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Activity Number: 458 - Models for Spatial and Environmental Data
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics and the Environment
Abstract #311058
Title: Spatial Analysis of Interval-Valued Symbolic Data
Author(s): Austin Kane Workman* and Joon Jin Song
Companies: Baylor University and Baylor University
Keywords: Spatial Prediction; Methodology; Symbolic; Temperature; Interval Data; Spatial Analysis
Abstract:

Spatial data analysis has received increasing attention in several applications, and spatial data have been progressively observed and recorded in various forms. Symbolic data is defined as a hypercube in p-dimensional space and embraces a variety of data forms, such as histograms, lists, and intervals. In this paper, we specifically focus on spatial interval-valued data, which is observed in the form of intervals over space. We propose a statistical framework for spatial interval-valued data analysis in order to study spatial structure and to conduct spatial prediction at unobserved locations. A simulation study is performed to compare the proposed methods and a real data set is used to illustrate the methods. Several novel evaluation measures are proposed to evaluate the prediction accuracy.


Authors who are presenting talks have a * after their name.

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