scholarly journals A Spatiotemporal Prediction Model for Regional Scheduling of Shared Bicycles Based on the INLA Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhuoran Yu ◽  
Yimeng Duan ◽  
Shen Zhang ◽  
Xin Liu ◽  
Kui Li
Keyword(s):  
Prediction Models ◽  
Bayesian Hierarchical ◽  
Demand Prediction ◽  
Point Of Interest ◽  
Modeling Process ◽  
Proposed Model ◽  
Bayesian Hierarchical Method ◽  
Spatial Entities ◽  
Operation Results ◽  
The Impact

Dock-less bicycle-sharing programs have been widely accepted as an efficient mode to benefit health and reduce congestions. And modeling and prediction has always been a core proposition in the field of transportation. Most of the existing demand prediction models for shared bikes take regions as research objects; therefore, a POI-based method can be a beneficial complement to existing research, including zone-level, OD-level, and station-level techniques. Point of interest (POI) is the location description of spatial entities, which can reflect the cycling route characteristics for both commuting and noncommuting trips to a certain extent, and is also the main generating point and attraction point of shared-bike travel flow. In this study, we make an effort to model a POI-level cycling demand with a Bayesian hierarchical method. The proposed model combines the integrated nested Laplace approximation (INLA) and random partial differential equation (SPDE) to cope with the huge computation in the modeling process. In particular, we have adopted the dock-less bicycle-sharing rental records of Mobike as a case study to validate our method; the study area was one of the fastest growing urban districts in Shanghai in August 2016. The operation results show that the method can help better understand, measure, and characterize spatiotemporal patterns of bike-share ridership at the POI level and quantify the impact of the spatiotemporal effect on bicycle-sharing use.

scholarly journals Statistical Modeling in Nonlinear Systems

2005 ◽  
Vol 18 (16) ◽  
pp. 3388-3399 ◽  
Author(s):  
Edward P. Campbell
Keyword(s):  
Nonlinear Dynamics ◽  
Statistical Models ◽  
Lorenz System ◽  
Prediction Models ◽  
Climate Data ◽  
Bayesian Hierarchical ◽  
Alternative Approaches ◽  
The Lorenz System ◽  
Bayesian Hierarchical Methods ◽  
The Impact

Abstract The use of linear statistical methods in building climate prediction models is examined, particularly the use of anomalies. The author’s perspective is that the climate system is a nonlinear interacting system, so the impact of modeling using anomalies rather than observed data directly is considered. With reference to the Lorenz system and a simple model for regime dependence, it is shown that anomalies impair our ability to reconstruct nonlinear dynamics. Some alternative approaches in the literature that offer an attractive way forward are explored, focusing on Bayesian hierarchical methods to construct so-called physical–statistical models. The author’s view is that anomalies should be reserved in most cases as a tool for enhancing graphical representations of climate data. The exceptions are when the implicit assumptions underlying the use of anomalies are met or when an anomaly representation is physically motivated.

Test of Mediation and Moderation between Employee Empathy and Customers’ Advocacy

2020 ◽  
Author(s):  
Sajjad Ahmad Afridi ◽  
Asad Shahjehan ◽  
Maqsood Haider ◽  
Dr Uzma Munawar
Keyword(s):  
Factor Analysis ◽  
Customer Loyalty ◽  
Service Sector ◽  
Mediating Effect ◽  
First Line ◽  
Private Hospitals ◽  
Proposed Model ◽  
Confirmatory Factor ◽  
The Impact ◽  
Significant Moderator

This study examined the impact of employee empathy on customers’ advocacy directly and indirectly through customers’ loyalty. Moreover, the interacting effect of customers’ trust was verified between the association of customers’ loyalty and advocacy. The attributes of the proposed model were examined in the context of first line employee and patients’ interactions. A total of 220 responses were collected for analysis from the private hospitals of Peshawar. The model fitness was confirmed through confirmatory factor analysis and hypotheses were examined. Findings confirmed the positive and significant impact of employee empathy on customers’ advocacy. Further, the mediating effect was examined and found that loyalty partially mediates employee empathy and customers’ advocacy. Additionally, trust was found a significant moderator between the association of customer loyalty and advocacy. Furthermore, findings revealed that trust based loyalty significantly and positively mediates employee empathy and customers’ advocacy. Findings of the present study provide understanding for the service sector, particularly in healthcare, to enhance customers’ loyalty, advocacy, and trust through service employee’s empathic aptitude. Keywords: Employee empathy, Service Eco-system, Customers’ Loyalty, Customers’ Advocacy, Trust-Based Loyalty, Healthcare, S-D Logic

The impact of duration observations on precision of results GNSS measurements

2017 ◽  
Vol 921 (3) ◽  
pp. 7-13 ◽  
Author(s):  
S.V. Grishko
Keyword(s):  
Measurement Accuracy ◽  
Functional Dependence ◽  
Observation Time ◽  
Satellite Networks ◽  
Satellite Measurements ◽  
Proposed Model ◽  
Actual Error ◽  
Gnss Measurements ◽  
The Impact ◽  
Qualitative Characteristics

This paper shows that the accuracy of relative satellite measurements depend not only on the length of the baseline, as it is regulated by the rating formula of accuracy of GNSS equipment, but also on the duration of observations. As a result of the strict adjustment much redundant satellite networks with different duration of observations obtained covariance matrix of baselines, the most realistic reflecting the actual error of satellite observations. Research of forms of communication of these errors from length of the baseline and duration of its measurement is executed. A significant influence of solar activity on accuracy of satellite measurements, in general, leads to unequal similar series of measurements made at different periods, for example, in the production of monitoring activities. The model of approximation of the functional dependence of accuracy of the baseline from its length and duration of observations having good qualitative characteristics is offered. Based on the proposed model, we analyzed the dynamics of changes in measurement accuracy with an increase in observation time.

scholarly journals The impact of quarantine on Covid-19 infections

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Pablo Marshall
Keyword(s):  
Health System ◽  
Infection Rate ◽  
District Level ◽  
Infection Model ◽  
Social Distancing ◽  
Proposed Model ◽  
Lower Infection ◽  
The Impact ◽  
Lower Infection Rate ◽  
Over Time

Abstract Objectives: Coronavirushas had profound effects on people’s lives and the economy of many countries, generating controversy between the need to establish quarantines and other social distancing measures to protect people’s health and the need to reactivate the economy. This study proposes and applies a modification of the SIR infection model to describe the evolution of coronavirus infections and to measure the effect of quarantine on the number of people infected. Methods: Two hypotheses, not necessarily mutually exclusive, are proposed for the impact of quarantines. According to the first hypothesis, quarantine reduces the infection rate, delaying new infections over time without modifying the total number of people infected at the end of the wave. The second hypothesis establishes that quarantine reduces the population infected in the wave. The two hypotheses are tested with data for a sample of 10 districts in Santiago, Chile. Results: The results of applying the methodology show that the proposed model describes well the evolution of infections at the district level. The data shows evidence in favor of the first hypothesis, quarantine reduces the infection rate; and not in favor of the second hypothesis, that quarantine reduces the population infected. Districts of higher socio-economic levels have a lower infection rate, and quarantine is more effective. Conclusions: Quarantine, in most districts, does not reduce the total number of people infected in the wave; it only reduces the rate at which they are infected. The reduction in the infection rate avoids peaks that may collapse the health system.

scholarly journals Measuring spatial allocative efficiency in basketball

2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn
Keyword(s):  
Spatial Distribution ◽  
Hierarchical Model ◽  
Opportunity Cost ◽  
Allocative Efficiency ◽  
Bayesian Hierarchical Model ◽  
National Basketball Association ◽  
Spatial Context ◽  
Bayesian Hierarchical ◽  
The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

scholarly journals Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou

2021 ◽  
Vol 13 (2) ◽  
pp. 444
Author(s):  
Xucai Zhang ◽  
Yeran Sun ◽  
Ting On Chan ◽  
Ying Huang ◽  
Anyao Zheng ◽  
...  
Keyword(s):  
Rapid Development ◽  
Ordinary Least Squares ◽  
Information And Communications Technology ◽  
Time Data ◽  
Location Data ◽  
Point Of Interest ◽  
Quality Life ◽  
City Construction ◽  
The Impact ◽  
The City

Urban vibrancy contributes towards a successful city and high-quality life for people as one of its vital elements. Therefore, the association between service facilities and vibrancy is crucial for urban managers to understand and improve city construction. Moreover, the rapid development of information and communications technology (ICT) allows researchers to easily and quickly collect a large volume of real-time data generated by people in daily life. In this study, against the background of emerging multi-source big data, we utilized Tencent location data as a proxy for 24-h vibrancy and adopted point-of-interest (POI) data to represent service facilities. An analysis framework integrated with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models is proposed to explore the spatiotemporal relationships between urban vibrancy and POI-based variables. Empirical results show that (1) spatiotemporal variations exist in the impact of service facilities on urban vibrancy across Guangzhou, China; and (2) GTWR models exhibit a higher degree of explanatory capacity on vibrancy than the OLS models. In addition, our results can assist urban planners to understand spatiotemporal patterns of urban vibrancy in a refined resolution, and to optimize the resource allocation and functional configuration of the city.

scholarly journals An adaptive social distancing SIR model for COVID-19 disease spreading and forecasting

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Said Gounane ◽  
Yassir Barkouch ◽  
Abdelghafour Atlas ◽  
Mostafa Bendahmane ◽  
Fahd Karami ◽  
...  
Keyword(s):  
Mathematical Theory ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Threshold ◽  
Social Distancing ◽  
Sir Epidemic ◽  
Proposed Model ◽  
Disease Spreading ◽  
The Government ◽  
The Impact

Abstract Recently, various mathematical models have been proposed to model COVID-19 outbreak. These models are an effective tool to study the mechanisms of coronavirus spreading and to predict the future course of COVID-19 disease. They are also used to evaluate strategies to control this pandemic. Generally, SIR compartmental models are appropriate for understanding and predicting the dynamics of infectious diseases like COVID-19. The classical SIR model is initially introduced by Kermack and McKendrick (cf. (Anderson, R. M. 1991. “Discussion: the Kermack–McKendrick Epidemic Threshold Theorem.” Bulletin of Mathematical Biology 53 (1): 3–32; Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society 115 (772): 700–21)) to describe the evolution of the susceptible, infected and recovered compartment. Focused on the impact of public policies designed to contain this pandemic, we develop a new nonlinear SIR epidemic problem modeling the spreading of coronavirus under the effect of a social distancing induced by the government measures to stop coronavirus spreading. To find the parameters adopted for each country (for e.g. Germany, Spain, Italy, France, Algeria and Morocco) we fit the proposed model with respect to the actual real data. We also evaluate the government measures in each country with respect to the evolution of the pandemic. Our numerical simulations can be used to provide an effective tool for predicting the spread of the disease.

scholarly journals GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2050
Author(s):  
Beatriz Castro Dias Cuyabano ◽  
Gabriel Rovere ◽  
Dajeong Lim ◽  
Tae Hun Kim ◽  
Hak Kyo Lee ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Phenotypic Expression ◽  
Genetic Evaluation ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Korean Cattle ◽  
Evaluation Programs ◽  
The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

scholarly journals A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wilfredo Angulo ◽  
José M. Ramírez ◽  
Dany De Cecchis ◽  
Juan Primera ◽  
Henry Pacheco ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Risk Perceptions ◽  
Transmission Rate ◽  
Early Stage ◽  
Individual Risk ◽  
Seir Model ◽  
Proposed Model ◽  
Probable Site ◽  
Adjusted Model ◽  
The Impact

AbstractCOVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than $$4\%$$ 4 % . The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2.

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