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.

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 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 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.

scholarly journals COVID-19 Mortality Rate and Its Incidence in Latin America: Dependence on Demographic and Economic Variables

2021 ◽  
Vol 18 (13) ◽  
pp. 6900
Author(s):  
Javier Cifuentes-Faura
Keyword(s):  
Latin America ◽  
Mortality Rate ◽  
Population Density ◽  
Life Expectancy ◽  
Latin American ◽  
Positive Influence ◽  
Death Rates ◽  
Per Capita ◽  
Latin American Countries ◽  
Quality Infrastructure

The pandemic caused by COVID-19 has left millions infected and dead around the world, with Latin America being one of the most affected areas. In this work, we have sought to determine, by means of a multiple regression analysis and a study of correlations, the influence of population density, life expectancy, and proportion of the population in vulnerable employment, together with GDP per capita, on the mortality rate due to COVID-19 in Latin American countries. The results indicated that countries with higher population density had lower numbers of deaths. Population in vulnerable employment and GDP showed a positive influence, while life expectancy did not appear to significantly affect the number of COVID-19 deaths. In addition, the influence of these variables on the number of confirmed cases of COVID-19 was analyzed. It can be concluded that the lack of resources can be a major burden for the vulnerable population in combating COVID-19 and that population density can ensure better designed institutions and quality infrastructure to achieve social distancing and, together with effective measures, lower death rates.

scholarly journals The Role of Corporate Social Responsibility in Supporting Second-Order Social Capital and Sustainable Innovation Ambidexterity

2021 ◽  
Vol 13 (13) ◽  
pp. 6994
Author(s):  
Asif Khan ◽  
Li-Ru Chen ◽  
Chao-Yang Hung
Keyword(s):  
Social Capital ◽  
Positive Influence ◽  
Second Order ◽  
Partial Least Square ◽  
Middle Level ◽  
Least Square ◽  
Legal Responsibility ◽  
Cluster Sampling ◽  
Manufacturing Companies ◽  
Sustainable Innovation

This research contributes to the developing literature on CSR, second-order social capital, and sustainable innovation ambidexterity by (1) offering a complete theoretical framework grounded on related theories by clarifying the associations between the four components of CSR proposed by Carroll, because this model suggests a company to be a responsible member of the society by following the required laws while generating profits and conducting philanthropic initiatives, SSC, and sustainable innovation ambidexterity, and (2) testing this framework in a new setting and with a new target population. This study focuses on the top-level management of different manufacturing companies located in Pakistan. A total of 34 manufacturing industries were selected using a cluster sampling technique based on their proximity in the selected cluster. Geographical location and industry type were selected as the criteria to group the industries in clusters. The data collected from 220 top and middle-level managers were analyzed using a partial least square method while the moderation analysis was conducted by using variance analysis. According to the findings of this study, economic, ethical, legal, and philanthropical responsibilities of CSR were all found to have a positive influence on second-order social capital. The economic, ethical, and legal responsibility of CSR did not influence sustainable innovation ambidexterity, whereas the philanthropical responsibility of CSR was found to have a positive influence on sustainable innovation ambidexterity. The findings of this research study will allow the managers to identify the right mix of CSR initiatives required to manage SSC and sustainable innovation exploitation and exploration techniques.

scholarly journals Sales Promotion, Hedonic Shopping Value, and Impulse Buying on Online Consumer Websites

2020 ◽  
Vol 76 ◽  
pp. 01052
Author(s):  
Sesilya Kempa ◽  
Kevin Vebrian ◽  
Hakim Bendjeroua
Keyword(s):  
Online Shopping ◽  
Positive Influence ◽  
Partial Least Square ◽  
Sales Promotion ◽  
Least Square ◽  
Impulse Buying ◽  
Fashion Products ◽  
Hedonic Shopping Value ◽  
Hedonic Shopping ◽  
Online Buying

The phenomenon in the increasing fashion business is caused by online shopping activity, especially in fashion products. In this research, shopping activity is focused on online shopping. Online shopping is also called internet shopping, electronic shopping, online buying, or buying through the internet. Online shopping has become the newest trend for Indonesian as an alternative to buying a product or service. Advertisement and trend are able to influence consumers in doing or deciding to buy. This is the reason people buy excessively unplanned as needed. This research purpose is to observe the sales promotion influence toward impulse buying with hedonic shopping value as intervening to fashion online shopping consumers in Surabaya. This research uses 99 respondents, and the data analysis uses the Partial Least Square (PLS) model. The result shows that sales promotion and hedonic shopping value have significant positive influence on impulse buying. Moreover, hedonic shopping value as variable intervening has an influence between sales promotion to impulse buying.

Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

2021 ◽  
pp. 1-20
Author(s):  
Chaojie Liu ◽  
Jie Lu ◽  
Wenjing Fu ◽  
Zhuoyi Zhou
Keyword(s):  
Real Estate ◽  
Geographically Weighted Regression ◽  
Euclidean Distance ◽  
Evaluation Model ◽  
Housing Prices ◽  
Information Criterion ◽  
Access Point ◽  
Least Square ◽  
Weighted Regression ◽  
Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

scholarly journals PENGARUH FAKTOR EKONOMI, SOSIAL EKONOMI DAN IKLIM TERHADAP BENCANA ALAM DI INDONESIA

Jurnal Ecogen ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 539
Author(s):  
Surya Irmayani ◽  
Zul Azhar ◽  
Melti Roza Adry
Keyword(s):  
Economic Growth ◽  
Population Density ◽  
Natural Disasters ◽  
Urban Population ◽  
Participation Rate ◽  
Least Square ◽  
Ordinary Least Square ◽  
Descriptive Research ◽  
Negative Effect ◽  
Education Participation

This purpose of the research  are to the analyse the Economic Growth, Education Participation Rate, Urban Population, Population Density, Number of Rainfall in terms of Damage Natural Disasters in Indonesia. This type of research is associative descriptive research. This study is based on data 2015 obtained from institutions and related institution. Methods that being used are Ordinary Least Square (OLS). The estimation results show that Economic Growth has a significant negative effect the Damage Natural Disasters in Indonesia, Education Participation Rate has a not significant effect the Damage Natural Disasters in Indonesia, Urban Population has a significant positive effect the Damage Natural Disasters in Indonesia, Population Density has a not significant effect the Damage Natural Disasters in Indonesia, Number of rainfall has a not significant effect the Damage Natural Disasters in Indonesia. Keywords: Economic Growth, Education Participation Rate, Urban Population, Population Density, Number of Rainfall

scholarly journals Trend Dynamics of GRACE Terrestrial Water Storage in the Nile River Basin

2019 ◽  
Author(s):  
Emad Hasan ◽  
Aondover Tarhule
Keyword(s):  
Soil Moisture ◽  
Population Density ◽  
River Basin ◽  
Goodness Of Fit ◽  
Water Storage ◽  
Least Square ◽  
Nile River ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Nile River Basin

GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

scholarly journals How Creative Destruction Functions in Corporate Entrepreneurial Process: An Empirical Investigation of Schumpeterian Concept in Engineering Firms Setting in Pakistan

2021 ◽  
Author(s):  
Muhammad Zubair Alam ◽  
Shazia Kousar ◽  
Muhammad Rizwan Ullah ◽  
Amber Pervaiz
Keyword(s):  
Structural Equation ◽  
Corporate Entrepreneurship ◽  
Creative Destruction ◽  
Positive Influence ◽  
Opportunity Recognition ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Engineering Firms ◽  
The Impact

Abstract Schumpeter's idea of creative destruction (CD) explains innovation functions in organisations. This paper investigates the CD concept in engineering firms by explaining how technical opportunity (TO) transforms into corporate entrepreneurship (CE) actions once opportunities have a market orientation (MO). A survey conducted using a structured questionnaire with 132 managers in engineering firms in Pakistan. Structural Equation Modeling (SEM) using Partial Least Square (PLS) approach has been used to analyse the data. Results reveal that MO and TO exerts a positive influence on CE. MO is the reason for the emergence of TO, which is exploited by CE's in engineering firms. CD intensifies the impact of MO on TO significantly. Opportunity recognition in engineering firms is distinguished and bounded by MO and technical viability. Engineering firms need to identify gaps in the market through naturally occurring obsolescence of products and services (CD) to create TO with appropriate MO. This study has revived a classical debate over opportunity recognition by proposing a CE model by incorporating external factors. The Schumpeterian opportunity recognition process and CD have been explained for engineering firms that are distinguished from other types of firms. Kirznerian opportunity recognition view has also been debated to dialect Schumpeterian view.

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