Spatio-temporal analysis of flood data from South Carolina

AbstractTo investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

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Spatio–temporal entanglement of twin photons: An intuitive picture

International Journal of Quantum Information ◽

10.1142/s0219749914610164 ◽

2014 ◽

Vol 12 (02) ◽

pp. 1461016 ◽

Cited By ~ 4

Author(s):

Alessandra Gatti ◽

Lucia Caspani ◽

Tommaso Corti ◽

Enrico Brambilla ◽

Ottavia Jedrkiewicz

Keyword(s):

Quantum Entanglement ◽

Wave Packets ◽

Temporal Structure ◽

Space Time ◽

Entangled State ◽

Temporal Properties ◽

Classical Wave ◽

Spatio Temporal

We draw an intuitive picture of the spatio–temporal properties of the entangled state of twin photons, where they are described as classical wave-packets. This picture predicts a precise relation between their temporal and transverse spatial separations at the crystal output. The space-time coupling described by classical arguments turns out to determine in a precise way the spatio–temporal structure of the quantum entanglement, analyzed by means of the biphotonic correlation and of the Schmidt dimensionality of the entanglement.

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The Response of Microwave Emission to the Development of Active Regions

Highlights of Astronomy ◽

10.1017/s1539299600007243 ◽

1986 ◽

Vol 7 ◽

pp. 755-757

Author(s):

G. Ya. Smolkov

Keyword(s):

Active Region ◽

Temporal Structure ◽

Active Regions ◽

Microwave Emission ◽

Degree Of Polarization ◽

Emission Characteristics ◽

Eclipse Observations ◽

Temporal Properties ◽

Spatio Temporal ◽

Main Components

As is known from eclipse observations, the microwave emission of an active region consists of three main components: floccular, inter-spot (halo) and spot components which differ in intensity, the degree of polarization, and in structure and sizes /1/. A possibility of identifying the finer spatial and temporal structure in the active region (AR) emission has existed since RATAN, the VLA and WSRT became operational. The construction of the SSRT permitted the initiation of a systematic study of spatio-temporal properties of the development of active regions /2, 3/.The majority of the properties in the AR development are reflected in detail and rapidly in the microwave emission characteristics /4, 6/.

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Prospective Spatial-Temporal Clusters of COVID-19 in Local Communities: Case Study of Kansas City, Missouri, United States

10.33774/coe-2021-p4wdt ◽

2021 ◽

Author(s):

Hadeel AlQadi

Keyword(s):

United States ◽

Kansas City ◽

Temporal Analysis ◽

The United States ◽

Local Health ◽

Test Results ◽

Large Numbers ◽

Spatio Temporal ◽

Spatial Temporal Analysis

Just in the United States (U.S.), the COVID-19 cases reached over 37 million as of August 2021. Kansas City in Missouri State has become one of the major U.S. hot spots for COVID-19 due to an increase in the rate of positive COVID-19 test results. Despite the large numbers of COVID-19 cases in Kansas City, the Spatio-temporal analysis of data has been less investigated. In this study, we conducted a prospective Poisson spatial-temporal analysis of Kansas City, MO, COVID-19 data at the zip code level. The analysis focused on daily COVID-19 cases in four equal periods of three months. We detected temporal patterns of emerging and reemerging space-time clusters between March 2020 and February 202. The statistical results were communicated with local health officials and provided the necessary guidance for decision-making and the allocation of resources.

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Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions, in the United States from 2011 to 2020

10.21203/rs.3.rs-220805/v1 ◽

2021 ◽

Author(s):

Zhijuan Song ◽

Xiaocan Jia ◽

Junzhe Bao ◽

Yongli Yang ◽

Huili Zhu ◽

...

Keyword(s):

United States ◽

Statistical Significance ◽

Temporal Analysis ◽

The United States ◽

Incidence Rates ◽

Influenza Like Illness ◽

Temporal Cluster ◽

Control And Prevention ◽

Spatio Temporal ◽

And Control

Abstract Introduction: About 8% of Americans get influenza during an average season from the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning of influenza and the prediction of public health. Methods In this study, we analyzed the characteristics of Influenza-like Illness (ILI) by Geographic Information System and SARIMA model, respectively. Spatio-temporal cluster analysis detected 23 clusters of ILI during the study period. Results The highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. The Local spatial autocorrelation analysis revealed the High-High cluster was mainly located in Louisiana and Mississippi. This means that if the influenza incidence is high in Louisiana and Mississippi, the neighboring states will also have higher influenza incidence rates. The regression model SARIMA(1, 0, 0)(1, 1, 0)52 with statistical significance was obtained to forecast the ILI incidence of Mississippi. Conclusions The study showed, the ILI incidence will begin to increase in the 45th week 2020 and peak in the 6th week 2021. To conclude, notable epidemiological differences were observed across states, indicating that some states should pay more attention to prevent and control respiratory infectious diseases.

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Normalization Strategies for Enhancing Spatio-Temporal Analysis of Social Media Responses during Extreme Events: A Case Study based on Analysis of Four Extreme Events using Socio-Environmental Data Explorer (SEDE)

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w2-139-2017 ◽

2017 ◽

Vol IV-4/W2 ◽

pp. 139-146

Author(s):

J. Ajayakumar ◽

E. Shook ◽

V. K. Turner

Keyword(s):

Social Media ◽

Extreme Events ◽

The United States ◽

Environmental Data ◽

Data Sources ◽

Social Media Data ◽

Public Response ◽

Spatio Temporal ◽

Media Data ◽

Media Responses

With social media becoming increasingly location-based, there has been a greater push from researchers across various domains including social science, public health, and disaster management, to tap in the spatial, temporal, and textual data available from these sources to analyze public response during extreme events such as an epidemic outbreak or a natural disaster. Studies based on demographics and other socio-economic factors suggests that social media data could be highly skewed based on the variations of population density with respect to place. To capture the spatio-temporal variations in public response during extreme events we have developed the Socio-Environmental Data Explorer (SEDE). SEDE collects and integrates social media, news and environmental data to support exploration and assessment of public response to extreme events. For this study, using SEDE, we conduct spatio-temporal social media response analysis on four major extreme events in the United States including the “North American storm complex” in December 2015, the “snowstorm Jonas” in January 2016, the “West Virginia floods” in June 2016, and the “Hurricane Matthew” in October 2016. Analysis is conducted on geo-tagged social media data from Twitter and warnings from the storm events database provided by National Centers For Environmental Information (NCEI) for analysis. Results demonstrate that, to support complex social media analyses, spatial and population-based normalization and filtering is necessary. The implications of these results suggests that, while developing software solutions to support analysis of non-conventional data sources such as social media, it is quintessential to identify the inherent biases associated with the data sources, and adapt techniques and enhance capabilities to mitigate the bias. The normalization strategies that we have developed and incorporated to SEDE will be helpful in reducing the population bias associated with social media data and will be useful for researchers and decision makers to enhance their analysis on spatio-temporal social media responses during extreme events.

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Faster is better: Visual responses to motion are stronger for higher refresh rates

10.1101/505354 ◽

2018 ◽

Author(s):

Mina A.Khoei ◽

Francesco Galluppi ◽

Quentin Sabatier ◽

Pierre Pouget ◽

Benoit R Cottereau ◽

...

Keyword(s):

Visual Cortex ◽

Human Subjects ◽

Temporal Structure ◽

Temporal Information ◽

Visual Responses ◽

General Belief ◽

Temporal Properties ◽

Low Pass ◽

Spatio Temporal ◽

Multiple Species

Although neural responses with a millisecond precision were reported in the retina, lateral geniculate nucleus and visual cortex of multiple species, the presence and role of such a fine temporal structure is still debated at the cortical level and the general belief remains that early visual system encodes information at slower timescales. In this study, we used a new stimulation platform to generate visual stimuli that were very precisely encoded in time and we characterized in human subjects the EEG responses to moving patterns that shared the same global motion but differed in their fine scale spatio-temporal properties. In two experiments, we manipulated the information within temporal windows that corresponded to the frame duration in conventional (1/60 = 16.7ms, experience 1) and more recent (1/120 = 8.3ms, experience 2) visual displays. Our results demonstrate that EEG responses to temporally dense and coherent trajectories are significantly stronger than those to control conditions without these properties. They extend previous results from our group that showed that accurate temporal information (<10ms) significantly improve perceptual judgments on spatial discrimination, digit recognition and sensitivity for speed [Kime et al., 2016]. Altogether, our results suggest that instead of low-pass filtering the temporal information it receives from its thalamic afferents, the visual cortex may actually exploit its richness to improve visual perception.

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Multivariate spatio-temporal analysis of the global COVID-19 pandemic

10.1101/2021.02.08.21251339 ◽

2021 ◽

Author(s):

Wen Xiang ◽

Ben Swallow

Keyword(s):

Control Strategies ◽

Temporal Structure ◽

Temporal Trends ◽

Virus Transmission ◽

Temporal Analysis ◽

Global Scale ◽

Global Dynamics ◽

Multivariate Statistical ◽

Wide Range ◽

Spatio Temporal

AbstractThe COVID-19 pandemic has caused significant mortality and disruption on a global scale not seen in living memory. Understanding the spatial and temporal vectors of transmission as well as similarities in the trajectories of recorded cases and deaths across countries can aid in understanding the benefit or otherwise of varying interventions and control strategies on virus transmission. It can also highlight emerging globa trends as they occur. Data on number of cases and deaths across the globe have been made available through a variety of databases and provide a wide range of opportunities for the application of multivariate statistical methods to extract information on similarity or difference from them. Here we conduct spatial and temporal multivariate statistical analyses of global COVID-19 cases and deaths for the period spanning January to August 2020, using a variety of distance based multivariate methods to cluster countries according to similar temporal trends in cases and deaths resulting from COVID-19. We also use novel air passenger data as a proxy for movement between countries. The air passenger movement can act as an important vector of transmission and thus scaling covariance matrices before conducting dimension reduction techniques can account for known structures in the data and help highlight important residual spatial and/or temporal trends that may then be attributable to the success of interventions or other cultural differences. Global temporal structure is found to be of significantly more importance than local spatial structure in terms of global dynamics. Our results highlight a significant global change in case and mortality daynamics from early-August, consistent in timing with the emergence of new strains with highger levels of transmission. We propose the methodology offers great potential in real-time analysis of complex, noisy spatio-temporal data and the extraction of emerging changes in pandemic dynamics that can support policy and decision makers.

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Why Does It Always Rain on Me? A Spatio-Temporal Analysis of Precipitation in Austria

Austrian Journal of Statistics ◽

10.17713/ajs.v41i1.190 ◽

2016 ◽

Vol 41 (1) ◽

Cited By ~ 1

Author(s):

Nikolaus Umlauf ◽

Georg Mayr ◽

Jakob Messner ◽

Achim Zeileis

Keyword(s):

Temporal Structure ◽

Temporal Analysis ◽

Weekend Effect ◽

Quality Data ◽

Data Set ◽

Data Driven Approach ◽

Spatio Temporal ◽

Ordered Response ◽

Additive Regression ◽

Spatially Varying

It is popular belief that the weather is “bad” more frequently on weekends than on other days of the week and this is often perceived to be associated with an increased chance of rain. In fact, the meteorological literature does report some evidence for such human-induced weekly cycles although these findings are not undisputed. To contribute to this discussion, a modern data-driven approach using structured additive regression modelsis applied to a newly available high-quality data set for Austria. The analysis investigates how an ordered response of rain intensities is influenced by a (potential) weekend effect while adjusting for spatio-temporal structure using spatially varying effects of overall level and seasonality patterns. The underlying data are taken from the HOMSTART project which provides daily precipitation quantities over a period of more than 60 years and a dense netof more than 50 meteorological stations all across Austria.

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Analysis of lockdown perception in the United States during the COVID-19 pandemic

The European Physical Journal Special Topics ◽

10.1140/epjs/s11734-021-00265-z ◽

2021 ◽

Author(s):

Francesco Vincenzo Surano ◽

Maurizio Porfiri ◽

Alessandro Rizzo

Keyword(s):

United States ◽

Temporal Analysis ◽

The United States ◽

Economic Effects ◽

Economic Factors ◽

Political Orientations ◽

Containment Measures ◽

Spatio Temporal ◽

Different Parts ◽

Socio Economic Factors

AbstractContainment measures have been applied throughout the world to halt the COVID-19 pandemic. In the United States, several forms of lockdown have been adopted in different parts of the country, leading to heterogeneous epidemiological, social, and economic effects. Here, we present a spatio-temporal analysis of a Twitter dataset comprising 1.3 million geo-localized Tweets about lockdown, from January to May 2020. Through sentiment analysis, we classified Tweets as expressing positive or negative emotions about lockdown, demonstrating a change in perception during the course of the pandemic modulated by socio-economic factors. A transfer entropy analysis of the time series of Tweets unveiled that the emotions in different parts of the country did not evolve independently. Rather, they were mediated by spatial interactions, which were also related to socio-ecomomic factors and, arguably, to political orientations. This study constitutes a first, necessary step toward isolating the mechanisms underlying the acceptance of public health interventions from highly resolved online datasets.

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Spatio-temporal analysis of wildfire ignitions in the St Johns River Water Management District, Florida

International Journal of Wildland Fire ◽

10.1071/wf04034 ◽

2006 ◽

Vol 15 (1) ◽

pp. 87 ◽

Cited By ~ 54

Author(s):

Marc G. Genton ◽

David T. Butry ◽

Marcia L. Gumpertz ◽

Jeffrey P. Prestemon

Keyword(s):

Water Management ◽

River Water ◽

Spatial Scales ◽

Temporal Structure ◽

Temporal Analysis ◽

Irregular Distribution ◽

Point Patterns ◽

North Eastern ◽

Spatio Temporal ◽

Fire Ignitions

We analyse the spatio-temporal structure of wildfire ignitions in the St Johns River Water Management District in north-eastern Florida. We show, using tools to analyse point patterns (e.g. the L-function), that wildfire events occur in clusters. Clustering of these events correlates with irregular distribution of fire ignitions, including lightning and human sources, and fuels on the landscape. In addition, we define a relative clustering index that summarizes the amount of clustering over various spatial scales. We carry our analysis in three steps: purely temporal, purely spatial, and spatio-temporal. Our results show that arson and lightning are the leading causes of wildfires in this region and that ignitions by railroad, lightning, and arson are spatially more clustered than ignitions by other accidental causes.

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