Evaluating walkability using mobile GPS data

Yoshiki Yamagata; Daisuke Murakami; Takahiro Yoshida

doi:10.1016/B978-0-12-813127-5.00009-6

Evaluating walkability using mobile GPS data

Yoshiki Yamagata, Daisuke Murakami, Takahiro Yoshida

Research output: Chapter in Book/Report/Conference proceeding › Chapter

8 Citations (Scopus)

Abstract

This chapter illustrates the results of applications of geographically weighted regression modeling (Chapter 6) for assessing goodness of walking environment, that is, walkability. Section 9.1 briefly introduces some representative measures of walkability. Section 9.2 explains about using data and walkability measures. The settings for geographically weighted regression modeling are also explained here. Sections 9.3 and 9.4 show the estimation results. The former focuses on the impact of pedestrian network structure on the number of pedestrians, whereas the latter focuses on heatwave risk for pedestrians at an intra-urban scale. Finally, Section 9.5 concludes this chapter with a discussion.

Original language	English
Title of host publication	Spatial Analysis Using Big Data
Subtitle of host publication	Methods and Urban Applications
Publisher	Elsevier
Pages	239-257
Number of pages	19
ISBN (Electronic)	9780128131329
ISBN (Print)	9780128131275
DOIs	https://doi.org/10.1016/B978-0-12-813127-5.00009-6
Publication status	Published - 2019 Nov 2
Externally published	Yes

Keywords

Bayesian model averaging
Heatwave risk
Mobile GPS
Walkability

ASJC Scopus subject areas

Economics, Econometrics and Finance(all)
Business, Management and Accounting(all)

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10.1016/B978-0-12-813127-5.00009-6

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abstract = "This chapter illustrates the results of applications of geographically weighted regression modeling (Chapter 6) for assessing goodness of walking environment, that is, walkability. Section 9.1 briefly introduces some representative measures of walkability. Section 9.2 explains about using data and walkability measures. The settings for geographically weighted regression modeling are also explained here. Sections 9.3 and 9.4 show the estimation results. The former focuses on the impact of pedestrian network structure on the number of pedestrians, whereas the latter focuses on heatwave risk for pedestrians at an intra-urban scale. Finally, Section 9.5 concludes this chapter with a discussion.",

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AU - Murakami, Daisuke

AU - Yoshida, Takahiro

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N2 - This chapter illustrates the results of applications of geographically weighted regression modeling (Chapter 6) for assessing goodness of walking environment, that is, walkability. Section 9.1 briefly introduces some representative measures of walkability. Section 9.2 explains about using data and walkability measures. The settings for geographically weighted regression modeling are also explained here. Sections 9.3 and 9.4 show the estimation results. The former focuses on the impact of pedestrian network structure on the number of pedestrians, whereas the latter focuses on heatwave risk for pedestrians at an intra-urban scale. Finally, Section 9.5 concludes this chapter with a discussion.

AB - This chapter illustrates the results of applications of geographically weighted regression modeling (Chapter 6) for assessing goodness of walking environment, that is, walkability. Section 9.1 briefly introduces some representative measures of walkability. Section 9.2 explains about using data and walkability measures. The settings for geographically weighted regression modeling are also explained here. Sections 9.3 and 9.4 show the estimation results. The former focuses on the impact of pedestrian network structure on the number of pedestrians, whereas the latter focuses on heatwave risk for pedestrians at an intra-urban scale. Finally, Section 9.5 concludes this chapter with a discussion.

KW - Bayesian model averaging

KW - Heatwave risk

KW - Mobile GPS

KW - Walkability

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M3 - Chapter

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BT - Spatial Analysis Using Big Data

PB - Elsevier

ER -

Evaluating walkability using mobile GPS data

Abstract

Keywords

ASJC Scopus subject areas

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