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THE BIG ATLAS OF LA
POOLS
JOSEPH LEE
A part of the Hands-On Ideas series
2013.10.16
About us
•  Benedikt Groβ
•  From: Germany
•  M.A: Royal College of Art, London
•  Interaction & Speculative Design

•  Joseph Lee:
•  From: USA
•  B.A: UCLA
•  Geography/Cartography &

Environmental Science
We met at the Massachusetts
Institute of Technology, Summer ‘12
DI Trip
What inspired this work?

Relevant image here
What inspired this work?

Relevant image here
What inspired this work?

Q:
How many
swimming pools
are in LA?
What we wanted to explore…
Interaction Design

Geography
THE BIG ATLAS OF LA
POOLS
A process-based exploration of new tools and
emerging issues in data privacy
About the project
•  Overview:
•  Start with ~17gb of aerial
imagery of LA

NA I P O r t h o i ma ge r y
L os A ngeles
( a e ri al i ma ge r y ti les )

Po o l - S ha p e Tra c i ng
by C l ipp i ng fa c to r y. co m
( Crowd S o u rc i ng )

Co rre c tio n M a rks
by Jos e p h & B ene dikt
( E x p e r t Revi ew )

•  Computer vision not suitable

for task

H i gh - Res C h e ck
by A ma z o n M e cha n i cal Tu rk
( Crowd S o u rc i ng )

H i ts Res u l t A

H i ts Res u l t B

•  “Crowdsourcing”
Do b o t h A ma z o n M e cha n i cal Tu rk
wo rke rs a g re e ( Yes / No ) ?

•  Data Enrichment
•  Books, Posters, and “The LA

Swimmer”

C h e ck o f co ntrove rs i al o n es
by Jo ey & B e n e d i kt
( E x p e r t Revi ew )

Fi nal Da tas e t

S a tell i te & Ae ri al I ma ge r y
B i ng M a ps
Traced!
A:
~64200 Pools
Sum Area: 2157056,65 m² (= 2,157 km²)
Maximum Area:
Mean: 33,59 m²

1312,47 m²

16
17
18
~64200

~52340
Limits of resolution
Really
a pool?

20
Google/Bing/Nokia ... Maps
23
A:
~43123 Pools
Sum Area: 3192640.04 m² (= 3,192 km²)
Maximum Area:
Mean: 74,03 m²

485214,42 m²

24
~52340

~43123
hood

pages

count

Long Beach

360

2859

Rancho Palos Verdes

326

2592

Beverly Hills

313

2481

Downey

262

2078

Brentwood

242

1920

Torrance

242

1917

Hollywood Hills West

197

1559

Beverly Crest

175

1379

Bel-Air

164

1289

Palos Verdes Estates

143

1124

Rolling Hills Estates

110

858

Lakewood

104

809

Carson

96

747

Westwood

95

737

Monterey Park

94

731

South Pasadena

90

703

Westchester

90

701

Santa Monica

86

668

Hollywood Hills

80

619

Hancock Park

77

593

Los Feliz

76

587

Ladera Heights

74

574

Inglewood

73

563

San Pedro

72

560

Alhambra

71

552

Rolling Hills

66

507

Culver City

60

461

Manhattan Beach

60

458

View Park-Windsor
Hills
Cheviot Hills

58

448

58

445

Bellflower

57

439

Baldwin
Hills/Crenshaw
Mar Vista

55

421

52

395

South Gate

51

392
33
THE LA SWIMMER: 43,123
POOLS I HAVE NEVER VISITED
AND NEVER WILL
Google Street Car / Hyperlapse.js meets The
Big Atlas of LA Pools
The LA Swimmer: 43,123 Pools I Have
Never Visited and Never Will
POST-PROJECT
Now What
What we learned
•  There are a lot of

data that are freely
available – and
even more that
isn’t.
•  There are a lot of

open-source tools
that can be
leveraged to do
complex/weird
tasks.

•  What might data mining look like at an
institutional or governmental level?
How you can build off our work
•  (Ab)use the available tools
•  Ask questions relevant to your own community or

social-physical landscape
•  Think spatially – how are things related in space

and time.
•  Find ways of collecting your own data
QUESTION AND
ANSWERS

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HOI: The Big Atlas of LA Pools

  • 1. THE BIG ATLAS OF LA POOLS JOSEPH LEE A part of the Hands-On Ideas series 2013.10.16
  • 2. About us •  Benedikt Groβ •  From: Germany •  M.A: Royal College of Art, London •  Interaction & Speculative Design •  Joseph Lee: •  From: USA •  B.A: UCLA •  Geography/Cartography & Environmental Science We met at the Massachusetts Institute of Technology, Summer ‘12
  • 3. DI Trip What inspired this work? Relevant image here
  • 4. What inspired this work? Relevant image here
  • 5. What inspired this work? Q: How many swimming pools are in LA?
  • 6. What we wanted to explore… Interaction Design Geography
  • 7. THE BIG ATLAS OF LA POOLS A process-based exploration of new tools and emerging issues in data privacy
  • 8. About the project •  Overview: •  Start with ~17gb of aerial imagery of LA NA I P O r t h o i ma ge r y L os A ngeles ( a e ri al i ma ge r y ti les ) Po o l - S ha p e Tra c i ng by C l ipp i ng fa c to r y. co m ( Crowd S o u rc i ng ) Co rre c tio n M a rks by Jos e p h & B ene dikt ( E x p e r t Revi ew ) •  Computer vision not suitable for task H i gh - Res C h e ck by A ma z o n M e cha n i cal Tu rk ( Crowd S o u rc i ng ) H i ts Res u l t A H i ts Res u l t B •  “Crowdsourcing” Do b o t h A ma z o n M e cha n i cal Tu rk wo rke rs a g re e ( Yes / No ) ? •  Data Enrichment •  Books, Posters, and “The LA Swimmer” C h e ck o f co ntrove rs i al o n es by Jo ey & B e n e d i kt ( E x p e r t Revi ew ) Fi nal Da tas e t S a tell i te & Ae ri al I ma ge r y B i ng M a ps
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  • 17. A: ~64200 Pools Sum Area: 2157056,65 m² (= 2,157 km²) Maximum Area: Mean: 33,59 m² 1312,47 m² 16
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  • 24. A: ~43123 Pools Sum Area: 3192640.04 m² (= 3,192 km²) Maximum Area: Mean: 74,03 m² 485214,42 m² 24
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  • 28. hood pages count Long Beach 360 2859 Rancho Palos Verdes 326 2592 Beverly Hills 313 2481 Downey 262 2078 Brentwood 242 1920 Torrance 242 1917 Hollywood Hills West 197 1559 Beverly Crest 175 1379 Bel-Air 164 1289 Palos Verdes Estates 143 1124 Rolling Hills Estates 110 858 Lakewood 104 809 Carson 96 747 Westwood 95 737 Monterey Park 94 731 South Pasadena 90 703 Westchester 90 701 Santa Monica 86 668 Hollywood Hills 80 619 Hancock Park 77 593 Los Feliz 76 587 Ladera Heights 74 574 Inglewood 73 563 San Pedro 72 560 Alhambra 71 552 Rolling Hills 66 507 Culver City 60 461 Manhattan Beach 60 458 View Park-Windsor Hills Cheviot Hills 58 448 58 445 Bellflower 57 439 Baldwin Hills/Crenshaw Mar Vista 55 421 52 395 South Gate 51 392
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  • 33. THE LA SWIMMER: 43,123 POOLS I HAVE NEVER VISITED AND NEVER WILL Google Street Car / Hyperlapse.js meets The Big Atlas of LA Pools
  • 34. The LA Swimmer: 43,123 Pools I Have Never Visited and Never Will
  • 36. What we learned •  There are a lot of data that are freely available – and even more that isn’t. •  There are a lot of open-source tools that can be leveraged to do complex/weird tasks. •  What might data mining look like at an institutional or governmental level?
  • 37. How you can build off our work •  (Ab)use the available tools •  Ask questions relevant to your own community or social-physical landscape •  Think spatially – how are things related in space and time. •  Find ways of collecting your own data