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The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years.
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We quantify these differences using geospatial methods and statistical analysis.
Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis).
In this paper, we focus on the spatial distribution of travel time differences between car and public transport during peak and off-peak hours using geospatial methods and circular statistics.
As such, we advocate for greater use of geospatial methods to supplement traditional epidemiological studies and communicate results to policymakers.
Specifically, we recommend the dual use of statistical and geospatial methods to better identify demographic risk factors associated with counterfeit detections.
Accordingly, this study utilises geospatial methods to analyse and map spatio-temporal trends in OCC, characterising findings using unique local geographies of suburbanisation, deprivation, and demography.
Fig. 1 Flowchart of selection process for studies that applied geospatial methods to investigate unintentional injuries.
Therefore, the aim of this study is to summarise the application of geospatial methods to unintentional injury as found in epidemiological studies published since 2000.
The breeding habitats of these vectors should be studied using geospatial technologies to improve spatial and temporal coverage.
Therefore, models using geospatial statistics and disease mapping methods are necessary to explicitly estimate the influence of neighboring site's risks on the risk at a specific site [ 13, 14].
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