A nonparametric test for the comparison of first-order structures of spatial point processes
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https://doi.org/10.1016/j.spasta.2017.02.007 |
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Title
A nonparametric test for the comparison of first-order structures of spatial point processesDate
2017Publisher
ElsevierISSN
2211-6753Bibliographic citation
FUENTES-SANTOS, I.; GONZÁLEZ-MANTEIGA, W.; MATEU, J. A nonparametric test for the comparison of first-order structures of spatial point processes. Spatial Statistics, 2017, vol. 22, p. 240-260Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S2211675316301270Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
Comparing the spatial distribution of two spatial point patterns is an important issue in many scientific areas such as ecology, epidemiology or environmental risk assessment. However, up to date, the analysis of ... [+]
Comparing the spatial distribution of two spatial point patterns is an important issue in many scientific areas such as ecology, epidemiology or environmental risk assessment. However, up to date, the analysis of multitype point processes has been mainly focused on searching for interactions between events of different patterns, i.e. on the second-order structure, while the first-order structure has received less attention. This work proposes testing the similarity between two spatial point patterns through the comparison of their densities of event locations. For this purpose, we consider the usual squared discrepancy measure to propose a nonparametric statistical test. The asymptotic normal distribution of the associated statistic provides a calibration procedure. The simulation study conducted to analyze the performance of the test shows that this calibration can be too conservative and supports the use of a proposed bootstrap calibration. The performance of the test is also illustrated throughout its application to the analysis of the spatial patterns of wildfires registered in Galicia (NW Spain) during 2006. [-]
Is part of
Spatial Statistics, 2017, vol. 22, p. 240-260Investigation project
They also acknowledge the support of the Project MTM2008-0310 from the Spanish Ministry of Science and Innovation, Projects MTM2010-14961, MTM2013-41383-P, MTM2013-43917-P from the Spanish Ministry of Economy and Competitiveness, projects MTM2016-78917-R and MTM2016-76969-P (AEI/FEDER, UE), grant P1-1B2015-60 from Bancaja Foundation, and IAP network StUDyS grant 3E120297 from the Belgian Science Policy.Rights
© 2017 Elsevier B.V. All rights reserved.
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