Moran Index Research Articles

Spatial distribution of congenital syphilis in the state of Bahia, Brazil from 2009 to 2018.

To describe the temporal trend and spatial distribution of congenital syphilis (CS) cases in the state of Bahia, Brazil between 2009 and 2018. Mixed ecological study conducted through the analysis of data obtained from the Notifiable Diseases Information System and the Live Birth Information System. Global Moran Index I was performed in order to analyze spatial autocorrelation of CS cases in the municipalities of Bahia and the Local Spatial Association Indicator (LISA) was used to identify the formation of spatial regimes in the GeoDA software. 8,786 cases of CS were registered in the period. An increasing growth in CS incidence, with a 511% increase between 2009 and 2018. Spatial autocorrelation was observed between the municipalities (I Moran = 0.452; p < 0.001) and four clusters were identified. More frequently, mothers were aged 20-29 years (50.7%); had incomplete primary education (54.9%); were Black and multiracial (93.2%); received prenatal care (82.2%); 49.0% were diagnosed with syphilis during prenatal care; 68.8% were not adequately treated, and 81.1% of their partners were not treated. The results showed that CS consolidates as a serious public health problem in Bahia, with an incidence 8.4 times higher in the period than the WHO target of 0.5/1,000 live births, predominantly related to inadequate prenatal care and social vulnerability indicators: young mothers with low education levels, as well as individuals identified as Black and multiracial. Thus, programs aimed at women of childbearing age and pregnant women need to be intensified.

Spatial Error Model to Analyze Morbidity Rate in Indonesia

Morbidity is a diseased state or symptom in which a person is unable to do daily activities. The morbidity rates can differ by person and place. This paper aims to investigate factors affecting morbidity rates in each province in Indonesia. Using applied spatial error model (SEM), this study analyzed the spatial effect on factors being investigated. It demonstrated that SEM is well-applied on Indonesian morbidity rates data. The results show that in 2020, provinces on Java Island and some provinces on Sulawesi Island have health problems that need to be addressed because they are in the high and moderately high morbidity categories. The value of the Moran Index is positive, indicating a similarity in the percentage of morbidity rates in adjacent provinces. The morbidity rates in most of provinces in Indonesia are affected by the duration averages of school attendance, health insurance, and population density. The coefficient of determination (R2) of the SEM model of 77.60% can be said to be a fairly good model.

An integrated approach combining LISA, BI-LISA, and the modified COPK method to improve groundwater management in large-scale karst areas

Groundwater protection and sustainable utilization of wells in large-scale karst areas face many challenges due to the complexity of numerous groundwater basins and aquifers. Therefore, scientific evidence is needed for groundwater management in these areas. A method combining source vulnerability, spatial autocorrelation, and bivariate spatial autocorrelation is proposed for groundwater management research. Based on the COP method (composed of flow Concentration, Overlying layers, and Precipitation regime), the COPK method is modified by replacing the sinkhole distance with sinkhole density, and incorporating land use and karst network development. The modified COPK method is used to evaluate the vulnerability of nitrate in groundwater in large-scale karst areas. Guizhou Province in China, which is a typical karst region, was evaluated as a study area. Comparatively to the classical COP method, the revised vulnerability map shows more areas of high vulnerability, mainly in the middle of the study area. According to the receiver operating characteristic curve (ROC) analysis, the modified method had a higher accuracy than the classical COP algorithm. Based on the accurate vulnerability index, vulnerability was classified using the natural break method, and the spatial autocorrelation of vulnerability clustering characteristics was studied. The source vulnerability was divided into High-High (HH), High-Low (HL), Low-High (LH), Low-Low (LL), and Not Significant (NS) clusters. This result can clarify the vulnerability clustering of groundwater in specific areas, thereby helping to improve groundwater management accuracy and efficiency. In addition, bivariate spatial autocorrelation was used to study the nitrate content of wells and source vulnerability. The bivariate local Moran's index showed a spatial negative correlation between the two. Similarly, the BI-LISA map can be used to characterize the spatial aggregation of nitrate content and well vulnerability. This result can help groundwater managers develop reasonable measures to protect water wells.

Can Digital Rural Construction Improve China’s Agricultural Surface Pollution? Autoregressive Modeling Based on Spatial Quartiles

The problem of agricultural surface pollution is becoming increasingly prominent, directly impeding the realization of the goals of “industrial prosperity and ecological livability” in the strategy of rural revitalization. To thoroughly analyze the impact of Digital Rural Construction on agricultural surface pollution and to effectively strengthen the prevention and control measures, the Moran index was used to assess the influence of agricultural surface pollution in 31 provinces and cities across China. The Moran index was employed to conduct global and local spatial autocorrelation analysis of agricultural surface source pollution, and a panel quantile autoregressive model was constructed to explore the effects of Digital Rural Construction on such pollution. The results show the following: (1) agricultural surface pollution in each province and city exhibits spatial spillover effects that are growing stronger; (2) the spatial impact of agricultural surface pollution on neighboring provinces and cities follows an inverted U-shaped pattern at different levels of pollution; (3) the relationship between the degree of agricultural surface pollution and the impact of Digital Rural Construction on it also follows an inverted U-shaped pattern, wherein improvements are observed as the pollution levels deepen. When the level of agricultural surface pollution is located in the quartile point 0.1, the improvement effect of Digital Rural Construction on agricultural surface pollution is small (0.0484), as the quartile point increases, the improvement effect is gradually increased, and it reaches the maximum value at the quartile point 0.5 (0.523), and the coefficient of agricultural surface pollution decreases to the minimum value at the quartile point 0.9 (0.423).

Spatial–Temporal Characteristics and Influencing Factors of Marine Fishery Eco-Efficiency in China: Evidence from Coastal Regions

Marine fishery is an important part of China’s maritime power strategy. Improving the ecological efficiency of marine fishery is the inevitable way to achieve the sustainable development of fishery. Based on the perspective of industrial sustainable development, this study used the Super-SBM model to evaluate the ecological efficiency of marine fishery in 11 coastal provinces of China from 2011 to 2020. Combined with Malmquist index, Moran index and other methods, the spatial and temporal evolution characteristics were analyzed. On this basis, the Tobit panel model was used to explore the influencing factors of marine fishery eco-efficiency. The results show that: (1) From 2011 to 2020, the marine fishery eco-efficiency in the 10 coastal provinces and cities of China shows a clear trend of improvement, and the efficiency values in high-efficiency areas remain basically stable. The relative gap between efficient and inefficient regions remains significant. (2) From the perspective of spatial distribution characteristics, the ecological efficiency of marine fishery in coastal provinces and cities in China had no obvious spatial correlation, and showed a trend of cross-distribution between high-efficiency regions and low-efficiency regions. (3) The change of marine fishery eco-efficiency is the result of a variety of influencing factors. Fishery industrial structure, scientific and technological support levels and environmental regulation play a role in promoting the improvement of marine fishery eco-efficiency. Therefore, optimizing the structure of the fishery industry, improving environmental regulation and increasing investment in science and technology are all effective measures for local governments to improve the eco-efficiency of marine fisheries.

Research on Spatial–Temporal Characteristics and Driving Factors of Urban Development Intensity for Pearl River Delta Region Based on Geodetector

The Pearl River Delta (PRD) is one of the most dynamic economic regions in the Asia-Pacific region. At present, it still faces many problems, such as the over-exploitation of urban land and unbalanced development. Through the study of the spatial–temporal characteristics of the development intensity of the PRD region and its driving factors, the key points and difficulties of urban development can be intuitively found. In previous studies, geodetector was widely used to determine the impact of driving factors. This paper uses several different research methods, including the Moran index, the semi-variability index, hot and cold spots, etc., based on the land use data of the PRD region in 1990, 2000, 2010, and 2020 to analyze the spatial–temporal characteristics of the development intensity. Combined with the socio-economic data of the statistical yearbook, factor detection and interaction detection of the 10 driving factors of development intensity are carried out based on geodetector, and reasonable optimization suggestions are put forward for the current situation of the region. The main conclusions are as follows: (1) The overall development intensity of the PRD region shows an upward trend, showing a “core periphery” spatial pattern of high center and low periphery centered around the Pearl River estuary. (2) The spatial distribution of cold and hot spots shows agglomeration, mainly in the form of high aggregation and low aggregation. (3) The driving factors for the development intensity for the PRD region in the past 30 years mainly include population agglomeration level, industrial structure level, economic strength level, terrain slope, etc. Among them, any two factors have a stronger interaction than a single factor, and all are enhanced by two factors. The dominant factors of interaction in different periods are different.

COĞRAFİ AĞIRLIKLI REGRESYON İLE YÜKSEK TEKNOLOJİ İHRACATININ MEKANSAL ANALİZİ

The development of High-Technology Exports (HTE) contributes to the increase of competitiveness and growth in national production, the development of commercial and economic cooperation with foreign countries, and the diversification of the economy. In addition, high technology exports reduce the current account deficit of the country's economy. Therefore, high-tech industries have become indispensable for economic development in a country. The aim of this study is to examine the existence of spatial (neighbourhood) relations of countries in high-technology exports. In this study, Exploratory Regression analysis was applied to determine the model that best explains HTE among the variables. Moran Index was used for spatial autocorrelation, Geographical Weighted Regression (GWR) method for spatial regression analysis, and Ordinary Least Squares (OLS) method, to compare with the GWR method. The GWR model established in this study showed that countries have a weak or strong spatial relationship with their neighboring countries in terms of HTE. It has been observed that this spatial relationship is strong in the countries neighboring China, which ranks first in HTE, while the spatial relationship is weak in countries where HTE is low.

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.

The dynamic impact of digital economy on the green development of traditional manufacturing industry: Evidence from China

The traditional manufacturing industry is China’s second-largest source of carbon emissions, and the successful completion of dual carbon target depend on the traditional manufacturing industry’s ability to reduce emissions. This paper constructs the evaluation index system to analyze the driving factors and green coupling level of the digital economy enabling the green development of traditional manufacturing industry from three aspects: industry digitalization capacity, socio-economic benefits capacity and ecological resources carrying capacity based on the theory of social shaping of technology. From the perspective of regional economic development, this paper constructs the spatial Durbin model and threshold regression model to analyze the mediation effect and spatial spillover mechanism of the digital economy empowering the green development of traditional manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2020, the empirical analysis show that digital economy has the positive impact on the green development of traditional manufacturing industry through the digital innovation, industrial upgrading and human capital. At same time, the digital economy has a positive spatial autocorrelation pattern of the green development of traditional manufacturing industries with “high in the eastern and low in the western of China” from the global Moran index. Through the policy test of carbon-trading pilot programs by the difference-in-differences model, the digital economy has a non-linear impact on the intensive production of traditional manufacturing industry, which green finance activate the new impetus of market and the data element resources enhance industrial coordination development.

The study on the characteristics of carbon pressure agglomeration and the dynamic evolution of heterogeneity in China from a regional perspective.

Provinces (cities and districts) with identifiable boundaries are under intense pressure to reduce emissions as a fundamental unit and research object of carbon peaking and carbon-neutrality goals. Due to the significant variability of regional development, achieving equilibrium between carbon emissions and carbon absorption is challenging, contributing to the difficulty of developing carbon emission reduction and relevant green strategic initiatives in China. Therefore, this paper explored the spatial effect of carbon balance with carbon pressure as the starting point. First, this paper defined the "carbon pressure index" (CPI) of 30 provinces (cities and districts) in China from 2000 to 2019. Second, this paper validated the CPI agglomeration evolutionary characteristics in global and local aspects based on the Moran's index. Third, this paper identified and decomposed the spatial heterogeneity of CPI using the kernel density estimation method and the Theil index, then extracted typical cities to analyze the specific causes. Finally, this paper classified the seven regions in China into four types according to a comprehensive analysis of CPI. The results indicated that China's ecological carbon cycle system was in a serious "carbon overload" state. Thirty provinces (cities and districts) showed significant spatial agglomeration characteristics. The spatial gap of CPI was gradually decreasing nationwide, and the intra-regional differences were the leading cause of CPI levels in China. This can provide policy basis for the improvement of China's balanced development system of regional carbon emission reduction.

Evaluation of Supply and Demand Balance and Spatial Spillover Effect of Age-Friendly Environment

Given the rapid development of population aging, regions face significant challenges in providing diverse public service resources for the elderly. Constructing an age-friendly environment actively addresses population aging and ensures the well-being of the elderly. This study employed panel data from 31 provinces (municipalities) in China spanning the years 2014 to 2020. The research utilized a coupling coordination degree model, Theil index, Moran index method, and spatial econometric model to empirically examine the level of coupling coordination development, regional differences, and spatial spillover effects of age-friendly environments. The findings indicate the following: (1) In terms of coupling and coordination development, the degree of coupling and coordination of age-friendly environments in China shows a consistent upward trend over the years. The eastern region exhibits the highest level of development, surpassing the national average, while the western region has the lowest level, followed by the northeast and central regions. (2) In terms of regional development disparities, inter-regional differences gradually intensify due to variations in development status and resource endowment, playing a significant role in overall variations. Within regions, the highest intra-regional differences occur in the eastern region, while the lowest occur in the western region. (3) Considering the spatial spillover effect, the coupling coordination degree of age-friendly environments demonstrates positive spatial autocorrelation. Various development clusters and variables exert diverse influences on the coupling coordination degree of age-friendly environments within the region, while the spatial spillover effect of surrounding regions manifests distinct development patterns. The findings of this study contribute to enhancing the quality of age-friendly environment construction and promoting coordinated development among different regions.

Spatial Characteristics and Influencing Factors of Green Development Progress Level of Private Enterprises in China: Based on Large Collection Surveys

As the market subject of China’s economic development, private enterprises play an important role in fighting against pollution and solving ecological and environmental problems. It is especially important to evaluate the green development progress of Chinese private enterprises in the epidemic era. This paper conducts a questionnaire survey on 10,623 private enterprises in 31 provinces of China, and based on 6223 industrial survey results, it focuses on the production and operation status of private enterprises in terms of pollution reduction performance, energy saving, and carbon reduction intensity in order to construct a green development progress index. The spatial Moran index test and the spatial Durbin model are used to analyze the regional correlations and influencing factors of green development progress in China. The results show that the green development of enterprises with a main business income of more than 100 million CNY and key areas such as Fenwei Plain have improved significantly in 2021, especially with the increase in a private enterprise scale, the carbon reduction regime, the pollution abatement regime, and the pollution control manner, and because the investment, profitability, and pollution discharge of private enterprises is more significant. The indexes of the provinces in the southeast coastal area and the northeastern region of China are the highest and lowest, respectively, in terms of pollution, which is demonstrated by the spatial aggregation effect on the surrounding areas by Moran local index analysis. The urbanization level and government financial support for environmental protection are just two of the negative factors regarding this issue, while the economic development level and industrial structure are positive factors that have a spatial spillover effect.

Hot spot analysis and evaluation of influencing factors on regional road crash safety and severity indices: insights from Iran

Inadequate regional road safety studies have been conducted in developing countries like Iran. Regarding regional road safety indices (RSIs), a significant disparity between Iranian provinces was observed. Thus, it was aimed to evaluate the regional RSIs in Iran and identify their influencing factors and potential hot spots. Data on regional road crashes, fatalities, demographics, transportation, health institutions, economics, education, and fuel consumption rates were collected. The association between the variables was evaluated using correlation analysis. Using Moran’s I and local Moran indices, provinces’ spatial distributions were evaluated. Hot spot analysis was used to identify factors influencing RSIs. Significant correlations between the variables were detected. A vast local cluster in terms of fatality per injury (as a crash severity index) was identified in the country’s southeast. The distribution patterns of provinces in terms of seven RSIs were cluster-like. Variable groups, including road length, demographic, income, education, and geographic, influence RSIs in hot or cold spot regions. Crashes were severe in underdeveloped and remote provinces. Increasing income and education levels make it possible to reduce crash severity indices in this country. A positive Moran’s I index does not guarantee the existence of significant local cluster cores in a country.

Does urban scale borrowing aggravate carbon and air pollution emissions?

The study on carbon emissions and air pollutants in Chinese cities is typical and representative, providing valuable insights and references for carbon reduction and environmental governance in cities around the world. This study is the first to apply scale borrowing to the environmental field, taking into account the synergistic effects of greenhouse gases and air pollution, and to innovatively explore the effects of scale borrowing on them. Using a panel data for 295 Chinese cities from 2005 to 2020 to measure their scale borrowing, the spatial correlation of particulate matter (PM2.5) and carbon dioxide (CO2) was evaluated using the Moran index. The spillover effects of scale borrowing and other driving factors on PM2.5 and CO2 were investigated using a spatial Durbin model. The results revealed that borrowing population size has steadily increased, with the average value rising from 165.85 in 2005 to 186.39 in 2020, and the distribution is high in the east and low in the west, clustered in the North China Plain and the middle and lower reaches of the Yangtze River. Borrowing economic activity density varies considerably from year to year, showing an overall “M-shaped” characteristic with 2012 as the inflection point. Borrowing advanced functions is more stable, with the average value increasing by 29.25% from 0.0489 to 0.0632. The effect of scale borrowing on local PM2.5 is significant, with borrowing population size reducing PM2.5 emissions while increasing carbon emissions in neighbouring cities. Borrowing economic activity density has a positive effect on local PM2.5 emissions and CO2 emissions in neighbouring cities. Borrowing advanced functions not only reduces local PM2.5 emissions by influencing the spatial division of functions in cities, but also mitigates air pollution in neighbouring cities. The analysis of the spillover effect indicated that the indirect effect is greater than the direct effect of driving factors, signifying that cooperative efforts across cities to avoid and co-control PM2.5 and CO2 will have a considerable positive impact on ambient air quality.

Analysis of Pre-post Covid-19 Influence in Bangka Belitung Islands Province: Socio-Economic Aspects in 7 Regencies/Cities

This study will analyze the variables of economic growth, poverty rate, unemployment rate, and the Human Development Index using situational trend analysis of the 4 factors before - after Covid-19 and spatial autocorrelation to see the inter-regional linkages as well as the distribution pattern of the observed data. The purpose of the study was: 1) to find out the description of the situation of the 4 factors before – after Covid-19; and 2) knowing the spatial autocorrelation based on Moran’s index with the spatial weighting matrix (WIJ). Based on the results of the analysis, the rate of economic growth in each district/city of Prov. Bangka Belitung tends to have a downward trend in the period 2018–2020 except Kab. Bangka and Pangkalpinang City. The variable rate of economic growth also did not have a spatial autocorrelation during the 2018-2021 period, which was indicated by the distribution pattern of the 2018-2019 data spreading (the Moran index was negative) and 2020-2021 was clustered (the Moran index was positive). Unemployment variable in all districts/cities of Prov. Bangka Belitung had a significant upward trend in the 2018-2021 period. The variable percentage of unemployment also did not have a spatial autocorrelation, which was indicated by a data distribution pattern that spreads over the 2018-2021 time period. The poverty variable in all districts/cities of Prov. Bangka Belitung had a downward trend in the 2018-2021 period. The variable percentage of poverty also does not have a spatial autocorrelation, which was indicated by a data distribution pattern that spreads over the 2018-2021 observation period (the Moran index was negative). Variable Human Development Index in all regencies/cities prov. Bangka Belitung has an upward trend during the 2018-2021 timeframe. The HDI variable also does not have a spatial autocorrelation, but the data distribution pattern tends to collect during the 2018-2021 time period (positive Moran index).&#x0D; Keywords: economic growth, poverty, unemployment, human development, spatial autocorrelation, Covid 19

Distribution and spatial autocorrelation of physical-water attributes of an Oxisol

Spatial autocorrelation, which in this work was calculated using Moran's bivariate analysis, can be defined as the coincidence of similar values in nearby locations, or the absence of randomness of a variable due to its spatial distribution. Therefore, the objective of this study is to analyze the distribution and spatial autocorrelation of physical attributes of an Oxisol. The experiment was carried out in the irrigation and drainage area of the Universidade Federal de Viçosa, in Viçosa, Minas Gerais, Brazil. The soil in which the experimental meshes were installed was classified as a sandy clayey Oxisol. The attributes were determined: soil moisture on a dry basis, % (DB), soil moisture on a wet basis, % (WB), volumetric soil moisture, % (VS), particle density, g cm-1 (PD), sampled at different depths and within a grid of 90 georeferenced points. For spatial autocorrelation, the global Moran and local Moran indexes (LISA) were used as statistical tools. Bivariate analysis revealed that soil volumetric moisture is closely related to wet and dry basis moisture. It was also found that the surface particle density is related to the deeper layers of the soil, thus reinforcing that the solid fraction of a soil sample, without considering porosity, tends to remain constant. This happens because the predominant mineral constituents in soils are quartz, feldspars, and colloidal aluminum silicates, whose particle densities are around 2.65 g cm-3.

Analysis of the cancer occurrence related to natural radioactivity in the Rio Grande do Norte State, Brazil.

The state of Rio Grande do Norte, located in the Northeast region of Brazil, has areas of granites and pegmatites with minerals that have varying concentrations of uranium. Consequently, high concentrations of radon gas, a carcinogenic substance for humans, can occur. The present study aimed to assess the occurrence of cancer and its association with exposure to sources of natural radioactivity using geological and geophysical information in the aforementioned state. The spatial dependence of pulmonary, breast, stomach, leukemia, and skin cancer cases with the location of radioisotope sources were analyzed using geoprocessing tools. The geoprocessing analysis showed a differential pattern of uranium emission throughout the state, with the highest emission from areas with pegmatites outcrops. A spatial dependency of cancer cases was shown (Moran index: 0.43; p < 0.01). Moreover, a higher rate of natural radioactivity-cancer cases was associated with the high-intensity natural radioactivity areas: odds ratio:1.21 (95% CI 1.20; 1.23), following the same pattern when separately compared the different related types of cancer. These results highlight the importance of natural radioactivity as a public health problem in the Brazilian environmental scenario, confirming the need for further studies as the first toward understanding and implementing health management strategies mitigating the exposures, especially in areas of environmental risk.

Spatial Poverty Concentration In East Java Province

This study focuses on the spatial concentration of poverty in East Java Province by looking at data on the number of poor people in each urban district in East Java province using the Moran Index Analysis. The results of the analysis and discussion through Moran's I conclude that there is a spatial autocorrelation in the percentage of the poor population in East Java, both in 2020 and 2021. Moran's I figures in 2020 and 2021 are 0.989 and 0.990 which indicates a positive autocorrelation or a pattern that is grouped and have similar characteristics at adjacent locations

Historical Trends and Characteristics of Meteorological Drought Based on Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index over the Past 70 Years in China (1951–2020)

Against the backdrop of global climate change, the frequency of drought events is increasing, leading to significant impacts on human society and development. Therefore, it is crucial to study the propagation patterns and trends of drought characteristics over a long timescale. The main objective of this study is to delineate the dynamics of drought characteristics by examining their propagation patterns in China from 1951 to 2020. In this study, precipitation data from meteorological stations across mainland China were used. A comprehensive dataset consisting of 700 stations over the past 70 years was collected and analyzed. To ensure data accuracy, the GPCC (the Global Precipitation Climatology Center) database was employed for data correction and gap-filling. Long-term drought evolution was assessed using both the SPI-12 (standardized precipitation index) and SPEI-12 (standardized precipitation evapotranspiration index) to detect drought characteristics. Two Moran indices were applied to identify propagation patterns, and the MK (the Mann–Kendall) analysis method, along with the Theil–Sen slope estimator, was utilized to track historical trends of these indices. The findings of this study reveal the following key results: (i) Based on the SPI-12, the main areas of China that are prone to drought are mostly concentrated around the Hu Huanyong Line, indicating a tendency towards drying based on the decadal change analysis. (ii) The distribution of drought-prone areas in China, as indicated by the SPEI-12, is extensive and widely distributed, with a correlation to urbanization and population density. These drought-prone areas are gradually expanding. (iii) Between 2010 and 2011, China experienced the most severe drought event in nearly 70 years, affecting nearly 50% of the country’s area with a high degree of severity. This event may be attributed to atmospheric circulation variability, exacerbated by the impact of urbanization on precipitation and drought. (iv) The frequency of drought occurrence in China gradually decreases from south to north, with the northeast and northern regions being less affected. However, areas with less frequent droughts experience longer and more severe drought durations. In conclusion, this study provides valuable insights into the characteristics and propagation patterns of drought in China, offering essential information for the development of effective strategies to mitigate the impacts of drought events.

The Spatial Pattern of the Spread of the COVID-19 Pandemic (Case Study: DKI Jakarta Province)

The COVID-19 pandemic has been running in Indonesia for more than two years. The first case was found in March 2020. DKI Jakarta as the capital city of the country with a high population density and an economic center that was threatened because the area has a high vulnerability to the spread of COVID-19. The number of confirmed cases that continue to soar and the spread that is difficult to be controlled have resulted in the DKI Jakarta government taking policies such as implementing large-scale social restrictions (PSBB), which aims to stop the spread of COVID-19 and to look for patterns of spread of COVID-19. This study uses a geographic information system in looking for patterns of the spread of COVID-19. The analytical method used is spatial autocorrelation, which is carried out using the Moran Index. In addition, the autocorrelation test was also carried out using a Local Indicator of Spatial Autocorrelation (LISA) with the results in the form of a cluster map and a map of significance. The Ordinary Least Squares analysis method is a regression technique that provides a global model for understanding and predicting variables in research. The correlation variables used in this research are Markets, Supermarkets, Buses, and Stations. The result of this study is the spatial autocorrelation of the pattern of spread of COVID-19 between villages and spatially the distribution pattern is clustered. In the OLS regression distribution pattern, the supermarket variable with an R-Squared value of 0.128555 or 12% affects the spread of COVID-19. Based on the calculation of R-Square, Koenker (BP) in addition to the OLS model, the assumption of homoscedasticity is not met, so the model is Ordinary Least Squares not good compared to other models in analyzing the pattern of the spread of COVID-19 in DKI Jakarta.