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

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.

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

Jurnal Buana Informatika ◽

10.24002/jbi.v8i4.1446 ◽

2017 ◽

Vol 8 (4) ◽

Cited By ~ 1

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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Spatial Analysis of the Distribution of Reported Dengue Incidence in the National Capital Region, Philippines

Acta Medica Philippina ◽

10.47895/amp.v51i2.610 ◽

2017 ◽

Vol 51 (2) ◽

Author(s):

Mark Anthony P. Pangilinan ◽

Derice Paolo G. Gonzales ◽

Robert Neil F. Leong ◽

Frumencio F. Co

Keyword(s):

Spatial Autocorrelation ◽

Dengue Fever ◽

Temporal Analysis ◽

Scan Statistics ◽

Moran’S I ◽

National Capital Region ◽

Moran's I ◽

Capital Region ◽

National Capital ◽

Northern Cities

Background and Objective. With an aim of developing an effective disease monitoring and surveillance of dengue fever, this study intends to analyze the spatial distribution of dengue incidences in the National Capital Region (NCR), across four years of reported dengue cases. Materials and Methods. Data used was provided by the Department of Health (DOH) consisting of all reported dengue cases in NCR from 2010-2013. For mapping and visualization, a shapefile of NCR was made readily available by www.philgis.org. Both Moran’s I and Kulldorff’s spatial scan statistics (SaTScan) were used to identify clusters across the same time period. Results and Conclusion. The analyses identified significant clustering of dengue incidence and revealed that the northern cities of NCR, such as Caloocan, Malabon, Navotas and Valenzuela, exhibited high spatial autocorrelation using local Moran’s I and Kulldorff’s SaTScan. A temporal analysis of the results also suggested movement in increased dengue incidence through time, from the northwest cities to the northeast cities. Presence of spatial autocorrelation in dengue incidence suggests possible enhancements of early detection schemes for dengue surveillance. Moreover, the results of these analyses will be of interest to both policymakers and health experts in providing a basis for which they can properly allocate resources for the prevention and treatment of dengue fever.

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Spatial Analysis of COVID-19 and Exploration of Its Environmental and Socio-Demographic Risk Factors Using Spatial Statistical Methods: A Case Study of Iran

Health in Emergencies & Disasters Quarterly ◽

10.32598/hdq.5.3.358.1 ◽

2020 ◽

Vol 5 (3) ◽

pp. 145-154

Author(s):

Mohsen Shariati ◽

◽

Mahsa Jahangiri-rad ◽

Fatima Mahmud Muhammad ◽

Jafar Shariati ◽

...

Keyword(s):

Risk Factors ◽

Spatial Autocorrelation ◽

Confidence Level ◽

Hot Spot ◽

Moran’S I ◽

Moran's I ◽

Hot Spot Analysis ◽

The Relationship ◽

Global Moran’S I ◽

Spot Analysis

Background: Iran detected its first COVID-19 case in February 2020 in Qom province, which rapidly spread to other cities in the country. Iran, as one of those countries with the highest number of infected people, has officially reported 1812 deaths from a total number of 23049 confirmed infected cases that we used in the analysis. Materials and Methods: Geographic distribution by the map of calculated incidence rates for COVID -19 in Iran within the period was prepared by GIS 10.6 Spatial autocorrelation (Global Moran’s I) and hot spot analysis were used to assess COVID -19 spatial patterns. The ordinary least square method was used to estimate the relationship between COVID -19 and the risk factors. The next step was to explore Geographically Weighted Regression (GWR) models that might better explain the variation in COVID -19 cases based on the environmental and socio-demographic factors. Results: The spatial autocorrelation (Global Moran’s I) result showed that COVID-19 cases in the studied area were in clustered patterns. For statistically significant positive z-scores, the larger the z-score is, the more intense the clustering of high values (hot spot), such as Semnan, Qom, Isfahan, Mazandaran, Alborz, and Tehran. Hot spot analysis detected clustering of a hot spot with confidence level 99% for Semnan, Qom, Isfahan, Mazandaran, Alborz, and Tehran, as well. The risk factors were removed from the model step by step. Finally, just the distance from the epicenter was adopted in the model. GWR efforts increased the explanatory value of risk factor with better special precision (adjusted R-squared=0.44) Conclusion: The highest CIR was concentrated around Qom. Also, the greater the distance from the center of prevalence (Qom), the fewer the patients. Hot spot analysis also implies that the neighboring provinces of prevalence centers exhibited hot spots with a 99% confidence level. Furthermore, the results of OLS analysis showed the significant correlation of CIR is with the distance from epicenter (Qom). The GWR can result in the spatial granularity providing an opportunity to well understand the relationship between environmental spatial heterogeneity and COVID-19 risk as entailed by the infection of CIR with COVID-19, which would make it possible to better plan managerial policies for public health.

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

Geospatial health ◽

10.4081/gh.2020.921 ◽

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.

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Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

Scientific Research Journal ◽

10.24191/srj.v9i2.9405 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1

Author(s):

Asra Hosseini

Keyword(s):

Spatial Autocorrelation ◽

Urban Areas ◽

Smart Cities ◽

Moran’S I ◽

Moran's I ◽

Urban Planner ◽

Universal Feature ◽

Autocorrelation Method ◽

Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

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Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

Scientific Research Journal ◽

10.24191/srj.v9i2.5389 ◽

2012 ◽

Vol 9 (2) ◽

pp. 1

Author(s):

Asra Hosseini

Keyword(s):

Spatial Autocorrelation ◽

Smart Cities ◽

Moran’S I ◽

Moran's I ◽

Urban Planner ◽

Universal Feature ◽

Autocorrelation Method ◽

Selection Of ◽

Monotonic Relation

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature ofurban areas. This study explored issue ofmeasuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% ofneighbourhoods, area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

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Geographical variations and associated factors of defaulting from immunization among children aged 12 to 23 months in Ethiopia: using spatial and multilevel analysis of 2016 Ethiopian Demographic and Health Survey

Environmental Health and Preventive Medicine ◽

10.1186/s12199-021-00984-8 ◽

2021 ◽

Vol 26 (1) ◽

Author(s):

Mukemil Awol ◽

Zewdie Aderaw Alemu ◽

Nurilign Abebe Moges ◽

Kemal Jemal

Keyword(s):

Multilevel Analysis ◽

Health Survey ◽

Associated Factors ◽

Demographic And Health Survey ◽

Community Level ◽

Scan Statistics ◽

Moran’S I ◽

Moran's I ◽

Depth Analysis ◽

Global Moran’S I

Abstract Background In Ethiopia, despite the considerable improvement in immunization coverage, the burden of defaulting from immunization among children is still high with marked variation among regions. However, the geographical variation and contextual factors of defaulting from immunization were poorly understood. Hence, this study aimed to identify the spatial pattern and associated factors of defaulting from immunization. Methods An in-depth analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS 2016) data was used. A total of 1638 children nested in 552 enumeration areas (EAs) were included in the analysis. Global Moran’s I statistic and Bernoulli purely spatial scan statistics were employed to identify geographical patterns and detect spatial clusters of defaulting immunization, respectively. Multilevel logistic regression models were fitted to identify factors associated with defaulting immunization. A p value < 0.05 was used to identify significantly associated factors with defaulting of child immunization. Results A spatial heterogeneity of defaulting from immunization was observed (Global Moran’s I = 0.386379, p value < 0.001), and four significant SaTScan clusters of areas with high defaulting from immunization were detected. The most likely primary SaTScan cluster was seen in the Somali region, and secondary clusters were detected in (Afar, South Nation Nationality of people (SNNP), Oromiya, Amhara, and Gambella) regions. In the final model of the multilevel analysis, individual and community level factors accounted for 56.4% of the variance in the odds of defaulting immunization. Children from mothers who had no formal education (AOR = 4.23; 95% CI: 117, 15.78), and children living in Afar, Oromiya, Somali, SNNP, Gambella, and Harari regions had higher odds of having defaulted immunization from community level. Conclusions A clustered pattern of areas with high default of immunization was observed in Ethiopia. Both the individual and community-level characteristics were statistically significant factors of defaulting immunization. Therefore, the Federal Ethiopian Ministry of Health should prioritize the areas with defaulting of immunization and consider the identified factors for immunization interventions.

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Abundance and spatial-temporal distribution of Macrobrachium surinamicum Holthuis, 1948 (Palaemonidae) in the Amazon estuary, north of Brazil

Brazilian Journal of Biology ◽

10.1590/1519-6984.00316 ◽

2016 ◽

Vol 77 (3) ◽

pp. 594-601

Author(s):

D. V. Cavalcante ◽

B. S. Bentes ◽

J. M. Martinelli-Lemos

Keyword(s):

Significant Influence ◽

Abiotic Factors ◽

Temporal Distribution ◽

Artisanal Fisheries ◽

Spatial And Temporal Distribution ◽

Low Salinity ◽

Amazon Estuary ◽

Disturbed Areas ◽

Macrobrachium Amazonicum ◽

The City

Abstract Macrobrachium surinamicum is a small shrimp that inhabits rivers of low salinity. It is mainly caught as bycatch in Amazon shrimp Macrobrachium amazonicum fisheries, which is widely exploited by artisanal fisheries for food and economic needs of the riverside population. This study aimed to characterize the spatial and temporal distribution of the freshwater shrimp M. surinamicum in the Guajará Bay and on Mosqueiro Island, correlating the abundance of this species with abiotic factors (temperature and salinity). Samples were taken from May 2006 to April 2007 in six locations: Mosqueiro Island (Furo das Marinhas and Porto do Pelé); Icoaraci district; Arapiranga Island, edge of the city of Belém; and Combu Island, using traps named 'matapis'. A total of 361 shrimps were caught. The abundance was higher in December and lower in July 2006. The biggest catch occurred on Arapiranga Island and the lowest on Mosqueiro Island. The abundance differed significantly in December 2006 and no variable studied had significant influence on M. surinamicum abundance. In Guajará Bay, particularly the more sheltered places, as Arapiranga and Combu islands, favor the development of M. surinamicum, indicating that this species has preference for less disturbed areas.

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Examining the spread of Aquatic Invasive Species, Bythothephes longimanus, In Inland Lakes Across Ontario

10.32920/ryerson.14654484 ◽

2021 ◽

Author(s):

Joseph Arambulo

Keyword(s):

Invasive Species ◽

Spatial Autocorrelation ◽

Aquatic Invasive Species ◽

Water Flea ◽

Moran's I ◽

Secondary Spread ◽

Independent Variables ◽

Inland Lakes ◽

Mean Temperature ◽

Global Moran’S I

The purpose of this study is to is to examine the secondary spread of Bythothephes longimanus, commonly known as spiny water flea, across inland lakes in Ontario, and potentially determine predictors for the its invasion. Data for 190 inland lakes across 84 quaternary watersheds in Ontario were included in the database. Global Moran's I was used to analyze the spatial autocorrelation of the variables, and McFadden's Rho-Squared was used to determine if a variable was a predictor of invasion. Three independent variables, out of 28, were found to be good predictors of invasion: (1) mean temperature of watersheds during summer (MNTMPWSSU), (2) mean precipitation for watersheds during spring (MNPCPWSSP), and (3) mean precipitation for watersheds during summer (MNPCPWSSU). Of the three, mean precipitation for watersheds during summer was determined to be the best predictor.

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Approaches to measuring the relevance of geographical dimension of societa inequalities

Geografie ◽

10.37040/geografie2009114010052 ◽

2009 ◽

Vol 114 (1) ◽

pp. 52-65 ◽

Cited By ~ 6

Author(s):

Pavlína Netrdová ◽

Vojtěch Nosek

Keyword(s):

Spatial Autocorrelation ◽

Demographic Data ◽

Moran’S I ◽

Theil Index ◽

Moran's I ◽

Similar Information ◽

Index Decomposition ◽

Geographical Dimension

The article focuses on geographical dimension of societal inequalities, especially on approaches to its analysing. Two distinct methods of analysing the relative geographical inequality are utilized: Theil index decomposition and spatial autocorrelation measured by Moran’s I coefficient. Both employed methods should bring, in theory, very similar information. This fact is explored empirically by comparing both methods and by their application on detailed economic, social and demographic data on municipalities in Czechia. Conclusions, predominantly of epistemological nature, are intended to assess advantages and limitations of individual methods and their possible application in practice.

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