Exploratory Spatial Data Analysis to Support Maritime Search and Rescue Planning

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Managers are often expected to analyze, report, plan, and make decisions using data that are aggregated to administrative areas historically delineated for other purposes. This enforced aggregation may misinterpret true patterns or complexities underlying the data, hindering recognition and communication of potentially important insights. The result may well provide misleading information on which to base decisions. Spatial data analysis tools are available that could allow managers to analyze and aggregate data more meaningfully and effectively for decision-making and planning, while still allowing them to report to the standard administrative units. These spatial analytical tools would be of importance to managers who are using data to prevent, plan for, or mitigate risk-related events.

The Canadian Coast Guard is offered as an example whereby managers are responsible for planning for the provision of maritime search and rescue emergency response using historical maritime incident data collected site-specific but aggregated to historical reporting units. We explore how spatial data analysis techniques, in combination with GIS, can provide a way to analyze incident data spatially regardless of existing reporting units, providing a better way to ‘package’ the data for use in emergency response planning and decision-making. We show how marine incident patterns over the region can be monitored to help planners anticipate emerging incident hot-spots or gauge the persistence of existing hot-spots. Finally, we show how a better understanding of incident patterns within existing administrative units can inform the development of new reporting boundaries that better reflect incident patterns.


Point Pattern Spatial Data Analysis Exploratory Spatial Data Analysis Point Pattern Analysis Resource Allocation Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Spatial Sciences Laboratories, Department of GeographyUniversity of VictoriaVictoriaCanada

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