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Documents authored by Inoue, Ryo

Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2024.12
Exploring Discrete Spatial Heterogeneity Across Quantiles: A Combination Approach of Generalized Lasso and Conditional Quantile Regression (Short Paper)

Authors: Ryo Inoue and Kenya Aoki

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

Abstract
Spatial heterogeneity has been investigated extensively. However, in addition to spatial heterogeneity, there are spatial phenomena where heterogeneity in the data generation process exists across quantiles. This study proposes a new method that combines generalized lasso (GL) and conditional quantile regression (CQR) to analyze discrete spatial heterogeneity across quantiles. GL enables the identification of spatial boundaries where the spatial data generation process varies discretely, and CQR estimates the parameters of the conditional quantile of the dependent variable. The proposed method is expressed as a linear programming problem and is simple to use. To validate its effectiveness, we applied this method to apartment rent data in Minato Ward, Tokyo. The results revealed that the neighborhoods where rent levels deviated from the overall trend in the analyzed area differed by quantiles.
Cite as

Ryo Inoue and Kenya Aoki. Exploring Discrete Spatial Heterogeneity Across Quantiles: A Combination Approach of Generalized Lasso and Conditional Quantile Regression (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 12:1-12:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{inoue_et_al:LIPIcs.COSIT.2024.12,
  author =	{Inoue, Ryo and Aoki, Kenya},
  title =	{{Exploring Discrete Spatial Heterogeneity Across Quantiles: A Combination Approach of Generalized Lasso and Conditional Quantile Regression}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{12:1--12:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.12},
  URN =		{urn:nbn:de:0030-drops-208272},
  doi =		{10.4230/LIPIcs.COSIT.2024.12},
  annote =	{Keywords: discrete spatial heterogeneity, generalized lasso, conditional quantile regression}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.59
Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data (Short Paper)

Authors: Zhan Peng and Ryo Inoue

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Spatial analysis of data with compositional structure has gained increasing attention in recent years. However, the spatial heterogeneity of compositional data has not been widely discussed. This study developed a Moran eigenvectors-based spatial heterogeneity analysis framework to investigate the spatially varying relationships between the compositional dependent variable and real-value covariates. The proposed method was applied to municipal-level household income data in Tokyo, Japan in 2018.
Cite as

Zhan Peng and Ryo Inoue. Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 59:1-59:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{peng_et_al:LIPIcs.GIScience.2023.59,
  author =	{Peng, Zhan and Inoue, Ryo},
  title =	{{Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{59:1--59:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.59},
  URN =		{urn:nbn:de:0030-drops-189540},
  doi =		{10.4230/LIPIcs.GIScience.2023.59},
  annote =	{Keywords: Compositional data analysis, Spatial heterogeneity, Moran eigenvectors}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.32
Identification of Geographical Segmentation of the Rental Apartment Market in the Tokyo Metropolitan Area (Short Paper)

Authors: Ryo Inoue, Rihoko Ishiyama, and Ayako Sugiura

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
It is often said that the real estate market is divided geographically in such a manner that the value of attributes of real estate properties is different for each area. This study proposes a new approach to the investigation of the geographical segmentation of the real estate market. We develop a price model with many regional explanatory variables, and implement the generalized fused lasso - a regression method for promoting sparsity - to extract the areas where the valuation standard is the same. The proposed method is applied to rental data of apartments in the Tokyo metropolitan area, and we find that the geographical segmentation displays hierarchal patterns. Specifically, we observe that the market is divided by wards, railway lines and stations, and neighbourhoods.
Cite as

Ryo Inoue, Rihoko Ishiyama, and Ayako Sugiura. Identification of Geographical Segmentation of the Rental Apartment Market in the Tokyo Metropolitan Area (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 32:1-32:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{inoue_et_al:LIPIcs.GISCIENCE.2018.32,
  author =	{Inoue, Ryo and Ishiyama, Rihoko and Sugiura, Ayako},
  title =	{{Identification of Geographical Segmentation of the Rental Apartment Market in the Tokyo Metropolitan Area}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{32:1--32:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.32},
  URN =		{urn:nbn:de:0030-drops-93608},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.32},
  annote =	{Keywords: geographical market segmentations, rental housing market, sparse modelling, generalised fused lasso, Tokyo metropolitan area}
}
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