scholarly journals Study of Flight Departure Delay and Causal Factor Using Spatial Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Shaowu Cheng ◽  
Yaping Zhang ◽  
Siqi Hao ◽  
Ruiwei Liu ◽  
Xiao Luo ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Least Square ◽  
Spatial Error ◽  
Causal Factors ◽  
Spatial Lag Model ◽  
Control Factors ◽  
Aggregation Pattern ◽  
Lag Effect ◽  
Error Correlation ◽  
The Impact

Analysis of flight delay and causal factors is crucial in maintaining airspace efficiency and safety. However, delay samples are not independent since they always show a certain aggregation pattern. Therefore, this study develops a novel spatial analysis approach to explore the delay and causal factors which is able to take dependence and the possible problem involved including error correlation and variable lag effect of causal factors on delay into account. The study first explores the delay aggregation pattern by measuring and quantifying the spatial dependence of delay. The spatial error model (SEM) and spatial lag model (SLM) are then established to solve the error correlation and the variable lag effect, respectively. Results show that the SEM and SLM achieve better fit than ordinary least square (OLS) regression, which indicates the effectiveness of considering dependence by employing spatial analysis. Moreover, the outcomes suggest that, aside from the well-known weather and flow control factors, delay-reduction strategies also need to pay more attention to reducing the impact of delay at the previous airport.

scholarly journals Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Shaohua Wang ◽  
Yanyan Chen ◽  
Jianling Huang ◽  
Ning Chen ◽  
Yao Lu
Keyword(s):  
Population Density ◽  
Spillover Effects ◽  
Positive Influence ◽  
Least Square ◽  
Spatial Autoregression ◽  
Road Density ◽  
Traffic Crashes ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weight

This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran’s I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

scholarly journals A spatial econometric empirical research on the impact of industrial agglomeration on haze pollution in China

2020 ◽  
Vol 13 (11) ◽  
pp. 1305-1312 ◽  
Author(s):  
Hong Zhao ◽  
Xiaoxi Cao ◽  
Tao Ma
Keyword(s):  
Environmental Regulation ◽  
Carbon Dioxide Emissions ◽  
Industrial Agglomeration ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Haze Pollution ◽  
The Government ◽  
Obvious Manner ◽  
And Control ◽  
The Impact

Abstract Based on statistical data on 30 provincial administrative regions in China from 2000 to 2016, this paper conducts an empirical study of the impact of industrial agglomeration on haze pollution using the spatial Dubin model (SDM), spatial lag model (SLM), and spatial error model (SEM). The findings are as follows: (1) Industrial agglomeration can effectively reduce the degree of haze pollution. (2) Haze pollution has an inverted U-shaped relationship with economic development and population agglomeration. (3) The secondary industry has a positive correlation with haze pollution, while the tertiary industry can reduce haze pollution but not in an obvious manner. (4) The level of innovation and urbanization can help to reduce haze pollution, and the level of economic opening up and carbon dioxide emissions can exacerbate haze pollution. (5) Due to the insufficient commercialization of scientific and technological achievements, investment in science and technology is not obviously effective in preventing and controlling haze pollution. The relationship between environmental regulation and haze pollution is still unclear due to regional differences and the varied effectiveness of law enforcement. The study suggests that the government should guide industrial agglomeration in a reasonable manner, improve joint prevention and control across regions, and strengthen environmental regulation to prevent and control haze pollution.

scholarly journals Examining the Bus Ridership Demand: Application of Spatio-Temporal Panel Models

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Moshiur Rahman ◽  
Shamsunnahar Yasmin ◽  
Ahmadreza Faghih-Imani ◽  
Naveen Eluru
Keyword(s):  
Public Transit ◽  
Spatial Models ◽  
Spatial Error ◽  
Panel Models ◽  
Spatial Lag Model ◽  
Transit Ridership ◽  
Lag Model ◽  
Spatio Temporal ◽  
Investment Process ◽  
The Impact

An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models. Drawing on stop-level boarding and alighting data for the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobserved factors on transit ridership. Specifically, two spatial models, Spatial Error Model and Spatial Lag Model, are estimated for boarding and alighting separately by employing several exogenous variables including stop-level attributes, transportation and transit infrastructure variables, built environment and land use attributes, and sociodemographic and socioeconomic variables in the vicinity of the stop along with spatial and spatiotemporal lagged variables. The model estimation results are further augmented by a validation exercise. These models are expected to provide feedback to agencies on the benefits of public transit investments while also providing lessons to improve the investment process.

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 A New Approach of Disaster Forecasting Based on Least Square Optimized Neural Network

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Fanbao Meng ◽  
Suolin Jing ◽  
Xizhen Sun ◽  
Changxiang Wang ◽  
Yanbo Liang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rock Burst ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Least Square Method ◽  
Mining Area ◽  
Least Square ◽  
Control Factors ◽  
General Law ◽  
The Impact

The evaluation of the risk is the prerequisite for the implementation of countermeasures in the prevention and control of rock burst, and the research on the fast forecast at scene of the rock burst is more important for the safety production of coal mine. Aiming at the problem that dynamic disasters caused by many factors and heterogeneity of coal and rock are difficult to predict in the process of coal mining, in this paper, the general law and the risk control factors of the rock burst are studied, a mathematical model based on the BP neural network was built according to the different actual mining conditions in the mining area, and the output layer has obtained the prediction result. Then, the results of the output samples after training were fitted by using SPSS software, and the fitting function was obtained by multiple least square fitting. Finally, the fitting results are checked by the data of actual coal mine dynamic disaster parameters. The prediction results show that the simulation results of BP neural network prediction model and the fitting function of the least square method can reduce the impact of subjective judgment on the prediction results, and the application of the fitting function can obtain the prediction results in the first time to ensure the construction safety. The method of on-site hazard assessment and inspection by using fitting function is simple and feasible and has high accuracy, which provides a new idea for the field prediction of rock burst.

scholarly journals Examining Spatial Dependency in Child Malnutrition Across 640 Districts in India for Context Specific Planning and Intervention

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 919-919 ◽  
Author(s):  
Thirumal Vennam ◽  
Satish Agnihotri ◽  
Pennan Chinnasamy
Keyword(s):  
Spatial Relationship ◽  
Family Health ◽  
Positive Association ◽  
Least Square ◽  
Spatial Dependency ◽  
Significant Positive Association ◽  
Spatial Error ◽  
Ordinary Least Square ◽  
Spatial Lag Model ◽  
Lag Model

Abstract Objectives India, in accordance with United Nation's Sustainable Development Goals, is committed to reduce malnutrition, which accounts to 68.2% of deaths in children below 5 years in the country. The fourth round of National Family Health Survey (NFHS4) provided information on nutrition and health at district level, for the first time. The objective of this study was to investigate significant correlates that influence the nutrition outcomes, and establish a spatial relationship, if any, which would help in informing policy decisions and targeted planning, considering the vast diversity and heterogeneity across regions in India. Methods Publicly available district data from National Family Health Survey-4(2015–16) was used. Based on previous studies, 21 independent variables providing information on household conditions, maternal health and childhood diseases and deficiencies, were shortlisted. The outcome variables were stunting, wasting and underweight in children under the age of five. Principal Component Analysis was conducted to reduce the dimensions owing to multicollinearity. Moran's I Values, Ordinary least square method, spatial lag model and spatial error model were employed to study the spatial relationship using statistical tools like Stata 15 (SE), Minitab and GeoDa version 1.14.0. Results Moran's I Values of stunting (0.67), wasting (0.51) and underweight (0.76) suggest strong spatial dependency across regions in India. Spatial Error Model with lower Akaike info Criterion value was found to be a better model in comparison with ordinary least square and spatial lag model. Women's short height was found to have significant positive association with both stunting (coefficient: 0.86, P < 0.01) and underweight (coefficient: 0.66, P < 0.01). Whereas, child anaemia showed significant positive association with wasting (coefficient: 0.19, P < 0.01) and underweight (coefficient: 0.14, P < 0.01). While households using iodized salt showed a negative association with both stunting (coefficients: −0.18, P < 0.01) and underweight (Coefficient: −0.12, P < 0.01), households with improved drinking water showed negative association (coefficient: −.06, P < 0.05) with wasting. Conclusions This study confirms spatial dependency in malnutrition in India and urges the need for focused interventions to tackle malnutrition. Funding Sources None.

Assessing the Impact of Recent Global Crisis on Small and Medium Enterprises (SMEs): Evidence from Indonesia

2013 ◽  
Vol 1 (1) ◽  
pp. 6
Author(s):  
Tri Winarno
Keyword(s):  
Financial Crisis ◽  
Interest Rates ◽  
Global Financial Crisis ◽  
Small And Medium Enterprises ◽  
Quantitative Methodology ◽  
Spatial Error ◽  
Medium Scale ◽  
Spatial Lag Model ◽  
Medium Enterprises ◽  
The Impact

In this article we examine three broad issues. The first is to measure the impact of 2008 global financial crisis on Indonesia’s economy, particularly on loans extended to small and medium scale enterprises at regional level. Next is to analyze significant factors of inducing loans extended to small and medium scale enterprises. Finally, it is to fill the gap in the literature by introducing a quantitative methodology. A spatial lag model and spatial error model are used to assess the three broad issues. Regionally, quarterly panel data spanning from 2002 up to 2011 are employed to support the analysis. One of the results is the global financial crisis that negatively impacts on Indonesia economy, particularly on the performance of small and medium enterprises (SMEs).  In terms of loan extended to the SMEs, there is strong and positive spatially correlation among province, showing commoving and integrating economy within the territories of Indonesia. Finally, this research suggests that interest  rates is not significantly correlated with loans to SMEs, which indicates that the access to financial institutions is more important and urgent to boost the performance of SMEs in Indonesia which is  reinforcing the opinion of financial inclusions for SMEs.

scholarly journals Impact of crime on spatial analysis of house prices: evidence from a UK city

2016 ◽  
Vol 9 (4) ◽  
pp. 627-647 ◽  
Author(s):  
David McIlhatton ◽  
William McGreal ◽  
Paloma Taltavul de la Paz ◽  
Alastair Adair
Keyword(s):  
Spatial Analysis ◽  
House Prices ◽  
Spatial Association ◽  
House Price ◽  
Spatial Effects ◽  
Hedonic Price ◽  
Spatial Autoregression ◽  
Spatial Error ◽  
Content Type ◽  
The Impact

Purpose There is a lack of understanding in the literature on the spatial relationships between crime and house price. This paper aims to test the impact of spatial effects in the housing market, how these are related to the incidence of crime and whether effects vary by the type of crime. Design/methodology/approach The analysis initially explores univariate and bivariate spatial patterns in crime and house price data for the Belfast Metropolitan Area using Moran’s I and Local Indicator Spatial Association (LISA) models, and secondly uses spatial autoregression models to estimate the role of crime on house prices. A spatially weighted two-stage least-squares model is specified to analyse the joint impact of crime variables. The analysis is cross sectional, based on a panel of data. Findings The paper illustrates that the pricing impact of crime is complex and varies by type of crime, property type and location. It is shown that burglary and theft are associated with higher-income neighbourhoods, whereas violence against persons, criminal damage and drugs offences are mainly associated with lower-priced neighbourhoods. Spatial error effects are reduced in models based on specific crime variables. Originality/value The originality of this paper is the application of spatial analysis in the study of the impact of crime upon house prices. Criticisms of hedonic price models are based on unexplained error effects; the significance of this paper is the reduction of spatial error effects achievable through the analysis of crime data.

scholarly journals Spatial Econometric Models Applied to Environmental Pollution: A Literature Review of the Socioeconomics Drivers.

2021 ◽  
Author(s):  
Manuel Cañaveral ◽  
Leonardo Emmendorfer ◽  
Débora Spenassato ◽  
Ana Azambuja
Keyword(s):  
Spatial Analysis ◽  
Literature Review ◽  
Environmental Pollution ◽  
Spatial Regression ◽  
Analysis Model ◽  
Spatial Econometric ◽  
Spatial Error ◽  
Econometric Methods ◽  
Spatial Lag Model ◽  
Lag Model

The interest in spatial analysis has been growing in recent years, mainly due to communication technology advance, economic globalization, and the development of new statistical and econometric methods. The main aim of this article is to contribute to the dissemination of spatial econometric applications by presenting some basic theoretical aspects and a literature review of articles that address the socio-economic drivers that lead to environmental pollution. Three spatial regression models are reviewed here: the spatial lag model (SLM), the spatial error model (SEM), and the spatial Durbin model (SDM). A literature search was conducted using specific terms of interest in eight databases, from 1996 to February 2021, where 22 articles were considered for analysis. The results showed that most articles studied environmental problems in China. The most used exploratory spatial analysis model was Moran Index and the most used explanatory spatial analysis models were SDM and SLM.

Ease of Doing Business and Its Impact on Inward FDI

2018 ◽  
Vol 1 (1) ◽  
pp. 52 ◽  
Author(s):  
Mohamed Tareq Hossain ◽  
Zubair Hassan ◽  
Sumaiya Shafiq ◽  
Abdul Basit
Keyword(s):  
Regression Model ◽  
Causal Relationship ◽  
World Bank ◽  
Least Square ◽  
Doing Business ◽  
Least Square Regression ◽  
Foreign Countries ◽  
Ease Of Doing Business ◽  
The Impact ◽  
Starting A Business

This study investigates the impact of Ease of Doing Business on Inward FDI over the period from 2011 to 2015 across the globe. This study measures ease of doing business using starting a business, getting credit, registering property, paying taxes and enforcing contracts. The research used a sample of 177 countries from 190 countries listed in World Bank. Least square regression model via E-views software used to examine causal relationship. The study found that ease of doing business indicators ‘Enforcing Contracts’ was found to have a positive significant impact on Inward FDI. Nevertheless, ‘Getting Credit’ and ‘Registering Property’ were found to have a negative significant impact on Inward FDI. However, ‘Starting a Business’ and ‘Paying Taxes’ have no significant impact on Inward FDI in the studied timeframe of this research. The findings of the study suggested the ease of doing business enables inward FDI through better contract enforcements, getting credit and registering property. The findings of the research will assist international managers and companies to know the importance of ease of doing business when investing in foreign countries through FDI.

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