Detecting space–time patterns of disease risk under dynamic background population

Alexander Hohl (), Wenwu Tang, Irene Casas, Xun Shi and Eric Delmelle
Additional contact information
Alexander Hohl: University of Utah
Wenwu Tang: University of North Carolina at Charlotte
Irene Casas: Louisiana Tech University
Xun Shi: Dartmouth College
Eric Delmelle: University of North Carolina at Charlotte

Journal of Geographical Systems, 2022, vol. 24, issue 3, No 7, 389-417

Abstract: Abstract We are able to collect vast quantities of spatiotemporal data due to recent technological advances. Exploratory space–time data analysis approaches can facilitate the detection of patterns and formation of hypotheses about their driving processes. However, geographic patterns of social phenomena like crime or disease are driven by the underlying population. This research aims for incorporating temporal population dynamics into spatial analysis, a key omission of previous methods. As population data are becoming available at finer spatial and temporal granularity, we are increasingly able to capture the dynamic patterns of human activity. In this paper, we modify the space–time kernel density estimation method by accounting for spatially and temporally dynamic background populations (ST-DB), assess the benefits of considering the temporal dimension and finally, compare ST-DB to its purely spatial counterpart. We delineate clusters and compare them, as well as their significance, across multiple parameter configurations. We apply ST-DB to an outbreak of dengue fever in Cali, Colombia during 2010–2011. Our results show that incorporating the temporal dimension improves our ability to delineate significant clusters. This study addresses an urgent need in the spatiotemporal analysis literature by using population data at high spatial and temporal resolutions.

Keywords: Kernel density estimation; Dengue; Background; Adaptive; Population (search for similar items in EconPapers)
JEL-codes: C21 C63 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10109-022-00377-7 Abstract (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:kap:jgeosy:v:24:y:2022:i:3:d:10.1007_s10109-022-00377-7

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/10109/PS2

DOI: 10.1007/s10109-022-00377-7

Access Statistics for this article

Journal of Geographical Systems is currently edited by Manfred M. Fischer and Antonio Páez

More articles in Journal of Geographical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().