A Study on the Impact of Living Floor on the Price of an Apartment using a Spatio-temporal Model

2019 ◽  
Author(s):  
Sae Park
Keyword(s):  
Temporal Model ◽  
Spatio Temporal ◽  
The Impact

scholarly journals Linking spatial self-organization to community assembly and biodiversity

2021 ◽  
Author(s):  
Ehud Meron ◽  
Bidesh K Bera ◽  
Omer Tzuk ◽  
Jamie J. R. Bennett
Keyword(s):  
Community Structure ◽  
Ecosystem Services ◽  
Plant Communities ◽  
Self Organization ◽  
Provisioning Ecosystem Services ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Complex Relationships ◽  
The Impact ◽  
Community Reassembly

Drier climates impose environmental stresses on plant communities that may result in community reassembly and threatened ecosystem services, but also may trigger self-organization in spatial patterns of biota and resources, which act to relax these stresses. The complex relationships between these counteracting processes -- community reassembly and spatial self-organization -- have hardly been studied. Using a spatio-temporal model of dryland plant communities and a trait-based approach, we study the response of such communities to imposed water stress of increasing degrees. We first show that spatial patterning acts to reverse shifts from fast-growing species to stress-tolerant species, as well as to reverse functional-diversity loss. We then show that spatial re-patterning buffers the impact of further stress on community structure. Finally, we identify multistability ranges of uniform and patterned community states and use them to propose forms of non-uniform ecosystem management that integrate the need for provisioning ecosystem services with the need to preserve community structure.

scholarly journals High-Resolution Spatio-Temporal Model for County-Level COVID-19 Activity in the U.S.

2021 ◽  
Vol 12 (4) ◽  
pp. 1-20
Author(s):  
Shixiang Zhu ◽  
Alexander Bukharin ◽  
Liyan Xie ◽  
Mauricio Santillana ◽  
Shihao Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Demographic Factors ◽  
The United States ◽  
County Level ◽  
Current Time ◽  
Pairwise Correlation ◽  
Community Mobility ◽  
Temporal Model ◽  
Spatio Temporal ◽  
The Impact

We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases 1 week ahead of the current time, at the county level and weekly aggregated, in the United States. A notable feature of our spatio-temporal model is that it considers the (1) temporal auto- and pairwise correlation of the two local time series (confirmed cases and deaths from the COVID-19), (2) correlation between locations (propagation between counties), and (3) covariates such as local within-community mobility and social demographic factors. The within-community mobility and demographic factors, such as total population and the proportion of the elderly, are included as important predictors since they are hypothesized to be important in determining the dynamics of COVID-19. To reduce the model’s high dimensionality, we impose sparsity structures as constraints and emphasize the impact of the top 10 metropolitan areas in the nation, which we refer to (and treat within our models) as hubs in spreading the disease. Our retrospective out-of-sample county-level predictions were able to forecast the subsequently observed COVID-19 activity accurately. The proposed multivariate predictive models were designed to be highly interpretable, with clear identification and quantification of the most important factors that determine the dynamics of COVID-19. Ongoing work involves incorporating more covariates, such as education and income, to improve prediction accuracy and model interpretability.

scholarly journals A Bayesian spatio-temporal analysis of mortality rates in Spain: application to the COVID-19 2020 outbreak

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Pedro Saavedra ◽  
Angelo Santana ◽  
Luis Bello ◽  
José-Miguel Pacheco ◽  
Esther Sanjuán
Keyword(s):  
Time Trend ◽  
Age Groups ◽  
Specific Effect ◽  
Temporal Analysis ◽  
Mortality Rates ◽  
Temporal Models ◽  
Temporal Model ◽  
Spatio Temporal ◽  
The Impact ◽  
Excess Deaths

Abstract Background The number of deaths attributable to COVID-19 in Spain has been highly controversial since it is problematic to tell apart deaths having COVID as the main cause from those provoked by the aggravation by the viral infection of other underlying health problems. In addition, overburdening of health system led to an increase in mortality due to the scarcity of adequate medical care, at the same time confinement measures could have contributed to the decrease in mortality from certain causes. Our aim is to compare the number of deaths observed in 2020 with the projection for the same period obtained from a sequence of previous years. Thus, this computed mortality excess could be considered as the real impact of the COVID-19 on the mortality rates. Methods The population was split into four age groups, namely: (< 50; 50–64; 65–74; 75 and over). For each one, a projection of the death numbers for the year 2020, based on the interval 2008–2020, was estimated using a Bayesian spatio-temporal model. In each one, spatial, sex, and year effects were included. In addition, a specific effect of the year 2020 was added ("outbreak"). Finally, the excess deaths in year 2020 were estimated as the count of observed deaths minus those projected. Results The projected death number for 2020 was 426,970 people, the actual count being 499,104; thus, the total excess of deaths was 72,134. However, this increase was very unequally distributed over the Spanish regions. Conclusion Bayesian spatio-temporal models have proved to be a useful tool for estimating the impact of COVID-19 on mortality in Spain in 2020, making it possible to assess how the disease has affected different age groups accounting for effects of sex, spatial variation between regions and time trend over the last few years.

scholarly journals Mitochondrial calcium handling within the interstitial cells of Cajal

2014 ◽  
Vol 307 (1) ◽  
pp. G107-G121 ◽  
Author(s):  
Shawn A. Means ◽  
Leo K. Cheng
Keyword(s):  
Endoplasmic Reticulum ◽  
Interstitial Cells Of Cajal ◽  
Interstitial Cells ◽  
Calcium Handling ◽  
Intracellular Ca ◽  
Temporal Model ◽  
Model Geometry ◽  
Spatio Temporal ◽  
Cells Of Cajal ◽  
The Impact

The interstitial cells of Cajal (ICC) drive rhythmic pacemaking contractions in the gastrointestinal system. The ICC generate pacemaking signals by membrane depolarizations associated with the release of intracellular calcium (Ca2+) in the endoplasmic reticulum (ER) through inositol-trisphosphate (IP3) receptors (IP3R) and uptake by mitochondria (MT). This Ca2+ dynamic is hypothesized to generate pacemaking signals by calibrating ER Ca2+ store depletions and membrane depolarization with ER store-operated Ca2+ entry mechanisms. Using a biophysically based spatio-temporal model of integrated Ca2+ transport in the ICC, we determined the feasibility of ER depletion timescale correspondence with experimentally observed pacemaking frequencies while considering the impact of IP3R Ca2+ release and MT uptake on bulk cytosolic Ca2+ levels because persistent elevations of free intracellular Ca2+ are toxic to the cell. MT densities and distributions are varied in the model geometry to observe MT influence on free cytosolic Ca2+ and the resulting frequencies of ER Ca2+ store depletions, as well as the sarco-endoplasmic reticulum Ca2+ ATP-ase (SERCA) and IP3 agonist concentrations. Our simulations show that high MT densities observed in the ICC are more relevant to ER establishing Ca2+ depletion frequencies than protection of the cytosol from elevated free Ca2+, whereas the SERCA pump is more relevant to containing cytosolic Ca2+ elevations. Our results further suggest that the level of IP3 agonist stimulating ER Ca2+ release, subsequent MT uptake, and eventual activation of ER store-operated Ca2+ entry may determine frequencies of rhythmic pacemaking exhibited by the ICC across species and tissue types.

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 Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

Sign in / Sign up

Export Citation Format

Share Document