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/