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.

scholarly journals Empleo en el sector terciario. Una estimación espacial para los municipios de la región centro de México, 1999-2009

2016 ◽  
Vol 29 (68) ◽  
Author(s):  
Leobardo De Jesús Almonte ◽  
Yolanda Carbajal Suárez
Keyword(s):  
Spatial Autocorrelation ◽  
Central Region ◽  
Spatial Econometrics ◽  
Error Model ◽  
Moran’S I ◽  
Spatial Error Model ◽  
Tertiary Sector ◽  
Spatial Error ◽  
Moran's I ◽  
Spatial Autoregressive

Resumen: el objetivo es identificar un patrón de aglomeración en la división espacial del empleo en el sector servicios entre los municipios de la región centro de México. Con el método de la econometría espacial, se estimó un modelo de error espacial para el sector terciario. Según los resultados, la elasticidad ingreso del empleo es baja, el peso de las unidades económicas es importante y hay poca sensibilidad al incremento en las remuneraciones. A pesar de que el análisis exploratorio, a partir de Índice de Moran, sugiere efectos de autocorrelación espacial del empleo en la región de estudio, esto no se puede confirmar con suficiente robustez a partir de los resultados de la estimación de un modelo de error espacial. Aunque esta técnica no es novedosa, en México hay pocos trabajos aplicados al empleo en el sector terciario. El parámetro autorregresivo espacial del término de error l aporta evidencia de que existe una asociación espacial local del empleo, más que una global. Se concluye que en el sector terciario, la vecindad espacial entre los municipios de mayor dinamismo ha generado más crecimiento y aglomeración, que en el resto de ellos.Palabras clave: división espacial del empleo; sector terciario de México; autocorrelación espacial (I de Moran); análisis económico espacial; modelo de error espacial; región centro de México.Employment in the tertiary sector. A spatial estimation for municipalities in Mexico’s central region, 1999-2009Abstract: the aim is to identify an agglomeration pattern in the services sector’s spatial division of employment among municipalities in Mexico’s central region. By using the method of spatial econometrics, a spatial error model was estimated for the tertiary sector. According to results, the income elasticity of employment is low, the weight of economic unities is significant and there is little sensitivity to an increase in wages. Although the exploratory analysis, based on Moran’s I, suggests effects of spatial autocorrelation of employment in the region studied, this cannot be confirmed with enough certainty from the results of the estimation of a spatial error model. Despite this estimation technique is not new, in Mexico there are few studies dealing with employment in the tertiary sector. The spatial autoregressive parameter of the error term provides evidence that there is a local rather than global spatial association of employment. It therefore follows that in the tertiary sector spatial vicinity among the most dynamic municipalities has generated more growth and agglomeration than in the rest of them.Key words: spatial division of labor; Mexico’s tertiary sector; spatial autocorrelation (Moran’s I); spatial economic analysis; spatial error model; Mexico’s central region.

scholarly journals Model Regresi Spasial dan Aplikasinya dalam Menganalisis Angka Putus Sekolah Usia Wajib Belajar di Provinsi Sulawesi Selatan

2019 ◽  
Vol 1 (2) ◽  
pp. 183
Author(s):  
Wahidah Sanusi ◽  
Hisyam Ihsan ◽  
Nur Hikmayanti Syam
Keyword(s):  
Regression Model ◽  
Spatial Regression ◽  
Predictor Variable ◽  
Error Model ◽  
Drop Out ◽  
Moran’S I ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Moran's I ◽  
School Drop Out

Abstrak. Penduduk Sulawesi Selatan pada kelompok pengeluaran terendah menunjukkan bahwa banyak dari mereka mengalami putus sekolah. Salah satu faktor yang mempengaruhi angka putus sekolah yaitu lokasi antar wilayah. Tujuan penelitian ini adalah untuk mengaplikasikan regresi spasial untuk memodelkan angka putus sekolah di Provinsi Sulawesi Selatan. Pengujian dependensi spasial dan pemilihan model regresi spasial dilakukan menggunakan uji Moran’s I dan Langrange Multiplier (LM). Dari hasil penelitian, kasus putus sekolah untuk tingkat SMP tidak memiliki dependensi spasial baik dalam lag maupun error dan berdasarkan model regresi klasiknya diperoleh variabel prediktor yang signifikan mempengaruhi variabel respon adalah jumlah penduduk miskin . Sedangkan untuk kasus angka putus sekolah tingkat SMA, diperoleh dependensi spasial dalam error sehingga model regresi spasial yang digunakan adalah Spatial Error Model (SEM) dan matriks pembobotnya adalah queen contiguity. Matriks pembobot tersebut menggambarkan ukuran kedekatan antar wilayah pengamatan. Hasil analisis spasial menunjukkan bahwa variabel prediktor yang signifikan mempengaruhi variabel respon adalah jumlah penduduk miskin  dan kepadatan penduduk , dengan nilai  89,78% dan AIC =  430,604.Kata Kunci: Langrange Multiplier, Moran’s I, Putus Sekolah, Regresi Spasial, Spatial Error Model (SEM).  Abstract. The population of South Sulawesi in the lowest expenditure group shows that many of them have dropped out of school. One of the factors that influence the drop out rate is location between regions. The purpose of this study was applying spatial regression to the model drop out rates in South Sulawesi Province. Spatial dependency test and spatial regression model selection were performed using Moran's I and Langrange Multiplier (LM) tests. From the results of the study, the drop out case for junior high school didn’t have spatial dependencies either in lag or error and based on the classical regression model obtained predictor variable significantly affect the response variable was the number of poor people . As for the case of high school drop out rate, obtained spatial dependency in error so that spatial regression model used was Spatial Error Model (SEM) and weighting matrix was queen contiguity. The weighted matrix represents the measure of proximity between observation areas. The result of spatial analysis indicates that the significant predictor variable influencing the response variable was the number of the poor  and the population density , with  = 89.78% and AIC = 430,604.Keywords: Lagrange Multiplier, Moran's I, School Drop Out, Spatial Regression, Spatial Error Model (SEM).

scholarly journals Spatial hedonic pricing models for the valuation of irrigation water

2013 ◽  
Vol 11 (4) ◽  
pp. 575-582 ◽  
Keyword(s):  
Irrigation Water ◽  
Spatial Dependence ◽  
Hedonic Pricing ◽  
Error Model ◽  
Hedonic Model ◽  
Spatial Error Model ◽  
Pricing Models ◽  
Spatial Error ◽  
Lagged Dependent Variable ◽  
Value Of Irrigation Water

The main objective of this work is to apply the hedonic pricing method using the methodology of spatial econometrics in order to assess the economic value of irrigation water, as one of the individual attributes of the value of agricultural land parcels. Most of the agricultural land’s value attributes, like neighbor characteristics as well as the availability of irrigation water, exhibit a spatial variability. This means that the application of a conventional hedonic pricing model, which is based on the assumption of spatial stationarity, may be inefficient and probably introduce bias in the estimation of several parameters. In fact, the spatial effect, and in particular the spatial dependence is a determinant of the efficiency and consistency of the hedonic model. Therefore, two spatial hedonic pricing models and a conventional one are formulated and implemented. Spatial dependence is incorporated in the modeling in two ways: a) by including a spatially lagged dependent variable (spatial lag model) and b) by including the spatial dependence of the error term (spatial error model). The two spatial econometric models together with a conventional model of multiple regression are applied in a typical rural area of Greece. A key feature of the proposed approach is that a GIS analysis of land parcels is a basic component of the modeling procedure. Results from this application show that the spatial methods increase the efficiency and consistency and reduce the bias of the parameter estimates. Moreover, the spatial error model provides better results and it is, therefore, preferred in order to estimate the value of irrigation water.

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 The Impact of Inter-City Traffic Restriction on COVID-19 Transmission from Spatial Econometric Perspective

2021 ◽  
Vol 33 (5) ◽  
pp. 705-716
Author(s):  
Xijin Lu ◽  
Changxi Ma
Keyword(s):  
Spatial Correlation ◽  
Goodness Of Fit ◽  
Ordinary Least Squares ◽  
Hubei Province ◽  
Error Model ◽  
Moran’S I ◽  
Spatial Error ◽  
Moran's I ◽  
Spatial Lag ◽  
The Impact

The aim of this paper is to conduct a spatial correlation study of virus transmission in the Hubei province, China. The number of confirmed COVID-19 cases released by the National Health and Construction Commission, the traffic flow data provided by Baidu migration, and the current situation of Wuhan intercity traffic were collected. The Moran’s I test shows that there is a positive spatial correlation between the 17 cities in the Hubei province. The result of Moran’s I test also shows that four different policies to restrict inter-city traffic can be issued for the four types of cities. The ordinary least squares regression, spatial lag model, spatial error model, and spatial lag error model were built. Based on the analysis of the spatial lag error model, whose goodness of fit is the highest among the four models, it can be concluded that the speed of COVID-19 spread within a certain region is not only related to the current infection itself but also associated with the scale of the infection in the surrounding area. Thus, the spill-over effect of the COVID-19 is also presented. This paper bridges inter-city traffic and spatial economics, provides a theoretical contribution, and verifies the necessity of a lockdown from an empirical point of view.

scholarly journals Spatial Econometric Model on Economic Growth in West Nusa Tenggara

Jurnal Varian ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 153-158
Author(s):  
Siti Soraya ◽  
Baiq Candra Herawati ◽  
Muttahid Shah ◽  
Syaharuddin Syaharuddin
Keyword(s):  
Economic Growth ◽  
Spatial Dependence ◽  
Econometric Model ◽  
Error Model ◽  
Spatial Error Model ◽  
Spatial Econometric ◽  
Spatial Error ◽  
Response Variable ◽  
Spatial Econometric Model ◽  
Capital Value

Gross Regional Domestic Product (GRDP) is a reflection of a region's economic growth. West Nusa Tenggara (NTB) is one of the provinces that contributes to good GRDP for Indonesia. The purpose of this research is to modeling GRDP in NTB using spatial econmetrics. The data used is the GRDP data of each district / city in NTB Province as a response variable and factors that affect the number of workers, capital value and electrification ratio as predictor variables. The results showed that there is a spatial dependence on the district / city GRDP in NTB Province on the error model so that the model formed is the Spatial Error Model (SEM) with a rho of 71.1% and an AIC value of 173.34.

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.

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