Running the pipeline

Prerequisites

• R ≥ 4.3
• RStudio or Quarto CLI
• Quarto ≥ 1.4 Install required R packages once:

install.packages(c("sf", "ggplot2", "dplyr", "ineq", "tiff", "tidyverse", "patchwork"))

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Data

Data files are not tracked in this repo. Place them at the paths expected by R/00_config.R: Download data folder from: Download link

data/
├── delft/
│   ├── raster/    # delft_worldpop_proj.tif, ndvi_delft_proj.tif, …
│   └── vector/    # delft_boundary_proj.gpkg, delft_osm_green_proj.gpkg, …
└── Yuexiu/
    ├── raster/    # Yuexiu_viirs_proj.tif, …
    └── vector/    # yuexiu_boundary.gpkg, guangzhou_osm_green_proj.gpkg, …

All paths are centralised in R/00_config.R — edit that file if your data lives elsewhere.

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How to run

1. Clone and open

git clone https://github.com/Applied-Spatial-Analytics/create-your-report-groupe.git

Open asa2025-report.Rproj in RStudio.

2. Run the pipeline scripts in order

source("R/00_config.R")               # paths & constants
source("R/01_load_data.R")            # load & validate layers
source("R/02_accessibility.R")        # SQ1 — green space access
source("R/03_typology_biodiversity.R")# SQ2 — typology & NDVI
source("R/04_spatial_justice.R")      # SQ3 — Gini, equity
source("R/05_connectivity.R")         # SQ4 — fragmentation & graph
source("R/06_mcda_nbs.R")             # SQ5 — MCDA & corridors

Figures are saved automatically to report_files/.

3. Render the report

In RStudio: open report.qmd and click Render, or from the terminal:

quarto render report.qmd

Output: report.html

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File strcture

APPLIED_SPATIAL_ANALYTICS/
├── data/
│   ├── delft/
│   │   ├── raster/          # delft_worldpop_proj.tif, ndvi_delft_proj.tif, etc.
│   │   └── vector/          # delft_boundary_proj.gpkg, delft_wijken_proj.gpkg, etc.
│   └── Yuexiu/
│       ├── raster/          # Yuexiu_viirs_proj.tif, Yuexiu_worldpop_proj.tif, etc.
│       └── vector/          # Yuexiu_boundary_proj.gpkg, Yuexiu_subdistricts_proj.gpkg, etc.
│
├── R/
│   ├── 00_config.R          # ALL paths, CRS constants, thresholds — loaded by every script
│   ├── 01_load_data.R       # Load + validate all layers, save checked objects to /outputs/
│   ├── 02_accessibility.R   # SQ1 — green space per capita, buffers, nearest distance
│   ├── 03_typology_biodiversity.R  # SQ2 — OSM typology, NDVI zonal stats, GBIF density
│   ├── 04_spatial_justice.R # SQ3 — Gini, bivariate choropleth, income/VIIRS correlation
│   ├── 05_connectivity.R    # SQ4 — fragmentation metrics, graph connectivity
│   └── 06_mcda_nbs.R        # SQ5 — MCDA scoring, corridor prioritisation
│
├── report_files/
│   └── [figures auto-saved here by ggsave()]
│
├── report.qmd               # Narrative + renders figures from report_files/
└── README.md

Reproducibility notes

• All file paths live in R/00_config.R — no hardcoded paths elsewhere.
• Uses only open, globally available datasets (OSM, WorldPop, GBIF, VIIRS, NDVI).
• The workflow is fully reproducible in any city/district by updating 00_config.R.