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.

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 Autokorelasi Spasial untuk Identifikasi Pola Hubungan Kemiskinan di Jawa Timur

2012 ◽  
Vol 3 (1) ◽  
pp. 217
Author(s):  
Rokhana Dwi Bekti
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Moran’S I ◽  
Poor People ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Moran's I ◽  
Relationship Of ◽  
The Relationship

Spatial autocorrelation is a spatial analysis to determine the relationship pattern or correlation among some locations (observation). On the poverty case of East Java, this method will provide important information for analyze the relationship of poverty characteristics in each district or cities. Therefore, in this research performed spatial autocorrelation analysis on the data of East Java’s poverty. The method used is moran's I test and Local Indicator of Spatial Autocorrelation (LISA). The analysis showed that by the moran's I test, there is spatial autocorrelation found in the percentage of poor people amount in East Java, both in 2006 and 2007. While by LISA, obtained the conclusion that there is a significant grouping of district or cities.

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.

scholarly journals Spatially Varying Relationships between Land Subsidence and Urbanization: A Case Study in Wuhan, China

2022 ◽  
Vol 14 (2) ◽  
pp. 291
Author(s):  
Zhengyu Wang ◽  
Yaolin Liu ◽  
Yang Zhang ◽  
Yanfang Liu ◽  
Baoshun Wang ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Land Subsidence ◽  
Urban Areas ◽  
Spatial Scales ◽  
Moran’S I ◽  
Anthropogenic Factors ◽  
Spatial Correlations ◽  
Moran's I ◽  
Average Value ◽  
Gwr Model

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning.

scholarly journals Priestorová analýza dát z povrchového zberu na lokalite Vráble-Fidvár

2021 ◽  
Vol 6 (1-2) ◽  
pp. 35-50
Author(s):  
Dominik Drozd
Keyword(s):  
Spatial Analysis ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Interpolation Method ◽  
Objective Evaluation ◽  
Moran’S I ◽  
Sampled Data ◽  
Moran's I ◽  
Geostatistical Interpolation ◽  
Global And Local

The goal of this study is to introduce selected methods of spatial analysis and their contribution to evaluation of fieldwalking data. Spatial analysis encompasses various methods suitable for identification, objective evaluation and visualization of spatial patterns which are present in obtained data. This article primarily deals with sampled data, collected during a 2007 fieldwalking campaign. The dataset consisting of potsherds was spatially autocorrelated, using the global and local Moran’s I coefficient, which was used to identify clusters of finds. Spatial pattern of the settlement was visualised by geostatistical interpolation method – kriging.

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 Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China

2014 ◽  
Vol 11 (8) ◽  
pp. 2401-2409 ◽  
Author(s):  
W. J. Fu ◽  
P. K. Jiang ◽  
G. M. Zhou ◽  
K. L. Zhao
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Forest Ecosystem ◽  
Forest Litter ◽  
Zhejiang Province ◽  
Moran’S I ◽  
Carbon Density ◽  
Southeastern China ◽  
Moran's I ◽  
Density Values

Abstract. Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south–north) × 6 km (east–west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha−1 to 8841.3 kg ha−1, with an average of 1786.7 kg ha−1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.

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 Sebaran tingkat kemiskinan dan tingkat akses air bersih di Sumatera Selatan

2021 ◽  
Vol 8 (1) ◽  
pp. 12
Author(s):  
Desri Yesi ◽  
Oktaf Juairiyah
Keyword(s):  
Clean Water ◽  
Scatter Diagram ◽  
High Poverty ◽  
Poverty Level ◽  
The People ◽  
Low Levels ◽  
Different Characteristics ◽  
Poverty Levels

The poverty level and the level of access for cleaning water are two interesting variables to analyze. One type of feasible analysis was used scatter diagram. South Sumatra Province with its 17 municipalities has different characteristics in terms of poverty levels and access to clean water for the people. Overall, using the scatter diagram, in 2018 the areas with low poverty levels however high levels of access to clean water (Quadrant I) were Lahat and Penukal Abab Lematang Ilir Regency. The regions with low levels of access to clean water and low levels of poverty (Quadrant II) are Empat Lawang, Banyuasin, Ogan Komering Ulu  Selatan and Prabumulih. The areas with high levels of access to clean water and high poverty (Quadrant III) are Musi Rawas Utara, Musi Banyuasin, Ogan Komering Ilir,  Musi Rawas and Ogan Ilir Regency. The areas with low levels of access to clean water and high poverty (Quadran IV) are Lahat and Penukal Abab Lematang Ilir regency.

Spatial Pattern of Cholera Incidence in Ibadan City

2014 ◽  
Vol 1 (1) ◽  
pp. 112-120
Author(s):  
E. A. Salubi
Keyword(s):  
Spatial Pattern ◽  
Moran’S I ◽  
Moran's I ◽  
Environmental Sanitation ◽  
The Core ◽  
Statistiquement Significatives ◽  
Eau Potable ◽  
Core Areas ◽  
The Impact ◽  
The City

The recurrence of the cholera outbreak in Ibadan is alarming and very little has been documented on its spatial pattern. Hence, this study investigated the spatial pattern of cholera in Ibadan using GIS techniques and spatial statistics. Cholera is a disease that has been known to be associated with poor environmental sanitation. However, the impact of other contributory factors is also becoming evident. The study investigated the pattern of cholera incidence within the city for a period of three years. The process of geocoding was used to match addresses of cholera patients to the districts within Ibadan city. Anselin's Local Moran's I was used to assess statistically significant clusters within the city. The results depict clusters of cholera incidences located more within the core areas of the city characterized by high population density and poor sanitation. This study suggests improved environmental sanitation and provision of potable water supply to the core areas of the city. La récurrence de l'épidémie de choléra à Ibadan est alarmante et très peu a été documentée sur sa configuration spatiale. Par conséquent, cette étude a examiné le schéma spatial du choléra à Ibadan en utilisant des techniques SIG et des statistiques spatiales. Le choléra est une maladie connue pour être associée à un mauvais assainissement de l'environnement. Cependant, l'impact d'autres facteurs contributifs devient également évident. L'étude a examiné le schéma de l'incidence du choléra dans la ville pendant une période de trois ans. Le processus de géocodage a été utilisé pour faire correspondre les adresses des patients atteints de choléra aux districts de la ville d'Ibadan. Le Local Moran's I d'Anselin a été utilisé pour évaluer les grappes statistiquement significatives au sein de la ville. Les résultats décrivent des grappes d'incidences de choléra situées davantage dans les zones centrales de la ville caractérisées par une forte densité de population et un assainissement médiocre. Cette étude suggère une amélioration de l'assainissement de l'environnement et de l'approvisionnement en eau potable dans les zones centrales de la ville.

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