TY - JOUR
T1 - A geo-computational algorithm for exploring the structure of diffusion progression in time and space
AU - Chin, Wei Chien Benny
AU - Wen, Tzai Hung
AU - Sabel, Clive E.
AU - Wang, I. Hsiang
N1 - Funding Information:
The research was supported by the grants of the Ministry of Science and Technology in Taiwan (MOST 104-2627-M-002-020; MOST 105-2627-M-002-018). The funder had no role in the study design, data collection and analysis or in the preparation of the manuscript.
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - A diffusion process can be considered as the movement of linked events through space and time. Therefore, space-time locations of events are key to identify any diffusion process. However, previous clustering analysis methods have focused only on space-time proximity characteristics, neglecting the temporal lag of the movement of events. We argue that the temporal lag between events is a key to understand the process of diffusion movement. Using the temporal lag could help to clarify the types of close relationships. This study aims to develop a data exploration algorithm, namely the TrAcking Progression In Time And Space (TaPiTaS) algorithm, for understanding diffusion processes. Based on the spatial distance and temporal interval between cases, TaPiTaS detects sub-clusters, a group of events that have high probability of having common sources, identifies progression links, the relationships between sub-clusters, and tracks progression chains, the connected components of sub-clusters. Dengue Fever cases data was used as an illustrative case study. The location and temporal range of sub-clusters are presented, along with the progression links. TaPiTaS algorithm contributes a more detailed and in-depth understanding of the development of progression chains, namely the geographic diffusion process.
AB - A diffusion process can be considered as the movement of linked events through space and time. Therefore, space-time locations of events are key to identify any diffusion process. However, previous clustering analysis methods have focused only on space-time proximity characteristics, neglecting the temporal lag of the movement of events. We argue that the temporal lag between events is a key to understand the process of diffusion movement. Using the temporal lag could help to clarify the types of close relationships. This study aims to develop a data exploration algorithm, namely the TrAcking Progression In Time And Space (TaPiTaS) algorithm, for understanding diffusion processes. Based on the spatial distance and temporal interval between cases, TaPiTaS detects sub-clusters, a group of events that have high probability of having common sources, identifies progression links, the relationships between sub-clusters, and tracks progression chains, the connected components of sub-clusters. Dengue Fever cases data was used as an illustrative case study. The location and temporal range of sub-clusters are presented, along with the progression links. TaPiTaS algorithm contributes a more detailed and in-depth understanding of the development of progression chains, namely the geographic diffusion process.
UR - http://www.scopus.com/inward/record.url?scp=85030528086&partnerID=8YFLogxK
U2 - 10.1038/s41598-017-12852-z
DO - 10.1038/s41598-017-12852-z
M3 - Article
C2 - 28974752
AN - SCOPUS:85030528086
SN - 2045-2322
VL - 7
SP - 1
EP - 13
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 12565
ER -