Understanding the modifiable areal unit problem in dockless bike sharing usage and exploring the interactive effects of built environment factors

ABSTRACT

Understanding the influence mechanisms of dockless bike-sharing usage is essential for land use planning and bike scheduling strategy implementation. Although various studies have been carried out to explore the impact of built environment (BE) factors on bike-sharing usage, few studies have examined the modifiable areal unit problem (MAUP). Moreover, previous studies mainly focused on the separate effect of each factor but neglected the interactions between these factors. Taking Shenzhen, China as the case, this study fills these two gaps by employing the geographical detector method to examine the MAUP in dockless bike-sharing usage as well as the interactive effects of BE factors. The results revealed that the influences of most BE variables are sensitive to the spatial areal units, which have informed urban planners what built-environment factors should be paid more attention to at certain spatial scales. Additionally, through the comparisons between single effect and interactive effect, this study revealed some interesting findings that can provide scientific basis for temporal rebalance strategy for the innovative and high-density metropolis in China.

KEYWORDS:

• Geographical detector
• interactions
• dockless bike sharing
• built environment
• social media data
• Modifiable areal unit problem

Data and codes availability statement

The model datasets and geographical detector model software that support the findings of the present study are available in figshare at https://doi.org/10.6084/m9.figshare.11438502.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by Guangzhou Science and technology project [Grant No. 201904010198], the National Natural Science Foundation of China [Grant No. 41871290, 41401432], Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) [GML2019ZD0301].

Notes on contributors

Feng Gao

Feng Gao is a master student at Guangzhou University and works in the area of geospatial big data analysis and urban planning.

Shaoying Li

Shaoying Li is an associate professor at Guangzhou University and works in the area of geospatial big data analysis and urban study, transportation and land use.

Zhangzhi Tan

Zhangzhi Tan receives the Ph.D. degree in Sun Yat-Sen University and works in the area of transportation study.Zhangzhi Tan receives the Ph.D. degree in Sun Yat-Sen University and works in the area of transportation study.

Zhifeng Wu

Zhifeng Wu is a professor at Guangzhou University and works in the research area of urban human settlements.

Xiaoming Zhang

Xiaoming Zhang  is the director of Transportation Planning and Design Department of Guangzhou Urban Planning Survey and Design Institute, and works in the area of transportation development strategic planning, comprehensive transportation planning and traffic information model development and application.

Guanping Huang

Guanping Huang is a master student at Guangzhou University and works in the area of big data analysis and urban study.

Ziwei Huang

Ziwei Huang is a master student at Guangzhou University and works in the area of big data analysis and urban economic study.