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.

Background: Cancer remains a critical public health issue in India, with rising cases of breast cancer and cervical cancer.Accurate predictions and spatial analysis of cancer incidence are essential for shaping prevention strategies and targeting interventions in high-risk regions. Methods:This study utilized a big data framework employing machine learning techniques from the SparkML library to predict cancer cases and analyze spatial distributions across Indian states from 2016 to 2021.Three machine learning models used Random Forest Regressor, Gradient Boosting Regressor, and Geographically Weighted Regression (GWR) were applied to the dataset.Spatial autocorrelation analysis used Moran's I statistic to identify clustering patterns. Results:The spatial analysis revealed significant clustering of cancer cases, particularly in 2020, with a z-score of 2.23, a p-value of 0.02, and a Moran's index of 0.15.Among the machine learning models, GWR achieved a predictive accuracy of 98% for both breast cancer and cervical cancer, while the Random Forest Regressor and Gradient Boosting Regressor achieved 95% and 97% accuracy, respectively, over the six-year period.Gradient Boosting outperformed other models in identifying key predictors and ensuring high predictive accuracy. Conclusions:The findings highlight the efficacy of Gradient Boosting and GWR in predicting cancer incidence and analyzing spatial patterns.These models provide critical insights into cancer clustering and risk factors, supporting the development of targeted prevention strategies and policy interventions for high-risk regions in India.The results emphasize the utility of machine learning techniques in public health research and cancer control.

The Tuojiang Basin accounts for 30.8% of Sichuan Province's GDP, but the total water resources account for only 3.5%, resulting in increasing problems of water shortage, environmental deterioration and pollution, which further affects green development in the Basin. The objective of this paper is to investigate the green development of the Basin, expose deficiencies and ultimately unravel the path toward green development in the river basin of China. This paper was based on a green development measurement system under Economy-Nature-Resource-Society-Pollution perspectives and used Crtic method to calculate the weights of system indicators. Then Gray Correlation-Topsis evaluation model was used to measure green development level from 2009 to 2020. Finally, spatial evolution of green development in Tuojiang Basin was analyzed through Moran Index. The results showed that economy and pollution are the important factors of green development. And overall green development level was showing a trend of decreasing first then rising, which reached the lowest in 2014 and highest in 2019. Moreover, all cities in Tuojiang Basin except Ziyang reached a high level of green development in 2020. This paper added various pressure indicators produced by environmental pollution to the index system and enriched the evaluation index system for green development. Doi: 10.28991/HIJ-2024-05-04-014 Full Text: PDF

Spatial interpolators for Delineating management zones to mitigate Mucuna pruriens in sugarcane plantations in the Eastern Amazon

The spatial economic network serves as a pivotal methodology in spatial economics research. This paper endeavors to integrate complex network theory with the gravity model to establish a directed and unweighted spatial network model. Building upon this foundation, we introduce two distinct types of indicator systems designed to systematically analyze both the overall structure of the network and the importance of individual nodes within it. Furthermore, we employ the Theil index and Moran index to investigate the dynamic evolution of the network structure across both temporal and spatial dimensions. To explore the spillover effects inherent in this network, this article introduces relevant basic elements, uses spatial econometric models, and combines indicators that can reflect the characteristics of the network structure to comprehensively evaluate the multiple factors that affect changes in network structure. The above research methods are applied to the spatial economic network composed of 31 provinces and cities in Chinese Mainland, and the results show that the economic network presents a core periphery structure and that the spatial distribution of economic links is uneven. We further apply the research method to the civil aviation transportation field in the Chinese Mainland, as well as analyze the overall structure and dynamic changes of the civil aviation transportation network.

High TSS causes siltation around coastal areas in the Madura Strait. TSS impacts water quality and habitat health. It's necessary to know that TSS distribution can vary each season. The algorithm detects TSS distribution by processing Landsat-8 satellite image data. However, existing algorithms are sometimes only suitable for some instances, so the results do not correspond to actual conditions. Therefore, this paper wants to build a better detection model using Laili's algorithm to determine whether satellite image analysis can explain the exact conditions. Laili's algorithm detection was validated and corrected against field data via a correlation test. It’s necessary to know the spatial distribution pattern of data attribute values using the Moran Index. The results TSS in the dry season is 5-18 mg/L and covers an area of up to 4 km; in the rainy season, it is 5-22 mg/L and can cover an area of up to 7.8 km. Moran's Index results show that spatial autocorrelation in the distribution pattern results in a cluster pattern. These results show that the detection model is relatively reasonable and can be used as training data to detect the distribution of TSS in the Madura Strait in subsequent years.

Hospitalizations due to primary care–sensitive conditions (PCSCs) can be considered a proxy for the effectiveness of primary healthcare (PHC), especially diabetes mellitus (DM). The aim of this study was to analyze the temporal, spatial, and space–time patterns of PCSCs associated with DM in a state in Northeast Brazil from 2008 to 2022. An ecological and time–series study that included all records related to PCSCs–DM from the 75 municipalities of Sergipe was conducted. Segmented linear regression, global (I) and local (LISA) Moran indices, spatial scanning, Spearman correlation tests, bivariate I, and LISA were used in our analysis to examine the temporal trends and clusters of high spatial risk. Overall, 14,390 PCSCs–DM were recorded between 2008 and 2022. There was a higher prevalence of PCSCs–DM among women (53.75%) and individuals over 70 years old (57.60%). Temporal trends in PCSCs–DM were increasing with regard to the overall rate (AAPC: 2.39); males (AAPC: 3.15); age groups ≤ 19 years (AAPC: 6.13), 20–39 years (AAPC: 4.50), and 40–59 years (AAPC: 2.56); and 3 out of the 7 health regions. There was a positive spatial correlation between per capita income (I = −0.23; p = 0.004) and diabetic foot examination being performed by a nurse in a PHC (I = −0.18; p = 0.019) setting. The heterogeneous spatial distribution of DM hospitalizations demonstrated that this condition is a persistent public health problem in Sergipe.

The sustainability of watershed management is a crucial issue that must be addressed to guarantee the persistence of watershed services including agriculture, food production, and energy supply. This issue has also been addressed in Presidential Regulation No. 18/2020 concerning the National Medium-Term Development Plans for 2020–2024, which stipulate the restoration of priority watersheds, including the Upstream Bengawan Solo Watershed. This study seeks to address this information gap by assessing the local sustainability of the watershed from a temporal dynamics perspective by calculating the Local Sustainability Index (LSI), Local Moran Index, and spatial associations. Measuring sustainable development indices locally is essential because each location has different characteristics, and using specific indicators at the local level is rarely done. The enactment of the national law on village autonomy in Indonesia necessitates the formulation of sustainable development indicators at the village level. These indicators serve as the metrics and frameworks for local government policies and initiatives. Our results show that village sustainability in the social and economic dimensions has increased from 2007 to 2021, especially in urban activity center areas that serve social and economic facilities. This seems different in the environmental dimension, where the sustainability value decreased from 2007 to 2021. The concentration of low sustainability values on ecological conditions occurred in pocket areas. Environmental problems were indicated by land-use conversion and disaster areas.

The global scientific and technological revolution and industrial change are developing rapidly, the integration of automotive technology with energy, transportation and information and communication fields is accelerating, and electrification, internet connectivity and intelligence have become the main trends. Therefore, studying the impact of digital development on the growth of new energy vehicle industry and consumer purchase intention is of great theoretical and practical significance for the digital transformation of automobile companies. In this paper, based on the provincial panel data of new energy vehicles in China from 2016 to 2023, we constructed the index system of digital economy and economic development, used the entropy method to calculate the level of development, and conducted spatial autocorrelation tests using global and local Moran indices. Considering the non-linear relationship between variables, a semi-parametric spatial Durbin model is established using local polynomial estimation to explore the impact of digital economy on new energy automobile industry. The results of the study show that the development of digital economy promotes the development of new energy automobile industry to a certain extent, but its development level may have a negative impact if it is too low or too high.

The imbalance in the “production–living–ecology” function (PLEF) has become a major issue for global cities due to the rapid advancement of urbanization and industrialization worldwide. The realization of PLEF coupling and coordination is crucial for a region’s sustainable development. Existing research has defined the concept of PLEF from the perspective of land function and measured its coupling coordination level using relevant models. However, there is still room for improvement in the indicator system, research methods, and other aspects. This work builds a PLEF coupling coordination evaluation-index system based on the perspective of human habitat using multi-source data in order to examine the spatial differences in PLEF coupling coordination level and the influencing factors in the Yellow River Basin (YRB). Using the modified coupling coordination model, the Moran index, spatial Markov chain model, and geographically weighted random forest model were introduced to analyze its spatial and temporal differentiation and influencing factors. The results found that (a) the level of PLEF coupling coordination in the YRB from 2010 to 2022 has been improving, and the number of severely imbalanced cities has been reduced from 23 to 15, but the level of downstream cities’ coupling coordination is significantly higher than that of upstream cities. The probability of cities maintaining their own level is greater than 50%, and there is basically no cross-level transfer. (b) The Moran index of the PLEF coupling coordination level has risen from 0.137 to 0.229, which shows a significant positive clustering phenomenon and is continually strengthening. The intercity polarization effect is being continually enhanced as seen in the LISA clustering diagram. (c) There is significant heterogeneity between the influencing factors in time and space. In terms of importance level, the series is per capita disposable income (0.416) > nighttime lighting index (0.370) > local general public budget expenditure (0.332) > number of beds per 1000 people (0.191) > NO2 content in the air (0.110). This study systematically investigates the dynamic evolution of the coupled coordination level of PLEF in the YRB and its influencing mechanism, which is of great practical use.

Exploring the mechanism of the spatial distributed impacts of transportation systems on the local economy helps realize the coordinated development of the local economy and environmental protection. Therefore, correlation tests are conducted using linear regression models, decision tree regression models, machine learning random forest regression models, and spatial econometric models in this study. An extensive analysis was carried out to determine the relationship between the transport infrastructure of the Yangtze River Economic Belt and the impact on regional economic mechanisms and transmission paths from the spatial spillover effect. The three models are compared and analyzed to select the best model. Then the Moran index is calculated to study whether the data have spatial correlation and spatial difference. After confirming the spatial impact of the data, the Lagrange Multiplier test, the Likelihood-Ratio test and other relevant tests were used to select the spatial econometric model that best fits the data set. Finally, the study results show that the random forest model has a higher R2 of 95.14%. To analyze spatial economics, it is recommended to use the fixed effect and double fixed effect of the Spatial Durbin Model (SDM). Road and Density of the transportation network explain GDP more significantly.

The construction of ecological barriers in the Yellow River Basin represents a significant step toward reducing agricultural carbon emissions, achieving carbon neutrality, and reaching carbon peaking in China. The diverse agrarian development objectives of various regions within the basin have resulted in a heterogeneous approach to greening agriculture. Therefore, this paper will evaluate the development of carbon sink agriculture across 34 cities and municipalities in the lower Yellow River basin from 2008 to 2021 based on the EBM-GML model, and analyze the spatial-temporal evolution of agricultural green total factor productivity (AGTFP) in each region through the application of the Moran index, kernel density estimation, and spatial Markov chain analysis. The results demonstrate that agricultural carbon emissions in the Lower Yellow River Basin gradually decreased throughout the study period. Furthermore, overall carbon emission efficiency improved, indicating significant potential for further emission reduction. In addition, Agricultural Green Technology Progress (AGTC) has become a primary driver of AGTFP growth, while Agricultural Green Technology Efficiency (AGEC) has demonstrated a gradual upward trend. Locally, most areas are weakly connected and display an isolated development trend. The results of the kernel density analysis demonstrate a notable degree of mobility in the distributional dynamics of AGTFP growth, characterized by a gradual narrowing of the gap between locations. The transfer of (AGTFP) types in the lower reaches of the Yellow River Basin is stable, with a noticeable “club convergence” phenomenon, while geographical conditions significantly influence the transfer of AGTFP types in this region. Based on long-term trend predictions, the future trajectory of AGTFP in the lower Yellow River Basin appears optimistic and is expected to improve progressively, with the overall distribution tending toward equilibrium.

Municipal activities should not be profitable. Their intention is to provide the highest possible quality of service to citizens and, in this way, contribute to improving their quality of life. For this reason, the evaluation of their performance is very complex and should include several aspects, or criteria. The aim of this study is to quantify the agreement of the financial health assessment of the territorial self-government entities in 2020 with the financial health assessment based on a gradually decreasing number of entry criteria. For this purpose, we use a TOPSIS technique, and a total of 26 combinations of criteria are created with a gradually decreasing number of criteria, i.e., five, four, three, and two criteria used. For a description of the results obtained, we use a wide range of mathematical and statistical methods. The tests used include the Jaccard index, Kolmogorov–Smirnov test, Levene test, Moran index, and others. Our results confirm the fact that the outcome of MCDM analysis is directly and significantly affected by the structure and number of entry criteria. The reduction in the number of criteria resulted in a change in the parameters of the overall results.

SummaryBackgroundFemale homicides are a public health-relevant issue, and its spatial distribution may evidence socioeconomic vulnerabilities. This study aims to analyze the temporal and spatial trends of female homicides in Brazil and investigate socioeconomic-demographic factors associated with it.MethodsThis is an ecological, descriptive, and analytical epidemiological study investigating the age-standardized female homicide rate in all Brazilian municipalities between 2000 and 2018, divided into three periods. Spatial and temporal analyses were conducted using the Global Moran's Index and LISA to identify clusters of high and low rates. Rates were also calculated by population size and means of violence across macro-regions. For the last period, a multivariable linear regression model analyzed the association of female homicide rates with social, economic, and geographic factors.FindingsFemale homicide rates in Brazil remained high during the studied period, with differences in trends between regions. Among the potentially associated factors, it was observed that male homicide rate, the high percentage of violent deaths among black women and those with low levels of education, in addition to the low Gross Domestic Product (GDP) per capita, were positively associated with female homicide, whereas larger cities were negatively associated.InterpretationThese findings show that Brazil is a country with a high risk of female homicide. Nevertheless, the vulnerability of women is unequally distributed in the country. Female homicides are mostly caused by domestic conflicts but can also be influenced by changes in the urban and social contexts.FundingThis project is funded by the 10.13039/100000865Bill & Melinda Gates Foundation.

Introduction: Schistosomiasis mansoni is an important waterborne disease in Brazil, closely associated with socioeconomic, structural, cultural, and political factors. Objective: To analyze the spatial and spatiotemporal distribution of positivity rates for schistosomiasis mansoni in Alagoas between 2012 and 2021. Methods: This is an ecological study with a spatiotemporal approach. Data were obtained through the Schistosomiasis Control Program Information System (SISPCE). To this end, we considered positivity rates for the disease by the municipality in Alagoas. We verified the existence of autocorrelation using the Global Moran Index (GMI) and identified municipalities presenting similar patterns through the Local Moran Index (LMI). Risk clusters were identified using Kulldorff's spatiotemporal scanning analysis. Results: Throughout the studied period, 73,325 positive cases of Schistosoma mansoni infection were recorded in the state. The positivity rate for the disease is distributed heterogeneously across the endemic Health Regions (HR) of the state, with the majority of positivity rates lt;5.0 (low endemicity). Our analysis also identified 20 municipalities in the state with average endemicity (rates between 5.1 and 15.0). Only 1 municipality (Branquinha) had a positivity rate higher than 15.0 (high endemicity). Furthermore, we identified positive spatial autocorrelation (IMG = 0.52308; p = 0.001). A total of 16 municipalities presented a Q1 (high/high) pattern, high risk for the disease, located in the 1st, 2nd, 3rd, 4th, and 6th HR. Another 30 municipalities located in the 7th, 8th, 9th, and 10th HR presented standard Q2 (low/low), low-risk areas, it is noteworthy that the 9th and 10th HR are not endemic for the disease. The spatiotemporal scanning analysis revealed 2 high-risk clusters, with the primary cluster being present in all endemic HR in the state, with a relative risk of 11.06 (p lt;0.001) and likelihood ratio of 46895.10. Conclusion: These data demonstrated that the areas at risk for schistosomiasis are mainly concentrated in municipalities in the 3rd, 4th, and 6th HR of the state. Furthermore, most municipalities have low endemicity, which may indicate that the state has taken promising steps in controlling the disease. However, the profile of serious infections within these municipalities needs to be better elucidated, to demonstrate that the state has reduced the number of serious infections to less than 1%, as the WHO aims to eliminate schistosomiasis as a public health problem.

Objectives: To map the distribution of schistosomiasis mansoni (SM) in the municipalities of Bahia from 2019 to 2023, identifying clusters of municipalities with the highest incidence. Methods: QGIS was used for thematic map creation, and Geoda for bivariate and univariate spatial analyses. Socioeconomic variables (HDI, Social Vulnerability Index, Gini Index) and housing variables (sewage network connection and piped water supply) were utilized. Excel® software was applied for data organization, tabulation, and graph generation. Information from SINAN notifications was used to detail the characteristics of the population affected by the disease, as well as to integrate IBGE territorial meshes. Results: The Global and Local Moran Indexes were calculated to verify spatial autocorrelation between neighboring municipalities, with emphasis on local univariate and bivariate analyses of the incidence during the period. Clusters were observed in many microregions, particularly in municipalities adjacent to Aracatu, which had the highest incidence coefficient during the period (361.13 per 100,000 inhabitants) and the most significant incidence rate in 2023, reaching 1,729.33 per 100,000 inhabitants. A higher concentration of clusters and outliers was identified in the microregions of Brumado, Vitória da Conquista, and Porto Seguro. In the state capital, Salvador, a Low-Low cluster was found in the univariate incidence analysis, specifically in municipalities of the Juazeiro, Bom Jesus da Lapa, and Boquira microregions. Conclusions: Critical vulnerability areas for schistosomiasis were identified in the municipalities of the Brumado and Porto Seguro microregions, where the most affected municipalities, Aracatu and Jucuruçu, are located. Furthermore, the importance of geoprocessing tools in guiding public health measures to border municipalities was reinforced. Study advancements and/or applications This study advances epidemiological knowledge on the incidence of schistosomiasis, given that the Schistosomiasis Control Program (PCE) focuses only on highly endemic municipalities in the state. This study helps clarify and reformulate public health policies for SM in a broader scope across the presented municipalities. For example, Jucuruçu had a 100% positivity rate in the last two years studied, except for 2021 when the PCE was not conducted.

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.

From the perspective of practical development, under the premise of stable macroeconomic growth in society, influenced by spatiotemporal factors, regional economies inevitably have differences and changes, which affect various aspects of social production and life. In order to understand the spatiotemporal data evolution characteristics of regional economy, promote common regional development and the implementation of coordinated economic development strategies, this article takes the Beijing Tianjin Hebei (BTH for short here) region as an example. By combining spatial econometric models (SEM for short here), this article collects and processes economic development data from 2013 to 2022 in the BTH region, and introduced a spatial weight matrix to conduct High-performance computing and analysis of its regional economic spatial correlation. Based on this, this article conducted in-depth research on the spatiotemporal data evolution characteristics of the BTH regional economy through the description and quantitative analysis of the influencing factors of the BTH regional economy. The empirical analysis results showed that the global Moran index (Global Moran’s for short here) of the BTH region was positive from 2013 to 2022, and the [Formula: see text]-values were all greater than 1.96, indicating a significant spatial correlation in the BTH regional economy. There is an imbalance in economic development in the BTH region, but with the continuous development of the region, its economic balance has improved.

The main objective of this research is to analyze the economic agglomeration pattern of competitiveness of the main export products of West Sumatra province, focusing on the role of improving trade facilities and transportation infrastructure to and from the export port. How strong is its competitiveness, able to interact to serve national, regional, and global trade, what strategies will be adopted to increase export competitiveness? The approach to measuring agglomeration export competitiveness used the Lapay Index, Moran, and LISA Index, as well as factors influencing the creation of agglomeration using the spatial lag model and spatial error model, as well as strategies for increasing export competitiveness using the Promethee model. The population in this study is export activities throughout Indonesia, but the sample from this study focuses on the West Sumatra region. The research results show that aspects of existing transportation infrastructure and trade facilities have placed the competitiveness of West Sumatra province lower at the national and regional levels. Export ports have not interacted heavily with national and regional export port nodes. Strategy to increase the agglomeration of West Sumatra's export competitiveness through increasing ship units loading and unloading at export ports, increasing the number of containers, increasing warehousing, improving the regulatory environment, and increasing ICT capabilities at export ports

ABSTRACT The spatial and temporal variations of net surface radiation (Rn) are critical for comprehending ecological environments. Nonetheless, the intricate interplay among Rn dynamics, vegetation growth, climate, and natural factors remains inadequately elucidated. In this study, we estimated net surface radiation based on Landsat data and ERA5 meteorological data in the Google Earth Engine (GEE) platform, which closely matched the observable spatial distribution (R 2 = 0.96), with an average growth rate of 0.15 MJ m−2 mth−1. Trend analyses and spatial autocorrelation were used to explore the spatial and temporal changes in net radiation from 2000 to 2020, the global Moran's index for net radiation was found to exceed 0.76, with fluctuating increases, showing a highly positive spatial distribution of Rn. Local Moran's I predominantly fell into two categories: ‘High-High’ and ‘Low-Low’, with the first increasing in range and the latter decreasing. Combining GeoDetector and PLS-SEM analyses, temperature and vegetation emerge as predominant drivers of net surface radiation variation within the study area, each contributing more than 17% to Rn change. Furthermore, interactions between any two factors typically exhibits nonlinear enhancement. PLS-SEM underscores the influence of vegetation and climate on Rn, with other factors indirectly affecting net radiation changes by influencing vegetation growth.