scholarly journals Determinants of spatial intensity of stop locations on cruise passengers tracking data

2021 ◽  
pp. 159-164
Author(s):  
Nicoletta D’Angelo ◽  
Mauro Ferrante ◽  
Antonino Abbruzzo ◽  
Giada Adelfio
Keyword(s):  
Point Process ◽  
Process Model ◽  
Linear Network ◽  
Spatial Intensity ◽  
Quadrature Scheme ◽  
Stochastic Point Process ◽  
Gibbs Point Process ◽  
Location Patterns ◽  
Level Information ◽  
Main Determinants

This paper aims at analyzing the spatial intensity in the distribution of stop locations of cruise passengers during their visit at the destination through a stochastic point process modelling approach on a linear network. Data collected through the integration of GPS tracking technology and questionnaire-based survey on cruise passengers visiting the city of Palermo are used, to identify the main determinants which characterize their stop locations pattern. The spatial intensity of stop locations is estimated through a Gibbs point process model, taking into account for both individual-related variables, contextual-level information, and for spatial interaction among stop points. The Berman-Turner device for maximum pseudolikelihood is considered, by using a quadrature scheme generated on the network. The approach used allows taking into account the linear network determined by the street configuration of the destination under analysis. The results show an influence of both socio-demographic and trip-related characteristics on the stop location patterns, as well as the relevance of distance from the main attractions, and potential interactions among cruise passengers in stop configuration. The proposed approach represents both improvements from the methodological perspective, related to the modelling of spatial point process on a linear network, and from the applied perspective, given that better knowledge of the determinants of spatial intensity of visitors’ stop locations in urban contexts may orient destination management policy.

scholarly journals A Variational Point Process Model for Social Event Sequences

2020 ◽  
Vol 34 (01) ◽  
pp. 173-180
Author(s):  
Zhen Pan ◽  
Zhenya Huang ◽  
Defu Lian ◽  
Enhong Chen
Keyword(s):  
Point Process ◽  
Real World ◽  
Process Model ◽  
Classification Model ◽  
Event Sequence ◽  
Event Sequences ◽  
Sequence Modeling ◽  
Stochastic Point Process ◽  
Feature Based ◽  
Real World Datasets

Many events occur in real-world and social networks. Events are related to the past and there are patterns in the evolution of event sequences. Understanding the patterns can help us better predict the type and arriving time of the next event. In the literature, both feature-based approaches and generative approaches are utilized to model the event sequence. Feature-based approaches extract a variety of features, and train a regression or classification model to make a prediction. Yet, their performance is dependent on the experience-based feature exaction. Generative approaches usually assume the evolution of events follow a stochastic point process (e.g., Poisson process or its complexer variants). However, the true distribution of events is never known and the performance depends on the design of stochastic process in practice. To solve the above challenges, in this paper, we present a novel probabilistic generative model for event sequences. The model is termed Variational Event Point Process (VEPP). Our model introduces variational auto-encoder to event sequence modeling that can better use the latent information and capture the distribution over inter-arrival time and types of event sequences. Experiments on real-world datasets prove effectiveness of our proposed model.

scholarly journals Gibbs Point Process Model for Young Star Clusters in M33

2020 ◽  
Author(s):  
Dayi Li ◽  
Pauline Barmby
Keyword(s):  
Point Process ◽  
Process Model ◽  
Function Analysis ◽  
Cluster Formation ◽  
Process Models ◽  
Spatial Distributions ◽  
Galactocentric Distance ◽  
Stellar Cluster ◽  
Positive Effects ◽  
Gibbs Point Process

Abstract We demonstrate the power of Gibbs point process models from the spatial statistics literature when applied to studies of resolved galaxies. We conduct a rigorous analysis of the spatial distributions of objects in the star formation complexes of M33, including giant molecular clouds (GMCs) and young stellar cluster candidates (YSCCs). We choose a hierarchical model structure from GMCs to YSCCs based on the natural formation hierarchy between them. This approach circumvents the limitations of the empirical two-point correlation function analysis by naturally accounting for the inhomogeneity present in the distribution of YSCCs. We also investigate the effects of GMCs’ properties on their spatial distributions. We confirm that the distribution of GMCs and YSCCs are highly correlated. We found that the spatial distributions of YSCCs reaches a peak of clustering pattern at ∼250 pc scale compared to a Poisson process. This clustering mainly occurs in regions where the galactocentric distance ≳ 4.5 kpc. Furthermore, the galactocentric distance of GMCs and their mass have strong positive effects on the correlation strength between GMCs and YSCCs. We outline some possible implications of these findings for our understanding of the cluster formation process.

A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability

2005 ◽  
Vol 288 (1) ◽  
pp. H424-H435 ◽  
Author(s):  
Riccardo Barbieri ◽  
Eric C. Matten ◽  
AbdulRasheed A. Alabi ◽  
Emery N. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Point Process ◽  
Process Model ◽  
Goodness Of Fit ◽  
Gaussian Model ◽  
Quantitative Measure ◽  
Local Maximum ◽  
Inverse Gaussian ◽  
Design Of Algorithms

Heart rate is a vital sign, whereas heart rate variability is an important quantitative measure of cardiovascular regulation by the autonomic nervous system. Although the design of algorithms to compute heart rate and assess heart rate variability is an active area of research, none of the approaches considers the natural point-process structure of human heartbeats, and none gives instantaneous estimates of heart rate variability. We model the stochastic structure of heartbeat intervals as a history-dependent inverse Gaussian process and derive from it an explicit probability density that gives new definitions of heart rate and heart rate variability: instantaneous R-R interval and heart rate standard deviations. We estimate the time-varying parameters of the inverse Gaussian model by local maximum likelihood and assess model goodness-of-fit by Kolmogorov-Smirnov tests based on the time-rescaling theorem. We illustrate our new definitions in an analysis of human heartbeat intervals from 10 healthy subjects undergoing a tilt-table experiment. Although several studies have identified deterministic, nonlinear dynamical features in human heartbeat intervals, our analysis shows that a highly accurate description of these series at rest and in extreme physiological conditions may be given by an elementary, physiologically based, stochastic model.

Sign in / Sign up

Export Citation Format

Share Document