1. K.Fedra ‘97 Decision Support Systems multiple objectives, multiple criteria and valuation in environmental DSS

2. K.Fedra ‘97 DSS Definition A DSS is a computer based problem solving system that assists choice between alternatives in complex and controversial domains. A DSS is a computer based problem solving system that assists choice between alternatives in complex and controversial domains.

3. K.Fedra ‘97 Decision making A choice between alternatives requires a ranking of alternatives by the decision makers preferences: the preferred alternative must satisfy the constraintssatisfy the constraints maximise the decision makers utility functionmaximise the decision makers utility function A choice between alternatives requires a ranking of alternatives by the decision makers preferences: the preferred alternative must satisfy the constraintssatisfy the constraints maximise the decision makers utility functionmaximise the decision makers utility function

4. K.Fedra ‘97 Decision making ranking of alternatives is trivial with a single attribute (e.g., cost): select the alternative with the minimum cost provided the attribute can be measured without error. ranking of alternatives is trivial with a single attribute (e.g., cost): select the alternative with the minimum cost provided the attribute can be measured without error.

5. K.Fedra ‘97 Decision support paradigms Multiple attributes multiple objectives multiple objectives multiple criteria multiple criteria trade-off, compromise, satisfaction, acceptance satisfaction, acceptance Multiple attributes multiple objectives multiple objectives multiple criteria multiple criteria trade-off, compromise, satisfaction, acceptance satisfaction, acceptance

6. K.Fedra ‘97 Multiple attributes Criteria: problem dimensions relevant for the decision relevant for the decision Objectives: the goals to be furthered criteria to be maximized criteria to be maximized or minimized: max f(c) or minimized: max f(c) Constraints: bounds for acceptable solutions, limit values on criteria solutions, limit values on criteria Criteria: problem dimensions relevant for the decision relevant for the decision Objectives: the goals to be furthered criteria to be maximized criteria to be maximized or minimized: max f(c) or minimized: max f(c) Constraints: bounds for acceptable solutions, limit values on criteria solutions, limit values on criteria

7. K.Fedra ‘97 Multicriteria decision example set of criteria individual criteria may be: cardinal (numerical):cardinal (numerical): distance to employment: 1,2,3,4...km distance to employment: 1,2,3,4...km ordinal (symbolic but ordered)ordinal (symbolic but ordered) neighborhood: peaceful, active, noisy neighborhood: peaceful, active, noisy nominalnominal heating system: oil, gas, electric heating system: oil, gas, electric set of criteria individual criteria may be: cardinal (numerical):cardinal (numerical): distance to employment: 1,2,3,4...km distance to employment: 1,2,3,4...km ordinal (symbolic but ordered)ordinal (symbolic but ordered) neighborhood: peaceful, active, noisy neighborhood: peaceful, active, noisy nominalnominal heating system: oil, gas, electric heating system: oil, gas, electric

8. K.Fedra ‘97 Decision making ranking of alternatives with multiple attributes: collapse attributes into a single attribute (e.g., monetization)collapse attributes into a single attribute (e.g., monetization) OR solve the multi-dimensional problemOR solve the multi-dimensional problem ranking of alternatives with multiple attributes: collapse attributes into a single attribute (e.g., monetization)collapse attributes into a single attribute (e.g., monetization) OR solve the multi-dimensional problemOR solve the multi-dimensional problem

9. K.Fedra ‘97 Decision making the multi-dimensional problem Multi-objective optimisation min f(x) where X=(x 1, x 2, …..,x n ) is the vector of decision variables. the multi-dimensional problem Multi-objective optimisation min f(x) where X=(x 1, x 2, …..,x n ) is the vector of decision variables.

10. K.Fedra ‘97 Multi-objective optimisation The vector f(X) = (f 1 (x), f 2 (x), ….., f n (x)) represents the objective function. Decision X 1 is considered preferable to X 2 if f(X 1 ).GE. f(X 2 ) and f i (x 1 ).GE. f i (x 2 ) for all i and f i (x 1 ).GE. f i (x 2 ) for all i The vector f(X) = (f 1 (x), f 2 (x), ….., f n (x)) represents the objective function. Decision X 1 is considered preferable to X 2 if f(X 1 ).GE. f(X 2 ) and f i (x 1 ).GE. f i (x 2 ) for all i and f i (x 1 ).GE. f i (x 2 ) for all i

11. K.Fedra ‘97 Multi-objective optimisation The Pareto optimal solution f(x * ) to min f(x) requires that there is no attainable f(x) that scores better than f(x * ) in at least one criterion i (f i (x).LT. f i (x * )) without worsening all other components of f(x * ) The Pareto optimal solution f(x * ) to min f(x) requires that there is no attainable f(x) that scores better than f(x * ) in at least one criterion i (f i (x).LT. f i (x * )) without worsening all other components of f(x * )

12. K.Fedra ‘97 Pareto optimal an alternative is Pareto optimal or non- dominated, if it is: best in at least one criterion (better than any other alternative);best in at least one criterion (better than any other alternative); or equal to the best in at least one criterion without being worse in all other criteria.or equal to the best in at least one criterion without being worse in all other criteria. an alternative is Pareto optimal or non- dominated, if it is: best in at least one criterion (better than any other alternative);best in at least one criterion (better than any other alternative); or equal to the best in at least one criterion without being worse in all other criteria.or equal to the best in at least one criterion without being worse in all other criteria.

13. K.Fedra ‘97 Multi-objective optimisation Pareto solutions are efficient (non improvable), the implied ordering is incomplete, i.e., a partial ordering. This means that the problem has more than one solution which are not directly comparable with each other. Pareto solutions are efficient (non improvable), the implied ordering is incomplete, i.e., a partial ordering. This means that the problem has more than one solution which are not directly comparable with each other.

14. K.Fedra ‘97 Multicriteria decisions A simple example: statement of the problem (objectives)statement of the problem (objectives) set of alternativesset of alternatives set of criteriaset of criteria set of constraints (feasible sub-set)set of constraints (feasible sub-set) evaluation of alternatives (trade-off)evaluation of alternatives (trade-off) decision rules, selectiondecision rules, selection A simple example: statement of the problem (objectives)statement of the problem (objectives) set of alternativesset of alternatives set of criteriaset of criteria set of constraints (feasible sub-set)set of constraints (feasible sub-set) evaluation of alternatives (trade-off)evaluation of alternatives (trade-off) decision rules, selectiondecision rules, selection

15. K.Fedra ‘97 Multicriteria decision example statement of the problem (objectives) characterises the DM goalscharacterises the DM goals allows identification of alternativesallows identification of alternatives Buy a new car that is cost efficient Alternatives: different models statement of the problem (objectives) characterises the DM goalscharacterises the DM goals allows identification of alternativesallows identification of alternatives Buy a new car that is cost efficient Alternatives: different models

16. K.Fedra ‘97 Multicriteria decision example set of alternatives Rolls Royce Rolls Royce Porsche Porsche Volvo Volvo Volkswagen Volkswagen Seat Seat Lada Lada set of alternatives Rolls Royce Rolls Royce Porsche Porsche Volvo Volvo Volkswagen Volkswagen Seat Seat Lada Lada

17. K.Fedra ‘97 Multicriteria decision example set of criteria purchase pricepurchase price operating costsoperating costs – mileage service – service, repairs – insurance, road tax safety prestige value set of criteria purchase pricepurchase price operating costsoperating costs – mileage service – service, repairs – insurance, road tax safety prestige value

18. K.Fedra ‘97 Multicriteria decision example set of criteria is considered important with regard to the objectives of the decision makersis considered important with regard to the objectives of the decision makers common for all feasible alternativescommon for all feasible alternatives necessary to describe the alternatives (decision utility), should be maximised or minimisednecessary to describe the alternatives (decision utility), should be maximised or minimised its elements are independent from each otherits elements are independent from each other set of criteria is considered important with regard to the objectives of the decision makersis considered important with regard to the objectives of the decision makers common for all feasible alternativescommon for all feasible alternatives necessary to describe the alternatives (decision utility), should be maximised or minimisednecessary to describe the alternatives (decision utility), should be maximised or minimised its elements are independent from each otherits elements are independent from each other

19. K.Fedra ‘97 Multicriteria decision example set of constraints maximum available budgetmaximum available budget (limit on one of the criteria) (limit on one of the criteria) repair shop within a 20 km radiusrepair shop within a 20 km radius (independent of criteria, implicit: distance to repair shop) (independent of criteria, implicit: distance to repair shop) must fit into the garagemust fit into the garage (implicit: size, maneuverability) (implicit: size, maneuverability) set of constraints maximum available budgetmaximum available budget (limit on one of the criteria) (limit on one of the criteria) repair shop within a 20 km radiusrepair shop within a 20 km radius (independent of criteria, implicit: distance to repair shop) (independent of criteria, implicit: distance to repair shop) must fit into the garagemust fit into the garage (implicit: size, maneuverability) (implicit: size, maneuverability)

20. K.Fedra ‘97 Multicriteria decision example objectives and constraints can be reformulated: constraint: maximum cost objective: minimise cost objectives and constraints can be reformulated: constraint: maximum cost objective: minimise cost

21. K.Fedra ‘97 Multicriteria decision example set of constraints defines the feasible subset: 1 Roll Royce: exceeds budget limit does not fit into garage does not fit into garage 2 Porsche: no repair shop within specified radius specified radius set of constraints defines the feasible subset: 1 Roll Royce: exceeds budget limit does not fit into garage does not fit into garage 2 Porsche: no repair shop within specified radius specified radius

22. K.Fedra ‘97 Multicriteria decision example evaluation of alternatives (trade-off) price OMR S P price OMR S P 1 Rolls Royce 10 10 8 10 1 Rolls Royce 10 10 8 10 2 Porsche 6 8 6 8 2 Porsche 6 8 6 8 3 Volvo 3 3 10 6 3 Volvo 3 3 10 6 4 Volkswagen 2 2 5 4 4 Volkswagen 2 2 5 4 5 Seat 1.5 2.1 3 2 5 Seat 1.5 2.1 3 2 6 Lada 1.0 3 1 1 6 Lada 1.0 3 1 1 evaluation of alternatives (trade-off) price OMR S P price OMR S P 1 Rolls Royce 10 10 8 10 1 Rolls Royce 10 10 8 10 2 Porsche 6 8 6 8 2 Porsche 6 8 6 8 3 Volvo 3 3 10 6 3 Volvo 3 3 10 6 4 Volkswagen 2 2 5 4 4 Volkswagen 2 2 5 4 5 Seat 1.5 2.1 3 2 5 Seat 1.5 2.1 3 2 6 Lada 1.0 3 1 1 6 Lada 1.0 3 1 1

23. K.Fedra ‘97 Multicriteria decision example decision rules, selection price only: select 6 (Lada) total cost (3y): select 5 (Seat) total cost (5y): select 4 (VW) safety only: select 3 (Volvo) total cost + safety: ?? all criteria: ?? decision rules, selection price only: select 6 (Lada) total cost (3y): select 5 (Seat) total cost (5y): select 4 (VW) safety only: select 3 (Volvo) total cost + safety: ?? all criteria: ??

24. K.Fedra ‘97 Multicriteria decision example cost plus safety: safety cost 1 3 10 utopia nadir dominated reference point efficient point

25. K.Fedra ‘97 Pareto efficiency

26. K.Fedra ‘97 Pareto efficiency Pareto frontier or surface represents the set of all non-dominated alternatives: an alternative is non-dominated, if it is better in at least one criterion than any other alternative; or equal to the best without being worse in all other criteria. Pareto frontier or surface represents the set of all non-dominated alternatives: an alternative is non-dominated, if it is better in at least one criterion than any other alternative; or equal to the best without being worse in all other criteria.

27. K.Fedra ‘97 Multicriteria decision example cost plus safety: safety cost 1 3 10 utopia nadir dominated reference point efficient point

28. K.Fedra ‘97 Multicriteria decision example axes normalized as % of possible achievement (utopia - nadir): safety cost 100% 0% 100% utopia nadir dominated reference point efficient point

29. K.Fedra ‘97 Multicriteria decisions trade off: indifference: a trade-off is the change in criterion C 1 that is necessary to offset a given change in criterion C 2 so that the new alternative A 2 is indifferent to the original one (A 1 ).indifference: a trade-off is the change in criterion C 1 that is necessary to offset a given change in criterion C 2 so that the new alternative A 2 is indifferent to the original one (A 1 ). trade off: indifference: a trade-off is the change in criterion C 1 that is necessary to offset a given change in criterion C 2 so that the new alternative A 2 is indifferent to the original one (A 1 ).indifference: a trade-off is the change in criterion C 1 that is necessary to offset a given change in criterion C 2 so that the new alternative A 2 is indifferent to the original one (A 1 ).

30. K.Fedra ‘97 Multicriteria decisions trade off: preferred proportions: a trade-off is the proportion of change in criteria C 1 and C 2 that the DM would prefer if he could move away from the initial alternative in some specific way.preferred proportions: a trade-off is the proportion of change in criteria C 1 and C 2 that the DM would prefer if he could move away from the initial alternative in some specific way. (implicit relative weights of attributes). (implicit relative weights of attributes). trade off: preferred proportions: a trade-off is the proportion of change in criteria C 1 and C 2 that the DM would prefer if he could move away from the initial alternative in some specific way.preferred proportions: a trade-off is the proportion of change in criteria C 1 and C 2 that the DM would prefer if he could move away from the initial alternative in some specific way. (implicit relative weights of attributes). (implicit relative weights of attributes).

31. K.Fedra ‘97 Multicriteria decisions weights (relative importance) of criteria are not constant over the range of alternatives: trade-off between criteria and the relative weights of criteria are context dependent. weights (relative importance) of criteria are not constant over the range of alternatives: trade-off between criteria and the relative weights of criteria are context dependent.

32. K.Fedra ‘97 Multicriteria decisions trade-off between price and location of a house a house (distance (distance to work) to work) trade-off between price and location of a house a house (distance (distance to work) to work) dominated

33. K.Fedra ‘97 Multicriteria decisions indifference and preference curves for cost vs cost vs distance distance indifference and preference curves for cost vs cost vs distance distance

34. K.Fedra ‘97 Multicriteria decisions indifference: moving from the initial alternative A 0 (18,50) to the closer alternative A 1 at (10,.) the DM is willing to pay 85. A 1 (10,85) is considered equivalent to A 0 (18,50), DM has no preference, he is indifferent. indifference: moving from the initial alternative A 0 (18,50) to the closer alternative A 1 at (10,.) the DM is willing to pay 85. A 1 (10,85) is considered equivalent to A 0 (18,50), DM has no preference, he is indifferent.

35. K.Fedra ‘97 Multicriteria decisions 3 criteria (3D) extension of the indifference curves

36. K.Fedra ‘97 Multicriteria decisions complicated by high dimensionality of the problem difficulty to elicit meaningful and consistent preferences from DM – explicit weights – elicitation (pairwise comparison, etc.) – reference point complicated by high dimensionality of the problem difficulty to elicit meaningful and consistent preferences from DM – explicit weights – elicitation (pairwise comparison, etc.) – reference point

37. K.Fedra ‘97 Multicriteria decision making Valuation: expressing the value of ALL criteria in the same (monetary) units, so that a simple ordering is possible. How to value: safety cost of insurance safety cost of insurance prestige value cost of an alternative prestige value cost of an alternative way to achieve the way to achieve the same goals same goalsValuation: expressing the value of ALL criteria in the same (monetary) units, so that a simple ordering is possible. How to value: safety cost of insurance safety cost of insurance prestige value cost of an alternative prestige value cost of an alternative way to achieve the way to achieve the same goals same goals

38. K.Fedra ‘97 Multicriteria decision making Valuation: monetization (assigning monetary values) depends on the existence of some form of market. There is no market for most environmental goods and services. Valuation: monetization (assigning monetary values) depends on the existence of some form of market. There is no market for most environmental goods and services.

39. K.Fedra ‘97 ValuationValuation of environmental goods and services commercial use of a resourcecommercial use of a resource functional value (service)functional value (service) on-site recreational useon-site recreational use option for maintaining the potential for future use (visit)option for maintaining the potential for future use (visit) existence value (knowing it is there)existence value (knowing it is there) bequest value (for future generations)bequest value (for future generations) of environmental goods and services commercial use of a resourcecommercial use of a resource functional value (service)functional value (service) on-site recreational useon-site recreational use option for maintaining the potential for future use (visit)option for maintaining the potential for future use (visit) existence value (knowing it is there)existence value (knowing it is there) bequest value (for future generations)bequest value (for future generations)

40. K.Fedra ‘97 ValuationValuation of environmental goods and services can be grouped in use and non-use values. use and non-use values. How to measure non-use values ? non-use values ? of environmental goods and services can be grouped in use and non-use values. use and non-use values. How to measure non-use values ? non-use values ?

41. K.Fedra ‘97 ValuationValuation How to measure non-use values ? willingness to pay willingness to pay (or compensation demanded) (or compensation demanded) - contingent valuation - contingent valuation - travel cost - travel cost restoration cost restoration cost (what is the restoration cost (what is the restoration cost for an extinct species ?) for an extinct species ?) How to measure non-use values ? willingness to pay willingness to pay (or compensation demanded) (or compensation demanded) - contingent valuation - contingent valuation - travel cost - travel cost restoration cost restoration cost (what is the restoration cost (what is the restoration cost for an extinct species ?) for an extinct species ?)

42. K.Fedra ‘97 ValuationValuation Willingness to pay measures the value of goods or services that do not have a market to establish prices. Basic methods: contingent valuation (hypothetical) contingent valuation (hypothetical) observed behavior (travel cost) observed behavior (travel cost) Willingness to pay measures the value of goods or services that do not have a market to establish prices. Basic methods: contingent valuation (hypothetical) contingent valuation (hypothetical) observed behavior (travel cost) observed behavior (travel cost)

43. K.Fedra ‘97 ValuationValuation Travel cost method: uses the average expenditures (travel cost) and number of visitors to determine the value of a recreational resource like a park, lake, etc. Travel cost method: uses the average expenditures (travel cost) and number of visitors to determine the value of a recreational resource like a park, lake, etc.

44. K.Fedra ‘97 ValuationValuation Contingent valuation: uses survey data on hypothetical transactions ( willingness to pay, compensation demanded ) contingent upon the creation of a market to establish the value of a non-market good. Contingent valuation: uses survey data on hypothetical transactions ( willingness to pay, compensation demanded ) contingent upon the creation of a market to establish the value of a non-market good.

45. K.Fedra ‘97 ValuationValuation Restoration costs or opportunity costs: estimates the costs of restoring an environmental good or service, or providing it in an alternative way: Estimate the value of an aquifer by the cost of restoring it, or the cost of alternative water supply. Estimate the value of an aquifer by the cost of restoring it, or the cost of alternative water supply. Restoration costs or opportunity costs: estimates the costs of restoring an environmental good or service, or providing it in an alternative way: Estimate the value of an aquifer by the cost of restoring it, or the cost of alternative water supply. Estimate the value of an aquifer by the cost of restoring it, or the cost of alternative water supply.

46. K.Fedra ‘97 ValuationValuation Restoration costs or opportunity costs: fails for irreversible damage (extinction of a species) or the existence value of an environmental good (irreplaceable by definition). Restoration costs or opportunity costs: fails for irreversible damage (extinction of a species) or the existence value of an environmental good (irreplaceable by definition).

47. K.Fedra ‘97 ValuationValuation The basic problems: Intangibles: difficult to measure and express in quantitative termsIntangibles: difficult to measure and express in quantitative terms Qualitative character of values: including ethical, moral, religious ….. aspectsQualitative character of values: including ethical, moral, religious ….. aspects Time dependency: discounting versus sustainability, intergenerational equityTime dependency: discounting versus sustainability, intergenerational equity The basic problems: Intangibles: difficult to measure and express in quantitative termsIntangibles: difficult to measure and express in quantitative terms Qualitative character of values: including ethical, moral, religious ….. aspectsQualitative character of values: including ethical, moral, religious ….. aspects Time dependency: discounting versus sustainability, intergenerational equityTime dependency: discounting versus sustainability, intergenerational equity

48. K.Fedra ‘97 ValuationValuation Simple example: use scores, points, indices, or similar subjective measurements to make non-commensurate attributes comparable Simple example: use scores, points, indices, or similar subjective measurements to make non-commensurate attributes comparable

49. K.Fedra ‘97 ValuationValuation Hypothetical water project: score score Water supply 50 M m 3 /day 40 Flood control: damage 200,000 $/year 20 Flood control: lives 1/year 20 Electricity supply: 3 MKWh 20 Recreation: reservoir 40,000 visitor days 3 Aquatic habitat: increase 100,000 fish 1 TOTAL score for benefits 104 Hypothetical water project: score score Water supply 50 M m 3 /day 40 Flood control: damage 200,000 $/year 20 Flood control: lives 1/year 20 Electricity supply: 3 MKWh 20 Recreation: reservoir 40,000 visitor days 3 Aquatic habitat: increase 100,000 fish 1 TOTAL score for benefits 104

50. K.Fedra ‘97 ValuationValuation Hypothetical water project: score score Construction cost 10 M$ 120 Operating costs 100,000 $/year 10 Nutrient losses: farming 100 tons/year 5 Beach nourishment: 20 tons/year 5 Loss of Recreation: 1,000 visitor days 5 Terrestrial habitat: losses 1 bear, 50 deer 10 TOTAL score for losses 155 Hypothetical water project: score score Construction cost 10 M$ 120 Operating costs 100,000 $/year 10 Nutrient losses: farming 100 tons/year 5 Beach nourishment: 20 tons/year 5 Loss of Recreation: 1,000 visitor days 5 Terrestrial habitat: losses 1 bear, 50 deer 10 TOTAL score for losses 155

51. K.Fedra ‘97 ValuationValuation Hypothetical water project: TOTAL score for benefits 104 TOTAL score for losses 155 Public welfare contribution -49 Conclusion: don’t build ! Hypothetical water project: TOTAL score for benefits 104 TOTAL score for losses 155 Public welfare contribution -49 Conclusion: don’t build !

52. K.Fedra ‘97 ValuationValuation Hypothetical water project: score score Water supply 50 M m 3 /day 60 Flood control: damage 200,000 $/year 20 Flood control: lives 1/year 30 Electricity supply: 3 MKWh 25 Recreation: reservoir 40,000 visitor days 5 Aquatic habitat: increase 100,000 fish 5 TOTAL score for benefits 145 Hypothetical water project: score score Water supply 50 M m 3 /day 60 Flood control: damage 200,000 $/year 20 Flood control: lives 1/year 30 Electricity supply: 3 MKWh 25 Recreation: reservoir 40,000 visitor days 5 Aquatic habitat: increase 100,000 fish 5 TOTAL score for benefits 145

53. K.Fedra ‘97 ValuationValuation Hypothetical water project: score score Construction cost 10 M$ 100 Operating costs 100,000 $/year 10 Nutrient losses: farming 100 tons/year 3 Beach nourishment: 20 tons/year 2 Loss of Recreation: 1,000 visitor days 1 Terrestrial habitat: losses 1 bear, 50 deer 4 TOTAL score for losses 120 Hypothetical water project: score score Construction cost 10 M$ 100 Operating costs 100,000 $/year 10 Nutrient losses: farming 100 tons/year 3 Beach nourishment: 20 tons/year 2 Loss of Recreation: 1,000 visitor days 1 Terrestrial habitat: losses 1 bear, 50 deer 4 TOTAL score for losses 120

54. K.Fedra ‘97 ValuationValuation Hypothetical water project: TOTAL score for benefits 145 TOTAL score for losses 120 Public welfare contribution 25 Conclusion: build ! Hypothetical water project: TOTAL score for benefits 145 TOTAL score for losses 120 Public welfare contribution 25 Conclusion: build !

55. K.Fedra ‘97 ValuationValuation Hypothetical water project: to improve the estimate for recreational benefits, use the travel cost method: since the reservoir (lake) does not yet exist, use: a similar lake or reservoira similar lake or reservoir hypothetical questionshypothetical questions Hypothetical water project: to improve the estimate for recreational benefits, use the travel cost method: since the reservoir (lake) does not yet exist, use: a similar lake or reservoira similar lake or reservoir hypothetical questionshypothetical questions

56. K.Fedra ‘97 ValuationValuation Travel cost method: count visitorscount visitors determine distance traveled (travel cost based on mileage)determine distance traveled (travel cost based on mileage) determine other expendituresdetermine other expenditures estimate total expenditures from recreational users == value of the resourceestimate total expenditures from recreational users == value of the resource Travel cost method: count visitorscount visitors determine distance traveled (travel cost based on mileage)determine distance traveled (travel cost based on mileage) determine other expendituresdetermine other expenditures estimate total expenditures from recreational users == value of the resourceestimate total expenditures from recreational users == value of the resource

57. K.Fedra ‘97 Travel cost method: Create a hypothetical simple but complete example from your local setting, use realistic estimates for number and prices. How can you treat time ? How could you use GIS data ? Create a hypothetical simple but complete example from your local setting, use realistic estimates for number and prices. How can you treat time ? How could you use GIS data ?