Deterministic Spatial Process Models

Deterministic Spatial Process Models is a spatial modeling approach that belongs to the class of spatial process models and is distinguished by its reliance on fixed mathematical rules and exact input parameters to predict spatial phenomena without incorporating randomness or probabilistic variation.

19

Publications

623

Citations

50

Authors

25

Institutions

Publications per year

2017–2026

0

Authors

50

  1. HE
    H. E. Daniels

    University of Birmingham, United Kingdom

    3 publications · 45 citations

    3

    h-index

  2. LH
    Lei He

    University of California, Los Angeles, United States

    2 publications · 59 citations

    2

    h-index

  3. LC
    Lerong Cheng

    University of California, Los Angeles, United States

    2 publications · 59 citations

    2

    h-index

  4. KQ
    Kun Qian

    Southeast University, China

    2 publications · 59 citations

    2

    h-index

  5. PG
    Puneet Gupta

    University of California, Los Angeles, United States

    2 publications · 59 citations

    2

    h-index

1–5 of 50

Venues

  1. Journal of Applied Probability

    JAP · 2 publications · 24 citations

    2

    h-index

  2. Safety Science

    1 publications · 50 citations

    1

    h-index

  3. Metrology and Measurement Systems

    M&M · 1 publications · 35 citations

    1

    h-index

  4. Pure and Applied Geophysics

    PAGEOPH · 1 publications · 13 citations

    1

    h-index

  5. Journal of Econometrics

    1 publications · 121 citations

    1

    h-index

1–5