G-LI N E AA VISITING SCHOOL | SEOUL | YONSEI UNIVERSITY
UNIT 2 | HYPER AGENCY ALEKSANDAR BURSAC & SOOMEEN HAHM DAVIS WATTS WEI WU LEE DONGHYEON LEE SEUNG HOON DIANA ONG
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 1 /73
T H E SI T E | GA R I BO N G-DONG
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 2 /73
THE SITE GARIBONG-DONG
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
G A R I B O N G D O N G AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 3 /73
THE SITE | GARIBONG - DONG
DIVERSE COMMUNITY
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
VIBRANT NEIGHBOURHOOD
SECURITY
SELF SUFFICIENT
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 4 /73
THE ISSUE
K O R E A N
C H I N E S E Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 5 /73
S TR A T E GY | CON CEPT
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 6 /73
STRATEGY
DATA COLLECTION
DATA TRANSLATION
ZIP LINE DESIGN Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
DATA COMPARISON
DESIGN PROPOSAL AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 7 /73
CONCEPT
GARIBONG-DONG ZIP LINE FUN
MEETING POINT
PROMOTES INTEGRATION
DIFFERENT VIEW OF GARIBONG-DONG Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 8 /73
F R OM DA T A T O DE SI GN
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 9 /73
DATA COLLECTION | PRIMARY DATA This map shows the points where data were collected by all group members. The denser the dots, the more data collected at that particular area. This also corresponds to the accuracy of data analysis of Garibong-dong.
Types of data collected Building use Building levels Languages Signages
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 10 /73
M A P P I N G T HE R O UT E ST A R T P OI N T S
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 11 /73
DETERMINING START POINTS >> POINTS OF GATHERING
START POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 12 /73
G-LINE | DETERMINING START POINTS STEP 1: FILTERING DATA DATA INPUT: LANGUAGES Data of languages Languages spoken at spoken different at areas different areareas analysed are to determine separated outplaces to be analysed. where Koreans and Chinese gathered separately. This map shows the points where data were collected. That means every points on this map contains unique information about buildings and people. The denser the dots, the more data collected at that particular area. This also corresponds to the accuracy of data analysis of Garibong-dong.
Types of data collected Building use Building levels Languages Signages
0
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 13 /73
G-LINE | DETERMINING START POINTS STEP 1: FILTERING DATA DATA INPUT: LANGUAGES Languages spoken at different areas are analysed to determine places where Koreans and Chinese gathered separately.
And then we highlighted only the points which contains information about people’s language usage. Every point means that we observed a person using a language. We recorded down which language they used and where.
Types of data collected Building use Building levels Languages Signages
Languages Korean Chinese Chinese Korean Korean Chinese Chinese+Korean 0
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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DETERMINING START POINTS STEP 1: DATA COLLECTION DATA INPUT : LANGUAGES A colour gradient diagram is constructed to help visualise thte zones where Chinese and Korean populations gather. Next step is to paint colors according to the language usage on the map. As you know, the points means that there was a usage of a certain language at that point, but it doesn’t mean there’s no usage outside the area with points. So we need to assume the average usage of a language on area which doesn’t have sufficient amount of data. So basically the goal is to make something like this but in data driven way, which we believe to be more accurate.
Types of data collected Building use Building levels Languages Signages
Languages Korean Chinese Chinese Korean Korean Chinese Chinese+Korean
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 15 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES - KOREAN 1. In order to make a map which shows the usage of Korean, we first draw only point for Korean.
Types of data collected Building use Building levels Languages Signages
Languages Korean Chinese Chinese Korean
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 16 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES 1. In order to make a map which shows the usage of Korean, we first draw only point for Korean. 2. And because we cannot actually tell every resident’s language in every houses, we should sample the whole site by putting grids on it.
Types of data collected Building use Building levels Languages Signages
Languages Korean Chinese Chinese Korean
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 17 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES 1. In order to make a map which shows the usage of Korean, we first draw only point for Korean. 2. And because we cannot actually tell every resident’s language in every houses, we should sample the whole site by putting grids on it. 3. Next, we calculated the distance from each intersection to the nearest data point. We assume that if the distance from one intersection to the nearest point is long, the frequency of use of the language at that intersection is small. In other words, if the distance is long, the language usage is small and if the distance is close, the usage is high. d=36.861 d=7.974
d=21.761
d=18.328 d=20.565 d=47.065 d=75.580 d=104.474
Density
d=7.974
Distance
d=18.328
d=20.565 d=21.761 d=36.861
d=47.065 d=75.580 d=104.474
1/(distance to closet data) ? Density of the language
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 18 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES 1. In order to make a map which shows the usage of Korean, we first draw only point for Korean. 2. And because we cannot actually tell every resident’s language in every houses, we should sample the whole site by putting grids on it. 3. Next, we calculated the distance from each intersection to the nearest data point. We assume that if the distance from one intersection to the nearest point is long, the frequency of use of the language at that intersection is small. In other words, if the distance is long, the language usage is small and if the distance is close, the usage is high. 4. Next, we visualized the Korean usage of the whole garbong bong by changing the radius of the sphere according to the calculated result.
RARE
FREQUENT
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 19 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES We wanted to express this processed data in various ways so that the data could be seen at a glance. So we put one more dimension on a two-dimensional map to show the usage of the language. Various methods have been tried and we have found that smooth surfaces are best suited for representing this kind of data. This map simply shows how often the people use Korean in this area, and now we need to figure out where it’s most efficient for Korean users to gather.
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 20 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES We wanted to express this processed data in various ways so that the data could be seen at a glance. So we put one more dimension on a two-dimensional map to show the usage of the language. Various methods have been tried and we have found that smooth surfaces are best suited for representing this kind of data. This map simply shows how often the people use Korean in this area, and now we need to figure out where it’s most efficient for Korean users to gather.
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 21 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES We wanted to express this processed data in various ways so that the data could be seen at a glance. So we put one more dimension on a two-dimensional map to show the usage of the language. Various methods have been tried and we have found that smooth surfaces are best suited for representing this kind of data. This map simply shows how often the people use Korean in this area, and now we need to figure out where it’s most efficient for Korean users to gather.
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 22 /73
STEP 2: DATA TRANSLATION
This video shows how we designed it for the initial concept (figuring out where people from the same language can gather most easily). Let's consider this surface as a mountain range. In this video, each particle automatically moves toward the nearest high ground at its starting point, as valley water flows from the mountain range to the lowest point.
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 23 /73
STEP 2: DATA TRANSLATION
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 24 /73
STEP 2: DATA TRANSLATION This is seen from the top of each particle path taken from the previous process. You can see that the point we are looking for is narrowed down to a much narrower area than the somewhat rough and uniform map we saw at the beginning.
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 25 /73
STEP 2: DATA TRANSLATION Then we can repeat the process in order to make another map for Chinese.
Types of data collected Building use Building levels Languages Signages
Languages Korean Chinese Chinese Korean
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 26 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES 1. Korean speaking data are culled out. 2. A grid is laid on top of Garibong.
Types of data collected Building use Building levels Languages Signages
Languages Korean Chinese Chinese Korean
0
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 27 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES 1. Korean speaking data are culled out. 2. A grid is laid on top of Garibong. 3. The grid is populated by circles.
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 28 /73
STEP 2: DATA TRANSLATION DATA INPUT : LANGUAGES 1. Korean speaking data are culled out. 2. A grid is laid on top of Garibong. 3. The grid is populated by circles. 4. The radius of the circles change according to its distance to the closest data point which contains information regarding language usage. The more closer and darker the circles, the more frequent the use of a certain language at that point.
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 29 /73
STEP 2: DATA TRANSLATION
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 30 /73
STEP 2: DATA TRANSLATION
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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STEP 2: DATA TRANSLATION
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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STEP 2: DATA TRANSLATION
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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STEP 2: DATA TRANSLATION
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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STEP 3: DATA COMPARISON & OVERLAY Two different maps for Korean and Chinese
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 35 /73
STEP 3: DATA COMPARISON & OVERLAY We can overlay and compare two language’s usage pattern.
Korean Chinese 0
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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STEP 3: DATA COMPARISON & OVERLAY Using this trajectory of particles, specific narrow area to gather people can be specified.
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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STEP 4: BUILDING SELECTION Next, we can choose buildings which has potential to be used as a station of zipline, which means it has enough heights and good reachability from roadside.
Korean Chinese
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 38 /73
STEP 4: BUILDING SELECTION | STARTING POINTS And then finally we select buildings which will be used as starting point, they should be close from the gathering points that we have found.
Korean Chinese
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 39 /73
M A P P I N G T HE R O UT E END POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 40 /73
END POINTS >> PUBLIC SPACES
END POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 41 /73
END POINTS >> PUBLIC SPACES
GATHERING SPOTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 42 /73
DETERMINING END POINTS - PUBLIC SPACES COMPARING CHINESE & KOREAN MAPS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 43 /73
Korean Chinese
DETERMINING END POINTS STEP 1: COMPARING DATA
0
signages retrieved from Korean online map (NAVER)
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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signages retrieved from Chinese online map (Baidu)
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STEP 2: FINDING THE OPTIMAL GATHERING SPOTS Selection criteria: The intersecfion between Korean and Chinese areas of information.
Korean Chinese Intersection (potential public spaces) Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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STEP 2: FINDING THE OPTIMAL GATHERING SPOTS Selection criteria: The intersecfion between Korean and Chinese areas of information.
Intersection (potential public spaces)
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 46 /73
STEP 2: FINDING THE OPTIMAL GATHERING SPOTS
Selection criteria: Suitable public spaces The optimal gathering spot/public space as end points are determined according to convenience and location of the building and the existing of current empty space.
Intersection (potential public spaces)
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 47 /73
STEP 3: MAPPING THE ROUTE
Selection criteria: STARTING points to ENDING points The routes for the ziplines are created by connecting the start points (buildings) to the end points (public spaces).
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 48 /73
G-LINE ROUTE| AXONOMETRIC VIEW
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 49 /73
T E CHN I CA L D E SI GN
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 50 /73
ZIPLINES | PRECEDENTS & IDEAS
http://www.wanderbeforewhat.com/five-things-las-vegas/
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
http://voodoozipline.com/hands-up-4/
https://www.youtube.com/watch?v=I5KR4GNhqW4
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 51 /73
ZIPLINES | HARNESS TYPOLOGY
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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G-LINE STARTING POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 53 /73
G-LINE ENDING POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 54 /73
CALCULATING THE SLOPE
CALCULATED START/END POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
MEASURE HORIZONTAL DISTANCE
IDEAL INCLINATION NEEDED
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 55 /73
CALCULATING THE SLOPE
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
CALCULATE IDEAL HEIGHT
CREATE HEIGHT GUIDELINE
CREATE REALIZABLE HEIGHT
CREATE PATH AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 56 /73
CALCULATING THE SLOPE
Inclination
FINAL ZIPLINE ROUTE 0.02 Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
0.16
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CALCULATING THE SLOPE
Starting Point
Ending Point
Starting Point
Ending Point
height height
length 100*height/length = inclination (%) length 100*height/length = inclination (%)
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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CALCULATING THE SLOPE
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 59 /73
TYPICAL G-LINE STATION
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 60 /73
DIFFERENT TYPES OF STATIONS & EXIT POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 61 /73
NIGHT VIEW OF STATIONS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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G-LINE STARTING POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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G-LINE ENDING POINTS
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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VI SUA LI SA T I O N
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
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Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 67 /73
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 68 /73
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 69 /73
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 70 /73
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 71 /73
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 72 /73
T HA N K Y OU!
Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG
AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 73 /73