Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

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Research on the public sports service efficiency and spatial-temporal evolution in China

10.31124/advance.13536488 ◽

2021 ◽

Author(s):

JIAO LINYAN ◽

QIAO ZIWEI ◽

MI SIMENG ◽

Jung Woo Jin

Keyword(s):

Spatial Data ◽

Evaluation Index System ◽

Spatial Data Analysis ◽

Eastern Region ◽

Data Envelopment ◽

Input Output ◽

Super Efficiency ◽

The Public ◽

Output Efficiency ◽

The Difference

<p>Taking 31 provinces as the research object, this paper constructed the input-output efficiency evaluation index system of public sports service in China. The paper evaluated the input-output efficiency level and spatial-temporal pattern of public sports services using the methods of data envelopment analysis and exploratory spatial data analysis. The results show that: (1) The average comprehensive super-efficiency value of public sports service in 2016 was higher than that in 2008, and the provinces with the comprehensive super-efficiency value greater than 1 increased, but the difference between provinces in 2016 was more obvious. (2) Compared with 2008, the efficiency distribution of public sports service in 2016 is more balanced among the three regions, the difference between the eastern region and the central region is reduced. (3) The efficiency of public sports service has different spatial correlation in the geographical spatial distribution, and this correlation shows the reverse in the two measurements.</p>

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Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060380 ◽

2020 ◽

Vol 9 (6) ◽

pp. 380

Author(s):

Radosław Cellmer ◽

Aneta Cichulska ◽

Mirosław Bełej

Keyword(s):

Spatial Heterogeneity ◽

Housing Market ◽

Spatial Data ◽

Geographically Weighted Regression ◽

Housing Prices ◽

Spatial Diversity ◽

Spatial Data Analysis ◽

Weighted Regression ◽

Market Activity ◽

Administrative Units

The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

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Smart and IoT-Based Coupling and Coordination Development for Financial and Logistics Industries

Mobile Information Systems ◽

10.1155/2021/3227408 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Ji-you Li ◽

Qi-qing Zhou ◽

Pan-pan Yin

Keyword(s):

Spatial Data ◽

Driving Forces ◽

Comprehensive Evaluation ◽

Fuzzy Comprehensive Evaluation ◽

Spatial Data Analysis ◽

Theil Index ◽

Eastern Region ◽

Coordinated Development ◽

The Republic ◽

Total Difference

Based on the panel data, collected through various Internet of Things (IoT) devices, of 31 various provinces and cities in the Republic of China from 2004 to 2019, due to the analysis of mechanism and the significance of coupled and coordinated development, methods like fuzzy comprehensive evaluation, entropy, coupling, and coordination degree model, exploratory spatial data analysis, and Theil index are widely used to analytically evaluate the dynamic coupling development of China’s financial and logistics industries. The analysis of the collected data shows that demand promotion, technological progress, corporate decision-making, and policy stimulus are the driving forces for the coordinated development. In addition, the coordinated development of both industries can achieve a win-win situation. Moreover, during the sample period, the level of coupled and coordinated development has made considerable progress, achieving a transition from moderate to slightly unbalanced level, but overall, it is still at a low level. The level of coupled and coordinated development is showing east-central-west, that is, a three-level declining trend. Guangdong is the province with the highest level, and Qinghai and Ningxia are the provinces with the lowest levels of coupled and coordinated development. The general evolution trend of the total difference in the levels of coupled and coordinated development is declining in fluctuation, and the differences in the eastern region and within the zones are the main reasons for the total difference.

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REMOTE SENSING ANALYSIS OF THE CHANGES IN VEGETATED COASTLAND: THE CASE OF SOUTHEASTERN EUROPE

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-4-2021-55-2021 ◽

2021 ◽

Vol V-4-2021 ◽

pp. 55-59

Author(s):

K. Laze

Keyword(s):

Remote Sensing ◽

Land Use ◽

Spatial Data ◽

Methodological Approach ◽

Driving Factors ◽

Spatial Data Analysis ◽

Study Region ◽

Spatial Changes ◽

Explicit Analysis ◽

Abiotic And Biotic Factors

Abstract. The coastlands are changing in the Southeastern European countries. Yet, temporal and spatial changes in natural-vegetated land, urban-vegetated land and agriculture-vegetated land use on coastal surfaces, respectively, are missing. The aim is to understand the changes in vegetated land on the coastal surface by retrieving spatial data from remote sensing using a new approach by reducing errors in data processed and by collecting data using surveys to understand the driving (abiotic and biotic) factors of vegetated land changes and land dynamics at regional level. These spatial and survey data are then to be employed at spatially explicit analysis to define driving factors, plausibly explaining spatial and temporal changes of vegetated land on coastal surfaces in the Southeastern European study region including Albania, Bosnia and Herzegovina, Croatia, and Montenegro and Slovenia for the last fifty years. The expected main result is a new methodological approach of remote sensing spatial data analysis by reducing data errors and by identifying driving factors of the changes in vegetated land. The findings may serve to understand the effects of land management and land use on the changes in vegetated land-use on coastal surfaces, and to potentially uncover the least disturbed vegetated land areas by human intervention for vegetation conservation.

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Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16224442 ◽

2019 ◽

Vol 16 (22) ◽

pp. 4442 ◽

Cited By ~ 2

Author(s):

Zhang ◽

Chen ◽

Cai ◽

Gao ◽

Zhang ◽

...

Keyword(s):

Spatial Distribution ◽

Influencing Factors ◽

Spatial Data ◽

Evaluation System ◽

Negative Impact ◽

Positive Impact ◽

Measurement Model ◽

Spatial Data Analysis ◽

Urban Resilience ◽

Pension Insurance

The healthy development of the city has received widespread attention in the world, and urban resilience is an important issue in the study of urban development. In order to better provide a useful reference for urban resilience and urban health development, this paper takes 56 cities in China as the research object, and selects 29 indicators from urban infrastructure, economy, ecology and society. The combination weight method, exploratory spatial data analysis (ESDA) and spatial measurement model are used to explore the spatial distribution of urban resilience and its influencing factors. From 2006 to 2017, the urban resilience of prefecture-level cities in the four provinces showed a wave-like rise. During the study period, the urban resilience values, measured as Moran’s Is, were greater than 0.3300, showing a significantly positive correlation in regard to their spatial distribution. Regarding the local spatial correlation, the urban resilience of the study area had spatial agglomeration characteristics within the province, with a significant distribution of "cold hot spots" in the spatial distribution. From the perspective of the factors that affected urban resilience, the proportion of the actual use of foreign capital in GDP and carbon emissions per 10,000 CNY of GDP had a negative impact and GDP per square kilometer, the proportion of urban pension insurance coverage, the proportion of the population with higher education, and expenditure to maintain and build cities had a positive impact. The development strategy of urban resilience must be combined with the actual situation of the region, and the rational resilience performance evaluation system and the top-level design of urban resilience improvement should be formulated to comprehensively improve urban resilience.

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Research on the public sports service efficiency and spatial-temporal evolution in China

10.31124/advance.13536488.v1 ◽

2021 ◽

Author(s):

JIAO LINYAN ◽

QIAO ZIWEI ◽

MI SIMENG ◽

Jung Woo Jin

Keyword(s):

Spatial Data ◽

Evaluation Index System ◽

Spatial Data Analysis ◽

Eastern Region ◽

Data Envelopment ◽

Input Output ◽

Super Efficiency ◽

The Public ◽

Output Efficiency ◽

The Difference

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Spatial-Temporal Evolution of the Coupling Coordination Relationship between Urbanization and Atmospheric Environment in the Yangtze River Economic Belt, China

10.21203/rs.3.rs-947897/v1 ◽

2021 ◽

Author(s):

Yuxia Deng ◽

Xianglian Wang ◽

Ting Huang ◽

Ya Liu ◽

Lian Zeng ◽

...

Keyword(s):

Spatial Data ◽

Yangtze River ◽

Temporal Evolution ◽

Spatial Data Analysis ◽

Atmospheric Environment ◽

Eastern Region ◽

The Yangtze River ◽

The Sustainable Development ◽

Research Perspectives ◽

Coordination Degree

Abstract Based on the coupling coordination degree model and the exploratory spatial data analysis method, we established the coupling coordination relationship between urbanization and atmospheric environment and explored the spatial-temporal evolution characteristics of the coupling coordination degree of 11 provinces in the Yangtze River Economic Belt (YEB) from 2003 to 2017. The results indicated the following: (1) The level of urbanization increases linearly, and the atmospheric environment level shows a fluctuating upward trend. (2) The types of coordination gradually change from “Seriously uncoordinated development with urbanization lag” to “Superiorly coordinated development with atmospheric environment lag”. The spatial distribution of coordination shows the characteristics of “high in the eastern region and low in the central and western region”. Regarding temporal evolution, the coupling coordination degree of the region gradually increases, and the spatial differences between provinces gradually narrow. (3) Significant spatial autocorrelation is observed between the coordination of urbanization and atmospheric environment, which weakens over time. The local agglomeration mode shows that the “High-High” cluster areas are in the lower reaches and the “Low-Low” cluster areas are mainly in the middle or upper reaches. This study contributes to promoting the sustainable development of the Yangtze River Economic Belt and provides basic data and research perspectives for further investigation of the relationship between urbanization and atmospheric environment.

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Research on the Public Sports Service Efficiency and Spatial-Temporal Evolution in China

SAGE Open ◽

10.1177/21582440211056602 ◽

2021 ◽

Vol 11 (4) ◽

pp. 215824402110566

Author(s):

Linyan Jiao ◽

Ziwei Qiao ◽

Simeng Mi ◽

WooJin Jung

Keyword(s):

Spatial Data ◽

Evaluation Index System ◽

Spatial Data Analysis ◽

Eastern Region ◽

Data Envelopment ◽

Input Output ◽

Super Efficiency ◽

The Public ◽

Output Efficiency ◽

The Difference

Taking 31 provinces as the research object, this paper constructed the input-output efficiency evaluation index system of public sports service in China. The paper evaluated the input-output efficiency level and spatial-temporal pattern of public sports services using the methods of data envelopment analysis and exploratory spatial data analysis. The results show that: (1) The average comprehensive super-efficiency value of public sports service in 2016 was higher than that in 2008, and the provinces with the comprehensive super-efficiency value greater than 1 increased, but the difference between provinces in 2016 was more obvious. (2) Compared with 2008, the efficiency distribution of public sports service in 2016 is more balanced among the three regions, the difference between the eastern region and the central region is reduced. (3) The efficiency of public sports service has different spatial correlation in the geographical spatial distribution, and this correlation shows the reverse in the two measurements.

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An Empirical Study on Spatial–Temporal Dynamics and Influencing Factors of Tea Production in China

Sustainability ◽

10.3390/su10093037 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3037 ◽

Cited By ~ 2

Author(s):

Hanchu Liu ◽

Jie Fan ◽

Kan Zhou

Keyword(s):

Spatial Distribution ◽

Influencing Factors ◽

Spatial Data ◽

Temporal Dynamics ◽

Regional Cooperation ◽

Labor Cost ◽

Spatial Data Analysis ◽

Eastern Region ◽

Spatial Agglomeration ◽

Tea Production

Revealing the characteristics of spatial–temporal dynamics and influencing factors is important for optimizing the spatial distribution of tea production. Taking prefecture-level cities as the basic spatial unit, this study uses the Herfindahl index and exploratory spatial data analysis to reveal the spatial–temporal dynamics of China’s tea production from 2000 to 2015. A theoretical analysis framework is established and a spatial econometric model is used to explore its influencing factors. The results show a U-shaped trend in the degree of tea spatial agglomeration, which gradually declined during 2000–2010, and rapidly increased during 2011–2015. The proportion of tea production shifted from the eastern region to the central and western regions, and spatial distribution coverage expanded to the north. Tea production had significant spatial correlation, and spatial agglomeration characteristics were exhibited for similar values (high or low). Tea production had a significant spatial spillover effect. Natural resources, labor cost, specialized production, and policies all affected the spatial–temporal dynamics of tea production somewhat, but the effects of traffic conditions and technological level were insignificant. Finally, this study proposed optimizing four aspects of the tea spatial layout: regional cooperation, comprehensive suitability evaluation of tea cultivation, spatial agglomeration, and distinctive local brands.

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Coupling coordination of industrial parks and industrial clusters in Hexi Corridor and their spatial correlation analysis

E3S Web of Conferences ◽

10.1051/e3sconf/202123502025 ◽

2021 ◽

Vol 235 ◽

pp. 02025

Author(s):

Rui Lyu ◽

Zhiqiang Liu

Keyword(s):

Spatial Correlation ◽

Spatial Data ◽

Industrial Clusters ◽

Hexi Corridor ◽

Evaluation Index System ◽

Spatial Data Analysis ◽

Regional Innovation ◽

Coordinated Development ◽

Coordination Model ◽

Industrial Parks

With Hexi Corridor as the study case, this study established an evaluation index system for the development of the industrial parks and industrial clusters, and used the coupling coordination model and exploratory spatial data analysis method (ESDA) to explore the evolution and spatial correlation of the coupling coordination between the industrial park and industrial clusters in Hexi Corridor from 2005 to 2017. It is found that from 2005 to 2017, the industrial parks and industrial clusters has shown closer correlations; despite the high level of coordination, the degree of coupling remains low; the Hexi Corridor has not yet developed a growth engine for the coordinated development of the industrial parks and the clusters, and there is no county-level industrial entities that have strong stimulating power. Given the requirements of the current industrial agglomeration model for regional innovation capabilities, this study proposes suggestions for coordinated development of industrial parks and industrial clusters in Hexi Corridor.

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