The Influence of the Structure of Spatial Weight Matrix on Regression Analysis in the Presence of Spatial Autocorrelation

2000 ◽  
Vol 17 ◽  
pp. 321-325 ◽  
Author(s):  
Morito Tsutsumi ◽  
Hiroshi Ide ◽  
Eihan Shimizu
Keyword(s):  
Regression Analysis ◽  
Spatial Autocorrelation ◽  
Weight Matrix ◽  
Spatial Weight Matrix ◽  
Spatial Weight

scholarly journals ANALYSING LOCATION ATTRIBUTES WITH A HEDONIC MODEL FOR APARTMENT PRICES IN DONETSK, UKRAINE

2007 ◽  
Vol 11 (3) ◽  
pp. 157-178 ◽  
Author(s):  
Marko Kryvobokov ◽  
Mats Wilhelmsson
Keyword(s):  
Spatial Autocorrelation ◽  
Weight Matrix ◽  
Hedonic Model ◽  
City Centre ◽  
Land Market ◽  
Spatial Weight Matrix ◽  
Spatial Weight ◽  
The City ◽  
Land Assessment

A hedonic model is specified for asking prices for apartments in Donetsk (Ukraine). This model is used to determine statistically significant location attributes. These attributes can be used for land assessment in a city where data on the land market are lacking. Distance gradients for CBD accessibility are investigated in different geographical directions. Separate models are created for sub‐samples located inside and outside the city centre. A spatial weight matrix is used to detect spatial autocorrelation. The regression results are compared with the valuation of experts. Vietos atributų analizė su hedonistiniu modeliu siekiant nustatyti butų kainas Donceke (Ukraina) Santrauka Apibrėžtas hedonistinis modelis, leidžiantis nustatyti butu kainas Donecke (Ukraina). Pagal ši modeli nustatomi statistiškai reikšmingi vietos atributai. Šiuos atributus galima naudoti vertinant sklypus mieste, kur trūksta duomenų apie žemes rinka. Nagrinėjami atstumo gradientai siekiant įvertinti prieiga prie centriniu verslo rajonu įvairiomis geografinėmis kryptimis. Sukurti modeliai bandomiesiems objektams, esantiems miesto centre ir už jo. Remiantis erdves svorine matrica, nustatoma erdves autokoreliacija. Regresijos rezultatai lyginami su ekspertu vertinimais.

scholarly journals Spatial Correlation and Convergence Analysis of Eco-Efficiency in China

2019 ◽  
Vol 11 (9) ◽  
pp. 2490 ◽  
Author(s):  
Defeng Zheng ◽  
Shuai Hao ◽  
Caizhi Sun ◽  
Leting Lyu
Keyword(s):  
Spatial Autocorrelation ◽  
Mainland China ◽  
Economic Transformation ◽  
Weight Matrix ◽  
Spatial Distance ◽  
Spatial Proximity ◽  
Conditional Convergence ◽  
Spatial Weight Matrix ◽  
Spatial Weight ◽  
Measure Model

In this paper, we first measured the eco-efficiency of 31 provinces in China during 2000–2015 using the SBM (Slack-Based Measure) model, and the spatial character of eco-efficiency was identified based on symmetrical spatial weight matrix. We then proposed a new asymmetrical spatial weight matrix based on the eco-economic transformation index (EETI)-distance reciprocal principle to assess the spatial character of eco-efficiency. Finally, we analyzed the convergence of eco-efficiency’s total factor productivity (EETFPs) in mainland China and in three major regions based on the results of EETFP. The study revealed the following findings: (1) There were some limitations to the spatial autocorrelation of eco-efficiency in mainland China by the symmetrical spatial weight methods based on the spatial proximity principle or spatial distance principle. However, the new spatial weight scheme improved the reliability of the accounting results of the spatial autocorrelation. (2) The clustering effect of eco-efficiency exhibited a downward trend in mainland China during the study period; meanwhile, the significant high-high and low-high clustering areas were located in the eastern, the central, and the western regions. (3) The study of convergence showed that there was a club-convergence phenomenon in mainland China, and except for the western region, all the regions expressed conditional convergence. The results provide a significant reference for ecological-economy management and sustainable development in China.

scholarly journals Research on the Spatial Correlation and Spatial Lag of COVID-19 Infection Based on Spatial Analysis

2021 ◽  
Vol 13 (21) ◽  
pp. 12013
Author(s):  
Keqiang Dong ◽  
Liao Guo
Keyword(s):  
Spatial Analysis ◽  
Spatial Correlation ◽  
Lagrange Multiplier ◽  
Weight Matrix ◽  
Primary Data ◽  
Lagrange Multiplier Test ◽  
Spatial Lag ◽  
Spatial Weight Matrix ◽  
The World ◽  
Spatial Weight

COVID-19 has spread throughout the world since the virus was discovered in 2019. Thus, this study aimed to identify the global transmission trend of the COVID-19 from the perspective of the spatial correlation and spatial lag. The research used primary data collected of daily increases in the amount of COVID-19 in 14 countries, confirmed diagnosis, recovered numbers, and deaths. Findings of the Moran index showed that the propagation of infection was aggregated between 9 May and 21 May based on the composite spatial weight matrix. The results from the Lagrange multiplier test indicated the COVID-19 patients can infect others with a lag.

scholarly journals Spatial weight matrix in dimensionality reduction reconstruction for micro-electromechanical system-based photoacoustic microscopy

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Yuanzheng Ma ◽  
Chang Lu ◽  
Kedi Xiong ◽  
Wuyu Zhang ◽  
Sihua Yang
Keyword(s):  
Image Reconstruction ◽  
Dimensionality Reduction ◽  
Optical Resolution ◽  
Distortion Correction ◽  
Electromechanical System ◽  
Weight Matrix ◽  
Photoacoustic Microscopy ◽  
Spatial Weight Matrix ◽  
Spatial Weight ◽  
Micro Electromechanical System

AbstractA micro-electromechanical system (MEMS) scanning mirror accelerates the raster scanning of optical-resolution photoacoustic microscopy (OR-PAM). However, the nonlinear tilt angular-voltage characteristic of a MEMS mirror introduces distortion into the maximum back-projection image. Moreover, the size of the airy disk, ultrasonic sensor properties, and thermal effects decrease the resolution. Thus, in this study, we proposed a spatial weight matrix (SWM) with a dimensionality reduction for image reconstruction. The three-layer SWM contains the invariable information of the system, which includes a spatial dependent distortion correction and 3D deconvolution. We employed an ordinal-valued Markov random field and the Harris Stephen algorithm, as well as a modified delay-and-sum method during a time reversal. The results from the experiments and a quantitative analysis demonstrate that images can be effectively reconstructed using an SWM; this is also true for severely distorted images. The index of the mutual information between the reference images and registered images was 70.33 times higher than the initial index, on average. Moreover, the peak signal-to-noise ratio was increased by 17.08% after 3D deconvolution. This accomplishment offers a practical approach to image reconstruction and a promising method to achieve a real-time distortion correction for MEMS-based OR-PAM.

scholarly journals On Misspecification of Spatial Weight Matrix for Small Area Estimation in Longitudinal Analysis

2013 ◽  
Vol 15 (4) ◽  
pp. 305-318
Author(s):  
Tomasz Żądło
Keyword(s):  
Sample Size ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Weight Matrix ◽  
Random Sample Size ◽  
Monte Carlo Simulation Study ◽  
Spatial Weight Matrix ◽  
First Case ◽  
Spatial Weight ◽  
Random Components

The problem of prediction of subpopulation (domain) total is studied as in Rao (2003). Considerations are based on spatially correlated longitudinal data. The domain of interest can be defined after sample selection what implies its random sample size. The special case of the General Linear Mixed Model is proposed where two random components obey assumptions of spatial and temporal moving average process respectively. Moreover, it is assumed that the population may change in time and elements’ affiliations to subpopulation may change in time as well. The proposed model is a generalization of longitudinal models studied by e.g. Verbeke, Molenberghs (2000) and Hedeker, Gibbons (2006). The best linear unbiased predictor (BLUP) is derived. It may be used even if the sample size in the subpopulation of interest in the period of interest is zero. In the Monte Carlo simulation study the accuracy of the empirical version of the BLUP will be studied in the case of correct and incorrect specification of the spatial weight matrix. Two cases of model misspecification are studied. In the first case the misspecified spatial weight is used. In the second case independence of random components is assumed but the variable which is used to compute elements of spatial weight matrix in the correct case will be used as auxiliary variable in the model.

scholarly journals New Robust PCA for Outliers and Heavy Sparse Noises’ Detection via Affine Transformation, the L ∗ , w and L 2,1 Norms, and Spatial Weight Matrix in High-Dimensional Images: From the Perspective of Signal Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Peidong Liang ◽  
Habte Tadesse Likassa ◽  
Chentao Zhang ◽  
Jielong Guo
Keyword(s):  
Signal Processing ◽  
Extreme Values ◽  
Weight Matrix ◽  
High Dimensional ◽  
Affine Transformations ◽  
Spatial Weight Matrix ◽  
Art Works ◽  
Spatial Weight ◽  
Recursive Equations

In this paper, we propose a novel robust algorithm for image recovery via affine transformations, the weighted nuclear, L ∗ , w , and the L 2,1 norms. The new method considers the spatial weight matrix to account the correlated samples in the data, the L 2,1 norm to tackle the dilemma of extreme values in the high-dimensional images, and the L ∗ , w norm newly added to alleviate the potential effects of outliers and heavy sparse noises, enabling the new approach to be more resilient to outliers and large variations in the high-dimensional images in signal processing. The determination of the parameters is involved, and the affine transformations are cast as a convex optimization problem. To mitigate the computational complexity, alternating iteratively reweighted direction method of multipliers (ADMM) method is utilized to derive a new set of recursive equations to update the optimization variables and the affine transformations iteratively in a round-robin manner. The new algorithm is superior to the state-of-the-art works in terms of accuracy on various public databases.

scholarly journals Spatial weight matrix impact on real estate hierarchical clustering in the process of mass valuation

2019 ◽  
Vol 10 (1) ◽  
pp. 131-151
Author(s):  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Hierarchical Clustering ◽  
Information Entropy ◽  
Value Added ◽  
Weight Matrix ◽  
Spatial Constraints ◽  
Spatial Weight Matrix ◽  
Real Estate Valuation ◽  
Spatial Weight ◽  
The Impact

Research background: The value of the property can be determined on an individual or mass basis. There are a number of situations in which uniform and relatively fast results obtained by means of mass valuation undoubtedly outweigh the advantages of the individual approach. In literature and practice there are a number of different types of models of mass valuation of real estate. For some of them it is postulated or required to group the valued properties into homogeneous subset due to various criteria. One such model is Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA). When using this algorithm, the area to be valued should be divided into the so-called location attractiveness areas (LAZ). Such division can be made in many ways. Regardless of the method of clustering, its result should be assessed, depending on the degree of implementation of the adopted criterion of division quality. A better division of real estate will translate into more accurate valuation results. Purpose of the article: The aim of the article is to present an application of hierarchical clustering with a spatial constraints algorithm for the creation of LAZ. This method requires the specification of spatial weight matrix to carry out the clustering process. Due to the fact that such a matrix can be specified in a number of ways, the impact of the proposed types of matrices on the clustering process will be described. A modified measure of information entropy will be used to assess the clustering results. Methods: The article utilises the algorithm of agglomerative clustering, which takes into account spatial constraints, which is particularly important in the context of real estate valuation. Homogeneity of clusters will be determined with the means of information entropy. Findings & Value added: The main achievements of the study will be to assess whether the type of the distance matrix has a significant impact on the clustering of properties under valuation.

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