ETD

archivio digitale delle tesi discusse presso l'Università di Pisa

 

Tesi etd-09202018-022855


Tipo di tesi
Tesi di laurea magistrale
Autore
ASGARIFIROUZJAEI, ATEFEH
URN
etd-09202018-022855
Titolo
Multi-scale generalization of mobility data
Struttura
INFORMATICA
Corso di studi
INFORMATICA PER L'ECONOMIA E PER L'AZIENDA (BUSINESS INFORMATICS)
Commissione
relatore Prof. Rinzivillo, Salvatore
correlatore Prof. Monreale, Anna
Parole chiave
  • generalization
  • aggregation
  • mobility data analysis
  • trajectories
Data inizio appello
05/10/2018;
Consultabilità
completa
Riassunto analitico
With widespread availability of low cost GPS devices, it is becoming possible to record data about the movement of people and objects at a large scale. While these data hide important knowledge for the optimization of location and mobility oriented infrastructures and services.<br>Although Visual analytics techniques support understanding of movement behaviors and mobility patterns, and Visual representations is an effective way to provide material for human’s perception and reasoning but movement data (trajectories of moving agents) are hard to visualize: numerous intersections and overlapping between trajectories make the display heavily cluttered and illegible. It is necessary to use appropriate data abstraction methods.<br>Therefore, in this thesis we suggest a method for spatial generalization and aggregation of movement data, which approximate the aggregation of trajectories between areas. <br>We have devised a special method for mapping significant points of trajectories (Voronoi tessellation) and in another step this mapping result used for approximating of aggregation of trajectories for different level of granularity, that is more efficient and less expensive for abstracting trajectories.<br>The approximation of trajectories performed in two different strategies, step by step strategy and jumping strategy. The validity of these methods were checked with different evaluation measures. The suggested method can be used in interactive visual exploration of movement data and for creating legible flow maps for presentation purposes.<br>
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