Scenarios post foreclosure crisis in Catalonia: accumulation of housing by banks as the first step for the rise of large private landlords

Erdkunde ◽  
2021 ◽  
Vol 75 (2) ◽  
pp. 105-120
Author(s):  
Antoni Domènech ◽  
Aaron Gutiérrez ◽  
Josep-Maria Arauzo-Carod
Keyword(s):  
Negative Binomial ◽  
Binomial Model ◽  
Regional Geography ◽  
Public Funds ◽  
Socioeconomic Vulnerability ◽  
Rental Market ◽  
Foreign Population ◽  
Explanatory Factors ◽  
Foreclosure Crisis ◽  
Concentration Patterns

The article analyses the uneven geography of foreclosed housing owned by large private landlords in Catalonia. A Negative Binomial Model is applied to identify the local determinants of the concentration patterns of 32,941 housing units in Catalan cities. Indicators of socioeconomic vulnerability, such as the percentage of foreign population or the percentage of unemployed residents, are identified as key explanatory factors of the regional geography of housing accumulated by banks which, in turn, correspond to areas in which global corporate landlords are focusing their business for profiteering from the rental market in the current expansionist phase of the housing cycle. Our findings demonstrate that the concentration of properties in the most vulnerable areas was fuelled by foreclosures responsibility of banks rescued with public funds. In tandem, we provide detailed information for the understanding of the new scenarios that have emerged during the post-crisis phase.

scholarly journals Identifying the Socio-Spatial Logics of Foreclosed Housing Accumulated by Large Private Landlords in Post-Crisis Catalan Cities

2020 ◽  
Vol 9 (5) ◽  
pp. 313
Author(s):  
Aaron Gutiérrez ◽  
Antoni Domènech
Keyword(s):  
Working Class ◽  
Urban Geography ◽  
Spatial Regression ◽  
Population Level ◽  
Housing Crisis ◽  
Foreign Population ◽  
Spatial Logic ◽  
Explanatory Factors ◽  
Spatial Logics ◽  
Concentration Patterns

The article analyses the socio-spatial logic behind the accumulation of foreclosed housing in the hands of large private landlords in the neighbourhoods of all the Catalan cities with over 100,000 inhabitants. Spatial regression and clustering techniques are applied to identify the determinants of the concentration patterns of 10,725 housing units in these cities. The socioeconomic variables, such as income level, percentage of foreign population, level of studies or percentage of unemployed residents, are identified as key explanatory factors of clustering of foreclosures in working-class neighbourhoods. A high presence of previously mortgaged homes is a variable especially relevant in the case of working-class neighbourhoods, but it has no incidence in the case of the medium-high class neighbourhoods. Our findings provide a detailed urban geography of the housing accumulated by banks which, at the same time, correspond to areas in which the vulture funds are focusing their business in the present and in the forthcoming years. New evidences of the spatial logic of the housing crisis and detailed information for the understanding of the new scenarios that have emerged during the post-crisis phase are revealed.

Robust inference in the multilevel zero-inflated negative binomial model

2019 ◽  
Vol 47 (2) ◽  
pp. 287-305 ◽  
Author(s):  
Eghbal Zandkarimi ◽  
Abbas Moghimbeigi ◽  
Hossein Mahjub ◽  
Reza Majdzadeh
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Model ◽  
Robust Inference ◽  
Binomial Model

scholarly journals Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Setting-specific Transmission Rates: A Systematic Review and Meta-analysis

2021 ◽  
Author(s):  
Hayley A Thompson ◽  
Andria Mousa ◽  
Amy Dighe ◽  
Han Fu ◽  
Alberto Arnedo-Pena ◽  
...  
Keyword(s):  
Systematic Review ◽  
Severe Acute Respiratory Syndrome ◽  
Negative Binomial ◽  
Meta Analysis ◽  
Contact Tracing ◽  
Binomial Model ◽  
Transmission Potential ◽  
Transmission Rates ◽  
Symptom Status ◽  
Index Cases

Abstract Background Understanding the drivers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is crucial for control policies, but evidence of transmission rates in different settings remains limited. Methods We conducted a systematic review to estimate secondary attack rates (SARs) and observed reproduction numbers (Robs) in different settings exploring differences by age, symptom status, and duration of exposure. To account for additional study heterogeneity, we employed a beta-binomial model to pool SARs across studies and a negative-binomial model to estimate Robs. Results Households showed the highest transmission rates, with a pooled SAR of 21.1% (95% confidence interval [CI]:17.4–24.8). SARs were significantly higher where the duration of household exposure exceeded 5 days compared with exposure of ≤5 days. SARs related to contacts at social events with family and friends were higher than those for low-risk casual contacts (5.9% vs 1.2%). Estimates of SARs and Robs for asymptomatic index cases were approximately one-seventh, and for presymptomatic two-thirds of those for symptomatic index cases. We found some evidence for reduced transmission potential both from and to individuals younger than 20 years of age in the household context, which is more limited when examining all settings. Conclusions Our results suggest that exposure in settings with familiar contacts increases SARS-CoV-2 transmission potential. Additionally, the differences observed in transmissibility by index case symptom status and duration of exposure have important implications for control strategies, such as contact tracing, testing, and rapid isolation of cases. There were limited data to explore transmission patterns in workplaces, schools, and care homes, highlighting the need for further research in such settings.

Assessing recurrence of depression using a zero-inflated negative binomial model: A secondary analysis of lifelog data

2021 ◽  
Vol 300 ◽  
pp. 113919
Author(s):  
Narimasa Kumagai ◽  
Aran Tajika ◽  
Akio Hasegawa ◽  
Nao Kawanishi ◽  
Hirokazu Fujita ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Secondary Analysis ◽  
Negative Binomial Model ◽  
Binomial Model

scholarly journals Influence of weekday of admission and level of distress on length of hospital stay in patients with low back pain: a retrospective cohort study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Emanuel Brunner ◽  
André Meichtry ◽  
Davy Vancampfort ◽  
Reinhard Imoberdorf ◽  
David Gisi ◽  
...  
Keyword(s):  
Primary Care ◽  
Cohort Study ◽  
Low Back Pain ◽  
Back Pain ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Negative Binomial ◽  
Length Of Hospital Stay ◽  
Binomial Model ◽  
Low Back

Abstract Background Low back pain (LBP) is often a complex problem requiring interdisciplinary management to address patients’ multidimensional needs. Providing inpatient care for patients with LBP in primary care hospitals is a challenge. In this setting, interdisciplinary LBP management is often unavailable during weekends. Delays in therapeutic procedures may result in a prolonged length of hospital stay (LoS). The impact of delays on LoS might be strongest in patients reporting high levels of psychological distress. Therefore, this study investigates the influence of weekday of admission and distress on LoS of inpatients with LBP. Methods This retrospective cohort study was conducted between 1 February 2019 and 31 January 2020. In part 1, a negative binomial model was fitted to LoS with weekday of admission as a predictor. In part 2, the same model included weekday of admission, distress level, and their interaction as covariates. Planned contrast was used in part 1 to estimate the difference in log-expected LoS between group 1 (admissions Friday/Saturday) and the reference group (admissions Sunday-Thursday). In part 2, the same contrast was used to estimate the corresponding difference in (per-unit) distress trends. Results We identified 173 patients with LBP. The mean LoS was 7.8 days (SD = 5.59). Patients admitted on Friday (mean LoS = 10.3) and Saturday (LoS = 10.6) had longer stays, but not those admitted on Sunday (LoS = 7.1). Analysis of the weekday effect and planned contrast showed that admission on Friday or Saturday was associated with a significant increase in LoS (log ratio = 0.42, 95% CI = 0.21 to 0.63). A total of 101 patients (58%) returned questionnaires, and complete data on distress were available from 86 patients (49%). According to the negative binomial model for LoS and the planned contrast, the distress effect on LoS was significantly influenced (difference in slopes = 0.816, 95% CI = 0.03 to 1.60) by dichotomic weekdays of admission (Friday/Saturday vs. Sunday-Thursday). Conclusions Delays in interdisciplinary LBP management over the weekend may prolong LoS. This may particularly affect patients reporting high levels of distress. Our study provides a platform to further explore whether interdisciplinary LBP management addressing patients’ multidimensional needs reduces LoS in primary care hospitals.

scholarly journals Factors Affecting Under-Five Mortality in Ethiopia: A Multilevel Negative Binomial Model

2020 ◽  
Vol Volume 11 ◽  
pp. 525-534
Author(s):  
Bisrat Misganew Geremew ◽  
Kassahun Alemu Gelaye ◽  
Alemakef Wagnew Melesse ◽  
Temesgen Yihunie Akalu ◽  
Adhanom Gebreegziabher Baraki
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Model ◽  
Binomial Model ◽  
Factors Affecting ◽  
Under Five ◽  
Multilevel Negative Binomial Model

scholarly journals Generalized linear models with random effects for the description of data with excess zeros

2011 ◽  
Vol 54 (6) ◽  
pp. 661-675
Author(s):  
N. Mielenz ◽  
K. Thamm ◽  
M. Bulang ◽  
J. Spilke
Keyword(s):  
Random Effects ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Linear Models ◽  
Negative Binomial ◽  
Detailed Comparison ◽  
Response Scale ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Explanatory Factors

Abstract. In this paper count data with excess zeros and repeated observations per subject are evaluated. If the number of values observed for the zero event in the trial substantially exceeds the expected number (derived from the Poisson or from the negative binomial distribution), then there is an excess of zeros. Hurdle and zero-inflated models with random effects are available in order to evaluate this type of data. In this paper both model approaches are presented and are used for the evaluation of the number of visits to the feeder per cow per hour. Finally, for the analysis of the target trait a hurdle model with random effects based on a negative binomial distribution was used. This analysis was derived from a detailed comparison of models and was needed because of a simpler computer implementation. For improved interpretation of the results, the levels of the explanatory factors (for example, the classes of lactation) were not averaged in the link scale, but rather in the response scale. The deciding explanatory variables for the pattern of visiting activities in the 24-hour cycle are the milking and cleaning times at hours 4, 7, 12 and 20. The highly significant differences in the visiting frequencies of cows of the first lactation and those of higher lactations were explained by competition for access to the feeder and thus to the feed.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

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