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.

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 Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

MATEMATIKA ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Suhartono Suhartono ◽  
Dedy Dwi Prastyo ◽  
Heri Kuswanto ◽  
Muhammad Hisyam Lee
Keyword(s):  
Forecast Accuracy ◽  
Oil Production ◽  
Training Data ◽  
Temporal Data ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Temporal Models ◽  
Accurate Forecast ◽  
Temporal Model ◽  
Spatio Temporal

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

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.

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>

An Improved Clustering Approach for Identifying Significant Locations from Spatio-temporal Data

2021 ◽  
Author(s):  
Rigzin Angmo ◽  
Naveen Aggarwal ◽  
Veenu Mangat ◽  
Anurag Lal ◽  
Simarpreet Kaur
Keyword(s):  
Temporal Data ◽  
Clustering Approach ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Characteristics of the Bluetongue Epizooty in the Balkan Peninsula from 2014 to February 2015

2018 ◽  
Vol 41 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Zharko Stojmanovski ◽  
Blagojcho Tabakovski
Keyword(s):  
Balkan Peninsula ◽  
Space Time ◽  
Scan Statistics ◽  
Security Measures ◽  
Scan Statistic ◽  
Disease Clusters ◽  
Temporal Characteristics ◽  
The Balkans ◽  
Spatio Temporal ◽  
Spatial And Temporal Characteristics

Abstract Starting in May 2014 an emerging Bluetongue (BT) serotype 4 (BTV-4) epizooty has affected the ruminant population of eleven countries from the Balkan Peninsula. Consequently, the veterinary services implemented various bio-security measures and a considerable discussion has been raised if future BTV surveillance and preventive measures should be taken in risk based zones and periods. Therefore, the objective of this work was to describe the spatial and temporal characteristics of the BTV-4 epizooty in the Balkan Peninsula from May 2014 to February 2015. We used the space-time permutation model of the scan statistic to identify the space-time disease clusters. The scan statistic was parameterized to a maximum temporal length of 150 days (duration of the epizooty in the Balkans in 2014) and a radius of 100 km as a maximum spatial cluster size (protection zone for BT). Results were significant (p < 0.05) to the maximum spatial size defined for the clusters. From the 6295 BT outbreaks the scan statistics identified 33 disease clusters in nine Balkan countries. The highest number of outbreaks occurred from September to November 2014.The earliest cluster was detected in Greece in July 2014 with a radius of 56 km. The latest cluster was detected in Croatia in February 2015 with a radius of 99,8 km. These results are a first description of the spatial and temporal characteristics of the 2014-February 2015 BT epizooty in the Balkans.

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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