| Name | stmeasures JSON |
| Version |
0.0.1.1
JSON |
| download |
| home_page | None |
| Summary | A library for measuring trajectory similarities. |
| upload_time | 2024-12-11 05:42:57 |
| maintainer | None |
| docs_url | None |
| author | Amilcar Meneses, Erika Hernandez |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 TT2024-B106 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
geojson
measurement similarities
mobile devices
mobility patterns
trajectory similarities
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Similar Trajectories Measurements (`stmeasures`)
`stmeasures` is a Python library designed to provide researchers in Geographic Information Systems (GIS) and related fields with simple and efficient tools for measuring the similarity between trajectories. This library supports a variety of use cases, scenarios, and contexts, depending on where, how, and why the trajectories were collected or analyzed.
## Installation
Install it through PyPI:
```sh
pip install stmeasures
```
## Usage
Once installed you can use all functionalites provided by this package.
```py
import stmeasures
dataset = stmeasures.read_file("trajectories.json")
spad_metric = stmeasures.distance(dataset[4], dataset[53]) # Will get you the Spatial Assembling Distance between trajectories
```
### Visualization
Data can be visualized using [`geojsonio`](https://github.com/jwass/geojsonio.py) package.
```py
import stmeasures
import geojsonio
dataset = stmeasures.read_file("trajectories.json")
geojsonio.display(geojsonio.display(stmeasures.geojsonio_contents(trajectories=dataset[50:56]))) # Will display a map with the given trajectories in geojson.io
```
## Key Features
- **Diverse Algorithms:** Includes multiple algorithms such as Hausdorff, Frechet, DTW, LCSS, Euclidean, and SPAD to suit different similarity measurement needs.
- **Flexible Integration:** Offers modular functions and customizable parameters for specific trajectory analysis tasks.
- **Scalable Design:** Suitable for individual comparisons, clustering, and pattern discovery in trajectory datasets.
## Library Overview
The following diagram illustrates the structure and main components of the library:
## Use Cases
`stmeasures` can be used in a variety of applications, including:
1. **Trajectory Clustering:** Grouping similar movement patterns, such as vehicle paths or animal migrations.
2. **Outlier Detection:** Identifying unusual trajectories in a dataset.
3. **Pattern Matching:** Finding trajectories that follow a specific path or trend.
4. **Path Optimization Analysis:** Comparing planned and actual paths to optimize logistics or urban planning.
## About the Project
This project is part of a final bachelor's degree research initiative at IPN University (Mexico City, 2024). It aims to support GIS and data science communities with tools for trajectory similarity analysis. While still under development, the library is designed with extensibility and research-focused applications in mind.
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"description": "# Similar Trajectories Measurements (`stmeasures`)\n\n`stmeasures` is a Python library designed to provide researchers in Geographic Information Systems (GIS) and related fields with simple and efficient tools for measuring the similarity between trajectories. This library supports a variety of use cases, scenarios, and contexts, depending on where, how, and why the trajectories were collected or analyzed.\n\n## Installation\n\nInstall it through PyPI:\n\n```sh\npip install stmeasures\n```\n\n## Usage\n\nOnce installed you can use all functionalites provided by this package.\n\n```py\nimport stmeasures\n\ndataset = stmeasures.read_file(\"trajectories.json\")\nspad_metric = stmeasures.distance(dataset[4], dataset[53]) # Will get you the Spatial Assembling Distance between trajectories\n```\n\n### Visualization\n\nData can be visualized using [`geojsonio`](https://github.com/jwass/geojsonio.py) package.\n\n```py\nimport stmeasures\nimport geojsonio\n\ndataset = stmeasures.read_file(\"trajectories.json\")\ngeojsonio.display(geojsonio.display(stmeasures.geojsonio_contents(trajectories=dataset[50:56]))) # Will display a map with the given trajectories in geojson.io\n```\n\n## Key Features\n\n- **Diverse Algorithms:** Includes multiple algorithms such as Hausdorff, Frechet, DTW, LCSS, Euclidean, and SPAD to suit different similarity measurement needs.\n- **Flexible Integration:** Offers modular functions and customizable parameters for specific trajectory analysis tasks.\n- **Scalable Design:** Suitable for individual comparisons, clustering, and pattern discovery in trajectory datasets.\n\n## Library Overview\n\nThe following diagram illustrates the structure and main components of the library:\n\n\n\n## Use Cases\n\n`stmeasures` can be used in a variety of applications, including:\n\n1. **Trajectory Clustering:** Grouping similar movement patterns, such as vehicle paths or animal migrations.\n2. **Outlier Detection:** Identifying unusual trajectories in a dataset.\n3. **Pattern Matching:** Finding trajectories that follow a specific path or trend.\n4. **Path Optimization Analysis:** Comparing planned and actual paths to optimize logistics or urban planning.\n\n## About the Project\n\nThis project is part of a final bachelor's degree research initiative at IPN University (Mexico City, 2024). It aims to support GIS and data science communities with tools for trajectory similarity analysis. While still under development, the library is designed with extensibility and research-focused applications in mind.\n",
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