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 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 Evaluating the utility of companion animal tick surveillance practices for monitoring spread and occurrence of human Lyme disease in West Virginia, 2014-2016

2017 ◽  
Author(s):  
Brian Hendricks ◽  
Miguella Mark-Carew ◽  
Jamison Conley
Keyword(s):  
Lyme Disease ◽  
West Virginia ◽  
Spatial Dependence ◽  
Regression Models ◽  
Disease Incidence ◽  
Ordinary Least Squares ◽  
Moran’S I ◽  
Moran's I ◽  
Spatial Lag ◽  
Regression Residuals

Domestic dogs and cats are potentially effective sentinel populations for monitoring occurrence and spread of Lyme disease. Few studies have evaluated the public health utility of sentinel programmes using geo-analytic approaches. Confirmed Lyme disease cases diagnosed by physicians and ticks submitted by veterinarians to the West Virginia State Health Department were obtained for 2014-2016. Ticks were identified to species, and only Ixodes scapularis were incorporated in the analysis. Separate ordinary least squares (OLS) and spatial lag regression models were conducted to estimate the association between average numbers of Ix. scapularis collected on pets and human Lyme disease incidence. Regression residuals were visualised using Local Moran’s I as a diagnostic tool to identify spatial dependence. Statistically significant associations were identified between average numbers of Ix. scapularis collected from dogs and human Lyme disease in the OLS (β=20.7, P<0.001) and spatial lag (β=12.0, P=0.002) regression. No significant associations were identified for cats in either regression model. Statistically significant (P≤0.05) spatial dependence was identified in all regression models. Local Moran’s I maps produced for spatial lag regression residuals indicated a decrease in model over- and under-estimation, but identified a higher number of statistically significant outliers than OLS regression. Results support previous conclusions that dogs are effective sentinel populations for monitoring risk of human exposure to Lyme disease. Findings reinforce the utility of spatial analysis of surveillance data, and highlight West Virginia’s unique position within the eastern United States in regards to Lyme disease occurrence.

scholarly journals Valuing Rural Recreation Amenities: Hedonic Prices for Vacation Rental Houses at Deep Creek Lake, Maryland

2010 ◽  
Vol 39 (3) ◽  
pp. 485-504 ◽  
Author(s):  
Jon P. Nelson
Keyword(s):  
Real Estate ◽  
Least Squares ◽  
Spatial Correlation ◽  
Ordinary Least Squares ◽  
Hedonic Prices ◽  
Spatial Error ◽  
Spatial Lag ◽  
Implicit Prices ◽  
Real Estate Management ◽  
House Size

Hedonic prices are estimated for summer and winter rentals for vacation houses located near a lake and ski-golf resort in rural western Maryland. Regressions for weekly rents are conditioned on house size, quality, and recreation features including lakefront proximity and ski-slope access. Percentage effects and marginal implicit prices indicate that access to recreation is reflected importantly in rental offers. Evaluated at the means, lakefront locations command a premium of $1,100–1,200 per week, and the premium for ski-slope access is $500–600 per week. Unit recreation values are about $18 per person per day for a lakefront location with a private dock and $7 per person per day for a ski-slope location. There are small differences in the unit values for three real estate management agencies. Although there is evidence of spatial correlation in ordinary least squares residuals, estimation of spatial-lag and spatial-error models does not yield substantial changes in the empirical results.

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 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).

Transportation and Urban Development

2018 ◽  
pp. 283-305
Author(s):  
Mehmet Akif Kara
Keyword(s):  
Spillover Effect ◽  
Error Model ◽  
Transportation Infrastructure ◽  
Spatial Error Model ◽  
Test Statistic ◽  
Spatial Econometric ◽  
Spatial Error ◽  
Infrastructure Investments ◽  
Regional Economic Performance ◽  
The Impact

It is noteworthy that there is a substantial literature review that examines the impact of transportation infrastructure on urban and regional economic performance. It is observed that such infrastructure investments are focused on the economic growth as well as the spillover effect in applied studies carried out in this respect. In this study, in which the effects of highway transportation infrastructure on urban output and the spillover effect of these investments are determined using the spatial econometric method, 81 cities in Turkey have been taken into consideration, and according to the results of the study, transportation infrastructure investments in Turkey have been found to contribute positively to urban output. Also, while the Moran's I test statistic reveals the spatial dependence of such investments, the Lagrange multiplier test results also determine the need to use the spatial error model. The spatial error model results reveal the existence of the positive spillover effect of transportation infrastructure investments.

scholarly journals Spatial-Temporal Differentiation Analysis of Agricultural Land Use Intensity and Its Driving Factors at the County Scale: A Case Study in Hubei Province, China

2020 ◽  
Vol 17 (18) ◽  
pp. 6910
Author(s):  
Li Yu ◽  
Zhanqi Wang ◽  
Hongwei Zhang ◽  
Chao Wei
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Hubei Province ◽  
Driving Factors ◽  
The Other ◽  
Moran’S I ◽  
Agricultural Land Use ◽  
Land Use Intensity ◽  
Moran's I ◽  
Continuous Growth

Scientifically characterizing the spatial-temporal distribution characteristics of agricultural land use intensity and analyzing its driving factors are of great significance to the formulation of relevant agricultural land use intensity management policies, the realization of food safety and health, and the achievement of sustainable development goals. Taking Hubei Province as an example, and taking counties as the basic evaluation unit, this paper establishes an agricultural land use intensity evaluation system, explores the spatial autocorrelation of agricultural land use intensity in each county and analyzes the driving factors of agricultural land use intensity. The results show that the agricultural land use intensity in Hubei Province increased as a whole from 2000 to 2016, and the spatial agglomeration about the agricultural land use intensity in Hubei Province experienced a process of continuous growth and a fluctuating decline; the maximum of the Global Moran’s I was 0.430174 (in 2007) and the minimum was 0.148651 (in 2001). In terms of Local Moran’s I, H-H agglomeration units were mainly concentrated in two regions: One comprising the cities of Huanggang, Huangshi and Ezhou, and the other the cities of Xiangyang and Suizhou; the phenomenon is particularly obvious after 2005. On the other hand, factors such as the multiple cropping index (MCI) that reflect farmers’ willingness to engage in agricultural production have a great impact on agricultural land use intensity, the influence of the structure of the industry on agricultural land use intensity varies with the degree of influence of different industries on farmers’ income, and agricultural fiscal expenditure (AFE) has not effectively promoted the intensification of agricultural land use. The present research has important significance for enhancing insights into the sustainable improvement of agricultural land use intensity and for realizing risk control of agricultural land use and development.

scholarly journals A New Error Model and Compensation Strategy of Angle Encoder in Torsional Characteristic Measurement System

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3772 ◽  
Author(s):  
Chen ◽  
Peng ◽  
Huang
Keyword(s):  
Measurement Error ◽  
Measurement System ◽  
Error Compensation ◽  
Error Model ◽  
Angular Measurement ◽  
Geometric Errors ◽  
Spatial Error ◽  
Error Matrix ◽  
The Impact ◽  
Characteristic Measurement

For systems of measurement, geometric errors such as manufacturing and assembly errors could have a significant impact on the accuracy of the angle encoders of the system. In this study, an error model of angular measurement with geometric errors of a torsional characteristic measurement system was developed based on multibody system theory, the aim of which was to reveal the impact of geometric errors on angular measurement and to compensate the measurement error. According to the principle of spatial error transfer, the decomposition of geometric errors is illustrated and the error matrix of geometric errors is constructed by the Denavit–Hartenberg (DH) method. Subsequently, an error compensation function can be obtained and the impact of geometric error on angular measurement is discussed. Finally, we demonstrate by the experimental results of an ultra-autocollimator that the proposed error compensation method reduced the angular measurement error from 3.7% to 0.7%, which shows that the proposed error model can effectively predict the angular measurement error. In addition, it can be seen from the measurement results of the RV reducer that the error of the torsional characteristic measurement system decreased significantly.

Spatial autoregressive analysis of nationwide street network patterns with global open data

2021 ◽  
pp. 239980832098784
Author(s):  
Qi Zhou ◽  
Hao Lin ◽  
Junya Bao
Keyword(s):  
Open Data ◽  
Ordinary Least Squares ◽  
Street Network ◽  
Spatial Error ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Physical Form ◽  
Lag Model ◽  
Spatial Autoregressive ◽  
Network Patterns

The study of street network patterns is beneficial in understanding the layout or physical form of a city. Many studies have analyzed street network patterns, but the similarity and/or difference of street network patterns across a country or region are rarely quantitatively understood. To fill this gap, this research proposes a quantitative analysis of street network patterns nationwide. Specifically, the street network patterns across a country or region were first mapped, and then the relationship between such patterns and various landscape factors (calculated based on global open data) was quantitatively investigated by employing three regression models (ordinary least squares, spatial lag model, and spatial error model). Not only the whole region of China but also its subregions were used as study areas, which involved a total of 362 prefecture-level cities and 2081 built-up areas for analysis. Results showed that (1) similar street network patterns are spatially aggregated; (2) a number of factors, including both land-cover and terrain factors, are found to be significantly correlated with street network patterns; and (3) the spatial lag model is preferred in most of the application scenarios. Not only the analytical method and data can be applied to other countries and regions but also these findings are useful for understanding street network patterns and their associated urban forms in a country or region.

scholarly journals PEMODELAN REGRESI SPASIAL PADA TPT DI DKI JAKARTA, JAWA BARAT, DAN BANTEN TAHUN 2018

2021 ◽  
Vol 2020 (1) ◽  
pp. 728-738
Author(s):  
Muhammad Suprapto ◽  
Gama Putra Danu Sohibien
Keyword(s):  
Lagrange Multiplier ◽  
Moran’S I ◽  
Test Model ◽  
Moran's I ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lm Test ◽  
Lag Model

Pengangguran merupakan masalah utama yang dihadapi di berbagai negara baik negara maju maupun negara berkembang. Masalah pengangguran tidak terlepas kaitannya dengan dimensi wilayah atau dependensi spasial. Keberadaan dependensi spasial menunjukkan bahwa tingkat pengangguran di suatu wilayah akan berhubungan dengan tingkat pengangguran wilayah tetangganya. Penelitian ini bertujuan untuk memberikan gambaran umum Tingkat Pengangguran Terbuka (TPT) dan variabel-variabel yang diduga mempengaruhinya, mendapatkan model terbaik dalam menjelaskan pengaruh variabel-variabel independen terhadap TPT, dan menganalisis pengaruh variabel-variabel independen dari model terbaik terhadap TPT serta hubungan pengangguran antar wilayah. Analisis gambaran umum TPT dilakukan dengan pemetaan. Sedangkan untuk mendapatkan model terbaik dalam menjelaskan pengaruh variabel-variabel independen terhadap TPT diawali dengan pembentukan model regresi linier berganda. kemudian dilanjutkan dengan diagnosis keberadaan efek spasial dengan menggunakan Moran’s I dan Lagrange Multiplier (LM) test. Model terbaik yang terbentuk adalah Spatial Lag Model dengan variabel independen yang signifikan. variabel-variabel independen yang mempengaruhi TPT di DKI Jakarta, Jawa Barat, dan Banten adalah persentase penduduk status kawin dan persentase penduduk miskin.

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