Location - Without all the hype
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Transcript of Location - Without all the hype
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LOCATION INTELLIGENCE
SERGIO ÁLVAREZ, CHIEF PRODUCT OFFICER @CARTO
–Without all the hype
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TWITTER GAINS INSIGHTS THROUGH CARTO ON HOW EVENTS ARE UNFOLDING ACROSS ITS PLATFORM
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THE MAYOR’S OPERATIONS OFFICE KNOWS WHAT IS HAPPENING IN NEW YORK CITY THROUGH CARTO
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BBVA IDENTIFIES KEY ECONOMIC HOTSPOTS WITH CREDIT CARD TRANSACTION DATA USING CARTO
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Leonardo Da Vinci1502IMOLA
C I T Y M A P
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Petrus Plancius1594
MOLLUSC ISLANDSS E A R O U T E S M A P
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John Ogilvy1675
WHITBY TO DURHAMR O U T E M A P
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unknown17th Century
OSAKA TO NAGASAKIS E A R O U T E M A P
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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
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Matthew Fontaine Maury1851 WHALE CHART
O F T H E W O R L D
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Andrew Scott Waugh1858
M EVEREST & DEODANGAT H E G R E A T A R C
T
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Williard B. Farwell1885
“ O F F I C I A L ” M A P O F
CHINATOWN
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Charles Booth1889
LONDON POVERTYD E S C R I P T I V E M A P
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Sanborn Map Company1916
INSURANCE MAPSB Y S A N B O R N
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London County Architect’s Department1945
D A M A G E A F T E R
BOMBING
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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
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LOCATION INTELLIGENCE GOT INTO A DARK ERA
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[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
”
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S E N S O R S F O R $ 1
DIGITALIZATION OF HUMANS
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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
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S E N S O R S F O R $ 1
DIGITALIZATION OF CITIES
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URBOA P R O J E C T B Y T E L E F Ó N I C A
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S E N S O R S F O R $ 1
DIGITALIZATION OF THE SKY
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S E N S O R S F O R $ 1
DIGITALIZATION OF STREETS
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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
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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
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A N E W E R A
POWER OF LOCATION NEEDS TO BE UNLEASHED (AGAIN)
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HAVING A NEAR REAL-TIME CENSUS OF THE WORLD
T H E I D E A O F
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THINKING IN GEO IS HARD
LINEARIZATION, AGGREGATION, ANALYSIS, VISUALIZATION
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LINEARIZATIONG E N E R A T I N G Q U A D K E Y I N D E X E S
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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
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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
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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]
},
...
]
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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
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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
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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
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{
"type": "kmeans",
"params": {
"source": {
"type": "source",
"params": {
"query": "select * from clients"
}
},
"clusters": 10
}
}
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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
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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