Entr'acte Studio

E N ’ T R A C T E G R A D U A T E S T U D I O PROFESSOR JORDAN GEIGER K A T H Y Y U E N

INITIAL CROWD STUDIES_physical boundaries UNIVERSITY STATION_ COMMUTER CROWDS

Using the University Train Sta on, this study was an analysis of how people interact with their physical environment.

The diagrams show the pa erns of movement between the invisiable boundary line created by the cket booths.

Through these series of photos, people and their dierent speeds of movement create another form of physical boundary which interrupts the pa erns of movement and creates paths.

INITIAL CROWDING DEVICE_ghos ng//echo

CURRENTLY THE PIEZO ELEMENT WORKS MORE LIKE A SWITCH, BUT IT IS ABLE TO DETECT VIBRATIONS

LIGHTSCAPE _CONCEPT THE UNIVERSITY STATION ON MAIN ST. IS A MAJOR HUB OF TRANSPORTATION BETWEEN UNIVERSITY AT BUFFALO, DOWNTOWN, AND THE SURROUNDING SURBURBS OF BUFFALO. ALTHOUGH IT IS A MAJOR TRANSPORTATION HUB, PEOPLE DO NOT OCCUPY THAT SPACE FOR A LONG TIME CREATING CROWDS. MOST PEOPLE TEND TO FILTER INTO THE SPACE THEN FILTER OUT TO THEIR INTENDED DESTINATION. LIGHTSCAPE IS INTENDED TO FORCE PEOPLE INTO CROWDS AS THEY ARE FILTERING THROUGH THE SPACE. BY REMOVING ALL THE LIGHTS AND ONLY HAVING TILES THAT LIGHT UP BASED ON MOVEMENT. AS THE ONLY LIGHT SOURCE PEOPLE WOULD HAVE TO GROUP TOGETHER TO CREATE A LARGER SOURCE OF LIGHT. THERE WOULD BE CONSTANTLY LIT TILES AT THE ENTRANCES OF THE SPACE FROM THE TRAINS, BUS TERMINAL, AND UB. THE TILES WITHIN THE STATION WOULD BE ACTIVE THROUGH PIEZOELECTRIC ELEMENTS THAT HARNESS ENERGY THROUGH VIBRATIONS. THE PIEZO ELEMENTS LOSE ENERGY AFTER A WHILE CREATING TRAILING LIGHTS OF PATHS PEOPLE HAVE TAKEN AND IF OTHER PEOPLE FOLLOW AN EXSISTING PATH IT WOULD REACTIVATE THE LIGHTS. THE EXSISTING FLOOR TILE ARE 8” X 8” AND THE NEW FLOOR SYSTEM WOULD SIT ON TOP WITH THE ELECTRONICS RUNNING IN BETWEEN THE TWO LAYERS.

ACOUSTIC TILES

HICKORY RED OAK POPLAR PINE MAHOGANY CORK

The piezo element is a pressure sensi ve, electricity genera ng sensor. It also has the ability to act as a contact mic which detects audio vibra ons through solid surfaces by conver ng vibra ons to an electrical current to a signal.

Embedding this type of sensor in a floor surface would create device that would detect footsteps. At first, the floor surface was conceived as a ligh ng strategy to a ract more people to certain areas. The floor eventually evolved into a surface that would fill a space audibly by collec ng the sounds of the footsteps and replaying them.

side by side duplica oon

foward or behind duplica on

musical composi on

pa erns generate by train departure and arrival

EN’TRACTE RESEARCH

Die Audio Gruppe: Audio Ballerinas, 1989 Sound is constantly transforming through the resampling of the enviroment and the dancer’s movements.

Bernard Tschumi: Fireworks, 1974/1992/2009 The ac on or event defines how the space is used or how people fill that space rather than the space itself.

Christoph Schlingensief: Chance2000, 1998 One person has an idea and starts to influence each other in a group or crowd. The diagram shows how much each person can be influence through proximity and orienta on to each individual.

TORONTO SITE ANALYSIS_PATH system

PATH

crosby hall

VIDEO INSTALLATION

crosby hall

external references

crosby hall

PATH

PATH

PATH

PATH

ligh ng

PATH

PATH

PATH

PATH

PATH

PATH

PATH

crosby hall

signage

architectural references

The video installa on depicts the disorienta on of the PATH system in me and space. Disorienta on in the PATH is related to the lack of visual references to the exterior. The only me there is a reference to me and space in the path when there is a glimpse of the exterior world. The path creates an environment of uniformity where it is hard to dis nguish me because the light levels are sta c, not dynamic. It also hard to dis nguish space because each space is very similar in terms of use, and they start to blend to together. The only me space gets defined is through materiality and transi onal spaces that hard to no ce. In the video, a black frame represents the transi onal space. The frames of layered and ghosted images show the sequence of movement which can disorient you more, but then there are moments of clarity as well.

The occupancy of buildings is defined by its use which also defines the occupancy of the PATH due to its physical link. The movement of people within the PATH is not only restricted by its physical boundaries, but by the types of buildings that are connected to it.

The change in occupancy is related to the different me cycles and me scales such as mes that people use that space, mes that the building is open and specific events at the buildings. The dynamic map shows the different levels of ac va on by crowds at mul ple me scales. As the buildings themselves fluctuate in occupa on, the PATH is also expanding and contrac ng with different flows of people. Whereas the sta c map of the PATH uses the same technique of diagramming for Christoph Schlingensief’s Chance 2000 to depict the influence of the adjacent buildings to PATH and physical boundaries define flow.

HOTEL OFFICE BUILDING VENUES TRAIN STATION

PHYSICAL BOUNDARIES

The PATH system has a very linear system of movement as defined by its physical boundaries. These are some studies of destroying those physical boundaries and redefining new boundaries. The new boundaries are dynamic and constantly changing which would constantly redirect and create new pa erns of movement within the PATH. The model uses the exis ng boundary of the wall which could manipulate the form of the space to the extent of the wall. Looking at sound spa aliza on, the possiblites for the space to expand and contract exponen ally. Not can the space expand and contract but it can shrink and elongate as well.

AUDIO BOUNDARIES sound spatialization

elongated.expanded

elongated.contracted

shortened.expanded

shortened.contracted

contracted

expanded

shortened

elongated

sound clip

SYNCHRONIZATION

synchronization

Synchroniza on can happens at mul ple scale with the PATH. Most people that use the PATH are snycrhonized to the 9-5 work day schedule. There maybe fluxes in pa erns when the PATH is used for shopping or to shelter from the cold, but it is mainly used for commuters that work in the buildings a ached to the PATH system. Synchroniza on can also happen through the collec on and playback of footsteps, as people are listening to their own footsteps and other footsteps, the rhythms start to conformed crea ng a kind of rhythmic march.

PROGRAMS

9PM - 5PM

WEEKEND

4PM - 6PM

MONDAY TO FRIDAY

8AM - 10AM

MONDAY TO FRIDAY

12PM - 5PM

WEEKEND

[ATM ALCOVE]

[FIRST CANADIAN PLACE BELOW GROUND LOBBY]

10FT X 8FT

60FT X 30FT

8AM - 10AM

MONDAY TO FRIDAY

11AM - 1PM

MONDAY TO FRIDAY

8AM - 10AM

[[ELEVATOR BAY]

50FT X 12FT

MONDAY TO FRIDAY

Several sites in the PATH have been chosen for their dis nc ve paths and forms. Crowds interact with these site at different me scales depending on the adjacent buildings. Many mes the ac va on of sites are the dependent on the me scale of office building as many office buildings are connected to the PATH. The diagrams below show the emergent pa erns caused by density, me scale, and physical boundaries.

9PM - 5PM

4PM - 6PM

MONDAY TO FRIDAY

4PM - 6PM

WEEKEND

MONDAY TO FRIDAY

8AM - 10AM

MONDAY TO FRIDAY

4PM - 6PM

MONDAY TO FRIDAY

4PM - 6PM

MONDAY TO FRIDAY

11AM - 1PM

MONDAY TO FRIDAY

9PM - 5PM

WEEKEND

[FOOD COURT ADJACENT TO SUBWAY STATION]

[FOOD COURT ADJACENT TO SUBWAY STATION]

36FT X 36FT

36FT X 36FT

8AM - 10AM

MONDAY TO FRIDAY

NEW RHYTHMS

Inser ng new programs such as a concert hall, dance, and a dance floor, allows for commuters to break from their mundane rhythms. These new programs create opportuni es for new rhythms to emerge throughout the day. The footsteps are already crea ng a certain rhythm or beat, and having street performers plug into the system creates opportuni es to modify these rhythms. It also allows for the commuter to join in ac vely as a performer changing from a worker to a street dancer not just passively inpu ng footsteps.

concert hall

dance studio

dance floor

MOVEMENT ANALYSIS_ me based RECORDING FOOTSTEPS Each floortile has its own table within the database of 2 seond sound samples when is triggered by a footstep

floortile2 [0, tile2_1.wav, c:\windows\path, 3:45:04, 12\01\2010] activationcount2 [13503, timestamp, count] floortile1 [index, sample name, audio sample location, timestamp, date] activationcount [index(0 - 86400), timestamp, count]

floortile1 [0, tile1_1.wav, c:\windows\path, 3:45:02, 12\01\2010] activationcount1 [13502, timestamp, count]

PLAYBACKING FOOT STEPS_wayfinding system

floortile 7

floortile 8

floortile 9

floortile 3

floortile 10

floortile 11

floortile 12

floortile 4

floortile 5

floortile 6

floortile 1 triggered @ 12:34:38 && floortile 2 triggered @ 12:34:40

Return PATTERN 1 1 1 1 1 1 1 1;

PATTERN RECOGNITION specific paths can be identified using a linear sequence of timestamps

floortile 1

1 0 0 0 0 0

floortile 2

floortile 3

1 1 0 0 0 0

1 1 1 0 0 0

floortile 4

floortile 5

floortile 6

1 1 1 1 0 0

1 1 1 1 1 0

1 1 1 1 1 1

7:14:32 7:14:34 7:14:32 7:14:34 7:14:32 7:14:34

7:14:34 7:14:36 7:14:34 7:14:36 7:14:34 7:14:36

7:14:36 7:14:38 7:14:36 7:14:38 7:14:36 7:14:38

7:14:38 7:14:40 7:14:38 7:14:40 7:14:38 7:14:40

7:14:40 7:14:40 7:14:40 7:14:40 7:14:40 7:14:40

7:14:42 7:14:42 7:14:42 7:14:42 7:14:42 7:14:42

DATABASE FOR PATTERNS PATTERNS [index, date, start_time, 00000000] PATTERNSCOUNT [index, start_time, 00000000, count]

floortile2 [100, tile2_100.wav, C:\Windows\tile2\, 12:34:40,12\01\2010] floortile2 [200, tile2_200.wav, C:\Windows\tile2\, 12:34:40,12\02\2010] floortile2 [300, tile2_300.wav, C:\Windows\tile2\, 12:34:40,12\03\2010] floortile2 [102, tile2_102.wav, C:\Windows\tile2\, 12:34:42,12\01\2010] floortile2 [202, tile2_202.wav, C:\Windows\tile2\, 12:34:42,12\02\2010] floortile2 [103, tile2_103.wav, C:\Windows\tile2\, 12:34:44,12\01\2010] floortile2 [203, tile2_203.wav, C:\Windows\tile2\, 12:34:44,12\02\2010] floortile2 [301, tile2_301.wav, C:\Windows\tile2\, 12:34:44,12\03\2010] floortile2 [104, tile2_104.wav, C:\Windows\tile2\, 12:34:46,12\01\2010] floortile2 [204, tile2_204.wav, C:\Windows\tile2\, 12:34:46,12\02\2010] floortile2 [205, tile2_205.wav, C:\Windows\tile2\, 12:34:48,12\02\2010] floortile2 [302, tile2_302.wav, C:\Windows\tile2\, 12:34:48,12\03\2010]

floortile3 [121, tile3_121.wav, C:\Windows\tile3\, 12:34:40, 12\01\2010] floortile3 [222, tile3_222.wav, C:\Windows\tile3\, 12:34:40, 12\02\2010] floortile3 [323, tile3_323.wav, C:\Windows\tile3\, 12:34:40, 12\03\2010] floortile3 [124, tile3_124.wav, C:\Windows\tile3\, 12:34:42, 12\01\2010] floortile3 [225, tile3_225.wav, C:\Windows\tile3\, 12:34:42, 12\02\2010] floortile3 [326, tile4_326.wav, C:\Windows\tile3\, 12:34:42, 12\03\2010] floortile3 [127, tile3_127.wav, C:\Windows\tile3\, 12:34:44, 12\01\2010] floortile3 [228, tile3_228.wav, C:\Windows\tile3\, 12:34:44, 12\02\2010] floortile3 [329, tile3_329.wav, C:\Windows\tile3\, 12:34:44, 12\03\2010] floortile3 [130, tile3_130.wav, C:\Windows\tile3\, 12:34:46, 12\01\2010] floortile3 [231, tile3_231.wav, C:\Windows\tile3\, 12:34:46, 12\02\2010] floortile3 [132, tile3_132.wav, C:\Windows\tile3\, 12:34:48, 12\01\2010] floortile3 [232, tile3_229.wav, C:\Windows\tile3\, 12:34:48, 12\02\2010]

floortile4 [90, tile4_90.wav, C:\Windows\tile4\, 12:34:40,12\01\2010] floortile4 [101, tile4_101.wav, C:\Windows\tile4\, 12:34:40,12\02\2010] floortile4 [202, tile4_202.wav, C:\Windows\tile4\, 12:34:40,12\03\2010] floortile4 [91, tile4_91.wav, C:\Windows\tile4\, 12:34:42,12\01\2010] floortile4 [102, tile4_102.wav, C:\Windows\tile4\, 12:34:42,12\02\2010] floortile4 [92, tile4_92.wav, C:\Windows\tile4\, 12:34:44,12\01\2010] floortile4 [103, tile4_103.wav, C:\Windows\tile4\, 12:34:44,12\02\2010] floortile4 [203, tile4_203.wav, C:\Windows\tile4\, 12:34:44,12\03\2010] floortile4 [93, tile4_93.wav, C:\Windows\tile4\, 12:34:46,12\01\2010] floortile4 [94, tile4_94.wav, C:\Windows\tile4\, 12:34:48,12\01\2010] floortile4 [104, tile4_104.wav, C:\Windows\tile4\, 12:34:48,12\02\2010] floortile4 [204, tile4_204.wav, C:\Windows\tile4\, 12:34:48,12\03\2010]

floortile5 [102, tile5_102.wav, C:Windows\tile2\, 12:34:40,12\01\2010] floortile5 [203, tile5_203.wav, C:Windows\tile2\, 12:34:40,12\02\2010] floortile5 [103, tile5_103.wav, C:Windows\tile2\, 12:34:42,12\01\2010] floortile5 [204, tile5_204.wav, C:Windows\tile2\, 12:34:42,12\02\2010] floortile5 [104, tile5_104.wav, C:Windows\tile2\, 12:34:44,12\01\2010] floortile5 [205, tile5_205.wav, C:Windows\tile2\, 12:34:44,12\02\2010] floortile5 [308, tile5_308.wav, C:Windows\tile2\, 12:34:44,12\03\2010] floortile5 [105, tile5_105.wav, C:Windows\tile2\, 12:34:46,12\01\2010] floortile5 [206, tile5_206.wav, C:Windows\tile2\, 12:34:46,12\02\2010] floortile5 [106, tile5_106.wav, C:Windows\tile2\, 12:34:48,12\01\2010] floortile5 [207, tile5_207.wav, C:Windows\tile2\, 12:34:48,12\02\2010] floortile5 [309, tile5_309.wav, C:Windows\tile2\, 12:34:48,12\03\2010]

TILE1 // TILE 2 search query 12:34:38 //floortile1 = 1 && 12:34:40//floortile 2 = 1 return dates INSERT into PATTERNS the calculated pattern for the time sequence PATTERNS [0, 12/01/2010, 12:34:38, 111111000000] floortile 7

floortile 1

floortile 8

floortile 2

floortile 9

floortile 3

floortile 10

floortile 11

floortile 12

floortile 4

floortile 5

floortile 6

PATTERNS [1, 12/02/2010, 12:34:38, 111111000000] floortile 7

floortile 1

floortile 8

floortile 2

PATTERNSCOUNT [0, 12:34:38, 111111000000, 2] most popular path @ 12:34:38 PATTERNSCOUNT [0, 12:34:38, 111110000011, 1]

floortile 9

floortile 3

floortile 10

floortile 11

floortile 12

floortile 4

floortile 5

floortile 6

PATTERNS [2, 12/03/2010, 12:34:38, 111110000011]

floortile 7

floortile 1

floortile 8

floortile 2

floortile 9

floortile 3

floortile 10

floortile 11

floortile 12

floortile 4

floortile 5

floortile 6

playback random sample from 2:34:40 from tile3

playback random sample from 2:34:42 from tile4

playback random sample from 2:34:44 from tile5

playback random sample from 2:34:46 from tile6

In this me-based pa ern analysis of footsteps, footsteps are collected and retraced sequen ally based on me it was collected developing a specific pa ern for that space at a certain me and day. Not only can pa ern be iden fied but direc on of the pa ern could be stored as well through loca on of the les and me sequence of the previous and next ac vated le . The footsteps would then playback when the le is retriggered on another day and the same me. As a le is triggered, it would playback most frequent pa ern for that specific me of day crea ng an audio wayfinding system. This system would trigger the next five steps in sequence.

The dierent color schemes show the dierent emergent paths within the defined physical path, and the color gradients describe the frequency in which those paths are taken.

PATTERN ANALYSIS_SCALAR SHIFT

1 1 0 0 0 0

1 1 0 0 0 0

1 1 0 0 0 0

1 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 1 0 0 0 0

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 1 1

0 0 0 0 1 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

0 0 0 0 0 1

PLAYBACK

end me 3:00:24

start me 3:00:02

start me 3:00:02

end me 3:00:34

end me 3:00:34

start me 3:00:02

end me 3:00:30

start me 3:00:02

start me 3:00:02

end me 3:00:34

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

1 0 0 0 0 0

PATTERNS [0, 12/01/2010, 12:34:38, 0 0 1 1 0 0

0 0 1 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 1 1 0 0

0 0 0 1 0 0

0 0 1 1 0 0

0 0 1 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 1 0 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 0

0 0 0 0 1 1

0 0 0 0 1 1

0 0 0 0 1 1

0 0 0 0 1 1

0 0 0 0 1 1

0 0 0 0 1 1

0 0 0 0 1 1

2 1 1 1 1 0

2 1 1 1 1 0

2 1 0 1 1 0

2 1 0 1 1 0

1 1 1 1 1 0

1 1 0 1 1 0

1 1 1 1 1 0

1 1 1 1 1 0

1 1 0 1 1 0

1 1 0 1 1 0

1 1 0 1 1 0

1 1 0 1 1 2

1 1 0 1 1 2

1 1 0 1 1 2

1 1 0 1 1 2

1 1 0 1 1 2

1 1 0 1 1 2

1 1 0 1 1 2]

PATTERNS [0, 12/01/2010, 12:34:38, 1 1 2 1 2 2 1 1 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0

2 0 0 0 1 0 1 1 0

2 0 0 0 1 1 1 0 0

2 0 0 0 1 1 0 1 0

2 0 0 0 1 0 1 1 0

start me 3:00:02

2 1 0 0 0 1 1 0 0

2 1 0 0 1 1 0 1 0

1 1 0 0 1 1 1 1 0

1 1 0 0 1 1 1 0 0

1 1 1 0 1 0 0 1 0

1 1 1 0 0 1 1 1 0

1 1 0 0 1 1 1 0 0

0 0 1 0 1 1 0 1 0

1 1 1 0 0 1 1 1 0

1 1 1 0 1 0 1 0 0

1 1 1 0 1 1 0 1 0

1 1 0 0 1 1 1 1 0

0 0 1 0 1 1 1 1 0

1 1 1 0 0 1 0 1 0

1 1 1 0 1 1 1 0 0

1 1 1 0 1 1 1 1 0

1 1 0 0 0 1 2 1 0

0 0 1 0 1 1 2 0 0

1 1 1 0 1 1 1 1 0

1 1 1 0 1 1 2 0 0

1 1 1 0 1 1 2 1 0

1 1 0 0 0 0 2 1 0

0 0 1 0 1 1 2 0 0

1 1 1 0 1 0 2 1 0

1 1 1 0 0 1 2 1 0

1 0 1 0 1 1 2 1 0

1 1 0 0 1 0 2 1 0

0 1 1 0 0 0 2 0 0

1 1 1 0 1 0 2 1 0

1 1 1 0 0 0 1 1 0

1 0 1 0 1 0 2 2 0

1 1 0 0 1 0 2 2 0]

end me 3:00:34

end me 3:00:34

start me 3:00:02

end me 3:00:30

start me 3:00:02

start me 3:00:02

end me 3:00:34

start me 3:00:02

MOVEMENT ANALYSIS_beat maker Footstep = 1 beat

# of beats/ me = number of footsteps within a meframe

Analysizing the footstep as a beat would iden fy the flow and density of the space at a certain me. When used with the pa ern analysis system, beats could be sequence spacially according to pa erns collected. There would be no playback of actual footsteps, but it becomes the base track for other musical inputs.

sound clip

sound clip

sound clip

sta onary_not ac ve le