The Role of Spatial Variability in Developing Cycling Cities: Implications Drawn from Geographically Weighted Regressions

Abstract

Highlights: What are the main findings? A multiple geographically weighted regression (GWR) approach shows that cycling use in cities is not uniform, and the effects of distance and precipitation on cycling vary across different locations. While the relationship between cycling volume and distance and precipitation remains negative, some locations are less sensitive to these effects. Cycling volumes in New Zealand’s largest city of Auckland show lower sensitivity to distance compared with Wellington and Christchurch, suggesting that urban design plays a role in cycling behavior. In addition, cycling volumes in Christchurch show the highest sensitivity to precipitation despite having the lowest annual rainfall of the three cities. What is the implication of the main finding? Improving infrastructure to connect to central economic nodes, rather than solely the central business district, will help mitigate the impact of distance on cycling, encouraging the use of cycling as an alternative transport option. Prioritize the development of weather-resistant cycling infrastructure to remove barriers related to weather by including features such as covered bike lanes, rain shelters, and real-time weather updates to help cyclists on their trip. As cities grow, they increase in complexity, requiring the effective use of land resources. Cycling is generally regarded as an alternative transport mode to support the development of the cities of tomorrow. In response to urbanization, in many cities worldwide, a common concern associated with investing in cycling networks is the resulting use after such investment. This study uses a continuous longitudinal dataset of daily cycling counts from January 2018 to June 2024 to assess bicycle volumes across three of New Zealand’s largest cities. The results reveal that the relationship between distance and cycle count is not uniform across space, with some areas showing a negative effect between distance and cycling, and others showing a positive one. A global OLS model hides these complexities, as shown in the geographically weighted regression (GWR) model. The coefficients for distance (−0.49) and precipitation (−95.23) in the global OLS are higher, and do not reveal the non-uniformity between cities, wheras themultiple GWR coefficients for distance range between −0.57 and −0.47 and precipitation between −33.47 and −97.63. The results reveal that cycling volume demonstrates lower sensitivity to changes in distance compared to variations in weather conditions. At the city level, there are notable intercity differences in sensitivity. The variability in the coefficients across locations suggests that, although distance and precipitation have general effects, local factors, such as infrastructure quality, topography, weather adaptation measures, and cultural attitudes toward cycling, play a critical role in modulating these relationships. The findings highlight the complexity of spatial interactions and emphasize the need for localized interventions when planning cycling networks.