New tools of the trade? The potential and pitfalls of’Machine Learning’and’DAGs’ to model origin-destination data

Robin Lovelace and Ilan Fridman-Rojas (2017). New tools of the trade? The potential and pitfalls of’Machine Learning’and’DAGs’ to model origin-destination data. GeoComputation 2017.
Authors

Robin Lovelace

Ilan Fridman-Rojas

Published

January 1, 2017

Abstract
This article explores the application of machine learning and Directed Acyclic Graphs (DAGs) in the analysis of origin-destination data, discussing both the technical opportunities and the challenges of interpretability.

Type: Conference Paper Venue: GeoComputation 2017 Year: 2017

BibTeX

Abstract

This article explores the application of machine learning and Directed Acyclic Graphs (DAGs) in the analysis of origin-destination data, discussing both the technical opportunities and the challenges of interpretability.

Citation

Robin Lovelace and Ilan Fridman-Rojas (2017). New tools of the trade? The potential and pitfalls of’Machine Learning’and’DAGs’ to model origin-destination data. GeoComputation 2017.

BibTeX

@inproceedings{lovelace_new_2017,
    title = {New tools of the trade? {The} potential and pitfalls of’{Machine} {Learning}’and’{DAGs}’ to model origin-destination data},
    copyright = {CC0 1.0 Universal Public Domain Dedication},
    shorttitle = {New tools of the trade?},
    abstract = {This article explores the application of machine learning and Directed Acyclic Graphs (DAGs) in the analysis of origin-destination data, discussing both the technical opportunities and the challenges of interpretability.},
    booktitle = {{GeoComputation} 2017},
    publisher = {Centre for Computational Geography, University of Leeds},
    author = {Lovelace, Robin and Fridman-Rojas, Ilan},
    editor = {Long, Rob},
    year = {2017},
}

Notes