scholarly journals Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

2019 ◽  
Vol 11 (17) ◽  
pp. 4751 ◽  
Author(s):  
Zhao ◽  
Xu
Keyword(s):  
Spatial Variation ◽  
Environmental Conditions ◽  
Regression Models ◽  
Economic Factors ◽  
Spatial Agglomeration ◽  
Chinese Cities ◽  
Explanatory Variables ◽  
Haze Pollution ◽  
Pm2.5 Concentration ◽  
Socio Economic Factors

Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry's proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.

scholarly journals A Cross-Tabulation Analysis of Socio-Economic Determinants of Crime: Evidence from Women Jail Multan, Pakistan

2021 ◽  
Vol VI (I) ◽  
pp. 130-147
Author(s):  
Muhammad Ramzan Sheikh ◽  
Muhammad Tariq ◽  
Sana Sultan
Keyword(s):  
Random Sampling ◽  
Crime Rate ◽  
Sampling Method ◽  
Economic Factors ◽  
Economic Determinants ◽  
Female Prisoners ◽  
Explanatory Variables ◽  
Cross Tabulation ◽  
Socio Economic Variables ◽  
Socio Economic Factors

The crime rate in Pakistan has increased severely within the last decade. It may be because of high unemployment, increasing poverty, income, rising inflation and urbanized setups. Few noneconomic constraints are also responsible for it. The study has been made with reference to Women Jail Multan. The 70 female prisoners are selected via a random sampling method. The data are collected by interviewing them. The study has used the type of crime as the dependent variable. Purely crime-related variables and socio-economic factors of crime have been used as explanatory variables. Both purely crime-related variables and socio-economic variables have found effect size with the type of crime.

Socio-economic Correlates of Marital Status and Marriage Timing Among Adult Men in Nigeria

2022 ◽  
pp. 0192513X2110598
Author(s):  
David A. Okunlola ◽  
Olusesan A. Makinde ◽  
Stella Babalola
Keyword(s):  
Regression Models ◽  
Cox Regression ◽  
Economic Factors ◽  
Adult Men ◽  
First Marriage ◽  
Age At First Marriage ◽  
Multiple Indicator Cluster Survey ◽  
Multiple Indicator ◽  
Wealth Group ◽  
Socio Economic Factors

There is a gradual tendency towards prolonged bachelorhood among men in Nigeria. Studies have linked this to socio-economic factors, but this evidence is sparsely explored in the context of Nigeria. Hence, this study fills the knowledge gap. The 2016/17 Nigeria Multiple Indicator Cluster Survey data of 7803 adult men (aged 18–34 years) was analysed by using descriptive and fitting binary logitic regression and Cox regression models. Results show that slightly more than one-third of adult men in Nigeria (35%) had a marriage history and their median age at first marriage was about 24 years. Educated men (than the uneducated) and those in middle wealth group (than the poor men) were less likely to have ever been married and to delay marriage, respectively. Wealthy men were more likely to delay marriage. Employed men were more likely to have a marriage history and to delay marriage.

Lifestyle Segmentation Variables as Predictors of Home-Based Trips for Atlanta, Georgia, Airport

2012 ◽  
Vol 2266 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Josephine D. Kressner ◽  
Laurie A. Garrow
Keyword(s):  
Regression Models ◽  
Travel Demand ◽  
Economic Factors ◽  
Linear Regression Models ◽  
Data Set ◽  
Credit Reporting ◽  
Adequate Information ◽  
Home Based ◽  
Air Travel Demand ◽  
Socio Economic Factors

This research investigated the influence of demographic and socio-economic factors on air travel demand by using a unique data set purchased from a credit-reporting agency. Linear regression models based on lifestyle segmentation variables were used to predict air passenger trips for Hartsfield–Jackson International Airport in Atlanta, Georgia. The study focused on predicting trips that originated from or terminated at residences in Atlanta's 13-county metropolitan area. The lifestyle regression models were compared with regression models based on income, because the latter were similar to the regression models currently used by the Atlanta Regional Commission to predict home-based airport passenger trips. The results provide directional evidence for using lifestyle clusters over income groups in predicting airport passenger trips. The evidence suggests that alternative data sources with adequate information for lifestyle segmentation can improve airport passenger models. The discussion points out the need for air passenger surveys to collect information about the number of annual air trips a surveyed individual takes.

scholarly journals Forecasting of Tourism Processes in Uzbekistan in Correlation Regression Models

2021 ◽  
Vol 04 (11) ◽  
Author(s):  
F.E. Gulmurodov ◽  
Keyword(s):  
Future Development ◽  
Regression Models ◽  
Regression Function ◽  
Development Trend ◽  
Tourism Industry ◽  
Economic Factors ◽  
Factors Affecting ◽  
The Future ◽  
The Development Trend ◽  
Socio Economic Factors

The article provides detailed information on the process of developing effective plans for the development of the tourism industry and choosing the optimal one based on them, forecasting the future development of the industry. It also considers the processes of using special computational and arithmetic methods that allow predicting the events and happenings in the tourism industry, to determine the regression function as a result of the interaction and interaction of indicators representing the type of activity. As a result of targeted research, using correlation-regression models, a forecast of the development trend of the tourism industry based on socio-economic factors affecting the tourism process was developed.

scholarly journals Water, Sanitation, Hygiene (WASH) or others? - a global analysis on determinants of Covid-19 pandemic

2020 ◽  
Author(s):  
Ajishnu Roy ◽  
Aman Basu ◽  
Kousik Pramanick
Keyword(s):  
Rank Order ◽  
Global Analysis ◽  
Global Scale ◽  
Health Concern ◽  
Economic Factors ◽  
Strong Positive Correlation ◽  
Global Public Health ◽  
Public Health Concern ◽  
Explanatory Variables ◽  
Socio Economic Factors

AbstractCoronavirus (SARS Covid-19) has become a global public health concern due to its unpredictable nature and lack of adequate pre-existing conditions. Achievement of WASH services is being acknowledged as indispensable in safeguarding health. However, on a global scale, it is currently not clear whether deprivation or non-obtainability of which of the factors are closely related to Covid-19 dynamics and up to which degree. We have analysed 6 months’ data related to five Covid-19 indicators for most of the countries in the world with three groups of indicators of WASH, socio-economic factors and mobility & stringency. Four successive steps were followed to carry out the analysis: (a) identification of relevant key explanatory variables of Covid-19, (b) identification of indicators (n=103) for other 5 sub-groups of factors (viz. WASH, economy, health, society, mobility & stringency) through literature review, (c) utilizing Spearman’s rank-order correlation for measuring the association of the possible explanatory variables with those of Covid-19 (of spatial nature), and then (d) continuing the same for 6 consecutive months (from March to August 2020) until we get an almost unchanging trend. We have found a strong positive correlation between lesser effects (i.e. either less confirmed case or higher recovery rate) of Covid-19 and better access to WASH as well as socioeconomic factors throughout this time for a significant amount of the indicators. Since a handful of research is available to discuss the association between Covid-19 with other probable determinants along with the nature and degree of their relationship, this study should be perceived as an expanded view on the complexities of Covid-19 interrelationships towards understanding determinants of Covid-19 dynamics, which could help to shape an agenda for research into some unanswered questions.

scholarly journals Quantifying the impacts of socio-economic factors on air quality in Chinese cities from 2000 to 2009

2012 ◽  
Vol 167 ◽  
pp. 148-154 ◽  
Author(s):  
Juanjuan Zhao ◽  
Shengbin Chen ◽  
Hua Wang ◽  
Yin Ren ◽  
Ke Du ◽  
...  
Keyword(s):  
Air Quality ◽  
Economic Factors ◽  
Chinese Cities ◽  
Socio Economic Factors

scholarly journals Statistical Data Research on Staff Training for the Digital Economy in the Russian Federation

2021 ◽  
Vol 30 (11) ◽  
pp. 29-41
Author(s):  
Yu. V. Frolov ◽  
T. M. Bosenko
Keyword(s):  
Russian Federation ◽  
Regression Models ◽  
Statistical Data ◽  
Digital Economy ◽  
Economic Factors ◽  
Objective Functions ◽  
Economic Sectors ◽  
Different Dimensions ◽  
The Russian Federation ◽  
Socio Economic Factors

The article analyzes the statistical data relating to training specialists for digitalized economy by secondary vocational and higher education institutions. The purpose of the study was to develop and test personnel support indices for digitalization of the economy, as well as to identify social and economic factors that significantly affect the level of personnel support for the processes of digital transformation of the economy. The authors applied data from the official statistical reporting of the Russian Federation. The proposed staffing indices were modeled as objective functions depending on socio-economic factors characterizing the development of the economy in different dimensions. At the same time, the indices themselves were calculated as values in which the parameters of the output of digital specialists and their relevance in the economy were correlated. In the course of the study, a comparison of statistical and neural network data modeling methods and generalizing indices was performed. An analysis of the obtained regression models and an analysis of the sensitivity of trained neural networks made it possible to evaluate their accuracy in predicting the trends in the staffing of the digital economy and to identify factors that significantly affect the achievement of the goal of matching the output of specialists and the demands of economic sectors.

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