scholarly journals Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA

2019 ◽  
Vol 16 (11) ◽  
pp. 1923
Author(s):  
Meijie Chen ◽  
Yumin Chen ◽  
Xiaoguang Wang ◽  
Huangyuan Tan ◽  
Fenglan Luo
Keyword(s):  
Gravity Model ◽  
Spatial Variations ◽  
Multiple Comparison ◽  
Spatial Difference ◽  
Policy Recommendations ◽  
Public Health Services ◽  
Difference Analysis ◽  
Spatial Lag Model ◽  
Lag Model ◽  
Multiple Comparison Methods

This paper proposes a spatial difference analysis method for evaluating transit-based accessibility to hospitals using spatially adjusted ANOVA. This method specializes in examining spatial variations of accessibility to hospitals by regions (i.e. administrative districts or subdistricts). The spatial lag model is applied to adjust traditional ANOVA, which reduces spatial dependency and avoids false rejection to null hypothesis. Multiple comparison methods are used for further detection of differences in accessibility between regions. After multiple comparison, accessibility within regions is classified into three levels. The study is conducted on two scales—administrative districts and subdistricts—to discuss spatial variations in macro and micro dimensions respectively in the central part of Wuhan, China. Accessibility is calculated by using a simple model and a gravity model. The final classification results showed that the spatially adjusted method is more reliable than the traditional non spatially adjusted one and the gravity model can better detect more hidden information about the inequal distribution of medical resources. It is also found that the subdistricts, which have significantly lower accessibility to hospitals than others, are mainly distributed in Hongshan and Qingshan district. Our study hopes to shed new lights in spatial difference analysis for accessibility and provide policy recommendations that would promote equality in provisions of public health services.

Impacts of Urban Railway Investment on Regional Economies: Evidence from Tokyo using Spatial Difference-in-Differences Analysis

2019 ◽  
Vol 2673 (10) ◽  
pp. 129-140 ◽  
Author(s):  
Yuichiro Kaneko ◽  
Takuro Nakagawa ◽  
Veng Kheang Phun ◽  
Hironori Kato
Keyword(s):  
Economic Effects ◽  
Least Square ◽  
Policy Implications ◽  
Spatial Difference ◽  
Land Price ◽  
Difference In Differences ◽  
Spatial Error ◽  
Tokyo Metropolitan Area ◽  
Spatial Lag Model ◽  
Lag Model

This study empirically analyzes the effects of urban railway investment on regional population density, employment density, and land price using the spatial difference-in-differences (DID) approach, employing a sociodemographic and socioeconomic dataset in 2,843 zones in the Tokyo Metropolitan Area from 2000 to 2010. A spatial-lag model and a spatial-error model, in addition to an ordinary least square model under the framework of the DID approach, are employed in the empirical analyses. The results show that investment in urban railway lines was in areas with lower population densities and higher employment densities. The urban railway investment significantly positively influenced land price but insignificantly influenced population and employment densities. Land price was positively influenced by population and employment densities. The analysis suggests that introduction of the railway directly affected the land price via anticipation of expected future development, rather than an indirect effect via increased population and employment densities. Finally, the policy implications regarding transit-oriented development are discussed, including strategic residential development in line with the railway investment and the integrated development of business clusters following railway investment to enhance the economic effects of railway investments.

scholarly journals Bayesian Estimation of the Functional Spatial Lag Model

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Alassane Aw ◽  
Emmanuel Nicolas Cabral
Keyword(s):  
Bayesian Estimation ◽  
Linear Model ◽  
Unemployment Rate ◽  
Simulation Study ◽  
Data Modeling ◽  
Functional Linear Model ◽  
Bayesian Mcmc ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lag Model

AbstractThe spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

scholarly journals Spatial analysis of the prevalence of obesity and overweight among women in Ghana

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e041659
Author(s):  
Killian Asampana Asosega ◽  
Atinuke Olusola Adebanji ◽  
Iddrisu Wahab Abdul
Keyword(s):  
Public Health ◽  
Hot Spots ◽  
Information Criterion ◽  
Health Interventions ◽  
Spatial Lag Model ◽  
Spatial Differences ◽  
Body Sizes ◽  
Lag Model ◽  
Effective Public Health ◽  
Average Body

ObjectiveIdentifying hot spots for the overweight aids in effective public health interventions due to the associated public health burden and morbidities. This study, therefore aimed to explore and determine the spatial disparities in the overweight/obesity prevalence among women in Ghana. The study also aims at modelling the average body mass index (BMI) values using the spatial regression and the performance compared with the standard regression model.DesignThis is a cross-sectional study using data from the 2014 Ghana Demographic and Health Survey (GDHS).SettingThe study was set in Ghana.Participants and methodsData on 4393 non-pregnant women aged 15–49 years from the 2014 GDHS. Both global (Moran’s I) and the local indicators for spatial dependence were examined through the mapped BMI values across the country by clusters. An estimated spatial lag model was used to explain the spatial differences in the average body sizes of women.ResultsThe overall prevalence of overweight/obesity among reproductive women in Ghana was 35.4%, and this was highly prevalent among educated women (p<0.001), those from wealthy households (p<0.001) and dwelling in an urban setting (p<0.001). Significant clustering (Moran’s I=0.3145, p<0.01) of overweight/obesity was observed with hot spots (clustering) in Greater Accra, Central, Western and Ashanti regions. The spatial lag model was the best fit based on the Likelihood ratio test and the Akaike information criterion and Bayesian information criterion values. The mean age of women and household wealth were significant factors accounting for the increase in the average cluster body size (BMI) of women and the spatial differences.ConclusionThe prevalence of overweight/obesity was high and spatially clustered in the southern, middle and coastal regions. Geographic specific and effective public health interventions and strategies are needed to address the growing morbidity burden associated with the rise in the average body sizes of reproductive women.

scholarly journals Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

2021 ◽  
Vol 13 (16) ◽  
pp. 9014
Author(s):  
Yongjiao Wu ◽  
Huazhu Zheng ◽  
Yu Li ◽  
Claudio O. Delang ◽  
Jiao Qian
Keyword(s):  
Carbon Emissions ◽  
Industrial Development ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Spatial Lag Model ◽  
Workforce Allocation ◽  
Lag Model ◽  
Using Data ◽  
Shape Curve ◽  
Carbon Productivity

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

scholarly journals Analiza zróżnicowania sytuacji kobiet na lokalnych rynkach pracy w Polsce w latach 2010–2016

2019 ◽  
Vol 2 (341) ◽  
pp. 99-115
Author(s):  
Karolina Lewandowska‑Gwarda
Keyword(s):  
Seemingly Unrelated Regression ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Unrelated Regression ◽  
Lag Model

Głównym celem artykułu jest ocena sytuacji kobiet na lokalnych rynkach pracy w Polsce oraz analiza jej zróżnicowania w czasie i przestrzeni. Podjęto w nim również próbę specyfikacji determinant badanego zjawiska. W analizach wykorzystano taksonomiczny miernik rozwoju, metody geograficznych systemów informacyjnych, metody eksploracyjnej analizy danych przestrzennych oraz wielorównaniowy model o równaniach pozornie niezależnych z autoregresją przestrzenną SUR‑SLM (Seemingly Unrelated Regression Spatial Lag Model). Badania przeprowadzono na podstawie danych statystycznych dla NUTS4 w latach 2010, 2012, 2014 i 2016. Na podstawie uzyskanych wyników zauważono, że zróżnicowanie sytuacji kobiet na lokalnych rynkach pracy w Polsce nie jest duże, niemniej jednak w nieco lepszej sytuacji są Polki mieszkające w okolicach stolicy oraz w zachodniej części kraju. Stwierdzono również, że sytuacja kobiet na lokalnych rynkach pracy nie zmieniła się znacząco w czasie. Dodatkowo potwierdzono, że nie tylko czynniki ekonomiczne, ale w dużej mierze również społeczne wpływają na analizowane zjawisko.

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