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Primary Applications of Spatial Analysis in Humanitarian Emergencies
Firoz Verjee, D.Sc.
February 19, 2009
Session: Humanitarian Affairs, Global Health, and Food Security
ESRI Federal Users Conference 2009
Introduction• Systems-based assessment of how beneficial GIS-based
analysis is for coordinating humanitarian operations during UN-led interventions
• Humanitarian applications of GIS have, so far, been primarily cartographic, i.e. map making
• The coordination of humanitarian “clusters” requires information exchange and analysis to create situational awareness
• This research investigates what types of GIS-based analysis are helpful to decision makers in UN and non-UN agencies during the relief and recovery phases of an emergency
Literature Review• GIS-based analysis falls into six categories, excluding
cartography
• Scholarly research in GIS applications for humanitarian assistance has been sporadic and unsystematic
• Aside from Health applications, the tradition of peer-reviewed research in humanitarian applications of GIS is weak, although there are some signs of change
• Humanitarian community is weary of techno-centric solutions or any centralized approach which threatens its independence or autonomy
• A new school of thought in coordination is emerging, which abandons ICS idealism in favor of trust-based pragmatism
GIS Phenomena
Boundary Condition A: Limited to UN-led Interventions using the Clusters ApproachBoundary Condition B: Excludes Cartographic (i.e. map making) Applications of GIS
Queries & Measurements
Transformations
Optimization
Statistical Analysis
Geovisualization
Remote Sensing & GPS
Information Sharing
Data Models
Geospatial & Humanitarian Standards
Web-based GIS
Data Availability & Collection
Humanitarian ClustersCluster Coordination (UNOCHA)
Water & Sanitation (UNICEF)
Health (WHO)
Emergency Shelter (IFRC/UNHCR)
IT & Communication (UNICEF/WFP)
Logistics (WFP)
Camp Management (IOM/UNHCR)
Early Recovery & Reconstruction (UNDP)
Education (UNESCO)
Nutrition (UNICEF)
Livelihoods (FAO)
Protection (UNICEF/UNHCR/OHCHR)Education, Research & Training
Map Templates & Symbology
Hypothesis Testing & Simulation
Major Hypothesis & MethodNull: GIS-based analysis cannot have a decisive impact upon the
coordination of humanitarian assistance
Alternate: GIS-based analysis can have a decisive impact upon the coordination of humanitarian assistance
Method: Sequential Exploratory Mixed Method, involving:
1. Qualitative survey using expert interviews
2. Design of representative examples of most promising types of GIS-based analysis
3. Quantitative survey of humanitarian decision makers
Phase 1: Expert Interviews• A total of 40 interviews, averaging 57.5 minutes each and
conducted primarily by phone
• Research subjects selected because of their familiarity with humanitarian coordination and/or application of GIS applications, as well as their ability to reflect the humanitarian community at-large
• Subjects provided with a review of the study’s objectives and clarification that their responses would be de-identified
• Researcher then began open-ended interview technique geared towards the subject’s specific area(s) of expertise
• Nevertheless, standard questions such as, “What types of GIS products or services does (or would) your organization use during humanitarian emergencies?” were asked of all those interviewed
Phase 1: Expert InterviewsType of Organization Name of Organization Country
Government Donor United States Agency for International Development, Office for Foreign Disaster Assistance (USAID/OFDA)
USA
Department for International Development (DFID) UK
National Government United States Geological Survey, Department of the Interior (USGS) USA
Humanitarian Information Unit, Department of State (HIU) USA
Federal Emergency Management Agency, Department of Homeland Security (FEMA/DHS)
USA
Private Sector/Vendor ESRI USA
Academia/Research Institute for Crisis, Disaster & Risk Management, The George Washington University (ICDRM)
USA
American Association for the Advancement of Science (AAAS) USA
Association of American Geographers (AAG) USA
Institute for the Study of International Migration, Georgetown University (ISIM)
USA
Virginia Polytechnic Institute (Virginia Tech) USA
Lahore University of Management Sciences (RISEPAK/LUMS) Pakistan
Type of Organization
Name of Organization Country
International Organizations
United Nations Joint Logistics Center, World Food Programme (UNJLC) Italy
United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA)
USA, Switzerland & Israel / Occupied Palestinian Territories
Food and Agriculture Organization of the United Nations (FAO) Italy
United Nations High Commissioner for Refugees (UNHCR) Lebanon & Switzerland
United Nations Children’s Fund (UNICEF) USA
United Nations Department of Peacekeeping Operations (UN DPKO) USA
World Health Organization (WHO) Switzerland
United Nations Development Programme (UNDP) Haiti
United Nations System Influenza Coordination, United Nations Development Group
USA
The World Bank USA
Non-Governmental Organizations
FOCUS Humanitarian Assistance USA & Pakistan
Veterans for America USA
Mercy Corps USA
Oxfam UK
MapAction UK
World Vision USA
Phase 1: Conclusions• Gap Analysis is the most urgent priority in humanitarian
coordination – irrespective of whether GIS is employed to calculate the difference between Assessed Needs and Relief Activities
• Situational awareness will only be possible by improved data sharing – field-level mechanisms to facilitate timely, no-hassle reporting are crucial to the ultimate success of GIS-based analysis
• GIS must be advocated with caution – it must provide unique and decisive intelligence, that is simple, easily-comprehendible, reliably reproducible, and field-tested.
Priority List of GIS-based AnalysesCategory Suggested Application
Queries & Measurements 1. Gap Analysis: Calculation of actual or percentage of relief needs met over time2. Measurement: Computation of distance or area of various lines or polygons of interest3. Range Analysis: Determine population within or beyond some distance of certain
humanitarian services4. Zone Analysis: Calculate total relief capacity within a certain area, and/or compliance
with humanitarian standards
Transformations 5. Vulnerability Estimation: Anticipate most vulnerable populations based upon proximity to hazard(s) and distance from relief distribution infrastructure
6. Site Selection: Identify potential locations for humanitarian services given certain terrain, land use and proximity criteria
Optimizations 7. Service Optimization: Determine best locations for humanitarian services given a transportation network
8. Route Optimization: Calculate the shortest or fastest route between two or more locations
Geostatistics 9. Central Feature Analysis: Compute the most central (i.e. median) point amongst a set of points
10. Exact Center Analysis (Weighted): Compute the centre of a set of weighted points (i.e. mean)
11. Cluster Analysis: Detect the correlation of a set of points with one another, andunderlying phenomena
Geovisualization 12. Orientation & Situation Reporting: Communicate the response environment and humanitarian activity through Geovisualization
Hypothesis Testing 13. “What If?” Analysis: Simulate the impact of a change to one or more variables that affect humanitarian operations
Phase 2: Web Survey
Phase 2: Web Survey• A total of 156 qualified survey respondents, averaging 24 minutes to
complete the 28 questions
• A total of 3,200 individuals invited response rate of 5.4%
• Subjects first provided with a review of the study’s objectives and assurances that their responses would be de-identified and aggregated
• Questions 1-6 captured basic demographic about the respondents
• Questions 7-13 gauged their views and comprehension of GIS
• Questions 14-27 elicited their opinion of the 13 priority applications
• Question 28 calibrated and cross-checked their utility rankings
Phase 2: Web Survey Participants • Affiliation: 1/3 from international NGOs, 1/3 from UN agencies
• Roles: 50% Program Managers or Technicians
• Base: 30% USA, 15% Pakistan, 8% Sudan + 32 other countries
• Focus: #1 South Asia, #2 Sub-Saharan Africa, #3 Middle East
• Cluster: #1 ERR, #2 Livelihoods, #3 Coordination
• Experience: 40% (< 5 years), 49% (5-15 years), 11% (>15 years)
• GIS Expectations: 59% (Optimistic), 37% (Curious), 6% (Cautious) & 2% (Skeptical)
Phase 2: Web Survey Participants
Poor Basic Good ExcellentUnderstanding of GIS
0
10
20
30
40
50
60
# of
Res
pond
ents
9.6 % %
26.3 %
34.0 % %
30.1 % %
None Basic Intermediate Advanced ExpertLevel of GIS Training
0
10
20
30
40
50
# of
Res
pond
ents 17.9 % %
32.1 %
21.2 % %
14.1 % %14.7 % %
Phase 2: Web Survey
Utility Assessment of the 13 types of GIS-based Analysis
Question 14: Gap Analysis (Single Cluster)
Population without Shelterbetween Week 1 – Week 8 for Neelum District
Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100% Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100% Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100%
0
20
40
60
80
100%
Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100% Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100% Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100%
0
20
40
60
80
100%
Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100% Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100% Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100%
0
20
40
60
80
100%
Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100% Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100% Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
0
20
40
60
80
100%
0
20
40
60
80
100%
Diagram size represents # of People Affected
(Data for illustrative purposes only)
Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
<100
<500
<1000
<5000
<10000 Week 1
Week 2
Week 3
Week 4Week 5
Wee
k 6
Wee
k 7
Week 8
<100
<500
<1000
<5000
<10000
<100
<500
<1000
<5000
<10000
Number of People without ShelterRadius shows Gap remaining, where:
Gap = Assessed Need – Relief Distributed
Weeks after EarthquakeCircumference shows Time after the Emergency
Question 14: Gap Analysis (Multiple Clusters)
(Data for illustrative purposes only)
HealthProtectionLivelihoodsWat/San
Aug 1-15
Aug 15-31
Sep 1-15
Sep 15-30Oct 1-15
Oct 15-31
Nov 1-15
Nov 15-30
20
40
60
80
100% Aug 1-15
Aug 15-31
Sep 1-15
Sep 15-30Oct 1-15
Oct 15-31
Nov 1-15
Nov 15-30
20
40
60
80
100%
Aug 1-15
Aug 15-31
Sep 1-15
Sep 15-30Oct 1-15
Oct 15-31
Nov 1-15
Nov 15-30
20
40
60
80
100% Aug 1-15
Aug 15-31
Sep 1-15
Sep 15-30Oct 1-15
Oct 15-31
Nov 1-15
Nov 15-30
20
40
60
80
100%
Percentage of Gaps Remaining
Semi-monthly Reporting Period
Gap Analysis “Compared to existing methods of tracking the progress of
humanitarian response, how useful is this type of analysis to you?”
• 109 out of 156 respondents provided comments, ranging from “100x better than a list of needs” to “too complex for decision makers”
• The scale of examples should have been at the village-level
N/A8.3% Not useful
4.5%
Somewhat useful 28.2%
Essential 13.5%
Very useful 45.5%
Question 15: Range Analysis
20 km
# of Villages BEYOND one day walk to Clinic: 8=> Population needing Mobile Field Vaccination: 1,750
# of Villages WITHIN one day walk to Clinic: 10=> Population that can visit Clinic: 2,150
Range Analysis “The figure above shows how GIS can be used to calculate the
number of settlements (or schools, hospitals, people, etc.) within a certain distance of a vaccination clinic (or road, food distribution point, etc.). How useful is this type of analysis to you?”
• 110 out of 156 respondents provided comments, and suggested that the simplicity of Range Analysis was both good and bad
N/A5.1% Not useful
3.2%Essential 23.1% Somewhat
useful 17.3%
Very useful 51.3%
Zone of Interest (user defined)
Settlement Population: 4,800Communal Refuse PitsWater Tanks (total # of taps = 35)Latrine Sites (total # of toilets = 150)Security Zone (50km cordon)⇒ SPHERE Compliance for max. 3,000⇒ Extra Capacity Needed for 1,800
(Data for illustrative purposes only)
Question 16: Zone Analysis
Zone Analysis “The figure above shows how GIS can be used to calculate the total
relief capacity (and compliance with humanitarian standards) within a certain area. How useful is this type of analysis to you?”
• 90 out of 156 respondents provided comments, ranging from “Excellent for ensuring compliance with standards” to “SPHERE guidelines are only indicators and should not be considered out of context”
N/A9.6%
Not useful 3.8%
Somewhat useful 23.1%
Essential 19.2%
Very useful 44.2%
Origin
Destination
Flying Distance (Helicopter) = 63 km
Driving Distance (Paved Roads): 135 kmDriving Distance (Gravel Roads): 20 km⇒Total Driving Distance: 155 km⇒Estimated Fuel (Return Trip): 40 litres
North Rd.Motorway
South Rd.
Main St.
Logging Rd.
Mosquito-breeding Swamps: 15 sq.kms.⇒Volume of Chemical Agent: 450 litres⇒Estimate Spraying Time: 3.5 hours
Question 17: Measurement
Measurement Analysis “Compared to existing methods of distance and areal measurement,
how useful is this type of analysis to you?”
• One of the most obvious and simplest types GIS-based analysis, particularly for logistics planning
• But is precise measurement outweighed by overall imprecision?
N/A16.7%
Not useful 7.1%
Somewhat useful 21.2%
Essential 15.4%
Very useful 39.7%
Question 18: Vulnerability Estimation
Muzaffarabad (near epicentre)
Worst Affected Areas(30 km buffer around
quake faultine)
Areas far from main routes & local
capitals
Areas near district boundaries
Areas far from main routes & local
capitals, AND near district boundaries
+
=
Villages most vulnerable to being underserved by relief agencies:
1. Within Worst Affected Area (predicted)2. Far from main routes & capitals3. Near district boundaries
Vulnerability Estimation “Compared to existing methods of vulnerability estimation, how useful
is this type of analysis to you?”
• 95 out of 156 respondents provided enthusiastic comments, but stressed the need for accurate gazetteer as well as Who-What-Where data
N/A5.1%
Essential 23.1%
Not useful 0.6%
Somewhat useful 19.9%
Very useful 51.3%
Question 19: Site Selection“GIS can be used to identify the best locations for campsites (or schools,
hospitals, helipads, etc.), based upon certain criteria such as water availability, land cover, accessibility, hazards, etc. In other words, by layering existing knowledge of land cover, terrain, hydrology and relief activities, it is possible to apply GIS-based analysis to optimize the locations for relief operations. How useful is this type of analysis to you?”
N/A4.5%
Essential 30.8%
Not useful 1.9%
Somewhat useful 13.5%
Very useful 49.4%
Question 20: Service Optimization
Optimal Locations for Health, Educational & Financial Services
Clinics Service Goal: < 60 minutes from any village by Landcruiser (4x4)Schools Service Goal: <7.5 kms from any village by footBank Service Goal: Minimum Aggregate Travel time by bus
Main Town
VillagesMain RoadsSecondary RoadsTrails
Service Optimization “Compared to existing methods of selecting locations for humanitarian
services, how useful is this type of analysis to you?”
• 88 out of 156 respondents provided comments, and cautioned that local politics often dictates such decision making
N/A8.3%
Somewhat useful 21.8%
Essential 18.6%
Not useful 2.6%
Very useful 48.7%
Question 21: Route Optimization“You may be familiar with Internet-based route planning services, which allow
the user to input an origin and destination and then obtain a turn-by-turn set of directions. When transportation networks are complex (or constantly changing), GIS-based analysis can quickly identify the shortest or fastest route between two or more locations. Compared to existing methods of navigation and route planning during humanitarian operations, how useful is this type of analysis to you?
N/A7.1% Not useful
7.7%
Somewhat useful 30.1%
Essential 16.7%
Very useful 38.5%
Question 23: Exact Center Analysis (Weighted)
Most Central Village (unweighted)
(see Question 22)
Exact CenterIdeal location for a grain processing facility, if the same villagesare weighted according to total wheat production, and assumingthat modes of local travel are unknown.
Central Feature & Exact Center Analysis “The figures above shows how GIS can be used to calculate the most central
and the exact center amongst a set of points (weighted to reflect village populations, agricultural output, AIDS/HIV infection rates, etc.). Compared to existing methods, how useful are these type of analysis to you?”
• Two of three types of geostatistics tested in the survey
Not useful 20.5%
Very useful 21.8%
N/A12.8%
Somewhat useful 39.1%
Essential 5.8%
Not useful 12.8%
Very useful 27.6%
N/A17.9%
Somewhat useful 34.6%
Essential 7.1%
Question 22: Central Feature Analysis Question 23: Exact Center Analysis
Question 24: Cluster Analysis
Rebel attacks with a correlation to the village’s ethnic affiliation, considered statistically “clustered”.
Rebel attack without an identifiable pattern, considered statistically “random”.
Cluster Analysis “The figure above shows how GIS can be used to identify if a series of
features is correlated (i.e. clustered) with other phenomena. The purpose of cluster analysis is to determine if the distribution of a set of features is related or random, and to investigate cause-and-effect relationships. The most common applications of cluster analysis have been in epidemiology (detecting possible sources of disease outbreaks) and crime analysis (anticipating the location and tactics of criminals). How useful is this type of analysis to you ?”
Very useful 49.4%
N/A9.0%
Somewhat useful 17.9%
Essential 18.6% Not useful
5.1%
Question 25: “What if?”Analysis
=
Snow Extent as of January 7, 2006Data for illustrative purposes only
Main Access Roads⇒Roads above 3,500 metres will probably be impassable after January 15, 2006
Villages bordering snow-level⇒These 21 villages in District A will need priority for blanket & tent distribution (total affected population: 7,500)
Epicentre
“What if?” Analysis • 75 out of 156 respondents provided comments, split between a
majority who felt it could be the most powerful type of analysis, and a minority who argued there was neither the time, data nor sophistication to conduct hypothesis testing and simulations during a crisis situation
Very useful 46.8%
N/A7.1%
Somewhat useful 24.4%
Essential 17.9% Not useful
3.8%
Question 27: Geovisualization for Orientation & Situation Reporting
Object: Building Status = DestroyedSource = UNOSATConfidence = LowDate = Aug 14, 2006
Object: Road Status = Severe DamageSource = UNOSATConfidence = Very HighDate = Aug 14, 2006
Object = OrchardStatus = UXO Mine HazardSource = Local ResidentConfidence = MediumDate = Aug 12, 2006
Village = Tair Debba, LebanonStatus = Partially DisplacedSource = UNDPConfidence = HighDate = Aug 25, 2006
Note: Data for demonstrative purposes only.
Geovisualization “Compared to other methods of orientation and situation reporting,
how useful is this Geovisualization to you?”
• Unquestionably strong application but enthusiasm was tempered byvalid concerns about data reliability, Internet dependency & resolution
No, 15.4%
Yes, 84.6%
Very useful 52.9%
N/A4.6%
Somewhat useful 19.0%
Essential 22.2%
Not useful 1.3%
Question 26: Are you familiar with Google Earth, MS Virtual Earth or ArcGIS Explorer?
Question 28: Top 5 Types of Analysis
• Without doubt, Gap Analysis had the highest relative utility amongst the 87 qualifying responses, followed by Vulnerability Estimation.
• Geovisualization, Cluster Analysis & Site Selection also ranked well
• Range Analysis had lowest variance, standard deviation and the second highest mode
Overall Analysis• This was a voluntary survey, and therefore the survey respondents
are representative of the population inclined to exploit GIS-based analysis. Broader extrapolation or inference was avoided, and not necessary to assess “utility”
• The benchmark for statistically determining “utility” is arbitrary: I defined this a being a clear majority (>55%) of the sample stating that a type of analysis was either “Very Useful” or “Essential”
Con
fiden
ceIn
terv
al
Total Population
Confidence Level = 95%
6%
7%
8%
500
9%
10%
5,000 100,000
Confidence Level = 99% Key Point: With a sample size (n) =156, the total population is not significant for determining utility, but it is for determining feasibility
Testing the Minor Alternative HypothesesCategory Suggested Application >55% Very Useful or Essential
Queries & Measurements 1. Gap Analysis2. Measurement3. Range Analysis4. Zone Analysis
YesYesYesYes
Transformations 5. Vulnerability Estimation6. Site Selection
YesYes
Optimizations 7. Service Optimization8. Route Optimization
YesYes
Geostatistics 9. Central Feature Analysis10. Exact Center Analysis (Weighted)11. Cluster Analysis
NoNoYes
Geovisualization 12. Orientation & Situation Reporting Yes
Hypothesis Testing 13. “What If?” Analysis Yes
“More than 55% of the statistically-representative sample population says that GIS-based analysis type x can have a decisive impact upon the coordination of humanitarian assistance, within a confidence level of 95% and interval of 7.15%”(calculated for a target population = 10,000)
At the 55% threshold, it is possible to conclude that GIS-based analysis can have a decisive impact upon the coordination of humanitarian assistance, but:
• The need for Gap Analysis seems indisputable, however the Phase 1 & 2 results were inconsistent and we cannot conclude that GIS-based analysis is critical
• More study is also required to determine the utility of “What if?”Analysis, since it is too complex to test with just one example
• Optimizations and Geostatistics may have limited utility and feasibility in typical humanitarian situations
Final Conclusions
GIS Tutorial for Humanitarian Assistance
ESRI Press, to be released end of 2009
Thank you