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Spatio-Temporal Analysis of Rates Derived from Count Data Using Generalized Fused Lasso Poisson Model

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Intelligent Decision Technologies (KESIDT 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 352))

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Abstract

We propose a method to statistically analyze rates obtained from count data in spatio-temporal terms, allowing for regional and temporal comparisons. Generalized fused Lasso Poisson model is used to estimate the spatio-temporal effects of the rates; the coordinate descent algorithm is used for estimation. The results of an analysis using data on crime rates in Japan’s Osaka Prefecture from 1995 to 2008 confirm the validity of the approach.

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Acknowledgments

This research was supported by JSPS Bilateral Program Grant Number JPJSBP 120219927 and JSPS KAKENHI Grant Number 20H04151.

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Authors and Affiliations

  1. Department of Statistics, Radiation Effects Research Foundation, Hiroshima, 732-0815, Japan

    Mariko Yamamura

  2. Center for Data-driven Science and Artificial Intelligence, Tohoku University, Sendai, 980-8576, Japan

    Mineaki Ohishi

  3. Mathematics Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8526, Japan

    Hirokazu Yanagihara

Authors
  1. Mariko Yamamura
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  2. Mineaki Ohishi
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  3. Hirokazu Yanagihara
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Corresponding author

Correspondence to Hirokazu Yanagihara .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. KES International Research, Shoreham-by-sea, UK

    R.J. Howlett

  3. KES International, Selby, UK

    Lakhmi C. Jain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yamamura, M., Ohishi, M., Yanagihara, H. (2023). Spatio-Temporal Analysis of Rates Derived from Count Data Using Generalized Fused Lasso Poisson Model. In: Czarnowski, I., Howlett, R., Jain, L.C. (eds) Intelligent Decision Technologies. KESIDT 2023. Smart Innovation, Systems and Technologies, vol 352. Springer, Singapore. https://doi.org/10.1007/978-981-99-2969-6_20

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