Moran Scatterplot Research Articles

Studying whether the digital economy effectively promotes China’s common prosperity based on the spatial Durbin model

Common prosperity is the essential aspiration of all the Chinese people, and China’s entry into a period of common prosperity coincides with the era of the digital economy. This paper analyzes the impact of the digital economy on the urban-rural income gap from two dimensions industrial digitization and digital industrialization. Based on China’s provincial panel data from 2013 to 2020, the spatial autocorrelation of various indicators in the digital economy is tested employing the global Moran index and local Moran scatter plot. This paper studies whether the digital economy directly affects the income gap between urban and rural areas by constructing a spatial Durbin model with a geographical inverse distance square matrix and whether there is a spatial spillover effect on the income gap between urban and rural areas, to explore how to solve the existing problems in the income gap between urban and rural areas in China through the perspective of digital economy development. The results show that the digital economy in China has a positive spatial autocorrelation to the urban-rural income gap; The impact of the development of the digital economy on the urban-rural income gap is not only reflected in the narrowing of the local region but also through the spatial spillover effect to narrow the income gap in the surrounding region. Digital industrialization has a significant role in narrowing the urban-rural income gap with both direct and indirect effects. Based on the empirical results obtained, this paper proposes that to adjust the imbalance in the development of the digital economy in different regions, digital industrialization and industrial digitalization should be further promoted, and the balanced development of the digital economy should be correctly guided through policy means, to play the regulating role of the digital economy in narrowing the urban-rural income gap.

Spatiotemporal Patterns for Agroforestry Tree Crops in the U.S. Corn Belt for USDA Census of Agriculture Periods 2012–2022

Within the U.S., there is a growing need to integrate environmentally friendly practices into conventional agriculture. Agroforestry enhances environmental and resource stewardship in agricultural landscapes while offering potential economic benefits to farmers. Despite rising interest, limited information on its application in the U.S. hinders development efforts. A spatiotemporal analysis of current farm operations can provide crucial insights. This study examined patterns of agroforestry and tree crop adoption in the U.S. Corn Belt using USDA Census data (2012, 2017, and 2022) and spatial tools (Global Moran’s I, Local Moran’s I, and Moran scatterplots). The tree crops included in the analysis were chestnut (Castanea spp.), hazelnut (Corylus spp.), improved northern pecan (Carya illinoinensis), elderberry (Sambucus spp.), and pawpaw (Asimina triloba). The results showed increasing farm operations with agroforestry and tree crops over time for all census periods. Agroforestry had the strongest spatial cluster patterns, with Local Moran’s I revealing R2 values rising from 0.30 to 0.35 between 2017 and 2022. Chestnut, hazelnut, and improved pecan had clustered spatial patterns, but had decreasing spatial autocorrelations from 2012 to 2022, while elderberry clustered in 2017 but not 2022. This study reveals an upward trend in agroforestry adoption and the spatial expansion of certain tree crops in the U.S. Corn Belt, highlighting potential for region-specific agroforestry development. The findings offer insights to guide strategies and programs supporting sustainable agricultural practices.

Citrus Industry Agglomeration and Citrus Green Total Factor Productivity in China: An Empirical Analysis Utilizing a Dynamic Spatial Durbin Model

In the context of increasingly severe resource and environmental constraints, examining the impact of citrus industry agglomeration on the green total factor productivity (GTFP) of citrus is of great importance for the sustainable development of the citrus industry and is crucial for promoting the green, high-quality growth of China’s agricultural sector. In this study, the global Malmquist–Luenberger productivity index (GMLPI) model was used to measure the GTFP of mandarins and tangerines based on inter-provincial panel data from China’s major citrus-producing regions between 2007 and 2022. The dynamic spatial Durbin model was employed to empirically analyze the effects of citrus industry agglomeration on the GTFP of mandarins and tangerines, including the disaggregation of its spatial spillover effects. The results indicate that, in terms of temporal dynamics, the GTFP, technical progress index (GTC), and technical efficiency index (GEC) of mandarins and tangerines significantly fluctuated, especially during the period from 2007 to 2015. Regional disparities in GTFP and the GTC are more pronounced for mandarins than for tangerines, while the GEC shows greater regional disparities for tangerines than for mandarins. The intensification of citrus industry agglomeration has had a significant positive impact on the GTFP of mandarins and tangerines, both locally and in neighboring regions. The spatial correlation of the green total factor productivity of mandarins and tangerines fluctuated; mandarins showed significant spatial aggregation in some years, while tangerines showed significant spatial dispersion in several years. The local Moran scatterplot further reveals the significant negative spatial autocorrelation of mandarin and tangerine green total factor productivity from 2007 to 2022. The direct, indirect, and total effects of citrus industry agglomeration on the GTFP of mandarins and tangerines are significant and positive in both the short- and long-term, with short-term benefits exceeding long-term effects. Consequently, enhancing regional cooperation and exchange while advancing citrus industry agglomeration is essential for sustained productivity growth.

Analysis of the degree of correlation of spatial distribution of electricity theft and exogenous variables: case study of Florianopolis, Brazil

Abstract This article presents a geospatial study case on electricity theft. The main objective is to identify the degree of correlation between exogenous variables and areas with a high density of irregular cases. Firstly, the geospatial study is carried out to asses the null hypothesis and check whether the data pattern presents clustering, for this the ANN method is applied, which ruled out the null hypothesis for the data set. Once the clustering pattern is confirmed, the spatial weight matrix is created to study spatial autocorrelation by applying Global Moran’s I and Local Moran’s I. Moran scatterplot is used to evaluate the degree of fitness, identify outliers, and local pockets of stationarity. The Local Moran index is used to determine the location of the clusters and the relationship between the points. In the data pre-processing step, spatial interpolation is implemented to the exogenous variables as a tool to better association of consumer units points and socioeconomic variables, the method utilized is IDW interpolation. The R-squared value of the spatial lag model after model tuning by feature selection was 87 % indicating that the model fit the observed data well.

Analysis of Spatial Agglomeration Characteristics of Logistics Enterprises in Henan Province based on Poi

As an important industry connecting urban and rural areas and serving production and life, logistics enterprises, as the micro-foundation of the development of logistics industry, have a profound impact on the spatial reconstruction of regional economic and social development. National Development and Reform Commission issued the "Fourteenth Five-Year Plan" modern logistics development and "Fourteenth Five-Year Plan" modern circulation system construction planning "put forward new requirements for the development of logistics industry, emphasizing the optimization of logistics spatial layout and speed up the construction of national logistics hub, especially pointed out that to make up for the shortcomings of the central and western regions. As an important base for the development of China's logistics industry, the spatial layout of logistics enterprises in Henan province not only affects the high-quality development of the logistics industry in the province, but also plays an important role in the improvement of the national logistics network. In this paper, based on POI data, the mean nearest neighbor method, local Moran scatter plot analysis and kernel density visualization technology are used to analyze the spatial agglomeration characteristics of logistics enterprises in 157 counties in Henan Province. It is found that the spatial distribution of logistics enterprises in Henan Province has gradually evolved from multi-point dispersion to a single-core and multi-core collaborative model with Zhengzhou as the core, and presents a significant spatial positive correlation and agglomeration trend. This change has improved the logistics efficiency and promoted the strengthening of regional economic ties. At the same time, the agglomeration trend of logistics enterprises in Henan Province continues to strengthen, from the initial agglomeration in the core area to the diversified diffusion pattern, showing the continuous optimization and reconstruction of logistics network in the province.

Autocorrelation Spatial Based on Specific Nutritional Interventions Achievement with Stunting Cases in Toddlers at Kendari City Using Local Indicator of Spatial Autocorrelation (LISA) Method

Background:Stunting is a priority target both globally and in Indonesia. There are 10 provinces in Indonesia that are the main focus of the stunting reduction program, one of which is Southeast Sulawesi Province. Kendari City, located in Southeast Sulawesi, has experienced an increase in stunting incidence over the past three years. However, progress in reducing stunting in Kendari City has not been evenly distributed across its regions and sub-regions, with significant disparities in stunting rates between different sub-districts. Objective:To determine the spatial autocorrelation based on the achievement of specific nutritional interventions for toddlers and the incidence of stunting in Kendari City using the Local Indicator of Spatial Autocorrelation (LISA). Method:This quantitative study used the Local Indicator of Spatial Autocorrelation (LISA) method. The data on stunting incidence consisted of the number of stunting cases among toddlers in 2023 across 11 sub-districts in Kendari City. The sub-districts analyzed were Abeli, Baruga, Kadia, Kambu, Kendari, West Kendari, Mandonga, Nambo, Poasia, Puuwatu, and Wua-Wua. The study was conducted from November 2023 to May 2024 in Kendari City. A local autocorrelation test with LISA was performed to determine the spatial relationships among the sub-districts based on the research variables, with results displayed in the form of Moran's scatterplot, cluster map, and significance map. Results:The results of Moran's local bivariate test (LISA) indicated that the majority of sub-districts, particularly Kambu, exhibited significant positive autocorrelation with neighboring sub-districts and fell into the cold-spot category. This indicates that the number of specific nutritional intervention programs for toddlers and the cases of stunting in toddlers in 2023 were low in Kambu and its surrounding sub-districts, which also had similarly low values. Conclusion:There is spatial autocorrelation among the sub-districts in Kendari City. Although the cases of stunting in the Kambu sub-district are low, the achievement of intervention programs should remain optimal, as cases still exist in the area. Additionally, since Kambu has a spatial correlation with its neighboring areas, the government should target these areas for appropriate interventions to accelerate stunting reduction, particularly in Kendari City. Keywords:Spatial Autocorrelation; LISA; Specific Nutrition Interventions; Stunting Toddlers

Spatial–temporal evaluation of total-factor energy efficiency in Chinese construction industry based on three-stage super-efficiency SBM-DEA model

PurposeThe study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.Design/methodology/approachUtilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.FindingsAnalysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.Originality/valueThis research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

Exploring spatiotemporal distribution characteristics of air quality and driving factors: empirical evidence of 288 cities in China.

Excellent air quality is important for China to achieve high quality economic development. The paper analyses the spatial and temporal distribution characteristics of the air quality index (AQI) in 288 Chinese cities, and further investigates the driving factors affecting air quality using the spatial Durbin model (SDM) based on the panel data of 288 Chinese cities from 2014 to 2021. The results of the study show that: (1) China's air quality level has improved in general, but there are large differences in air quality between regions; (2) China's AQI has significant spatial positive autocorrelation, and the Moran's scatter plot shows a high-high and low-low agglomeration; (3) The driving factors of air quality have different effects, and regional heterogeneity is obvious. Some developed regions in China have already crossed the inflexion point of the environmental Kuznets curve (EKC); promoting industrial upgrading and reducing pollutant emissions can significantly improve urban PM2.5 concentrations; and the "Three-Year Strategy for Conquering the Blue Sky War" policy has lowered the AQI in North China and improved PM2.5 concentrations nationwide. Based on the above findings, the paper puts forward corresponding policy recommendations.

Confounded Local Inference: Extending Local Moran Statistics to Handle Confounding

Local statistical analysis has long been of interest to social and environmental scientists who analyze geographic data. Research into local spatial statistics experienced a step-change in the mid-1990s, which provided a large class of local statistical methods and models. The local Moran statistic is one commonly used local indicator of spatial association, able to detect both areas of similarity and observations that are very dissimilar from their surroundings. From this, many further local statistics have been developed to characterize spatial clusters and outliers. These statistics have seen limited adoption because they do not sufficiently model the relationships involved in confounded spatial data, where the analyst seeks to understand the local spatial structure of a given outcome variable that is influenced by one or more additional factors. Recent innovations used to do joint multivariate local analysis also do not model this kind of conditional local structure in data. This article provides tools to rigorously characterize confounded local inference and a new and different class of multivariate conditional local Moran statistics that can account for confounding. To do this, we return to the Moran scatterplot as the critical tool for local Moran-style covariance statistics. Extending this concept, a new method is available directly from a “Moran-form” multiple regression. We show the empirical and theoretical properties of this statistic, show how some existing heuristic approaches arise naturally from this framework, and show how the use of conditional inference can change interpretations in an empirical analysis of rent and housing stock in a rapidly changing neighborhood.

Spatio-temporal evolutionary analysis of surface ecological quality in Pingshuo open-cast mine area, China.

The open-pit mining area is highly affected by human activities, which aggravate soil erosion and disturb surface ecology, bringing many problems and challenges to its environmental management and restoration, which has received widespread attention. The establishment of an objective, timely and quantitative remote sensing monitoring, and evaluation system for the spatio-temporal evolution of the surface ecological environment in the open-pit mining area is of great significance for its environmental protection, management decisions, and sustainable social development. Based on the Google Earth Engine (GEE) platform, this paper uses Landsat images to construct and calculate the remote sensing ecological index (RSEI) of the Pingshuo open-cast mine area (POMA) from 1990 to 2020 and monitor and evaluate its surface ecological environment. Combined with the Theil-Sen median, Mann-Kendall test, and Hurst index, the spatio-temporal process was analyzed. The results showed that the ecological environmental quality of the mining area first decreased and then increased from 1990 to 2020. 1990-2000 was a period of serious ecological degradation, followed by improvement. The overall improvement area reached 87.03%, and the degradation was concentrated in the coal mining area. Between 1990 and 2020, the Hurst index of the mining area was 0.452, indicating that the region has a fragile ecological environment and has difficult maintaining its stability. The global Moran's I mean value of the RSEI of the study area is 0.92, which combined with Moran's scatter plot to indicate that there is a strong positive spatial correlation rather than a random distribution of its ecological environment. During the study period, the impact on the climate of the ecological environmental change of POMA was weak, and human factors such as coal mining, land reclamation, and social construction were the main driving forces for the change in ecological quality. The results of this study reveal the changing trend of surface ecology in the mining area over the past 30 years, which is helpful for understanding its impact mechanism on ecological quality and provides support for the management of the region.

Research on the Spatiotemporal Dynamic Relationship between Human Activity Intensity and Ecosystem Service Value in the Three Gorges Reservoir Area

The Three Gorges Dam project and other human activities, including regional urbanization and industrialization, have had a substantial influence on the biological environment of the Three Gorges Reservoir Area (TGRA). They have changed the surface land use pattern, disrupted ecosystem structure and function, and influenced changes in the value of ecosystem services. The human activity intensity (HAI) assessment model, the ecosystem services value (ESV) assessment model, and the bivariate spatial autocorrelation model were used based on the spatiotemporal evolution data of towns along the Yangtze River in the TGRA in 1995, 2000, 2005, 2010, 2015, and 2020. At the same time, the spatiotemporal impact of the HAI on land use patterns was evaluated and the magnitude of the spatiotemporal influences on the ESV was investigated. The findings demonstrate the following: (1) The TGRA’s higher reaches are occupied by forested land, while the middle and lower portions are characterized by agricultural land. Land change in the reservoir region has mostly featured transitions between wooded land, agricultural land, grassland, and building land during the last 25 years. Because of differences in natural geography and administrative divisions, the intensity of human activity in the TGRA changes throughout the Yangtze River, with higher intensity in Chongqing and lower intensity in Hubei. By comparing the ESV and the HAI and validating with Moran scatter plots, it was determined that there is a negative relationship between the value of ecosystem services and the intensity of human activities. (2) The ESV rose from CNY 1017.16 × 108 in 1995 to CNY 1052.73 × 108 in 2020, suggesting that the policies of converting farmland back into forests, eliminating outdated production capacity, and developing green industries, among other ecological conservation measures, are effective. (3) In the research area, the effect coefficient of HAI on ESV ranges from −0.02 to −0.032 to −0.031. This coefficient represents the correlation between the HAI and ESV and can preliminarily judge the change in the degree of correlation between the HAI and ESV. The increase in HAI leads to a decrease in the value of ecosystem services, and there is a clear negative spatial correlation between the two. The low human activity area and low ecosystem service value area in the Chongqing section have been transformed into a high ecosystem service value area through years of returning farmland to forest and ecological management measures for sustainable development.

Spatial Analysis of DHF Cases in Yogyakarta City Based on Population Density and Altitude (2017-2018)

Aims: This study aims to determine the relationship between population density and altitude of the territory and DHF incidence in Yogyakarta City in 2017-2018.
 Study Design: The study was designed as an observational cross-sectional study.
 Place and Duration of Study: This study was conducted in Yogyakarta from 2017 to 2018. Methodology: This quantitative study employs an analytic observational cross-sectional design. Moran's I and LISA tests were used to analyze the data.
 Results: Based on bivariate Moran's Scatterplot analysis, the density of residents with dengue incidence showed negative spatial autocorrelation (I=-0.308), and the altitude with dengue incidence showed negative spatial autocorrelation (I=-0.128), indicating that the majority of the sub-district was scattered in quadrants II and IV. The LISA density bivariate test results on residents with dengue incidence revealed that the Gondomanan Sub-district has positive autocorrelation (li= 0.30) with a Low-Low quadrant and statistical significance (P-Value=0.040.05). In contrast, Kotagede Sub-district has negative autocorrelation (li=-2.31) with a Low-High quadrant and statistical significance (P-Value=0.000.05). Gondomanan Sub-District has positive autocorrelation (li=0.67) at the Low-Low quadrant and statistical significance (P-Value=0.03 0.05), whereas Kotagede Sub-District has negative autocorrelation (li=-2.86) at the Low-High quadrant and statistical significance (P-Value=0.000.05).
 Conclusion: Density residents have a negative autocorrelation with the incidence of DHF in Yogyakarta City. Local spatial density residents with DHF cases were found in the Gondomanan Sub-District with positive spatial autocorrelation. At the same time, Kotagede Sub-District had negative spatial autocorrelation in contrast to an altitude with a global spatial connection toward DHF incidence in Yogyakarta with negative autocorrelation.

Investigating the Synergy between CO2 and PM2.5 Emissions Reduction: A Case Study of China’s 329 Cities

The synergetic reduction of CO2 and PM2.5 emissions has received much attention in China in recent years. A comprehensive evaluation of the synergy between CO2 emission reduction (CER) and PM2.5 emission reduction (PER) would provide valuable information for developing synergetic control policies. Thus, we constructed a comprehensive CO2-PM2.5-emission-reduction index system and evaluated the synergy between CER and PER, using the coupling coordination degree (CCD) and relative development degree (RDD) model in China’s 329 cities from 2003 to 2017. The spatiotemporal characteristics of the CCD were analyzed on the national, regional, and urban scales. Furthermore, we used the spatial autocorrelation analysis, kernel density estimation, and Dagum Gini coefficient to investigate the spatial autocorrelation, evolutionary characteristics, and regional differences of the CCD. The results indicate that (1) the synergy between CO2 and PM2.5 emissions’ reductions showed an upward trend, and the lowest CCD values occurred in NW and Shanghai on the regional and urban scales, respectively; (2) the CCD showed obvious spatial clustering characteristics, with 75% of the cities located in the “High–High” or “Low–Low” clustering zones in the Moran scatter plots in 2017; (3) the polarization of CCD in SC, MYR, and SW showed intensified trends; (4) and the hypervariable density was the largest contributor to the overall difference in the CCD. Our findings suggest that more attention should be paid to the top-level design of the policies, technological innovation, and cross-regional or intercity cooperation.

Pola Sebaran Spasial Stunting di Kabupaten Lampung Selatan dengan Pendekatan Autokorelasi Spasial

Stunting is a condition in toddlers who experience failure to thrive due to chronic malnutrition which causes children to be shorter than their cohort. The Indonesian Ministry of Health's Basic Health Research in 2018 reported that the prevalence of stunting in the South Lampung Regency was above the WHO standard of 20%, although it decreased from 43.01% in 2013 to 29.08% in 2018. This study aims to identify spatial distribution pattern of stunting prevalence in the South Lampung Regency. The methods included Moran's Index, Moran's Scatterplot, Local Indicator of Spatial Association (LISA) and correlation analysis. The results showed that there was a spatial autocorrelation to the prevalence of stunting in South Lampung Regency with the spatial distribution pattern being clustered. The grouping of areas in the high-high (HH) cluster is mostly located in villages in Kalianda District with one of the contributing factors being the joint use of water from inappropriate sources. Clean water provision is expected to reduce stunting prevalence in the South Lampung Regency.

Analyse exploratoire spatiale des effets sanitaires et économiques de la COVID-19 à partir de données mondiales

L'objectif : L'objectif de cet article est d'analyser les effets sanitaires et économiques de la pandémie de COVID-19. Conception/méthodologie/approche : L'échantillon est composé de 132 pays et la méthodologie se fonde sur l ' analyse exploratoire des données spatiales. Le calcul de l'output gap par la méthode de Hodrick-Prescott a permis de ressortir les conséquences économiques de la crise sanitaire, à travers les écarts de production pour l'année 2020. La variable sanitaire, quant à elle, est mesurée par le taux d'incidence de la COVID-19 et la mortalité. Résultats: Les résultats des estimations ont permis de valider l'hypothèse d'autocorrélation spatiale aussi bien pour la variable sanitaire que pour la variable économique. L'examen du diagramme de Moran confirme le schéma d'association spatiale local positif, c'est-à-dire l'existence à la fois de similitudes entre pays voisins dans la manifestation de la pandémie et d'hétérogénéité spatiale entre les groupes de pays. De manière plus précise, les résultats montrent l'existence de clusters avec de faibles niveaux d'incidence de la COVID-19 en Afrique et en Asie, comparativement à l'Europe et à l'Amérique du Nord. De plus, si les pays à revenu élevé ont généralement été davantage touchés sur le plan sanitaire, ils ont toutefois développé une plus grande résilience économique. Originalité/valeur : Ces résultats montrent que la prise en compte de l'espace pourrait permettre de mieux comprendre la dynamique des chocs sanitaires et économiques.

Agglomeration and Spatial Distribution of E-Commerce Sector in China: A Case Study of Jiangsu Province

The research intends to examine the pattern of e-commerce and to investigate the clustering of e-commerce activities in the Jiangsu Province of China. The development of “Taobao villages” has become a new growth pattern for the economy of rural areas in China. This typical model of e-commerce industry agglomeration has generated huge sales, which is not only important for local economic development but also has some implications for other places with underdeveloped e-commerce industries. The proposed study combines theory and empirical evidences based on panel data generated from “Taobao villages” in all 13 regions of Jiangsu Province, China. To explore the spatial distribution and economic returns of e-commerce in the perspectives of economic agglomeration, data of five years—2016 to 2020—is exploited. Samples of regions are selected based on different agglomeration types. The type of e-commerce industry agglomeration in the form of “Taobao villages” is measured by constructing a spatial weight matrix. Using the facts and figures of the five years, a Moran scatterplot and a Local Indicators of Spatial Association (LISA) cluster map are drawn. From the perspective of economic agglomeration, the e-commerce industry in Jiangsu Province presents three basic patterns: High-High cluster, Low-High cluster, and Low-Low cluster. In addition, the Location Quotient (LQ) was used to quantitatively characterize the agglomeration of “Taobao villages.” Findings of the analysis revealed that there is a positive spatial correlation between the clusters of Taobao villages, mainly between the H-H cluster and L-L cluster. The results are summarized and the most useful solutions for promoting the e-commerce industry in China. The empirical model proposed has great significance in e-commerce and may be used to enhance rural economy of China.

The Impacts of Transportation Infrastructure on Economic Openness in Yangtze River Delta of China

The paper discusses the impacts of transportation infrastructure on economic openness within the 27 major cities of Yangtze River Delta of China. Through reviewing relevant literatures, it forms an influencing mechanism and research path based on the theories of New Economic Geography and comparative advantages. In the empirical test, the whole research chooses the panel data from the year 2005 to 2020 to observe the spatial autocorrelation through Global Moran Index (Moran’s I) and Local Moran Scatterplots. Then, the paper builds an econometric function on the basis of Spatial Durbin Model (SDM) to measure the regression results and then decompose the effects. According to the test results, it can be concluded that transportation infrastructure generates positive impacts on economic openness and there truly exists the spatial spillover effect in the Yangtze River Delta. Finally, the paper gives proposals from three aspects which are combined with the policies on integration construction of national strategic deployment of China.

Spatial-Temporal Changes and Influencing Factors of Ecological Protection Levels in the Middle and Lower Reaches of the Yellow River

In recent years, ecological problems in the middle and lower reaches of the Yellow River have been frequent. Therefore, exploring its core influences can advance the implementation of “Ecological Protection and High-quality Development of the Yellow River Basin”. This paper constructs an indicator system based on PSR guidelines, evaluates the ecological protection level of 55 cities in the middle and lower reaches of the Yellow River from 2009 to 2019, and uses correlation analysis with geographically and temporally weighted regression (GTWR) model to explore the spatial distribution characteristics of influencing factors, such as intensity of fertilizer application, amount of agricultural film applied, afforestation area per capita, and green technology innovation level on the ecological protection level. It is found that the overall level of ecological protection has shown a steady increase, but the spatial distribution varies widely. The ecological level increased from 0.2218 to 0.3357, showing a decreasing distribution trend from coastal to inland. Furthermore, it is found that the ecological protection level has a significant positive spatial correlation, mainly for similar clustering. The Global Moran’s I for ecological protection level is greater than 0, and the Moran scatter plot has a high number of cities distributed in the first and third quadrants. There is a heterogeneity in the spatial and temporal distribution of factors influencing the level of ecological protection. Fertilizer application, the agricultural film uses, and afforestation area per capita are mainly negatively affected, while green innovation level has a strong positive effect, and agricultural film use, afforestation area per capita, and green innovation level become the core influencing factor of different regions. Therefore, in the middle and lower reaches of the Yellow River, the ecological protection level should be improved by implementing a regional differentiated development strategy, realizing cross-regional linkages between cities and focusing on differences in core driving factors.

Spatial Analysis of Seven Islands in Indonesia to Determine Stunting Hotspots

Indonesia is a vast country struggling to reduce its stunting prevalence. Hence, identifying priority areas is urgent. In determining areas to prioritize, one needs to consider geographical issues, particularly correlations among areas. This study aimed to discover whether stunting prevalence in Indonesia occurs randomly or in clusters; and, if it occurs in clusters, which areas are the hotspots. This ecological study used aggregate data from the 2018 National Basic Health Research and Poverty Data and Information Report from the Statistics Indonesia. This study analyzed 514 districts/cities across 34 provinces on seven main islands in Indonesia. The method used was the Euclidean distance to define the spatial weight. Moran's index test was used to identify autocorrelation, while a Moran scatter plot was applied to identify stunting hotspots. Autocorrelation was found among districts/cities in Sumatra, Java, Sulawesi, and Bali East Nusa Tenggara West Nusa Tenggara Islands, resulting in 133 districts/cities identified as stunting hotspots on four major islands. Autocorrelation proves that stunting in Indonesia does not occur randomly.

Spatiotemporal Coupling Effect of Regional Economic Development and De-Carbonisation of Energy Use in China: Empirical Analysis Based on Panel and Spatial Durbin Models

The synergistic development of economic construction and low-carbon transformation of energy systems must be promoted for building a green, low-carbon, and cyclic economic system and achieving the “double carbon” goal in China. Based on the panel data of 30 provincial-level administrative regions in China from 2015 to 2019, the global entropy method, coupling coordination degree model, and spatial statistical analysis methods are applied to analyse the factors affecting the coupling effect. The coordination degree increased in the study period, with Beijing, Shanghai, Tianjin, Jiangsu, and Zhejiang being the regions with the highest values. The spatial distribution of the coupling coordination degree is strongly positively correlated with the eastern provincial-level administrative regions located in the high–high concentration area of the Moran scatterplot and western provincial-level administrative regions concentrated in the low–low concentration area. The spatial association pattern is stable in the study period, with only two provinces exhibiting a transition: Shandong province made the transition to high–high agglomeration areas, and Liaoning province made the transition to low–low agglomeration areas. The level of regional economy, urbanization process, energy consumption structure, and level of investment in science and innovation enhance the coupling coordination degree, whereas the industrial structure deteriorates this degree.