A study on spatial autocorrelation: Case of Russian regional inflation

2021 ◽  
Vol 64 (4) ◽  
pp. 5-22
Author(s):  
Andrew Kirillov ◽  
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Panel Data ◽  
Spatial Autocorrelation ◽  
Spatial Dependence ◽  
Quantitative Measure ◽  
Moran’S I ◽  
Food Services ◽  
Moran's I

We apply APLE statistic to explore spatial autocorrelation of Russian regional inflationary processes. APLE is discussed to be the fine alternative to Moran’s I. To conduct this study we modify statistics of spatial dependence for panel data structure. We use time series of Russian regional CPIs (i.e. quantitative measure of inflation) of food, non-food, services baskets. We find evidence to confirm the hypothesis of the existence of spatial autocorrelation of regional inflationary processes on the horizon of our study.

scholarly journals Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices

2015 ◽  
Vol 3 (5) ◽  
pp. 463-471 ◽  
Author(s):  
Bianling Ou ◽  
Xin Zhao ◽  
Mingxi Wang
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Moran’S I ◽  
Panel Data Models ◽  
Time Varying ◽  
Moran's I ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Time Invariant ◽  
Over Time

AbstractThe spatial weights matrix is usually specified to be time invariant. However, when it are constructed with economic/socioeconomic distance, trade /demographic/climatic characteristics, these characteristics might be changing over time, and then the spatial weights matrix substantially varies over time. This paper focuses on power of Moran’s I test for spatial dependence in panel data models with where spatial weights matrices can be time varying (TV-Moran). Compared with Moran’s I test with time invariant spatial weights matrices (TI-Moran), the empirical power of TV-Moran test for spatial dependence are evaluated. Our extensive Monte Carlo simulation results indicate that Moran’s I test with misspecified time invariant spatial weights matrices is questionable; Instead, TV-Moran test has shown superiority in higher power, especially for cases with negative spatial correlation parameters and the large time dimension.

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

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.

scholarly journals Spatial Distribution of Cardio-Vascular Diseases in India

2021 ◽  
Author(s):  
Ayantika Biswas ◽  
Shri Kant Singh ◽  
Jitendra Gupta
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Dependence ◽  
Vascular Diseases ◽  
Error Model ◽  
Moran’S I ◽  
Spatial Error Model ◽  
Cvd Risk ◽  
Spatial Error ◽  
Moran's I ◽  
Cardio Vascular

Abstract Objective: Cardio-vascular Diseases (CVDs) are a leading cause of death and disease burden across the world, and the burden is only expected to increase as the population ages. The objective of this paper is to explore the patterns of CVD risk factors among women in the late reproductive ages (35-49 years) across 640 districts in India, and investigate the association between area-level socioeconomic factors and CVD risk patterns., using a nationally representative sample of 239,729 women aged 35–49 years from all 36 States/UTs under NFHS-4 (2015–16). Methods: Age-standardized prevalence of CVDs have been calculated, along with 95% CI among women in their late reproductive ages (35–49 years) in India. The spatial dependence and clustering of CVD burden has been examined by Moran's I indices, bivariate Local Indicator of Spatial Autocorrelation (LISA) cluster and significance maps. Ordinary Least Square (OLS) regression has been employed with CVD prevalence as the outcome variable. To consider for spatial dependence, Spatial Autoregressive (SAR) models have been fitted to the data. Diagnostic tests for spatial dependence have also been carried out to identify the best fit model. Results: Higher values of Moran's I imply high spatial autocorrelation in CVD among districts of India. Smoking, alcohol consumption, hailing from a Scheduled Caste background, more than 10 years of schooling, as well as urban places of residence appeared as significant correlates of CVD prevalence in the country. The spatial error model and the spatial lag model are a marked improvement over the OLS model; among the two, the spatial error model emerging to be the most improved of the lot. Conclusions: A broader course of policy action relating to social determinants can be a particularly effective way of CVD risk addressal. Social policy interventions related to health like reduction in inequalities in factors like education, poverty, unemployment, access to health-promoting physical or built-environments are crucial in tackling the long-term effects of CVD inequalities between geographical areas.

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. 

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.

scholarly journals Approaches to measuring the relevance of geographical dimension of societa inequalities

Geografie ◽  
2009 ◽  
Vol 114 (1) ◽  
pp. 52-65 ◽  
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.

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 SPATIAL ASSOCIATION PATTERNS OF SMOKING, TOBACCO OUTLET DENSITY, AND SECONDHAND SMOKE IN THAILAND

2021 ◽  
Vol 56 (5) ◽  
pp. 351-361
Author(s):  
Pittaya Thammawongsa ◽  
Wongsa Laohasiriwong ◽  
Nakarin Prasit ◽  
Surachai Phimha
Keyword(s):  
Spatial Autocorrelation ◽  
Secondhand Smoke ◽  
Spatial Regression ◽  
Spatial Association ◽  
Moran’S I ◽  
Data Set ◽  
Moran's I ◽  
High Prevalence ◽  
Smoking Behaviors ◽  
Outlet Density

Thailand has a higher prevalence of smoking behaviors which puts people at risk of morbidity and mortality. This study aimed to determine the spatial association of smoking behaviors and their associated factors among the population of Thailand. This study was conducted using a data set from the National Statistical Office of Thailand, 2017. A Moran’s I, local indicators of spatial association (LISA), and spatial regression were used to identify the spatial autocorrelation between tobacco outlet density, the prevalence of secondhand smoke, and smoking behaviors among Thai people. According to the results, among 88,689 participants, the prevalence of smoking behaviors was 18.00 per 1,000 population. There was global spatial autocorrelation between tobacco outlet density, the prevalence of secondhand smoke, and smoking behaviors with the Moran’s I values of 0.120 and 0.375, respectively. The LISA analysis identified significant positive spatial local autocorrelation of smoking behaviors in the form of nine high-high clusters of tobacco outlets density and ten high-high prevalence clusters of secondhand smoke. The prevalence of secondhand smoke predicted smoking behaviors by 62.8 percent. There were spatial associations between tobacco outlet density and secondhand smoke problems that led the youngsters to start smoking. It is a general recommendation to strictly enforce policies and laws to control smoking, and cover all regions in Thailand.

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