scholarly journals Reducing spatial autocorrelation in the dynamic simulation of urban growth using eigenvector spatial filtering

2021 ◽  
Vol 102 ◽  
pp. 102434
Author(s):  
Xinlei Yan ◽  
Yongjiu Feng ◽  
Xiaohua Tong ◽  
Pengshuo Li ◽  
Yilun Zhou ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Dynamic Simulation ◽  
Urban Growth ◽  
Spatial Filtering ◽  
Eigenvector Spatial Filtering

scholarly journals Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment

2019 ◽  
Vol 8 (8) ◽  
pp. 332 ◽  
Author(s):  
Huifang Li ◽  
Yumin Chen ◽  
Susu Deng ◽  
Meijie Chen ◽  
Tao Fang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Spatial Autocorrelation ◽  
Landslide Susceptibility ◽  
Predictive Accuracy ◽  
Model Misspecification ◽  
Roc Curves ◽  
Spatial Filtering ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment ◽  
Eigenvector Spatial Filtering

Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran’s I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis.

scholarly journals House price estimation using an eigenvector spatial filtering approach

2019 ◽  
Vol 13 (5) ◽  
pp. 845-867 ◽  
Author(s):  
Michael James McCord ◽  
John McCord ◽  
Peadar Thomas Davis ◽  
Martin Haran ◽  
Paul Bidanset
Keyword(s):  
Spatial Autocorrelation ◽  
House Prices ◽  
Spatial Scales ◽  
House Price ◽  
Spatial Filtering ◽  
Spatial Dependency ◽  
Content Type ◽  
Price Estimation ◽  
Eigenvector Spatial Filtering ◽  
Filtering Approach

Purpose Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity. Design/methodology/approach Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria. Findings The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error. Originality/value Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.

scholarly journals Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1969 ◽  
Author(s):  
Tao Fang ◽  
Yumin Chen ◽  
Huangyuan Tan ◽  
Jiping Cao ◽  
Jiaxin Liao ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Flood Risk ◽  
Poisson Regression ◽  
Yangtze River ◽  
Extreme Rainfall ◽  
Spatial Filtering ◽  
The Yangtze River ◽  
A Value ◽  
Eigenvector Spatial Filtering ◽  
Gis Environment

A Poisson regression based on eigenvector spatial filtering (ESF) is proposed to evaluate the flood risk in the middle reaches of the Yangtze River in China. Regression analysis is employed to model the relationship between the frequency of flood alarming events observed by hydrological stations and hazard-causing factors from 2005 to 2012. Eight factors, including elevation (ELE), slope (SLO), elevation standard deviation (ESD), river density (DEN), distance to mainstream (DIST), NDVI, annual mean rainfall (RAIN), mean annual maximum of three-day accumulated precipitation (ACC) and frequency of extreme rainfall (EXE) are selected and integrated into a GIS environment for the identification of flood-prone basins. ESF-based Poisson regression (ESFPS) can filter out the spatial autocorrelation. The methodology includes construction of a spatial weight matrix, testing of spatial autocorrelation, decomposition of eigenvectors, stepwise selection of eigenvectors and calculation of regression coefficients. Compared with the pseudo R squared obtained by PS (0.56), ESFPS exhibits better fitness with a value of 0.78, which increases by approximately 39.3%. ESFPS identifies six significant factors including ELE, DEN, EXE, DIST, ACC and NDVI, in which ACC and NDVI are the first two main factors. The method can provide decision support for flood risk relief and hydrologic station planning.

scholarly journals A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia

2019 ◽  
Vol 8 (11) ◽  
pp. 508
Author(s):  
Lan Hu ◽  
Yongwan Chun ◽  
Daniel A. Griffith
Keyword(s):  
Multilevel Model ◽  
House Prices ◽  
House Price ◽  
Spatial Filtering ◽  
Neighborhood Environment ◽  
Model Specification ◽  
Spatial Effects ◽  
Residential Areas ◽  
Fairfax County ◽  
Eigenvector Spatial Filtering

House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house prices. Empirical studies show its capability in accounting for neighborhood specific spatial autocorrelation (SA) and analyzing potential factors related to house prices at both individual and neighborhood levels. However, a standard multilevel model specification only considers within-neighborhood SA, which refers to similar house prices within a given neighborhood, but neglects between-neighborhood SA, which refers to similar house prices for adjacent neighborhoods that can commonly exist in residential areas. This oversight may lead to unreliable inference results for covariates, and subsequently less accurate house price predictions. This study proposes to extend a multilevel model using Moran eigenvector spatial filtering (MESF) methodology. This proposed model can take into account simultaneously between-neighborhood SA with a set of Moran eigenvectors as well as potential within-neighborhood SA with a random effects term. An empirical analysis of 2016 and 2017 house prices in Fairfax County, Virginia, illustrates the capability of a multilevel MESF model specification in accounting for between-neighborhood SA present in data. A comparison of its model performance and house price prediction outcomes with conventional methodologies also indicates that the multilevel MESF model outperforms standard multilevel and hedonic models. With its simple and flexible feature, a multilevel MESF model can furnish an appealing and useful approach for understanding the underlying spatial distribution of house prices.

scholarly journals Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

2022 ◽  
Vol 11 (1) ◽  
pp. 67
Author(s):  
Meijie Chen ◽  
Yumin Chen ◽  
John P. Wilson ◽  
Huangyuan Tan ◽  
Tianyou Chu
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Spatial Heterogeneity ◽  
Influencing Factors ◽  
Mainland China ◽  
Spatial Filtering ◽  
Varying Coefficient Model ◽  
Health Risk Factors ◽  
Eigenvector Spatial Filtering ◽  
Spatially Varying

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Incorporating spatial autocorrelation and settlement type segregation to improve the performance of an urban growth model

2019 ◽  
Vol 47 (7) ◽  
pp. 1184-1200 ◽  
Author(s):  
Chao Xu ◽  
Didit O Pribadi ◽  
Dagmar Haase ◽  
Stephan Pauleit
Keyword(s):  
Logistic Regression ◽  
Markov Chain ◽  
Spatial Autocorrelation ◽  
Urban Growth ◽  
Driving Factors ◽  
Autologistic Regression ◽  
Cellular Automata Model ◽  
Urban Growth Model ◽  
Ordinary Logistic Regression ◽  
Settlement Growth

As rapid urbanization and population growth have become global issues, urban growth modeling has become an essential tool for decision-makers to understand how urban growth works in overall dense environments and to assess the sustainability of current urban forms. However, in urban growth models (particularly when incorporating quantitative approaches to include driving factors of urban growth), spatial autocorrelation may influence the overall model performance. In this paper, an empirical study was conducted in the region of Munich, and an integrated urban growth model was tested to explain current urban growth. The modeling contributes to advances in the state of the art by combining a range of driving factors using autologistic regression with a transition probability matrix from the Markov chain method in a cellular automata model simulation. The autologistic regression employed here addresses the impact of spatial autocorrelation compared to ordinary logistic regression. Furthermore, this study compared modeling of overall settlement growth with modeling high- and low-density settlement types separately. Incorporating spatial dependency into the model through application of autologistic regression showed improvements when compared to the ordinary logistic regression model. The Kappa indexes were higher when separately modeling the two types of settlement density compared to modeling overall settlement growth since the driving factors of settlement growth of different densities might be different. From an urban planning perspective, this novel autologistic regression-Markov chain-based cellular automata model is a powerful tool that offers an opportunity for planners and government authorities to gain a more precise understanding of the different urban growth processes that might occur in an urban region similar to the one tested here. It should allow for a better assessment of the potential costs, benefits, and risks of corresponding planning strategies.

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