scholarly journals Proposal of real estate mass valuation in Slovenia based on generalised additive modelling approach

2021 ◽  
Vol 65 (01) ◽  
pp. 46-81
Author(s):  
Melita Ulbl ◽  
Miroslav Verbič ◽  
Anka Lisec ◽  
Marko Pahor
Keyword(s):  
Additive Model ◽  
Generalised Additive Model ◽  
Experimental Part ◽  
Valuation Model ◽  
Gamma Distributions ◽  
The Republic ◽  
Transaction Value ◽  
The Impact ◽  
Modelling Approach ◽  
Model Approach

The present paper discusses the heterogeneity of the apartment market. For this purpose, we have developed the model for the mass valuation of apartments in the Republic of Slovenia. The construction of the mass valuation model is based on the generalised additive model approach. In this paper, the development of the model is presented. In the experimental part, the analysis of the results of the two models is performed. The dependent variable (the price of an apartment) is distributed according to the Gaussian and the gamma distributions. Particular attention has been paid to the impact of the transaction time on the apartments’ transaction value. The results of the model are also compared with the results of the mass valuation model in the Republic of Slovenia, which is carried out cyclically and iteratively, the results of which depend on the results (and mass valuation models) of previous cycles.

scholarly journals Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 131
Author(s):  
Sverre Solberg ◽  
Sam-Erik Walker ◽  
Philipp Schneider ◽  
Cristina Guerreiro
Keyword(s):  
Nitrogen Dioxide ◽  
Temporal Trends ◽  
Measurement Data ◽  
Additive Model ◽  
Generalised Additive Model ◽  
The Difference ◽  
The One ◽  
The Impact ◽  
Good Agreement ◽  
Model Approach

In this paper, the effect of the lockdown measures on nitrogen dioxide (NO2) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO2, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO2 reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO2 data during the lockdown and the corresponding changes in tropospheric vertical NO2 column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.

scholarly journals Drug offence detection during the COVID-19 lockdown: a spatiotemporal study of change in a street-level drug market

2021 ◽  
Author(s):  
Jason Leslie Payne ◽  
Cameron Thomas Langfield
Keyword(s):  
Additive Model ◽  
Drug Market ◽  
Future Research ◽  
Generalised Additive Model ◽  
Train Station ◽  
Street Level ◽  
Drug Offence ◽  
Crime Types ◽  
The Impact ◽  
North And South

The COVID-19 pandemic and the subsequent introduction of strict government orders to `stay-at-home' has led to a significant decline in most crime types--except, notably, illicit drug detections. However, the impact of these restrictions on open-air, or street-level, drug markets has been neglected in the study of COVID-19. In this paper, we use data from the state of Queensland, Australia, to explore how COVID-19 restrictions may have impacted the open-air drug market of Fortitude Valley in Brisbane. Using a spatiotemporal generalised additive model (GAM), we find that drug detections did not change in the Fortitude Valley region (despite significant increases across the whole state) but that this finding masked considerable reductions in and around the Fortitude Valley train station as well as in the vicinity Brunswick Street mall. It seems that any COVID-19-related decrease appears to have been offset by increases elsewhere, particularly to the streets north and south west of the main street market. These results highlight the limitations of city-wide aggregate analyses of crime during the pandemic and highlights the need for future research, including with qualitative and ethnographic methods to better understand the lived experiences of drug sellers/users and the law enforcement officers who policed these areas.

scholarly journals Shiny-SoSV: A web-based performance calculator for somatic structural variant detection

2019 ◽  
Author(s):  
Tingting Gong ◽  
Vanessa M Hayes ◽  
Eva KF Chan
Keyword(s):  
Study Design ◽  
Additive Model ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Web Based ◽  
Generalised Additive Model ◽  
Structural Variant ◽  
Variant Detection ◽  
User Friendly ◽  
The Impact

AbstractSomatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applications that could predict the performance of somatic structural variant detection. To address this gap, we developed Shiny-SoSV, a user-friendly web-based calculator for determining the impact of common variables on the sensitivity and precision of somatic structural variant detection, including choice of variant detection tool, sequencing depth of coverage, variant allele fraction, and variant breakpoint resolution. Using simulation studies, we determined singular and combinatoric effects of these variables, modelled the results using a generalised additive model, allowing structural variant detection performance to be predicted for any combination of predictors. Shiny-SoSV provides an interactive and visual platform for users to easily compare individual and combined impact of different parameters. It predicts the performance of a proposed study design, on somatic structural variant detection, prior to the commencement of benchwork. Shiny-SoSV is freely available at https://hcpcg.shinyapps.io/Shiny-SoSV with accompanying user’s guide and example use-cases.

scholarly journals Secular trends in incidence of acute gastroenteritis in general practice, France, 1991 to 2015

2017 ◽  
Vol 22 (50) ◽  
Author(s):  
Mathieu Rivière ◽  
Noémie Baroux ◽  
Vanina Bousquet ◽  
Katia Ambert-Balay ◽  
Pascal Beaudeau ◽  
...  
Keyword(s):  
Acute Gastroenteritis ◽  
Emergency Departments ◽  
Additive Model ◽  
Emergency Department Visits ◽  
Data Sources ◽  
Viral Gastroenteritis ◽  
Generalised Additive Model ◽  
Drug Reimbursement ◽  
Gp Visits ◽  
The Impact

We analysed 25 years of general practitioner (GP) visits for acute gastroenteritis (AG) surveillance in France, by the GP Sentinelles network. We searched for time trends of acute gastroenteritis incidence during winter periods. Data from emergency departments and drug reimbursement were additional data sources. A time-series analysis was performed using a generalised additive model for all data sources for the winter period. Virological data were incorporated and compared with the three data sources. The cumulative incidence of GP visits for winter AG exhibited an increasing trend from 1991 until 2008, when it reached 6,466 per 100,000 inhabitants. It decreased thereafter to 3,918 per 100,000 inhabitants in 2015. This decreasing trend was observed for all age groups and confirmed by the generalised additive model. For emergency department visits a decreasing trend was observed from 2004. Drug reimbursement data analyses demonstrated a decreasing trend from when data began in 2009. The incidence reported by GPs and emergency departments was lower following the emergence of norovirus GII.4 2012 (p < 0.0001). Winter AG incidences seem to follow long-term rising and decreasing trends that are important to monitor through continuous surveillance to evaluate the impact of prevention strategies, such as future immunisation against acute viral gastroenteritis.

scholarly journals Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 504
Author(s):  
Said Munir ◽  
Gulnur Coskuner ◽  
Majeed Jassim ◽  
Yusuf Aina ◽  
Asad Ali ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Human Life ◽  
Additive Model ◽  
Positive Association ◽  
Air Pollutant ◽  
Back Trajectory ◽  
Generalised Additive Model ◽  
Mobility Data ◽  
The Impact

The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter.

scholarly journals A first estimation of the impact of public health actions against COVID-19 in Veneto (Italy)

2020 ◽  
pp. jech-2020-214209 ◽  
Author(s):  
Dario Gregori ◽  
Danila Azzolina ◽  
Corrado Lanera ◽  
Ilaria Prosepe ◽  
Nicolas Destro ◽  
...  
Keyword(s):  
Additive Model ◽  
Quadratic Growth ◽  
Time Interval ◽  
Epidemic Outbreak ◽  
Generalised Additive Model ◽  
Epidemic Curve ◽  
Italian Regions ◽  
Containment Measures ◽  
Changepoint Analysis ◽  
The Impact

BackgroundVeneto is one of the first Italian regions where the COVID-19 outbreak started spreading. Containment measures were approved soon thereafter. The present study aims at providing a first look at the impact of the containment measures on the outbreak progression in the Veneto region, Italy.MethodsA Bayesian changepoint analysis was used to identify the changing speed of the epidemic curve. Then, a piecewise polynomial model was considered to fit the data in the first period before the detected changepoint. In this time interval, that is, the weeks from 27 February to 12 March, a quadratic growth was identified by a generalised additive model (GAM). Finally, the model was used to generate the projection of the expected number of hospitalisations at 2 weeks based on the epidemic speed before the changepoint. Such estimates were then compared with the actual outbreak behaviour.ResultsThe comparison between the observed and predicted hospitalisation curves highlights a slowdown on the total COVID-19 hospitalisations after the onset of containment measures. The estimated daily slowdown effect of the epidemic growth is estimated as 78 hospitalisations per day as of 27 March (95% CI 75 to 81).ConclusionsThe containment strategies seem to have positively impacted the progression of the COVID-19 epidemic outbreak in Veneto.

scholarly journals Magnitude and time-course of excess mortality during COVID-19 outbreak: population-based empirical evidence from highly impacted provinces in northern Italy

2020 ◽  
Vol 6 (3) ◽  
pp. 00458-2020 ◽  
Author(s):  
Sara Conti ◽  
Pietro Ferrara ◽  
Giampiero Mazzaglia ◽  
Marco I. D'Orso ◽  
Roberta Ciampichini ◽  
...  
Keyword(s):  
Time Series ◽  
Time Course ◽  
Excess Mortality ◽  
Additive Model ◽  
Population Based ◽  
Northern Italy ◽  
Cumulative Number ◽  
Generalised Additive Model ◽  
The Impact ◽  
Excess Deaths

BackgroundThe real impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on overall mortality remains uncertain as surveillance reports have attributed a limited number of deaths to novel coronavirus disease 2019 (COVID-19) during the outbreak. The aim of this study was to assess the excess mortality during the COVID-19 outbreak in highly impacted areas of northern Italy.MethodsWe analysed data on deaths that occurred in the first 4 months of 2020 provided by the health protection agencies (HPAs) of Bergamo and Brescia (Lombardy), building a time-series of daily number of deaths and predicting the daily standardised mortality ratio (SMR) and cumulative number of excess deaths through a Poisson generalised additive model of the observed counts in 2020, using 2019 data as a reference.ResultsWe estimated that there were 5740 (95% credible set (CS) 5552–5936) excess deaths in the HPA of Bergamo and 3703 (95% CS 3535–3877) in Brescia, corresponding to a 2.55-fold (95% CS 2.50–2.61) and 1.93 (95% CS 1.89–1.98) increase in the number of deaths. The excess death wave started a few days later in Brescia, but the daily estimated SMR peaked at the end of March in both HPAs, roughly 2 weeks after the introduction of lockdown measures, with significantly higher estimates in Bergamo (9.4, 95% CI 9.1–9.7).ConclusionExcess mortality was significantly higher than that officially attributed to COVID-19, disclosing its hidden burden likely due to indirect effects on the health system. Time-series analyses highlighted the impact of lockdown restrictions, with a lower excess mortality in the HPA where there was a smaller delay between the epidemic outbreak and their enforcement.

Unemployment as an indicator of economic security in the employment sector: A regional dimension

2020 ◽  
Vol 16 (4) ◽  
pp. 715-729
Author(s):  
T.N. Savina
Keyword(s):  
Comparative Analysis ◽  
Age Groups ◽  
National Development ◽  
Federal District ◽  
Economic Security ◽  
Employment Policy ◽  
Multivariate Statistical ◽  
Band Theory ◽  
The Republic ◽  
The Impact

Subject. To achieve a high level of economic security is a key priority of national development. Employment reveals one of the most important aspects of social development of the individual that is associated with his or her needs satisfaction in the sphere of employment and is boon to economic security. Objectives. The purpose of the study is to show the impact of unemployment on economic security in employment. Methods. I apply such scientific methods as dialectical, historical and logical unity, structural and functional analysis, traditional techniques of economic analysis and synthesis. The methods of multivariate statistical and comparative analysis serve as a methodological basis of the study. To determine the indicator of unemployment, I use the band theory. Results. I underpin the growing role of employment in ensuring economic security. The paper presents a comprehensive assessment of the unemployment status and a comparative analysis of the indicator in the Republic of Mordovia, the Volga Federal District, and the Russian Federation as a whole. I identify trends in the average duration of unemployment, show the distribution of unemployed by level of education and age groups. Conclusions. The average annual unemployment rate in the Republic of Mordovia is lower than in Russia and the Volga Federal District. The findings may be useful for public authorities to substantiate their employment policy at both macro- and meso-levels, for designing programs and strategies for socio-economic development of regions and the social security doctrine, as well as in practical activities of employment services.

Some reflections on social structure: Theoretical and practical aspects

2020 ◽  
Vol 18 (6) ◽  
pp. 1063-1078
Author(s):  
T.N. Skorobogatova ◽  
I.Yu. Marakhovskaya
Keyword(s):  
Service Industry ◽  
External Factors ◽  
Infrastructure Development ◽  
Social Infrastructure ◽  
Social Sphere ◽  
The Social ◽  
Study Results ◽  
The Republic ◽  
Internal And External Factors ◽  
The Impact

Subject. This article discusses the role of social infrastructure in the national economy and analyzes the relationship between the notions of Infrastructure, Service Industry and Non-Productive Sphere. Objectives. The article aims to outline a methodology for development of the social infrastructure of Russia's regions. Methods. For the study, we used the methods of statistical and comparative analyses. The Republic of Crimea and Rostov Oblast's social infrastructure development was considered as a case study. Results. The article finds that the level of social infrastructure is determined by a number of internal and external factors. By analyzing and assessing such factors, it is possible to develop promising areas for the social sphere advancement. Conclusions. Assessment and analysis of internal factors largely determined by the region's characteristics, as well as a comprehensive consideration of the impact of external factors will help ensure the competitiveness of the region's economy.

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