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Space-Time Pattern Mining Analysis of Cancer Incidence in South Dakota, 2007-2017

Authors: Bruce Millett*, South Dakota State University
Topics: Medical and Health Geography, Geographic Information Science and Systems, Temporal GIS
Keywords: Cancer, Space-Time, GIS, Health
Session Type: Paper
Presentation File: No File Uploaded


An approach to understanding spatial and temporal trends is to divide the data into time steps. This research utilizes GIS tools to identify and map statistical trends of incidences of cancer at varying geographic locations in South Dakota. The basic data are the latitude and longitude references encoded in the South Dakota Department of Health Cancer Registry for 2007-2017. New statistical analyst tools available in ArcGIS Pro provide space-time pattern mining statistical approaches for analyzing data distributions and patterns. Hotspot techniques identify statistically significant spatial clusters of high incidences (hot spots of disease) and low incidences (cold spots of disease). Trend mapping looks at temporal nature of health data and provide geovisualizations of changes of dispersion or magnitude of a disease. Space-time pattern analysis gives a unique prospective to cancer incidence data in South Dakota. Knowing when and where cancer occurred in the state can be used in conjunction with other spatial-temporal data on known factors increasing the risk of cancer that include poor diet, lack of physical activity, and obesity among others. Results are displayed on a series of map products.

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