feat: Add examples/geospatial-python-urban-analysis-with-postgis project

examples/geospatial-python-urban-analysis-with-postgis/Dockerfile

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+# Base image with Python
+FROM python:3.12
+
+# Set the working directory
+WORKDIR /app
+
+# Install dependencies
+RUN apt-get update && apt-get install -y \
+    libgdal-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the script
+COPY transport.py .
+
+# Run the script when the container starts
+CMD ["python", "transport.py"]

examples/geospatial-python-urban-analysis-with-postgis/README.md

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+# 🌍 Geospatial Urban Analysis Project
+
+## 📌 Overview
+
+This project focuses on **geospatial data analysis** for urban environments, particularly analyzing **pedestrian zones, transportation networks, census data, and geographic boundaries**. The dataset includes **shapefiles, GeoJSON, Parquet, and raster files**, allowing advanced **spatial processing and visualization**.
+
+The project uses **PostgreSQL with PostGIS**, **Docker**, and **GeoPandas**, enabling **efficient spatial queries, ETL pipelines, and geospatial machine learning models**.
+
+### ✨ **Key Features**
+
+- 🏙 **Urban Infrastructure Analysis**: Analyzes bike paths, subway entrances, and school locations.
+- 📊 **Geospatial Data Processing**: Supports various spatial formats (Shapefile, GeoJSON, Parquet, Raster).
+- 🔄 **ETL Pipelines**: Extract, transform, and load urban data into **PostGIS**.
+- 🤖 **Geospatial Machine Learning**: Clustering models to optimize urban planning decisions.
+- 🗺 **Interactive Mapping**: Generates visualizations using **Folium and Matplotlib**.
+
+---
+
+## 🛠 **Requirements**
+
+Before running the project, ensure you have the following dependencies installed:
+
+### 💻 **System Requirements**
+
+- 🐳 **Docker** (for PostgreSQL with PostGIS)
+- 🐍 **Python 3.8+**
+
+### 📦 **Python Dependencies**
+
+All required Python libraries are listed in `requirements.txt`. Install them using:
+
+```sh
+pip install -r requirements.txt
+```
+
+Main dependencies:
+
+- 🌍 **GeoPandas**: Geospatial data processing.
+- 🗄 **PostgreSQL & PostGIS**: Geospatial database support.
+- 📈 **Matplotlib & Folium**: Data visualization.
+- 🤖 **Scikit-learn**: Clustering and machine learning models.
+
+---
+
+## 🚀 **Setup & Installation**
+
+### 📂 **1. Clone the Repository**
+
+```sh
+git clone [email protected]:nanlabs/backend-reference.git
+cd examples/geospatial-python-urban-analysis-with-postgis
+```
+
+### 🏗 **2. Set Up a Virtual Environment**
+
+Create and activate a Python virtual environment:
+
+```sh
+python -m venv env
+source env/bin/activate  # On macOS/Linux
+env\Scripts\activate  # On Windows
+```
+
+Once activated, install dependencies:
+
+```sh
+pip install -r requirements.txt
+```
+
+### 🐳 **3. Set Up Docker with PostgreSQL and PostGIS**
+
+Ensure that **Docker** is installed and running. Then, start the database with:
+
+```sh
+docker-compose up -d
+```
+
+This will:
+
+- 🛢 Start a **PostgreSQL database** with **PostGIS** extensions enabled.
+- 📌 Create the necessary **database schema** for storing geospatial data.
+
+## 📖 **Working with Notebooks**
+
+To start the analysis and visualization:
+
+```sh
+jupyter notebook
+```
+
+Then, open one of the notebooks in the `notebooks/` directory.
+
+The notebooks cover:
+
+- 🌍 **Geospatial Data Exploration**: Loading and visualizing spatial datasets.
+- 🚇 **Urban Accessibility Analysis**: Assessing accessibility of public transport.
+- 🤖 **Clustering and Machine Learning**: Applying spatial clustering algorithms.
+
+
+### 📌 **Pipelines Overview**
+The project includes **several geospatial data processing pipelines**, located in `src/pipelines/`:
+
+- 🚌 **`bus_stop_analysis.py`**: Analyzes bus stops and their spatial distribution.
+- 📍 **`optimal_stop_pipeline.py`**: Computes the best locations for public transportation stops.
+- 🗺 **`shapefile_to_raster.py`**: Converts vector-based shapefiles into raster format for GIS applications.
+
+### ⚙️ **Running Pipelines**
+To execute a pipeline, use the following command:
+
+```sh
+PYTHON=. python -m src.pipelines.bus_stop_analysis
+```
+
+Replace `bus_stop_analysis` with the pipeline you want to run.
+
+Each pipeline processes geospatial data **efficiently**, ensuring the data is ready for **urban planning and visualization**.
+
+---
+
+## 🏗 **Project Structure**
+
+```sh
+.
+├── Dockerfile                   # 🐳 Docker configuration for Python environment
+├── docker-compose.yml           # 🛢 PostgreSQL + PostGIS setup
+├── requirements.txt             # 📦 Python dependencies
+├── config.py                    # ⚙️ Configuration settings
+├── data/                        # 🌍 Raw geospatial datasets
+├── notebooks/                   # 📖 Jupyter Notebooks for geospatial analysis
+├── scripts/                     # 🔄 Data processing scripts
+├── src/                         # 🏗 Source code
+│   ├── database/                # 🗄 Database connection and queries
+│   ├── etl/                     # 🔄 ETL pipeline for spatial data
+│   ├── ml/                      # 🤖 Machine learning models for clustering
+│   ├── pipelines/               # 📌 Spatial data processing workflows
+│   ├── visualization/           # 🗺 Map and data visualization modules
+```
+
+---

examples/geospatial-python-urban-analysis-with-postgis/config.py

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+DB_CONNECTION = "postgresql://user:password@host:port/dbname"
+POPULATION_DENSITY_RASTER = "data/population_density.tif"
+DISTRICTS_URL = "https://cdn.buenosaires.gob.ar/datosabiertos/datasets/ministerio-de-educacion/comunas/comunas.geojson"
+EPSG_TARGET = 3857  # Standard coordinate reference system (CRS)

examples/geospatial-python-urban-analysis-with-postgis/data/Pedestrian Zone Shapefile (Tabular)_20241220/geo_export_a7bb075a-41dc-445f-8244-8430e90a8dde.cpg

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+UTF-8

examples/geospatial-python-urban-analysis-with-postgis/data/Pedestrian Zone Shapefile (Tabular)_20241220/geo_export_a7bb075a-41dc-445f-8244-8430e90a8dde.prj

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+GEOGCS["WGS84(DD)", DATUM["WGS84", SPHEROID["WGS84", 6378137.0, 298.257223563]], PRIMEM["Greenwich", 0.0], UNIT["degree", 0.017453292519943295], AXIS["Geodetic longitude", EAST], AXIS["Geodetic latitude", NORTH], AUTHORITY["EPSG","4326"]]

examples/geospatial-python-urban-analysis-with-postgis/data/Pedestrian Zone Shapefile (Tabular)_20250127/geo_export_8aa884d0-7056-4b1e-94ff-62eb7b00d65c.cpg

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+UTF-8

examples/geospatial-python-urban-analysis-with-postgis/data/Pedestrian Zone Shapefile (Tabular)_20250127/geo_export_8aa884d0-7056-4b1e-94ff-62eb7b00d65c.prj

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+GEOGCS["WGS84(DD)", DATUM["WGS84", SPHEROID["WGS84", 6378137.0, 298.257223563]], PRIMEM["Greenwich", 0.0], UNIT["degree", 0.017453292519943295], AXIS["Geodetic longitude", EAST], AXIS["Geodetic latitude", NORTH], AUTHORITY["EPSG","4326"]]

examples/geospatial-python-urban-analysis-with-postgis/data/census/caba/cabaxrdatos.prj

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+PROJCS["POSGAR_94_Argentina_3",GEOGCS["GCS_POSGAR 94",DATUM["D_POSGAR_1994",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",-90],PARAMETER["central_meridian",-66],PARAMETER["scale_factor",1],PARAMETER["false_easting",3500000],PARAMETER["false_northing",0],UNIT["Meter",1]]

examples/geospatial-python-urban-analysis-with-postgis/data/census/caba/cabaxrdatos.qpj

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+PROJCS["POSGAR 94 / Argentina 3",GEOGCS["POSGAR 94",DATUM["Posiciones_Geodesicas_Argentinas_1994",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],TOWGS84[0,0,0,0,0,0,0],AUTHORITY["EPSG","6694"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4694"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",-90],PARAMETER["central_meridian",-66],PARAMETER["scale_factor",1],PARAMETER["false_easting",3500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AUTHORITY["EPSG","22183"]]