A Spatially Correlated Model with Generalized Autoregressive Conditionally Heteroskedastic Structure for Counts of Crimes
Crime is a negative phenomenon that affects the daily life of the population and its devel-opment. When modeling crime data, assumptions on either the spatial or the temporal relationship between observations are necessary if any statistical analysis is to be performed. In this paper, we structure s...
Main Authors: | Angulo, J.M (Author), Escudero, I. (Author), Mateu, J. (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI
2022
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package
by: Duncan Lee, et al.
Published: (2018-04-01) -
A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates
by: Congdon, P.
Published: (2022) -
Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review
by: Umair Muneer Butt, et al.
Published: (2020-01-01) -
Bayesian Analysis of Partially Linear Additive Spatial Autoregressive Models with Free-Knot Splines
by: Zhiyong Chen, et al.
Published: (2021-09-01) -
Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model
by: Jianqing Qiu, et al.
Published: (2021-02-01)