📌 Overview¶
This repository demonstrates a map layout and cartographic visualization workflow using GeoPandas.
The focus of this project is not only spatial plotting, but also map layout composition following basic cartographic principles for clear and effective visual communication.
The full workflow is implemented in a Google Colab notebook, allowing users to run the code without local setup.
🚀 Open Notebook in Google Colab¶
🧭 Script Workflow Explanation¶
Below is an overview of what is included in the script and what each part does.
1️⃣ Library Imports 📦¶
This section loads the core Python libraries required for geospatial analysis and visualization.
Main purposes:
Enable spatial data handling
Prepare plotting and layout customization
Support cartographic visualization
Key libraries include GeoPandas and Matplotlib.
2️⃣ Spatial Data Loading 🗺️¶
The script reads spatial vector data into a GeoDataFrame.
At this stage:
Geometry (polygon, line, or point) is initialized
Attribute data becomes available for styling and labeling
The study area is defined
This step forms the foundation of the map.
3️⃣ Coordinate Reference System (CRS) Handling 🌍¶
The script checks and manages the CRS to ensure spatial accuracy.
Why this matters:
Correct distance and scale representation
Proper alignment between spatial layers
Valid cartographic layout
If needed, reprojection is applied.
4️⃣ Base Map Plotting 🎨¶
This part creates the main plotting canvas:
Figure and axis are defined
The main spatial layer is rendered
Basic color, edge, and transparency settings are applied
The result is a clean base map ready for layout elements.
5️⃣ Thematic Styling 🎯¶
Styling is applied to improve map readability and meaning:
Color schemes based on attributes
Line width and opacity adjustments
Visual contrast enhancement
This step turns raw spatial data into a thematic map.
6️⃣ Map Layout Components 🧩¶
This is the core cartographic section of the script.
Included layout elements:
🏷️ Map title
📑 Legend
📏 Scale bar
🧭 North arrow
❌ Axis removal for a cleaner look
These elements transform a plot into a proper map layout.
7️⃣ Annotation and Labeling ✍️¶
Additional information is added through text annotations, such as:
Area names
Notes or descriptions
Supporting contextual information
The goal is clarity without visual clutter.
8️⃣ Exporting the Output 💾¶
The final map is exported as an image file:
High-resolution output
Ready for reports, presentations, or portfolios
Suitable for academic and professional use
🛠️ Tools and Technologies¶
Python
GeoPandas
Matplotlib
Google Colab
GIS & cartographic principles
🎯 Use Cases¶
GIS and remote sensing coursework
Forestry and environmental mapping
Map layout reference using Python
Portfolio demonstration of spatial visualization skills
👤 Author¶
Defani Arman Alfitriansyah
Forestry Student | GIS & Remote Sensing
Interested in spatial analysis, cartography, and visual communication of geospatial data.
📝 Notes¶
This project emphasizes map layout quality and cartographic clarity,
not merely spatial analysis output.
Users are encouraged to adapt the layout structure for their own datasets.
Notebook¶
Notebook dari repositori ini, tampil lengkap dengan sel kode dan hasilnya: