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Understanding Mobility and Active Transportation in Urban Areas Through Crowdsourced Movement Data.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Understanding Mobility and Active Transportation in Urban Areas Through Crowdsourced Movement Data./
Author:	Conrow, Lindsey.
Description:	1 online resource (124 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
Subject:	Geographic information science and geodesy. -
Online resource:	click for full text (PQDT)
ISBN:	9780355935455

Understanding Mobility and Active Transportation in Urban Areas Through Crowdsourced Movement Data.
Conrow, Lindsey.

Understanding Mobility and Active Transportation in Urban Areas Through Crowdsourced Movement Data. - 1 online resource (124 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2018.

Includes bibliographical references

Factors that explain human mobility and active transportation include built environment and infrastructure features, though few studies incorporate specific geographic detail into examinations of mobility. Little is understood, for example, about the specific paths people take in urban areas or the influence of neighborhoods on their activity. Detailed analysis of human activity has been limited by the sampling strategies employed by conventional data sources. New crowdsourced datasets, or data gathered from smartphone applications, present an opportunity to examine factors that influence human activity in ways that have not been possible before; they typically contain more detail and are gathered more frequently than conventional sources. Questions remain, however, about the utility and representativeness of crowdsourced data. The overarching aim of this dissertation research is to identify how crowdsourced data can be used to better understand human mobility. Bicycling activity is used as a case study to examine human mobility because smartphone apps aimed at collecting bicycle routes are readily available and bicycling is under studied in comparison to other modes. The research herein aimed to contribute to the knowledge base on crowdsourced data and human mobility in three ways. First, the research examines how conventional (e.g., counts, travel surveys) and crowdsourced data correspond in representing bicycling activity. Results identified where the data correspond and differ significantly, which has implications for using crowdsourced data for planning and policy decisions. Second, the research examined the factors that influence cycling activity generated by smartphone cycling apps. The best predictors of activity were median weekly rent, percentage of residential land, and the number of people using two or more modes to commute in an area. Finally, the third part of the dissertation seeks to understand the impact of bicycle lanes and bicycle ridership on residential housing prices. Results confirmed that bicycle lanes in the neighborhood of a home positively influence sale prices, though ridership was marginally related to house price. This research demonstrates that knowledge obtained through crowdsourced data informs us about smaller geographic areas and details on where people bicycle, who uses bicycles, and the impact of the built environment on bicycling activity.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355935455Subjects--Topical Terms:

1148646
Geographic information science and geodesy.
Index Terms--Genre/Form:

554714
Electronic books.

Understanding Mobility and Active Transportation in Urban Areas Through Crowdsourced Movement Data.
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