High-high Clusters Research Articles

Spatial Distribution of Small and Big-Scale Modern Retail Through the Growth of Yogyakarta Urbanized Area

The focus on the growth of modern retail has become increasingly important due to several generated externalities. Therefore, this research aimed to identify spatial distributional pattern of modern retail in order to implement precise policies. Data were obtained from The Department of Licensing Service of Yogyakarta City and supplemented with observation through Google Maps. The global pattern was analyzed using the Moran Index (Moran’s I), while the spatial pattern was evaluated using Local Indicators of Spatial Association (LISA). The analysis resulted in a significant cluster pattern for small and big-scale modern retail. The LISA analysis indicated the existence of agglomeration in the suburban area, which was the urban growth area with a high-high (HH) cluster of both small and big-scale modern retail in exact locations. In conclusion, agglomeration in the suburban area was of great concern for policymakers due to the possibility of conflicts.

District-level heterogeneity in overweight or obesity among women of reproductive age: A geo-spatial analysis in India.

Globally by 2030, 38% of the world's population would be overweight, and another 20% would be obese. This has led to rising concerns regarding how swiftly and substantially the world is moving towards this epidemic of "globesity". India too is facing an increased burden of overweight and obese population. The changing dietary patterns are significantly associated with the increasing prevalence of overweight/obesity and related complications, especially among women. Hence, the present study aims to observe the spatial patterns of overweight or obesity among women in reproductive age group in India and factors associated with it. The study analyzed data from a cross-sectional nationwide household survey, i.e. National Family Health Survey (NFHS-4), 2015-16. The primary outcome variable of this study was overweight/obesity among reproductive-age women, which was measured through the body mass index (BMI) of the women. Bivariate and multivariate logistic regression analysis was used to analyze the data. Additionally, for spatial analysis in terms of overweight/obesity among women in India, univariate and bivariate Moran's I index measurements were used along with the usage of spatial regression models. The value of spatial-autocorrelation for overweight or obese was 0.64, which depicts the moderately high prevalence of the overweight/obesity coverage over districts of India. The overall prevalence overweight/obesity among women in India is around 25% and higher proportion of women from urban areas (37.8%), and non-poor (33.4%) economic group reported to be overweight or obese. From spatial lag model, the lag coefficient was found to be 0.28, implying that a change in the prevalence of overweight/obesity among women in a certain district may statistically lag the prevalence of overweight/obesity by 28% in the neighbouring districts. There were significantly high clustering of overweight/obese women and non-poor wealth quintiles in 132 districts, mainly from states of Punjab, Haryana, Gujarat, Maharashtra, Kerala, Tamil Nadu, Karnataka and Andhra Pradesh. Additionally, there was high-high clustering of overweight/obese women and those who ever had caesarean in 82 districts, mostly from Kerala, Tamil Nadu, Andhra Pradesh and Karnataka. The spatial patterns on the prevalence of overweight and obesity in India show that the women belonging to the southern states' districts are more overweight or obese in comparison to other states. The determinants like older age, higher education, urban residence, higher economic status are the key factors contributing to the prevalence of overweight or obesity among women in the reproductive age group. The study concludes and recommends an urgent need of interventions catering to urban women belonging to higher socio-economic status, to reduce the risks of health consequences due to overweight and obesity.

Spatial patterns and evolution trend of coupling coordination of pollution reduction and carbon reduction along the Yellow River Basin, China

Collaborative efforts to reduce pollution and carbon emissions are a notable feature of the current era. Achieving the “pollution reduction” and “carbon reduction” dual-driven new development model is the inevitable requirement and significant practical measure for building a new development pattern and realizing a comprehensive green transformation of economic and social development. Thesynergistic effectsbetween pollution reduction and carbon reduction (PRCR) have been demonstrated. However, the measurement andquantitative analysisof thecoupling coordination degree(CCD) of PRCR have not received enough attention. Therefore, the evaluation index system was constructed based on the scientific connotation of PRCR, and the CCD of PRCR in cities along the Yellow River Basin (YRB) from 2009 to 2018 was measured quantitatively by constructing the coupling coordination model in this paper. In addition, we comprehensively investigated its spatial pattern, distribution characteristics, and evolution trend using ESDA, Kernel density estimation, and Markov chain. The primary findings can be summarized as: (1) PRCR levels and their CCD fluctuated and increased in the YRB, with “east-high and west-low” spatial differentiation characteristics, and the coupling phenomenon of “pollution reduction is hindered and carbon reduction is lagging”. (2) There is a phenomenon of “spatial club” clustering in the coordination degree of PRCR in the YRB, with most cities in the downstream areas belonging to the type of high-high clustering, while the middle and upstream areas had the opposite type. (3) The CCD of PRCR in the whole and upper reaches of the YRB have transformed from low-level divergence to high-level convergence. (4) Spatial geographic factors have a significant spillover effect on the evolution of the CCD of PRCR, and there is a phenomenon of the “Matthew effect”. This paper provides a comprehensive framework for analyzing thesynergistic effectsbetween PRCR, and the research findings can also provide insights for other regions.

Assessing Spatiotemporal Characteristics and Driving Factors of Urban Public Buildings Carbon Emissions in China: An Approach Based on LMDI Analysis

Urban public buildings carbon emissions exhibit an upward trend and have a large potential in carbon emission reduction. The analysis of spatiotemporal characteristics and driving factors for urban public buildings carbon emissions is essential in formulating effective policies for carbon reduction, meeting commitments to peak carbon emissions and achieving carbon neutrality. This study takes China’s urban public buildings carbon emissions as the research object, employing methods such as spatial autocorrelation analyses, kernel density estimation analyses, and the LMDI decomposition methods to explore the spatiotemporal characteristics and regional disparities in carbon emissions from 2006 to 2019. Furthermore, it quantifies the contributions of driving factors to the spatiotemporal changes in urban public buildings carbon emissions. The results show the following: (1) Urban public buildings carbon emissions among provinces are consistently increasing, indicating an overall upward trend. The spatial distribution highlights significant regional disparities. (2) The spatial characteristics of urban public buildings carbon emissions were basically stable. The eastern coastal regions demonstrate a high-high cluster, while the western regions exhibit a low-low cluster. The overall cluster evolution showed a decreasing trend from east to west. (3) Per capita urban public building area, economic density, urbanization rate, and population size serve as driving factors for carbon emissions from urban public buildings, while energy efficiency and energy consumption intensity act as inhibitory factors. The findings of this research can assist policymakers in getting a deeper comprehension of urban public buildings carbon emissions and providing a scientific basis to formulate appropriate carbon emission reduction policies.

Impact of invasive fungal infections in men from a state in southern Brazil: A geospatial analysis.

To analyse the records of male hospitalisation for fungal infections, including their spatial distribution and the main invasive epidemiological and sociodemographic characteristics in the State of Paraná, Brazil. Spatial analysis based on data from male admission records for invasive fungal infections (IFIs) in the State of Paraná, from 2015 to 2019. Data were taken from the hospital records obtained in the Hospital Information System of the Unified Health System, being collected, georeferenced and analysed to infer the existence of autocorrelation with the hospitalisation rates in the state. From 2015 to 2019, there were 385 cases of IFIs in men, being more prevalent in white individuals aged 61-70 years. We observed that the metropolitan, southeast, central-eastern, north-central, northwestern and western regions formed high-high clusters, with regions with a high number of registered cases. In the regression, there was an association with socioeconomic and demographic factors that showed a correlation with the rates of hospitalisation for IFIs. The study draws attention to Paraná as an endemic region for paracoccidioidomycosis, in addition to presenting high rates of nosocomial fungal infections. We also emphasise the importance of compulsory notification in the state and in the country to gain greater control over the incidence and prevalence of cases and to incentivise the creation of public policies for the prevention of IFIs.

Spatial distribution and influencing factors of high-quality tourist attractions in Shandong Province, China.

Optimizing the spatial layout of high-quality tourist attractions is of great significance in the sustainable development of the tourism industry. This work employs the ArcGIS spatial analysis tool to study the form, equality, and density of the spatial distribution of the 892 3A+ tourist attractions (high-quality tourist attractions hereafter) in Shandong Province, China. It also examines the factors influencing the spatial distribution of tourist attractions from the perspectives of geographic features and landscapes, culture and heritage, socioeconomic development, and transportation. We therefore find the following: 1) High-quality tourist attractions in Shandong Province have obvious clustering in spatial distribution with the high-density areas mainly concentrated in Qingdao, Jining, Jinan, Tai'an and other cities. Influenced by resource endowment and economic development, the two major geographical areas in Central Shandong and Jiaodong Peninsula have the most concentrated distribution of high-quality tourist attractions. 2) The distribution of high-quality tourist attractions shows a southwest‒northeast clustering direction; Qingdao is a high-high clustering area, and Heze is a low-high clustering area with low uniformity of spatial distribution and obvious spatial divergence. 3) Tourist attractions show an obvious "N" type high-density distribution belt and nuclear density distribution across the three existing agglomeration centers in the Jining-Tai'an intersection, Binzhou-Dongying intersection, and Qingdao Jiaozhou Bay coast. 4) Topography, climate conditions, history and culture are intrinsic factors affecting the spatial distribution of tourist attractions, while socioeconomic and transportation conditions are external requirements for the development thereof; collectively, they constrain the spatial distribution of high-quality tourist attractions.

A spatiotemporal analysis of the association between carbon productivity, socioeconomics, medical resources and cardiovascular diseases in southeast rural China.

With China's rapid industrialization and urbanization, China has been increasing its carbon productivity annually. Understanding the association between carbon productivity, socioeconomics, and medical resources with cardiovascular diseases (CVDs) may help reduce CVDs burden. However, relevant studies are limited. The study aimed to describe the temporal and spatial distribution pattern of CVDs hospitalization in southeast rural China and to explore its influencing factors. In this study, 1,925,129 hospitalization records of rural residents in southeast China with CVDs were analyzed from the New Rural Cooperative Medical Scheme (NRCMS). The spatial distribution patterns were explored using Global Moran's I and Local Indicators of Spatial Association (LISA). The relationships with influencing factors were detected using both a geographically and temporally weighted regression model (GTWR) and multiscale geographically weighted regression (MGWR). In southeast China, rural inpatients with CVDs increased by 95.29% from 2010 to 2016. The main groups affected were elderly and women, with essential hypertension (26.06%), cerebral infarction (17.97%), and chronic ischemic heart disease (13.81%) being the leading CVD subtypes. The results of LISA shows that central and midwestern counties, including Meilie, Sanyuan, Mingxi, Jiangle, and Shaxian, showed a high-high cluster pattern of CVDs hospitalization rates. Negative associations were observed between CVDs hospitalization rates and carbon productivity, and positive associations with per capita GDP and hospital beds in most counties (p < 0.05). The association between CVDs hospitalization rates and carbon productivity and per capita GDP was stronger in central and midwestern counties, while the relationship with hospital bed resources was stronger in northern counties. Rural hospitalizations for CVDs have increased dramatically, with spatial heterogeneity observed in hospitalization rates. Negative associations were found with carbon productivity, and positive associations with socioeconomic status and medical resources. Based on our findings, we recommend low-carbon development, use of carbon productivity as an environmental health metric, and rational allocation of medical resources in rural China.

Breast cancer incidence in Yogyakarta, Indonesia from 2008-2019: A cross-sectional study using trend analysis and geographical information system.

Breast cancer is a significant public health concern worldwide, including in Indonesia. Little is known about the spatial and temporal patterns of breast cancer incidence in Indonesia. This study aimed to analyze temporal and spatial variations of breast cancer incidence in Yogyakarta Province, Indonesia. The study used breast cancer case data from the Yogyakarta Population-Based Cancer Registry (PBCR) from 2008 to 2019. The catchment areas of the PBCR included the 48 subdistricts of 3 districts (Sleman, Yogyakarta City, and Bantul). Age-standardized incidence rates (ASR) were calculated for each subdistrict. Joinpoint regression was used to detect any significant changes in trends over time. Global Moran's and Local Indicators of Spatial Association (LISA) analyses were performed to identify any spatial clusters or outliers. The subdistricts had a median ASR of 41.9, with a range of 15.3-70.4. The majority of cases were diagnosed at a late stage, with Yogyakarta City having the highest proportion of diagnoses at stage 4. The study observed a significant increasing trend in breast cancer incidence over the study period the fastest of which is in Yogyakarta City with an average annual percentage change of 18.77%, with Sleman having an 18.21% and Bantul having 8.94% average changes each year (p <0.05). We also found a significant positive spatial autocorrelation of breast cancer incidence rates in the province (I = 0.581, p <0.001). LISA analysis identified 11 subdistricts which were high-high clusters in the central area of Yogyakarta City and six low-low clusters in the southeast region of the catchment area in the Bantul and Sleman Districts. No spatial outliers were identified. We found significant spatial clustering of BC ASR in the Yogyakarta Province, and there was a trend of increasing ASR across the region. These findings can inform resource allocation for public health efforts to high-risk areas and develop targeted prevention and early detection strategies. Further res is needed to understand the factors driving the observed temporal and spatial patterns of breast cancer incidence in Yogyakarta Province, Indonesia.

Unveiling the spatial divide in open defecation practices across India: an application of spatial regression and Fairlie decomposition model

ObjectiveThe study contextualises the spatial heterogeneity and associated drivers of open defecation (OD) in India.DesignThe present study involved a secondary cross-sectional survey data from the fifth round of the National...

Spatio-temporal evolution of resources and environmental carrying capacity and its influencing factors: A case study of Shandong Peninsula urban agglomeration

Promoting ecological conservation and high-quality development in the Yellow River basin is an important objective in China's 14th Five-Year Plan. Understanding the spatio-temporal evolution of and factors affecting the resources and environmental carrying capacity (RECC) of the urban agglomerations is critical for boosting high-quality green-oriented development. We first combined the Driver-Pressure-State-Impact-Response (DPSIR) framework and the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate the RECC of Shandong Peninsula urban agglomeration in 2000, 2010 and 2020; we then used trend analysis and spatial autocorrelation analysis to understand the spatio-temporal evolution and distribution pattern of RECC. Furthermore, we employed Geodetector to detect the influencing factors and classified the urban agglomeration into six zones based on the weighted Voronoi diagram of RECC as well as specific conditions of the study area. The results show that the RECC of Shandong Peninsula urban agglomeration increased consistently over time, from 0.3887 in 2000 to 0.4952 in 2010 and 0.6097 in 2020, respectively. Geographically, RECC decreased gradually from the northeast coast to the southwest inland. Globally, only in 2010 the RECC presented a significant spatial positive correlation, and that in the other years were not significant. The high-high cluster was mainly located in Weifang, while the low-low cluster in Jining. Furthermore, our study reveals three key factors—advancement of industrial structure, resident consumption level, and water consumption per ten thousand yuan of industrial added value—that affected the distribution of RECC. Other factors, including the interactions between residents’ consumption level and environmental regulation, residents’ consumption level and advancement of industrial structure, as well as between the proportion of R&D expenditure in GDP and resident consumption level also played important roles resulting in the variation of RECC among different cities within the urban agglomeration. Accordingly, we proposed suggestions for achieving high-quality development for different zones.

Spatial pattern and environmental drivers of breast cancer incidence in Chinese women.

Breast cancer (BC) had the highest incidence of all cancers in Chinese women. However, studies on spatial pattern and environmental drivers of BC were still lacked as they were either limited in a small area or few considered the comprehensive impact of multiple risk factors. In this study, we firstly performed spatial visualization and the spatial autocorrelation analysis based on Chinese women breast cancer incidence (BCI) data of 2012-2016. Then, we explored the environmental drivers related to BC by applying univariate correlation analysis and geographical detector model. We found that the BC high-high clusters were mainly distributed in the eastern and central regions, such as Liaoning, Hebei, Shandong, Henan, and Anhui Provinces. The BCI in Shenzhen was significantly higher than other prefectures. Urbanization rate (UR), per capita GDP (PGDP), average years of school attainment (AYSA), and average annual wind speed (WIND) had higher explanatory power on spatial variability of the BCI. PM10, NO2, and PGDP had significant nonlinear enhanced effect on other factors. Besides, normalized difference vegetation index (NDVI) was negatively associated with BCI. Therefore, high socioeconomic status, serious air pollution, high wind speed, and low vegetation cover were the risk factors for BC. Our study may able to provide evidence for BC etiology research and precise identification of areas requiring focused screening.

Spatio-temporal evolution and influencing factors of China’s ICT service industry

The ICT service industry has become a burgeoning industry at a high and stable speed. Their equitable distribution can improve national and global positive peace. This paper aimed to verify the characteristics of spatio-temporal evolution and its influencing factors in the ICT service industry. Based on the data from 31 Provinces in China from 2015 to 2019, this paper uses location quotient, spatial autocorrelation methods and spatial econometric analysis to explore the development characteristics, evolution and influencing factors of the ICT service industry, respectively. The main results are shown as follows: (1) China's ICT service industry is mainly concentrated in Beijing, Shanghai, Zhejiang, Tibet, and Guangdong, with a trend of specialisation development. They are not only distributed in cities with relatively superior overall development but also those with superior industrial and development carrier elements. Technological relevance, aggregation, and political difference might have an impact on promoting the emergence and development of these industries. (2) ICT service industry is characterised by stable and highly concentrated development. Numbers between three to five significant provinces and types with high-high (HH) and high-low (HL) clusters of local spatio-temporal association kept stable in the period. The HH was in eastern coastal areas, including Zhejiang, Shanghai, Jiangsu, and Shandong, and the HL was in Guangdong in 2015. There is a definite spatial correlation in spatial distribution with constant strengthening. (3) TUR, NDN, MIAT and the area were shown to have a significant role in promoting the ICT service industry, while NW, GDP and ICT Employment were shown to have a significant negative impact on this industry. Correspondingly, two strategies were put forward here: (1) accelerating the inter-provincial networking development of the ICT service industry, and (2) strengthening government policy guidance for the ICT service industry. These outcomes can not only provide a scientific basis and theoretical support for the distribution of strategies and resources for these industries at the theoretical level but also improve resource integration from the national perspective and the efficiency of resource use at the practical level.

Optimization of Tourism Management Based on Regional Tourism Competitiveness Evaluation: Evidence from Ningxia Hui Autonomous Region, China

This paper evaluated the regional tourism competitiveness of 22 county-level administrative regions in Ningxia Hui Autonomous Region, China. The study analyzed the connotation of regional tourism competitiveness from the perspectives of resources and economic and environmental benefits, and established an evaluation index system for comprehensive competitiveness. The study used entropy weight TOPSIS to measure the level of tourism competitiveness in Ningxia, and used spatial association and hot and cold analysis to describe the spatial pattern of comprehensive tourism competitiveness in Ningxia. The results indicated that Lingwu City, Xixia District, and Huinong District have high comprehensive tourism competitiveness, while Jingyuan County, Haiyuan County, and Hongsipu District exhibit the weakest comprehensive competitiveness. The study also showed that regional economic development level competitiveness had the greatest impact on the comprehensive competitiveness of county tourism in Ningxia. The northern region and the southern region of Ningxia were the regions with high-high clusters and low-low clusters of tourism competitiveness, respectively. Based on the comprehensive evaluation results, the development level of tourism of Ningxia county was classified into four types: advanced development type, marginal dependence type, improved optimization type, and backward development type. Finally, the paper put forward targeted optimization suggestions in order to provide reference for Ningxia tourism functional zoning and high-quality development.

Spatial and temporal distribution characteristics of seasonal A(H3N2) influenza in China, 2014-2019

Objective: To analyze the spatial and temporal distribution characteristics of seasonal A(H3N2) influenza [influenza A(H3N2)] in China and to provide a reference for scientific prevention and control. Methods: The influenza A(H3N2) surveillance data in 2014-2019 was derived from China Influenza Surveillance Information System. A line chart described the epidemic trend analyzed and plotted. Spatial autocorrelation analysis was conducted using ArcGIS 10.7, and spatiotemporal scanning analysis was conducted using SaTScan 10.1. Results: A total of 2 603 209 influenza-like case sample specimens were detected from March 31, 2014, to March 31, 2019, and the influenza A(H3N2) positive rate was 5.96%(155 259/2 603 209). The positive rate of influenza A(H3N2) was statistically significant in the north and southern provinces in each surveillance year (all P<0.05). The high incidence seasons of influenza A (H3N2) were in winter in northern provinces and summer or winter in southern provinces. Influenza A (H3N2) clustered in 31 provinces in 2014-2015 and 2016-2017. High-high clusters were distributed in eight provinces, including Beijing, Tianjin, Hebei, Shandong, Shanxi, Henan, Shaanxi, and Ningxia Hui Autonomous Region in 2014-2015, and high-high clusters were distributed in five provinces including Shanxi, Shandong, Henan, Anhui, and Shanghai in 2016-2017. Spatiotemporal scanning analysis from 2014 to 2019 showed that Shandong and its surrounding twelve provinces clustered from November 2016 to February 2017 (RR=3.59, LLR=9 875.74, P<0.001). Conclusion: Influenza A (H3N2) has high incidence seasons with northern provinces in winter and southern provinces in summer or winter and obvious spatial and temporal clustering characteristics in China from 2014-2019.

Spatial analysis of provision the population of Kharkiv with civil defense facilities

The article analyzed the territorial aspect of the distribution and capacity characteristics of civil defense structures in the city of Kharkiv, comparing it with the population size. In the first stage, population density was calculated for specific areas within the maximum permissible radius of the nearest available shelter. The spatial characteristics of the location (level of clustering) and capacity of the defense structures were examined for these same areas. Finally, the population size was correlated with the capacity of the civil defense structures, and spatial clustering of the identified areas was conducted based on this attribute. The geostatistical method of spatial interpolation was used to determine population density in specific areas and fill gaps in the primary data. The application of this method required the following sequential procedures: transforming the primary data according to a normal distribution, constructing a semivariogram model of the transformed variables, aggregating the model into a surface, and defining target polygons. During the calculation of the territorial provision of various types of shelters in the city of Kharkiv, the following sources were utilized: a layer of point objects from the interactive map of open data on the Kharkiv Geoportal (to determine the locations and types of protective structures), and information from the website of the non-commercial enterprise «Emergency Medical Care and Disaster Medicine Center» of the Kharkiv Regional Council (to update information on the capacity of storage facilities and anti-radiation shelters). The Global Moran’s Index and Local Moran’s Index are statistical methods used to assess spatial autocorrelation, which is the degree of clustering or spatial pattern in a variable across a defined area. In this case, they were employed to evaluate the spatial autocorrelation of the capacity of civil defense structures in specific parcels in Kharkiv. The Global Moran’s Index indicated a high level of clustering of areas based on this attribute. Using the Local Moran’s Index, parcels were classified into five object classes: High-High cluster (HH), Low-Low cluster (LL), a high-value outlier surrounded by low-values (HL), a low-value outlier surrounded by high-values (LH), and areas without cluster or outliers (non-significant). The scientific novelty of the article, compared to related studies on a similar topic, lies in the utilization and transformation of a hexagonal grid of population density distribution in the city of Kharkiv, in accordance with the research requirements. In the conclusions, based on the results of cluster analysis and through the adaptation of the tectological principle of the weakest link to the realities of the present, a comprehensive sequential strategy for addressing the shortcomings of territorial provision of civil defense structures in ensuring the population of Kharkiv was proposed.

Evolution of spatiotemporal patterns in vegetation net primary productivity and the driving forces on the Loess Plateau

In this study, we determined whether changes in vegetation net primary productivity (NPP) can be used to characterize the quality of terrestrial ecosystems, which is critical for global change and carbon balance. We first explored the spatial correlation of NPP and its impact on vegetation restoration. MOD17A3 remote sensing products were used to analyze the temporal and spatial changes in NPP on the Loess Plateau (LP) over the last two decades (2000–2020). The resulting spatial autocorrelation indices identified cold and hot spots in the spatial clustering patterns. The effects of climate change and human activities on the anomalous clustering of NPP were assessed using Pearson correlation analysis and multi-temporal land use land cover data. The results indicate that i) Temporally, from 2000 to 2020, the NPP of the LP increased significantly by 6.88 gCm−2yr−1 and so did the proportion of revegetated land area &amp;gt;400 gCm−2yr−1 from 4% to 37%. Spatially, NPP showed an increasing trend from northwest to southeast. ii) The vegetation NPP on the LP showed a strong positive global spatial autocorrelation (p&amp;lt; 0.01). The hot and cold regions were polarized; the cold spots were clustered in the northwest, while the hot spots in the south and east. The spatial clustering patterns were dominated by high-high (HH) and low-low (LL) clusters. Abnormal patterns existed mainly in the transition areas between HH and LL clusters and insignificant regions, which were jointly affected by human activities and climate change. iii) Precipitation was the dominant climatic factor (86%) affecting the NPP variation in the LP, with the annual minimum precipitation showing a significantly positive relationship with the interannual variability in NPP, while the maximum precipitations greatly influenced the variation in local spatial anomaly patterns. This suggests that climatic extremes affect vegetation. Our study helps to facilitate green ecological management and high-quality development in the LP.

The Measurement and Spatial-Temporal Distribution of the Development Level of China's Digital Economy

Aims: Based on the latest classification standard of digital economy issued by the National Bureau of Statistics of China, this paper makes statistical measurement and analysis of the spatial and temporal distribution of the development level of China's digital economy.&#x0D; Study Design: Establish an indicator system for the development level of the digital economy and measure the spatial correlation between different regions.&#x0D; Place and Duration of Study: School of Statistics and Application, Anhui University of Finance and Economics, China March to May 2023.&#x0D; Methodology: Entropy method, kernel density estimation, Moran's I index and other statistical methods.&#x0D; Results: It is found that China's digital economy is developing rapidly, but the development is obviously unbalanced, and the gap between high and low levels is increasing year by year. The development level of digital economy in provinces and cities has positive spatial autocorrelation, showing low-low clustering and high-high clustering spatial agglomeration phenomenon.&#x0D; Conclusion: It is suggested to increase investment in digital innovation, strengthen regional cooperation, and adopt policies based on local conditions, so as to narrow regional gaps and promote the healthy and sustainable development of digital economy.

Spatial differentiation and driving factors of the high-quality development of undertakings for the aged of China

BackgroundPromoting the high-quality development (HQD) of undertakings for the aged is an important action to proactively respond to the rapid population aging in China. This study analyzes spatial differentiation and driving factors of the HQD of China’s undertakings for the aged.MethodsBased on a quantitative indicator system consisting of old-age social security, elder care services, health care service and older adults’ social participation, the HQD levels of 31 Chinese provincial administrative regions during 2013–2019 are measured by using the entropy weight method. Spatial panel regression models are used to analyze the impact of population aging, economic development and digital technology on the HQD of undertakings for the aged.ResultsThe comprehensive level of the HQD slightly increased from 0.212 to 2013 to 0.220 in 2019, and the overall level was low. The HQD of the eastern region was the highest (0.292), followed by the western region (0.215), and the central region was the lowest (0.151). The high-high cluster type was mainly distributed in the eastern region; the low-low cluster type was mainly distributed in the western and central regions. Economic development and digital technology have significant positive effects while population aging has significant negative effects on the HQD of undertakings for the aged.ConclusionThere is a significantly spatial differentiation in the HQD of China’s undertakings for the aged. In order to promote the HQD of undertakings for the aged, it is necessary to identify development gaps through making HQD evaluation and to focus on the indicators that are critical in maintaining sustainable economic development and to develop digital technology in order to get rid of those gaps.

Temporal and spatial analyses of colorectal cancer incidence in Yogyakarta, Indonesia: a cross-sectional study.

We aimed to explore the district-level temporal dynamics and sub-district level geographical variations of colorectal cancer (CRC) incidence in the Special Region of Yogyakarta Province. We performed a cross-sectional study using data from the Yogyakarta population-based cancer registry (PBCR) comprised of 1,593 CRC cases diagnosed in 2008-2019. The age-standardized rates (ASRs) were determined using 2014 population data. The temporal trend and geographical distribution of cases were analysed using joinpoint regression and Moran's I statistics. During 2008-2019, CRC incidence increased by 13.44% annually. Joinpoints were identified in 2014 and 2017, which were also the periods when annual percentage change (APC) was the highest throughout the observation periods (18.84). Significant APC changes were observed in all districts, with the highest in Kota Yogyakarta (15.57). The ASR of CRC incidence per 100,000 person- years was 7.03 in Sleman, 9.20 in Kota Yogyakarta, and 7.07 in Bantul district. We found a regional variation of CRC ASR with a concentrated pattern of hotspots in the central sub-districts of the catchment areas and a significant positive spatial autocorrelation of CRC incidence rates in the province (I=0.581, p<0.001). The analysis identified four high-high clusters sub-districts in the central catchment areas. This is the first Indonesian study reported from PBCR data, showing an increased annual CRC incidence during an extensive observation period in the Yogyakarta region. A heterogeneous distribution map of CRC incidence is included. These findings may serve as basis for CRC screening implementation and healthcare services improvement.

Spatio-Temporal Differentiations and Influence Factors in China’s Grain Supply Chain Resilience

Based on Chinese provincial panel data (2011–2020), the CRITIC-EWM, kernel density estimation, Dagum Gini coefficient and a spatial Dubin model were used for analyzing endogenous and exogenous factors to enhance grain supply chain resilience. The results illustrated the following: (1) Ensuring the resilience of the grain supply chain’s ability to digest, recover and learn was fundamental to internal system robustness. (2) Overall, China’s grain supply chain resilience increased year over year, but the growth rate was low and showed an attenuation trend of east-middle-west. (3) There was a significant spatial positive correlation among provinces and grain supply chain resilience, however, the large gap and slow development led to the formation of polarization with high-high and low-low clusters. (4) The exogenous drivers were all significant and had a significant positive spillover effect on the grain supply chain resilience. (5) Subregional regressions reflected the heterogeneity of the influence factors, which highlighted the implementation of targeted strategies as the key to achieving synergy.