Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2012.10746

A Spatiotemporal Functional Model for Bike-Sharing Systems -- An Example based on the City of Helsinki

Andreas Piter·Philipp Otto·Hamza Alkhatib
View original source ↗Read source document ↗Export metadata ↓

Abstract

Understanding the usage patterns for bike-sharing systems is essential in terms of supporting and enhancing operational planning for such schemes. Studies have demonstrated how factors such as weather conditions influence the number of bikes that should be available at bike-sharing stations at certain times during the day. However, the influences of these factors usually vary over the course of a day, and if there is good temporal resolution, there could also be significant effects only for some hours/minutes (rush hours, the hours when shops are open, and so forth). Thus, in this paper, an analysis of Helsinki's bike-sharing data from 2017 is conducted that considers full temporal and spatial resolutions. Moreover, the data are available at a very high frequency. Hence, the station hire data is analysed in a spatiotemporal functional setting, where the number of bikes at a station is defined as a continuous function of the time of day. For this completely novel approach, we apply a functional spatiotemporal hierarchical model to investigate the effect of environmental factors and the magnitude of the spatial and temporal dependence. Challenges in computational complexity are faced using a bootstrapping approach. The results show the necessity of splitting the bike-sharing stations into two clusters based on the similarity of their spatiotemporal functional observations in order to model the station hire data of Helsinki's bike-sharing system effectively. The estimated functional influences of the proposed factors are different for the two clusters. Moreover, the estimated parameters reveal high random effects in the data that are not explained by the mean of the process. In this random-effects model, the temporal autoregressive parameter dominates the spatial dependence.

Explore related subjects

stat.AP

Keep this discovery

BibTeXRIS

Andreas Piter, Philipp Otto, Hamza Alkhatib. 2020-12-19. A Spatiotemporal Functional Model for Bike-Sharing Systems -- An Example based on the City of Helsinki. https://doi.org/10.1111/rssa.12834

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Privacy-Preserving Causal Meta-Mediation Analysis with Survival Outcomes

Privacy and data-governance constraints often prevent pooling individual-level data across studies, limiting the use of conventional approaches for causal media- tion analysis in multicenter settings. We propose a federated causal meta-mediation framework for right-censored time-to-event outcomes that enables collaborative es- timation without sharing individual-level data. Our framework targets natural indirect effects in a prespecified population by combining information on mediator and outcome mechanisms across distributed data sources. A site-by-site identifi- cation strategy further allows heterogeneity across data sources to be character- ized, with a variance decomposition separating outcome-related, mediator-related, and interaction components. We develop federated one-step and targeted maxi- mum likelihood estimators that accommodate data-adaptive and machine-learning methods for nuisance-function estimation. The finite-sample performance of the proposed estimators is evaluated through numerical simulations. To illustrate the practical utility of the framework, we apply it on data from the French National Health Data System to evaluate the role of methotrexate coprescription in explain- ing the effect of TNFi versus IL-12/23 inhibitor therapy on treatment persistence among psoriatic patients.

stat.AP↗
arXiv2026

Geospatial Foundation Models Capture Health-Relevant Dimensions of Place Beyond Conventional Social Risk Indices

Area-based social risk indices summarize residents' socioeconomic conditions but incompletely capture physical features of place that may affect health. We evaluated whether numerical representations of physical place produced by four geospatial foundation model families from 2022 satellite data explained residual variance in tract-level associations between the Area Deprivation Index, Social Deprivation Index, and Social Vulnerability Index with health outcomes. We used LightGBM to predict variables from the American Community Survey and 40 chronic disease and health-behavior outcomes from CDC PLACES across 82,646 census tracts in the contiguous United States, evaluating performance across 10 held-out states. Among survey variables, models were moderately predictive of some variables including housing type (R-squared up to 0.54) but weak for disability, unemployment, and income disparity. For health outcomes, models explained up to 54% of variance left unexplained by social risk indices, with the largest gains for annual checkups, arthritis, and high blood pressure. Mean total variance explained by geospatial foundation models across the 40 health-related outcomes increased from 0.31 in the smallest tract-size decile to 0.39 in the largest. Geospatial foundation models capture health-relevant features of place not represented by conventional social risk indices and may usefully augment them in epidemiological analyses.

stat.AP↗
arXiv2026

A spatiotemporal negative binomial model with dynamic dispersion: An application to Tuberculosis infections

Tuberculosis (TB) remains a critical public health concern in Brazil, characterized by pronounced spatial heterogeneity and fluctuating temporal volatility. In this paper, we study monthly TB notifications across 61 microregions of Sao Paulo state from 2001 to 2024. To do this, we introduce a negative binomial spatial integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) model featuring jointly dynamic conditional means and time-varying dispersion. To capture inter-regional spillovers, we incorporate both discrete adjacency structures and a novel continuous distance-based formulation leveraging the Matern correlation function. Parameter estimation via conditional maximum likelihood employs a two-step profile-likelihood iterative scheme, demonstrating solid finite-sample performance in simulation studies. Applied to the Sao Paulo TB surveillance data, the framework substantially outperforms standard Poisson and fixed-dispersion spatiotemporal baselines in empirical fit and uncertainty quantification, maintaining nominal 95% predictive coverage across both dense metropolitan centers and rural microregions. Our results reveal marked spatial heterogeneity in baseline incidence, dynamic overdispersion driven by localized outbreaks, and short-range spatial interaction decay. By accurately modeling spatiotemporal volatility, the proposed methodology provides a robust statistical tool to support public health surveillance, policy-making, and resource allocation.

stat.AP↗