Morito Tsutsumi

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.

Application of Lasso to the Eigenvector Selection Problem in Eigenvector Based Spatial Filtering

Eigenvector based spatial filtering is one of the well-used approaches to model spatial autocorrelation among the observations or errors in a regression model. In this approach, subset of eigenvectors extracted from a modified spatial weight matrix is added to the model as explanatory variables. The subset is typically specified via the forward stepwise model selection procedure, but it is disappointingly slow when the number of observations n takes a large number. Hence as a complement or alternative, the present paper proposes the use of the LASSO (L1-penalized regression) to select the eigenvectors. The LASSO model selection procedure is applied to the well-known Boston housing dataset and simulation dataset, and its performance is compared with that of the stepwise procedure. The obtained results suggest that the LASSO is fairly fast compared the stepwise procedure, and can select eigenvectors effectively even if dataset is relatively large (n = 10000), to which the forward stepwise procedure is uneasy to apply.

Area-to-point parameter estimation with geographically weighted regression

The modifiable areal unit problem (MAUP) is a problem by which aggregated units of data influence the results of spatial data analysis. Standard GWR, which ignores aggregation mechanisms, cannot be considered to serve as an efficient countermeasure of MAUP. Accordingly, this study proposes a type of GWR with aggregation mechanisms, termed area-to-point (ATP) GWR herein. ATP GWR, which is closely related to geostatistical approaches, estimates the disaggregate-level local trend parameters by using aggregated variables. We examine the effectiveness of ATP GWR for mitigating MAUP through a simulation study and an empirical study. The simulation study indicates that the method proposed herein is robust to the MAUP when the spatial scales of aggregation are not too global compared with the scale of the underlying spatial variations. The empirical studies demonstrate that the method provides intuitively consistent estimates.

Practical spatial statisics for areal interpolation

Differences in spatial units among spatial data often complicate analyses. Spatial unit conversion, called areal interpolation, is often applied to address this problem. Of the many proposed areal interpolation methods, few consider spatial autocorrelation, which is the general property of spatial data. In this paper an areal interpolation method is constructed by combining a spatial process model, a primal model in spatial statistics, and the linear-regression-based areal interpolation method. The primal advantages of our methods are twofold: It considers both spatial autocorrelation and the volume-preserving property; it is more practical than other spatial-statistics-based areal interpolation methods. A case study on the areal interpolation of the density of employee numbers is provided to check the properties of our method. This case study shows that our method succeeds in improving predictive accuracy. Furthermore, the areal interpolation result indicates that our method, which provides a smooth interpolation map, is appropriate to model the underlying process of spatially aggregated data. These results indicate that the consideration of spatial autocorrelation is important for areal interpolation.

New Spatial Econometrics–Based Areal Interpolation Method:

Spatial data are often aggregated into spatial units and differences between spatial units can complicate the analysis of the data. One solution to this problem is spatial unit conversion, also called areal interpolation. Of the many areal interpolation methods proposed thus far, few method are based on spatial econometrics: a subset of econometrics which is concerned with the role of spatial autocorrelation (a general property of spatial data that implies that data in nearby locations are similar) in the regional economic model response. In this article, an areal interpolation method that considers both the spatial autocorrelation and the pycnophylactic property (a most basic premise of areal interpolation that the sum of the data given in a specific area must be constant) is proposed by combining a spatial econometric model and a linear regression-based areal interpolation method. Parameters of the proposed method are estimated using the expectation-maximization algorithm. The performance of the proposed method was examined through empirical analysis using real data and ratios on aging populations. The results indicate the importance of considering both the pycnophylactic property and the spatial autocorrelation in areal interpolation. The results also show the applicability of spatial econometrics to areal interpolation problems.

Intraregional Flow Problem in Spatial Econometric Model for Origin—Destination Flows

The gravity model is used in a variety of fields to explain spatial interaction behavior such as transportation, commodity, or migration flows, but the model assumes observed flows to be independent and thus affected by spatial autocorrelation. Recent studies succeeded in modeling origin–destination (OD) flows in a spatial econometric field, implying that considering spatial dependence among flows will improve the accuracy of the model. However, not all OD flow data contain intraregional flows, and no research has been conducted on how to cope with such data. This study focuses on the problem wherein the spatial econometric model for flows proposed by LeSage and Pace (2008 Journal of Regional Science 48 941–967) is not feasible when the flow data do not have intraregional flows. We propose the EM algorithm as a method to overcome this problem and show validity of the proposed method through an application to Japanese migration flow data.

Dealing with Intraregional Flows in Spatial Econometric Gravity Models

While LeSage and Pace (J Reg Sci 48:941–967, 2008) had succeeded in modeling origin–destination (OD) flows by using a spatial econometric approach (spatial econometric gravity model), most studies have not paid appropriate attention towards the treatment of intraregional flows. This study discusses issues regarding intraregional flows within the spatial econometric gravity model, mainly focusing on the definition of internal distances and unobserved intraregional flows, and introduces approaches to deal with associated problems. Specifically, the Expectation-Maximization (EM) algorithm method in Tsutsumi and Tamesue (Environ Plan B 39(6):1006–1015, 2012) and Heckman’s two-step estimation method are discussed for solving the unobserved intraregional flow issue. The results from the application of these methods to Japanese migration data show validity of both approaches. The study also takes an experimental analysis regarding to the difference in nature of interregional and intraregional flows.