Geary’s C

2016 ◽  
pp. 1-1
Author(s):  
Xiaobo Zhou ◽  
Henry Lin
Keyword(s):  
2008 ◽  
pp. 329-330
Author(s):  
Xiaobo Zhou ◽  
Henry Lin
Keyword(s):  

2018 ◽  
Vol 8 (8) ◽  
pp. 2594 ◽  
Author(s):  
Nailya K. SHAMSUTDINOVA ◽  
Elmira I. ISIANGULOVA ◽  
Irina A. LAKMAN ◽  
Vadim B. PRUDNIKOV ◽  
Liana F. SADIKOVA

Spatial effects in human development levels among different regions of a territory are important to study in the context of the core-periphery model. We use different methods to study human development index (HDI) for 85 Russian regions. The authors studied the human development index (HDI) for 85 Russian regions. Methods of spatial statistics (econometrics) are used to estimate the ‘spatial gradient’ in economic geography (Moran’s global and local I, Geary’s C, Getis-Ord global G indices). As a weighting matrix we used a contiguity matrix, taking into account the HDI levels only in neighboring regions. Analysis of the global indices of Moran’s I, Geary’s C and Getis-Ord G and Morans scatter plots showed the presence of time-inconsistent spatial autoregressive dependence of the level of HDI in regions of Russia. The ‘spatial gradient’ of the level of human development in Russia is influenced by historically existing imbalances (due to strong oil and gas export-oriented nature of the economy) and insufficient use of human capital. To our view the regional differentiation in human development among the regions is caused primarily by the ‘catching up’ style of Russian economy: human capital is concentrated in regions with already high level of development, although in terms of growth rates Moscow and St. Petersburg are not the leaders. The territorial and geopolitical policies of Russian Federation also influence HDI distribution. For example, huge public investments in the regions of Russian Far East are often ineffective.


2017 ◽  
pp. 645-645
Author(s):  
Xiaobo Zhou ◽  
Henry Lin
Keyword(s):  

2010 ◽  
Vol 16 (1) ◽  
pp. 17-24 ◽  
Author(s):  
P. Jong ◽  
C. Sprenger ◽  
F. Veen

2020 ◽  
Vol 2 (2) ◽  
pp. 151
Author(s):  
S. Sukarna ◽  
Wahidah Sanusi ◽  
Hafilah Hardiono

Analisis spasial merupakan salah satu metode yang sering digunakan dalam melihat pola penyebaran penyakit menular. Penyakit Kusta atau lepra merupakan penyakit menular kronis yang disebabkan oleh bakteri Mycrobacterium Leprae yang penyebarannya melalui droplet. Penelitian ini bertujuan untuk mengetahui pola spasial pada Kusta dengan menggunakan metode Quadrat Analysis, untuk mengetahui ada atau tidaknya autokorelasi spasial antar daerah dengan menggunakan Moran’s I, Geary’s C, Getis-Ord G, dan pemetaan penyebaran penyakit Kusta di Kabupaten Gowa. Pada penelitian ini diperoleh bahwa pola spasial penyebaran penyakit Kusta pada Tahun 2016 dan 2017 di Kabupaten Gowa bersifat mengelompok (clustered). Pada Tahun 2016 terdapat autokorelasi spasial dengan pengujian Moran’s I  dan Geary’s C, sedangkan pengujian Getis-Ord G tidak terdapat autokorelasi spasial antar daerah. Pada Tahun 2017 tidak terdapat autokorelasi spasial antar daerah dengan menggunakan ke tiga pengujian tersebut. Pada Tahun 2016 daerah yang rawan adalah Barombong, daerah yang harus berhati-hati dengan daerah sekitarnya adalah Bontonompo dan daerah yang termasuk kategori aman adalah Tompobulu. Sedangkan pada tahun 2017 daerah yang rawan terhadap penyakit Kusta adalah Bajeng dan Manuju.Kata kunci : Moran’s I, Geary’s C, Getis-Ord G, Moran Scatterplot, Kusta Spatial analysis is one of the methods that is often used to observe spreading pattern of infectious diseases. Leprosy is a chronic infectious disease caused by bacterium Mycrobacterium Leprae which spreads through droplets. This study aims to determine the spatial pattern of leprosy using the Quadrat Analysis method, to determine whether there is spatial autocorrelation between regions using Moran's I, Geary’s C, Getis-Ord G, and mapping the spread of leprosy in Gowa Regency. In this study it was found that the spatial patterns of the spread of leprosy in 2016 and 2017 in Gowa Regency was clustered. In 2016 there were spatial autocorrelations with the tests of Moran's I and Geary's C, while the testing of Getis-Ord G did not have spatial autocorrelation between regions. In 2017 there is no spatial autocorrelation between regions using the three tests. In 2016 the vulnerable areas was Barombong, the area that had to be careful with the surrounding areas was Bontonompo and the area included in the safe category was Tompobulu. Whereas in 2017 areas prone to leprosy were Bajeng and Manuju.Keywords : Moran's I, Geary's C, Getis-Ord G, Moran Scatterplot, Leprosy


2014 ◽  
Vol 15 (1) ◽  
pp. 20-26
Author(s):  
Dmitry Pavlyuk

Abstract This research is devoted to discovering of spatial effects in European airports’ partial factor productivity (PFP). A set of study PFP indicators includes infrastructural (air transport movements per runway), labour (workload units per employee), and financial (revenue and profit per workload unit) ratios. We utilised a number of appropriate statistical tests (Moran’s I., Geary’s C., Mantel test, and spatial auto-regression) for revelation of spatial relationships between PFP indicator’s values. The tests were separately applied to samples of Spanish (2009-2010) and UK airports (2011-2012) and provided evidences of significant spatial effects in data.


Author(s):  
Mevin B. Hooten ◽  
Sierra Pugh ◽  
Carl A. Roland
Keyword(s):  

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