Global Megalopolis Siphon Effects: Evolutionary Divergence and Eco-Economic Decoupling

Replication Package for: "Global Megalopolis Siphon Effects: Evolutionary Divergence and Eco-Economic Decoupling" 1. Overview This replication package contains the processed datasets and custom Python source code necessary to fully replicate the spatial analyses, statistical models, and high-resolution visualizations (Figures 1-6, SI Tables 1-2) presented in the manuscript "Global Megalopolis Siphon Effects: Evolutionary Divergence and Eco-Economic Decoupling". To adhere to Open Science principles and overcome data upload limits, we do not include the raw, multi-gigabyte global satellite imagery (TIFs) in this repository. Instead, we provide the rigorously processed 1-km Enriched Analytical Grids (GeoJSON) and Extracted Vector Layers (OSM POIs and Networks), which act as the direct quantitative foundation for all findings. 2. System Requirements & Dependencies The codebase was developed and tested using Python 3.9+. To execute the scripts seamlessly, please ensure the following libraries are installed in your Python environment: geopandas (Spatial data manipulation) osmnx (OpenStreetMap vector retrieval) rasterio & rasterstats (Raster processing and zonal statistics) pandas & numpy (Data wrangling) scipy (cKDTree for spatial distance calculations) statsmodels (OLS regression and LOWESS smoothing) scikit-learn (StandardScaler for Ward's hierarchical clustering) matplotlib & seaborn (High-resolution data visualization) Recommended installation via conda: conda install -c conda-forge geopandas osmnx rasterio rasterstats seaborn statsmodels scikit-learn 3. Directory Structure Please extract the .zip archive and ensure the following directory structure is maintained: Megalopolis_Siphon_Replication_Package/ │ ├── 00_README.md <-- [This Document] │ ├── 01_Code/ <-- [Python Source Code] │ ├── 01_spatial_infrastructure_builder.py │ ├── 02_raster_zonal_extractor.py │ ├── 03_plot_spatial_dynamics_3x5.py │ ├── 04_plot_decay_and_transport.py │ └── 05_macro_clustering_and_si.py │ ├── 02_Data/ <-- [Processed Spatial Datasets] │ ├── Enriched_1km_Grids/ (Contains 15 GeoJSON files with Pop, NTL, LULC, NDVI metrics) │ ├── POIs/ (Contains High-End, Low-End, and Transport Hub points) │ └── Networks/ (Contains Drive Network nodes and edges) │ └── 03_Output/ <-- [Generated Figures and Tables will be saved here] 4. Data Availability & Raw Data Sources The derived data within the 02_Data/ folder were computationally extracted from the following public earth observation repositories (spanning the years 2000 and 2020): Population Density: WorldPop Unconstrained Global Datasets (https://www.worldpop.org/) Economic Agglomeration: DMSP-OLS and NPP-VIIRS Harmonized Nighttime Lights (Earth Observation Group) Ecological Degradation: MODIS NDVI (MOD13Q1 product, NASA EarthData) Physical Built-up Sprawl: Global Human Settlement Layer (GHSL, European Commission JRC) Urban Infrastructure: OpenStreetMap API (via osmnx Python package) 5. Step-by-Step Reproduction Guide To replicate the manuscript's findings, please run the scripts in the 01_Code/ directory in the following sequential order. Ensure your working directory is set to the root of this replication package so relative paths function correctly. Step 1: Data Infrastructure Verification (Optional) Script: 01_spatial_infrastructure_builder.py & 02_raster_zonal_extractor.py Note: The final output of these scripts (the 1-km enriched grids and POIs) is already provided in the 02_Data/ folder to save computational time. You only need to run these if you wish to reconstruct the data pipeline from scratch using your own locally downloaded raw TIF files. Step 2: Render Spatial Dynamics & Micro-Anatomy Script: 03_plot_spatial_dynamics_3x5.py Action: Reads the Enriched_1km_Grids and POIs to generate high-resolution geographical plots. Generates: Figure_1_World_Locator_and_Micro_Matrix_3x5.png Figure_4_Phase_Space_Global_Clean_3x5.png Figure_5_Industrial_Polarization_3x5.png Step 3: Render Spatial Decay & Transport Regressions Script: 04_plot_decay_and_transport.py Action: Calculates radial distance to Central Business Districts (CBDs) and distance to Transport Hubs/Roads using cKDTree algorithms. Applies LOWESS smoothing. Generates: Figure_2_Combined_Global_Decay.png (The Siphon Shadow envelopes) Figure_3_Transport_Mechanism_Global.png (The Non-linear Transport Effect) Step 4: Macro-Clustering and Robustness Checks Script: 05_macro_clustering_and_si.py Action: Extracts 4 macro-indices (DSI, LSI, EP, EA) per megalopolis, standardizes them (Z-scores), and executes Ward's minimum variance hierarchical clustering. Subsequently runs OLS regressions to check endogeneity. Generates: Figure_6_Global_Typology_Matrix.png (Dendrogram and Heatmap) Table_1_Macro_Indices.csv SI_Table1_Data_Quality.csv (Grid coverage statistics) SI_Table2_Transport_Regression.csv (Multivariate OLS results)