Intensity estimation of spatial point processes based on area-aggregated data

• Published in: Japanese journal of statistics and data science Vol. 3; no. 1; pp. 413 - 428
• Main Authors: Lai, Chi-Wei, Huang, Hsin-Cheng
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.06.2020
• Subjects: Chemistry and Earth Sciences; Computer Science; Economics; Finance; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Original Paper; Physics; Statistical Theory and Methods; Statistics; Statistics and Computing/Statistics Programs; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Inhomogeneous spatial Poisson processes; Kernel intensity estimation; Parametric bootstrap; Area censoring; Standardized mortality ratio
• ISSN: 2520-8756; 2520-8764
• DOI: 10.1007/s42081-020-00080-1

We consider estimation of intensity function for spatial point processes based on area-aggregated count data. Given a spatial point pattern of events, we can estimate its intensity function using the kernel method. However, when data are given in a spatially aggregated form with only the numbers of events in geographical subregions available, traditional methods developed for point patterns become infeasible. In this research, a modified kernel method is proposed to estimate the intensity function based on aggregated count data. Some numerical examples are provided to demonstrate the effectiveness of the proposed method.