scholarly journals Geographical clustering and socioeconomic factors associated with hypoglycemic events requiring emergency assistance in Andalusia (Spain)

2021 ◽  
Vol 9 (1) ◽  
pp. e001731
Author(s):  
Fernando Gomez-Peralta ◽  
Cristina Abreu ◽  
Manuel Benito ◽  
Rafael J Barranco
Keyword(s):  
Geographical Distribution ◽  
Spatial Data ◽  
Geographical Location ◽  
Population Characteristics ◽  
Moran’S I ◽  
Sports Facilities ◽  
Z Score ◽  
Moran's I ◽  
Societal Factors ◽  
Emergency Assistance

IntroductionThe geographical distribution of hypoglycemic events requiring emergency assistance was explored in Andalusia (Spain), and potentially associated societal factors were determined.Research design and methodsThis was a database analysis of hypoglycemia requiring prehospital emergency assistance from the Public Company for Health Emergencies (Empresa Pública de Emergencias Sanitarias (EPES)) in Andalusia during 2012, which served 8 393 159 people. Databases of the National Statistics Institute, Basic Spatial Data of Andalusia and System of Multiterritorial Information of Andalusia were used to retrieve spatial data and population characteristics. Geographic Information System software (QGIS and GeoDA) was used for analysis and linkage across databases. Spatial analyses of geographical location influence in hypoglycemic events were assessed using Moran’s I statistics, and linear regressions were used to determine their association with population characteristics.ResultsThe EPES attended 1 137 738 calls requesting medical assistance, with a mean hypoglycemia incidence of 95.0±61.6 cases per 100 000 inhabitants. There were significant differences in hypoglycemia incidence between basic healthcare zones attributable to their geographical location in the overall population (Moran’s I index 0.122, z-score 7.870, p=0.001), women (Moran’s I index 0.088, z-score 6.285, p=0.001), men (Moran’s I index 0.076, z-score 4.914, p=0.001) and aged >64 years (Moran’s I index 0.147, z-score 9.753, p=0.001). Hypoglycemia incidence was higher within unemployed individuals (β=0.003, p=0.001) and unemployed women (β=0.005, p=0.001), while lower within individuals aged <16 years (β=−0.004, p=0.040), higher academic level (secondary studies) (β=−0.003, p=0.004) and women with secondary studies (β=−0.005, p<0.001). In subjects aged >64 years, lower rate of hypoglycemia was associated with more single-person homes (β=−0.008, p=0.022) and sports facilities (β=−0.342, p=0.012).ConclusionsThis analysis supports the geographical distribution of hypoglycemia in the overall population, both genders and subjects aged >64 years, which was affected by societal factors such as unemployment, literacy/education, housing and sports facilities. These data can be useful to design specific prevention programs.

Analysis on Spatial Difference of the Rural Resident’s per Capita Net Income in Qinhuangdao City Based on ESDA

2014 ◽  
Vol 955-959 ◽  
pp. 3893-3898
Author(s):  
Yu Hong Wu
Keyword(s):  
Spatial Data ◽  
Rural Economy ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Net Income ◽  
Gis Technology ◽  
Moran's I ◽  
Per Capita ◽  
Global Moran’S I

Based on the exploratory spatial data analysis (ESDA) and GIS technology, the spatial differences of the rural economic development level of Qinhuangdao city was investigated by adopting the rural resident’s per capita net income data at town level in Qinhuangdao city from 2007 to 2011. The results of global Moran’s I value for rural resident’s per capita net income at town level showed that there existed significant positive spatial autocorrelation and significant spatial aggregation in the spatial distribution of rural resident’s per capita net income. However, the global Moran’s I value showed a decreasing trend during 2007 to 2011, indicating an enlarged spatial disparity of rural economy at the town level. The results of the Moran scatter plots and LISA cluster maps of 2007 and 2011 showed that most of towns were characterized by positive local spatial association , ie. They were located in the HH or the LL quadrant. The significant HH towns were mostly to be found in the south of Qinhuangdao city, Haigang district, Changli county, Lulong county. The significant LL towns were mostly to be found in the Qinglong county, north of Qinhuangdao city.

scholarly journals Kemiskinan dan Ketimpangan Pembangunan kabupaten/Kota di Provinsi Lampung

2021 ◽  
Vol 10 (1) ◽  
pp. 31-45
Author(s):  
Resha Moniyana ◽  
Ahmad Dhea Pratama
Keyword(s):  
Spatial Pattern ◽  
Spatial Data ◽  
Research Area ◽  
Moran’S I ◽  
Poor People ◽  
High Poverty ◽  
Spatial And Temporal Patterns ◽  
Moran's I ◽  
Different Characteristics ◽  
Poverty Levels

The analysis results used in the problem of poverty are increasingly developing as the understanding of the problem of poverty becomes more complex in the spatial and temporal patterns, seeing the patterns and characteristics of a phenomenon with spatial imaging and study of patterns is the main objective of this study by looking at the pattern of the percentage of poor people and the level of inequality. The method used is processing Moran's I spatial data, Moranscatterplot and LISA, testing development inequality with the Williamson Index, The research area covers 15 districts/cities in 2015-2019. Spatial linkages The percentage of poor people between districts/cities in Lampung Province has a positive Moran's I value, has a spatial pattern with the same characteristics and is clustered. Development inequality is negative Moran's I, Development inequality has a spatial pattern with different characteristics in 2015 -2019. Poverty analysis indicates that during the 5-year study period, 5 districts in Lampung Province were still trapped in high poverty levels, The results of regional development inequality with the Williamson index indicate 3 regions with high levels of inequality, 4 areas of moderate inequality and 8 regions with low levels of inequality.

scholarly journals 1001Spatio-temporal epidemiology of mumps and related region factors in Korea

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Kwan Hong ◽  
Hari Hwang ◽  
Byung Chul Chun
Keyword(s):  
Geographical Distribution ◽  
Cumulative Incidence ◽  
Autoregressive Model ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Hospital Resources ◽  
Simultaneous Autoregressive Model ◽  
Epidemiological Characteristics ◽  
Simultaneous Autoregressive

Abstract Background Mumps is in Korea's national immunization program, though there are still epidemics, especially in young age. The study's objectives are to establish the epidemiological characteristics of mumps and suggest the predicting factors. Methods We extracted cases from national health insurance data, between 2013 and 2017. Age-specific incidence rate and geographical distribution were evaluated. We tested for spatial autocorrelation by Moran’s I statistics with Delaunary triangular links. Simultaneous autoregressive model for cumulative incidence of mumps using triangular links was used to predict cumulative incidence with region specific factors. Results A total of 219,149 (85.12 per 100,000) were diagnosed and 23,805 (9.25 per 100,000) were hospitalized. Weekly cumulative incidence showed two epidemics every year, between weeks 20-25 and 40-45. Cumulative incidence of ages 10-19 was the highest, 332.21 per 100,000 people, followed by 300.75 per 100,000 people in ages 0-9. Geographical distribution showed clusters of epidemics, and Moran’s I statistics was 0.304 with a p-value &lt;0.01. The Simultaneous autoregressive model estimated the mean age and hospital resources of each region as prediction factors for geographical distribution of mumps. Conclusions Mumps is common in children and peaks in summer and winter. Additionally, there are geographical clusters in epidemics, and the effect of region factors such as mean age and hospital resources are suspected. Key messages Two peaks in age and season appear in mumps in Korea. Clusters of geographical distribution indicate that region factors may affect the incidence.

scholarly journals Measuring The Impact of Spatial Perturbations on The Relationship Between Data Privacy and Validity of Descriptive Statistics

2020 ◽  
Author(s):  
Kelly Broen ◽  
Rob Trangucci ◽  
Jon Zelner
Keyword(s):  
Spatial Statistics ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Data Privacy ◽  
Perturbation Methods ◽  
Descriptive Statistics ◽  
Moran’S I ◽  
Mortality Data ◽  
Moran's I ◽  
The Impact

Abstract Background: Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. Methods: We analyzed the impact of perturbation on spatial patterns in the full set of address- level mortality data from Lawrence, MA during the period from 1911-1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran’s I, Local Moran’s I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley’s K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and the its compliance with HIPAA privacy standards. Results: Random perturbation at 50 meters, donut masking between 5 and 50 meters, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100x100 and 250x250 meter cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. Conclusions: Using the set of published perturbation methods applied in this analysis, HIPAA- compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.

scholarly journals Measuring the impact of spatial perturbations on the relationship between data privacy and validity of descriptive statistics

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Kelly Broen ◽  
Rob Trangucci ◽  
Jon Zelner
Keyword(s):  
Spatial Statistics ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Data Privacy ◽  
Perturbation Methods ◽  
Descriptive Statistics ◽  
Moran’S I ◽  
Mortality Data ◽  
Moran's I ◽  
The Impact

Abstract Background Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. Methods We analyzed the impact of perturbation on spatial patterns in the full set of address-level mortality data from Lawrence, MA during the period from 1911 to 1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran’s I, Local Moran’s I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley’s K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and its compliance with HIPAA and GDPR privacy standards. Results Random perturbation at 50 m, donut masking between 5 and 50 m, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100 × 100 and 250 × 250 m cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. Conclusions Using the set of published perturbation methods applied in this analysis, HIPAA and GDPR compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.

scholarly journals Spatial inequalities in skilled birth attendance in India: a spatial-regional model approach

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Prem Shankar Mishra ◽  
Debashree Sinha ◽  
Pradeep Kumar ◽  
Shobhit Srivastava
Keyword(s):  
Mass Media ◽  
Spatial Association ◽  
Population Characteristics ◽  
Moran’S I ◽  
Effective Sample Size ◽  
Spatial Error ◽  
Moran's I ◽  
Spatial Inequalities ◽  
Birth Attendance ◽  
Household Headship

Abstract Background Despite a significant increase in the skilled birth assisted (SBA) deliveries in India, there are huge gaps in availing maternity care services across social gradients - particularly across states and regions. Therefore, this study applies the spatial-regression model to examine the spatial distribution of SBA across districts of India. Furthermore, the study tries to understand the spatially associated population characteristics that influence the low coverage of SBA across districts of India and its regions. Methods The study used national representative cross-sectional survey data obtained from the fourth round of National Family Health Survey, conducted in 2015-16. The effective sample size was 259,469 for the analysis. Moran’s I statistics and bivariate Local Indicator for Spatial Association maps were used to understand spatial dependence and clustering of deliveries conducted by SBA coverage in districts of India. Ordinary least square, spatial lag and spatial error models were used to examine the correlates of deliveries conducted by SBA. Results Moran’s I value for SBA among women was 0.54, which represents a high spatial auto-correlation of deliveries conducted by SBA over 640 districts of India. There were 145 hotspots for deliveries conducted by SBA among women in India, which includes almost the entire southern part of India. The spatial error model revealed that with a 10% increase in exposure to mass media in a particular district, the deliveries conducted by SBA increased significantly by 2.5%. Interestingly, also with the 10% increase in the four or more antenatal care (ANC) in a particular district, the deliveries conducted by SBA increased significantly by 2.5%. Again, if there was a 10% increase of women with first birth order in a particular district, then the deliveries conducted by SBA significantly increased by 6.1%. If the district experienced an increase of 10% household as female-headed, then the deliveries conducted by SBA significantly increased by 1.4%. Conclusion The present study highlights the important role of ANC visits, mass media exposure, education, female household headship that augment the use of an SBA for delivery. Attention should be given in promoting regular ANC visits and strengthening women’s education.

scholarly journals Spatial Filtering with EViews and MATLAB

2016 ◽  
Vol 36 (1) ◽  
Author(s):  
Robert Ferstl
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Spatial Filtering ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Spatial Clusters ◽  
Moran's I ◽  
Detection Of Outliers ◽  
Filtering Procedure

This article summarizes the ideas behind a few programs we developed for spatial data analysis in EViews and MATLAB. They allow the user to check for spatial autocorrelation using Moran’s I and provide a spatial filtering procedure based on the Gi statistic by Getis and Ord (1992). We have also implemented graphical tools like Moran Scatterplots for the detection of outliers or local spatial clusters.

scholarly journals SPATIAL ASSOCIATION TO CHARACTERIZE THE CLIMATE TELECONNECTION PATTERNS IN ECUADOR BASED ON SATELLITE PRECIPITATION ESTIMATES

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 101-106
Author(s):  
D. Ballari ◽  
L. Campozano ◽  
E. Samaniego ◽  
D. Orellana
Keyword(s):  
Spatial Patterns ◽  
Spatial Data ◽  
Large Scale ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Climate Indices ◽  
Moran's I ◽  
Local Climate ◽  
Teleconnection Patterns

Abstract. Climate teleconnections show remote and large-scale relationships between distant points on Earth. Their relations to precipitation are important to monitor and anticipate the anomalies that they can produce in the local climate, such as flood and drought events impacting agriculture, health, and hydropower generation. Climate teleconnections in relation to precipitation have been widely studied. Nevertheless, the spatial association of the teleconnection patterns (i.e. the spatial delineation of regions with teleconnections) has been unattended. Such spatial association allows to characterize how stable (heterogeneity/dependent and statistically significant) is the underlying spatial phenomena for a given pattern. Thus our objective was to characterize the spatial association of climate teleconnection patterns related to precipitation using an exploratory spatial data analysis approach. Global and local indicators of spatial association (Moran’s I and LISA) were used to detect spatial patterns of teleconnections based on TRMM satellite images and climate indices. Moran’s I depicted high positive spatial association for different climate indices, and LISA depicted two types of teleconnections patterns. The homogenous patterns were localized in the Coast and Amazonian regions, meanwhile the disperse patterns had a major presence in the Highlands. The results also showed some areas that, although with moderate to high teleconnection influences, had a random spatial patterns (i.e. non-significant spatial association). Other areas showed both teleconnections and significant spatial association, but with dispersed patterns. This pointed out the need to explore the local underlying features (topography, orientation, wind and micro-climates) that restrict (non-significant spatial association) or reaffirm (disperse patterns) the teleconnection patterns.

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 ◽  
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. 

Sign in / Sign up

Export Citation Format

Share Document