MasterKinjalk / Mumbai-Data-Viz

Mental Map of Mumbai Slums and Landmarks in Folium

Mumbai Data Exploration & Visualization

Welcome to the Mumbai Data Exploration & Visualization repository! This project leverages the powerful OSMnx library to extract and visualize a wide array of data, starting with the intricate network of roads in Mumbai. Designed to be flexible and adaptable, this tool can be customized to gather and plot different types of urban data, offering insights into the city's layout, infrastructure, and more.

Downlaod and open in a brower to interact with the Map: Mumbai Map with Slums

Project Overview

Data Extraction & Visualization (play.py)

At the heart of this project is the play.py script, a versatile tool for data scientists, urban planners, and curious explorers alike. While our current focus is on mapping Mumbai's roads, the script's true power lies in its adaptability. Users can extract a variety of data points - from green spaces and water bodies to urban landmarks and transportation networks - and visualize them on detailed maps. This feature makes it an invaluable resource for comprehensive urban analysis and planning.

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Getting Started

Prerequisites

• Python 3.x
• OSMnx library
• Additional Python libraries as per requirements.txt

Installation

Clone the repository and install dependencies to dive into Mumbai's urban landscape:

git clone https://github.com/MasterKinjalk/mumbai-data-viz.git
cd mumbai-data-viz
pip install -r requirements.txt

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Usage

To start extracting and visualizing data with the mindmapmumbai.py script:

python mindmapmumbai.py

Customize the script to target different data sets and visualize various aspects of Mumbai or any other urban environment.

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Integration of play.py with mindmapmumbai.py

Overview

The script play.py is designed to extract various data from the OSMnx library, catering to different use cases specific to our project's needs. To effectively utilize this extracted data, modifications are necessary in the mindmapmumbai.py script. This ensures that the insights and information obtained from play.py are accurately represented and utilized in the project's core functionalities.

Modifications in mindmapmumbai.py

After extracting the desired data using play.py, it is essential to incorporate this data into the mindmapmumbai.py script to reflect its usage and impact on the project. The integration process involves updating mindmapmumbai.py to:

1. Import Data: Ensure that mindmapmumbai.py has the appropriate functions or mechanisms to import or read the data extracted by play.py.
2. Utilize Extracted Data: Modify the logic within mindmapmumbai.py to use the data for its specific functionalities. This could involve updating data structures, algorithms, or processing methods to accommodate the new information.
3. Reflect Changes in Output: Ensure that any changes in data or logic are reflected in the outputs of mindmapmumbai.py, whether it be through visual representations, data analysis results, or any other form of output relevant to the project.

Best Practices

• Consistency: Maintain a consistent data format between play.py and mindmapmumbai.py to minimize integration issues.
• Modularity: Keep the data extraction and processing logic as modular as possible. This simplifies updates or modifications to either script without significantly impacting the other.
• Documentation: Document any changes made to mindmapmumbai.py for integrating play.py data. This includes comments in the code and updates in the README or any other relevant documentation.

By following these guidelines and ensuring a seamless integration of data extracted with play.py into mindmapmumbai.py, the project can fully leverage the capabilities of the OSMnx library and enhance its functionality and effectiveness.

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Acknowledgments

• Gratitude to the developers of OSMnx for providing the tools to make this project possible.