<p>Administrations engaged in spatial planning and policy face many different tasks and evidence-based resource allocation to solve these tasks requires high-quality data. Here, data reduction techniques to reduce dimensionality or group cases with similar properties help in structuring the data through holistic built environment typologies. However, data-driven methods are susceptible to the nature of the sample as well as methodological decisions which introduces uncertainty about the validity of typologies. Studies usually apply one method to one sample which complicates the evaluation of resulting built environment typologies. In this comparative study, we use hierarchical cluster analysis, principal components analysis, latent profile analysis and latent class analysis as methods for reducing the number of environmental variables and test if the respective typologies yield different results in subsequent regression analyses. As use case we study environmental determinants of children’s independent mobility (CIM) on the shortest routes to school. We find that typologies vary between methods which limits their respective generalizability. Furthermore, environmental typologies are prone to bias introduced by sampling. Besides those methodological findings, we find that CIM is lower for longer distances and occurrence of accidents along the route to school. Parental perceptions and attitudes strongly impact CIM while the built and transport environment types only appear as marginal influence. These general findings are consistent for all typologies but conclusions regarding the specific associations of the respective types with CIM depend on the applied method. We conclude that environmental typologies should be based on sampling units that are spatially definite without any prior aggregation in order to achieve robust results.</p>