| Title | Fostering Smart City Development In Developing Nations: A Crime Series Data Analytics Approach |
|---|---|
| ID_Doc | 38401 |
| Authors | Isafiade, OE; Baguio, AB |
| Title | Fostering Smart City Development In Developing Nations: A Crime Series Data Analytics Approach |
| Year | 2017 |
| Published | |
| Abstract | Crime remains a challenge in many parts of the world. This is compounded in low-resource settings where police are short-staffed and there are not enough technological solutions in place to assist security agencies with knowledge-driven decision support. While most smart city initiatives have placed emphasis on the use of modern technology such as armed weapons for fighting crime, this may not be sufficient to achieve a sustainable safe and smart city in resource constrained environments, such as in Africa. In particular crime series which is a set of crimes considered to have been committed by the same offender is currently less explored in developing nations despite its importance for public safety improvement. This research presents a novel crime clustering model, CriClust, based on a dual threshold scheme for crime series pattern (CSP) detection and mapping to derive useful knowledge from a crime dataset. Based on analysis of 5500 (rape) crime records across 40 locations (suburbs) in Western Cape, CriClust led to the identification of up to three series at some of the locations investigated. We present an effective web-based system that security agencies can use for timely CSP identification to aid strategic and viable means of combating crime in low resource settings. |
Similar Articles
| ID | Score | Article |
|---|---|---|
44128
|
Butt, UM; Letchmunan, S; Hassan, FH; Ali, M; Baqir, A; Koh, TW; Sherazi, HHR Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities(2021) |