Exploratory Spatial Data Analysis to Support Maritime Search and Rescue Planning
Cynthia Anne Manren
B.Sc. University of Victoria, 1995
A Thesis Submitted in Partial F u l f i i e n t of the Requirements for the Degree of
MASTER OF SCIENCE
in the Department of Geography
O Cynthia Anne Marven, 2003
University of Victoria
All rights reserved. This thesis may not be reproduced in whole or in part, by
Supervisor: Dr. C. P. Keller
ABSTRACT
This study examined the use of exploratory spatial data analysis (ESDA) methods suitable for the analysis of point patterns to determine whether they would support maritime search and rescue resource allocation planning on the Pacific Coast of Canada. First the methods were applied to four marine incident data-subsets to determine whether they were technically feasible for use with
maritime data and to provide a preliminary indication of their potential for
planning. Second, the methods were applied in a series of problem-solving exercises suggested by Canadian Coast Guard Planning personnel to explore their utility for resolving 'real-life' planning issues.
In general the methods that provided graphic (rather than text-based)
and local (rather than global) outcomes were the most useful. Despite several
data-related challenges, the ESDA methods examined did provide information
about intra-areal incident patterns that could enhance planning and reporting activities.
TABLE OF CONTENTS
...
ABSTRACT ii...
...
TABLE OF CONTENTS UI...
LIST OF TABLES ix...
LIST OF FIGURES x...
LIST OF MAPS
.xi
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ACKNOWLEDGMENTS xv
CHAPTER 1: STUDY RATIONALE AND RESEARCH FRAMEWORK
1.0 INTRODUCTION
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11.1 QUANTITATIVE METHODS IN GEOGRAPHY..
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9...
1.2 RESEARCH FRAMEWORK.. 13
1.2.1 Meeting with Canadian Coast Guard Staff Responsible for
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Search a n d Rescue Planning 13
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1.2.2 Research Review 13
1.2.3 Review of Pacific Coast Marine Incident Data
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13...
1.2.4 Preliminary Selection of Spatial Data Analysis Methods.. 14
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1.2.5 Technical Environment Alternatives.. 14
1.2.6 Application of Selected Techniques t o Marine Incident Data
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(Phase One) 14
1.2.7 Presentation of Phase One Outcomes to Search and Rescue
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Planners.. 15
1.2.8 Application of Spatial Data Analysis Methods t o Canadian
Coast Guard Planning Scenarios (Phase Two)
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15...
1.2.9 Interview with Search and Rescue Program Supervisor 15
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1.3 THESIS ORGANIZATION 16
CHAPTER 2: SEARCH AND RESCUE RESOURCE ALLOCATION PLANNING IN THE PACIFIC REGION
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2.0 INTRODUCTION 17
iv
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2.1.1 Planning 17...
2.1.2 Decision-Making 18...
2.1.3 Reporting 202.2 PLANNING OBJECTIVES AND EFFECTIVENESS MEASURES
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20...
2.3 CCG PACIFIC REGION SAR ORGANIZATIONAL STRUCTURE 21
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2.4 INFORMATION AND ANALYTICAL TOOLS 24
2.5 IMPROVING INFORMATION AND PLANNING SUPPORT
CAPABILITIES
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27...
2.6 CHAPTER SUMMARY 28
CHAPTER 3: STUDY DATASETS AND APPLICATION ENVIRONMENT
3.0 INTRODUCTION
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293.1 SYSTEM INFORMATION SEARCH AND
RESCUE
(SEAR) INCIDENT...
DATABASE 29
3.1.1 System Information Search and Rescue Data for the Pacific
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Region 31
3.1.2 Coordinate Precision of Incident Data
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32...
3.2 SEARCH AND
RESCUE
AREAS AND OFFSHORE PATROL ZONES 35...
3.3 COASTAL BOUNDARY 37
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3.4 GRID FILE 38
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3.5 MAP PROJECTION AND DATUM 39
3.6 APPLICATION ENVIRONMENT
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39...
3.7 CHAPTER SUMMARY 41
CHAPTER 4: INTRODUCTION TO SPATIAL DATA ANALYSIS THEORY AND TERMINOLOGY
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4.0 INTRODUCTION 42...
4.1 TERMINOLOGY 42...
4.1.1 Spatial Data and Spatial Analysis 42
4.1.2 Spatial Statistics.
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43...
v 4.1.4 Exploratory and Confirmatory Approaches to Spatial Data
Analysis
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454.1.5 Spatial Autocorrelation
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454.2 POINT PATTERN ANALYSIS TERMINOLOGY
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474.2.1 Points
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474.2.2 Point Patterns
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484.2.3 Point Pattern Analysis
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484.3 CONCEPTS AND ISSUES
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494.3.1 Spatial Structure
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494.3.2 First and Second Order Effects
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504.3.3 Spatial Randomness
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51...
4.3.4 Alternative Models for Point Processes 52...
4.3.5 Process Simulation and Monte Carlo Methods 53 4.3.6 Thiessen (Voronoi) Polygons...
54...
4.4 POINT PATTERN ANALYSIS APPROACHES AND METHODS 54...
4.5 ( 3 U M ' E R SUMMARY 55 CHAPTER 5: SPATIAL DATA ANALYSIS METHODS IN MARITIME RISK ASSESSMENT. EPIDEMIOLOGY. AND CRIMINOLOGY 5.0 INTRODUCTION...
565.1 MARITIME RISK ASSESSMENT
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57...
5.2 EPIDEMIOLOGY AND SPATIAL DATA ANALYSIS 60...
5.3 CRIMINOLOGY AND SPATIAL DATA ANALYSIS 68 5.4 POTENTIAL FOR ESDA METHODS IN MARITIME SEARCH AND RESCUE PLANNING...
765.5 CHAPTER SUMMARY
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77CHAPTER 6: SELECTION OF METHODS FOR STUDY 6.0 INTRODUUION
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806.1 CRITERIA FOR CHOOSING SPATIAL DATA ANALYSIS METHODS
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806.1.1 Characteristics of the Data
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80vi
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6.1.3 Software Constraints 82
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6.2 METHODS SELECTED FOR THE STUDY 82
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6.3 CHAPTER SUMMARY 85
CHAPTER 7: STUDY APPROACH
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7.0 INTRODUCTION 86...
7.1 METHODS 86...
7.1.1 Phase One 86 7.1.2 Phase Two...
877.2 FRAMEWORK FOR EXPLORING TI-IE UTILITY OF THE
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ESDA METHODS 88...
7.2.1 Technical Suitability 88 7.2.2 Practical Utility...
89...
7.3 CHAPTER SUMMARY 9 3CHAPTER 8: PHASE ONE RESULTS
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8.0 INTRODUCTION 94
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8.1 PHASE ONE ANALYSIS 94
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8.2 RESULTS OF PHASE ONE 96
8.2.1 General .Technical Issues
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97...
8.2.2 Method-Specific Results 99 Centrographic Statistics...
99...
Distance Analysis 103...
Nearest Neighbour Analysis 103 .
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Ripley's K Function 106
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Spatial Cluster Analysis 109
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Nearest Neighbour Hierarchical Clustering 110
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STAC Cluster Analysis 117
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Kernel Density Estimation 125
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Spatio-Temporal Analysis 135...
Knox Index 135...
Mantel Index -138...
vii
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8.3 CHAPTER SUMMARY 143
CHAPTER 9: PHASE TWO RESULTS
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9.0 INTRODUCTION 147
9.1 SCENARIO ONE: INCIDENTS IN OFFSHORE ZONES AND LEVEL OF
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SERVICE TARGET 1 4 7...
9.1.1 Scenario Description 147...
9.1.2 Approach 1 4 7...
9.1.3 Results 1 4 8...
9.1.4Utility
of Methods 1509.2 SCENARIO TWO: SEARQl AND
RESCUE
BASE LOCATIONS...
150...
9.2.1 Scenario Description 150...
9.2.2 Approach 151...
9.2.3 Results 151...
9.2.4Utility
of Methods 1639.3 SCENARIO THREE: SIMUZ, TANEOUS INCIDENTS IN
...
OFFSHORE ZONES 1 63...
9.3.1 Scenario Description 163 9.3.2 Approach...
164...
9.3.3 Results 164...
9.3.4Utility
of Methods 1669.4 SCENARIO FOUR: HOURLY SPATIAL PATTERNS OF INCIDENTS IN
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OFFSHORE AREAS 166
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9.4.1 Scenario Description 166
9.4.2 Approach
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167Section A: Day-Time and Night-Time Spatial Patterns of Fishing
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Vessels 167...
9.4.3 Results 168...
9.4.4Utility
of Methods 174Section B: Spatial Patterns of Fishing Incidents
in
Offshore Zones...
by Two-Hour Intervals 176...
9.4.5 Results 176...
9.4.6Utility
of Methods 185viii
9.5 SCENARIO FIVE: SPATIAL PATTERNS OF FISHING VESSEL INCIDENTS
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IN OFFSHORE PATROL AREAS BY MONTH 1 8 6
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9.5.1 Scenario Description -186...
9.5.2 Approach 186 9.5.3 Results...
187...
9.5.4 Utility of Methods 1 9 4...
9.6 CHAPTER SUMMARY 195CHAPTER 10: DISCUSSION AND CONCLUSION
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10.0 RESEARCH SUMMARY 1 9 6
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10.1 DATA-RELATED ISSUES 201
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10.2 OPPORTUNITIES FOR FUTURE RESEARCH 203
10.2.1 Data-Related Research
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20310.2.2 The Use of ESDA Methods for Planning and
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Decision-Support 204
10.2.3 Development of Theory Relating to Spatio-Temporal Marine
Incident Patterns and Potential Contributing Factors
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204...
10.3 CONCLUSION -205
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REFERENCES 206
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APPENDIX A: List of Acronyms 220
APPENDIX B: Examples of Data Sources Used to Support Marine SAR Needs
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Analysis 222
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APPENDIX C: SISAR Tables and Fields -227
APPENDIX D: Point Pattern Analysis: Description of Approaches
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and Methods -232
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APPENDIX E: List of Questions 250
APPENDIX F: Excerpt from SISAR Database Guide:
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LIST OF TABLES
Table 3.1: Canadian Coast Guard Search and Rescue
Incident Classification
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30Table 6.1 : Exploratory Spatial Data Analysis Methods
Selected for Study..
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83Table 8.1: Data Categories Used for Phase One Analysis, Pacific Region, 1993
-
1999
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95Table 8.2: Centrographic Measures..
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99Table 8.3: Nearest Neighbour Analysis Statistics
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103Table 8.4: Nearest Neighbour Analysis (NNA) of Marine Incidents, Fishing
Vessel Incidents and Small Recreational Craft Incidents, Region, 1993-
1999
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104Table 8.5: Knox Index Matrix..
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135Table 8.6: Knox Index for Motor Craft Incidents, Pacific Region, 1993-1999
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137Table 8.7: Mantel Index for Motor Craft Incidents, Pacific Region, 1993-1999. 139 Table 8.8: Assessment Summary of Exploratory Spatial Data Analysis Methods
Assessed in this Study
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144Table 9.1: M1 and M2 Incidents, 1993
-
1999, Region, by Hour, CanadianCoast Guard Response Vessels Only
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148Table 9.2: Standard Distance Deviation and Mean Nearest Neighbour Distance between Fishing Vessel Incidents, MI-M4, 1993- 1999; Day and Night. 168 Table 9.3: Number, Mean, and Standard Deviations of Day- and Night-Time
Incidents
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17 1 Table 9.4: Number of Incidents Involving Fishing Vessels per Month, inLIST OF FIGURES
Figure 2.1: Organization Chart: Victoria Joint Rescue Coordinationcentre
Marine Search and Rescue (as of May 15, 2002)
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22Figure 2.2: Marine Search and Rescue Planning Reports
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24Figure 3.1: An Example Illustrating the Regular Pattern of Incident Locations
Due to the Constraints Imposed by the Coordinates.
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34Figure 3.2: Example of Marine Incident Locations and
Grid Cell Configuration.
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39Figure 4.1: Examples of Point Patterns: Regular, Clustered, Random 1993-
1999
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48Figure 8.1: Ripley's K Small Recreational Craft, Region, 1993- 1999
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108Figure 8.2: Ripley's K Fishing Vessels, Region, 1993- 1999
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.I08Figure 8.3: Ripley's K Calculated for Sail Craft Incidents on the Pacific Coast,
1993- 1999.
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123Figure 8.4: Comparison of Interpolated Values from Kernel Density Estimation, Triangular, Uniform, Quartic, and Normal Functions; Adaptive
Bandwidth (50 Minimum Points), Commercial Fishing Vessels, 1993-
1999
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134Figure 9.1: Number of Fishing Vessels Involved in Marine Incidents by Two-
LIST OF MAPS
Map 1.1: Canadian Coast Guard Search and Rescue Areas in the Pacific
Region.
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3Map 1.2: An Example of Spatial Variation of Marine Incident Locations Within
Search and Rescue Areas, 1993- 1999.
...
5Map 1.3: An Example of a Boundary Bisecting a Concentration of Marine
Incidents between Search and Rescue Areas 305 and 304..
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6Map 3.1: Marine Incidents Inside and Outside Search and Rescue Area
Boundaries in the Pac5c Region, 1993-1999..
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33Map 3.2: Canadian Coast Guard Search and Rescue Areas and Offshore Patrol
Zones in the Pacific Region..
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36Map 8.1: Centrographic Measures: Fishing Vessels (n= 1936) and Motor Craft
(n=4095), 1993- 1999, Minimum Points per Cluster: 1% of n
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101Map 8.2: Small Recreational Craft (n=902) Involved in Marine Incidents,
Nearest Neighbour Hierarchical Clusters, 1993- 1999, Minimum Points
...
per Cluster = 1% of n.. .I12
Map 8.3: Small Recreational Craft (n=902) Involved in Marine Incidents, South
Island, Nearest Neighbour Hierarchical Clusters, 1993- 1999, Minimum
...
Points per Cluster = 1% of n.. .I13
Map 8.4: Fishing Vessels Involved in Marine Incidents (n= 1936), Nearest
Neighbour Hierarchical Clusters, 1993- 1999, Minimum Points per
...
Cluster = 1% of n 115
Map 8.5: Fishing Vessels Involved in Marine Incidents (n=1936), North Coast,
1993- 1999, Nearest Neighbour Hierarchical Clusters, Minimum Points
per Cluster = 1% of n
...
116Map 8.6 Motor Craft Involved in Marine Incidents (n=4095), STAC Clusters;
Map 8.7: Motor Craft Involved in Marine Incidents (n=4095), STAC Clusters,
...
South Coast Mainland, 1993- 1999 120
Map 8.8: Sail Craft Involved in Marine Incidents (n= 12 l6), STAC Clusters,
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xii Map 8.9: Sail Craft Involved in Marine Incidents, STAC Clusters, South
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Vancouver Island and Lower Mainland, 1993-1999 122
Map 8.10: Commercial Fishing Vessel Incidents, by Region, 1993- 1999, Kernel Density Estimation, Quartic Kernel, Adaptive Bandwidth, Minimum
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Points: 50, n=4095 127
Map 8.11: Commercial Fishing Vessel Incidents, by Region, 1993- 1999, Kernel Density Estimation, Normal Kernel, Adaptive Bandwidth, Minimum
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Points: 50, n=4095.. 128
Map 8.12: Small Recreational Craft Incidents, by Region, 1993- 1999, Kernel Density Estimation, Quartic Kernel, Adaptive Bandwidth, Minimum
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Points: 23, n-902.. 130
Map 8.13: Sail Craft Incidents by Region, 1993- 1999, Kernel Density
Estimation, Quartic Kernel, Fixed Bandwidth, Search Radius: 9 km,
Map 9.1: Incident Locations Where Level of Service or 8-Hour Performance
...
Target Was Not Met: M 1
-
M2, 1993- 1999. 149Map 9.2: Spatial Distribution of Marine Incidents Responded to by CCGC
Mallard, 1993- 1999 (N=239)
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.I52Map 9.3: Spatial Distribution of Marine Incidents Responded to by CCGC Skua,
1993- 1999 (N=286)
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153Map 9.4: Standard Deviational Ellipse and Centre of Minimum Distance for Marine Incidents Responded to by CCGC Skua from Ganges, 1993- 1999,
M1-M4..
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.I55Map 9.5: Standard Deviational Ellipse and Centre of Minimum Distance for Marine Incidents Responded to by CCGC Mallard from Powell River, 1993
Map 9.6 Incident Concentrations: Incidents Responded to by CCGC Skua from
...
Ganges, 1993- 1999: Nearest Neighbour Hierarchical Clusters.. .I57
Map 9.7: Incident Concentrations: Incidents Responded to by CCGC Skua from
Ganges, 1993- 1999: STAC Clusters..
...
.I58Map 9.8: Ten Most Frequent Incident Locations: Incidents Responded to by
xiii Map 9.9: Incident Concentrations: Incidents Responded to by CCGC Mallard
from Powell River, 1993- 1999: Nearest Neighbour
Hierarchical Clusters..
...
.I60Map 9.10: Incident Concentrations: Incidents Responded to by CCGC Mallard
from Powell River, 1993- 1999, STAC Clusters..
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.16 1Map 9.11: Ten Most Frequent Locations: Incidents Responded to by CCGC Mallard from Powell River, 1993- 1999..
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162 Map 9.12: Day-time Incidents Involving Fishing Vessels, M 1-M4, 1993- 1999with Standard Deviational Ellipse, Mean Centre, and Centre of Minimum
Distance..
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169Map 9.13: Night-time Incidents Involving Fishing Vessels, M 1-M4, 1993- 1999
with Standard Deviational Ellipse, Mean Centre, and Centre of Minimum
Distance
...
.I69Map 9.14 Comparison of the Mean Centers for Day and Night Fishing Vessel
Incidents..
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.I70Map 9.15: Concentrations of Incidents Involving Fishing Vessels, MI-M4,
1993- 1999, Day and Night..
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1 7 5Map 9.16: Concentrations of Incidents Involving Fishing Vessels in Offshore
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Zones (Ml-M4, 1993-1999), 0000 hrs to 0600 hrs.. 177
Map 9.17: Concentrations of Incidents Involving Fishing Vessels in Offshore
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Zones (M 1-M4, 1993- 1999), 0600 hrs to 1200 hrs.. .I78
Map 9.18: Concentrations of Incidents Involving Fishing Vessels in Offshore
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Zones (Ml-M4, 1993-1999), 1200 hrs to 1800 hrs.. 179
Map 9.19: Concentrations of Incidents Involving Fishing Vessels in Offshore
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Zones (MI-M4, 1993-1999), 1800 hrs to 2400 hrs.. 180
Map 9.20: Centres of Fishing Vessel Incident Distributions, between 0000 hrs
and 0600 hrs, M 1-M4, 1993- 1999..
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1 8 1Map 9.2 1 : Centers of Minimum Distance of Fishing Vessel Incident
Distributions, between 0600 hrs and 1200 hrs, M 1-M4, 1993- 1999.. .I82 Map 9.22: Centres of Minimum Distance of Fishing Vessel Incident
Distributions, between 1200 hrs and 1800 hrs, M 1-M4, 1993- 1999.. .I83 Map 9.23: Centres of Minimum Distance of Fishing Vessel Incident
xiv
Map 9.24 Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones
-
January.....
.I88Map 9.25: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones
-
February.....
189Map 9.26: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones - March..
...
189Map 9.27: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones - April..
...
.I90Map 9.28: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones
-
May...
190Map 9.29: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones - June..
...
.19 1Map 9.30: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones
-
July.....
.19 1Map 9.3 1 : Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones
-
August..... .I92
Map 9.32: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones - September..
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192Map 9.33: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones
-
October.....
.I93Map 9.34: Concentrations of Incidents Involving Fishing Vessels in Offshore
Patrol Zones
-
November.....
193Map 9.35: Concentrations of Incidents Involving Fishing Vessels in Offshore
ACKNOWLEDGEMENTS
Many thanks to my supervisor, Dr. Peter Keller, and the members of my
committee for their guidance, support, and advice throughout my studies. Numerous Canadian Coast Guard personnel contributed their expertise and time towards improving my understanding of their difficult field; in
particular, Mr. John Palliser, Mr. Jeff Nemrava, Mr. Brian Steven, Ms. Alison
Keaghan, Ms. Bevan Stephenson, and Mr. Gilles Bouchard.
Invaluable technical support and advice were provided by my project colleague, Steven Dickie in Halifax, Nova Scotia, Dr. Ned Levine, Houston, Texas, and fellow graduate students.
This research was supported by GEOIDE (Geomatics for Informed
Decisions), one of Canada's Centres of Research Excellence. GEOIDE enriched
my studies by enabling me to meet and correspond with many geomatics
professionals across Canada a t their annual conferences and through the GEOIDE Student Network.
Finally, immense thanks to my husband and family for their unfailing support and encouragement.
CHAPTER 1
STUDY AREA, RATIONALE, AND RESEARCH FRAMEWORK
1.0 INTRODUCTION
The Canadian Coast Guard (CCG)l is required by international
agreements (Martel, 1998) to provide search and rescue (SAR) services along Canada's coasts. SAR, a s defined by the CCG involves "the search for and provision of aid to persons, ships or other craft which are, or are feared to be in distress or imminent danger." (National SAR Needs Analysis, 1993, p. 2-2).
Maritime and aeronautical SAR services on the Pacific coast are jointly provided by the CCG and Canadian Forces located at the Joint Rescue
Coordination Centre (JRCC) a t Canadian Forces Base Esquimalt in Victoria, BC. In most cases, maritime SAR resources are provided by the CCG while the Canadian Forces supply aeronautical resources. Staff a t the JRCC coordinate
SAR services for the Victoria Rescue Region2 (VRR).
The
VRR
is one of three Search and Rescue Regions (SRR) in Canada,encompassing
a
land area of 920,000 km2 in British Columbia and the Yukon,a n ocean area of approximately 560,000 k@ extending about 800 nautical
miles offshore, and 27,000 km of coastline (CCG Website, www.pacific.ccg-
gcc.gc.ca/ sar/jrcc/index-e. htm, July, 2003).
At present, there are eleven SAR shore stations in the
VRR
located atVictoria, Ganges, Barnfield, Sea Island, Tofmo, Kitsilano, French Creek, Powell
River, Campbell River, Port Hardy, and Prince Rupert. Although the
VRR
is thesmallest of the three SRR, it usually experiences the highest number of the
most serious marine incidents (National SAR Needs Analysis, 1993).
The SRR boundaries have existed for a t least 20 years (Clouatre pers. comm., 2003). Many factors contribute to the configuration and scale of the boundaries including other political boundaries (e.g., provincial), cultural factors, potential response time, type of operations, type of client, and natural features or climatic conditions. For example, the Yukon was originally the
See Appendix A for a list of acronyms used in this thesis.
2 There are three search and rescue regions (SRR) in Canada: Victoria SRR, Trenton
2
responsibility of Edmonton SRR but was passed to Trenton SRR when the Edmonton office was closed in 1994. However, it was incorporated into the Victoria SRR when it became obvious that Victoria's rescue resources were most often used in the Yukon, due to proximity. Part of the Quebec SRR extends across the provincial boundaries to include adjacent areas with high
francophone populations because the SAR response resources service both official languages (Clouatre pers. comm., 2003).
There are eight inshore statistical reporting areas in the Pacific Region
referred to as Search and Rescue Areas (SAR Areas). Seven of these areas are
fully bounded (SAR Areas 30 1-307) while a n eighth is a n unbounded area (SAR
Area 308) extending offshore for approximately 600 nm. The geographic area3
within the inshore Areas (SAR Areas 30 1-307) is approximately 144,577 sq. km.
The bounded SAR Areas vary in size from the smallest, SAR Area 301 (1,178
km2) to the largest, SAR Area 306 (67,058 W). Map 1.1 shows the
cor@quration of the SAR Areas.
The SAR Areas are used for aggregating and reporting marine incident statistics to support SAR resource allocation planning. Information about
marine incidents reported to the CCG in the Pacific Region is stored in a
national database called SISAR (System Information Search and Rescue). The database contains information about marine incidents reported to the CCG such a s the location of the incident (longitude, latitude); temporal data (e.g. time, day, date, quarter, year); incident type, incident cause, vessel type,
incident severity as well as other incident attributes. The VRR is one of the
busiest in Canada and for the years 1993 through 1999, the average number of marine incidents per year in the Pacific Region was about 1,600.
3 Excluding the Queen Charlotte Islands, Vancouver Island, and other smaller islands;
Map 1.1: Canadian Coast Guard Search and Rescue Areas in the Pacific Region
S A R Area Boundary
CCG SAR planning staff have expressed concern that SAR Area
boundaries in the Pacific coast did not support planning or decision-making well, because of their size and configuration (Palliser, Superintendent SAR,
Pacific Region, pers. comm., October, 2000). Several SAR Areas are large,
masking spatial and spatio-temporal variation of marine incident locations within the areas (Map 1.2) and SAR Area boundaries bisect places with high
concentrations of incidents (Map 1.3). Thus, potential incident 'hot spots7 are
concealed from SAR planners because the statistics are reported for two (or more) areas instead of one.
Although SAR planners and other CCG staff may know through experience where and when incident concentrations occur in the region, the tools available to them for communicating and reporting this knowledge or for using it to support medium to long-range planning are currently limited to conventional methods of reporting descriptive statistics. CCG planners in the
Pacific Region do not have access to in-house mapping software or a Geographic
Information System (GIS) to aid with the visualization or analysis of marine incident data. Usually, the incident data are aggregated and reported by SAR Areas or by Region in tables or graphs.
There are benefits to aggregating incident data to SAR Areas. For
example, statistics can be compared over time if the spatial units remain unchanged. Aggregated statistics are also useful for quantitative modeling although there are well-known problems that can emerge with the use of
spatially aggregated data
in
modeling, such as the modifiable areal unitproblem (MAUP), discussed in the next section. Still, aggregating data to spatial units is a relatively easy way to distill information from large amounts of data, provided there is not an unacceptable level of information loss due to data generalization.
The initial objective of this study was to investigate ways of re-designing the administrative boundaries to support SAR planning and statistical reporting more effectively. However, it was found that, with respect to designing zones for administrative and statistical reporting purposes, many zone design approaches were inappropriate or irrelevant. Also, a new zone system may quickly
succumb to problems similar to the old system if the criteria for designing the
zones change. A review of Literature concerning zone design and spatial data
analysis methods suitable for analyzing point patterns resulted in a n alternative
approach that is the subject of this thesis.
Presently, SAR resource allocation planners use marine incident data filtered through the framework of the SAR Areas to support their planning and reporting efforts in the absence of alternative analytical methods. Spatial data
analysis literature reviewed for the study suggested that if discrete event data
were available there may be an alternative approach to analyzing and reporting
data to support SAR planning more effectively.
The study focus shifted from exploring methods for designing a more appropriate zone system to determining what kind of information CCG planners need from the marine incident database to support planning and whether these needs could be met using exploratory spatial data analysis (ESDA) methods in
combination with a GIs for visualization and data management. A review of
ESDA methods applicable to discrete events resulted in the selection of several
methods that may address this question.
There are several reasons for choosing this approach. Firstly, SAR Areas
were established by the national SAR Program and through international
agreements. New boundaries would require integration with the existing
national and international system. Secondly, changing the boundaries would make temporal comparability of marine incident statistics diffcult. If temporal comparability were maintained by retaining existing SAR Areas and creating a system of nested sub-regional boundaries, problems associated with MAUP s t . exist and solutions to this problem are elusive (for examples and discussion,
see section 1.1). Finally, this approach would result in another 'fixed' boundary
8
boundaries change. Using event-level data therefore may provide the greatest flexibility for supporting SAR planning.
However, the value of using event-level data can only be achieved if appropriate and effective analytical methods and tools are available to CCG SAF? planners. While ESDA methods suitable for analyzing discrete event data have been explored for their value to support crime response planning and to some
extent, public health planning, they have not been examined for their capability
to aid maritime SAF? planning.
The purpose of this study, therefore, is to explore the utility of using
ESDA methods to support SAR planning and reporting in both a practical way and from a technical and theoretical perspective. The CCG supports the exploration of alternative methods for analyzing marine incident data to help
SAR planning. The CCG's National SAR Needs Analysis (1993) states that:
"Modern analytical and quantitative methods including operations research, linear programming, marginal analysis, trend analysis, risk analysis and other contemporary business management techniques are currently not used in SAR resource and operations planning and
programming. The new SISAR database, will allow some of these
techniques to be employed so that SISAR can be used more effectively as a management tool" (p. Exec-5).
ESDA methods would not replace the current role of the zones for reporting or planning but would complement it. Furthermore, information derived from these methods could be used to inform zone system design if that
alternative was pursued. For example, if spatial and spatio-temporal incident
patterns are found to be relatively stable over time, it may be possible to design a system of sub-areas based on this information that would more closely reflect incident pattern, thereby reducing intra-areal variation. Also, exploring the
spatial characteristics of the dataset could aid confimatory modeling with
aggregated incident data by detecting spatial effects that need to be accounted
for if spatially aggregated data are used in modeling. Finally, the use of ESDA
methods could give rise to questions and hypotheses concerning the relationship between areas with high (or low) numbers of incidents and potential explanatory factors.
9 The aims of this study will be achieved through two objectives. The f ~ s t is to discover how CCG SAI? resource allocation planning and reporting is
currently performed through a review of pertinent literature and interviews with
CCG planning personnel. The second objective is to select, apply, and assess
ESDA methods for their potential value to support maritime SAI? resource allocation planning.
Issues relating to the c ~ ~ g u r a t i o n and scale of zone systems and the
challenges of analyzing spatially aggregated and disaggregated spatial data represented in the practical problem outlined here, lie within the field of quantitative geography and regional science, although researchers in many fields such a s mathematics, statistics, economics, and ecology have contributed greatly to the development of theory and methods relating to spatial data
analysis. Quantitative geography has fallen in and out of favor over the past
several decades, but is arguably experiencing a small resurgence in recent years
due to technological advances in computing software and hardware and the
development of large electronic databases. The next section will provide a brief outline of the history and development of quantitative geography since the
1950s.
1.1 QUANTITATIVE METHODS IN GEOGRAPHY
Research interest in quantitative methods in geography has fluctuated over the past several decades. The development of quantitative models and the
search for 'global laws' of the 1950s and 1960s gave way to paradigms
characterized by Marxism, post-modernism, and humanism of the 1970s and 1980s. Classic quantitative methods continued to be employed, mainly in physical geography, but the focus of geographic research moved away from
quantitative spatial analyses (Fotheringham, et al., 2000). Theoretical and
empirical research in quantitative geography was sometimes avoided because of
its perceived difficulty (Fotheringham, et aL., 2000). This challenge was
compounded by technological limitations such as the lack of computational power and spatial data management and analysis software.
The development of quantitative methods suitable for analyzing event-
10 manage, manipulate, and analyze. If available, event-level data were often
aggregated to zone systems for analysis because of the difficulty and
computational intensity associated with methods suitable for disaggregated data.
The use of spatially aggregated (areal) data alleviates some of the
challenges presented by event-level data, but introduces other problems. One of these problems, known as MAUP, is characterized by analytical outcomes that vary depending on the configuration and scale (number of areas) of the
zone system (Openshaw & Taylor, 1979; Openshaw, 1984b; Fotheringham &
Wong, 1991). The MAUP was f m t noted by Gehlke & Biehl(1934) followed by
Yule & Kendall(1950) who stated that "the results [of analysis] depend on our
units". Fotheringham & Rogerson (1993) list the MAUP as the first among eight
impediments that arise in spatial analysis. Openshaw (1984a) regarded the
MAUP as a "fundamental geographical problem inherent in all studies of
spatially aggregated data".
This problem is increasingly relevant because zones, once regarded a s fmed, have become easily modifiable with the use of computer programs and
electronic data. Researchers are able to change zone ~ o ~ g u r a t i o n s and
aggregation levels with relative ease. Openshaw (1984b) notes that any statistical relationship can be manipulated either intentionally or otherwise with the choice of areal units. According to Openshaw and Rao (1995), the problem with MAUP is "how best to exploit the new-found fieedom of user- controlled zone design and flexible higher scale geographies by developing sensible and appropriate zone systems for particular purposes" (p. 426) Wong (1996) warns that the MAUP is the "most stubborn problem in geography and spatial science" and that despite almost seven decades of research into
assessing and resolving the problem, an acceptable solution is still not available.
Another characteristic of areal data is that while aggregating data can aid in the detection of trends or patterns among the detail of event-level data, the resulting generalization can lead to loss of information about trends and patterns within areas (Wong, 1996; Grfith, 1996b). Research has been
11 enhance understanding of the phenomena of interest without excessive loss of
information (Brown, 1996; Fesenmaier, 1980). However, using data at the
fmest resolution possible, particularly given the advances in data storage and
handling, is considered by some researchers as the best approach (Wong,
1996).
Since the 1990s, there has been renewed interest in quantitative
geography (Fotheringham et al. 2000). Fotheringham et a1 (2000) defmes
modern quantitative geography as consisting of one or more of the following
activities:
"the analysis of numerical spatial data; the development of spatial theory; and the construction and testing of mathematical models of
spatial processes. This can be done directly, as in the case of spatial
choice modeling where mathematical models are derived based on theories of how individuals make choices from a set of spatial
alternatives. Or, it can be done indirectly, as in the analysis of spatial
point patterns from which a spatial process might be inferred" (p. 4). The aims of spatial data analysis described by Haining (1994) are:
"1) The careful and accurate description of events in geographical space including the description of pattern; 2) the systematic exploration of the
pattern of events in space in order to gain a better understanding of the
processes that might be responsible for the observed distribution of
events; and 3) improving the ability to predict and control events
occurring in geographical space" (p. 45).
Spatial statistics encompasses the analysis of spatial data including the description, inference, modeling and prediction (Brown, 1996). A type of spatial
statistical analysis referred to as spatial point pattern analysis is comprised of
methods associated with descriptive spatial statistics. Point pattern analysis
results can be used directly; to generate hypotheses about the process influencing the pattern; or to provide information for further modeling and analysis (Brown, 1996).
The growing interest in quantitative geography can be attributed in part to advances in computer hardware and software. The increased power and availability of desktop computers has made quantitative geographical research
12
made managing, displaying, and analyzing spatial data easier (Anselin et al.,
2000). These developments have been accompanied by a n increase in the amount of event-level data. The emphasis in modem quantitative geography has shifted to local spatial analysis' from the search for global models and laws
that characterized most of the earlier research efforts (Fotheringham, et al.,
2000). Fotheringham et al. (2000) note that "empirical research is being used
to guide theoretical development [of quantitative methods] to form a more equal
symbiosis". In 1989, a symposium entitled Spatial Statistics
-
Past,
Present,and Future" hosted by the Department of Geography, Syracruse University
noted that there was a need for more "relevant empirical applications of spatial statistical techniques" (Gflith, 1996a).
Empirical research involving point pattern analysis methods is still relatively uncommon in geographical research although it occurs somewhat more frequently in other fields, chiefly epidemiology and criminology. One of the reasons for the dearth of empirical point pattern analysis research is that, until recently, there were relatively few computer programs available for running the spatial data analysis routines. Spatial data analysis software programs are often developed by researchers for their own work, not by
commercial software development companies. The software is used to perform the spatial statistical routines while GISs provide data management,
manipulation, and visualization functions. Fortunately, a growing number of robust and well-documented stand-alone spatial data analysis software
programs suitable for the analysis of both event-level data and areal data have been developed recently for and by researchers in the fields of epidemiology, criminology, and ecology. Many of these spatial data analysis software
programs provide researchers with the ability to export the analytical results to
a GIS for visualization and modeling.
The improving technological and philosophical climate surrounding
quantitative geographical research combined with the problems associated with
the use of spatially aggregated data have provided the impetus for this case study. The following section will describe the approach taken to accomplish the study objectives.
1.2 RESEARCH
FRAMEWORK
This section describes how the research objectives of this study were achieved.
1.2.1 Meeting with CCG Staff Responsible for SAR Planning
The researcher and thesis supervisor met with CCG SAR planners from the Pacifc Region including Mr. John Palliser, Superintendant of the Victoria
Rescue Coordination Centre (VRCC) and Ms. Alison Keaghan, Acting SAR
Coordinator. Mr. Palliser and Ms. Keaghan provided background information about the current SAR planning process and tools and described how marine incident data are used with other types of data to support SAR planning. They also indicated concern about the configuration of the SAR Area boundaries and the ensuing difficulty of using the aggregated statistics to support their
planning effort. The CCG staff a t the VRCC provided the researcher with the most recent CCG SAR planning documents (National SAR Needs Analysis, 1993) and information about the SISAR database (SISAR Users Manual,[no datel). The researcher and supervisor were invited to attend the marine CCG SAR
Workshop held in Victoria at the Department of National Defense, October 2-3,
2000. Presentations and discussions a t this conference provided information about the organizational needs of CCG SAR planning personnel.
1.2.2 Research Review
A review of relevant literature was undertaken, pertaining to spatial data analysis theory and methods; applications of spatial data analysis in
criminology and epidemiology; research relating to maritime SAR planning; data
aggregation and zone design; decision-making and planning; and the relationship between GISs and spatial data analysis software.
1.23 Review of Pacific Coast Marine Incident Data
The CCG's incident database (SISAR) was examined to familiarize the researcher with its data content and quality. Information about the database
was obtained through a personal interview and by e-mail correspondence with
14
data users (Mr. John Palliser, Superintendant, SAR, Pacific Region; Ms. Allison Keaghan, Acting SAR Coordinator, Pacific Region) and Ms. Bevan Stephensen, (SISAR Database Administrator, VRCC) regarding data collection and
management procedures and perceived data quality. A discussion of the data quality, data cleaning procedures and other SISAR data-related issues is presented in Chapter 3.
1.2.4 Preliminary Selection of Spatial Data Analysis Methods
Information gained from the literature review, discussions with SAR planners concerning their planning requirements, and characteristics of the dataset led to the development of a preliminary list of spatial data analysis methods for further study.
1.2.5 Technical Environment Alternatives
Ways to implement the spatial data analysis methods were explored. This included searching for suitable spatial data analysis software packages, assessing their routines, reviewing commercial GIs software functionalities, and ensuring that linkages between the spatial data analysis software and GIs could be made to provide the required functionality.
1.2.6 Application of the Selected Techniques to the Marine Incident Data (Phase
One).
The application of spatial data analysis methods to the marine incident data proceeded in two phases. The first phase was intended to provide the researcher with a n opportunity to become familiar with software and method implementation issues, to provide a way to assess the quality and user- friendliness of the software, to examine potential statistical or other technical problems due to the characteristics of the data, and to provide analytical outcomes that could be presented to CCG SAR planning personnel for their review. The spatial data analysis methods were applied to four subsets of the
marine incident data in a systematic manner. The software setting
specifications and analysis outcomes for each run were recorded. The
15
1.2.7 Presentation of Phase One Outcomes to SAR Plannem.
The spatial data analysis methods and results of phase one analysis were presented to CCG SAR planners from the Pacific Region on October 22, 2002. The individuals attending the meeting included Mr. John Palliser,
Superintendant, SAR, Pacific Region; Mr. Jeff Nemrava, SAR Program
Supervisor, Paciiic Region; Brian Steven, Operational Services, Pacific Region, a geography co-op student, the researcher, and the researcher's thesis
supervisor. CCG personnel were provided w i t h written descriptions of each
method summarizing its strengths and weaknesses and potential applications to SAR planning, accompanied by a n applied example drawn from the results.
1.2.8 Application of Spatial Data Analysis Methods to CCG Planning Scenarios
(Phase Two).
Selected spatial data analysis methods were applied to five planning scenarios suggested by the CCG planning staff following the meeting of October 22, 2002. The scenarios represent five problems that the planners are
currently concerned with. Details of these scenarios, the spatial data analysis
methods used, and the outcomes are described in Chapter 9.
1.2.9 Interview with SAR Program Supervisor
Two informal interviews were conducted by the researcher with Mr. Jeff Nemrava, SAR Program Supervisor. The intent was to obtain his comments on the spatial data analysis methods used in the study and to gain further
information about the decision-making environment and tasks associated with his position.
Information sought from Mr. Nemrava included: a) updated information
about the organizational structure (national and regional) of the CCG SAR
planning group, to aid understanding of who is involved in the planning
process, b) the tasks, activities and types of decisions the planner is responsible for; c) how the SAR resource planning process currently works; d) the type of output from the planning decisions and who receives them; e) the planner's
opinion as to whether the spatial data analysis methods that were used were
16
spatial analysis if it confirmed or did not confirm information from other
sources; and g) what would be needed to integrate the spatial data analysis tools explored in the study into the working environment.
1.3 THESIS ORGANIZATION
Chapters 2 and 3 outline the maritime SAR planning process in the
Pacific Region and describe the study datasets and application environment.
Chapter 4 provides a n introduction to spatial data analysis concepts and
terminology followed by a review of applications of spatial data analysis
methods in maritime SAR planning, epidemiology, and criminology in Chapter
5. Chapters 6 through 9 present the methods and results of the case study
CHAPTER 2
SEARCH AND RESCUE RESOURCE ALLOCATION PLANNING IN THE PACIFIC REGION
2.0 INTRODUCTION
This chapter begins with a general discussion about planning, reporting,
and decision-making followed by a n overview of the current methods used to accomplish medium to long-term marine SAR resource allocation planning in the Pacific Region. Descriptions of the planning objectives and measures used by the CCG to guide and assess the SAR program, the organizational structure of the SAR planning program, the roles of key planning personnel, the planning cycle, tools, data, and information used to support resource allocation planning are also provided.
2.1 PLANNING, DECISION-MAKING AND REPORTING
There is a large volume of literature pertaining to management activities
such as planning, decision-making, and reporting. This body of literature will
not be explored in detail, but a brief description of each activity and some
characteristics of decision-makers are discussed to provide a background for
understanding the specifc characteristics of the CCG SAR planning program.
2.1.1 Planning
Planning, defined by Smith (1998) involves,
"ident@ing a n organization's objectives and goals, developing policies, determining courses of action, making decisions, setting standard operating procedures and rules, developing programs, forecasting future situations, preparing budgets, and documenting
project plans." (p. 3 1)
Planning occurs over many temporal intervals from many years through present, real-time operational level planning. Medium-term to long-term
marine SAR resource allocation planning refers to temporal horizons of months to years and, in its broadest sense, is concerned with ensuring that primary
18 available in the quantity, place, and time necessary to respond to requests for
service for marine incidents in such a way a s to meet the Level of Service (LOS)
objectives set out in CCG SAR planning policy. The most current1 LOS objective
is that 90% of lives a t risk should be saved.
2.1.2 Decision-Making
According to Smith's defulition, both decisionmaking activities and
forecasting are parts of the planning process. Decision-making is a process
involving issue detection, issue definition, response identification, and response
implementation (Smith, 1998). Smith (1998) places decision process models in
one of three categories: normative, descriptive, or prescriptive. Normative decision-process models are concerned with finding the best decision usually according to some mathematical function given a set of objectives and are often based on idealistic situations. A descriptive decision-making model as its name suggests, describe actual situations where people make real decisions. Models of this sort include those by Rasmussen (1980), Dreyfus and Dreyfus
(1980), and Janis and Mann (1977) (in Smith, 1998). Finally, a prescriptive
model is a combination of normative and descriptive models, and is concerned with idenwing how real people in actual situations should perform decision- making (Smith, 1998).
The planner's level of experience affects their decision-making styles and their propensity to rely on data and quantitative methods for decision-support. The choice of planning tools to support decision-making and planning vary with
the person involved in the planning and decision-making activity. In general,
more experienced planners rely less on data and data analysis, and more on their intuition and expertise developed over their years practicing their profession (Mintzberg in Sauter, 1997; Smith, 1998). Rasmussen (1980 in Smith, 1998) classifies decision-makers into three categories (fkom least experienced to most experienced); novice, competent, and expert. Novice
decision-makers rely mainly on existing methods and break problems down into parts while expert users rely on their own experience and judgment.
19 In addition to experience levels, people have different styles of thinking (cognitive styles) and thus approach problem solving and decision-making differently. Their choice of decision-making methods depends on their cognitive style. McKeeney and Keen (1974 in Smith, 1998) characterized cognitive styles in two categories, information gathering and infomation evaluation. Information
gathering occurs along a continuum fkom preceptive to receptive thinkers, and
refers to a person's way of gathering, organizing and filtering information.
Preceptive thinkers look for patterns and connections between pieces of information and may overlook details while receptive thinkers like to involve
themselves in details but may not see the 'big picture'. Infomation evaluation
refers to problem-solving and the approaches range from systematic to intuitive.
Systematic thinkers like to approach a problem methodically, with clearly
structured methods and solutions and are comfortable with more easily
structured problems. Intuitive thinkers are comfortable with large, ill-defined
problems, large amounts of data and attempt various methods to solve them, and follow analytical paths guided by cues or ideas that can be hard to
verbalize. Most people's approaches to planning and decision-making are some combination of these styles (Smith, 1998).
The tasks involved in planning may be separated among different personnel levels in the organizational hierarchy (e.g., managers, analysts,
technicians), accomplished by only one individual, or by several. A person's
position within the organizational hierarchy and their responsibilities affects
their approach. For example, managers tend to prefer informal and time- efficient methods of obtaining information to support their planning activities,
as opposed to detailed reports or analyses (Sauter, 1996) whereas the
responsibilities of a n analyst are mainly to distill large quantities of information to meaningful patterns or categories and to provide concentrations of the
information to managers. Finally, both quantitative and qualitative methods are often used to support decision-making and planning.
The planning approaches used by the CCG personnel on the west coast seem to involve a combination of the styles discussed previously, reflecting their cognitive styles, experience levels, level of responsibility, the nature of the
20
21.3 Reporting
Repo&'ng involves
"documenting decisions and preparing informational reports such
that there is appropriate awareness of the organization's activities. In a
more general context, it involves developing information resource management capabilities and implementing these in the organization." (Smith, 1998, p. 31.)
In the case of the CCG SAR Program, reporting involves the preparation of reports and other materials to communicating decisions and plans on a
cyclical basis a s well a s on a n as-needed basis. This will be discussed in the
next section.
2.2 PLANNING OBJECTIVES AND EFFECTIVENESS MEASURES
CCG maritime SAR resource allocation planning is a complex and dmcult undertaking that involves many uncertainties and incomplete or unavailable information. Ensuring that SAR resources are situated efficiently and effectively to meet the responsibilities and objectives outlined in the SAR program requirements although challenging, is vitally important given that human lives are a t risk.
The objectives of the marine SAR program administered by the CCG a s outlined in the National SAR Needs Analysis (1993) are to:
"ensure the provision of marine SAR capability by the Coast Guard
within the Canadian area of responsibility as defined under International
Maritime Organization (IMO) agreements and in Canadian waters of the Great Lakes and St. Lawrence system; and to promote safety to the marine public, in order to minimize loss of life and injury, including where possible and directly related thereto, to make reasonable effort to minimize damage to, or loss of, property" (p. 2-8).
In addition to the 90% LOS target, maritime SAR planning personnel in the Pacific region use a performance measure (response time) a s a way to
assess operational performance (National SAR Needs Analysis, 1993). Maritime SAR personnel should reach an incident location in inshore areas within one hour of receiving notscation of the incident and within eight hours for incidents in the offshore patrol zones.
21
2 3 CCG SAR PACIFIC REGION ORGANIZATIONAL STRUCTURE
The marine SAR program in the Pacific Region, is administered primarily from the Regional Operations Centre (ROC), 25 Huron Street, Victoria, BC, and the Victoria Joint Rescue Coordination Centre (JRCC) a t Canadian Forces Base Esquimalt (CFB Esquimalt), in the Greater Victoria area.
The Pacific Region Marine SAR Superintendent a t CFB Esquimalt reports to the Director of Marine Programs in Vancouver, BC (Figure 2.1). The Marine SAR Superintendent is responsible for coordinating and delivering marine SAR services in the Pacific Region. The SAR Superintendent oversees the activities of the SAR Program Supervisor who is responsible for SAR planning, and the Maritime Coordinators who organize real-time maritime operational SAR incident response. The SAR Program Supervisor undertakes a variety of
planning and reporting activities, both in accordance with the agency's
planning cycle, and in response to special circumstances. Long-term capital and business planning occurs in the Operations Directorate although it intersects some of the functions provided by Marine Program planners.
The SAR Program Supervisor is responsible for estimating SAR coverage requirements and forwarding the estimates to the ROC staff who are
responsible for scheduling and tasking CCG Pacific Fleet. Primary CCG vessels are multi-tasked so SAR coverage requirements are integrated with other
service requests. In addition to SAR services, primary CCG vessels (ships, small craft, and hovercraft) and personnel provide marine navigational services; support for ocean and research sciences, fisheries conservation and
management; environmental response; navigational waters protection; boating safety activities; and atmospheric and environment services for Environment Canada. CCG vessels may also be used by the Royal Canadian Mounted Police, Parks Canada, Immigration Canada, or other agencies. Most of the primary SAR response vessels operate from fied-bases and provide service to inshore areas. The provision of SAR service offshore is provided by vessels assigned
according to the annual SAR planning needs analysis (RCC Victoria SOP,
23 Secondary SAR resources include, for example, the Canadian Coast Guard Auxiliary (CCGA), navy ships, RCMP patrol boats, pilotage vessels in major ports, harbor patrols, the Victoria Fire Department, and the Vancouver Police Department.
SAR resource allocation planning includes the preparation of daily,
weekly, yearly, and multi-year plans and reports (Figure 2.2). The SAR Program Supervisor prepares daily plans referred to as morning position reports (plans that anticipate where and when SAR resources will most likely be needed) that are compiled and forwarded weekly to CCG SAR Headquarters in Ottawa, Ontario. The weekly report summarizes the daily reports and assesses the plans' effectiveness. The assessment is based on whether the incident response times met or exceeded the targets set by the Region, and whether the national LOS objectives were met.
The SAR Program Supervisor prepares annual marine SAR resource
allocation plans for the region. These plans are adapted during the year if new
information becomes available or conditions change. Every five years, the
Regional SAR Program Supervisor contributes information to the Needs
Analysis for the Pacipc Region, prepared by the marine SAR Superintendent.
This plan is intended to anticipate SAR needs over a 5-year planning period.
The regional Needs Analysis is forwarded to Ottawa where it is compiled by
Headquarters CCG staff into the National SAR Program Needs Analysis and
Planning Report. The most recent National SAR Needs Analysis was completed in 2002 but is still in draft form and unavailable to the researcher.
Other planning or program evaluation activities occur periodically. For
example, in October, 2001, the CCG initiated a process referred to a s the
Change Initiative in the Paapc Region. The purpose of this initiative was to
evaluate the CCG's programs (including the SAR program) in the Pacific Region
to determine whether changes could be made to the CCG's programs that would make them more efficient and effective, given on-going funding constraints.
This regional effort coincided and was integrated with the national
Departmental Assessment and Alignment Project (DAAP) that was undertaken, largely for the same purpose, nation-wide.
Figure 2.2: Marine Search and Rescue Planning Reports Long-Term Planning Medium-Term Planning Short-Term Planning 0perCrt.lonal Planning (real-time)
Routine Plans Periodic Plans
Regional SAR needs analysis (every five years)
/
Annual marine SAR1
resource allocationplans (adaptive)
Weekly compilation and
assessment of morning .- . position reports reports (daily) Incident response coordination -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- 1 Periodic plans and
i
reports as needed (e.g., f Change Initiative in the ;Pacific Region); varying ! temporal intervals j
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
24 INFORMATION AND ANALYTICAL TOOLS
In general, CCG SAR resource allocation planning involves anticipating
demand for SAR services and ensuring that the supply of resources or
'coverage' provided by primary and secondary SAR resources will meet the
demand for services and the program objectives. Anticipating the coverage, involves estimating the quantity, type and placement of vessels, craft, gear, and personnel needed to meet demands for SAR services and program requirements.
SAR planners anticipate SAR demand using past experience and expertise, historical incident data, and information (where available) about vessel activity (e.g., commercial fisheries openings, ferry traflic and schedules, commercial shipping activity, recreational boating activity, special events
occurring in the marine environment such a s sailing regattas or fishing derbies, etc.), weather and climatic factors, and other environmental conditions (e.g., tides, currents, navigational hazards). Prevention efforts aimed a t reducing the
changes to regulations, gear, communications or vessels may also affect the number, type, or location of incidents.
Although the kind of data needed to anticipate demand is similar, the temporal and spatial scale and type of data required may differ somewhat depending on the planning horizon. For example, for daily plans, knowledge of a special event in a n area (e.g., Swiftsure Race, Canada Day Fireworks) or specsc occurrence (e.g., fishery openings), or weather forecast is important. In contrast, information for longer-term strategic planning includes broad-scale trends in population levels, demographics, economic indicators, changes in fisheries management legislation or boating regulations.
For example, the National SAR Needs Analysis (1 993) includes a
description of the environmental and physical characteristics (e.g., anchorages, tides, currents, climate); marine activities; and activity trends for each SAR Area in the Region. The number of vessels or crafts (per year) or vessel
movements (per year) usually are cited, if available. Comments about hazards
in the area, usually relating to the environment (e.g., weather, tides, currents,
wind) and navigational hazards (traffic conflicts, tides, etc.) are provided.
Human factors characterizing marine activities in the area that could affect incident severity or quantity are discussed. For example, in SAR Area 30 1, human factors mentioned include the fact that that there are "many small
recreational vessels in the area a s well as heavy commercial shipping tracn
and that there are many "inexperienced tourists who rent[ing] recreational boats".
The National SAR Needs Analysis (1993) also includes a description of the primary and secondary SAR resources for each SAR Area. Trends for the previous five-years are cited (number of lives saved, number of lives lost, number of distress incidents, etc.) based on SISAR data. Potential tvorst case' scenarios, recommendations, and comments are mentioned as well. Examples of worst case scenarios for SAR Area 301 include incidents involving large cruise ships or aircraft, earthquakes, tanker accidents or oil spills. The
'comments' section includes references to unusual events or data that may have
