scholarly journals A spatio-temporal model of housing prices based on individual sales transactions over time

2009 ◽  
Vol 11 (4) ◽  
pp. 333-355 ◽  
Author(s):  
Tony E. Smith ◽  
Peggy Wu
Keyword(s):  
Housing Prices ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Over Time

scholarly journals Climate drives spatial variation in Zika epidemics in Latin America

2019 ◽  
Vol 286 (1909) ◽  
pp. 20191578 ◽  
Author(s):  
Mallory Harris ◽  
Jamie M. Caldwell ◽  
Erin A. Mordecai
Keyword(s):  
Latin America ◽  
Spatial Variation ◽  
Prior Exposure ◽  
Effect Of Temperature ◽  
Virus Spread ◽  
Force Of Infection ◽  
Control Programmes ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Over Time

Between 2015 and 2017, Zika virus spread rapidly through populations in the Americas with no prior exposure to the disease. Although climate is a known determinant of many Aedes -transmitted diseases, it is currently unclear whether climate was a major driver of the Zika epidemic and how climate might have differentially impacted outbreak intensity across locations within Latin America. Here, we estimated force of infection for Zika over time and across provinces in Latin America using a time-varying susceptible–infectious–recovered model. Climate factors explained less than 5% of the variation in weekly transmission intensity in a spatio-temporal model of force of infection by province over time, suggesting that week to week transmission within provinces may be too stochastic to predict. By contrast, climate and population factors were highly predictive of spatial variation in the presence and intensity of Zika transmission among provinces, with pseudo- R 2 values between 0.33 and 0.60. Temperature, temperature range, rainfall and population size were the most important predictors of where Zika transmission occurred, while rainfall, relative humidity and a nonlinear effect of temperature were the best predictors of Zika intensity and burden. Surprisingly, force of infection was greatest in locations with temperatures near 24°C, much lower than previous estimates from mechanistic models, potentially suggesting that existing vector control programmes and/or prior exposure to other mosquito-borne diseases may have limited transmission in locations most suitable for Aedes aegypti , the main vector of Zika, dengue and chikungunya viruses in Latin America.

scholarly journals Spatial heterogeneity contributes more to portfolio effects than species variability in bottom-associated marine fishes

2018 ◽  
Vol 285 (1888) ◽  
pp. 20180915 ◽  
Author(s):  
James T. Thorson ◽  
Mark D. Scheuerell ◽  
Julian D. Olden ◽  
Daniel E. Schindler
Keyword(s):  
Marine Fishes ◽  
Risk Exposure ◽  
Total Biomass ◽  
Theoretical Maximum ◽  
Ecosystem Indicators ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Changes Over Time ◽  
Total Species ◽  
Over Time

Variance of community abundance will be reduced relative to its theoretical maximum whenever population densities fluctuate asynchronously. Fishing communities and mobile predators can switch among fish species and/or fishing locations with asynchronous dynamics, thereby buffering against variable resource densities (termed ‘portfolio effects’, PEs). However, whether variation among species or locations represent the dominant contributor to PE remains relatively unexplored. Here, we apply a spatio-temporal model to multidecadal time series (1982–2015) for 20 bottom-associated fishes in seven marine ecosystems. For each ecosystem, we compute the reduction in variance over time in total biomass relative to its theoretical maximum if species and locations were perfectly correlated (total PE). We also compute the reduction in variance due to asynchrony among species at each location (species PE) or the reduction due to asynchrony among locations for each species (spatial PE). We specifically compute total, species and spatial PE in 10-year moving windows to detect changes over time. Our analyses revealed that spatial PE are stronger than species PE in six of seven ecosystems, and that ecosystems where species PE is constant over time can exhibit shifts in locations that strongly contribute to PE. We therefore recommend that spatial and total PE be monitored as ecosystem indicators representing risk exposure for human and natural consumers.

scholarly journals A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

2021 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Adina Iftimi ◽  
Juan Francisco Correcher ◽  
Jose De Andrés ◽  
Manuel Lozano ◽  
...  
Keyword(s):  
Model Fitting ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals Glimpse: A Gaze-Based Measure of Temporal Salience

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3099
Author(s):  
V. Javier Traver ◽  
Judith Zorío ◽  
Luis A. Leiva
Keyword(s):  
Visual Attention ◽  
Computational Models ◽  
Temporal Evolution ◽  
Temporal Consistency ◽  
Visual Salience ◽  
Temporal Dimension ◽  
Spatial Perspective ◽  
Spatio Temporal ◽  
Scoring Algorithms ◽  
Over Time

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.

scholarly journals Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill

2016 ◽  
Vol 12 (6) ◽  
pp. e1004969 ◽  
Author(s):  
Zhihui Wang ◽  
Romica Kerketta ◽  
Yao-Li Chuang ◽  
Prashant Dogra ◽  
Joseph D. Butner ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Experimental Validation ◽  
Cell Kill ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Enhance Tumor Cell

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

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