Spatio-temporal parse network-based trajectory modeling on the dynamics of criminal justice system

Hong Huang; hong huang

doi:10.1080/02664763.2021.1887101

Spatio-temporal parse network-based trajectory modeling on the dynamics of criminal justice system

University of South Florida

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	Journal of Applied Statistics
DOIs	https://doi.org/10.1080/02664763.2021.1887101
State	Published - 2022

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Data Science

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title = "Spatio-temporal parse network-based trajectory modeling on the dynamics of criminal justice system",

author = "Hong Huang and hong huang",

note = "We extend the existing group-based trajectory modeling by proposing the network-based trajectory modeling based on judicious design and analysis of a spatio-temporal parse network (STPN) as a representation of neighborhood structure that evolves in time. The STPN offers a principled qualitative spec ...",

year = "2022",

doi = "10.1080/02664763.2021.1887101",

language = "American English",

journal = "Journal of Applied Statistics",

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TY - JOUR

T1 - Spatio-temporal parse network-based trajectory modeling on the dynamics of criminal justice system

AU - Huang, Hong

AU - huang, hong

N1 - We extend the existing group-based trajectory modeling by proposing the network-based trajectory modeling based on judicious design and analysis of a spatio-temporal parse network (STPN) as a representation of neighborhood structure that evolves in time. The STPN offers a principled qualitative spec ...

PY - 2022

Y1 - 2022

UR - https://pubmed.ncbi.nlm.nih.gov/35757592/

U2 - 10.1080/02664763.2021.1887101

DO - 10.1080/02664763.2021.1887101

M3 - Article

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

ER -

Spatio-temporal parse network-based trajectory modeling on the dynamics of criminal justice system

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