Spatial hotspot detection using polygon propagation

Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology, public health, astronomy and criminology applications on geographic data. Traditional scan statistic uses regular shapes like circles to detect ar...

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Bibliographic Details
Published in:International journal of digital earth 2019-07, Vol.12 (7), p.825-842
Main Authors: Katragadda, Satya, Chen, Jian, Abbady, Shaaban
Format: Article
Language:English
Subjects:
Algorithms
Astronomy
Boundaries
Data
Datasets
Detection
Epidemiology
Hot spots
hotspot detection
Mapping
MapReduce
Occupational health
polygon propagation
Polygons
Propagation
Public health
Shape recognition
Spatial clustering
Statistical analysis
Statistical methods
Triangles
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Summary:Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology, public health, astronomy and criminology applications on geographic data. Traditional scan statistic uses regular shapes like circles to detect areas of high activity; the same model was extended to eclipses to improve the model. More recent works identify irregular shaped hotspots for data with geographical boundaries, where information about population within the geographical boundaries is available. With the introduction of better mapping technology, mapping individual cases to latitude and longitude became easier compared to aggregated data for which the previous models were developed. We propose an approach of spatial hotspot detection for point data set with no geographical boundary information. Our algorithm detects hotspots as a polygon made up of a set of triangles that are computed by a Polygon Propagation algorithm. The time complexity of the algorithm is non-linear to the number of observations, which does not scale well for larger datasets. To improve the model, we also introduce a MapReduce version of our algorithm to identify hotspots for larger datasets.
ISSN:1753-8947
1753-8955
DOI:10.1080/17538947.2018.1485754