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Background Mortality decreased in European Union (EU) countries during the last century. Despite these similar trends, there are still considerable differences in the levels of mortality between Eastern and Western European countries. Sub-group analysis of mortality in Europe for different age and sex groups is common, however to our knowledge a spatio-temporal methodology as in this study has not been applied to detect significant spatial dependence and interaction with time. Thus, the objective of this paper is to quantify the dynamics of mortality in Europe and detect significant clusters of mortality between European countries, applying spatio-temporal methodology. In addition, the joint evolution between the mortality of European countries and their neighbours over time was studied. Methods The spatio-temporal methodology used in this study takes into account two factors: time and the geographical location of countries and, consequently, the neighbourhood relationships between them. This methodology was applied to 26 European countries for the period 1990-2012. Results Principally, for people older than 64 years two significant clusters were obtained: one of high mortality formed by Eastern European countries and the other of low mortality composed of Western countries. In contrast, for ages below or equal to 64 years only the significant cluster of high mortality formed by Eastern European countries was observed. In addition, the joint evolution between the 26 European countries and their neighbours during the period 1990-2012 was confirmed. For this reason, it can be said that mortality in EU not only depends on differences in the health systems, which are a subject to national discretion, but also on supra-national developments. Conclusions This paper proposes statistical tools which provide a clear framework for the successful implementation of development public policies to help the UE meet the challenge of rethinking its social model (Social Security and health care) and make it sustainable in the medium term.

Background Measles is a contagious viral disease that seriously affects children. The measles vaccine is widely recommended in Brazil and in the world; however, the disease remains relevant for the health authorities. The aim of the present study was to evaluate first and second dose of measles vaccine coverage (VC) in the cities of São Paulo and its spatial dynamics between 2015 and 2020. Method: In this mixed-type ecological study, we used secondary, public domain data from 2015 to 2020, extracted from the Digital Information System of the National Immunization Program, Mortality Information System and the National Live Birth Information System. After calculating the VC, the following four categories were created: very low, low, adequate, and high, and the spatial autocorrelation of VC was analyzed using the Global and Local Moran’s statistics. Results A steady decline in adherence to the vaccination was observed, which dynamically worsened until 2020, with a high number of cities fitting the classification of ineffective coverage and being potentially harmful to the effectiveness of the immunization activities of their neighbors. Conclusion A direct neighborhood pattern was observed between the units with low vaccination coverage, which implied that the reduction in measles VC was somehow related to and negatively influenced by the geographic location and social culture of these areas.

Background Over time, vaccination has been consolidated as one of the most cost effective and successful public health interventions and a right of every human being. This study aimed to assess the spatial dynamics of the vaccine coverage (VC) rate of children aged < 1 year per municipality in the Brazilian Northeast at 2016 and 2017. Methods This is a mixed-type ecological study that use a Public domain data Health Information. Vaccine doses were obtained from the Information System of the Brazilian National Immunization Program, and live births from the Brazilian Information System of Live Births of the Brazilian Unified Health System. Descriptive analysis of the coverage of all the vaccines for each year of the study was conducted, and Mann–Whitney U test was used to compare VC between the study years. Chi-squared test was used to evaluate the association between the years and VC, which was stratified into four ranges, very low, low, adequate, and high. Spatial distribution was analyzed according to both each study year and vaccine and presented as thematic maps. Spatial autocorrelation was analyzed using Moran’s Global and Local statistics. Results Compared with 2017, 2016 showed better VC ( p  < 0.05), except for Bacillus Calmette–Guérin. In the spatial analysis of the studied vaccines, the Global Moran’s Index did not show any spatial autocorrelation ( p  > 0.05), but the Local Moran’s Index showed some municipalities, particularly the Sertão Paraibano region, with high VC, high similarity, and a positive influence on neighboring municipalities ( p  < 0.05). In contrast, most municipalities with low VC were concentrated in the Mata Paraibano region, negatively influencing their neighbors ( p  < 0.05). Conclusion Uneven geographic regions and clusters of low VC for children aged < 1 year in the State of Paraíba were spatially visualized. Health policy makers and planners need to urgently devise and coordinate an action plan directed at each state’s regions to fulfill the vaccination calendar, thereby reversing the vulnerability of this age group, which is at a higher risk of diseases preventable by vaccination.

The NAO teleconnective pattern has a great influence on the European climate; however, the exact quantification of NAO pattern in the different areas is sometimes lacking, and at other times, highlights even large differences between the various studies. This motivation led to the identification of the aim of this research in the study of the relationship between the NAO index and temperature and precipitation anomalies over the period 1991–2020, through the analysis of 87 rain gauges and 86 thermometric stations distributed as homogeneously as possible over the Italian territory. The results were sometimes at odds with the scientific literature on the subject, as significance was also found outside the winter season, e.g., in the spring for temperatures and in the autumn for precipitation, and in some cases, correlations were found, especially in August, even in southern Italy, which is usually considered a poorly correlated area. In addition, the linear relationship between the NAO index and temperature and precipitation anomalies was verified, with many weather stations obtaining significant coefficients of determinations as high as 0.5–0.6 in December, with 29 degrees of freedom, and a p-value set at 95%. Finally, for both climatic parameters, the presence of clusters and outliers at seasonal and monthly levels was assessed, obtaining a spatial distribution using the local Moran index, and summarising them in maps. This analysis highlighted important clusters in Northern and Central Italy, while clusters in the summer months occur in the South. These results provide information that may further elucidate local atmospheric dynamics in relation to NAO phases, as well as encourage future studies that may link other teleconnective indices aimed at better explaining the variance of climate parameters.

The urban heat island (UHI) effect has significant effects on the quality of life and public health. Numerous studies have addressed the relationship between UHI and the increase in urban impervious surface area (ISA), but few of them have considered the impact of the spatial configuration of ISA on UHI. Land surface temperature (LST) may be affected not only by urban land cover, but also by neighboring land cover. The aim of this research was to investigate the effects of the abundance and spatial association of ISAs on LST. Taking Harbin City, China as an example, the impact of ISA spatial association on LST measurements was examined. The abundance of ISAs and the LST measurements were derived from Landsat Thematic Mapper (TM) imagery of 2000 and 2010, and the spatial association patterns of ISAs were calculated using the local Moran’s I index. The impacts of ISA abundance and spatial association on LST were examined using correlation analysis. The results suggested that LST has significant positive associations with both ISA abundance and the Moran’s I index of ISAs, indicating that both the abundance and spatial clustering of ISAs contribute to elevated values of LST. It was also found that LST is positively associated with clustering of high-ISA-percentage areas (i.e.,>50%) and negatively associated with clustering of low-ISA-percentage areas (i.e.,<25%). The results suggest that, in addition to the abundance of ISAs, their spatial association has a significant effect on UHIs.

Incident electromagnetic radiation hitting the Earth’s surface shows three phenomena as absorptivity, reflectivity and transmissivity where sum of the three is equal to one. The transmissivity is zero when the surface is opaque. There is a strong relationship between absorptivity and emissivity that is explained by Kirchhoff’s Law. Emissivity is managed by the thermal radiation on the Earth’s surface. Thermal radiation related with the heat transfer of the electromagnetic radiation is controlled by passing energy of atoms and molecules. There are different sources of energy other than the Sun such as geothermal activities, volcanoes and manufacturing plants that contributes to the emissivity of the surface. The thermal radiation produced by manufacturing plants contributes to the Earth’s surface temperature as well. In this study, land surface temperatures were estimated by using inverse Planck function from five Advanced Spaceborne Thermal Emission and Reflection Radiometer remote-sensing satellite sensor thermal infrared bands. It is aimed to highlight hotspots related to manufacturing plants in the region of Kocaeli, Turkey. The hotspots are examined statistically with the minimum noise fraction, the independent component analysis, the local Moran’s I index and the Getis-Ord G i index methods by using land surface temperatures.

This paper develops a three-step spatial data mining approach to directly identify road clusters with high-frequency crashes (RCHC). The first step, preprocessing, is to store the roads and crashes in a spatial database. The second step is to describe the conceptualization of road–road and crash–road spatial relationships. The spatial weight matrix of roads (SWMR) is constructed to describe the conceptualization of road–road spatial relationships. The conceptualization of crash–road spatial relationships is established using crash spatial aggregation algorithm. The third step, spatial data mining, is to identify RCHC using the cluster and outlier analysis (local Moran’s I index). This approach was validated using spatial data set including roads and road-related crashes (2008–2018) from Polk County, IOWA, U.S.A. The findings of this research show that the proposed approach is successful in identifying RCHC and road outliers.

In the global COVID-19 epidemic, humans are faced with a new challenge. The concept of quarantine as a preventive measure has changed human activities in all aspects of life. This challenge has led to changes in the environment as well. The air quality index is one of the immediate concrete parameters. In this study, the actual potential of quarantine effects on the air quality index and related variables in Tehran, the capital of Iran, is assessed, where, first, the data on the pollutant reference concentration for all measuring stations in Tehran, from February 19 to April 19, from 2017 to 2020, are monitored and evaluated. This study investigated the hourly concentrations of six particulate matters (PM), including PM2.5, PM10, and air contaminants such as nitrogen dioxide (NO 2 ), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Changes in pollution rate during the study period can be due to reduced urban traffic, small industrial activities, and dust mites of urban and industrial origins. Although pollution has declined in most regions during the COVID-19 quarantine period, the PM2.5 rate has not decreased significantly, which might be of natural origins such as dust. Next, the air quality index for the stations is calculated, and then, the interpolation is made by evaluating the root mean square (RMS) of different models. The local and global Moran index indicates that the changes and the air quality index in the study area are clustered and have a high spatial autocorrelation. The results indicate that although the bad air quality is reduced due to quarantine, major changes are needed in urban management to provide favorable conditions. Contaminants can play a role in transmitting COVID-19 as a carrier of the virus. It is suggested that due to the rise in COVID-19 and temperature in Iran, in future studies, the effect of increased temperature on COVID-19 can be assessed.

Background China launched a new round of healthcare-system reform in 2009 and proposed the goal of equal and guaranteed essential medical and health services for all by 2020. We aimed to investigate the changes in China’s health resources over the past ten years after the healthcare reform. Methods Data were collected from the China Statistical Yearbook and China Health Statistics Yearbook from 2009 to 2018. Four categories and ten indicators of health resources were analyzed. A descriptive analysis was used to present the overall condition. The Health Resource Density Index was applied to showcase health-resource distribution in demographic and geographic dimensions. The global and local Moran’s I were used to assess the spatial autocorrelation of health resources. Concentration Index (CI) was used to quantify the equity of health-resource distribution. A Geo-Detector model and Geographic Weighted Regression (GWR) were applied to assess the association between gross domestic product (GDP) per capita and health resources. Results Health resources have increased over the past ten years. The global and local Moran’s I suggested spatial aggregation in the distribution of health resources. Hospital beds were concentrated in wealthier areas, but this inequity decreased yearly (from CI=0.0587 in 2009 to CI=0.0021 in 2018). Primary medical and health institutions (PMHI) and their beds were concentrated in poorer areas (CI remained negative). Healthcare employees were concentrated in wealthier areas (CI remained positive). In 2017, the q-statistics indicated that the explanatory power of GDP per capita to beds, health personnel, and health expenditure was 40.7%, 50.3%, and 42.5%, respectively. The coefficients of GWR remained positive with statistical significance, indicating the positive association between GDP per capita and health resources. Conclusions From 2009 to 2018, the total amount of health resources in China has increased substantially. Spatial aggregation existed in the health-resources distribution. Health resources tended to be concentrated in wealthier areas. When allocating health resources, the governments should take economic factors into account.

Given the impact of greenhouse gases on the change in solar radiation and the increase in ultraviolet radiation as a result of the depletion of Ozone by these gases. This study highlighted the relationship between UVB and O 3 over Libya. Since Libya is a large country with a climate that varies from one region to another, the variation in Ozone and UVB amounts varies among these regions. In the northwestern regions, UVB is low and thus O 3 is high, which is the opposite of the southern regions. A strong inverse relationship was found between them, with the correlation coefficient for the periods 2005-2015 and 2016-2022 being approximately −0.8243 and −0.60796, respectively. Using spatial analysis to identify high and low areas and find the spatial correlation, an inverse correlation was observed. In addition to calculating the difference between the two periods and identifying the most accurate regions, it was found that Tripoli and its suburbs have the highest amount of UVB, while the south has less UVB due to the change in solar radiation intensity over time and the increase in greenhouse gases in the north of the country, especially in the western region.