scholarly journals Hyperendemicity, heterogeneity and spatial overlap of leprosy and cutaneous leishmaniasis in the southern Amazon region of Brazil

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Amanda Gabriela De Carvalho ◽  
João Gabriel Guimarães Luz ◽  
João Victor Leite Dias ◽  
Anuj Tiwari ◽  
Peter Steinmann ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Cutaneous Leishmaniasis ◽  
Urban Areas ◽  
Neglected Tropical Diseases ◽  
Scan Statistics ◽  
Moran’S I ◽  
Moran's I ◽  
Mato Grosso ◽  
Global And Local

Neglected tropical diseases characterized by skin lesions are highly endemic in the state of Mato Grosso, Brazil. We analyzed the spatial distribution of leprosy and Cutaneous Leishmaniasis (CL) and identified the degree of overlap in their distribution. All new cases of leprosy and CL reported between 2008 and 2017 through the national reporting system were included in the study. Scan statistics together with univariate Global and Local Moran’s I were employed to identify clusters and spatial autocorrelation for each disease, with the spatial correlation between leprosy and CL measured by bivariate Global and Local Moran’s I. Finally, we evaluated the demographic characteristics of the patients. The number of leprosy (N = 28,204) and CL (N = 24,771) cases in Mato Grosso and the highly smoothed detection coefficients indicated hyperendemicity and spatial distribution heterogeneity. Scan statistics demonstrated overlap of high-risk clusters for leprosy (RR = 2.0; p <0.001) and CL (RR = 4.0; p <0.001) in the North and Northeast mesoregions. Global Moran’s I revealed a spatial autocorrelation for leprosy (0.228; p = 0.001) and CL (0.311; p = 0.001) and a correlation between them (0.164; p = 0.001). Both diseases were found to be concentrated in urban areas among men aged 31-60 years, of brown-skinned ethnicity and with a low educational level. Our findings indicate a need for developing integrated and spatially as well as socio-demographically targeted public health policies.

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

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. 

scholarly journals Spatial distribution and determinant factors of anemia among adults aged 15–59 in Ethiopia; using mixed-effects ordinal logistic regression model

BMC Nutrition ◽  
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Biruk Shalmeno Tusa ◽  
Sewnet Adem Kebede ◽  
Adisu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background Anemia is a global public health problem, particularly in developing countries. Assessing the geographic distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anemia. Thus, the current study is aimed to assess the spatial distribution and determinant factors of anemia in Ethiopia among adults aged 15–59. Methods A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anemia. Result The spatial distribution of anemia in Ethiopia among adults age 15–59 was found to be clustered (Global Moran’s I = 0.81, p value <  0.0001). In the multivariable mixed-effectordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], highly educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults. Conclusions A significant clustering of anemia among adults aged 15–59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, sex, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed for the identified hotspot areas and risk groups in order to decrease the incidence of anemia.

scholarly journals Spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia; using mixed-effects ordinal logistic regression model

2021 ◽  
Author(s):  
biruk shalmeno tusa ◽  
Sewnet Adem kebede ◽  
Adisu Birhanu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background: Anaemia is a global public health problem particularly in developing countries. Assessing the geographical distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anaemia. Thus, the current study aimed to assess the spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia.Methods: A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of Anaemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of Anaemia.Result: The spatial distribution of anemia among adults age 15-59 was found to be clustered in Ethiopia (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effect ordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], higher educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults.Conclusions: A significant clustering of anemia among adults aged 15-59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, gender, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed in the identified hotspot areas and risk groups to decrease the incidence of anaemia.

scholarly journals Spatial Distribution and Determinant Factors of Anaemia Among Adults Aged 15-59 in Ethiopia; Using Mixed-Effects Ordinal Logistic Regression Model

2020 ◽  
Author(s):  
Biruk Shalmeno Tusa ◽  
Sewnet Adem kebede ◽  
Adisu Birhanu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background: Anaemia is a global public health problem particularly in developing countries. Assessing the geographical distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anaemia. Thus, the current study aimed to assess the spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia.Methods: A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anaemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anaemia.Result: The spatial distribution of anaemia among adults age 15-59 was found to be clustered in Ethiopia (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effect ordinal regression analysis; being females [AOR = 1.53; 95% CI: 1.42, 1.66], never married [AOR = 0.86; 95% CI: 0.77, 0.96], higher educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anaemia among adults.Conclusions: A significant clustering of anaemia among adults aged 15-59 were found in Ethiopia and the significant hotspot areas with high clusters of anaemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, gender, marital status, educational level, place of residence, region, wealth index and body mass index (BMI) were significant predictors of anaemia. Therefore, effective public health intervention and nutritional education should be designed in the identified hotspot areas and risk groups to decrease the incidence of anaemia.

Spatial Distribution of Chinese Traditional Villages and its Influencing Factors

2020 ◽  
Author(s):  
Yunong Wu ◽  
Bin Zhang ◽  
Burghard C. Meyer ◽  
Duo Xie ◽  
Yong Zeng ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Influencing Factors ◽  
Distribution Density ◽  
Regional Analysis ◽  
Spatial Differentiation ◽  
Moran’S I ◽  
Statistical Tool ◽  
Aggregation State ◽  
Moran's I

&lt;p&gt;Abstract: Chinese Traditional Villages (TV) were selected from millions of villages based on their important historical and cultural heritage value. The distribution of TV characterized by spatial differentiation is subject to complex and diverse influencing factors. This study takes 6819 TV in China (as of the end of 2019) as research objects to analyse the distribution density of TV in different provinces; the spatial autocorrelation module in ArcGIS' spatial statistical tool was used to analyse the distribution characteristics; a total of 9 factors were selected from the three indicator groups of climate, geography and humanities, and introduced into the clustering and outlier analysis (Anselin Local Moran's I) module to analyse their spatial relationships with TV distribution. The results show that: 1. The spatial distribution of Chinese TV presents an obvious uneven aggregation state. Among them, the highest distribution density was 10.18 per 10,000 km&amp;#178; in Zhejiang province, while less than 0.5 per 10,000 km&amp;#178; in Inner Mongolia, Heilongjiang, Tibet and Xinjiang. The Global Moran's I index of TV distribution is 0.352, and the z-value of normal statistic is 949.76, which has a strong spatial autocorrelation. 2. The distribution of TV is mainly interpreted by humidity index, annual average temperature, elevation, slope, cultural relics, and population. 3. The results of clustering and outlier show that there are significant differences in the effect of the influencing factors on the distribution of TV in different regions. This paper aims to understand the influencing factors that affect the spatial distribution of TV in China and provide more comprehensive research content. This study indicates the importance of further cross-regional analysis of the TV distribution and provides a reference for its environmental management and protective measures and policies.&lt;/p&gt;

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

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.

scholarly journals Spatial Distribution and Determinant Factors of Anaemia Among Adults Aged 15-59 in Ethiopia; Using Mixed-effects Ordinal Logistic Regression Model

2020 ◽  
Author(s):  
biruk tusa ◽  
Sewnet Adem kebede ◽  
Adisu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background: Anaemia is a global public health problem particularly in developing countries. Assessing the geographical distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anaemia. Thus, the current study aimed to assess the spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia.Methods: A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anaemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anaemia.Result: The spatial distribution of anaemia among adults age 15-59 was found to be clustered in Ethiopia (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effect ordinal regression analysis; being females [AOR = 1.53; 95% CI: 1.42, 1.66], never married [AOR = 0.86; 95% CI: 0.77, 0.96], higher educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anaemia among adults.Conclusions: A significant clustering of anaemia among adults aged 15-59 were found in Ethiopia and the significant hotspot areas with high clusters of anaemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, gender, marital status, educational level, place of residence, region, wealth index and body mass index (BMI) were significant predictors of anaemia. Therefore, effective public health intervention and nutritional education should be designed in the identified hotspot areas and risk groups to decrease the incidence of anaemia.

scholarly journals The Spatial Distribution Characteristics of Carbon Emissions at County Level in the Harbin−Changchun Urban Agglomeration

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1268
Author(s):  
Yixia Wang
Keyword(s):  
Spatial Distribution ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Urban Agglomeration ◽  
Moran’S I ◽  
County Level ◽  
Distribution Characteristics ◽  
Moran's I ◽  
Spatial Distribution Characteristics ◽  
Global And Local

China has clearly put forward the strategic goals of reaching the “Carbon Emission Peak” by 2030, and achieving “Carbon Neutrality” by 2060. To achieve these goals, it is necessary to precisely understand the spatial distribution characteristics of historical carbon emissions in different regions. This paper has selected a representative national-level urban agglomeration in China, the Harbin−Changchun urban agglomeration, to study the temporal and spatial distribution characteristics of carbon emissions in its counties. This paper has constructed global and local Moran’s I indexes for the 103 counties in this urban agglomeration by using the carbon emission values reflected by night light data from 1997 to 2017 to perform global and local autocorrelation analysis on a spatial level. The results show that: (1) the main characteristic of carbon emission clustering in the Harbin−Changchun urban agglomeration is similar clustering; (2) the changes in carbon emissions of the Harbin−Changchun urban agglomeration have a strong correlation with relevant policies. For example, due to the impact of the “Twelfth Five-Year Plan” policies, in 2013, the global county-level Moran’s I index of the carbon emissions in the Harbin−Changchun urban agglomeration decreased by 0.0598; (3) the areas where high carbon emission values cluster together (“High−High Cluster”) and low carbon emission values cluster together (“Low−Low Cluster”) in the Harbin−Changchun urban agglomeration are highly concentrated, and the clusters are closely related to the development level of different regions.

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.

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

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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