SIEMPRE: A GIS aided multi-criteria decision analysis application for setting priorities
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Transcript of SIEMPRE: A GIS aided multi-criteria decision analysis application for setting priorities
SIEMPRE: A GIS aided multi-criteria decision analysis
application for setting prioritiesBernardo Creamer, Jesús Rodríguez, Glenn Hyman,
Ernesto Giron, Marcos Nobre
Glenn HymanESRI International User Conference
16 July 2014
Complex problems that require multidisciplinary teams
Involve diversity of stakeholders (e.g. lower income population,
small farmers, entrepreneurs, local governments, etc.)
Various scales: Local, regional, continental, global
Multiple parameters of assessment:
economic returns, Environmental impact, impact on
health, nutrition quality, etc.
Uncertainty and incomplete information
Complexity of Policy and Decision Analysis
Example: Environmental impactE
cono
mic
Im
pact
Environmental impact (more is bad)
Project 1High cost
Project 2Medium cost
Project 3Low cost
Which project do we execute?
Depends:1. High economic Impact: choose Pr. 12. Low environmental Impact: Pr. 23. Limited resources: Project 3
Analytical Hierarchy Process AHP
Scoring methodology for multi-criteria decision analysis (MCDA)
Categorizes empirical data and qualitative information
Summarizes the importance of all parts in a coherent and simple hierarchical frame -> Helps organize the decision analysis in different levels
GIS aided AHP -> GAHP
In the information process for making the decision, GIS tools and maps are utilized
AHP levels
Goal, Priority
Criterion 1
Criterion 2
Criterion n…
Indicator a Indicator b Indicator c Indicator d Indicator x…
Final Objective
Evaluation Criteria
Indicators or atributes
GThe indicators are represented by maps or figures
GAHP steps
1. Setting up the process: definition of goals, priorities, criteria and indicators.
2. Quantitative prioritization. From indicators to criteria.
3. Qualitative hierarchization. From criteria to goals and priorities
1. Setting up the process: definition of goals, priorities, criteria and indicators.
• Goal: • What is the final objective of our project?• Why are we doing all this for?
• Examples: • For this workshop: Areas where RTB technologies are going to
be most beneficial.• For the RTB PS: Increase the welfare of lower income
populations in RTB growing regions
GAHP steps
GAHP steps
2. Quantitative prioritization. From indicators to criteria
Weighted Overlay Spatial Analysis
a) Collection of relevant spatial information data to use as indicators
b) Categorization of indicators: a) Conformation of indicator baskets that relate to each criterion.
Indicators can be seen as proxy variables that can explain part of the criterion.
b) Attaching weights to indicators
c) Overlaying weighted indicator layers to visualize each criterion in terms of their proxies.
GAHP steps
3. Qualitative hierarchization. From criteria to goals and priorities.
Prioritizing criteria (by region)
a) Survey experts to give weighted values to the qualitative criteria considered in the project
b) Multi-criteria matrix formation by region. Calculation of coefficients for each criterion, weighted by region.
SIEMPRE: Integrated System for Multicriteria
Evaluation of Policies and Strategies
Beta version:http://siempre.ciat.cgiar.org/
ArcGisserver Information flow
GAHPserver
Use of maps
Harvested Area
Children underweight
Weighted overlay=
This exercise is a simulated survey by which we intend to assess strategies or priorities for the beans production sector that can help to improve the socio economic conditions of the population living in crops producer areas.
Example survey:Strategic agricultural areas in Colombia
Goal:
• Contribute to the improvement of the socio-economic conditions of the population living in rural areas in Colombia
Evaluation criteria1. Contribution to poverty reduction
2. Increase in food security
1. Setting up the process
Indicators (presented in maps)
1. Stunting among children
2. Harvested area (beans)
3. Yield (beans)
4. Yield gap (Best reported yield – yield)
5. Poverty (we use as proxy Colombia’s Index of Insufficient Provision of Basic Needs- NBI 2010)
6. Protein intake
1. Setting up the process (cont.)
AHP representation of the survey
Goal, Priority
Criterion 1
Criterion 2
Criterion n…
Indicator a Indicator b Indicator c Indicator d Indicator x…
Final Objective
Evaluation Criteria
Indicators
Improvement of socio-economic conditions in rural
Colombia
1. Contribution to poverty reduction
2. Increase in food security
Stunting Area Yield Y Gap Poverty Protein
1. Evaluation Criteria weights
Respondents assign to each evaluation criteria a percentage weight according to their importance to achieve the proposed goal. The sum of percentages should be 100%.
2. Indicators weights
Respondents assess the importance of each strategy (represented by the indicators) in a 1-5 scale, where 1 least important, and 5 most important.
Criteria PercentagePoverty reduction Food security
Multicriteria survey
StrategiesCriteria
Poverty reduction Food security
Stunting
Harvested area
Yield
Yield gap
Poverty
Protein intake
We simulated 200 responses to the multicriteria survey. For criteria assessment responses following a normal
distribution with average equal to 60% and 40% , for Poverty Reduction and Food Security, respectively, and a standard deviation equal to 10% were generated.
The answers of each hypothetical respondent were normalized so that their sum equal 100%.
For strategies assessment, a sequence of numbers between 1 and 6 is created, and a sample with replacement of 200 is taken. The responses are then truncated at 5.
We set the limit of the sampling to 6 in order to get average valuations close to 5 for strategies with very high importance.
Methodology for data generation
Poverty reduction Food security
Min 18 3
Mean. 40 36Max. 70 61
Summary statistics of the Criteria Assessment
Statistics of the poverty reduction and food security evaluation criteria
Source: Own calculations with simulated data.
Criteria Stunting among children
Harvested area Yield Poverty Protein
intake
Poverty reduction
Min 0 3 0 4 0Mean 0 4 0 5 0Max. 0 5 0 5 0
Food security
Min 2 0 2 0 4Mean 3 0 3 0 5Max. 4 0 4 0 5
Harvested area Yield
Normalized data for common beanIndex of Insufficient
Provision of Basic Needs (poverty)
Poverty reduction: resulting map
Harvested area + Yield +Index of Insufficient Provision
of Basic Needs (poverty)
Deficiency in protein intakeStunting among children Yield
Food security: Resulting map
Deficiency in protein intake + Stunting among children + Yield
Aggregated Map for survey Goal
Poverty reduction + Food security
The strategic Geographic areas Where bean productionImpacts the achievementOf the Goal of the surveyAre:
The AHP method allows for an structured discussion of complex problems, by dissagregating them into different levels of importance or scale.
The Siempre package, by utilizing an AHP methodology aided by GIS tools, allows decision makers to utilize extensive amounts of informationin the form of maps to help the decision making process.
The priority setting of options of different nature or measurement parametrs is simplified by this package
Conclusions
Siempre can be used in an iterive way to do sensitivity analysis for different conditions or values for options or strategies, as the geographic impacts can be displayed inmediately on maps.
Conclusions (cont.)
Thank you!
Gracias Siempre!
Beta version:http://siempre.ciat.cgiar.org/