Crash rates analysis in China using a spatial panel model

Soro, Wonmongo Lacina; Zhou, Yiwei; Wayoro, Didier

doi:10.1016/j.iatssr.2016.11.001

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…The motivation for our choice is then essentially related to the aim of the present study to focus on spatial interaction effects and their relevance for the emergence of spillover across nearby geographical units. The existence of this problem is also confirmed in similar estimation settings by Elhorst (), Chakir and Le Gallo (), Kang et al (), and Soro et al ().…”

Section: Model Frameworksupporting

confidence: 66%

Modeling the Impact of Technological Innovation on Environmental Efficiency: A Spatial Panel Data Approach

Benedetti

Palma

Postiglione

2019

Geographical Analysis

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Technological progress has widely been acknowledged as a key driver in the achievement of environmental sustainability, and a new concern has recently arisen for assessing the role of differences in regional innovation performances with respect to the pursuit of pollution targets. We investigate the impact of innovative activities on environmental efficiency at the regional level, adopting a spatial panel data approach to account for dependence between geographical observations and to get deeper insights into spatial knowledge spillovers arising from agglomeration economies. The spatial panel analysis covers 103 Italian provinces (equivalent to NUTS3 regions) on four major air pollutants (i.e., CO 2 , CH 4 , NMVOC, CO) over the 1995-2010 period. Our findings show that the state of local technology development, proxied by patents, has a positive impact on GDP emission efficiency and that its magnitude is higher for more localized pollutants as a result of larger spillover effects. Finally, to analyze the Italian North-South dualism of emission levels of pollutants, we offer a spillover effect study to emphasize the remarkable difference among effects in the territory. This calls for closer attention to the local relevance of the pollution damage when environmental impact of innovation strategies is to be evaluated.

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Section: Model Frameworksupporting

confidence: 66%

Modeling the Impact of Technological Innovation on Environmental Efficiency: A Spatial Panel Data Approach

Benedetti

Palma

Postiglione

2019

Geographical Analysis

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“…The results also indicated that an increased length of roads with a speed limit below 30 km/h and a higher ratio of residents below the age of 15 are correlated with a lower traffic crash frequency, whereas a higher ratio of residents who moved to the TAZ, more vehicle kilometers traveled, and a greater number of access points with speed limit difference between side roads and the mainline above 30 km/h all increase the number of traffic crashes. Soro et al (2017) developed four models from panel data, which were the non-spatial model, the spatial autoregressive model, the spatial error model, and the fixed effect spatial autoregressive model with autoregressive disturbances (SARAR). Accident rate and injury rate were used as the dependent variables.…”

Section: Spatial Autoregressive Modelsmentioning

confidence: 99%

Bayesian Spatio-Temporal Analysis of Road Traffic Crashes

Azimian¹

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BAYESIAN SPATIO-TEMPORAL ANALYSIS OF ROAD TRAFFIC CRASHES Amin Azimian Road traffic crashes are one of the major causes of death and serious injury in the US, leading to economic losses and human suffering. In recent years, several research efforts have been made to screen areas and locate hotspots/zones and to identify the factors contributing to traffic crashes of different severity levels. Such research typically aggregates crash locations into spatial units at the macro level, such as counties, or at the micro level, such as road segments or intersections. Many crash estimation methods have been proposed in the literature. These methods range from classical approaches, such as the linear, Poisson, negative binomial, and logistic regression methods, to more state-of-the-art approaches, such as the empirical Bayesian (EB), spatial autoregressive, and full Bayesian methods. A considerable drawback of classical methods is that they cannot account for the regression-to-the-mean bias and potential unobserved heterogeneity, which can result in unstable and biased parameter estimates. Additionally, EB and spatial autoregressive methods are unable to address multilevel data and group-level random effects. By contrast, the full Bayesian framework is more flexible and can be easily extended to include random effect terms that can act as proxies for unobserved or missing covariates that have a spatial or temporal structure. Although the full Bayesian framework appears to be a promising methodology for dealing with crash data, the few previous studies in the area of road traffic safety that used this framework had some major limitations. Some studies performed their analyses in DEDICATION This dissertation is lovingly dedicated to my family. Your support and encouragement mean so much to me, and I am forever grateful. Thank you! v ACKNOWLEDGMENTS First, I would like to thank my advisor, Dr. V. Dimitra Pyrialakou. I am deeply honored to be her first Ph.D. student. I appreciate all her contributions in terms of her time, ideas, and efforts to make my Ph.D. experience truly productive and stimulating. The joy and enthusiasm she has for research were contagious and motivational for me, especially during the tough times in my Ph.D. pursuit. I also thank my committee members, namely, Drs. David Martinelli, Kakan Dey, Sijin Wen, Ashish Nimbarte, and Steven Lavrenz, for all their advice and support. Finally, I thank my family for their love, care, and support. Specifically, I express my immense gratitude to my lovely wife, Hanieh, for her patience and unparalleled support. My family is my biggest support system, materially and morally. vi

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“…Nesta linha, esta revisão narrativa foi organizada sob dois enfoques: em primeiro lugar, foram abordados os estudos que analisaram o comportamento da taxa de mortalidade/fatalidade por acidentes de transporte terrestre e seus fatores associados (Van Beeck et al, 2000;Young e Bielinska-Kwapisz, 2006;Dadgar e Norström, 2017;Soro et al, 2017;Goel et al, 2018;Silva et al, 2015). Em segundo, apresentaram-se as pesquisas que avaliaram os fatores relacionados às internações hospitalares por essa causa externa (Soares e Barros, 2006; Nunes e Nascimento, 2010; Andrade e Jorge, 2017).…”

Section: Evidências Empíricas Sobre Os Fatores Associados Aos Acident...unclassified

“…Fatores Associados às Internações do Sistema Único de Saúde (SUS) por Acidentes de Transporte Terrestre no Paraná: Análise pelo Modelo de Painel 417 Soro et al (2017) analisaram as taxas de acidentes e de lesões dos acidentes de transporte terrestre, fatais e não fatais, de 31 regiões administrativas (divisões de nível provincial) da China continental, entre 2004 e 2013. Para isso, usaram o modelo de regressão de painel espacial com efeitos fixos.…”

Section: Evidências Empíricas Sobre Os Fatores Associados Aos Acident...unclassified

“…A técnica de análise espacial, por sua vez, é adequada para estudos no contexto da economia das causas externas, pois permite a identificação das áreas com maior concentração desses agravos em um país (González et al, 2020;Lee et al, 2018;Soro et al, 2017;Rhee et al, 2016). Além disso, pode captar a relação entre indicadores de morbimortalidade e fatores associados, no contexto espaço-temporal, bem como o aspecto regionalizado do setor saúde no Brasil (Mendonça et al, 2017).…”

Section: Introductionunclassified

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Fatores associados às internações do Sistema Único de Saúde (SUS) por acidentes de transporte terrestre no Paraná: Análise pelo modelo de painel de dados espaciais

Oikawa,

Favoretto

2023

RBERU

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Este estudou analisou os fatores espaciais associados às taxas de internações hospitalares (suavizada) do SUS por acidentes de transporte terrestre entre os municípios do Paraná, no período de 2008 a 2018. A base teórica utilizada foi o modelo Grossman (2000; 1972b; 1972a). Utilizaram-se o método de suavização bayesiano empírico espacial, a Análise Exploratória de Dados Espaciais e modelo de Painel de Dados Espaciais. Os fatores usados foram o PIB per capita, taxa de motorização de veículos, densidade demográfica e prevalência desses acidentes (vitimização). Os resultados mostraram que as taxas de internações foram associadas positivamente com PIB real per capita e taxa de motorização e, negativamente com densidade demográfica. Constataram-se efeitos das variáveis demográficas dos municípios vizinhos sobre as taxas de hospitalizações do município considerado. As evidências deste estudo podem auxiliar gestores públicos em ações coordenadas sobre alocação de recursos, implementação de políticas de segurança no trânsito, de melhoria do capital humano e da qualidade de vida dos vitimados.

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Crash rates analysis in China using a spatial panel model

Cited by 10 publications

References 28 publications

Modeling the Impact of Technological Innovation on Environmental Efficiency: A Spatial Panel Data Approach

Modeling the Impact of Technological Innovation on Environmental Efficiency: A Spatial Panel Data Approach

Bayesian Spatio-Temporal Analysis of Road Traffic Crashes

Fatores associados às internações do Sistema Único de Saúde (SUS) por acidentes de transporte terrestre no Paraná: Análise pelo modelo de painel de dados espaciais

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