An empirical and modelling approach to the evaluation of cruise ships' influence on air quality: The case of La Paz, Mexico.

Maritime activity has diverse environmental consequences impacts in port areas, especially for air quality, and the post-COVID-19 cruise tourism market's potential to recover and grow is causing new environmental concerns in expanding port cities. This research proposes an empirical and modelling approach for the evaluation of cruise ships' influence on air quality concerning NO2 and SO2 in the city of La Paz (Mexico) using indirect measurements. EPA emission factors and the AERMOD modelling system coupled to WRF were used to model dispersions, while street-level mobile monitoring data of air quality from two days of 2018 were used and processed using a radial base function interpolator. The local differential Moran's Index was estimated at the intersection level using both datasets and a co-location clustering analysis was performed to address spatial constancy and to identify the pollution levels. The modelled results showed that cruise ships' impact on air quality had maximum values of 13.66 µg/m3 for NO2 and 15.71 µg/m3 for SO2, while background concentrations of 8.80 for NOx and 0.05 for SOx (µg/m3) were found by analysing the LISA index values for intersections not influenced by port pollution. This paper brings insights to the use of hybrid methodologies as an approach to studying the influence of multiple-source pollutants on air quality in contexts totally devoid of environmental data.

Burden of COVID-19 Risk in the Municipalities of East Java Province, Indonesia, and its Associated Factors: Spatial Analysis Approach

East Java Province has the fourth-highest number of COVID-19 cases among all other provinces Indonesia. This study aimed to examine the spatial effect on confirmed cases of COVID-19 and the risk factors. Data were analyzed using Geoda software to obtain Global Moran's Index and Local Spatial Autocorrelation (LISA) and QGIS 2.8.1 software to make a map. Moran's I scatter plots also used to exploring the bivariate association between COVID-19 cases and potential predictors. The Global Moran's I statistics value shows spatial clustering in COVID-19 cases across the municipalities of East Java Province (Moran's I=0.3986). A positive spatial autocorrelation was observed between COVID-19 cases and population density (Moran's I = 0.2059), vaccination coverage (Moran's I = 0.322), the number of laboratories (Moran's I = 0.2322), ratio of health worker (Moran's I = 0.1617), and household (Moran's I = 0.0866). In comparison, a negative spatial correlation was observed between COVID-19 cases and The Enforcement of Restrictions on Community Activities' levels (Moran's I =-0,2420), average number of family member (Moran's I = 0.0115). The LISA cluster map shows that there were 3 hot spots (Surabaya, Gresik, and Sidoarjo) and 3 cold spots (Sampang, Pamekasan, and Sumenep). Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.

Temporal evolution and spatial pattern of the COVID-19 epidemic in the United States

It is the cornerstone of precise and scientific prevention and control to understand the temporal evolution and spatial pattern of the COVID-19 epidemic. Based on the county-level COVID-19 case of the United States from January 22, 2020 to October 8, 2021, we explored and analyzed the epidemic by using time series analysis, spatial autocorrelation analysis and gravity center trajectory analysis. The results show that: (1) the epidemic in the United States experienced four stages of low incidence, growth, peak and rebound with June 15, September 30 and October 1, 2020 as the cut-off points. (2) The global Moran index experienced a process of 'increase-decrease-increase-stability', with the maximum value exceeding 0.6, indicating that the epidemic has obvious spatial aggregation;the epidemic is dominated by high-high clusters (over 150 counties) and low-low clusters (over 500 counties), presenting a pattern of 'three cores and multiple islands' and 'north-south belt'. (3) In 60% of states, the trajectory of the epidemic center of gravity is near-linear type. The epidemic hotspots in these states were relatively stable over time. In more than half of the states, the curve of the moving distance of the epidemic center of gravity is exponential. These states experienced a very rapid epidemic. This study is expected to provide a reference for evaluating the effectiveness of epidemic prevention measures and determining targeted epidemic prevention measures, as well as accumulate experience for future research on the spread of different infectious diseases in different regions. © 2022 IEEE.

Using spatial statistics to identify drought-prone regions (A case study of Khuzestan Province, Iran)

Iran is located in the Earth’s arid zone, and a drought crisis imperils the country as a result of declining water resources. Khuzestan Province, located in the south of Iran, is in critical condition due to water shortages;many of its groves have been destroyed. It also has many respiratory and pulmonary patients due to the constant presence of dust. The pandemic and this dust have caused acute problems for those diagnosed with COVID-19. Due to the importance of water deficit in this province, the present research calculated the Standardized Precipitation Index (SPI) and Standard Precipitation Evaporation Index (SPEI) in a thirty-year statistical period from 1984 to 2014;12 stations were selected during the months when rainfall was more likely. This study utilized a geostatistical method to prepare zoning maps of SPI and SPEI. Then, various spatial statistics techniques in ArcGIS software were used to identify and locate the exact areas that were the sources of drought with the help of drought hot spots and strong drought clusters. Anselin Local Moran's maps indicated that the high-high precipitation clusters were located in the northeastern regions of Khuzestan. The hot and cold drought spots, which were identified by GetisOrd G* spatial statistics based on both SPI and SPEI, showed that the hot spots were formed in the southern and southwestern regions;the cold spots were formed in the northwestern regions. Furthermore, the drought hot spots were identified with a 99% confidence level in places where the total ten-year precipitation was less than 270 millimeters. © 2021, Iranian Research Organization for Science and Technology. All rights reserved.

Spatial analysis and distribution of the potential for sustainability in the post-pandemic of COVID-19 in the municipalities of Rio Grande do Sul

The pandemic caused by the coronavirus triggered the possibility of the crisis in several segments, especially with the adoption of social isolation, the main action for the control of the infectious disease, therefore, the present study is justified by the concern in relation to the trends of sustainable development in the post-pandemic of COVID-19, in the state of Rio Grande do Sul. Measuring the degree of sustainability of the municipalities follows the thesis that the greater the degree of sustainability of the municipalities, the less the impact of the pandemic, and the greater the resources to restore balance. The objective of the research is to analyze the spatial distribution of the potential for sustainability in the post-pandemic of COVID-19, in the municipalities of Rio Grande do Sul, in order to generate a risk map with less and greater potential for sustainability in the post-pandemic. This study was carried out by means of an ecological analysis and application of the Barometer of sustainability, later the distribution and spatial analysis was carried out by means of the Moran Index. The spatial analysis, performed by calculating the Moran Index, showed significant spatial independence for confirmed cases (I = 0.058;p = 0.024) and deaths (I = 0.032;p = 0.039), and a significant, albeit weak, correlation. for the incidence coefficient (I = 0.234;p = 0.001) of COVID-19. Spatiality does not explain the distribution of cases and deaths. However, when taking into account the population size of the municipalities, in relation to the number of cases, expressed by the incidence coefficient, the spatial aggregation gains merit. The potential for sustainability was estimated and the risk map of the potential for sustainability was generated, in the post-pandemic of COVID-19, for the municipalities of Rio Grande do Sul. © 2021, Universidade Federal de Pernambuco. All rights reserved.

Exploratory spatial analysis for interval data: A new autocorrelation index with COVID-19 and rent price applications

This paper aims to identify the behavior of interval data associated to its respective geospatial information with in the framework of Symbolic Data Analysis. The main idea is to extend Moran’s autocorrelation index of Exploratory Spatial Analysis to interval data. Symbolic data analysis is a domain of research and application related to the areas of machine learning and statistics that provide tools to describe units (objects), enabling them to consider variability. Spatially correlated data are geospatial data with spatial autocorrelation, and the variability that comes from each region and neighborhood may be better expressed by intervals. Thus, this paper demonstrates the importance of considering the variability present in the interval variable and the variability present in geographical information. Experiments with synthetic interval data are performed to illustrate the usefulness of the proposed approach. We also, analyze two applications, dealing with COVID-19 and rent price interval data.

Analysis of changes in air pollution quality and impact of COVID-19 on environmental health in Iran: application of interpolation models and spatial autocorrelation.

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 (NO2), 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.

Spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level.

In recent history, Coronavirus Disease 2019 (COVID-19) is one of the worst infectious disease outbreaks affecting humanity. The World Health Organization has defined the outbreak of COVID-19 as a pandemic, and the massive growth of the number of infected cases in a short time has caused enormous pressure on medical systems. Mexico surpassed 3.7 million confirmed infections and 285,000 deaths on October 23, 2021. We analysed the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We computed weekly Moran's I index to assess spatial autocorrelation over time and identify clusters of the disease using the "flexibly shaped spatial scan" approach. Finally, we compared Euclidean, cost, resistance distances and gravitational model to select the best-suited approach to predict inter-municipality contagion. We found that COVID-19 pandemic in Mexico is characterised by clusters evolving in space and time as parallel epidemics. The gravitational distance was the best model to predict newly infected municipalities though the predictive power was relatively low and varied over time. This study helps us understand the spread of the epidemic over the Mexican territory and gives insights to model and predict the epidemic behaviour.

Spatial autocorrelation analysis of Covid-19 cases in South Kalimantan, Indonesia

Coronavirus Disease 2019 (COVID-19) is a new coronavirus that was discovered in Wuhan, China, at the end of 2019. In March 2020, the outbreak extended throughout the world, including Indonesia and one of its provinces, South Kalimantan. This rapid expansion should belinked to people's mobility between regions, hence the linkage across regions must be examined. In South Kalimantan Province, the purpose of this research is to evaluate the distribution and relationship across regions in terms of the number of positive COVID-19 cases, the number of additional positive COVID-19 cases, and the number of COVID-19 patients under treatment. The spatial autocorrelation analysis with the Moran Index and Local Indicator of Spatial Autocorrelation (LISA) tests were used to determine the spatial autocorrelation between what and what using what data/where the data obtained? from March 22 to September 30, 2020. Based on the results of the Moran Index test, it is known that there is a spatial autocorrelation in the number of cases, the number of additional cases and the number of positive confirmed COVID-19 patients in treatment between one region and another neighboring area. While the results of the LISA Index test show that Balangan Regency, Hulu Sungai Tengah Regency, Hulu Sungai Utara Regency, Banjarmasin City, Tabalong Regency and Banjar Regency affect the level of COVID-19 cases in their respective neighboring areas. Therefore, there is a need for policies to control community mobility in those spatially correlated areas and increase testing and tracing to control the spread of COVID-19 cases in South Kalimantan Province. © 2021 Institute of Physics Publishing. All rights reserved.

Exploratory spatial data analysis (Esda) on covid-19 cases in malaysia

The Malaysian government implemented The Movement Control Order (MCO) on 18 March 2020 to control the spread of the COVID-19 outbreak. However, the third wave that started in September 2020 during the Recovery Movement Control Order (RMCO) phase saw a continuous increase in the number of cases. In this study, the exploratory spatial data analysis (ESDA) was used to analyse the existence of COVID-19 spatial clusters. Moran's index was used to map the spatial autocorrelation (cluster) to showcase the spreading patterns of the COVID-19 pandemic in Malaysia. The study results indicated significant changes in the COVID-19 hotspots over time. At the beginning of 2020, the state of Selangor and Sarawak were the first locality to become a significant COVID-19 hotspot. Furthermore, this research showed all affected areas during the study period. Overall, a non-random distribution of COVID-19 occurrences was detected, thus suggesting a positive spatial autocorrelation. Many parties are affected by the COVID-19 pandemic, especially those involved in healthcare provision, financial assistance allocation, and law enforcement. Other sectors such as the economy, education, and religion are also affected. Therefore, the findings from this study will provide useful information to all the related governmental and private agencies, as well as policymakers and researchers. © 2021 Penerbit UTM Press. All rights reserved.

A Synergetic R-Shiny Portal for Modeling and Tracking of COVID-19 Data.

The purpose of this paper is to introduce a useful online interactive dashboard (https://mahdisalehi.shinyapps.io/Covid19Dashboard/) that visualize and follow confirmed cases of COVID-19 in real-time. The dashboard was made publicly available on 6 April 2020 to illustrate the counts of confirmed cases, deaths, and recoveries of COVID-19 at the level of country or continent. This dashboard is intended as a user-friendly dashboard for researchers as well as the general public to track the COVID-19 pandemic, and is generated from trusted data sources and built in open-source R software (Shiny in particular); ensuring a high sense of transparency and reproducibility. The R Shiny framework serves as a platform for visualization and analysis of the data, as well as an advance to capitalize on existing data curation to support and enable open science. Coded analysis here includes logistic and Gompertz growth models, as two mathematical tools for predicting the future of the COVID-19 pandemic, as well as the Moran's index metric, which gives a spatial perspective via heat maps that may assist in the identification of latent responses and behavioral patterns. This analysis provides real-time statistical application aiming to make sense to academic- and public consumers of the large amount of data that is being accumulated due to the COVID-19 pandemic.