Overall Assessment of Philippines after Course of Action 2 is implemented

What is this Theme

This thematic map represents the overall assessment of civil services assuming `Course of Action 2’ is taken after a hypothetical typhoon striking the Philippines on February 14, 2018. COA2 weights separate SWEAT-MSO conditions using the table latter in this document. SWEAT-MSO assessments are conducted as part of Stability Operations and Support to Civil Services operations, as well as before operations during Intelligence Preparation of the Operating Environment.

What is SWEAT-MSO

SWEAT-MSO is a Conditions Framework designed to organize information supporting Military Engineering operations. SWEAT-MSO is an acronym for—Sewer, Water, Electricity, Academics, Trash, Medical, Safety, and Other. SWEAT-MSO assessments are conducted as part of the Stability and Support to Civil Services operations where the purpose is to restore essential services and reinstate confidence for the local government (US Army, 2008).

Generating the Data Model for the SWEAT-MSO Conditions Framework

U.S. Army Field Manual 3-07 Stability Operations and Support Operations (US Army, 2008) defines the SWEAT-MSO assessment as a conceptual framework, with research by US Army Corp of Engineers’ ERDC defining this data model. This SWEAT-MSO data model is based on indicator maps calculated using publically available data as metrics.

Conditions		Indicators	Metrics
S	Sewage	Collection	Kind of Toilet; Sewage Service Availability Perception (simulated)
		Treatment	Distance to Wastewater Treatment
W	Water	Production	Distance to Water Tower; Functionality of Water Facilities
		Distribution	Cooking/Drinking Water Source; Laundry/Bathing Water Source; Water Availability Perception (simulated); Water Security Disruption
E	Electricity	Generation	Distance to Electrical Transformer
		Distribution	Fuel for Lighting; Fuel for Cooking; Has Washing Machine; Has Refrigerator; Has Television Set; Electricity Availability Perception (simulated)
A	Academics	Facilities	Distance to School
		Services	Literacy; School Attendance; Highest Grad Completed; School Availability Perception (simulated)
T	Trash	Collection	Manner of Garbage Disposal; Trash Collected Perception (simulated)
		Disposal	Distance to City Dump
M	Medical	Facilities	Distance to Medical Facility
		Services	Has Disability; Medical Availability Perception (simulated)
S	Safety	Facilities	Distance to Police/Fire Station; Distance to Government Administration Building
		Services	Has Television Set; Has Radio; Has Telephone; Police Perception (simulated); Army Perception (simulated)
O	Other	Transportation	Distance to Major Road; Traffic Perception (simulated)

SWEAT-MSO Conditions Map

The SWEAT-MSO map gives an overview of the infrastructure area, the status of operation, and the major components in the system. Aspects of uncertainty are included in the rating. A ‘No Risk’ rating is interpreted as essential services are operational, critical positions are staffed, infrastructure and the populace are secured, and civil order is attained. A ‘Maximum Risk’ rating indicates the opposite end state conditions. Each risk evaluation state is assigned a numerical value reflected in the table below. This normalizes all values.

Risk Evaluation	Risk Evaluation Values
Minimal Risk	0.500 – 1.000
Moderate Risk	0.250 – 0.500
High Risk	0.125 – 0.250
Very High Risk	0.063 – 0.125
Maximum Risk	0.001 – 0.063
No Data	0

Developing the Data Model of the Framework

The thematic values of a condition or indicator requires the evaluation of a combination of themes lower or deeper in the data model. Metrics combine to form indicators, indicators to conditions. Weights are assigned to metrics, indicators, and conditions—allowing each component level to be rolled-up to the next level. Users define weights as a numerical value greater than 0.0 and less than or equal to 1.0 based on its theme’s contribution to risk. Low values has less impact on the higher level condition. A value of 1.0 indicates that theme constrains the higher level theme to be no better than itself. Due to the uncertainty of `true importance’, weights are defined as a range of possible values where a greater range indicates more uncertainty to knowing the risk contribution.

If all weights within a grouping add up to 1, then each unit contributes to the accumulation of risk. For example when characterizing healthcare deficiencies, the availability of doctors, facilities, and pharmaceuticals all contribute to overall risk. If a weight equals 1, then it drive the overall risk. For example when characterizing climatological consequences, either flood, severe storm, or drought can impose the overall risk. In this example, all three characterizations would receive a weight of one, and the largest value becomes the maximum possible overall risk value when all other risk indicators are `no risk’.

Since no data model provides ALL necessary information to accurately model an indicator or condition, there is an unknown component associated with indicator. This inserts a random value at each location that accounts for unavailable additional variables. It is given a weight and treated the same as any other component.

Condition weights, as assigned to the SWEAT-MSO Conditions Framework specifically for typhoon disaster relief in the Philippines, are noted in the table below. Note that other disasters and locations may deserve different weights. While the conditions do encompass multiple aspects of stability, there is the potential for additional conditions to be added. Low uncertainty values represent minimal anticipated additions. A greater explanation of this process can be found at Ehlschlaeger et al. (2016).

Typhoon Valentine Response COA 02 SWEAT-MSO Conditions Weights
Conditions of SWEAT-MSO	Weight Minimum Value	Weight Maximum Value
Sewage	0.2	0.3
Water	0.2	0.3
Electricity	0.2	0.3
Academics	0.1	0.2
Trash	0.1	0.2
Medical	0.15	0.25
Safety	0.15	0.25
Other Conditions	0.15	0.25
Uncertainty	0.2	0.3

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Roll-up Computation

A stated goal of the FICUS effort was to explicitly represent errors and uncertainties within all products. For the favorability function to specifically quantify uncertainty, the following equation becomes the Uncertainty Quantified Power Based Favorability Function, and is described in Ehlschlaeger et al. (2016).

Where:

I_r is the r^th multi-verse indicator map;

M_i,r is the r^th multi-verse criteria map of the i^th metric;

w_i is the weight of the i^th criteria, with a value randomly determined between its minimum and maximum potential values;

M_u,r is the r^th multi-verse map of simulated uncertainty for an indicator; and

w_u is the weight of the uncertainty, with a value randomly determined between its minimum and maximum potential values, with higher values representing less knowledge about the uncertainty.

The simulated uncertainty map, M_u,r, is a random field of values between 0.0 and 1.0 with a histogram like the distribution of values within the criteria maps. The random field has spatial autocorrelation to the largest spatial dependence of the criteria maps. For example, if the criteria maps used kernel analysis on demographic factors, the random field should have positive spatial autocorrelation equal to the kernel analysis diameter. This algorithm used the random field described in Ehlschlaeger (2002). Modelers were expected to estimate the range of values for all weights, w_u and w_i, that might exist accounting for the lack of perfect understanding between the criteria and the indicator. We asked the modelers to imagine which criteria they wish existed that would better explain the indicator. Then, modelers were to estimate which of those unavailable criteria had the least correlation with available criteria. Uncorrelated unavailable criteria would be indicated by higher values and greater ranges of the uncertainty weight w_u. This uncertainty weight has the same behavior on the risk assessment model as the criteria weights.

References

Ehlschlaeger, C. R. (2002). Representing multiple spatial statistics in generalized elevation uncertainty models: Moving beyond the variogram. International Journal of Geographical Information Science 16(3):259-285. DOI: 10.1080/13658810110099116. URL: https://www.researchgate.net/publication/220649818_Representing_multiple_spatial_statistics_in_generalized_elevation_uncertainty_models_Moving_beyond_the_variogram

Ehlschlaeger, C. R., D. A. Browne, N. R. Myers, J. A. Burkhalter, C. Baxter, Y. Gao, D. Yin, and M. D. Hiett (2016). From Data to Decision with Analytic Frameworks: Presenting Data Errors and Uncertainties for Operational Planning. Military Intelligence Professional Bulletin, PB 34-16-3, 42(3):44-47, URL: https://www.researchgate.net/publication/313200616_From_Data_to_Decision_with_Analytic_Frameworks_Presenting_Data_Errors_and_Uncertainties_for_Operational_Planning

US Army (2008). Stability Operations FM3-07. Washington DC, 208 pages.