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toolkit_for_gps_mobility_data_analysis

๐Ÿ“ GitHub: Unlocking the Power of GPS Mobility Data Analysis!

๐Ÿš€ Developed with the software and tools below.

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๐Ÿ“š Table of Contents


๐Ÿ“Overview

The toolkit_for_gps_mobility_data_analysis is a Python package designed for comprehensive analysis of GPS-based mobility data. It offers a user-friendly API to facilitate the processing and analysis of GPS data, making it an essential tool for researchers and developers working in the field of mobility analysis.

๐Ÿ”ฎ Feautres

  • Data Processing: Efficient handling and processing of large-scale GPS data.
  • Analysis Tools: Advanced tools for analyzing mobility patterns, stops, and routes.
  • Customizable Workflows: Flexible architecture to adapt to various analysis needs.

โš™๏ธ Project Structure

repo
โ”œโ”€โ”€ cpputils
โ”‚ย ย  โ””โ”€โ”€ main.cpp
โ”œโ”€โ”€ docs
โ”‚ย ย  โ”œโ”€โ”€ build
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โ”‚ย ย  โ”‚ย ย  โ”‚ย ย  โ”œโ”€โ”€ index.doctree
โ”‚ย ย  โ”‚ย ย  โ”‚ย ย  โ”œโ”€โ”€ intro.doctree
โ”‚ย ย  โ”‚ย ย  โ”‚ย ย  โ”œโ”€โ”€ modules.doctree
โ”‚ย ย  โ”‚ย ย  โ”‚ย ย  โ””โ”€โ”€ wbgps.doctree
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โ”‚ย ย  โ”‚ย ย      โ”œโ”€โ”€ _modules
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โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ””โ”€โ”€ wbgps
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย      โ”œโ”€โ”€ analysis.html
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย      โ”œโ”€โ”€ labeling.html
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย      โ””โ”€โ”€ stops.html
โ”‚ย ย  โ”‚ย ย      โ”œโ”€โ”€ modules.html
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โ”‚ย ย  โ”‚ย ย      โ”œโ”€โ”€ searchindex.js
โ”‚ย ย  โ”‚ย ย      โ”œโ”€โ”€ _sources
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”œโ”€โ”€ index.rst.txt
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”œโ”€โ”€ intro.rst.txt
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”œโ”€โ”€ modules.rst.txt
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ””โ”€โ”€ wbgps.rst.txt
โ”‚ย ย  โ”‚ย ย      โ”œโ”€โ”€ _static
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”œโ”€โ”€ basic.css
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โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”‚ย ย  โ”‚ย ย  โ”œโ”€โ”€ lato-bold.woff
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โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”œโ”€โ”€ language_data.js
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”œโ”€โ”€ minus.png
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โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”œโ”€โ”€ pygments.css
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ”œโ”€โ”€ searchtools.js
โ”‚ย ย  โ”‚ย ย      โ”‚ย ย  โ””โ”€โ”€ sphinx_highlight.js
โ”‚ย ย  โ”‚ย ย      โ””โ”€โ”€ wbgps.html
โ”‚ย ย  โ”œโ”€โ”€ make.bat
โ”‚ย ย  โ”œโ”€โ”€ Makefile
โ”‚ย ย  โ””โ”€โ”€ source
โ”‚ย ย      โ”œโ”€โ”€ conf.py
โ”‚ย ย      โ”œโ”€โ”€ index.rst
โ”‚ย ย      โ”œโ”€โ”€ intro.rst
โ”‚ย ย      โ”œโ”€โ”€ modules.rst
โ”‚ย ย      โ””โ”€โ”€ wbgps.rst
โ”œโ”€โ”€ LICENSE
โ”œโ”€โ”€ README.md
โ”œโ”€โ”€ requirements.txt
โ”œโ”€โ”€ setup.py
โ””โ”€โ”€ wbgps
    โ”œโ”€โ”€ analysis.py
    โ”œโ”€โ”€ __init__.py
    โ”œโ”€โ”€ labeling.py
    โ””โ”€โ”€ stops.py

14 directories, 71 files

๐Ÿ’ป Modules

Wbgps
File Summary Module
labeling.py This code imports the necessary packages and functions to create a dataframe with user_id , start and end times , duration , latitude and longitude , total duration and pings at a stop location , cluster label , median accuracy , location type , wbgps/labeling.py
stops.py This code contains functions to apply the Infostop algorithm to a set of geolocated pings with timestamps . It validates the data frame , computes intervals , converts dates to UNIX time , and applies the DBSCAN wbgps/stops.py
analysis.py Prompt too long to generate summary. wbgps/analysis.py

๐Ÿš€ Getting Started

โœ… Prerequisites

Before you begin, ensure that you have the following prerequisites installed:

  • Pyspark 3 <

There is an development environment in the infrastructure directory. You would need to install docker-compose [https://docs.docker.com/compose/] and run the following command in the mentioned directory:

docker-compose up

This gives you all the requirements for running a Spark cluster in your local machine using docker containers.

๐Ÿ’ป Installation

  1. Clone the toolkit_for_gps_mobility_data_analysis repository:
git clone https://github.com/spfraib/toolkit_for_gps_mobility_data_analysis
  1. Change to the project directory:
cd toolkit_for_gps_mobility_data_analysis
  1. Install the package:
pip install .

๐Ÿค– Using toolkit_for_gps_mobility_data_analysis

import wbgps

๐Ÿงช Tests

Check out our example notebook for a quick demonstration of how to use the toolkit.


๐Ÿ›  Future Development

  • [๐Ÿ“Œ COMPLETED-TASK]
  • [๐Ÿ“Œ INSERT-TASK]
  • [๐Ÿ“Œ INSERT-TASK]

๐Ÿค Contributing

Contributions are always welcome! Please follow these steps:

  1. Fork the project repository. This creates a copy of the project on your account that you can modify without affecting the original project.
  2. Clone the forked repository to your local machine using a Git client like Git or GitHub Desktop.
  3. Create a new branch with a descriptive name (e.g., new-feature-branch or bugfix-issue-123).
git checkout -b new-feature-branch
  1. Make changes to the project's codebase.
  2. Commit your changes to your local branch with a clear commit message that explains the changes you've made.
git commit -m 'Implemented new feature.'
  1. Push your changes to your forked repository on GitHub using the following command
git push origin new-feature-branch
  1. Create a pull request to the original repository. Open a new pull request to the original project repository. In the pull request, describe the changes you've made and why they're necessary. The project maintainers will review your changes and provide feedback or merge them into the main branch.

๐Ÿชช License


๐Ÿ™ Acknowledgments


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  • Python 92.3%
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