scholarly journals Spatio-Temporal Change Detection Using Granger Sequence Pattern

2020 ◽  
Author(s):  
Nat Pavasant ◽  
Masayuki Numao ◽  
Ken-ichi Fukui
Keyword(s):  
Temporal Change ◽  
Synthetic Data ◽  
Sequence Mining ◽  
Occurrence Time ◽  
Causal Relations ◽  
Sequence Of Events ◽  
Time Signature ◽  
Spatio Temporal ◽  
Probabilistic Density Function ◽  
Cluster Sequence

This paper proposed a method to detect changes in causal relations over a multi-dimensional sequence of events. Cluster Sequence Mining algorithm was modified to extract causal relations in the form of g-patterns: a pair of clusters of events that have their occurrence time determined by Granger causality. This paper also proposed the pattern time signature, a probabilistic density function of the cluster sequence occurring at any given time. Synthetic data were used for validation. The result shows that the proposed algorithm can correctly identify the changes in causal relations even under noisy data.

Spatio-Temporal Modeling on Village Land Feature Change

2012 ◽  
Vol 204-208 ◽  
pp. 2721-2725
Author(s):  
Hua Ji Zhu ◽  
Hua Rui Wu
Keyword(s):  
Real World ◽  
Temporal Change ◽  
End Users ◽  
Temporal Modeling ◽  
The Real ◽  
Change Type ◽  
Feature Change ◽  
Spatio Temporal ◽  
The Village ◽  
Land Changes

Village land continually changes in the real world. In order to keep the data up-to-date, data producers need update the data frequently. When the village land data are updated, the update information must be dispensed to the end-users to keep their client-databases current. In the real world, village land changes in many forms. Identifying the change type of village land (i.e. captures the semantics of change) and representing them in the data world can help end-users understand the change commonly and be convenient for end-users to integrate these change information into their databases. This work focuses on the model of the spatio-temporal change. A three-tuple model CAR for representing the spatio-temporal change is proposed based on the village land feature set before change and the village land feature set after change, change type and rules. In this model, the C denotes the change type. A denotes the attribute set; R denotes the judging rules of change type. The rule is described by the IF－THEN expressions. By the operations between R and A, the C is distinguished. This model overcomes the limitations of current methods. And more, the rules in this model can be easy realized in computer program.

scholarly journals A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

2021 ◽  
Vol 18 (6) ◽  
pp. 7685-7710
Author(s):  
Yukun Tan ◽  
◽  
Durward Cator III ◽  
Martial Ndeffo-Mbah ◽  
Ulisses Braga-Neto ◽  
...  
Keyword(s):  
State Space ◽  
State Space Model ◽  
Synthetic Data ◽  
Public Health Intervention ◽  
Disease Outbreaks ◽  
Real Data ◽  
Epidemiological Data ◽  
Transmission Parameters ◽  
Intervention Measures ◽  
Spatio Temporal

<abstract><p>Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.</p></abstract>

scholarly journals Spatio-temporal change assessment using normalize difference vegetative index and spectral analysis techniques for vegetation cover on Wadi Auranah

2014 ◽  
Vol 16 (5) ◽  
pp. 955-965
Keyword(s):  
Spectral Analysis ◽  
Land Cover ◽  
Temporal Change ◽  
Treated Wastewater ◽  
Change Assessment ◽  
Field Assessment ◽  
Normalize Difference Vegetative Index ◽  
Increasing Trend ◽  
Geologic Map ◽  
Spatio Temporal

<div> <p>Wadi Auranah is one of the potential wadis in respect of land use, located on the western shield (Hijaz escarpment) of Saudi Arabia. A spatio-temporal change assessment of this wadi (valley) was conducted with the help of landsat data. The results of the spectral analysis and NDVI for vegetation changes assessment reveals a continuous increasing trend of land cover biomass in specific parts of the wadi. Geological review of geologic map supports soil fertility. It is observed from field assessment of the study area that has potential for more land more to be cultivated. About 73 km<sup>2</sup> of vegetation land cover has been increased in the last 20 years. Treated wastewater is the main source of water supply that is used for afforestation and cultivation purpose.</p> </div> <p>&nbsp;</p>

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