121,800 research outputs found
stopp: An R Package for Spatio-Temporal Point Pattern Analysis
No full textThe stopp R package deals with spatio-temporal point processes which might have occurred on the
Euclidean space or on some specific linear networks such as roads of a city. The package contains
functions to summarize, plot, and perform different kinds of analyses on point processes, mainly
following the methods proposed in some recent papers in the stream of scientific literature. The
main topics of such works, and of the package in turn, include modeling, statistical inference, and
simulation issues on spatio-temporal point processes on Euclidean space and linear networks, with
a focus on their local characteristics. We contribute to the existing literature by collecting many
of the most widespread methods for the analysis of spatio-temporal point processes into a unique
package, which is intended to welcome many further proposals and extension
Modelling three-dimensional point patterns
No full textIn this study, we explore the cubature scheme procedure for modelling three-dimensional point patterns through Poisson point process models, a computational realm that remains under-explored. Through simulations, we give guidelines for choosing the number of cubes to partition the observed three-dimensional region, the number of dummy points to generate, and whether to simulate them regularly or casually in space. We apply this methodology to the real observed point pattern of young stars of the Gaia Archive
Correction to: Local spatial log-Gaussian Cox processes for seismic data (AStA Advances in Statistical Analysis, (2022), 10.1007/s10182-022-00444-w)
No full textIn this article, Figs. 1a, 2a-c, 9 and 11 should have appeared as shown below. The original article has been corrected
Community detection of seismic point processes
No full textIn this paper, we combine robin and Local Indicators of Spatio-Temporal Association (LISTA) functions.
robin is an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. We use it to propose a classification algorithm of events in a spatio-temporal point pattern, by means of the local second-order characteristics and the community detection procedure in network analysis. We demonstrate the proposed procedure on a real data analysis on seismic data
Modeling Marked Poisson Point Processes with Real-Valued Marks
No full textIn the analysis of spatial point patterns with associated real-valued marks, standard models either rely on strong distributional assumptions about the marks to incorporate their effect on the estimated intensity, or exclude them from the fitting procedure, studying the marks through marked summary statistics. In this article, we address this issue by proposing two approaches, one parametric and one semi-parametric,
to model the intensity of marked point patterns with real-valued marks
in two spatial dimensions, without making any assumption on the marks
distribution. Both methods allow us to estimate the effect of the mark
on the intensity of the process and the density of the mark across the
observed window. We show that by including mark information in the
model, when the real-valued mark has an impact on the intensity, we
can obtain better intensity estimates with respect to unmarked models,
giving an additional layer of information about the process
Spatio-temporal analysis of the Covid-19 spread in Italy by Bayesian hierarchical models
No full textIn this paper, we investigate the spatio-temporal spread pattern of the virus Covid-19 in Italy, during the first wave of infections, from February to October 2020. We provide a disease mapping of the virus infections, by using the Besag-Yorke-Molliè model and its spatio-temporal extensions. Our results confirm the effectiveness of the lockdown action, and show that, during the first wave, the virus spread by an inhomogeneous spatial trend and each province was characterised by a specific temporal trend, independent of the temporal evolution of the observed cases in the other province
A Multi-Language Comparison of Influences on Author Verification using Character N-Grams
No full textWe create a new multi-language corpus for author verification based on Wikipedia talkpages, and evaluate the influence that differences in topic and time have on character n-gram author profiles. Topic alignment between two texts is found to increase author verification precision, and an authors writing style is found to change over time, but not more significantly after 3 years than after 1 year.Information ArchitectureWISElectrical Engineering, Mathematics and Computer Scienc
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Identification and modeling of stop activities at the destination from GPS tracking data
No full textIl presente articolo ha lo scopo di analizzare il comportamento turistico a destinazione, con un focus specifico sulle soste effettuate dai turisti nella destinazione. Vengono analizzati dati desunti da dispositivi GPS raccolti su un campione di crocieristi, a partire dai quali e possibile individuare le soste a destinazione `attraverso l’impiego di un opportuno algoritmo. L’effetto delle caratteristiche sociodemografiche e legate all’itinerario intrapreso sul numero di soste effettuate viene studiato attraverso l’impiego di modelli di reggressione di Poisson. I risultati sono di interesse sia da un punto di vista metodologico, legato all’analisi e sintesi di dati GPS, che dal punto di vista applicato, per quanto attiene alla conoscenza del comportamento spaziale dei turisti e delle relative implicazioni per il management della destinazione.This paper aims at analysing tourist behaviour at destination by focusing on the main determinants of their stop activities. A density-based cluster algorithm identifies the stops from GPS tracking data on cruise passengers starting from data on individual trajectories. A Poisson regression model analyses the effects of socio-demographic, and itinerary characteristics on the number of stops made. The results are of interest both from a methodological perspective, related to the analysis and synthesis of GPS tracking data and from an applied perspective concerning tourists' knowledge of spatial behaviour and its implications for destination management
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