Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors

Yuan Yuan Lee; Yi Ling Chang

doi:10.1109/CSCI49370.2019.00239

Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Consumer behavior analytics is at the epicenter of a Big Data revolution. In this paper we propose to analyze intra-regional spatial patterns mining tourists' behaviors and characteristics based on traveling group size with data collected from Airbnb open source focused on Los Angeles neighborhood in 2016. Random Forest Classification (RF) technique, an ensemble approach, is applied to identify the key drivers according to relevant traveler groups and presented patterns using Hotspot Analysis on Geographic Information System (GIS). Our empirical result highlights driving factors within Airbnb listings, providing valuable insights to better plan, monitor and manage tourism activity.

Original language	English
Title of host publication	Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1275-1280
Number of pages	6
ISBN (Electronic)	9781728155845
DOIs	https://doi.org/10.1109/CSCI49370.2019.00239
State	Published - Dec 2019
Externally published	Yes
Event	6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019 - Las Vegas, United States Duration: Dec 5 2019 → Dec 7 2019

Publication series

Name	Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019

Conference

Conference	6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019
Country/Territory	United States
City	Las Vegas
Period	12/5/19 → 12/7/19

Keywords

Computational complexity
Hotspot analysis
Machine learning
Pattern classification
Random forest

Access to Document

10.1109/CSCI49370.2019.00239

Cite this

Lee, Y. Y., & Chang, Y. L. (2019). Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors. In Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019 (pp. 1275-1280). Article 9071300 (Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI49370.2019.00239

Lee, Yuan Yuan ; Chang, Yi Ling. / Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors. Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1275-1280 (Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019).

@inproceedings{95b4969cafb94c7fbc204d9fc9fe2ea0,

title = "Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors",

abstract = "Consumer behavior analytics is at the epicenter of a Big Data revolution. In this paper we propose to analyze intra-regional spatial patterns mining tourists' behaviors and characteristics based on traveling group size with data collected from Airbnb open source focused on Los Angeles neighborhood in 2016. Random Forest Classification (RF) technique, an ensemble approach, is applied to identify the key drivers according to relevant traveler groups and presented patterns using Hotspot Analysis on Geographic Information System (GIS). Our empirical result highlights driving factors within Airbnb listings, providing valuable insights to better plan, monitor and manage tourism activity.",

keywords = "Computational complexity, Hotspot analysis, Machine learning, Pattern classification, Random forest",

author = "Lee, {Yuan Yuan} and Chang, {Yi Ling}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019 ; Conference date: 05-12-2019 Through 07-12-2019",

year = "2019",

month = dec,

doi = "10.1109/CSCI49370.2019.00239",

language = "English",

series = "Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1275--1280",

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}

Lee, YY & Chang, YL 2019, Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors. in Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019., 9071300, Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1275-1280, 6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019, Las Vegas, United States, 12/5/19. https://doi.org/10.1109/CSCI49370.2019.00239

Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors. / Lee, Yuan Yuan; Chang, Yi Ling.
Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1275-1280 9071300 (Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Consumer behavior analytics is at the epicenter of a Big Data revolution. In this paper we propose to analyze intra-regional spatial patterns mining tourists' behaviors and characteristics based on traveling group size with data collected from Airbnb open source focused on Los Angeles neighborhood in 2016. Random Forest Classification (RF) technique, an ensemble approach, is applied to identify the key drivers according to relevant traveler groups and presented patterns using Hotspot Analysis on Geographic Information System (GIS). Our empirical result highlights driving factors within Airbnb listings, providing valuable insights to better plan, monitor and manage tourism activity.

KW - Computational complexity

KW - Hotspot analysis

KW - Machine learning

KW - Pattern classification

KW - Random forest

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M3 - Conference contribution

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Lee YY, Chang YL. Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors. In Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1275-1280. 9071300. (Proceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019). doi: 10.1109/CSCI49370.2019.00239

Uncovering los angeles tourists' patterns using geospatial analysis and supervised machine learning with random forest predictors

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Keywords

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