Metadata-Version: 2.1
Name: CaGeo
Version: 0.0.1.post2
Summary: Package description
Home-page: https://github.com/USERNAME/project
Author: Cristiano Landi
Author-email: cri98li@gmail.com
License: BSD-Clause-2
Keywords: keyword1 keyword2 keyword3
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX :: Other
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
Provides-Extra: flag
License-File: LICENSE

# CageX: CAnonical Geospatial features



In this repository we want to collect and implement methods to extract 

from a set of raw GPS coordinates features that enrich the dataset, 

but which, simultaneously also allow dimensionality reduction.



## Features:



### Point Features

- Speed: AVG, STD, MIN, MAX

- Acceleration: AVG, STD, MIN, MAX

- angle/direction: atan2 == Bearing: angle between the magnetic north and an object ??



### Aggregate features

- Turning Angle: AVG, STD, MIN, MAX. 𝐻𝐶𝑅 = |𝑃𝑐|/𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 Pc is the collection of gps points at which a user changes 

   his/her heading direction exceeding a certain threshold (Hc), and |𝑃𝑐 | represents the number of elements in Pc

- Traveled Distance: SUM

- Stop Rate: 𝑆𝑅 = |𝑃𝑠|/𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 Ps is the collection of point with velocity smaller than a certain threshold

- Velocity Change Rate: foreach point 𝑝1. 𝑉𝑅𝑎𝑡𝑒 = |𝑉2 − 𝑉1|/𝑉1; then 𝑉𝐶𝑅 = |𝑃𝑣|/𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 where 𝑃𝑣 ={𝑝𝑖|𝑝𝑖 ∈ 𝑃, 𝑝𝑖 . 𝑉𝑅𝑎𝑡𝑒 > 𝑉𝑟 }

- FFT?

- duration of movement?

- traveled path?

- displacement?

- Bearing rate: B_rate(i+1) = (Bi+1 − Bi)/∆t

- Rate of bearing rate: Br_rate(i+1) = (Brate(i+1) − Brate(i))/∆t



### Derivate features

- sinuosity ?

- distance from POI



## References:

- A survey and comparison of trajectory classification methods

- Understanding mobility based on GPS data

- Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects

- Predicting Transportation Modes of GPS Trajectories using Feature Engineering and Noise Removal

- Determination transportation mode on mobile phones 







## Note

Per le distanze vedere Intelligent Trajectory Classification for Improved Movement Prediction



In "Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensamble Classifier": ci sono varie misure globali
