1. APPLICATIONS FOR STRATEGIC ASSESSMENT,
POLICY AND DECISION SUPPORT,
MONITORING AND EVALUATION
IN URBAN PLANNING
Case Study
City of Bucharest, Romania

2. Standards Indicator: population density per hectare (Census 2011)
Indicator: population over 65 years of age (Census 2011)
Minimum
40.97
CR_114
Maximum
862.19
CR_611
Average
Minimum
0.03
CR_620
Maximum
35.99
CR_101
Average
14.578

3. Standards
Indicator: percentage of households with more than 5 members (Census 2002)
Indicator: percentage of dependent population - receiving state allowances or being supported (Census 2002)
Minimum
0.41
CR_620
Maximum
20.39
CR_328
Average
7.588
Minimum
0.43
CR_620
Maximum
13.52
CR_517
Average
6.804

4. RANKING COMPOSITE INDICES
The system allows the user to describe an entire aspect or problem by developing indices or composites, as weighted sums of standardized values of selected indicators.

5. RANKING COMPOSITE INDICES
By ranking, grouping and plotting the municipalities, neighborhoods, statistical areas, census tracts on the map, MapDecision allows the user to identify problems. Individual report cards produced for each case show the specific sector problems of the cases in point.
Units (municipalities, neighborhoods, statistical areas, census tracts) are ranked and grouped according to the values of the composite index. The groups are color coded like traffic lights: green is good, red is bad, and yellow is an intermediary situation.

6. Population education level index – ranking and mapping
composition:
Variable
Description
Weight
%EDUC_SUP
Percentage of population with tertiary studies 2
%EDUC_SEC
Percentage of population with secondary education 1
%EDUC_PRIMARY
Percentage of population with primary education -1
%EDUC_WITHOUT
Percentage of population without studies -2
Census
2002
Legend:
Green and light green – census tracts with higher values of the index (population with higer education level)
Yellow – census tracts with average values of the index (population with average education level)
Brown and red – census tracts with lower values of the index (population with lower education level)

7. Ranking the spatial units - report cards
Report cards are generated for each census tract. They allow the user to compare the territorial units (in this case census tracts) in a SWOT (strength, weaknesses, opportunities and threats) strategic assessment approach.

8. Population education index – Report card
Legend:
The values displayed in green or red on each card are standardized, so the card shows for each variable selected whether the case has a value above or under the mean for all cases.
Green shows a relative strength of a case in comparison with the other cases for the respective variable.
Red shows a relative weakness of a case in comparison with all the other cases.

9. Population education index
Comparing report cards – Bucharest census tracts
Comparing 3 neighboring census tracts in Bucharest: the first ranking 4 in the index hierarchy (green on the map), the second ranking 66 (yellow on the map) and the last one ranking 126 (brown on the map).
The length and the color of the bars in the report cards bars suggests explanations for the position in the hierarchy, highlighting the strengths and weaknesses of each territorial unit.

10. Dwellings and housing index – ranking and mapping
Variable
Description
Weight
DWELLINGS_SIZE/PERS_SQM
Dwellings size – square meters per person 1
%DWELL_WATER
% of dwellings with running water
%DWELL_GAS_NETWORK
% of dwellings connected to the gas network
%DWELL_SEWAGE
% of dwellings with sewage system
%DWELL_ELECTRICITY
% of dwellings with electricity
Index
composition:
Census
2002
Green and light green– census tracts with higher values of the index (better housing conditions)
Yellow – census tracts with average values of the index (average housing conditions)
Brown and red – census tracts with lower values of the index (poor housing conditions)
Legend:

11. Dwellings and housing index
Comparing report cards – Bucharest census tracts
Comparing 3 neighboring census tracts in Bucharest: the first ranking 44 in the index hierarchy (light green on the map), the second ranking 90 (yellow on the map) and the last one ranking 130 (brownson the map).
The length and the color of the report cards bars suggests explanations for the position in the hierarchy, highlighting the strengths and weaknesses of each territorial unit.

12. Buildings situation index – ranking and mapping
composition:
Variable
Description
Weight
%BUILDINGS<1940
% of buildings built before 1940 -1
%BUILDINGS_1940_1959
% of buildings built between 1940 and 1959
%BUILDINGS_1960_1989
% of buildings built between 1960 and 1989
%BUIDNIGS_1990_2002
% % of buildings built between 1990 and 2002 1
DWELL/BUILDING
Number of dwellings per residential building ratio
Census
2002
Legend:
Green and light green – census tracts with higher values of the index (good buildings stock)
Yellow - census tracts with average values of the index (average buildings stock)
Brown and red - census tracts with lower values of the index (poor buildings stock)

13. Buildings situation index
Comparing report cards – Bucharest census tracts
Comparing 3 neighboring census tracts in Bucharest: the first ranking 11 in the index hierarchy (green on the map), the second ranking 79 (yellow on the map) and the last one ranking 151 (brown on the map).
The length and the color of the report cards bars suggests explanations for the position in the hierarchy, highlighting the strengths and weaknesses of each territorial unit.

14. Vulnerability index – ranking and mapping
composition:
Variable
Description
Weight
%UNEMPL
% of unemployed population -1
%POP_WITHOUT_EDUC
Percentage of population without studies
%HOUSEHOLDS>5MEMBRS
Percentage of households with more than 5 members
DWELLINGS_SIZE/PERS_SQM
Dwellings size – square meters per person 1
%DWELL_WATER
% of dwellings with running water
%F>4CHILDREN
% of female population with more than 4 children
%POP_DEPENDENT
% dependent or supported population
%POP>65
% of population over 65 years of age
Census
2002
Legend:
Green and light green – census tracts with higher values of the index (areas with the lowest level of vulnerability)
Yellow – census tracts with average values of the index (areas with average level of vulnerability)
Brown and red – census tracts with lower values of the index (areas with the higher level of vulnerability)

15. Vulnerability index Comparing report cards – Bucharest census tracts
Comparing 3 neighboring census tracts in Bucharest: the first ranking 24 in the index hierarchy (green on the map), the second ranking 86 (yellow on the map) and the last one ranking 129 (brown on the map).
The length and the color of the report cards bars suggests explanations for the position in the hierarchy, highlighting the strengths and weaknesses of each territorial unit.

16. ANALYZING RELATIONSHIPS
MapDecision helps the user understand the relationships between inputs and outcomes. The RELATE procedure computes and displays the simple correlation between selected variables.

17. CORRELATION PLOT
Clicking on one cell in the correlation matrix displays the Correlation Plot of percentage of households with more than 5 members and percentage of unemployed.

18. EVALUATING PERFORMANCE
MapDecision allows the user to evaluate performance against inputs (human and financial resources) in the specific socioeconomic and demographic environment. The concept is that of a “black box” where inputs and outputs shed light on the process. The system identifies over-performers and under-performers.
If a census tract ranks low in social environment (education) or housing, yet it ranks high in achievement (low vulnerability index), it is an over-performer.

19. EVALUATING PERFORMANCE
Example of over-performer census tract: high ranking according to VULNERABILITY index (less vulnerable census tracts) and lower ranks in education, housing and buildings indices

20. INDICATORS RELATIONSHIPS ANALYSIS
A multiple regression model provides insight into the relationships between various indicators.
Explained variable: Percentage of dependent or supported population
Explaining variables: percentage of population over 65 years of age, percentage of population without studies, average dwellings size (square meters), percentage of females with more than 4 children, percentage of unemployed.

21. PLAYING WHAT-IF SCENARIOS
MapDecision supports the development of action plans for improving system performance by developing “what-if” scenarios based on the multiple regression model.
For example, changing various inputs of the model can indicate the impact on lowering the value of dependent/supported population

22. TARGETING
The analytical framework supports targeting of scarce resources at the units where the needs are greatest in the respective municipality. Quadrant analysis is used for targeting based on two criteria, and a more general query procedure allows targeting based on any number of criteria.
Quadrant analysis can also be used in identifying the under-performing and over-performing territorial units (e.g. territorial units with “local conditions” below average but results above average)

23. Targeting interventions - Bucharest
According to percentage of population over 65 years of age and percentage of women with more than 4 children
Targeting the most disadvantaged areas
Highlighted on the map in magenta are the census tracts with percentage of population over 65 yeas of age and percentage of women with more than 4 children above the average

24. INDEX-BASED RESOURCE ALLOCATION
MapDecision helps address equity issues by supporting index-based resource allocation to direct resources to the territorial units where the needs are highest. The procedure allows an initial “proportional allocation” (e.g. per capita) to be adjusted in accordance to an index of need developed in consensus by the stakeholders.
For example, in the next scenario census tracts receive between 250EUR and EUR per inhabitant. The resulting allocations are shown in the next slide.

25. Procedure
RESOURCE ALLOCATION

26. If a different range of per inhabitant allocations is agreed on, a new allocation scenario develops automatically.

27. The resource allocation procedure in MapDecision allows playing realistic allocation scenarios, including set-aside amounts for discretionary allocations, and manually changing the computed amounts for census tracts where other resources have been made available, etc.

28. EXPLORING LOCAL TRADE-OFFS
Several procedures allow the user to identify possible local trade-offs of strengths between neighboring census tracts. The purpose of this function is to promote local dialogue and transparency, to empower local units, tap local resources and decrease their dependency on the center.

29. EXPLORING LOCAL TRADE-OFFS
Green – census tracts that can be potential donors of resources
Red – census tracts that can be potential receivers of resources
Legend: