A step by step procedure for multivariate modeling

2021 ◽  
Author(s):  
Claudia Cappello ◽  
Sandra De Iaco ◽  
Monica Palma ◽  
Sabrina Maggio
Keyword(s):  
Covariance Structure ◽  
Space Time ◽  
Temporal Data ◽  
Multivariate Geostatistics ◽  
Correlated Variables ◽  
Multiple Data ◽  
Step Procedure ◽  
Complete Procedure ◽  
Spatio Temporal ◽  
Selection Of

<p><span><span>In environmental sciences, it is very common to observe spatio-temporal multiple data concerning several correlated variables which are measured in time over a monitored spatial domain. In multivariate Geostatistics, the analysis of these correlated variables requires the estimation and modelling of the spatio-temporal multivariate covariance structure.<br>In the literature, the linear coregionalization model (LCM) has been widely used, in order to describe the spatio-temporal dependence which characterizes two or more variables. In particular, the LCM model requires the identification of the basic independent components underlying the analyzed phenomenon, and this represents a tough task. In order to overcome the aforementioned problem, this contribution provides a complete procedure where all the necessary steps to be followed for properly detect the basic space-time components for the phenomenon under study, together with some computational advances which support the selection of an ST-LCM.<br>The implemented procedure and the related algorithms are applied on a space-time air quality dataset.<br>Note that the proposed procedure can help practitioners to reproduce all the modeling stages and to replicate the analysis for different multivariate spatio-temporal data.</span></span></p>

Study of the Spatio-Temporal Data Model in Sea Battlefield

2012 ◽  
Vol 246-247 ◽  
pp. 744-748
Author(s):  
Yue Lin Sun ◽  
Lei Bao ◽  
Yi Hang Peng
Keyword(s):  
Data Model ◽  
Object Oriented ◽  
Space Time ◽  
Comprehensive Analysis ◽  
Temporal Data ◽  
Data Types ◽  
Spatio Temporal ◽  
The Right ◽  
Effective Analysis

An effective analysis of the battlefield situation and spatio-temporal data model in a sea battlefield has great significance for the commander to perceive the battlefield situation and to make the right decisions. Based on the existing spatio-temporal data model, the present paper gives a comprehensive analysis of the characteristics of sea battlefield data, and chooses the object-oriented spatio-temporal data model to modify it; at the same time this paper introduces sea battlefield space-time algebra system to define various data types formally, which lays the foundation for the establishment of the sea battlefield spatio-temporal data model.

scholarly journals The Visualization Representation of Space-Time-Path in The Space-Time-Cube

2021 ◽  
Vol 906 (1) ◽  
pp. 012030
Author(s):  
Bingbing Song ◽  
Yanlin Wang ◽  
Fang Li
Keyword(s):  
Three Dimensional ◽  
Temporal Changes ◽  
Space Time ◽  
Temporal Data ◽  
Processing Technologies ◽  
Spatial Object ◽  
Time Path ◽  
Visual Clutter ◽  
Spatio Temporal ◽  
The Impact

Abstract Map is a traditional visualization tool to represent distribution and interaction of spatial objects or spatial phenomenon. However, with the continuous development of acquisition and processing technologies for spatio-temporal data, traditional map can hardly meet the visualization requirement for this type of data. In other words, the dynamic information about spatial object or phenomenon cannot be expressed fully by traditional map. The Space-Time-Cube (STC), as a three-dimensional visualization environment, whose base represents the two-dimensional geographical space and whose height represents the temporal dimension, can simultaneously represent the spatial distribution as well as the temporal changes of spatio-temporal data. For some spatial object or phenomenon, its moving trajectory can be visualized in STC as a Space-Time-Path (STP), by which the speed and state of motion can be clearly reflected. Noticeably, the problem of visual clutter about STP is inevitably due to the complexity of three-dimensional visualization. In order to reduce the impact of visual clutter, this paper discusses different aspects about visualization representation of STP in the STC. The multiple scales representation and the multiple views display can promote interactive experience of users, and the application of different visual variables can help to represent different kinds of attribute information of STP. With the visualization of STP, spatio-temporal changes and attributive characters of spatial object or phenomenon can be represented and analysed.

scholarly journals Facilitating insights with a user adaptable dashboard, illustrated by airport connectivity data

2018 ◽  
Vol 1 ◽  
pp. 1-6
Author(s):  
Ieva Dobraja ◽  
Menno-Jan Kraak ◽  
Yuri Engelhardt
Keyword(s):  
Temporal Patterns ◽  
Data Representation ◽  
Data Exploration ◽  
Temporal Data ◽  
Movement Data ◽  
Graphical Data ◽  
Multiple Data ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Heterogeneous Multiscale

Since the movement data exist, there have been approaches to collect and analyze them to get insights. This kind of data is often heterogeneous, multiscale and multi-temporal. Those interested in spatio-temporal patterns of movement data do not gain insights from textual descriptions. Therefore, visualization is required. As spatio-temporal movement data can be complex because size and characteristics, it is even challenging to create an overview of it. Plotting all the data on the screen will not be the solution as it likely will result into cluttered images where no data exploration is possible. To ensure that users will receive the information they are interested in, it is important to provide a graphical data representation environment where exploration to gain insights are possible not only in the overall level but at sub-levels as well. A dashboard would be a solution the representation of heterogeneous spatio- temporal data. It provides an overview and helps to unravel the complexity of data by splitting data in multiple data representation views. The adaptability of dashboard will help to reveal the information which cannot be seen in the overview.

scholarly journals A WEB-BASED INTERACTIVE PLATFORM FOR CO-CLUSTERING SPATIO-TEMPORAL DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 175-179
Author(s):  
X. Wu ◽  
A. Poorthuis ◽  
R. Zurita-Milla ◽  
M.-J. Kraak
Keyword(s):  
Clustering Algorithm ◽  
Temporal Patterns ◽  
Temporal Data ◽  
Web Based ◽  
Server Side ◽  
Exploratory Data ◽  
Spatio Temporal ◽  
Client Side ◽  
Concurrent Analysis ◽  
Selection Of

Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.

scholarly journals Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

2017 ◽  
Author(s):  
Sami Ullah ◽  
Hanita Daud ◽  
Sarat C. Dass ◽  
Habib Nawaz Khan ◽  
Alamgir Khalil
Keyword(s):  
Space Time ◽  
Annual Data ◽  
Temporal Data ◽  
Heat Map ◽  
Monthly Data ◽  
Disease Clusters ◽  
Khyber Pakhtunkhwa Province ◽  
Clustering Approach ◽  
Periodic Trend ◽  
Spatio Temporal

Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space–time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend.

Methods for Space-Time Analysis and Modeling

2013 ◽  
Vol 4 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Eric Delmelle ◽  
Changjoo Kim ◽  
Ningchuan Xiao ◽  
Wei Chen
Keyword(s):  
Geographical Information Systems ◽  
Optimization Problems ◽  
Critical Distance ◽  
Space Time ◽  
Geographical Information ◽  
Temporal Data ◽  
Space And Time ◽  
Time Modeling ◽  
Spatio Temporal ◽  
The Impact

With increasing availability of spatio-temporal data and the democratization of Geographical Information Systems (GIS), there has been a demand for novel statistical and visualization techniques which can explicitly integrate space and time. The paper discusses the nature of spatio-temporal data, the integration of time within GIS and the flourishing availability of spatial and temporal-explicit data over the Internet. The paper attempts to answer the fundamental question on how these large datasets can be analyzed in space and time to reveal critical patterns. The authors further elaborate on how spatial autocorrelation techniques are extended to deal with time, for point, linear, and areal features, and the impact of parameter selection, such as critical distance and time threshold to build adjacency matrices. The authors also discuss issues of space-time modeling for optimization problems.

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