Abstract
As the variety and quality of spatial data increase in recent times, the potential to analyze local characteristics based on spatial data is getting stronger. Previous spatial analysis methods structuralize the spatial autocorrelation of data by the distances between data observation points and the contiguity of the data-observed regions. It is significant for the estimation of global characteristics of spatial data. However, these approaches are not suitable for identifying local differences from the data since they assume a smooth spatial autocorrelation structure. Generalized fused lasso, which can detect local differences in spatial data, has been proposed in machine learning studies. Its limitation is that the estimated parameters are biased toward zero; however, methods that overcome the limitation have also been proposed. Fused-MCP is one of those methods and is expected to be useful in spatial analyses. This study applies fused-MCP to spatial analyses. As an example of spatial analyses based on fused-MCP, this study analyzes the structure of geographical segmentation of the real estate market in central Tokyo. Fused-MCP is utilized to extract areas where the valuation standard is the same. The results reveal that the geographical segmentation displays hierarchal patterns. Specifically, the market is divided by municipalities, railway lines and stations, and neighborhoods. The case study confirmed the applicability of fused-MCP to spatial analyses.
Highlights
The recent development of information communication technologies and the progress of open data policies have increased the quality and variety of spatial data rich
As an example of spatial analyses based on fused-minimax concave penalty (MCP), this study introduces an analysis to identify the structure of geographical segmentation of an apartment rental market in central Tokyo and confirms the applicability of fused-MCP to the analysis
This study focused on the applicability of fused-MCP to the spatial analyses that detect local differences from spatial data
Summary
The recent development of information communication technologies and the progress of open data policies have increased the quality and variety of spatial data rich. Weighted regression (Fotheringham et al 2003) aims to estimate the heterogeneous parameters by locations, assuming that the location-based parameters are continuous in space It is a weighted regression in which the weights are set by the functions of the distance between the location where parameters are estimated and the locations where data are observed. The setting of adaptive weights requires initial parameter estimators to allocate small weights on the penalties of non-zero parameters and the large weights on the penalties of zero-parameters Another type of regularization is the concave penalty function; the smoothly clipped absolute deviation (SCAD) penalty proposed by Fan and Li (2001) and the minimax concave penalty (MCP) proposed by Zhang (2010). As an example of spatial analyses based on fused-MCP, this study introduces an analysis to identify the structure of geographical segmentation of an apartment rental market in central Tokyo and confirms the applicability of fused-MCP to the analysis
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