English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We develop a Bayesian modeling framework to address a pressing real-lifeproblem faced by the police in tackling insurgent gangs. Unlike criminalsassociated with common crimes such as robbery, theft or street crime, insurgentgangs are trained in sophisticated arms and strategise against the governmentto weaken its resolve. They are constantly on the move, operating over largeareas causing damage to national properties and terrorizing ordinary citizens.Different from the more commonly addressed problem of modeling crime-events,our context requires that an approach be formulated to model the movement ofinsurgent gangs, which is more valuable to the police forces in preemptingtheir activities and nabbing them. This paper evolved as a collaborative workwith the Indian police to help augment their tactics with a systematic method,by integrating past data on observed gang-locations with the expert knowledgeof the police officers. A methodological challenge in modeling the movement ofinsurgent gangs is that the data on their locations is incomplete, since theyare observable only at some irregularly separated time-points. Based on aweighted kernel density formulation for temporal data, we analytically derivethe closed form of the likelihood, conditional on incomplete past observeddata. Building on the current tactics used by the police, we device an approachfor constructing an expert-prior on gang-locations, along with a sequentialBayesian procedure for estimation and prediction. We also propose a new metricfor predictive assessment that complements another known metric used in similarproblems.

Movement of insurgent gangs: A Bayesian kernel density model for incomplete temporal data