cforgaci · Apr 20, 2026 · Apr 23, 2026 · Apr 23, 2026 · Apr 23, 2026 · Apr 24, 2026
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+=======
+.datasets_klimaateffectatlas/UHI_effect.tif
+>>>>>>> 34ae8c4ff2e2f7376e37ebe15f229cc17bbdd9fa
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diff --git a/README.md b/README.md
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-# Report template for the Applied Spatial Analytics 2026 course
+# Comparative Spatial Justice and Urban Heat Resilience: Rotterdam vs. Guangzhou
 
-This is a template repository used as a starting point for the group
-reports produced in the Applied Spatial Analytics 2025 course at
-TU Delft.
+This repository contains the computational pipeline and analytical workflows for evaluating the relationship between urban green infrastructure allocation, socioeconomic vulnerability, and Land Surface Temperature (LST).
 
-By starting this assignment in GitHub Classroom, you created a copy
-of this repository that you have write access to. You will continue to
-work on your report in that repository throughout the quarter. A great
-way to practice and apply what you learned in the "Intro to Git and GitHub"
-assignment!
+## Repository Architecture
 
-### Getting started with the report
+To replicate the study, your local directory must be structured as follows. Create a `/data` folder in your root directory.
 
-1. In RStudio, create a new project from version control. Use the
-   URL of your repository to clone it.
-
-2. To start working on the report, open the `report.qmd` file, add the
-   names of your group members, and press on the "Render" button. Next,
-   stage and commit all changed files and push them to GitHub. You will
-   follow this **stage -> commit -> push** workflow every time you make a
-   change.
+```text
+├── code/
+│   ├── 01_SJI_complete_rotterdam.R   # Full-resolution SJI for Rotterdam
+│   ├── 02_SJI_comparative_baseline.R # Simplified, harmonized SJI for both cities
+│   └── 03_network_thermal_analysis.R # Network routing, hex-aggregation, and UHI plotting
+├── data/                             # User-created local data directory
+│   ├── rotterdam/                    # Place downloaded Rotterdam files here
+│   └── guangzhou/                    # Place downloaded Guangzhou files here
+└── README.md                         # Project documentation
+```
 
-### Feedback
+## Data Requirements & Acquisition
 
-In the **Pull requests** section of your repository, you will find a
-**Feedback** pull request. We will use this pull request to provide
-feedback on your report throughout the quarter. You can also use this
-pull request to ask questions about the feedback.
+Because raw data are not hosted in this repository, you must download the source files directly from the providers below and place them into their respective subfolders within /data.
 
-### Asking for help
 
-If you have questions about the assignment, please ask them in
-[Discussions](https://github.com/Applied-Spatial-Analytics/asa2026/discussions).
+**1. Rotterdam Data**
+- Socioeconomic Indicators: Download the neighborhood data from the [CBS StatLine Portaal](https://www.cbs.nl/nl-nl/cijfers/detail/86165NED).
 
+- Physical Livability: Download the open dataset from the [Leefbaarometer 2024 Open Data Portal](https://www.leefbaarometer.nl/page/Opendata).
 
+- Building Footprints (BAG): Use the [PDOK Services Plugin](https://plugins.qgis.org/plugins/pdokservicesplugin/) in QGIS to download building functions and layers for the Rotterdam study area.
+
+- Network & Green Infrastructure: Use the [QuickOSM Plugin](https://plugins.qgis.org/plugins/QuickOSM/) in QGIS to download keys highway and green areas for Rotterdam.
+
+**2. Guangzhou Data**
+
+- Demographic Density: Download the gridded population raster from the [WorldPop Open Spatial Repository](https://www.worldpop.org/datacatalog/).
+
+- Network & Green Infrastructure: Use the [QuickOSM Plugin](https://plugins.qgis.org/plugins/QuickOSM/) in QGIS to extract building footprints, residential land use, pedestrian paths, and green spaces from OpenStreetMap.
+
+**3. Land Surface Temperature Data**
+
+- Create an account in USGS Earth Explorer (https://earthexplorer.usgs.gov/)
+
+- Set an area around Rotterdam/Guangzhou with small percentage of cloud coverage (e.g., < 40%)
+
+- Set the search criteria to Landsat 8/9 Collection 2 Level-2 Surface Temperature satellite imagery
+
+- Download the ST_B10 file from the Surface Temperature data package
+
+## Execution Workflow
+
+All scripts are written in R and utilize relative paths Ensure your working directory is set to the project root before executing.
+
+1. Pre-processing: Download and crop all spatial data layers (including LST rasters) to your respective city administrative boundaries.
+
+2. Run Spatial Justice Index:
+
+- To analyze Rotterdam with its full suite of local indicators, execute `code/01_SJI_complete_rotterdam.R`.
+
+- To run the comparative model across both cities, execute `code/02_SJI_comparative_baseline.R`.
+
+3. Run Spatial Routing and Thermal Overlays: Execute `code/03_network_thermal_analysis.R`. This script processes the pedestrian networks, calculates distance thresholds, aggregates data into spatial hexagons, and generates final correlation plots against the LST layers.
diff --git a/Report/.gitignore b/Report/.gitignore
@@ -0,0 +1,2 @@
+/.quarto/
+**/*.quarto_ipynb
diff --git a/Report/.nojekyll b/Report/.nojekyll
diff --git a/Report/_quarto.yml b/Report/_quarto.yml
@@ -0,0 +1,3 @@
+project:
+  type: website
+  output-dir: docs
diff --git a/asa2025-report.Rproj → Report/asa2025-report.Rproj b/asa2025-report.Rproj → Report/asa2025-report.Rproj
diff --git a/Report/maps/GuangzhouSJI_with_LST.png b/Report/maps/GuangzhouSJI_with_LST.png
diff --git a/Report/maps/Guangzhou_admin_boundaries.png b/Report/maps/Guangzhou_admin_boundaries.png
diff --git a/Report/maps/LST_SJI_Guangzhou.png b/Report/maps/LST_SJI_Guangzhou.png
diff --git a/Report/maps/LST_SJI_Rotterdam.png b/Report/maps/LST_SJI_Rotterdam.png
diff --git a/Report/maps/LST_SJI_Rotterdam_Full.png b/Report/maps/LST_SJI_Rotterdam_Full.png
diff --git a/Report/maps/RotterdamSJI.png b/Report/maps/RotterdamSJI.png
diff --git a/Report/maps/RotterdamSJI_with_LST.png b/Report/maps/RotterdamSJI_with_LST.png
diff --git a/Report/maps/Rotterdam_admin_boundaries.png b/Report/maps/Rotterdam_admin_boundaries.png
diff --git a/Report/maps/SJI_Guangzhou.png b/Report/maps/SJI_Guangzhou.png
diff --git a/Report/maps/SJI_Rotterdam_Full.png b/Report/maps/SJI_Rotterdam_Full.png
diff --git a/Report/maps/SpatialIndexKanton.png b/Report/maps/SpatialIndexKanton.png
diff --git a/Report/maps/lst_guangzhou.png b/Report/maps/lst_guangzhou.png
diff --git a/Report/maps/lst_rotterdam.png b/Report/maps/lst_rotterdam.png
diff --git a/Report/maps/vulnerability_guangzhou.png b/Report/maps/vulnerability_guangzhou.png
diff --git a/Report/maps/vulnerability_rotterdam.png b/Report/maps/vulnerability_rotterdam.png
diff --git a/Report/references.bib b/Report/references.bib
@@ -0,0 +1,100 @@
+@article{wong-2016,
+	author = {Wong, Man and Peng, Fen and Zou, Bin and Shi, Wen and Wilson, Gaines},
+	journal = {International Journal of Environmental Research and Public Health},
+	month = {3},
+	number = {3},
+	pages = {317},
+	title = {{Spatially Analyzing the inequity of the Hong Kong Urban Heat Island by Socio-Demographic characteristics}},
+	volume = {13},
+	year = {2016},
+	doi = {10.3390/ijerph13030317},
+	url = {https://doi.org/10.3390/ijerph13030317},
+}
+
+@article{bartesaghi-koc-2020,
+	author = {Bartesaghi-Koc, Carlos and Osmond, Paul and Peters, Alan},
+	journal = {Landscape and Urban Planning},
+	month = {7},
+	pages = {103893},
+	title = {{Quantifying the seasonal cooling capacity of ‘green infrastructure types’ (GITs): An approach to assess and mitigate surface urban heat island in Sydney, Australia}},
+	volume = {203},
+	year = {2020},
+	doi = {10.1016/j.landurbplan.2020.103893},
+	url = {https://doi.org/10.1016/j.landurbplan.2020.103893},
+}
+
+@techreport{dadashpoor-2025,
+	author = {Dadashpoor, Hashem and Dehghan, Roya},
+	month = {4},
+	number = {103387},
+	pages = {103387--},
+	title = {{Defining Spatial Justice: A Review}},
+	volume = {160},
+	year = {2025},
+	doi = {10.1016/j.habitatint.2025.103387},
+	url = {https://www.sciencedirect.com/science/article/pii/S0197397525001031?via%3Dihub},
+}
+
+@article{huang-2011,
+	author = {B. Huang, Ivy and Keisler, Jeffrey and Linkov, Igor},
+	journal = {Science of the Total Environment},
+	month = {9},
+	number = {19},
+	pages = {3578--3594},
+	title = {{Multi-criteria decision Analysis in Environmental Sciences: Ten years of applications and trends}},
+	volume = {409},
+	year = {2011},
+	doi = {10.1016/j.scitotenv.2011.06.022},
+	url = {https://doi.org/10.1016/j.scitotenv.2011.06.022},
+}
+
+@misc{cbs-2026,
+	author = {{Centraal Bureau voor de Statistiek}},
+	month = {3},
+	title = {{Kerncijfers wijken en buurten 2025}},
+	year = {2026},
+	url = {https://www.cbs.nl/nl-nl/cijfers/detail/86165NED},
+}
+
+@misc{leefbarometer-2026,
+	author = {{Ministerie van Binnenlandse Zaken en Koninkrijksrelaties}},
+	month = {5},
+	title = {{Leefbaarometer Opendata}},
+	year = {2026},
+	url = {https://www.leefbaarometer.nl/page/Opendata},
+}
+
+@misc{pdok-2026,
+	author = {Duivenvoorde, Richard},
+	month = {3},
+	title = {{PDOK Services Plugin}},
+	year = {2026},
+	url = {https://plugins.qgis.org/plugins/pdokservicesplugin/#plugin-details},
+}
+
+ @misc{OpenStreetMap,
+   author = {{OpenStreetMap contributors}},
+   title = {{Planet dump retrieved from https://planet.osm.org }},
+   howpublished = "\url{ https://www.openstreetmap.org }",
+   year = {2017},
+ }
+
+@misc{worldpop-2026,
+	author = {WorldPop},
+	month = {1},
+	title = {{Open access to global development data - WorldPop}},
+	year = {2026},
+	url = {https://www.worldpop.org/datacatalog/},
+}
+
+@misc{usgsee-2026,
+  author = {USGS},
+  year = {2026},
+  url = {https://earthexplorer.usgs.gov/},
+}
+
+@misc{usgs_index-2026,
+  author = {USGS},
+  year = {2026},
+  url = {https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature},
+}