A Geometry-Based Computational Assessment of Parcel-Level Redevelopment Feasibility in Seoul

Implementation of the methodology proposed in "A Geometry-Based Computational Assessment of Parcel-Level Redevelopment Feasibility in Seoul" — an automated system that evaluates urban parcel layouts using geometry-based scoring metrics within the Rhino/Grasshopper environment.

Figure 6. Evaluation results visualized on Seoul district maps

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Overview

Urban planners in Seoul face the challenge of evaluating large-scale parcel layouts efficiently and consistently. This project automates the evaluation process by:

1. Reading cadastral data from Korean national GIS shapefiles
2. Generating blocks by grouping spatially adjacent lots using graph-based connectivity analysis
3. Evaluating each block across four independent geometric scoring dimensions
4. Exporting scored results back to shapefiles for GIS integration

The system runs inside Rhino 3D + Grasshopper, leveraging Rhino's geometry kernel for robust spatial operations.

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Scoring Methodology

Each block is evaluated with four normalized scores (0.0 – 1.0):

1. Region Score — Buildable Area Efficiency

Measures how efficiently lots within a block can be developed by computing the ratio of buildable area after applying legal setbacks.

Figure 4. Buildable region derivation: Original Lot → Road Setback → Neighbor Lot Setback → Buildable Region

Figure 1. Block-level buildable area computation for lots A, B, C, and D

2. Shape Score — Region Shape Index (RSI)

Evaluates lot shape quality through a weighted combination of three geometric metrics:

Figure 5. Three shape metrics: Convexity (30%), Circularity (40%), and Squareness (30%)

Metric	Weight	Formula	Ideal
Convexity	0.3	Area / ConvexHullArea	1.0 (no concavities)
Circularity	0.4	4 * pi * Area / Perimeter^2	1.0 (circle)
Squareness	0.3	Based on bounding box aspect ratio	1.0 (square)

3. Road Score — Road Access Index

Quantifies road accessibility using cadastral road condition codes (01–12), mapping each to a normalized score. Corner lots with multiple road frontages receive higher scores.

Figure 2. Road frontage width computation for lots A, B, and C

4. Topography Score — Elevation Variation

Assesses terrain flatness within each block using DEM (Digital Elevation Model) data.

Figure 3. Elevation relief computation at block and lot levels

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Project Structure

parcel_layout_evaluation_analysis/
├── grasshopper/
│   ├── main.py                 # Entry point
│   ├── block_generator.py      # Groups adjacent lots into blocks (NetworkX)
│   ├── parcel_evaluator.py     # Orchestrates four scoring modules
│   ├── shape_manager.py        # Shapefile I/O with encoding detection
│   ├── units.py                # Core data classes (Parcel, Lot, Block, LayoutScore)
│   ├── utils.py                # Geometry utilities (Rhino + Clipper)
│   ├── constants.py            # Configuration parameters
│   ├── visual_debugger.gh      # Grasshopper visual debugger component
│   └── scorers/
│       ├── region.py           # Buildable area efficiency scorer
│       ├── shape.py            # Region Shape Index (RSI) scorer
│       ├── road.py             # Road access scorer
│       └── topo.py             # Topography/elevation scorer
├── input_data/                 # Input GIS data (shapefiles + DEM)
├── output_data/                # Evaluated block shapefiles
└── docs/                       # Research paper and figures

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Pipeline

Shapefile (Cadastral Data)  +  DEM Raster
              │                     │
              v                     │
     ShapefileManager               │
  (parse lots & roads)              │
              │                     │
              v                     │
      BlockGenerator                │
  (spatial join + graph             │
   connected components)            │
              │                     │
              v                     │
      ParcelEvaluator  <────────────┘
       ├─ RegionScorer
       ├─ ShapeScorer
       ├─ RoadScorer
       └─ TopoScorer
              │
              v
     Output Shapefile
  (blocks with 4 scores)

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Requirements

This project runs inside the Rhino 3D / Grasshopper scripting environment. The following dependencies are required:

• Rhino 7+ with Grasshopper
• Python libraries (available within Rhino's Python environment):
  • geopandas — Geospatial data operations
  • shapely — Geometric computations
  • networkx — Graph-based block grouping
  • pyshp — Shapefile I/O

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Input Data

Data	Format	Description
Cadastral parcels	Shapefile (.shp)	Korean national land parcel data (AL_D194)
Digital Elevation Model	GeoTIFF (.tif)	10m resolution DEM raster

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Output

The system exports a shapefile containing evaluated blocks with the following attribute fields:

Field	Description	Range
BLOCK_ID	Unique block identifier	—
REGION	Buildable area efficiency score	0.0 – 1.0
SHAPE	Region Shape Index score	0.0 – 1.0
ROAD	Road access score	0.0 – 1.0
TOPO	Topography score	0.0 – 1.0

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Usage

1. Open grasshopper/visual_debugger.gh in Rhino/Grasshopper
2. Set the input shapefile path and output save path
3. Run grasshopper/main.py through the GhPython component
4. The evaluated blocks are saved as a new shapefile in output_data/

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Citation

If you find this work useful, please consider citing:

@article{byun2025geometry,
  title     = {A Geometry-Based Computational Assessment of Parcel-Level Redevelopment Feasibility in Seoul},
  author    = {Byun, Sanghoon and Park, Dongjun and Kang, Bumjun},
  year      = {2025}
}

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Authors

	Name	Role	GitHub
	Sanghoon Byun	Author	@969flash
	Dongjun Park	Author	@gbjun7333
	Bumjun Kang	Advisor	Homepage

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Acknowledgments

This research was conducted at the LAUS (Lab. Architectural & Urban Space), Department of Architecture and Architectural Engineering, Seoul National University, under the supervision of Prof. Bumjun Kang.