Transcript
LOCATION INTELLIGENCE
SERGIO ÁLVAREZ, CHIEF PRODUCT OFFICER @CARTO
–Without all the hype
TWITTER GAINS INSIGHTS THROUGH CARTO ON HOW EVENTS ARE UNFOLDING ACROSS ITS PLATFORM
THE MAYOR’S OPERATIONS OFFICE KNOWS WHAT IS HAPPENING IN NEW YORK CITY THROUGH CARTO
BBVA IDENTIFIES KEY ECONOMIC HOTSPOTS WITH CREDIT CARD TRANSACTION DATA USING CARTO
Leonardo Da Vinci1502IMOLA
C I T Y M A P
Petrus Plancius1594
MOLLUSC ISLANDSS E A R O U T E S M A P
John Ogilvy1675
WHITBY TO DURHAMR O U T E M A P
unknown17th Century
OSAKA TO NAGASAKIS E A R O U T E M A P
Alexandre Von Humboldt & Aimé Bondpland1807
MOUNTAINS AND PLANTS O F A M E R I C A
H A L L M A P O F T H E
Matthew Fontaine Maury1851 WHALE CHART
O F T H E W O R L D
Andrew Scott Waugh1858
M EVEREST & DEODANGAT H E G R E A T A R C
T
Williard B. Farwell1885
“ O F F I C I A L ” M A P O F
CHINATOWN
Charles Booth1889
LONDON POVERTYD E S C R I P T I V E M A P
Sanborn Map Company1916
INSURANCE MAPSB Y S A N B O R N
London County Architect’s Department1945
D A M A G E A F T E R
BOMBING
William Bunge1971
W H E R E
COMMUTERS RUN OVER BLACK CHILDRENO N T H E P O I N T E S - D O W N T O W N T R A C K
LOCATION INTELLIGENCE GOT INTO A DARK ERA
[we need a] multi-
resolution,
3-dimensional
representation of the
planet, into which we can
embed vast quantities of
geo-referenced data.
– Al Gore, 1998
”
S E N S O R S F O R $ 1
DIGITALIZATION OF HUMANS
LEVERAGING THE POWER OF INDOOR LOCATIONA P R O J E C T B Y G E O G R A P H I C A
S E N S O R S F O R $ 1
DIGITALIZATION OF CITIES
URBOA P R O J E C T B Y T E L E F Ó N I C A
S E N S O R S F O R $ 1
DIGITALIZATION OF THE SKY
S E N S O R S F O R $ 1
DIGITALIZATION OF STREETS
PREVENTING COMMUTERS RUNNING OVER CHILDREN THROUGH PREDICTIONU S I N G S C H O O L S L O C A T I O N S A N D G P S P R O B E D A T A
N E T W O R K S A R E E V E R Y W H E R E
CONNECTED WORLDN E T W O R K S A R E E V E R Y W H E R E
CONNECTED WORLD
A N E W E R A
POWER OF LOCATION NEEDS TO BE UNLEASHED (AGAIN)
HAVING A NEAR REAL-TIME CENSUS OF THE WORLD
T H E I D E A O F
THINKING IN GEO IS HARD
LINEARIZATION, AGGREGATION, ANALYSIS, VISUALIZATION
LINEARIZATIONG E N E R A T I N G Q U A D K E Y I N D E X E S
1013670006216458240
1013670006216458387
1013670086816438331
1013670786816438001
1013676786816438222
1013676786816438222
LINEARIZATIONG E N E R A T I N G Q U A D K E Y I N D E X E S
AGGREGATIONT I L E C U B E S A R E T O G E O W H A T D A T A C U B E S A R E T O B I
AGGREGATIONT I L E C U B E S A R E T O G E O W H A T D A T A C U B E S A R E T O B I
[
{
x: 25,
y: 77,
values: [ 1, 10 ],
steps: [214, 215]
},
...
]
ANALYSISD Y N A M I C A N A L Y S I S I M P R O V E S K N O W L E D G E D I S C O V E R Y
D A T A S A M P L I N G K - M E A N S F I L T E R U S E R
D A T A S A M P L I N G K - M E A N S F I L T E R U S E R
{
"type": "kmeans",
"params": {
"source": {
"type": "source",
"params": {
"query": "select * from clients"
}
},
"clusters": 10
}
}
ANALYSISU S I N G Q U A D K E Y I N D I C E S F O R A N A L Y S I S O P T I M I Z A T I O N
VISUALIZATIONT O R Q U E . J S U S E S T I L E C U B E S T O R E N D E R D A T A D Y N A M I C A L L Y
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