https://github.com/kumaarbalbir/gps-data-viz
GPS Data ( latitude and longitude ) Visualization and Analysis ( traffic of trips ) using python, networkx, osmnx.
https://github.com/kumaarbalbir/gps-data-viz
gps-data gps-visualization-python networkx osmnx
Last synced: 6 months ago
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GPS Data ( latitude and longitude ) Visualization and Analysis ( traffic of trips ) using python, networkx, osmnx.
- Host: GitHub
- URL: https://github.com/kumaarbalbir/gps-data-viz
- Owner: KumaarBalbir
- Created: 2024-04-03T05:49:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-03T06:07:53.000Z (over 1 year ago)
- Last Synced: 2024-04-03T07:24:54.913Z (over 1 year ago)
- Topics: gps-data, gps-visualization-python, networkx, osmnx
- Language: Python
- Homepage:
- Size: 6.64 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# GPS Data and Bike Trip Analysis
This repository aims to provide tools for **visualizing and analyzing GPS data**, particularly focusing on bike trip data and trajectories. Description of below Python scripts:
## 1. `bike_trip.py`
This script contains a class `BikeTrip` for solving problems related to bike trip data. Here are some of the functionalities provided by this class:
- Processing bike trip data
- Calculating statistics such as maximum trip duration, minimum trip duration, count of minimum trip duration, and percentage of circular trips
- Filtering data based on time range
- Finding feasible trips and unique depots
- Generating graphs and **shortest routes** on maps
### How to use `bike_trip.py`
To use the functionalities provided by `BikeTrip` class, follow these steps:
1. Ensure you have the necessary dependencies installed (`pandas`, `numpy`, `matplotlib`, `geopy`, `networkx`, `osmnx`, `PIL`).
2. Import `BikeTrip` class from `bike_trip.py`.
3. Create an instance of `BikeTrip` by passing the path to your bike trip data CSV file.
4. You can then call various methods of `BikeTrip` class to analyze and visualize the data.
## 2. `gps.py`
This script contains two classes: `GPSVis` for visualizing GPS data on a map and `GPSdata` for analyzing GPS trajectories. Here's what each class does:
### `GPSVis`
- `GPSVis` class provides methods for plotting GPS data on a map image.
- It allows customization of the map output, including saving the map to a file.
- The class takes GPS records and a pre-downloaded **OSM map** image as inputs.
### `GPSdata`
- `GPSdata` class offers functionalities for processing GPS trajectory data.
- It calculates the **distance traveled** by each user in the provided dataset.
- It **filters GPS data points** for a specific geographical area (e.g., Beijing) and exports them to a CSV file.
### How to use `gps.py`
To utilize the functionalities provided by `GPSVis` and `GPSdata` classes:
1. Ensure you have the required dependencies installed (`pandas`, `numpy`, `matplotlib`, `geopy`, `PIL`).
2. Import the necessary classes from `gps.py`.
3. Create instances of these classes, providing the required input parameters.
4. Utilize the methods provided by these classes to visualize and analyze GPS data.
## Usage Examples
- Example usage of both scripts is demonstrated in the `if __name__=="__main__"` blocks within each file.
- You can refer to these examples to understand how to use the functionalities provided by each script.
## Results
- Shortest Path ( origin_node, destination_node, method = `dijakstra`) 
- Beijing [OSM](https://www.openstreetmap.org/#map=4/21.84/82.79) map 
- Bike trip traffic on Beijing map 
## Note
Ensure that you have the required data files (due to large size I am not pushing here) and map images in the specified paths before running the scripts.
Feel free to explore and customize these scripts according to your specific requirements!