LIPIcs.COSIT.2024.12.pdf
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Exploring Discrete Spatial Heterogeneity Across Quantiles: A Combination Approach of Generalized Lasso and Conditional Quantile Regression (Short Paper)
Authors
Ryo Inoue 
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16th International Conference on Spatial Information Theory (COSIT 2024)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: Conference on Spatial Information Theory (COSIT) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-09-09
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Acknowledgements
We used "At Home Dataset" provided by At Home Co.,Ltd. via IDR Dataset Service of National Institute of Informatics.
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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)
https://doi.org/10.4230/LIPIcs.COSIT.2024.12
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.
Subject Classification
ACM Subject Classification
- Information systems → Geographic information systems
Keywords
- discrete spatial heterogeneity
- generalized lasso
- conditional quantile regression
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References
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