Spatial autoregressive models for scan statistic
Lionel Cucala and
Michaël Genin ()
Additional contact information
Mohamed-Salem Ahmed: Univ. Lille, CHU Lille, ULR 2694 - METRICS Evaluation des technologies de santé et des pratiques médicales
Lionel Cucala: IMAG, Université de Montpellier, CNRS
Michaël Genin: Univ. Lille, CHU Lille, ULR 2694 - METRICS Evaluation des technologies de santé et des pratiques médicales
Journal in Spatial Econometrics, 2021, vol. 2, issue 1, 1-20
Abstract Spatial scan statistics are well-known methods for cluster detection and are widely used in epidemiology and medical studies for detecting and evaluating the statistical significance of disease hotspots. For the sake of simplicity, the classical spatial scan statistic assumes that the observations of the outcome variable in different locations are independent, while in practice the data may exhibit a spatial correlation. In this article, we use spatial autoregressive (SAR) models to account the spatial correlation in parametric/non-parametric scan statistic. Firstly, the correlation parameter is estimated in the SAR model to transform the outcome into a new independent outcome over all locations. Secondly, we propose an adapted spatial scan statistic based on this independent outcome for cluster detection. A simulation study highlights the better performance of the proposed methods than the classical one in presence of spatial correlation in the data. The latter shows a sharp increase in Type I error and false-positive rate but also decreases the true-positive rate when spatial correlation increases. Besides, our methods retain the Type I error and have stable true and false positive rates with respect to the spatial correlation. The proposed methods are illustrated using a spatial economic dataset of the median income in Paris city. In this application, we show that taking spatial correlation into account leads to the identification of more concentrated clusters than those identified by the classical spatial scan statistic.
Keywords: Spatial autoregressive models; Scan statistics; Cluster detection (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s43071-021-00017-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:jospat:v:2:y:2021:i:1:d:10.1007_s43071-021-00017-0
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal in Spatial Econometrics is currently edited by Giuseppe Arbia, Lung Fei Lee and James LeSage
More articles in Journal in Spatial Econometrics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().