hotspots detection in spatial analysis via the extended gustafson-kessel algorithm

Clicks: 345
ID: 222978
2013
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #5 of 28 articles by views in jurnal teknologi dan sistem komputer

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
We show a new approach for detecting hotspots in spatial analysis based on the extended Gustafson-Kessel clustering method encapsulated in a Geographic Information System (GIS) tool. This algorithm gives (in the bidimensional case) ellipses as cluster prototypes to be considered as hotspots on the geographic map and we study their spatiotemporal evolution. The data consist of georeferenced patterns corresponding to positions of Taliban’s attacks against civilians and soldiers in Afghanistan that happened during the period 2004–2010. We analyze the formation through time of new hotspots, the movement of the related centroids, the variation of the surface covered, the inclination angle, and the eccentricity of each hotspot.
Reference Key
martino2013advanceshotspots Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ferdinando Di Martino;Salvatore Sessa
Journal jurnal teknologi dan sistem komputer
Year 2013
DOI
10.1155/2013/876073
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.