jones Tableau Public product marketing manager
Twitter: @DataRemixed