Geomatics Solutions for Disaster Management pp 271-288
Exploratory Spatial Data Analysis to Support Maritime Search and Rescue Planning
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.
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- Anselin L, Cohen J, Cook D, Gorr W, Tita G (2000) Spatial analyses of crime. In: Duffee D (ed) Measurement and Analysis of Crime and Justice. National Institute of Justice, Washington DC, 4:213–262Google Scholar
- Bailey TC, Gatrell AC (1995) Interactive Spatial Data Analysis. Longman Group, Harlow, EnglandGoogle Scholar
- Block RR, Block CR (1995) Space, place and crime: Hot spot areas and hot places of liquor-related crime. In: Eck JE, Weisburd D (eds) Crime and Place, Vol 4. Criminal Justice Press, Monsey, New YorkGoogle Scholar
- Diggle PJ (1983) Statistical Analysis of Spatial Point Patterns. Academic Press Inc., Toronto, OntarioGoogle Scholar
- Dillon WR, Goldstein M (1984) Multivariate Analysis: Methods and Applications. Wiley, New YorkGoogle Scholar
- Ebdon D (1977) Statistics in Geography: A Practical Approach (1 edn) Basil Blackwell, Oxford, EnglandGoogle Scholar
- Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative Geography: Perspectives on Spatial Data Analysis. Sage Publications, Thousand Oaks, CAGoogle Scholar
- Griffith D, Amrhein CG (1997) Multivariate Statistical Analysis for Geographers. Prentice Hall Inc, Upper Saddle River, NJGoogle Scholar
- Haining R (1990) Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, CambridgeGoogle Scholar
- Kellerman A (1981) Centrography measures in geography. (Concepts and Techniques in Modern Geography) CATMOG 32. Geo Abstracts, University of East Anglia, Norwich.Google Scholar
- Levine N (2004) CrimeStat: A spatial statistics program for the analysis of crime incident locations (v 3.0). Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC. May (http://www.icpsr.umich.edu/NACJD/crimestat.html)Google Scholar
- Rothman KJ (1990) A sobering start for the cluster busters’ conference. American Journal of Epidemiology, 132: 6–13Google Scholar
- Sherman LW (1995) Hot spots of crime and criminal careers of places. In: Eck JE, Weisburd D (eds) Crime and Place, Vol. 4. Criminal Justice Press, Monsey, New YorkGoogle Scholar
- Tukey JW (1977) Exploratory Data Analysis. Addison-Wesley, Reading, MassachussettsGoogle Scholar