In recent years there has been a renewed interest in utilitarian cycling due to its recognized potential in the reduction of energy consumption and pollution in the cities. Bike-sharing systems generate a large amount of data which can be used to improve the systems themselves, or to improve the body of knowledge on urban mobility. The open data automatically generated by the Brussel's bike-sharing system (Villo) is explored through spatial regression models of the number of bicycle trips at stations. The main goal of the modelling process is to understand if socio-economic, infrastructure and land use factors influence mobility patterns in peak periods and weekdays. The first step of the modelling process consists in setting up exploratory Ordinary Least Squares (OLS) models in order to identify potential explanatory variables. Finally, Geographically Weighted Poisson Regression (GWPR) models and semi-parametric versions of GWPR models are parametrised using the previously identified variables. The results show that the relationships between the dependent and independent variables are complex and spatially varying. Furthermore, the results show hidden patterns that enable further local investigation on these relationships. The weaknesses and strengths of our approach are discussed, particularly its implementation in other geographic contexts and its potential of generalisation for all bicycle trips.