scholarly journals Rejoinder: A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

2018 ◽  
Vol 33 (3) ◽  
pp. 330-333 ◽  
Author(s):  
Alex Reinhart
Keyword(s):  
Point Processes ◽  
Spatio Temporal

Spatio-temporal cluster analyses of landslides in Italy at national and regional scale

2020 ◽  
Author(s):  
Marj Tonini ◽  
Kim Romailler ◽  
Gaetano Pecoraro ◽  
Michele Calvello
Keyword(s):  
Cluster Analysis ◽  
Point Processes ◽  
Statistical Significance ◽  
Regional Scale ◽  
Regional Analysis ◽  
Scan Statistics ◽  
Economic Losses ◽  
Campania Region ◽  
Temporal Cluster ◽  
Spatio Temporal

<p><strong>Keywords:</strong> Landslides, FraneItalia, cluster analysis, spatio-temporal point process</p><p>In Italy landslides pose a significant and widespread risk, resulting in a large number of casualties and huge economic losses. Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future, i.e. to establish reliable correlations between triggering factors and landslide occurrences. To deal with this issue, statistical methods originally developed for spatio-temporal stochastic point processes can be useful for identifying correlations between events in space and time and detecting a significant excess of cases within large landslide datasets.</p><p>In the present study, the authors propose an approach to analyze and visualize spatio-temporal clusters of landslides occurred in Italy in the period 2010-2017, considering the weather warning zones as territorial units. Besides, a regional analysis was conducted in Campania region considering the municipalities as territorial units. Data on landslide occurrences derived from the FraneItalia catalog, an inventory retrieved from online Italian news. The database contains 8931 landslides, grouped in 4231 single events and 938 areal events (records referring to multiple landslides triggered by the same cause in the same geographic area). Analyses were performed both annually, considering each year individually, and globally, considering the entire frame period. We applied the spatio-temporal scan statistics permutation model (STPSS, integrated in SaTScan<sup>TM</sup> software), which allowed detecting clusters’ location and estimating their statistical significance. STPSS is based on cylindrical moving windows which scan the area across the space and in time counting the number of observed and expected occurrences and computing the likelihood ratio. The statistical inference (p-value) is evaluated by Monte Carlo sampling and finally the most likely clusters in the real and randomly generated datasets are compared.</p><p>Although more detailed analyses are required for the determination of cause-effect relationships among landslides and other variables, some relations with the local topographic and meteorological conditions can already be argued. At national scale, spatio-temporal clusters of landslides are mainly recurrent in two zones: the area enclosing Liguria Region – Northern Tuscany at north-west and the area between Abruzzo and Molise regions at centre-east. During the year, landslide clusters are particularly abundant between October and March. as most of the events in the FraneItalia catalog are rainfall-induced, strongly influenced by seasonal rainfall patterns. Concerning the regional analysis, most of the clusters are located in the Lattari mountains, the Pizzo d’Alvano massif and the Picentini mountains, areas highly susceptible to landslide occurrence due to geomorphological factors.</p><p>In conclusion, the application of spatio-temporal cluster analysis at various scale allowed the identification of frame periods with greater landslide activity. The question of whether this increase in activity depends climate conditions or topographic factors is still open and request further investigations.</p><p>REFERENCES</p><p>Calvello, M., Pecoraro, G. FraneItalia: a catalog of recent Italian landslides. <em>Geoenvironmental Disasters</em>. 5: 13 (2018)</p><p>Tonini, M. & Cama, M. Spatio-temporal pattern distribution of landslides causing damage in Switzerland. <em>Landslides</em> 16 (2019)</p>

Modelling Seismicity in California as a Spatio-Temporal Point Process Using inlabru: Insights for Earthquake Forecasting

2020 ◽  
Author(s):  
Mark Naylor ◽  
Kirsty Bayliss ◽  
Finn Lindgren ◽  
Francesco Serafini ◽  
Ian Main
Keyword(s):  
Point Process ◽  
Spatial Data ◽  
Point Processes ◽  
Earthquake Forecasting ◽  
Data Sets ◽  
Computationally Efficient ◽  
Data Types ◽  
Initial Application ◽  
Spatio Temporal ◽  
Diverse Data

<p>Many earthquake forecasting approaches have developed bespokes codes to model and forecast the spatio-temporal eveolution of seismicity. At the same time, the statistics community have been working on a range of point process modelling codes. For example, motivated by ecological applications, inlabru models spatio-temporal point processes as a log-Gaussian Cox Process and is implemented in R. Here we present an initial implementation of inlabru to model seismicity. This fully Bayesian approach is computationally efficient because it uses a nested Laplace approximation such that posteriors are assumed to be Gaussian so that their means and standard deviations can be deterministically estimated rather than having to be constructed through sampling. Further, building on existing packages in R to handle spatial data, it can construct covariate maprs from diverse data-types, such as fault maps, in an intutitive and simple manner.</p><p>Here we present an initial application to the California earthqauke catalogue to determine the relative performance of different data-sets for describing the spatio-temporal evolution of seismicity.</p>

Spatial Statistics

2018 ◽  
Author(s):  
Christopher K. Wikle
Keyword(s):  
Machine Learning ◽  
Spatial Statistics ◽  
Spatial Data ◽  
Point Processes ◽  
Hierarchical Modeling ◽  
Climate Science ◽  
Spatial Process ◽  
Wide Range ◽  
Spatio Temporal ◽  
Specific Implementation

The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.

‘Surfing’ burglaries with forced entry in Catalonia: Large-scale testing of near repeat victimization theory

2020 ◽  
pp. 147737082096810
Author(s):  
Pere Boqué ◽  
Laura Serra ◽  
Marc Saez
Keyword(s):  
Large Scale ◽  
Point Processes ◽  
Criminological Theory ◽  
Repeat Victimization ◽  
Average Intensity ◽  
Predictive Capacity ◽  
Large Scale Testing ◽  
Spatio Temporal ◽  
Near Repeat ◽  
Do So

In recent years, various academic studies have proposed crime forecasting models based on the concept of repeat victimization. Some of them have been modelled from the area of differential equations and others from the perspective of spatio-temporal statistics, within the framework of point processes. These models have tended towards a certain sophistication in their formulation, which at times impedes understanding of the predictive mechanism and how it adapts to different realities. Predictive models that function well in one environment or society do not appear to do so in others. In this article, the possibility of crime forecasting for burglaries with forced entry in Catalonia is studied from the perspective of near repeat victimization on a larger territorial scale than is usual. To this effect, the explicative and predictive possibilities of this criminological theory are explored and a predictive system that does not require mathematical or statistical models is proposed. We found that a large part of the series of burglaries with forced entry in residences in Catalonia between 2014 and 2015 follow patterns of near repeat victimization. In addition, the average intensity of burglaries in space–time was high, as was the standard deviation. This system is adaptable to different environments and gives police forces the opportunity to improve preventative strategies and to optimize resources using standard tools. Last, the limitations of this approach are debated and new lines of investigation proposed that could increase its predictive capacity without abandoning the concept of repeat victimization.

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