IDEAS home Printed from https://ideas.repec.org/p/toh/dssraa/124.html
   My bibliography  Save this paper

Convolutional regression for big spatial data

Author

Listed:
  • Yasumasa Matsuda
  • Xin Yuan

Abstract

Recently it is common to collect big spatial data on a national or continental scale at discrete time points. This paper aims at a regression model when both dependent and independent variables are big spatial data. Regarding spatial data as functions over a region, we propose a functional regression by a parametric convolution kernel together with the least squares estimation on the frequency domain by applying Fourier transform. It can handle massive datasets with asymptotic validations under the mixed asymptotics. The regression is applied to Covid-19 weekly new cases and human mobility collected in city levels all over Japan to find that an increase of human mobility is followed by an increase of Covid-19 new cases in time lag of two weeks.

Suggested Citation

  • Yasumasa Matsuda & Xin Yuan, 2022. "Convolutional regression for big spatial data," DSSR Discussion Papers 124, Graduate School of Economics and Management, Tohoku University.
    • Handle: RePEc:toh:dssraa:124
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10097/00133836
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:toh:dssraa:124. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tohoku University Library (email available below). General contact details of provider: https://edirc.repec.org/data/fetohjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.