scholarly journals Modeling Severe Acute Respiratory Syndrome Coronavirus 2019 (SARS-CoV-19) Incidence across Conterminous US Counties: A Spatial Perspective

2021 ◽  
Vol 4 ◽  
pp. 1-9
Author(s):  
Olawale Oluwafemi ◽  
Oluseyi Oladepo
Keyword(s):  
Health Outcomes ◽  
Urban Migration ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Incidence And Mortality ◽  
Lag Model ◽  
Spatial Autoregressive ◽  
Socio Economic Variables ◽  
Spatial Autoregressive Models ◽  
Urban Conditions

Abstract. This study examines the spatial distribution of COVID-19 incidence and mortality rates across the counties in the conterminous US in the first 604 days of the pandemic. The dataset was acquired from Emory University, Atlanta, United States, which includes socio-economic variables and health outcomes variables (N = 3106). OLS estimates accounted for 31% of the regression plain (adjusted R2 = 0.31) with AIC value of 9263, and Breusch-Pagan test for heteroskedasticity indicated 472.4, and multicollinearity condition number of 74.25. This result necessitated spatial autoregressive models, which were performed on GeoDa 1.18 software. ArcGIS 10.7 was used to map the residuals and selected significant variables. Generally, the Spatial Lag Model (SLM) and Spatial Error Model (SEM) models accounted for substantial percentages of the regression plain. While the efficiency of the models is the order of SLM (AIC: 8264.4: BreucshPagan test: 584.4; Adj. R2 = 0.56) > SEM (AIC: 8282.0; Breucsh-Pagan test: 697.2; Adj. R2 = 0.56). In this case, the least predictive model is SEM. The significant contribution of male, black race, poverty and urban and rural dummies to the regression plain indicated that COVID-19 transmission is more of a function of socio-economic, and rural/urban conditions rather than health outcomes. Although, diabetes and obesity showed a positive relationship with COVID-19 incidence. However, the relationship was relatively low based on the dataset. This study further concludes that the policymakers and health practitioners should consider spatial peculiarities, rural-urban migration and access to resources in reducing the transmission of COVID-19 disease.

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.

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2010 ◽  
Vol 1 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Software Tool ◽  
Hedonic Pricing ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

scholarly journals Contributions from Spatial Models to Non-Life Insurance Pricing: An Empirical Application to Water Damage Risk

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2476
Author(s):  
Maria Victoria Rivas-Lopez ◽  
Roman Minguez-Salido ◽  
Mariano Matilla Matilla Garcia ◽  
Alejandro Echeverria Echeverria Rey
Keyword(s):  
Life Insurance ◽  
Spatial Information ◽  
Spatial Models ◽  
Spatial Error ◽  
Insurance Pricing ◽  
Insurance Portfolio ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
Insurance Data ◽  
Damage Risk

This paper explores the application of spatial models to non-life insurance data focused on the multi-risk home insurance branch. In the pricing modelling and rating process, spatial information should be considered by actuaries and insurance managers because frequencies and claim sizes may vary by region and the premium should be different considering this rating variable. In addition, it is relevant to examine the spatial dependence due to the fact that the frequency of claims in neighbouring regions is often expected to be more closely related than those in regions far from each other. In this paper, a comparison between spatial models, such as spatial autoregressive models (SAR), the spatial error model (SEM), and the spatial Durbin model (SDM), and a non-spatial model has been developed. The data used for this analysis are for a home insurance portfolio located in Spain, from which we have selected peril of water coverage.

scholarly journals Geostatistics of Active Fire Remote Sensing Data - Examples from South Asia

2021 ◽  
Author(s):  
Jyoti U. Devkota
Keyword(s):  
Remote Sensing ◽  
South Asia ◽  
Spatial Statistics ◽  
Geographical Location ◽  
Remote Sensing Data ◽  
Spatial Behavior ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Active Fire ◽  
Lag Model

Abstract Active fires illuminated on the earth surface are caught by the satellite. These fires are created by various sources such as vegetation fires, gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Near real time active fire data is collected using remote sensing techniques of satellites. Amount of active fires in an area is a proxy indicator of aerosols, green houses gases and trace gases. Here the behavior of active fires over a period of one year in Nepal, Bhutan and Srilanka are studied using spatial statistics. This study is based on data acquired through remote sensing of data acquisition platform, NASA’s MODIS. Spatial statistics is used here to study the incidence of active fires with respect to geographical location. The behavior of parameters of various autoregressive models like Spatial Durban Model, Spatial Lag Model, Spatial Error Model, Manski Model and Kelegian Prucha Model are minutely analyzed. The best model with highest pseudo R2 is selected. The spatial behavior of the fire radiative power for three countries is also predicted using spatial interpolation and kriging. So the burning potential of vegetations in unsampled areas is envisaged by thus predicting FRP. Such studies give a country wise perspective to the behavior of fire; this is with reference to south Asia. They are of great importance for countries of developing world which lack a strong backbone of good quality official records. Through the statistical analyses of data collected by such platforms, important information can be indirectly assessed.

scholarly journals The Effect of Land Use/Cover Change on Soil Erosion Change by Spatial Regression in Changwu County on the Loess Plateau in China

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1209
Author(s):  
Shichuan Yu ◽  
Fei Wang ◽  
Mei Qu ◽  
Binhou Yu ◽  
Zhong Zhao
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Regression ◽  
Ordinary Least Squares ◽  
Spatial Error Model ◽  
The Loess Plateau ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Geographically Weighted Regression Model ◽  
Lag Model

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km2 and 64.93 km2, respectively. In 1997–2007, the planted land increase was mainly through the conversion of cropland. In 2007–2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km−2 year−1, 1305.17 ton km−2 year−1, 1310.60 ton km−2 year−1, and 1891.46 ton km−2 year−1 in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.

scholarly journals GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh

2020 ◽  
Author(s):  
Md. Hamidu Rahman ◽  
Niaz Mahmud Zafri ◽  
Fajle Rabbi Ashik ◽  
Md Waliullah
Keyword(s):  
Health Workers ◽  
Disease Incidence ◽  
Spatial Regression ◽  
Ordinary Least Squares ◽  
Incidence Rates ◽  
Related Factors ◽  
Spatial Error ◽  
Factors Affecting ◽  
Spatial Lag Model ◽  
Lag Model

The outbreak of the COVID-19 pandemic is an unprecedented shock throughout the world which leads to generate a massive social, human, and economic crisis. However, there is a lack of research on geographic modeling of COVID-19 as well as identification of contributory factors affecting the COVID-19 in the context of developing countries. To fulfill the gap, this study aimed to identify the potential factors affecting the COVID-19 incidence rates at the district-level in Bangladesh using spatial regression model (SRM). Therefore, data related to 32 demographic, economic, weather, built environment, health, and facilities related factors were collected and analyzed to explain the spatial variability of this disease incidence. Three global (Ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) SRMs were developed in this study. The results of the models showed that four factors significantly affected the COVID-19 incidence rates in Bangladesh. Those four factors are urban population percentage, monthly consumption, number of health workers, and distance from the capital. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh with a R square value of 78.6%. Findings from this research offer a better insight into the COVID-19 situation and would help to develop policies aimed to prevent the future epidemic crisis.

scholarly journals The value of rural landscape in Aquitania (Colombia): application of spatial hedonic models in real estate analysis

2016 ◽  
Vol 12 (76) ◽  
pp. 155
Author(s):  
Ana Milena Plata Fajardo ◽  
Julio Cañón ◽  
Raffaele Lafortezza
Keyword(s):  
Real Estate ◽  
Structural Characteristics ◽  
Economic Value ◽  
Ordinary Least Squares ◽  
Rural Landscape ◽  
Environmental Amenities ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Lag Model ◽  
Real Estate Economics

This study addresses the marginal economic value of environmental amenities, structural characteristics, neighborhood facilities, and accessibility on property in Aquitania - Colombia. Based on 400 assessed values of rural land property and on 21 characteristic variables of land amenities and facilities, the study compares three models: Ordinary Least Squares (ols), Spatial Lag Model (slm), and Spatial Error Model (sem). Results show that both slm and sem outperformed ols in identifying the significance of real estate attributes. Results shows that farmers value environmental amenities more than other attributes, being implicit the greater value of cattle over agriculture (onion) in land use. These models may help to support decisions in rural real estate economics.

scholarly journals NONTESTABILITY OF EQUAL WEIGHTS SPATIAL DEPENDENCE

2011 ◽  
Vol 27 (6) ◽  
pp. 1369-1375 ◽  
Author(s):  
Federico Martellosio
Keyword(s):  
Spatial Autocorrelation ◽  
Parameter Space ◽  
Spatial Dependence ◽  
Elliptical Distribution ◽  
Spatial Error ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lag Model ◽  
Invariant Test

We show that any invariant test for spatial autocorrelation in a spatial error or spatial lag model with equal weights matrix has power equal to size. This result holds under the assumption of an elliptical distribution. Under Gaussianity, we also show that any test whose power is larger than its size for at least one point in the parameter space must be biased.

scholarly journals Examining the Bus Ridership Demand: Application of Spatio-Temporal Panel Models

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Moshiur Rahman ◽  
Shamsunnahar Yasmin ◽  
Ahmadreza Faghih-Imani ◽  
Naveen Eluru
Keyword(s):  
Public Transit ◽  
Spatial Models ◽  
Spatial Error ◽  
Panel Models ◽  
Spatial Lag Model ◽  
Transit Ridership ◽  
Lag Model ◽  
Spatio Temporal ◽  
Investment Process ◽  
The Impact

An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models. Drawing on stop-level boarding and alighting data for the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobserved factors on transit ridership. Specifically, two spatial models, Spatial Error Model and Spatial Lag Model, are estimated for boarding and alighting separately by employing several exogenous variables including stop-level attributes, transportation and transit infrastructure variables, built environment and land use attributes, and sociodemographic and socioeconomic variables in the vicinity of the stop along with spatial and spatiotemporal lagged variables. The model estimation results are further augmented by a validation exercise. These models are expected to provide feedback to agencies on the benefits of public transit investments while also providing lessons to improve the investment process.

USE OF SPATIAL AUTOCORRELATION TO BUILD REGRESSION MODELS OF TRANSACTION PRICES

2013 ◽  
Vol 21 (4) ◽  
pp. 65-74 ◽  
Author(s):  
Radosław Cellmer
Keyword(s):  
Spatial Autocorrelation ◽  
Regression Models ◽  
Spatial Regression ◽  
Research Work ◽  
Property Market ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Abstract This paper presents the principles of studying global spatial autocorrelation in the land property market, as well as the possibilities of using these regularities for the construction of spatial regression models. Research work consisted primarily of testing the structure of the spatial weights matrix using different criteria and conducting diagnostic tests of two types of models: the spatial error model and the spatial lag model. The paper formulates the hypothesis that the application of spatial regression models greatly increases the accuracy of transaction price prediction while forming the basis for the creation of cartographic documents including, among others, maps of land value.

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