Geographical Exploration of the Underrepresentation of Ethnic Minority Cyclists in England

Afua Kokayi, Shino Shiode and Narushige Shiode ()
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Afua Kokayi: Ministry of Justice, London SW1H 9AJ, UK
Shino Shiode: Department of Geography, Birkbeck, University of London, London WC1E 7HX, UK
Narushige Shiode: Department of Geography, Geology and the Environment, Kingston University, Kingston upon Thames KT1 2EE, UK

Sustainability, 2023, vol. 15, issue 7, 1-17

Abstract: Cycling is encouraged as a means of sustainable urban transport, yet its uptake rate is uneven between different ethnic groups. The ethnic minority population in England is underrepresented as cyclists, but the reasons for this are unclear. Through linear regression and Geographically Weighted Regression (GWR), this research investigates the spatial distribution of the propensity to cycling among the ethnic minority population and the white population across England with the aim to identify the contributing factors toward the discrepancy of cycling rates between both groups and how these factors vary geographically. Results from OLS regression suggest that cycle rates are generally affected most by hilliness , the presence of school-age children , and income , with the presence of school-age children affecting the ethnic minority group and hilliness affecting the white group the most. The use of GWR revealed that income generally reduces cycle rates but has a positive impact in London for both groups. The length of cycleways and the length of 20 mph speed limit roads per unit area were statistically insignificant, but their local coefficients in GWR showed strong regional variations for both groups. The study also found that, with the exception of the level of income , ethnic minority cyclists are less sensitive to contributing factors than the white cyclists.

Keywords: barrier to cycle; ethnic minority; cycle to work; geographically weighted regression; regression analysis; urban mobility (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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