China 
Africa 
Explore chapters and articles related to this topic
Models for Simulating the Impact of Accessibility on Real Estate Prices
Published in Rubén Cordera, Ángel Ibeas, Luigi dell’Olio, Borja Alonso, Land Use–Transport Interaction Models, 2017
Rubén Cordera, Ángel Ibeas, Luigi dell’Olio
If the hedonic model is specified as Equation 10.2, then the estimated parameters can be directly interpreted as semi-elasticities, in other words, the percentage change in the dependent variable per unit change in the independent variable. Furthermore, as the model is linear, it can be directly estimated using ordinary least squares. Nevertheless, hedonic models have received their share of criticism due to various weaknesses. First, they require large trustworthy databases of property prices, and these are sometimes difficult to obtain, create and update. Second, they are prone to errors derived from the important spatial effects that are normally present in real estate data, for example, the presence of sub-markets in certain zones with their own specific equilibriums (spatial heterogeneity) or from the effects of diffusion between the prices of nearby properties (spatial autocorrelation). Spatial hedonic models were developed to control this type of effects using techniques derived from spatial econometrics (Anselin 1988, 2010). Third, the lack of a theoretical guide means that the specification of the models could be erroneous and certain relevant variables could be omitted (Sirmans et al. 2005). However, all these problems can be minimised through researching the available theoretical and empirical literature and by correctly evaluating compliance with the suppositions of the proposed hedonic model.
Valuation of Water Used in Irrigated Crop Production
Published in Robert A. Young, John B. Loomis, Determining the Economic Value of Water, 2014
Robert A. Young, John B. Loomis
With the availability of GIS data, economists performing hedonic analysis have begun to use spatial hedonic models that take into account the fact that prices of nearby land parcel observations are probably spatially dependent on one another, i.e. the observations are not independent as required by ordinary least squares regression. The spatial dependence may be exhibited as “spatial autocorrelation” requiring a spatial lag model, or spatial dependence in the error term requiring a spatial error model. See LeSage and Pace (2009) for an introduction to spatial econometrics. For an application to hedonic property models of irrigation water see Mallios et al. (2009).
Introduction
Published in Robin Lovelace, Jakub Nowosad, Jannes Muenchow, Geocomputation with R, 2019
Robin Lovelace, Jakub Nowosad, Jannes Muenchow
R’s spatial capabilities originated in early spatial packages in the S language (Bivand and Gebhardt, 2000). The 1990s saw the development of numerous S scripts and a handful of packages for spatial statistics. R packages arose from these and by 2000 there were R packages for various spatial methods “point pattern analysis, geostatistics, exploratory spatial data analysis and spatial econometrics”, according to an article24 presented at GeoComputation 2000 (Bivand and Neteler, 2000). Some of these, notably spatial, sgeostat and splancs are still available on CRAN (Rowlingson and Diggle, 1993, 2017; Venables and Ripley, 2002; Majure and Gebhardt, 2016).
Examining the spatial mode in the early market for electric vehicles adoption: evidence from 41 cities in China
Published in Transportation Letters, 2022
Zhengxia He, Yanqing Zhou, Xin Chen, Jianming Wang, Wenxing Shen, Meiling Wang, Wenbo Li
As a branch of econometrics, spatial econometrics involves spatial interaction (spatial autocorrelation) and spatial structure (spatial variation) analysis in the regression model of cross-sectional data and panel data. The spatial weight matrix W is used to describe and understand the potential spatial structure of different geographical units or regions. This description of spatial structure can be classified into four types: contiguity-based spatial weights matrix(Getis 2009)whereby the spatial arrangement is measured by contiguity; inverse-distance-based spatial weights matrix(Yu 2009)which is constructed by measuring distance; economic-based weights matrix(Wang 2013), derived from the measurement of economic attributes(such as economic development levels, cultural background of residents, social environments, social customs, and so on); and the nested weights matrix(Parent and LeSage 2008),which combines the inverse-distance-based spatial weights matrix and economic-based weights matrix organically with the purpose of describing the comprehensiveness and complexity of spatial effects as accurately as possible. Since most of the 41 cities selected in this paper are not adjacent with great distances between them, we employed an inverse-distance-based spatial weights matrix to describe their spatial structure. The standardized expression of this matrix is:
Modeling the impact of large-scale transportation infrastructure development on land cover
Published in Transportation Letters, 2018
Dimitrios Efthymiou, Constantinos Antoniou, Emmanouela Siora, Demetre Argialas
Spatial econometrics are defined as the collection of techniques that deal with peculiarities caused by space in the statistical analysis of regional science models (Anselin 1988). The correlation and covariance of variables are determined by the position of the data points in space. Applications of spatial econometric models are encountered in the fields of economics, transportation, and environmental sciences. The main ‘local’ regression models (the term ‘local’ is used to differentiate them from the linear regression models which are referred as ‘global’) are: (1) the spatial error model (SEM), where a spatial dependence is applied to the error term; (2) the spatial autoregressive model (SAR), which assumes that the spatial dependence is applied on the response variable; (3) the spatial Durbin model which assumes both; and (4) the geographically weighted regression (GWR), introduced by Fotheringham, Charlton, and Brunson (1998). This model does not result in single statistical estimates, but on spatial distributions of these parameters. GWR was first applied to model land prices of the London Metropolitan Area. Another widely used spatiostatistical method that is widely used by econometricians is Kriging. Kriging is a spatial interpolation method defined as the optimal prediction of unknown values from observed data at known locations. While Kriging is applied with continuous regions, spatial autoregression is applied in aggregation into discrete regions. Another difference between Kriging and spatial autoregression is that the latter assumes that spatial interpolation follows a spatial trend including random residuals (Negreiros et al. 2007)
Measurement of inland port spatial relationship: a case study of Yangtze River inland ports
Published in Maritime Policy & Management, 2022
According to Equations (1) and (2), δ is known as the spatial autoregressive coefficient through which the existence of positive or negative spatial dependence among dependent variables (WYt) of n spatial units can be identified. θ represents m × 1 vector of unknown parameters associated with WXt. These terms control the possible correlation between the dependent variable in one spatial unit and independent variables in neighboring units. λ denotes the spatial autocorrelation coefficient of the error term. This term can reflect the unobserved shocks following a spatial pattern. A model that only considers spatially lagged dependent variables is known as ‘spatial lag model’ and is typically labelled as a spatial autoregressive model (SAR), whereas a model with only the spatially lagged term of error is known as a spatial error model (SEM). When both spatially lagged dependent and independent variables are included in the model, the spatial econometrics literature labels it as SDM. The SDM model does not ignore spatial dependence in disturbances, but rather implies a different type of specification for error dependence. Moreover, the SDM nests the SEM because the latter can be derived by imposing an appropriate non-linear restriction on parameters (LeSage and Pace 2009). Therefore, the SDM is a more general model. According to the literature, this more general model should be considered first in spatial econometric analysis. In view of the above analysis, the SDM model was established in this study, that is, the spatial lag terms of dependent variables (WYt) and independent variables (WXt) were considered.