An Analysis of an Incomplete Marked Point Pattern of Heat-Related 911 Calls

Matthew J. Heaton, Stephan R. Sain, Andrew J. Monaghan, Olga V. Wilhelmi and Mary H. Hayden

Journal of the American Statistical Association, 2015, vol. 110, issue 509, 123-135

Abstract: We analyze an incomplete marked point pattern of heat-related 911 calls between the years 2006-2010 in Houston, TX, to primarily investigate conditions that are associated with increased vulnerability to heat-related morbidity and, secondarily, build a statistical model that can be used as a public health tool to predict the volume of 911 calls given a time frame and heat exposure. We model the calls as arising from a nonhomogenous Cox process with unknown intensity measure. By using the kernel convolution construction of a Gaussian process, the intensity surface is modeled using a low-dimensional representation and properly adheres to circular domain constraints. We account for the incomplete observations by marginalizing the joint intensity measure over the domain of the missing marks and also demonstrate model based imputation. We find that spatial regions of high risk for heat-related 911 calls are temporally dynamic with the highest risk occurring in urban areas during the day. We also find that elderly populations have an increased probability of calling 911 with heat-related issues than younger populations. Finally, the age of individuals and hour of the day with the highest intensity of heat-related 911 calls varies by race/ethnicity. Supplementary materials are included with this article.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2014.983229 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:110:y:2015:i:509:p:123-135

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2014.983229

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().