Thermal Comfort in the Historic Core: A Comparative Network Analysis of Delft and Xi’an

Group F

• Chaeyeon Moon ( Geomatics)
• Evangelia Angeliki Palli (Geomatics)
• Julia Fossa Marques (Geomatics)
• Prianka Girish Bali (Urbanism)

Repository Structure

├── scripts/                            # Source code and input/output data
│   ├── data/                           # All required input data
│   ├── output/                         # Generated output files
│   │      └── 01_Report/               # Figures generated for the final report
│   ├── preprocess_neatnet.py           # Preprocessing script 1: Network Simplification
│   ├── preprocess_coins.py             # Preprocessing script 2: Network Consolidation
│   ├── pipeline.R                      # Main analysis (clustering)
│   ├── report_config.R                 # Plot script 1 (for report)
│   ├── report_figure_exports.R         # Plot script 2 (for report)
│   └── report_variable_distribution.R  # Plot script 3 (for report)
├── report.qmd                          # Quarto file of report
├── report_references.bib               # Bibliography list of report
└── styles.css                          # Style sheet for report 

How to Run

Please follow the sequence below to ensure data dependencies are met.

1. Preprocessing

Run the preprocessing scripts to prepare the network data. Ensure the output paths point to the data/ directory.

• Run scripts/preprocess_neatnet.py

  • It simplifies the raw network data
  • Note: Update INPUT_PATH (line 6) and OUTPUT_PATH (line 24) in the script before running.

• Run scripts/preprocess_coins.py

  • It consolidates the simplified network data
  • Note: Update INPUT_PATH (line 7) and OUTPUT_PATH (line 10) in the script before running.

2. Main Analysis

• Run scripts/pipeline.R
  • It executes variable calculation and spatial clustering.

3. [Optional] Report Visualization

The following scripts generate the figures used in the final report:

• scripts/report_config.R
• scripts/report_figure_exports.R
• scripts/report_variable_distribution.R