NYCdata: NYC Taxi Network Dataset
In gasper: Graph Signal Processing
NYCdata R Documentation
NYC Taxi Network Dataset
Description
A dataset derived from NYC taxi trip records. Additionally, the dataset includes a signal f that represents the total amount with added noise.
Usage
NYCdata
Format
A list with 2 elements:
-
A: NYC adjacency matrix, constructed using Gaussian weights based on mean distances between locations.
-
f: Signal representing the "total amount" with added artificial noise.
Details
The graph constructed represents the connectivity based on taxi trips between different locations. The weights of the edges represent the frequency and distances of trips between locations.
The data comes from the methodology in the referenced paper. It is constructed from real-world data fetched from NYC taxis databases. The graph consists of 265 vertices which correspond to different LocationID (both Pick-Up and Drop-Off points). Gaussian weights are defined by
w_{ij} = \exp(-\tau d_{i,j}^2)
, where d_{i,j} represents the mean distance taken on all the trips between locations i and j or j and i.
The signal f is constructed based on the "total amount" variable from the taxi dataset, with added artificial noise.
References
de Loynes, B., Navarro, F., Olivier, B. (2021). Data-driven thresholding in denoising with Spectral Graph Wavelet Transform. Journal of Computational and Applied Mathematics, Vol. 389.
gasper documentation built on May 29, 2024, 8:32 a.m.
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| NYCdata | R Documentation |
NYC Taxi Network Dataset
Description
A dataset derived from NYC taxi trip records. Additionally, the dataset includes a signal f that represents the total amount with added noise.
Usage
NYCdata
Format
A list with 2 elements:
-
A: NYC adjacency matrix, constructed using Gaussian weights based on mean distances between locations. -
f: Signal representing the "total amount" with added artificial noise.
Details
The graph constructed represents the connectivity based on taxi trips between different locations. The weights of the edges represent the frequency and distances of trips between locations.
The data comes from the methodology in the referenced paper. It is constructed from real-world data fetched from NYC taxis databases. The graph consists of 265 vertices which correspond to different LocationID (both Pick-Up and Drop-Off points). Gaussian weights are defined by
w_{ij} = \exp(-\tau d_{i,j}^2)
, where d_{i,j} represents the mean distance taken on all the trips between locations i and j or j and i.
The signal f is constructed based on the "total amount" variable from the taxi dataset, with added artificial noise.
References
de Loynes, B., Navarro, F., Olivier, B. (2021). Data-driven thresholding in denoising with Spectral Graph Wavelet Transform. Journal of Computational and Applied Mathematics, Vol. 389.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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