Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time.

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Title: Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time.
Authors: Masrur, Arif1,2, arif@psu.edu, Yu, Manzhu1,3, Mitra, Prasenjit4, Peuquet, Donna1, Taylor, Alan2,5
Source: International Journal of Geographical Information Science; Apr2022, Vol. 36 Issue 4, p692-719, 28p
Database: Applied Science & Technology Source
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DbLabel: Applied Science & Technology Source
An: 155952411
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  Data: Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>; Apr2022, Vol. 36 Issue 4, p692-719, 28p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=155952411
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/13658816.2021.1965608
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      – Code: eng
        Text: English
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        PageCount: 28
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      – TitleFull: Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time.
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              Text: Apr2022
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              Y: 2022
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              Value: 36
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