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CTAP Ex- 1 - Hurricane DRR

Stock Budgeting 1 Output Calculator

Introduction
This tool tracks resource stock indicators for output uris. Up to 15 new indicators can be added for each output.

Calculation View Description
This CTA is Example 1 in the Technology Assessment 2 tutorial.v216b

Version: 1.9.0

Relations

Use In Childs?
Overwrite Childs?

Step 3 of 3. Save

Method 1. Do you wish to save step 2's calculations? These calculations are viewed by opening this particular calculator addin.

Step 1

• Step 1. Indicators: Enter up to 10 indicators.
• Step 1. Indicator Name and Description: Name and description for each indicator.
• Step 1. Indicator Date: Make sure that the benchmark, targets, actual, indicators have distinct dates.
• Step 1. Distribution Type: The numeric distribution of QT. Refer to the Stock Calculation 1 reference.
• Step 1. Math Expression:A mathematical expression containing one or more of the Q1 to Q5 variables and/or sibling indicator Q1 to QTM variables. Use strings that identify both the indicator (I1, I2, … In) and the Qx property (Q … QTM), with a period separator between them. Examples include:((I1.Q1 + I1.Q2) * I1.Q3) + I1.Q4)) - (2 * I1.Q5)
• Step 1. Math Operator Type: Mathematical operation to use with QT. MathTypes include: equalto, lessthan, greaterthan, lessthanorequalto, and greaterthanorequalto. Refer to the Stock Calculation 1 reference for the algorithms.
• Step 1. Math Type and Math Sub Type: Mathematical algorithm and subalgorithm to use with Distribution Type, QT, QTD1, and QTD2 to solve for QTM, QTL, and QTU. Refer to the Stock Calculation 1 reference for the algorithms.
• Step 1. QT Amount and Unit: The Unit must be manually entered. The Amount will be the result of the mathematical calculation.
• Step 1. QTD1 Amount and Unit: First distribution, or shape, parameter for QT.
• Step 1. QTD2 Amount and Unit: Second distribution, or scale, for QT.
• Step 1. BaseIO: Base input or output property to update with this indicator's QTM property.
• Step 1. QTM Amount and Unit: Most Likely Estimate for QT. The Unit must be manually entered. The Amount will be the result of the mathematical algorithm.
• Step 1. QTL Amount and Unit: Low Estimate or QT. The Unit must be manually entered. The Amount will be the result of the mathematical algorithm.
• Step 1. QTU Amount and Unit: High Estimate for QT. The Unit must be manually entered. The Amount will be the result of the mathematical algorithm.
• Step 1. Math Result: TEXT string holding results of calculation.

Step 2

• Step 2. Use Same Calculator Pack In Descendants?: True to insert or update this same calculator in children.
• Step 2. Overwrite Descendants?: True to insert or update all of the attributes of this same calculator in all children. False only updates children attributes that are controlled by the developer of this calculator (i.e. version, stylehsheet name, relatedcalculatorstype ...)
• Step 2. What If Tag Name: Instructional videos explaining the use of what-if scenario calculations should be viewed before changing or using this parameter.
• Step 2. Related Calculators Type: When the Use Same Calculator Pack in Descendant is true, uses this value to determine which descendant calculator to update. Inserts a new descendant when no descendant has this same name. Updates the descendant that has this same name.
• Step 2. Indicators: Enter up to 5 indicators.
• Step 2. Target Type: Used with Progress analyzers to identify benchmark and actual indicators.
• Step 2. Altern Type: Used with Change by Alternative analyzers to identify alternatives to compare.
• Step 2. Score Math Expression: A mathematical expression containing one or more of the children indicator Q1 to QTM variables. Use strings that identify both the indicator (I1, I2, … In) and the Qx property (Q … QTM), with a period separator between them. Examples include:((I1.QTM + I2.QTM) * I3.Q3) + I4.QTM)) - (2 * I5.QTM)
• Step 2. Score Amount and Unit: The Unit must be manually entered. The Amount will be the result of the Math Expression calculation.
• Step 2. ScoreD1 Amount and Unit: First distribution variable for Score.
• Step 2. ScoreD2 Amount and Unit: Second distribution for Score.
• Step 2. Distribution Type: The numeric distribution of Score. Refer to the Stock Calculation 1 reference.
• Step 2. Score Math Type and Math Sub Type: Mathematical algorithm and subalgorithm to use with Distribution Type, Score, ScoreD1, and ScoreD2 to solve for ScoreM, ScoreL, and ScoreU. Refer to the Stock Calculation 1 reference for the algorithms.
• Step 2. Score Most Likely, Score Low, Score High, Amounts and Units: Results of Distribution Type and Math Type calculations.
• Step 2. Iterations: Number of iterations to use when drawing random number samples for some algorithms.
• Step 2. Confidence Interval: Level of confidence interal to use when reporting all Score and Indicator high and low amounts. Should be an integer such as 95, 90, or 40.
• Step 2. Random Seed: Any positive integer, except 0, will result in the same set of random variables being used each time a calculation is run.
• Step 2. Score BaseIO: Base input or output property to update with the Score Most Likely property.

References

• Refer to the Stock Calculation 1 reference.

Current view of document
CTAP Ex- 1 - Hurricane DRR
Output Group
CTAP Output Examples
Output
CTAP Ex- 1 - Hurricane DRR
Score Score Unit Score D1 Amount Score D1 Unit Score D2 Amount Score D2 Unit Iterations Confid Int Random Seed Base IO
Score Most Amount Score Most Unit Score Low Amount Score Low Unit Score High Amount Score High Unit Distribution Type Math Type Math Sub Type Observations
12.0661 highest bcr 12.0237 mean 3.0000 sd 10000 90 7 none
12.0560 highest bcr 12.0063 lower 90 % ci 12.1057 upper 90 % ci normal algorithm1 subalgorithm1 1
I6.QTM
sampled descriptive statistics N,Total,Mean,Median,StdDev,Var,Min,Max 10000, 120559.6906, 12.0560, 12.0388, 3.0096, 9.0578, 0.1870, 23.3980, sampled cumulative density function 0.00,0.10,0.20,0.30,0.40,0.50,0.60,0.70,0.80,0.90,1.00 0.1870,8.1954,9.5513,10.4938,11.2713,12.0393,12.8077,13.6408,14.6030,15.9601,23.3980
Name (N) Label Date Rel Label Math Type Dist Type Base IO Math Operator Math Sub Type
Q1 Amount Q1 Unit Q2 Amount Q2 Unit Q3 Amount Q3 Unit Q4 Amount Q4 Unit Q5 Amount Q5 Unit
QT Amount QT Unit QT D1 Amount QT D2 Amount QT Most Amount QT Most Unit QT Low Amount QT Low Unit QT High Amount QT High Unit
Hazard Distribution 1A 01/01/2005 none algorithm1 none none equalto subalgorithm9
105.1130 100year 78.8347 50year 52.5565 25year 36.7895 10year 26.2783 5year
49.8750 49.8750 4.9875 49.9287 mean wind speed mph 49.8461 lower 90 % ci 50.0112 upper 90 % ci
Each reach is described by a probability distribution (QTs) defined by the event probability (1, 2, 4, 10, 20 percent) and the associate quantity of the hazard (the matrix numbers).
I1.Q1.distribtype + I1.Q2.100year + I1.Q3. 50year + I1.Q4.25year + I1.Q5.10year + I1.Q6.5year
Exposure Distribution 2A 01/01/2005 none algorithm1 none none equalto subalgorithm9
2,202,366.3970 location 1 total value 2,087,242.7003 location 2 total value 0.0000 none 0.0000 none 0.0000 none
4,289,609.0973 1, 2 totals 0.0000 0.0000 4,289,609.0973 drr all locations 4,282,516.1635 lower 90 % ci 4,296,702.0311 upper 90 % ci
The total value of each asset type is described by a probability distribution (QTs) calculated from the price (p1) and quantity (q1) of the asset.
I2.Q1.distribtype + I2.Q2.QT + I2.Q3.QTUnit + I2.Q4.QTD1+ I2.Q5.QTD1Unit + I2.Q6.QTD2 + I2.Q7.QTD2Unit + I2.Q8.normalization + I2.Q9.weight + I2.Q10.quantity
Vulnerability Distribution 3A 01/01/2005 none algorithm1 none none equalto subalgorithm9
5.2557 5year 38.8918 10year 136.1211 25year 203.9192 50year 274.3448 100year
0.0000 0.0000 0.0000 17.2070 total percent damage 17.1785 lower 90 % ci 17.2354 upper 90 % ci
The damage percent for each asset type is described by a probability distribution (QTs) defined by the flood depth (0.5, 1.0, 1.5, 2.0, 2.5) and the percent of asset damage (the matrix numbers).
I3.Q1.distribtype + I3.Q2.5year + I3.Q3.10year + I3.Q4.25year + I3.Q5.50year + I3.Q6.100year
Loss EP Distribution 4A 01/01/2005 none algorithm1 none none equalto subalgorithm9
5,530.9429 5year 60,195.6790 10year 286,384.2140 25year 523,943.9671 50year 931,477.8539 100year
0.0000 0.0000 0.0000 38,374.7829 total avg ann damages 38,260.3792 lower 90 % ci 38,507.0687 upper 90 % ci
The damages for each asset type is described by a probability distribution (QTs) defined by the event probability (20, 10, 4, 2, 1) and the total damages (the matrix numbers).
I4.Q1.distribtype + I4.Q2.5year + I4.Q3.10year + I4.Q4.25year + I4.Q5.50year + I4.Q6.100year
Project Costs 5A 01/01/2005 none algorithm1 none none equalto subalgorithm9
0.0000 none 0.0000 none 0.0000 none 0.0000 none 0.0000 none
0.0000 mean avg annual costs 0.0000 0.0000 3,031.7873 mean avg annual costs 3,026.7743 lower 90 % ci 3,036.8004 upper 90 % ci
The cost for each project alternative is described as the probability of average annual costs (QTs).
I5.Q1.installcost + I5.Q2.installdistrib + I5.Q3.omcost+ I5.Q4.omdistrib + I5.Q5.isprojectcost
Benefit Cost Analysis 6A 01/01/2005 none algorithm1 none none equalto subalgorithm9
540,852.0629 base damage 233.0080 base cost 388,199.8771 AC1A_B damage 12,884.3140 AC1A_B cost 0.0000 none
140,000.8799 net benefits 0.0000 0.0000 12.0661 2_QTM_0.05_25, bcr 12.0624 lower 90 % ci 12.1013 upper 90 % ci
The Math Results from Indicator 4 define average annual benefits and the Math Results from Indicator 5 defines average annual costs. Benefits are defined as the reduction in damages of each project alternative in comparison to the baseline.
I6.Q1.distribtype + I6.Q2.100year + I6.Q3.50year + I6.Q4.25year + I6.Q5.10year + I6.Q6.5year
Cost Effectiveness Analysis 7A 10/02/2015 none algorithm1 none none equalto subalgorithm9
38,374.7829 base damage 195.7324 base cost 27,543.7352 AC1A_B damage 12,041.1894 AC1A_B cost 0.0000 none
1.0937 net benefits 0.0000 0.0000 1.0937 2_QTM_0.12_10, cer 1.0940 lower 90 % ci 1.0905 upper 90 % ci
The URL dataset define average annual benefits from non monetary disaster loss reductions and the Math Results from Indicator 5 defines average annual costs. Benefits are defined as the reduction in damages of each project alternative in comparison to the
I7.Q1.distribtype + I7.Q2.100year + I7.Q3.50year + I7.Q4.25year + I7.Q5.10year + I7.Q6.5year
Output Series: CTAP Ex- 1 - Hurricane DRR
Score Score Unit Score D1 Amount Score D1 Unit Score D2 Amount Score D2 Unit Iterations Confid Int Random Seed Base IO
Score Most Amount Score Most Unit Score Low Amount Score Low Unit Score High Amount Score High Unit Distribution Type Math Type Math Sub Type Observations
12.0661 highest bcr 12.0237 mean 3.0000 sd 10000 90 7 none
12.0560 highest bcr 12.0063 lower 90 % ci 12.1057 upper 90 % ci normal algorithm1 subalgorithm1 1
I6.QTM
sampled descriptive statistics N,Total,Mean,Median,StdDev,Var,Min,Max 10000, 120559.6906, 12.0560, 12.0388, 3.0096, 9.0578, 0.1870, 23.3980, sampled cumulative density function 0.00,0.10,0.20,0.30,0.40,0.50,0.60,0.70,0.80,0.90,1.00 0.1870,8.1954,9.5513,10.4938,11.2713,12.0393,12.8077,13.6408,14.6030,15.9601,23.3980
Name (N) Label Date Rel Label Math Type Dist Type Base IO Math Operator Math Sub Type
Q1 Amount Q1 Unit Q2 Amount Q2 Unit Q3 Amount Q3 Unit Q4 Amount Q4 Unit Q5 Amount Q5 Unit
QT Amount QT Unit QT D1 Amount QT D2 Amount QT Most Amount QT Most Unit QT Low Amount QT Low Unit QT High Amount QT High Unit
Hazard Distribution 1A 01/01/2005 none algorithm1 none none equalto subalgorithm9
105.1130 100year 78.8347 50year 52.5565 25year 36.7895 10year 26.2783 5year
49.8750 49.8750 4.9875 49.9287 mean wind speed mph 49.8461 lower 90 % ci 50.0112 upper 90 % ci
Each reach is described by a probability distribution (QTs) defined by the event probability (1, 2, 4, 10, 20 percent) and the associate quantity of the hazard (the matrix numbers).
I1.Q1.distribtype + I1.Q2.100year + I1.Q3. 50year + I1.Q4.25year + I1.Q5.10year + I1.Q6.5year
Exposure Distribution 2A 01/01/2005 none algorithm1 none none equalto subalgorithm9
2,202,366.3970 location 1 total value 2,087,242.7003 location 2 total value 0.0000 none 0.0000 none 0.0000 none
4,289,609.0973 1, 2 totals 0.0000 0.0000 4,289,609.0973 drr all locations 4,282,516.1635 lower 90 % ci 4,296,702.0311 upper 90 % ci
The total value of each asset type is described by a probability distribution (QTs) calculated from the price (p1) and quantity (q1) of the asset.
I2.Q1.distribtype + I2.Q2.QT + I2.Q3.QTUnit + I2.Q4.QTD1+ I2.Q5.QTD1Unit + I2.Q6.QTD2 + I2.Q7.QTD2Unit + I2.Q8.normalization + I2.Q9.weight + I2.Q10.quantity
Vulnerability Distribution 3A 01/01/2005 none algorithm1 none none equalto subalgorithm9
5.2557 5year 38.8918 10year 136.1211 25year 203.9192 50year 274.3448 100year
0.0000 0.0000 0.0000 17.2070 total percent damage 17.1785 lower 90 % ci 17.2354 upper 90 % ci
The damage percent for each asset type is described by a probability distribution (QTs) defined by the flood depth (0.5, 1.0, 1.5, 2.0, 2.5) and the percent of asset damage (the matrix numbers).
I3.Q1.distribtype + I3.Q2.5year + I3.Q3.10year + I3.Q4.25year + I3.Q5.50year + I3.Q6.100year
Loss EP Distribution 4A 01/01/2005 none algorithm1 none none equalto subalgorithm9
5,530.9429 5year 60,195.6790 10year 286,384.2140 25year 523,943.9671 50year 931,477.8539 100year
0.0000 0.0000 0.0000 38,374.7829 total avg ann damages 38,260.3792 lower 90 % ci 38,507.0687 upper 90 % ci
The damages for each asset type is described by a probability distribution (QTs) defined by the event probability (20, 10, 4, 2, 1) and the total damages (the matrix numbers).
I4.Q1.distribtype + I4.Q2.5year + I4.Q3.10year + I4.Q4.25year + I4.Q5.50year + I4.Q6.100year
Project Costs 5A 01/01/2005 none algorithm1 none none equalto subalgorithm9
0.0000 none 0.0000 none 0.0000 none 0.0000 none 0.0000 none
0.0000 mean avg annual costs 0.0000 0.0000 3,031.7873 mean avg annual costs 3,026.7743 lower 90 % ci 3,036.8004 upper 90 % ci
The cost for each project alternative is described as the probability of average annual costs (QTs).
I5.Q1.installcost + I5.Q2.installdistrib + I5.Q3.omcost+ I5.Q4.omdistrib + I5.Q5.isprojectcost
Benefit Cost Analysis 6A 01/01/2005 none algorithm1 none none equalto subalgorithm9
540,852.0629 base damage 233.0080 base cost 388,199.8771 AC1A_B damage 12,884.3140 AC1A_B cost 0.0000 none
140,000.8799 net benefits 0.0000 0.0000 12.0661 2_QTM_0.05_25, bcr 12.0624 lower 90 % ci 12.1013 upper 90 % ci
The Math Results from Indicator 4 define average annual benefits and the Math Results from Indicator 5 defines average annual costs. Benefits are defined as the reduction in damages of each project alternative in comparison to the baseline.
I6.Q1.distribtype + I6.Q2.100year + I6.Q3.50year + I6.Q4.25year + I6.Q5.10year + I6.Q6.5year
Cost Effectiveness Analysis 7A 10/02/2015 none algorithm1 none none equalto subalgorithm9
38,374.7829 base damage 195.7324 base cost 27,543.7352 AC1A_B damage 12,041.1894 AC1A_B cost 0.0000 none
1.0937 net benefits 0.0000 0.0000 1.0937 2_QTM_0.12_10, cer 1.0940 lower 90 % ci 1.0905 upper 90 % ci
The URL dataset define average annual benefits from non monetary disaster loss reductions and the Math Results from Indicator 5 defines average annual costs. Benefits are defined as the reduction in damages of each project alternative in comparison to the
I7.Q1.distribtype + I7.Q2.100year + I7.Q3.50year + I7.Q4.25year + I7.Q5.10year + I7.Q6.5year
Dataset: CTAP Ex- 1 - Hurricane DRR IRI This output is used to support a CTAP tutorial for hurricane Disaster Risk Reductions in St. Lucia, Caribbean