scholarly journals Spatial association between the incidence rate of Covid-19 and poverty in the São Paulo municipality, Brazil

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Marcos César Ferreira
Keyword(s):  
Spatial Autocorrelation ◽  
Incidence Rate ◽  
Spatial Dependence ◽  
Spatial Association ◽  
Sao Paulo ◽  
Moran’S I ◽  
São Paulo ◽  
Moran's I ◽  
The Social ◽  
Spatially Distributed

In this article, we investigated the spatial dependence of the incidence rate by Covid-19 in the São Paulo municipality, Brazil, including the association between the spatially smoothed incidence rate (INC_EBS) and the social determinants of poverty, the average Salary (SAL), the percentage of households located in slums (SLUMS) and the percentage of the population above 60 years of age (POP>60Y). We used data on the number notified cases accumulated per district by May 18, 2020. The spatial dependence of the spatially smoothed incidence rate was investigated through the analysis of univariate local spatial autocorrelation using Moran’s I. To evaluate the spatial association between the INC_EBS and the determinants SAL, POP>60Y and SLUMS, we used the local bivariate Moran’s I. The results showed that the spatially smoothed incidence rate for Covid-19 presented significant spatial autocorrelation (I = 0.333; p<0.05), indicating that the cases were concentrated in clusters of neighbouring districts. The INC_EBS showed a negative spatial association with SAL (I = - 0.253, p<0.05) and POP>60Y (I = -0.398, p<0.05). We also found that the INC_EBS showed a positive spatial association with households located in the slums (I = 0.237, p<0.05). Our study concluded that the households where the population most vulnerable to Covid-19 resides were spatially distributed in the districts with lower salaries, higher percentages of slums and lower percentages of the population above 60 years of age.

scholarly journals SPATIAL ASSOCIATION PATTERNS OF SMOKING, TOBACCO OUTLET DENSITY, AND SECONDHAND SMOKE IN THAILAND

2021 ◽  
Vol 56 (5) ◽  
pp. 351-361
Author(s):  
Pittaya Thammawongsa ◽  
Wongsa Laohasiriwong ◽  
Nakarin Prasit ◽  
Surachai Phimha
Keyword(s):  
Spatial Autocorrelation ◽  
Secondhand Smoke ◽  
Spatial Regression ◽  
Spatial Association ◽  
Moran’S I ◽  
Data Set ◽  
Moran's I ◽  
High Prevalence ◽  
Smoking Behaviors ◽  
Outlet Density

Thailand has a higher prevalence of smoking behaviors which puts people at risk of morbidity and mortality. This study aimed to determine the spatial association of smoking behaviors and their associated factors among the population of Thailand. This study was conducted using a data set from the National Statistical Office of Thailand, 2017. A Moran’s I, local indicators of spatial association (LISA), and spatial regression were used to identify the spatial autocorrelation between tobacco outlet density, the prevalence of secondhand smoke, and smoking behaviors among Thai people. According to the results, among 88,689 participants, the prevalence of smoking behaviors was 18.00 per 1,000 population. There was global spatial autocorrelation between tobacco outlet density, the prevalence of secondhand smoke, and smoking behaviors with the Moran’s I values of 0.120 and 0.375, respectively. The LISA analysis identified significant positive spatial local autocorrelation of smoking behaviors in the form of nine high-high clusters of tobacco outlets density and ten high-high prevalence clusters of secondhand smoke. The prevalence of secondhand smoke predicted smoking behaviors by 62.8 percent. There were spatial associations between tobacco outlet density and secondhand smoke problems that led the youngsters to start smoking. It is a general recommendation to strictly enforce policies and laws to control smoking, and cover all regions in Thailand.

scholarly journals What do we know about the behavior of animal rabies in Chile through the last years?

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Raul Alegria-Moran ◽  
Daniela Miranda ◽  
Alonso Parra ◽  
Lisette Lapierre
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Association ◽  
Winter Season ◽  
Central Zone ◽  
Domestic Dog ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Epidemiological Characterization

ObjectiveThis study aims to analyze the evolution of the epidemiologicalbehavior of rabies in Chile during the period 2003 to 2013, throughthe epidemiological characterization of a number of variables anddescription of spatial and temporal patterns of animal cases.IntroductionRabies is a zoonotic disease caused by an RNA virus from thefamily Rhabdoviridae, genus Lyssavirus. Worldwide distributed,control of rabies has been considered to be particularly amenable toa “One Health” strategy (1). In Chile, rabies was considered endemicin domestic dog population until the late 1960s, when a surveillanceprogram was established, decreasing the number of human casesrelated to canine variants until the year 1972 (2). Rabies is recognizedas a endemic infection in chiropterans of Chile and prompted thesurveillance of the agent in this and other species (3).MethodsAn epidemiological characterization of the registered cases fromthe National Program for Prevention and Control of Rabies wascarried. During the period 2003-2013, 927 cases were reported.Descriptive statistics and descriptive mapping, recording origin of thesample, number of cases per region, animal reservoir implicated andviral variant were performed. A spatial autocorrelation analysis wascarried using Moran’s I indicator for the detection of spatial clusters(4), using the Local Indicators of Spatial Association (LISA) statistics(5), at national and regional level of aggrupation (north, central andsouth zone). Temporal descriptive analysis was carried.Results927 positive cases were recorded. 920 (99.2%) cases came frompassive surveillance, while 7 (0.8%) cases by active surveillance, totalpositivity was 77.02% and 1.37% respectively. Positivity was reportedmainly in the central zone (88.1%), mainly in Valparaiso (19.1%),Metropolitana (40.6%) (Figure 1), Maule (11.8%) regions concentratedin urban centers. Main positive reservoirs were bats (99.8%),specificallyTadarida brasiliensisand viral variant 4 was the mostcommonly diagnosed. LISA test gives a Moran’s I indicator of 0.1537(p-value = 0.02) for the central zone (Table 1). Rabies tend to decreasein fall and winter season (2.9 cases vs 13 cases during summer).ConclusionsWildlife rabies in bats remains endemic in Chile, concentrated inurban areas. The main reservoirs are insectivorous bats. There is asignificant spatial autocorrelation of animal rabies cases in the centralzone of Chile. Results are relevant to the design of preventive andcontrol measures.

scholarly journals SPATIAL ASSOCIATION PATTERNS OF BINGE DRINKING, ALCOHOL OUTLET DENSITY, AND EARLY STARTED DRINKING IN THAILAND

2021 ◽  
Vol 56 (4) ◽  
pp. 187-198
Author(s):  
Nakarin Prasit ◽  
Wongsa Laohasiriwong ◽  
Kittipong Sornlorm ◽  
Surachai Pimha
Keyword(s):  
Spatial Autocorrelation ◽  
Binge Drinking ◽  
Spatial Association ◽  
Moran’S I ◽  
Alcohol Outlet ◽  
Data Set ◽  
Moran's I ◽  
Alcohol Outlets ◽  
Alcohol Outlet Density ◽  
Outlet Density

Thailand had a higher prevalence of binge drinking (BD) behaviors which put them at risks of morbidity and mortality. This study aimed to determine the spatial association of BD and its associated factors among the population of Thailand. This study was conducted using a data set of the National Statistical Office of Thailand and another data set of the Center for Alcohol Studies, Thailand, in 2017. A Moran's I, Local Indicators of Spatial Association (LISA), and Spatial regression were used to identify the spatial autocorrelation between alcohol outlet density, started drinking before 20 years of age, and BD among Thai people. According to the results, among 61,708 participants, the prevalence of BD was 11.47 per 1,000 population. There was global spatial autocorrelation between alcohol outlet density, start drinking before 20 years, and BD with the Moran's I values of 0.10 and 0.54, respectively. The LISA analysis identified significant positive spatial local autocorrelation of BD in the form of two high-high clusters for density of alcohol outlets and seven high-high clusters of started drinking before the age of 20. Started drinking before 20 years of age could predict binge drinking behaviors by 62.8 percent. There were spatial associations between alcohol outlet density and problems with alcoholic beverage control law enforcement that let the youngsters start drinking before 20. It is a general recommendation to strictly enforce the law in prohibited the underage from consuming alcohol, especially in the high density of alcohol outlets.

scholarly journals Spatial autocorrelation analysis in plant population: An overview

2015 ◽  
Vol 7 (1) ◽  
pp. 501-513 ◽  
Author(s):  
Manish Mathur
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Association ◽  
Plant Population ◽  
Moran’S I ◽  
Moran's I ◽  
Entire Study ◽  
Cold Spots ◽  
The Many ◽  
Global Spatial Autocorrelation ◽  
Entire Study Area

Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the many local spatial variation and spatial dependency while global measurements provide only one set of values that represent the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are also given.

scholarly journals Investigating spatiotemporal patterns of the COVID-19 in São Paulo State, Brazil

2020 ◽  
Author(s):  
Enner Alcântara ◽  
José Mantovani ◽  
Luiz Rotta ◽  
Edward Park ◽  
Thanan Rodrigues ◽  
...  
Keyword(s):  
Population Density ◽  
Sao Paulo ◽  
The State ◽  
Moran’S I ◽  
São Paulo ◽  
Moran's I ◽  
Key Indicator ◽  
Hospital Beds ◽  
Clustering Patterns ◽  
Local Transmission

AbstractAs of May 16th, 2020, the number of confirmed cases and deaths in Brazil due to COVID-19 hit 233,142 and 15,633, respectively, making the country one of the most affected by the pandemic. The State of Sao Paulo (SSP) hosts the largest number of confirmed cases in Brazil, with over 60,000 cases to date. Here we investigate the spatial distribution and spreading patterns of COVID-19 in the SSP by mapping the spatial autocorrelation and the clustering patterns of the virus in relation to the population density and the number of hospital beds. Moran’s I and LISA clustering analysis indicated that Sao Paulo City is a significant hotspot for both the confirmed cases and deaths, whereas other cities across the State could be considered colder spots. Bivariate Moran’s I also showed that the population density is a key indicator for the number of deaths, whereas the number of hospital beds is less related, implying that the fatality depends substantially on the actual patient’s condition. Social isolation measures throughout the SSP have been gradually increasing since early March, an action that helped to slow down the emergence of the new confirmed cases, highlighting the importance of the safe-distancing measures in mitigating the local transmission within and between cities in the SSP.

scholarly journals Cases and distribution of visceral leishmaniasis in western São Paulo: A neglected disease in this region of Brazil

2021 ◽  
Vol 15 (6) ◽  
pp. e0009411
Author(s):  
Regiane Soares Santana ◽  
Karina Briguenti Souza ◽  
Fernanda Lussari ◽  
Elivelton Silva Fonseca ◽  
Cristiane Oliveira Andrade ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Spatial Autocorrelation ◽  
Regional Scale ◽  
Spatial Association ◽  
Rapid Test ◽  
Sao Paulo ◽  
Sand Fly ◽  
Lutzomyia Longipalpis ◽  
São Paulo ◽  
Neglected Disease

Visceral leishmaniasis (VL) is one of the most prevalent parasitic diseases worldwide. In 2019, 97% of the total numbers of cases in Latin America were reported in Brazil. In São Paulo state, currently 17.6% of infected individuals live in the western region. To study this neglected disease on a regional scale, we describe the spread of VL in 45 municipalities of the Regional Network for Health Assistance11(RNHA11). Environmental, human VL (HVL), and canine VL (CVL) cases, Human Development Index, and Lutzomyia longipalpis databases were obtained from public agencies. Global Moran’s I index and local indicators of spatial association (LISA) statistics were used to identify spatial autocorrelation and to generate maps for the identification of VL clusters. On a local scale, we determined the spread of VL in the city of Teodoro Sampaio, part of the Pontal of Paranapanema. In Teodoro Sampaio, monthly peri-domicile sand fly collection; ELISA, IFAT and Rapid Test serological CVL; and ELISA HVL serum surveys were carried out. In RNHA11 from 2000 to 2018, Lu. longipalpis was found in 77.8%, CVL in 69%, and HVL in 42.2% of the 45 municipalities, and 537 individuals were notified with HVL. Dispersion occurred from the epicenter in the north to Teodoro Sampaio, in the south, where Lu. longipalpis and CVL were found in 2010, HVL in 2018, and critical hotspots of CVL were found in the periphery. Moran’s Global Index showed a weak but statistically significant spatial autocorrelation related to cases of CVL (I = 0.2572), and 11 municipalities were identified as priority areas for implementing surveillance and control actions. In RNHA11, a complex array of socioeconomic and environmental factors may be fueling the epidemic and sustaining endemic transmission of VL, adding to the study of a neglected disease in a region of São Paulo, Brazil.

scholarly journals Space-time description of dengue outbreaks in Cruzeiro, São Paulo, in 2006 and 2011

2014 ◽  
Vol 60 (6) ◽  
pp. 565-570 ◽  
Author(s):  
Renata Marzzano de Carvalho ◽  
Luiz Fernando Costa Nascimento
Keyword(s):  
Spatial Autocorrelation ◽  
Dengue Fever ◽  
Temporal Distribution ◽  
Sao Paulo ◽  
Moran’S I ◽  
Spatial And Temporal Distribution ◽  
Moran's I ◽  
P Values ◽  
The City ◽  
Global Moran’S I

Objective: to identify patterns in the spatial and temporal distribution of cases of dengue fever occurring in the city of Cruzeiro, state of São Paulo (SP). Methods: an ecological and exploratory study was undertaken using spatial analysis tools and data from dengue cases obtained on the SinanNet. The analysis was carried out by area, using the IBGE census sector as a unit. The months of March to June 2006 and 2011 were assessed, revealing progress of the disease. TerraView 3.3.1 was used to calculate the Global Moran’s I, month to month, and the Kernel estimator. Results: in the year 2006, 691 cases of dengue fever (rate of 864.2 cases/100,000 inhabitants) were georeferenced; and the Moran’s I and p-values were significant in the months of April and May (IM = 0.28; p = 0.01; IM = 0.20; p = 0.01) with higher densities in the central, north, northeast and south regions. In the year 2011, 654 cases of dengue fever (rate of 886.8 cases/100,000 inhabitants) were georeferenced; and the Moran’s I and p-values were significant in the months of April and May (IM = 0.28; p = 0.01; IM = 0.16; p = 0.05) with densities in the same regions as 2006. The Global Moran’s I is a global measure of spatial autocorrelation, which indicates the degree of spatial association in the set of information from the product in relation to the average. The I varies between -1 and +1 and can be attributed to a level of significance (p-value). The positive value points to a positive or direct spatial autocorrelation. Conclusion: we were able to identify patterns in the spatial and temporal distribution of dengue cases occurring in the city of Cruzeiro, SP, and locate the census sectors where the outbreak began and how it evolved.

scholarly journals Spatial Distribution of Cardio-Vascular Diseases in India

2021 ◽  
Author(s):  
Ayantika Biswas ◽  
Shri Kant Singh ◽  
Jitendra Gupta
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Dependence ◽  
Vascular Diseases ◽  
Error Model ◽  
Moran’S I ◽  
Spatial Error Model ◽  
Cvd Risk ◽  
Spatial Error ◽  
Moran's I ◽  
Cardio Vascular

Abstract Objective: Cardio-vascular Diseases (CVDs) are a leading cause of death and disease burden across the world, and the burden is only expected to increase as the population ages. The objective of this paper is to explore the patterns of CVD risk factors among women in the late reproductive ages (35-49 years) across 640 districts in India, and investigate the association between area-level socioeconomic factors and CVD risk patterns., using a nationally representative sample of 239,729 women aged 35–49 years from all 36 States/UTs under NFHS-4 (2015–16). Methods: Age-standardized prevalence of CVDs have been calculated, along with 95% CI among women in their late reproductive ages (35–49 years) in India. The spatial dependence and clustering of CVD burden has been examined by Moran's I indices, bivariate Local Indicator of Spatial Autocorrelation (LISA) cluster and significance maps. Ordinary Least Square (OLS) regression has been employed with CVD prevalence as the outcome variable. To consider for spatial dependence, Spatial Autoregressive (SAR) models have been fitted to the data. Diagnostic tests for spatial dependence have also been carried out to identify the best fit model. Results: Higher values of Moran's I imply high spatial autocorrelation in CVD among districts of India. Smoking, alcohol consumption, hailing from a Scheduled Caste background, more than 10 years of schooling, as well as urban places of residence appeared as significant correlates of CVD prevalence in the country. The spatial error model and the spatial lag model are a marked improvement over the OLS model; among the two, the spatial error model emerging to be the most improved of the lot. Conclusions: A broader course of policy action relating to social determinants can be a particularly effective way of CVD risk addressal. Social policy interventions related to health like reduction in inequalities in factors like education, poverty, unemployment, access to health-promoting physical or built-environments are crucial in tackling the long-term effects of CVD inequalities between geographical areas.

A study on spatial autocorrelation: Case of Russian regional inflation

2021 ◽  
Vol 64 (4) ◽  
pp. 5-22
Author(s):  
Andrew Kirillov ◽  
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Panel Data ◽  
Spatial Autocorrelation ◽  
Spatial Dependence ◽  
Quantitative Measure ◽  
Moran’S I ◽  
Food Services ◽  
Moran's I

We apply APLE statistic to explore spatial autocorrelation of Russian regional inflationary processes. APLE is discussed to be the fine alternative to Moran’s I. To conduct this study we modify statistics of spatial dependence for panel data structure. We use time series of Russian regional CPIs (i.e. quantitative measure of inflation) of food, non-food, services baskets. We find evidence to confirm the hypothesis of the existence of spatial autocorrelation of regional inflationary processes on the horizon of our study.

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Matheus Supriyanto Rumetna ◽  
Eko Sediyono ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Autocorrelation ◽  
Land Use Changes ◽  
Geographical Information ◽  
Moran’S I ◽  
Complete Spatial Randomness ◽  
Moran's I ◽  
Global Moran’S I ◽  
Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

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