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 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 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.

scholarly journals Bayesian Estimation of the Functional Spatial Lag Model

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Alassane Aw ◽  
Emmanuel Nicolas Cabral
Keyword(s):  
Bayesian Estimation ◽  
Linear Model ◽  
Unemployment Rate ◽  
Simulation Study ◽  
Data Modeling ◽  
Functional Linear Model ◽  
Bayesian Mcmc ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lag Model

AbstractThe spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

scholarly journals Analiza zróżnicowania sytuacji kobiet na lokalnych rynkach pracy w Polsce w latach 2010–2016

2019 ◽  
Vol 2 (341) ◽  
pp. 99-115
Author(s):  
Karolina Lewandowska‑Gwarda
Keyword(s):  
Seemingly Unrelated Regression ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Unrelated Regression ◽  
Lag Model

Głównym celem artykułu jest ocena sytuacji kobiet na lokalnych rynkach pracy w Polsce oraz analiza jej zróżnicowania w czasie i przestrzeni. Podjęto w nim również próbę specyfikacji determinant badanego zjawiska. W analizach wykorzystano taksonomiczny miernik rozwoju, metody geograficznych systemów informacyjnych, metody eksploracyjnej analizy danych przestrzennych oraz wielorównaniowy model o równaniach pozornie niezależnych z autoregresją przestrzenną SUR‑SLM (Seemingly Unrelated Regression Spatial Lag Model). Badania przeprowadzono na podstawie danych statystycznych dla NUTS4 w latach 2010, 2012, 2014 i 2016. Na podstawie uzyskanych wyników zauważono, że zróżnicowanie sytuacji kobiet na lokalnych rynkach pracy w Polsce nie jest duże, niemniej jednak w nieco lepszej sytuacji są Polki mieszkające w okolicach stolicy oraz w zachodniej części kraju. Stwierdzono również, że sytuacja kobiet na lokalnych rynkach pracy nie zmieniła się znacząco w czasie. Dodatkowo potwierdzono, że nie tylko czynniki ekonomiczne, ale w dużej mierze również społeczne wpływają na analizowane zjawisko.

Spatial Aspects of Mortality Rates and Neighborhood Environmental Characteristics in Seoul Mega City Region, South Korea

2015 ◽  
Vol 6 (4) ◽  
pp. 44-64
Author(s):  
Yoohyung Joo ◽  
Hee Yeon Lee
Keyword(s):  
Spatial Patterns ◽  
Statistical Significance ◽  
Mortality Rates ◽  
Spatial Disparity ◽  
City Region ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Spatial Aspect ◽  
Lag Model ◽  
Gwr Model

This study of the spatial patterns of standardized mortality rates (SMRs) in Seoul Mega City Region (SMCR) explores whether neighborhood characteristics affect mortality rates and identifies important determinants of spatial disparity in mortality rates in SMCR. Spatial patterns of mortality rates show a strong positive spatial autocorrelation, suggesting that mortality rates are spatially clustered. A spatial lag model and a GWR model were used to reflect the spatial aspect of mortality rates. The spatial lag model showed better model fitness by considering spatial dependence of mortality rates. It indicates that a higher level of residential deprivation, a less walkable environment, less economic affluence and less social participation are all associated with higher mortality rates with statistical significance. This study suggests that health and welfare policy could incorporate urban planning to consider the neighborhood factors which determine mortality rates in order to improve the health of neighborhood residents.

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