Taylor M. Oshan

TL;DR: In this paper, the authors introduce mgwr, a Python-based implementation of MGWR that explicitly focuses on the multiscale analysis of spatial heterogeneity, and provide novel functionality for inference and exploratory analysis of local spatial processes, new diagnostics unique to multi-scale local models, and drastic improvements in estimation routines.

TL;DR: This paper proposes a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining, and spatio-temporal analysis and evaluates this framework using a collection of trajectories from 205 volunteers linked to contextual spatial information.

TL;DR: A controlled simulation is employed to demonstrate that GWR is in fact very robust to the effects of multicollinearity, and the contention that G WR is highly susceptible to multicoll inearity issues needs rethinking.

TL;DR: In this paper, the authors propose a generalized additive model (GMM) for MGWR and derive standard errors for the local parameters in MGWR, which can be used to compare the overall fit of an MGWR model and for each of the covariates within the model.

TL;DR: A critical review of previous GWR models of obesogenic processes and a novel application of multiscale (M)GWR are presented, showing that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants.