scholarly journals Spatial-Temporal Characteristics in Grain Production and Its Influencing Factors in the Huang-Huai-Hai Plain from 1995 to 2018

2020 ◽  
Vol 17 (24) ◽  
pp. 9193
Author(s):  
Chunshan Zhou ◽  
Rongrong Zhang ◽  
Xiaoju Ning ◽  
Zhicheng Zheng
Keyword(s):  
Influencing Factors ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Grain Production ◽  
Temporal Characteristics ◽  
Spatial Panel ◽  
Geographically Weighted Regression Model ◽  
Northern Areas ◽  
Spatial Panel Data ◽  
The Impact

The Huang-Huai-Hai Plain is the major crop-producing region in China. Based on the climate and socio-economic data from 1995 to 2018, we analyzed the spatial–temporal characteristics in grain production and its influencing factors by using exploratory spatial data analysis, a gravity center model, a spatial panel data model, and a geographically weighted regression model. The results indicated the following: (1) The grain production of eastern and southern areas was higher, while that of western and northern areas was lower; (2) The grain production center in the Huang-Huai-Hai Plain shifted from the southeast to northwest in Tai’an, and was distributed stably at the border between Jining and Tai’an; (3) The global spatial autocorrelation experienced a changing process of “decline–growth–decline”, and the area of hot and cold spots was gradually reduced and stabilized, which indicated that the polarization of grain production in local areas gradually weakened and the spatial difference gradually decreased in the Huang-Huai-Hai Plain; (4) The impact of socio-economic factors has been continuously enhanced while the role of climate factors in grain production has been gradually weakened. The ratio of the effective irrigated area, the amount of fertilizer applied per unit sown area, and the average per capita annual income of rural residents were conducive to the increase in grain production in the Huang-Huai-Hai Plain; however, the effect of the annual precipitation on grain production has become weaker. More importantly, the association between the three factors and grain production was found to be spatially heterogeneous at the local geographic level.

scholarly journals Spatial Correlation, Influencing Factors and Environmental Supervision on Mechanism Construction of Atmospheric Pollution: An Empirical Study on SO2 Emissions in China

2019 ◽  
Vol 11 (6) ◽  
pp. 1742 ◽  
Author(s):  
Ruoyu Yang ◽  
Weidong Chen
Keyword(s):  
Air Pollution ◽  
Spatial Correlation ◽  
Influencing Factors ◽  
Atmospheric Pollution ◽  
Industrial Development ◽  
Population Pressure ◽  
Spatial Data Analysis ◽  
So2 Emissions ◽  
Political Movement ◽  
The Impact

In order to study the present situation regarding SO2 emissions in China, problems are identified and countermeasures and suggestions are put forward. This paper analyzes spatial correlation, influencing factors and regulatory tools of air pollution in 30 provinces on the Chinese mainland from 2006–2015. The results of exploratory spatial data analysis (ESDA) show that SO2 emissions have obvious positive spatial correlations, and atmospheric pollution in China shows obvious spatial overflow effects and spatial agglomeration characteristics. On this basis, the present study analyzes the impact of seven socioeconomical (SE) factors and seven policy tools on air pollution by constructing a STIRPAT model and a spatial econometric model. We found that population pressure, affluence, energy consumption (EC), industrial development level (ID), urbanization level (UL) and the degree of marketization can significantly promote the increase of SO2 emissions, but technology and governmental supervision of the environment have significant inhibitory effects. The reason why China’s air pollution is curbed at present is because the government has adopted a large number of powerful command-controlled supervision measures, to a large extent. Air pollution treatment is like a government-led “political movement”. The effect of the market is relatively weak and public force has not been effectively exerted. In the future, a comprehensive use of a variety of regulation tools is needed, as well as encouraging the public to participate, strengthening the supervision of third parties and building a diversified and all-encompassing supervision mechanism.

Inbound Tourism as a Driving Force for Regional Innovation: A Spatial Impact Study on China

2018 ◽  
Vol 58 (4) ◽  
pp. 594-607 ◽  
Author(s):  
Jingjing Liu ◽  
Peter Nijkamp
Keyword(s):  
Driving Force ◽  
Social Innovation ◽  
Innovation System ◽  
Regional Innovation ◽  
Spatial Panel ◽  
Inbound Tourism ◽  
Spatial Panel Data ◽  
The Impact ◽  
Tourism Flows ◽  
The Empirical Analysis

Cross-border flows of people, capital, and information along with inbound tourism flows can act as an important vehicle that benefits the innovation system in tourism destination areas. This study addresses the unintended but far-reaching impact of international tourism by focusing on the influence of inbound tourism on regional innovation in China. Data from 30 Chinese provinces for the years 2003–2012 are used for the empirical analysis, employing a spatial panel data model. The results show that inbound tourism may be a new and powerful driving force for regional innovation, while the effect of inbound tourism on technological innovation appears to be weaker than that on social innovation. Our findings also show that a higher market percentage of domestic tourism may weaken the impact of inbound tourism. Furthermore, the impact of inbound tourism on innovation tends to be relatively stronger in the richer and more internationally oriented provinces of China.

scholarly journals Building W Matrices Using Selected Geostatistical Tools: Empirical Examination and Application

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 112-133 ◽  
Author(s):  
Elżbieta Antczak
Keyword(s):  
Spatial Data ◽  
Spatial Dependence ◽  
Regression Models ◽  
Seemingly Unrelated Regression ◽  
Spatial Data Analysis ◽  
Spatial Dependency ◽  
Empirical Examination ◽  
Spatial Panel ◽  
Spatial Weights ◽  
Unrelated Regression

This paper investigates how to determine the values (elements) of spatial weights in a spatial matrix (W) endogenously from the data. To achieve this goal, geostatistical tools (standard deviation ellipsis, semivariograms, semivariogram clouds, and surface trend models) were used. Then, in the econometric part of the analysis, the effect of applying different variants of matrices was examined. The study was conducted on a sample of 279 Polish towns from 2005–2015. Variables were related to the quantity of produced waste and economic development. Both exploratory spatial data analysis and estimations of spatial panel and seemingly unrelated regression models were performed by including particular W matrices in the study (exogenous-random as well as distance and directional matrices constructed based on data). The results indicated that (1) geostatistical tools can be effectively used to build Ws; (2) outcomes of applying different matrices did not exclude but supplemented one another, although the differences were significant; (3) the most precise picture of spatial dependence was achieved by including distance matrices; and (4) the values of the assessed parameter at the regressors did not significantly change, although there was a change in the strength of the spatial dependency.

scholarly journals Spatial data analysis and the use of maps in scientific health articles

2016 ◽  
Vol 62 (4) ◽  
pp. 336-341
Author(s):  
Luciana Bertoldi Nucci ◽  
Patrick Theodore Souccar ◽  
Silvia Diez Castilho
Keyword(s):  
Spatial Analysis ◽  
Spatial Data ◽  
Geographical Location ◽  
Epidemiological Studies ◽  
Spatial Data Analysis ◽  
Careful Evaluation ◽  
Analysis Techniques ◽  
Impact Index ◽  
Scientific Papers ◽  
The Impact

Summary Introduction: Despite the growing number of studies with a characteristic element of spatial analysis, the application of the techniques is not always clear and its continuity in epidemiological studies requires careful evaluation. Objective: To verify the spread and use of those processes in national and international scientific papers. Method: An assessment was made of periodicals according to the impact index. Among 8,281 journals surveyed, four national and four international were selected, of which 1,274 articles were analyzed regarding the presence or absence of spatial analysis techniques. Results: Just over 10% of articles published in 2011 in high impact journals, both national and international, showed some element of geographical location. Conclusion: Although these percentages vary greatly from one journal to another, denoting different publication profiles, we consider this percentage as an indication that location variables have become an important factor in studies of health.

Testing Impact Measures in Spatial Autoregressive Models

2019 ◽  
Vol 43 (1-2) ◽  
pp. 40-75 ◽  
Author(s):  
Giuseppe Arbia ◽  
Anil K. Bera ◽  
Osman Doğan ◽  
Süleyman Taşpınar
Keyword(s):  
Linear Regression ◽  
Spatial Data ◽  
Regression Models ◽  
Delta Method ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Spatial Panel Data Models ◽  
Spatial Panel Data ◽  
Spatial Autoregressive ◽  
The Impact

Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel data models. In this article, we compare three procedures for testing the significance of impact measures in the spatial linear regression models. These procedures include (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulation method. In a Monte Carlo study, we compare the finite sample properties of these procedures.

scholarly journals Identifying the Driving Factors of Food Nitrogen Footprint in China, 2000–2018: Econometric Analysis of Provincial Spatial Panel Data by the STIRPAT Model

2021 ◽  
Vol 13 (11) ◽  
pp. 6147
Author(s):  
Chun Liu ◽  
Gui-Hua Nie
Keyword(s):  
Panel Data ◽  
Spatial Data ◽  
Driving Forces ◽  
Early Stage ◽  
Spillover Effect ◽  
Spatial Data Analysis ◽  
Ekc Hypothesis ◽  
Stirpat Model ◽  
Spatial Panel Data ◽  
Per Capita

This paper studies the EKC hypothesis and STIRPAT model. Based on the panel data of carbon emission intensity and other influencing factors of 30 provinces in China from 2000 to 2018, the spatial effect of per capita food nitrogen footprint (FNF) and the effect of different socio-economic factors in China were studied by using exploratory spatial data analysis and fixed effect spatial Durbin model for the first time. The results show that: (1) there is a spatial agglomeration effect and a positive spatial dependence relationship in China’s provincial per capita FNF (FNFP), which verifies that the relationship between China’s FNF and economy is in the early stage of EKC hypothesis curve. (2) The driving forces of China’s FNF were explored, including Engel’s coefficient of urban households (ECU), population density (PDEN), urbanization, nitrogen use efficiency (NUE) and technology. (3) The results show that there is a significant spatial spillover effect of FNFP. The ECU and NUE can reduce the regional FNFP, and can slow down the FNFP of surrounding provinces. (4) Policy makers need to formulate food nitrogen emission reduction policies from the food demand side, food consumption side and regional level.

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