Junqiu - Momo @twinscity - Tumblr Blog

Site

We choose this site because it sits on the edge of Rotterdam city centre, between housing, water, roads and public green space. It is not a fully defined park, but an open transitional urban space. This condition allows the project to test different forms of movement, occupation, viewing and interaction. The site therefore becomes a suitable field for recombining our order, conflict and language-based modules into a three-dimensional spatial system.

Google Earth

War2

The square tile was divided through a fixed geometric rule, using mainly 45-degree and 135-degree cuts, together with some vertical and horizontal divisions.

1 Vertical arrangement

2 Free arrangement

War

Choose 12 boxes

Segment

The cutting rule was based on diagonal divisions, vertical divisions, and a proportional structure. These fragments could then be rotated, mirrored, and recombined while still keeping a consistent angular order.

The first recomposition test used a random growth logic. Each fragment was placed one by one, starting from a seed piece. The fragments were allowed to rotate by 0, 90, 180, or 270 degrees, and they could also be mirrored, but they were not allowed to overlap.

The fragments are encouraged to align with the hidden vertical structure, but they can still extend beyond it. The left image shows the final composition, while the right image shows the hidden alignment logic. The blue vertical lines are not part of the final drawing. They are only used as an invisible framework to guide the placement of fragments.

Law

As the video moves through time, the corresponding state is activated on the drawing.

Choose

The selected tiles are a procedural base, from which routes, boundaries, waiting areas, and activity nodes can be extracted.

Meaning of layers

Combination： Law plan

High or low indicates the priority of this layer in spatial translation.

Site

Site: Het Park

1. Rotterdam’s largest national green heritage site (21 hectares), designed in 1852 by Zocher & Sons in the English landscape style, featuring undulating terrain and a varied natural environment.

2. The 1960 Floriade World Horticultural Exposition was held here – a historical precedent for temporary pavilions, which informs the narrative of constructing a ‘play pavilion’ for each of the subsequent 12 themes.

3. Situated adjacent to the River Maas, the dense urban fabric of the Scheepvaartkwartier, and the Euromast landmark.

4. The park itself is a place for leisure and play, in keeping with Huizinga’s concept of Homo Ludens.

Model Transformation

The 12 experimental models correspond to 12 themes

Concept Map

SOM of Art and Myth

From each of the 12 clusters, a representative God was selected. For each cluster, a theme was derived from the keywords and the deity’s narrative text.

For example, the keywords for cluster_04 are Fertility, abundance, sacrifice, negotiation and love, and the corresponding representative deities are Nemesis, Cupid, Eris and Metaneira.

Nemesis — Nemesis / Goddess of Retribution

Cupid — Cupid / God of Love

Eris — Eris / Goddess of Discord

Metaneira — Metaneira (a devotee of Demeter in Greek mythology)

Art fragments and metaphors were then matched to these themes. The corresponding lines of poetry containing metaphors are: ‘Cooling us with Shafts of Granite is like cooling thoughts.’ | ‘For his judgment, lo, a pudden!’ |'The thrush on the oaktop in the lane sang his last song, or last but one; and as he ended another had just begun. They knew no more than I the day was done.'

Cupid is surrounded by the corresponding artistic fragments: Cross, Decoration, Halo

Methodology

Definition of a metaphor (language → fusion operator for each step):

When a path spans from symbol A to B, locate the metaphor in the (literal → metaphor) pairings extracted from 1,000 poems that most closely match (A, B), and use its type selection operator: strong equivalence ‘A is B’ → intersect, attribute transfer → Union, weak association → Subtract.

2. Processing the theological database to convert the ‘Divine Character Cards CSV’ into structured data suitable for plotting narrative arcs.

Each deity → their multiple narrative actions → each action corresponds to a theme, intensity and text vector.

Read out each deity from the theological CSV, extract their narrative actions, break the actions down into individual ‘narrative beats’, and then assign the following to each beat:

theme category

intensity narrative tension intensity

text original action text

vec action text vector

three_modal_art_language_theology_matching.csv

Combination Law-War

OneDrive link: Picture

Map of War 02

01 Data + Colour Logic

02 Pose to Geometry Mapping

03 Pose + Spatial Organization

04 Relational / Graphic Language

05 Map

Map of War 01

01 War Pose Dataset

I first sampled the war videos every 2seconds, transforming continuous footage into a sequence of analyzable action samples. Then human pose detection was applied to each sampled frame to extract body key points and skeleton-based pose data. Key points were converted into pose features, including movement, arm movement, visibility, body scale, and pose direction. Each action sample became a pose feature vector / pose embedding.

02 Som

These pose embeddings were used to train a SOM, so similar bodily actions were organized into nearby positions. Each sample then received a som_x / som_y coordinate.

03 Data to Geometry Mapping

Each pose sample is placed on the SOM map according to som_x / som_y. Similar bodily actions are organized into nearby areas, so the overall layout comes from the data structure of the SOM.

04 Graphic Language

The body is no longer drawn as a complete skeleton. Instead, it is translated into black fragments.

05 Map

Map of Law 02

This version treats the courtroom procedure as an organic rule field. Each state becomes an attractor, and the transitions between states are translated into flowing trajectories. Through this method, the drawing does not represent a static courtroom plan, but visualizes the movement, repetition, delay, and accumulation of courtroom ritual over time.

01 Colour Legend

02 Data to Geometry Mapping

03 Graphic Language

04 Map

Map of Law 01

This drawing translates the rules of courtroom procedures into a procedural spatial drawing. Instead of drawing this as a conventional network diagram, the data is placed into a structureA. Different types of courtroom actions are organized into different spatial zones, and the strength of the data is expressed through geometry.

01 Color Legend

02 Data to Geometry Mapping

03 Spatial Organization

04 Graphic Language

05 Map

20x20

War

03 Analyze data

In this step, I converted the detected people clips into a structured behavior dataset. Each clip is no longer treated only as a video fragment, but as a data record containing time, source video, body visibility, movement level, pose condition, and behavior state. Through pose/key point analysis, the system identifies whether the human body appears as full body, upper body, partial body, pose disappearance, group movement, or unclear human behavior.

After that, I reorganized the clips according to their original video and timeline. This means that each war video can be read as a sequence of bodily states, showing how human presence changes over time. Instead of focusing on precise actions, the analysis reveals how bodies in war footage appear, move, gather, become unclear, or disappear. These patterns help me understand war not only as an event, but as a visual system of social grouping, secrecy, difference, and hidden bodies.

OneDrive link: pre

Law

01 Huizinga’s theory

I chose law as a dataset because Huizinga describes legal procedure as a rule-bound and ritualized form of play. The courtroom functions as a separated space in which ordinary relations are suspended and replaced by roles, procedures, verbal contest and judgement.

02 Workflow

11 videos/blip — Whisper — Pyannote — Merged

03 Rules

3D Map: Markov Rule Topology: Final Edition

2D Map: jade-cascaron-09271a.netlify.app

04 Visualize

The basic principle of this drawing is to translate the procedural states extracted from courtroom videos into a procedural diagram. The code first reads each current_state → next_state transition and uses probability and count to evaluate the strength and frequency of relationships between different courtroom actions. These detailed states are then grouped into several procedural categories, such as entering, waiting, documents, lawyer, listening, judge, and leaving. Each category is positioned within a 10×10 institutional space: the horizontal axis represents the progression of courtroom procedure, while the vertical axis represents the institutional hierarchy of actions, from boundary and waiting to material evidence, voice, observation, and authority. The lines in the diagram indicate transitions between states; stronger lines represent more stable or frequent procedural relationships, while the small open marks along the lines indicate how often each relation appears in the original video data. The dashed rails at the bottom and along the vertical axes act as technical reference lines, emphasizing that these actions are not isolated events, but repeated and connected behaviours within the order of courtroom ritual. Therefore, the drawing is not a direct timeline, but a spatialized rule system that visualizes the logic of courtroom procedure, repetition, and institutional behaviour.

War

01 Huizinga's theory

Within the five datasets, War corresponds to Huizinga’s final characteristic of play: the formation of social groupings that distinguish themselves from the common world through secrecy, difference, disguise or other means. Therefore, the war video is not treated simply as a record of combat or violence, but as a process through which a separated community is produced. Soldiers are transformed from ordinary individuals into a collective body through uniforms, equipment, formations, commands, concealment, observation and group movement. War therefore reveals how play can construct a closed social field based on belonging, exclusion, visibility and hiddenness.

02 Yolo detection

In this step, I used YOLO to detect human presence in the war videos and extract all video fragments where people appear. Instead of analyzing the entire footage manually, the model scans the videos frame by frame and identifies moments containing visible human bodies. Consecutive detected moments are then merged into short “people clips”, creating a focused dataset for further analysis.

03 Analyze data

After extracting these clips, I compared the original video with a pose-detection version. The left side shows the detected body/keypoint information, while the right side shows the original people clip. A timeline is placed below the video to record how the body state changes over time. Because the war footage is often blurry, black-and-white, and partially occluded, this step does not aim to identify precise actions. Instead, it focuses on how human bodies appear, move, gather, disappear, or become hidden within the battlefield image.

THE PLAY-CONCEPT IN LANGUAGE

Mataphor corpus:

Metaphorical subject

Metaphorical target

Metaphorical description

2. Ollama prompt:

You are an assistant specialized in analyzing metaphors in poetry.

Analyze the following poem and extract metaphor information.

Requirements: 1. subject: the entity in the metaphor that is being described. 2. target: the concept, feeling, object, or idea represented through the metaphor. 3. metaphor: the complete metaphorical expression from the poem.

Return only one valid JSON object. Do not explain. Do not add markdown. Do not add extra text.

If there is no clear metaphor, return: {{"subject": "", "target": "", "metaphor": ""}}

Example: {{"subject": "sun", "target": "life", "metaphor": "The sun shines on the garden of life"}}