Abstract
Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. To solve this clustering problem we propose to use the change-point methodology. we consider a Sequential Importance Sampling approach to change-point methodology using Monte Carlo simulation to find estimates of change-points as well as parameters on each domain. Numerical experiments illustrate the effectiveness of the approach. We applied this method to artificially generated data set and compared with the results obtained via binary segmentation procedure. We also provide example with real data set to illustrate the usefulness of this method.
| Language | English |
|---|---|
| Title of host publication | Recent advances in computational optimization |
| Subtitle of host publication | results of the Workshop on Computational Optimization WCO 2017 |
| Editors | Stefka Fidanova |
| Place of Publication | Switzerland |
| Publisher | Springer-VDI-Verlag GmbH & Co. KG |
| Chapter | 10 |
| Pages | 175-189 |
| Number of pages | 15 |
| ISBN (Electronic) | 9783319996486 |
| ISBN (Print) | 9783319996479 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 795 |
| ISSN (Print) | 1860-949X |
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Identifying clusters in spatial data via sequential importance sampling. / Raveendran, Nishanthi; Sofronov, Georgy.
Recent advances in computational optimization: results of the Workshop on Computational Optimization WCO 2017. ed. / Stefka Fidanova. Switzerland : Springer-VDI-Verlag GmbH & Co. KG, 2019. p. 175-189 (Studies in Computational Intelligence; Vol. 795).Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review
TY - CHAP
T1 - Identifying clusters in spatial data via sequential importance sampling
AU - Raveendran, Nishanthi
AU - Sofronov, Georgy
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. To solve this clustering problem we propose to use the change-point methodology. we consider a Sequential Importance Sampling approach to change-point methodology using Monte Carlo simulation to find estimates of change-points as well as parameters on each domain. Numerical experiments illustrate the effectiveness of the approach. We applied this method to artificially generated data set and compared with the results obtained via binary segmentation procedure. We also provide example with real data set to illustrate the usefulness of this method.
AB - Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. To solve this clustering problem we propose to use the change-point methodology. we consider a Sequential Importance Sampling approach to change-point methodology using Monte Carlo simulation to find estimates of change-points as well as parameters on each domain. Numerical experiments illustrate the effectiveness of the approach. We applied this method to artificially generated data set and compared with the results obtained via binary segmentation procedure. We also provide example with real data set to illustrate the usefulness of this method.
UR - http://www.scopus.com/inward/record.url?scp=85053552280&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-99648-6_10
DO - 10.1007/978-3-319-99648-6_10
M3 - Chapter
SN - 9783319996479
T3 - Studies in Computational Intelligence
SP - 175
EP - 189
BT - Recent advances in computational optimization
A2 - Fidanova, Stefka
PB - Springer-VDI-Verlag GmbH & Co. KG
CY - Switzerland
ER -