Leafmap

Abstract

1. Introduction

Leafmap addresses these challenges by leveraging the bidirectional communication provided by ipyleaflet, enabling users to load and visualize geospatial datasets with just one line of code. Leafmap also provides an interactive graphical user interface (GUI) for loading geospatial datasets without any coding. It is designed for anyone who wants to analyze and visualize geospatial data interactively in a Jupyter environment, making it particularly accessible for novice users with limited programming skills. Advanced programmers can also benefit from leafmap for geospatial data analysis and building interactive web applications.

There is a plethora of Python packages for geospatial analysis, such as geopandas for vector data analysis and xarray for raster data analysis. As listed at pyviz.org, there are also many options for plotting data on a map in Python, ranging from libraries focused specifically on maps like ipyleaflet and folium to general-purpose plotting tools that also support geospatial data types, such as hvPlot, bokeh, and plotly. While these tools provide powerful capabilities, displaying geospatial data from different file formats on an interactive map and performing basic analyses can be challenging, especially for users with limited coding skills. Furthermore, many tools lack bi-directional communication between the frontend (browser) and the backend (Python), limiting their interactivity and usability for exploring map data.

2. Objective

A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment

This project demonstrates how Python, Jupyter Notebook can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Creating an interactive map with just one line of code: Leafmap makes it easy to create an interactive map by providing a simple API that allows you to load and visualize geospatial datasets with minimal coding.
• Changing basemaps interactively: Leafmap allows you to change basemaps interactively, providing a variety of options such as OpenStreetMap, Stamen Terrain, CartoDB Positron, and many more.
• Adding XYZ, WMS, and vector tile services: You can easily add XYZ, WMS, and vector tile services to your map, allowing you to overlay additional geospatial data from various sources.
• Displaying raster data: Leafmap allows you to load and display raster data, such as GeoTIFFs, on the map. This feature is useful for visualizing satellite imagery, digital elevation models, and other gridded datasets.
• Creating custom legends and colorbars: Leafmap provides tools for customizing legends and colorbars on the map, allowing you to represent data values with different colors and corresponding labels.
• Downloading and visualizing OpenStreetMap data: Leafmap allows you to download and visualize OpenStreetMap data, providing access to detailed street maps, buildings, and other points of interest.
• Creating and editing vector data interactively: Leafmap includes tools for creating and editing vector data interactively on the map. You can draw points, lines, and polygons, and modify them as needed.
• Inspecting pixel values interactively: Leafmap allows you to interactively inspect pixel values in raster datasets, helping you analyze and understand the data at a more granular level.
• Displaying data from a PostGIS database: Leafmap provides tools for connecting to a PostGIS database and displaying spatial data stored in the database on the map.
• Creating time series animations: Leafmap enables the creation of time series animations from both vector and raster data, allowing you to visualize temporal changes in your geospatial datasets.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/opengeos/leafmap.git
cd leafmap

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by opengeos and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.