AAVS Seoul 2017_Unit 2

Mapping as Design

AAVS Seo ul 201 7 Social Algorithms 5.0 | Yonsei University AA VS Seoul 2017

Unit 2 - Aleksandar BURSAC & Soomeen HAHM

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Mapping as Design AAVS Seoul 2017

Unit 2 - Aleksandar BURSAC & Soomeen HAHM

Soomeen Hahm

http: //soomeenhahm .com/ Flat 38, Vesage Court, 8a Leather Ln, London, UK, EC1N 7RE +44.75.9529.0322 s.hahm@soomeenhahm.com

PAPER STRUCTURE

The AAVS Seoul of 2017 WORKSHOP INTRODUCTION

G-Line

02.1 Site Analysis and and strategy

02.2 Data collection logic

02.3 Mapping the routes

02.3.1. Starting points

02.3.2. Ending points

02.4 Slope and speed analysis

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01.1 AAVS Seoul 2017 - Social Algorithms 01.2 Unit 2 - Mapping as design 01.3 Tutors

DATA HARVESTING

DATA INPUT

1. site visit

1. OSM / Open Street Map

2. research assigned area

> input information carefully

3. collect data

> amend existing information

> building height

> enrich existing information

> building occupancy/demographics > road width/use/type > road width/use/type

02.5. G-line Station design

Groove top

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03.1. Data harvesting 03.2. Site analysis 03.3. Rooftop height sorting 03.4 Users daily spacial shift 03.5 Rooftop design

CREDITS

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM

> keep the structure uniform

3

4

5

output

READ DATA

GENERATE MAPS

RELATE MxD

DESIGN PROPOSAL

1. ELK/ Grasshopper Plugin

1. determine criteria

1. create evaluation criteria

1. analyse the plan

> sift through information

3. pixelize map

3. generate organisational schema

> particularly interesting juncture

> analyse output and look for errors

> assign closest value to each square

> export OSM file from OpenStreetMap

> feed file into Grasshopper through ELK > geographically relate data

2. select relevant data > divide the location into squares

> use distributed resulting data to create maps

> use data per square to deform, transform, replace, shuffle etc. > cross reference data to create new criteria

2. relate criteria to design decision EXAMPLE

> set up existing pedestrian network

2. select characteristic area > most used passageway

3. develop design proposal

> generate new possible passageways > analyse newly created network

> relate pathway length/width/incline to a typology ramp/ stair/elevator

> generate an urban plan for the district’s new infrastructure

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

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AA VISITING SCHOOL SEOUL 2017 Social algorithms

The AA Visiting School Seoul is in the 5th year of the theme Social Algorithms. The course under this topic is to investigate urban issues from social and algorithmic point of view in order to open up an interesting discussion, exchange ideas, knowledge and methodology between different design approaches.

The course offers a great opportunity for those who are interested in the topic of urban, city, architecture and design research who want to be involved in the discussion of experimental design thinking, innovative way of collaboration between design researches.

As the course being held in the central area of Seoul – one of the most active megacities in the world – we will be looking at Seoul closely to be our test bed during the 9 days. Seoul, though one of the largest cities in the world, is at the same time modern and historical.

This comes as a product of overlap between the tradition and rapid urban development in the 2nd half of 20th century. This interesting mixture and contrast is what gives this city its unique characteristics.

As consequence, although technologically developed and highly urbanised, Seoul’s urban fabric and life-style heavily rely on human scale – call it the software of the urban – as its main shaping force. It is very interesting to examine how the software – human, community & social aspects of everyday life, rather than hardware of the city – built environment itself, drives the evolution of Seoul to such high degree.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

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MAPPING AS DESIGN Unit 2

1. Agenda

2. Design Methodology & Tools

The aim of the workshop is to create a better understanding of where computational design can be situated with regards to urban planning and how advanced software tools can be used to shape and direct the designers thinking in the early stages of site evaluation.

Students will be using OSM files generated through OpenStreetMaps.

The students will be expected to adopt a data driven approach to design. They will engage social problems of the site by collecting, encoding and analysing site-specific data. Through this process the students will learn how to instrumentalize data graphically in order to implicitly extract new knowledge about the site through information layering.

The available information and subsequent discoveries made while writing/reading the graphic representation of data will give rise to a zoning/planning schema that will inform the design of the proposal itself.

Since the site itself does not contain a wealth of information in its current OSM package, the first goal is to have students enrich the existing database. This will be done by researching, collecting and harvesting data about the site through surveys, existing case studies, and interviews with locals.

The careful selection of which data is important is directly connected to the project assignment (ie. additional information about pedestrian and motorway information, public space and other points of interest) which will be essential to generate a relevant database.

The information will be encoded into generated maps that will visually represent the OSM data through Grasshopper.

In this assignment the students will be encouraged to not only passively display the data in a colorcoded map, but also find new and exciting ways of graphically representing the available information in relation to a specific problem/observation they have made about the site.

This can be done by cross-referencing and relating previously isolated data and observing the relationship they may have. Emerging consistencies as well as correlation and/or causation of data should give rise to a new understanding of the how and why the locale functions the way it does.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM

3. Learning Outcome & Deliverables In practical terms the goal of the workshop is to create a design proposal through careful data manipulation and relating that data to specific design decisions.

A possible avenue that can be explored is a proposal for a new circulation infrastructure within the district that will increase overall connectivity of the walkable space on the site between various points that are deemed of interest (restaurants, shopping areas, tourist attractions, public service localities, etc.).

Depending on the students’ interests and results in the earlier stages of the workshop this can be reoriented to a different possible proposal based on the output created in the 4th assignment of the mapping stage.

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

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TUTORS

SoomeenHahm Design is a London based design studio started its practice since 2011.

The practice runs based on a design research which search for coherent local behaviors generating highly affected global outcome exploring the relationship between design process and the final product.

The practice is interested in conducting parametric and algorithmic design research within multi-scale design agendas and seeks for design products actively engage and change in different time and environment which is highly controlled within intelligence of design, within systemic research through computation, robotic, structural engineering, sustainable engineering and parametric control systems.

http: //soomeenhahm .com/ Flat 38, Vesage Court, 8a Leather Ln, London, UK, EC1N 7RE

+44.75.9529.0322

s.hahm@soomeenhahm.com

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM

Soomeen Hahm is the founder of the SoomeenHahm Design, an architectural designer and researcher based in London teaching in various universities and research programs.

She specialises in coding, digital simulations and 3D modelling in various software platforms. She has taught and lectured at numerous institutions in UK and internationally, teaching studios, workshops and short courses focusing on computational design.

She tries to tackle the issue of algorithmic design paradigm in architecture across multiple scales, through collaboration and exchange of knowledge with other experts from the field.

She is currently teaching at the Bartlett UCL and the Architectural Association while running her own practice.

Aleksandar Bursac is from Belgrade where he did his BArch and MArch with the Faculty of Architecture at the University of Belgrade.

He relocated to London in 2014 to pursue a degree at the AADRL which shaped his current interests and goals. These include data driven design, advanced simulation techniques and the exploration into the effects the current tecnhological paradigm has on design, architecture and everyday life. His work looks into the intersection and interaction between technology and design and the effects their development has on the both the users and the producers of space.

He currently works at Zaha Hadid Architects in their London office.

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

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Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Un Con

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612826.0, 179780.0, 27339.815035 456092.740835, 175398.875175, 40144.896334

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Re RD15_area_333m2 t Re Re t f la 0flat fla Un Re0 0 Re Co 641445.0, 231362.0, Re 28723.509777 543228.346816, 284581.327885, 50049.043909 Con Un 641445.0, 231362.0, 28723.509777 Re Re Re Re Un0 flat RB30_area_489m2 Re Re Re Re Re Re Con37 RA1_area_561m2 Un Re 543228.346816, 284581.327885, 47049.043909 0m RC33_area_14m2 Re Re RD14_area_161m2 Re Re 1Re Re 14 Re Re 456092.740835, 175398.875175, 37144.896334 m Re Re Re Re Re RC0_area_21488m2 641445.0, 231362.0,Re28723.509777 Con Re Re Re Re RB18_area_899m2 Re Re Con Re Re RD12_area_881m2 641445.0, 231362.0, 28723.509777 Con

Sch

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335m

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456092.740835, 175398.875175,Con 34144.896334

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한글 한글 Con 한글 RA5_area_102m2 한글 Con 한글 Sch 한글 Con 한글 Sch

文

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Un

中文Eng 한글 Un

한글 n 中文Re 한글 Re 글RC48_area_321m2 Re Re 한글Sch 한글 한글 on 中文Sch 한글 中文 Sch 한글 Re 한글 Con 文 한글 Con Re Con

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543228.346816, 284581.327885, 41049.043909 Re

t 0flat

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543228.346816, 284581.327885, 38049.043909

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0

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한글41474.391637 中文 Con 82484.09505, 344904.660804, Re Re

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Re m2 282484.09505, 344904.660804, 41474.391637 Re Re

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Re 502423.616408, 52040.497125 524327.72576,

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s

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551049.0, Re 405554.0, 26876.512226 Re Re RC36_area_340m2 Re Re Re Con RC11_area_1939m2 Un Re Con 551049.0, 405554.0, 26876.512226 Re Re Con Re Re

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Con 26876.512226 RB24_area_230m2 551049.0, 405554.0, Re Con RD3_area_178m2 RC8_area_287m2

m 276Re

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551049.0, 405554.0, 26876.512226 Re Re RC40_area_1812m2 551049.0, 405554.0, 26876.512226 Re Re Re Re RD28_area_271m2 Re

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904.660804,Re38474.391637 Re

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524327.72576, 502423.616408, 52040.497125 Re

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한글 中文

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ra m p

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104m

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524327.72576, 502423.616408, RD10_area_182m2 Re Re 52040.497125 Re Re

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Re 524327.72576, 37040.497125 465010.0,502423.616408, 522975.0, 27919.73191 Re Re

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ra

0

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RD5_area_1167m2

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522975.0, 27919.73191

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t 0 fla

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mp

한글 Con 524327.72576, Re 502423.616408, 40040.497125 Re

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465010.0, 522975.0, 27919.73191 ReRe Re

RC35_area_1028m2 Re Re

Re

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0 flat

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한글 中文

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25

sta irs

2

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25s tair s

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a_145m2

25 st airs

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0

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t fla

한글 한글 中文 Re

0fla t

2 2

0

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0fla

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0fla0 flta t

3

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t

한글 Con 한글 Con 한글Con Con Un 한글 한글 Con 한글RC3_area_280m2 RE1_area_163m2 RC18_area_130m2 한글 Con Re 한글 Re Con Con Re 한글 Con Un RE6_area_257m2 465010.0, 522975.0, 27919.73191 한글 Con Con Ps 465010.0, 522975.0, 27919.73191 Re 한글 한글 Con Con Re Re 8 Con 한글 ra Re

Re

2

t

0 fla

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t

RC4_area_401m2

0flat Re

한글 RD6_area_3125m2 flat0flat 0 flat 0Con

Re

Re ReRe

한글

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Re0 flat

0 fla t

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한글 한글

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t

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3

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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

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fla t

t

G-Line

AAVS Seoul 2017 Unit 2 Hyper Agency Aleksandar Bursac & Soomeen HAHM Davis WATTS, Wei WU, Lee DONGHYEON, Lee SEUNG HOON, Diana ONG

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

15

G-LINE

Site Analysis - Garibong Dong Garibong Dong is a neighborhood (a dong) of the Guro district, in the southwest of Seoul, South Korea. Also known as “China town”, this area is home to many ethnic Korean with Chinese, known as the Joseonjok (조선족).

Joseonjok are descendant of Koreans, living in Yanbian Korean Autonomous prefecture in China.

A part of this community moved to this northeast district of China to escape natural disasters between 1945 and 1960. But most of Joseonjok ancesters moved to China for political reasons while being at war with Japan.

They are not considered to be Korean, even though they share the same ethnic ancestors to Koreans. Mainly this is because they were chinese citizens when South Korea became a state known as the Republic of Korea.

Moreover, since Korea has opened its door for immigration, Joseonjok have immigrated both legally and illegaly, and were said to be involved in criminal activites. It reinforces the stereotypes of Joseonjok having a bad influence toward the Korean society.

The Garibong Dong area is located next to what was previously the Guro industrial complex. Built in 1967, Guro industrial complex was the first industrial complex of the country. It attracted immigrants from China, and especially joseonjok, to work as blue collars. However, this complex has quickly mutated into a futuristic high tech hub, leaving the joseonjok unemployed but still leaving in their small worker housing in Garibong Dong.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

K

O R E

A

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E “After the blue-collar workers and democratization protesters who initiated the country’s labor movement in the area left to pursue better jobs, jjokbang, cage-like rooms built for the factory workers, became filled with migrant workers, mostly from China, looking for cheap rent and daily manual work. As of December last year, about 15,000 ethnic Koreans from China had registered at the district office, followed by 637 Chinese, 232 Taiwanese, 208 Malaysians and 153 Filipinos, according to district office data. If illegal aliens are added, the office said, there are about 20,000 foreigners in the area.”

Joong-Ang Ilbo

In Garibong Dong, the ethnic Korean from China are very noticeable, there are street signs written in chinese character, chinese restaurant and hotels, and a visual fracture between the rest of Seoul’s district. Most of Joseonjok speaks mandarin chinese as their main language, so it creates even a different atmosphere, in which Korean people don’t quite blend. These differences makes it very difficult for the population of Garibong Dong to be united as one society.

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

17

G-LINE

Strategy - Garibong Dong Zip Line A zipline is a mean of transportation which uses a pulley suspended on a cable of stainless steel.

It uses only the gravitational force thanks to a slope design.

This mean of transportation exists in alpine in region of the world to cross rivers, or valley. It is also used as an entertainement in outdoor adventure camps, and is very popular as a tourist attraction. It offers a fun and safe ride from any high point, to the ground, and a view toward the entire section visited.

Using these aspects of ziplines, the GaribongDong Line (G-Line) will serve as a fun way to bring together the Korean and Chinese community, as well as being energy efficient, and allowing a new perception of the area.

Through data harvesting, the student will be improve the OpenStreetMap data base, and use it as a design methodology to generate the zip-line project.

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

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G-LINE

From data to design - 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.

Different category of information were harvested, in this project it was important to collect information about the cultural fracture of Garibong Dong. Indeed, language written and spoken were mapped as well as height and usage of each building.

Types of data collected Building use Building levels Languages Signages

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

0

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100

150

200 m

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

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G-LINE

Mapping the routes - Starting point Conceptual diagram As explained, the Joseonkoks are seen as an issue for they don’t live together with the Korean population.

Therefore it is needed for these populations to blend more into the korean culture and local populations.

The zip line project will ensure that the chinese and korean inhabitants of Garibong Dong have the chance to meet each other. By doing so it will bring the populations from a starting point, an important urban node of this population, to a shared public space.

Achieving its final goal, mixing the difference into a fun and innovative urban blend, the zip-line will be an important object in the urban tissue.

In order to find an effective proposal for the zipline, generative algorithms will be used to define the nodes of the city for both Korean and Chinese inhabitants.

Mapping the cultural landmarks of Garibong Dong From the data harvested in the field trip to Garibong Dong, it was very much noticeable that many chinese commercial buildings use chinese character text for their street sign, while the korean shop are using Korean writting. Moreover, users from the Joseonjok community are speaking chinese mandarin, and Korean community users speaks in Korean language.

Through an investigation of the area, those cultural appropriations were mapped onto the buildings by using a cloud of point. The type of language spoken or written is a qualitative data, it is rarely mapped as it is an informal aspect of the city. However, with this study the students aimed to reveal the invisible cultural pattern of Garibong Dong neighborhood.

Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

0

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AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

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G-LINE

Mapping the routes - Starting point DATA INPUT : LANGUAGES A colour gradient diagram is constructed to help visualise the zones where Chinese and Korean populations gather.

To construct this diagram the algorithm will calculate the average for every point of the studied zone, between the previous point shown for the data language.

It will help to assume the average and compute the estimation of language spoken and written in area for which there was a lack of data.

This simulation will help completing the data driven design method used to find cultural nodes in the Garibong Dong area.

Types of data collected

Building use Building levels Languages Signages

Languages

Korean Chinese Chinese Korean

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

Korean Chinese Chinese+Korean

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100

150

200 m

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University

25

G-LINE

Data translation - Vizualization Korean and Chinese cultural landmarks are mapped with respectively blue and pink. The two sets of data are seperated in order to be evaluated seperately and compared.

The pattern of repartition for both communities spaces seems to be a little bit different, but their needs to be a better comprehension of those differences.

As to enhance and thus vizualize this urban cultural pattern, the data sets are projected onto a grid.

0

50

200 m

150

100

Then, we proceeded to do a density analysis, and to remap the dots into the grid, the larger and the more opaque the dot, the more the culture exists around this area.

This density mapping of cultural landmarks and appropriation showed that the two communities center of activities differ, and made it possible to identify those places.

0

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0

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0

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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 100

150

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d=36.86 1 d=7.974

200 m

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d=21.761 d=18.328 d=20.565 d=47.06 5 d=75.58 0 d=104.474

0

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200 m

d=18.328

Distance

100

Density

d=7.974

d=20.565 d=21.761 d=36.861

d=47.06 5 d=75.58 0 d=104.474

1/(distance to closet data) ? Density o f the languag e 0

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RARE

FREQUENT

AA VS Seoul 2017 Social Algorithms 5.0 | Yonsei University 0

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Data translation - Vizualization The density analysis then served to compute a simulation. The density grid was morphed into a mesh surface where the height of a point is function of his density value.

In this experimentation, the z coordinate of a point increase when the density value of this point is high. When the density value of a point approaches or is equal to zero, there is no deformation of the z coordinate.

As the time parameter increase, the factor of deformation increases as well. Meaning that the z coordinates is multiplied by a larger number. As long as the computer is running the simulation, the mesh surface morphs from a flat design to a rugged surface.

This logic aims to find the highest point that will be used in the project as starting station of the zip-line transportation.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

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G-LINE

Data translation - Vizualization The density data was remapped onto the meshed surface using spheres.

Afterward, using the rainflow analysis on the new topography generated by the algorithm, the cultural nodes for the ethnic Korean Chinese (joseonjok) and Korean community appeared with more accuracy.

Using techniques from landscape analysis and data management, the student were able to reveal some invisible aspect of the urban space that help to generate a data driven design.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

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G-LINE

Comparison of the Korean and Chinese cultural landmarks

This is a top view of each particle flow simulation at its peak.. It can be observed that the starting point, cultural landmark point is narrowed down to a much more accurate area than the rough and uniform mapping which triggered this project.

Comparing the two different simulation by a superposition of both results makes it clear to notice that even if the gathering point for each community are very close, they are different. The two community live in two different but similar neighborhood, they created a parallel system into the Garibong Dong area.

Thanks to this analysis the project will go further on by selecting the buildings which shows the most interest as gathering points for each culture.

These buildings will host the design of the G-Line stations.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

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G-LINE

Station selection

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G-LINE

Mapping the route - Ending point In Garibong Dong, chinese population and korean populations share the same district but they blend together as one mixed population.

Public life of the two population are distinct and they even have their own mapping system, using Baidu for the Chinese and Naver for the Korean.

Each community lives in its own public spaces and buildings, they tend to have different type of activities as well.

It was the primary goal of this project to create an opportunity for those two communities to meet and share moments, as to get to know each other and enrich themselves from this cultural exchange.

The zipline starts from the most important urban nodes and landmarks of each community, and has the ambition to lead them to public spaces that will be shared by both population.

Meaning that the ending points of the zipline will be some open public spaces gathering everyone in a shared area of Garibong Dong.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

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G-LINE

Mapping the route - Ending point The Garibong Dong zipline will bring people from each communities to shared public space.

But as for now, there are no such places in the neighborhood.

Ethnic korean chinese are using the web map called “Baidu” and the Korean people are using the web map called “Naver”. Those two maps show very different information about which public spaces (built or open) are important for each community.

The first step to find the ending point of the G-Line is to map those places. Second step is to do a cross analysis of this spatial data to find the overlay surface.

Finally, the arrival stations are chosen by selecting the closest optimal area or building next to these overlays.

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G-LINE

Arrival stations

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G-LINE

Slope and speed analysis

Unit 2 Mapping as design | 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

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G-LINE

Slope and speed analysis

Starting Point

Ending Point

height

length 100*height/length = inclination (%) Starting Point

Ending Point

height

length 100*height/length = inclination (%)

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G-LINE

Station design and urban implementation

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

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G-LINE

Render view

Unit 2 Mapping as design | Aleksandar Bursac & Soomeen HAHM G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

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Render view

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Render view

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G-line | Davis WATTS, Wei WU, Donghyeon LEE, Seung Hoon LEE, Diana ONG

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G-LINE - STUDENTS

Davis WATTS is currently a master’s degree student in architecture at the university of Melbourne, Australia.

Wei WU is a junior student in architecture major at the Guangdong University of technology, Guangzhou, China.

Donghyeon LEE is studying in the department of architecture of the college of engineering at Korea University, Seoul, Republic of Korea.

Seung Hoon LEE is currently a third year student

in architecture at the Korea University, Seoul, Korea.

Diana Sze Mei ONG is an undergraduate student at the university of Melbourne, Australia.

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Groove top AAVS Seoul 2017

Unit 2 Mapping as Design Aleksandar BURSAC & Soomeen HAHM Yuanyuan CHE, Hyunsung CHOI, Jesslyn HUMARDANI, Albertus MAGNUS YUDHISTIRA, Janghee LEE

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

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GROOVE TOP Data harvesting

Data Collected Spot

Low

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

High

Residential

Restaurant

Commercial

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GROOVE TOP Site Analysis

Garibong Dong area lacks public spaces where people can actually gather, sit together, and spend time as a community.

It goes as far as old people needing to seat on the pavement, and for children to stay on the street sides without proper leisure zone. It is our goal to take into consideration the needs of every different type of person in this site community.

However, in Chinese culture, it is common to use the public space of the street for dancing activities.

Not only do the chinese people dance in the street, but also they create a liveful atmosphere which brings joy to the community, as well as taking advantage of every public spaces they are given.

The groove top project will try to resolve this lack of public space and suggest some different solution for each group of people of this community to gather and share their interest.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM

Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

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GROOVE TOP

Roof top height sorting The first map is showing the height of building with a color gradient code. Height is given according not only to the building’s number of level, but considering the ground height as well.

Zero is the sea height.

It is noticeable that buildings height is arranged in a linear logic, which follow the natural slope of the terrain.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

In order to rearrange the building height and create public spaces on top the city, the buildings were sorted according to their height range.

It is meant to group the building with similar height and merge them into one bigger roof space.

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GROOVE TOP Merging analysis

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

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GROOVE TOP

Roof top height sorting

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

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GROOVE TOP

Roof top height sorting

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

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GROOVE TOP

Roof top height sorting

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

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GROOVE TOP

Morning Time 3am - 7am

Activity Time 7am - 3pm

User daily spacial shift

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STUDENT

VISITOR

WORKER Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

Coming home Time 3pm - 7pm

Dinner Time 7pm - 10pm

Night Time 10pm - 3am

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GROOVE TOP

Rooftop division according to time and user

Morning Time 3am - 7am

The designed rooftop are allocated to different typology of users according to the time period.

Using the previous analysis of spatial shifting of different categories of person in the community, the merged rooftop are given a suggestion toward which use could be optimal in different part of the days.

In doing so the Groove Top projects aims to gather people around different activities such as yoga, tai chi, hip hop dancing, and to open the gathering opportunities for citizen.

Coming home Time 3pm - 7pm

Dinner Time 7pm - 10pm

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

Activity Time 7am - 3pm

Night Time 10pm - 3am

Unit 2 Hyper Agency | Aleksandar Bursac & Soomeen HAHM GroupDancing | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

Space DIvision 22:00 PM - 3:00 AM

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AA VS Seoul 201

GROOVE TOP Ambiance view

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GROOVE TOP Design proposal

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM Groove top | Yuanyuan Che. Hyunsung Choi. Jesslyn Humardani. Albertus Magnus Yudhistira. Janghee Lee

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Yuanyuan CHE is a master degree student of

architecture design at the university of Korea, Seoul.

Hyunsung CHOI is currently working as an architect in Seoul, Korea.

Jesselyn HUMARDANI is an undergraduate architecture student Melbourne, Australia.

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Albertus Magnus YUDHISTIRA is studying

architecture as an undergraduate at the university of Melbourne, Australia.

Janghee LEE is a student in interior architecture at Yonsei university, Seoul, Korea.

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Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM

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CREDITS Director of AAVS Christopher PIERCE Director AAVS Seoul Jooeun SUNG Soomeen HAHM Tutor of AAVS Seoul Unit 2 Aleksandar BURSAC Soomeen HAHM Editor of this booklet Chloé HAENTJENS HERWEGH Students Yuanyuan CHE Hyunsung CHOI Jesselyn HUMARDANI Janghee LEE Donghyeon LEE Seung Hoon LEE Diana Sze Mei ONG Davis WATTS Wei WU Albertus Magnus YUDHISTIRA

REFERENCES Jarvis A., H.I. Reuter, A. Nelson, E. Guevara, 2008, Hole-filled seamless SRTM data V4, International, Centre for Tropical Agriculture (CIAT), available from http://srtm.csi.cgiar.org.

Reuter H.I, A. Nelson, A. Jarvis, 2007, An evaluation of void filling interpolation methods for SRTM data, International Journal of Geographic

SOFTWARE

Rhinoceros 3D. 5.0. November 2012, Robert McNeel & Associate Grasshopper. 1.0. April 2014, Robert McNeel & Associate Elk. 2.2.2. November 2017, Thimothy Logan

How to cite this publication:

Bursac A., Hahm S., Mapping as design, AAVS Seoul 2017, Architectural Association Visiting School 2017, Yonsei University, Seoul, Korea, 86p.

Unit 2 Mapping as design | Aleksandar BURSAC & Soomeen HAHM