Measuring Equity through Spatial Variability of Infrastructure Systems across the Urban-Rural Gradient

Recent regional research has taken an ‘infrastructure turn’ where scholars have called for examining the transformative ability of different infrastructures in causing systemic inequities beyond the spatial conception of ‘urban and the other’. This research examines the interconnected impact of infrastructure systems on existing spatial inequities through a study in metropolitan Philadelphia, Pennsylvania. This study investigates whether the urban-rural (U-R) gradient concept can enhance understanding of the spatial relationship between socioeconomic indicators and infrastructure systems. Indicators of spatial inequalities were regressed against infrastructure variables and imperviousness, as a proxy for the U-R gradient, using multivariate and spatial regression methods. The models show that imperviousness has a positive correlation with the concentration of racialized minorities and a negative correlation with access to health insurance. The study also shows that the predictive power of multiple infrastructures varies across space and does not adhere to urban boundaries or the U-R gradient. The complex interactions among different infrastructures shape inequities and require further inquiry in urban regions around the world.

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German forecasters’ narratives: How informative are German business cycle forecast reports?

Empirical Economics ◽

10.1007/s00181-021-02100-9 ◽

2021 ◽

Author(s):

Karsten Müller

Keyword(s):

Business Cycle ◽

Fixed Effects ◽

Predictive Power ◽

Recursive Estimation ◽

Regression Methods ◽

Inflation Forecasts ◽

Fixed Effects Panel Regression ◽

Weak Evidence ◽

Out Of Sample ◽

Different Characteristics

AbstractBased on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

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Simple correlations for calculating NHTPB enthalpy of formation through molecular structures

Multidiscipline Modeling in Materials and Structures ◽

10.1108/mmms-04-2018-0074 ◽

2019 ◽

Vol 15 (1) ◽

pp. 258-264 ◽

Cited By ~ 1

Author(s):

Hamid Reza Ghaieni ◽

Saeed Tavangar ◽

Mohammad Moein Ebrahimzadeh Qhomi

Keyword(s):

Enthalpy Of Formation ◽

Design Methodology ◽

Predictive Power ◽

Cross Validation ◽

Molecular Structures ◽

Mean Square ◽

Mean Square Deviation ◽

Simple Correlation ◽

Content Type ◽

Regression Methods

Purpose The purpose of this paper is to present simple correlation for calculating nitrated hydroxyl-terminated polybutadiene (NHTPB) enthalpy of formation. Design/methodology/approach It uses multiple linear regression methods. Findings The proposed correlation has determination coefficient 0.96. The correlation has root mean square deviation and the average absolute deviations values 53.4 and 46.1 respectively. Originality/value The predictive power of correlation is checked by cross-validation method (R2=0.96, Q L O O 2 = 0.96 ).

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Spatial regression methods to evaluate beekeeping production in the state of Rio de Janeiro

Arquivo Brasileiro de Medicina Veterinária e Zootecnia ◽

10.1590/s0102-09352013000200035 ◽

2013 ◽

Vol 65 (2) ◽

pp. 553-558

Author(s):

W.S. Tassinari ◽

M.C. Lorenzon ◽

E.L. Peixoto

Keyword(s):

Rio De Janeiro ◽

High Performance ◽

Spatial Regression ◽

The State ◽

Spatial Effects ◽

Regression Methods ◽

Honey Production ◽

Production Factors ◽

Proposed Model ◽

Wild Vegetation

Brazilian beekeeping has been developed from the africanization of the honeybees and its high performance launches Brazil as one of the world´s largest honey producer. The Southeastern region has an expressive position in this market (45%), but the state of Rio de Janeiro is the smallest producer, despite presenting large areas of wild vegetation for honey production. In order to analyze the honey productivity in the state of Rio de Janeiro, this research used classic and spatial regression approaches. The data used in this study comprised the responses regarding beekeeping from 1418 beekeepers distributed throughout 72 counties of this state. The best statistical fit was a semiparametric spatial model. The proposed model could be used to estimate the annual honey yield per hive in regions and to detect production factors more related to beekeeping. Honey productivity was associated with the number of hives, wild swarm collection and losses in the apiaries. This paper highlights that the beekeeping sector needs support and help to elucidate the problems plaguing beekeepers, and the inclusion of spatial effects in the regression models is a useful tool in geographical data.

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Distributional effects of transport policies on inequalities in access to opportunities in Rio de Janeiro

10.31235/osf.io/cghx2 ◽

2017 ◽

Cited By ~ 2

Author(s):

Rafael Henrique Moreas Pereira ◽

David Banister ◽

Tim Schwanen ◽

Nate Wessel

Keyword(s):

Public Transport ◽

Rio De Janeiro ◽

Spatial Regression ◽

Developed Countries ◽

Transport Infrastructure ◽

Future Research ◽

Distributional Effects ◽

Spatial Inequalities ◽

Bus Services ◽

The City

The evaluation of the social impacts of transport policies is attracting growing attention in recent years. Yet, this literature is still predominately focused on developed countries. The goal of this research is to investigate how investments in public transport networks can reshape social and geographical inequalities in access to opportunities in a developing country, using the city of Rio de Janeiro (Brazil) as a case study. Recent mega-events, including the 2014 Football World Cup and the 2016 Olympic Games, have triggered substantial investment in the city’s transport system. More recently, though, bus services in Rio have been rationalized and reduced as a response to a fiscal crisis and a drop in passenger demand, giving a unique opportunity to look at the distributional effects this cycle of investment and disinvestment have had on peoples’ access to educational and employment opportunities. Based on a before-and-after comparison of Rio’s public transport network, this study uses a spatial regression model and cluster analysis to estimate how accessibility gains vary across different income groups and areas of the city between April 2014 and March 2017. The results show that recent cuts in service levels have offset the potential benefits of newly added public transport infrastructure in Rio. Average access by public transport to jobs and public high-schools decreased approximately 4% and 6% in the period, respectively. Nonetheless, wealthier areas had on average small but statistically significant higher gains in access to schools and job opportunities than poorer areas. These findings suggest that, contrary to the official discourses of transport legacy, recent transport policies in Rio have exacerbated rather than reduced socio-spatial inequalities in access to opportunities. These results also suggest that future research should consider how the modifiable areal unit problem (MAUP) can influence the equity assessment of transport projects.

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Rural as Periphery Per Se? Unravelling the Discursive Node

Sociální studia / Social Studies ◽

10.5817/soc2016-2-11 ◽

2016 ◽

Vol 13 (2) ◽

pp. 11-28 ◽

Cited By ~ 5

Author(s):

Bianka Plüschke-Altof

Keyword(s):

Discourse Analysis ◽

Conceptual Framework ◽

Critical Discourse Analysis ◽

Rural Areas ◽

The Other ◽

Critical Discourse ◽

Hegemonic Discourse ◽

Spatial Inequalities ◽

Urban Rural ◽

Per Se

Despite often being used interchangeably, the dominant equation of the rural with the peripheral is not self-evident. In order to critically scrutinize the discursive node, the aim of this article is twofold. On one hand, it argues for overcoming the prevalent urban‒rural divide and dominant structural approaches in sociological and geographical research by introducing discursive peripheralization as a conceptual framework, which allows the analysis of the discursive (re-)production of socio-spatial inequalities on and between different scales. On the other hand, this article explores how rural areas are constituted as peripheries within a hegemonic discourse naturalizing the ascription of development (non-)potentials. Following a critical discourse analysis approach, this will be illustrated in the case of periphery constructions in Estonian national print media.

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Does better availability mean better accessibility? Spatial inequalities in the care of acute myocardial infarction in Hungary

Hungarian Geographical Bulletin ◽

10.15201/hungeobull.69.4.5 ◽

2020 ◽

Vol 69 (4) ◽

pp. 401-418

Author(s):

Annamária Uzzoli ◽

Zoltán Egri ◽

Dániel Szilágyi ◽

Viktor Pál

Keyword(s):

Myocardial Infarction ◽

Health Care ◽

Acute Myocardial Infarction ◽

Statistical Analysis ◽

Health Literacy ◽

Access To Health Care ◽

Health Care Services ◽

Care Services ◽

Spatial Inequalities ◽

Access To Health

The availability of health care services is an important issue, however, improving availability of health care services does not necessarily mean better accessibility for everybody. The main aim of this study is to find out how better availability in the care of acute myocardial infarction vary with accessibility of patients’ geographical location within Hungary. We applied statistical analysis and interview techniques to unfold the role of spatiality in the conditions of access to health care. Results of statistical analysis indicate significant health inequalities in Hungary. Decreasing national mortality rates of acute myocardial infarction, has been coupled by increasing spatial inequalities within the country especially at micro-regional level. According to in-depth interviews with local health care stakeholders we defined factors that support access to health care as well as important barriers. The supporting factors are related to the improvement of availability (i.e. infrastructural developments), while geographical distance, lack of material and human resources, or low level of health literacy proved to be the most relevant barriers. Main conclusion is that barriers to accessibility and availability are not only spatial but are also based on individual stages of acute myocardial infarction care. The development of cardiac catheter centres in Hungary has improved the short-term chances of infarction survival, but long-term survival chances have worsened in recent years due to deficiencies in rehabilitation care as well as low level of health literacy.

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Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities

PLoS ONE ◽

10.1371/journal.pone.0246785 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0246785

Author(s):

Lorenzo Donadio ◽

Rossano Schifanella ◽

Claudia R. Binder ◽

Emanuele Massaro

Keyword(s):

Public Transportation ◽

Wide Spectrum ◽

Spatial Regression ◽

User Profile ◽

Location Based Services ◽

Socioeconomic Indicators ◽

Customer Data ◽

Spatial And Temporal Scales ◽

Socioeconomic Data ◽

Global And Local

The availability of reliable socioeconomic data is critical for the design of urban policies and the implementation of location-based services; however, often, their temporal and geographical coverage remain scarce. We explore the potential for insurance customers data to predict socioeconomic indicators of Swiss municipalities. First, we define a features space by aggregating at city-level individual customer data along several behavioral and user profile dimensions. Second, we collect official statistics shared by the Swiss authorities on a wide spectrum of categories: Population, Transportation, Work, Space and Territory, Housing, and Economy. Third, we adopt two spatial regression models exploring both global and local geographical dependencies to investigate their predictability. Results show consistently a correlation between insurance customer characteristics and official socioeconomic indexes. Performance fluctuates depending on the category, with values of R2 > 0.6 for several target variables using a 5-fold cross validation. As a case study, we focus on predicting the percentage of the population using public transportation and we discuss the implications on a regional scope. We believe that this methodology can support official statistical offices and it could open up new opportunities for the characterization of socioeconomic traits at highly-granular spatial and temporal scales.

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Entropy as a Measure of Attractiveness and Socioeconomic Complexity in Rio de Janeiro Metropolitan Area

Entropy ◽

10.3390/e22030368 ◽

2020 ◽

Vol 22 (3) ◽

pp. 368 ◽

Cited By ~ 1

Author(s):

Maxime Lenormand ◽

Horacio Samaniego ◽

Júlio César Chaves ◽

Vinícius da Fonseca Vieira ◽

Moacyr Alvim Horta Barbosa da Silva ◽

...

Keyword(s):

Socioeconomic Status ◽

Mobile Phone ◽

Urban Environment ◽

Metropolitan Area ◽

Rio De Janeiro ◽

Socioeconomic Indicators ◽

Spatial Inequalities

Defining and measuring spatial inequalities across the urban environment remains a complex and elusive task which has been facilitated by the increasing availability of large geolocated databases. In this study, we rely on a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location in the Rio de Janeiro Metropolitan Area (Brazil) as the diversity of visitors’ location of residence. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location, and can thus be used as a proxy for complex socioeconomic indicators.

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Parameter estimation for functional–structural plant models when data are scarce: using multiple patterns for rejecting unsuitable parameter sets

Annals of Botany ◽

10.1093/aob/mcaa016 ◽

2020 ◽

Vol 126 (4) ◽

pp. 559-570 ◽

Cited By ~ 1

Author(s):

Ming Wang ◽

Neil White ◽

Jim Hanan ◽

Di He ◽

Enli Wang ◽

...

Keyword(s):

Parameter Estimation ◽

Predictive Power ◽

Model Performance ◽

Persea Americana ◽

Strong Interactions ◽

Model Parameters ◽

Timely Manner ◽

Complex Interactions ◽

Output Uncertainty ◽

Parameter Values

Abstract Background and Aims Functional–structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible. Methods We used an existing FSP model of avocado (Persea americana ‘Hass’) and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization. Key Results POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns. Conclusions Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.

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