Environmental Urban Indicators: Synthesis and Interpretation

1. Environmental Urban Indicators: Synthesis and Interpretation Mara Cammarrota, Natalia Golini, Giovanna Jona Lasinio Workshop GRASPA Siena 27-28 March 2008

2. AIM To evaluate the environmental risk for the 103 Italian head of province towns. Workflow: Divide the environment system into macroareas; Definition of an ideal set of indicators; Verify what is available: define the set of available indicators. Evaluate environmental risk on the basis of available data (work in progress). A first stage of this work has been presented at the convegno intermedio SIS 2007 (Cammarrota et al., 2007).

3. General aspects (I) It is of general interest to be able to understand the environmental state and related pressures to which large human communities are subjected. (Agenda 21) The EU has a leader position with respect to environmental issue and it has implemented several Community programs on this topic.

4. General aspects (II) In the 6th Framework Programme (2002-2006) (n. 1600/2002/CE) 4 environmental areas were considered: • Climate changes; • Ecosystems and biodiversity • Health, environment and life quality; • Natural resources and waste. The attention to the urban environment touches at least 3 out this 4 area.

5. Defining environmental risk This is a very ambiguous issue and no clear definition exists in the literature. Usually a commonly adopted definition is : “Risk is the combination of the probability, or frequency, of occurrence of a defined hazard and the magnitude of the consequences of the occurrence" (Royal Society, 1992). Attention: this is not an operational definition! We propose a classification approach.

6. Urban environmental risk Population Economy Water Air Energy …. • Multidisciplinary approach: • Chemistry • Biology • Epidemiology • Meteorology • Geology • Statistics • Demography Environment Urban environment = town with at least 10.000 inhabitants We have to take into account all system components:

7. Environmental indicators They have to: • Illustrate and describe the environment. • Have to be read by decision makers with no technical background, than they have to be easily understood. When defining them it is of relevance to locate indicators into the conceptual scheme DPSIR (Driving forces, Pressures, States, Impacts and Responses).

8. Steps Define macroareas (environmental dimensions). Define ideal urban environmental indicators. Collect available data (proxy).

9. Macroareas: • Water • Air • Electromagnetic Fields • Energy • Population • Waste • Noise • Soil • Transports • Green Areas

10. Ideal Indicators: how to choose them? • Current literature • Experts of specific topics • National and European regulations • Critical analysis

11. Available Indicators (I) Problems: • Amount and quality of available data; • More then one or no sources; • Spatial definition (town); • No data at town level; • No general standards are available.

12. Available indicators (II) ISTAT (national statistical institute) and APAT (environmental protection agency) (SISTAN). Why? • Several environmental topics are central in their surveys; • Time series length; • High quality data; • Spatial coverage.

13. Comparing ideal with available: macroarea Air • Emission data refers to 2001 while concentrations data refers to 2004 • Sources: • ISTAT, Indicatori ambientali urbani, years 2000-2006. • NAMEA, years 1990-2003.

14. Environmental risk assessment We can formalize our problem as a classification one. Our proposal is: • Partition the variable space (Macroareas) • Perform classification on each sub-space • Combine classification results in a meta-classification

15. First step First of all with available data we apply the Rank Transformation Why? • Different measure unit for quantitative indicators; • Time misalignment; • Indicators are not comparable in terms of levels.

16. Rank Transformation When transforming into ranks each indicator in a given area and macroarea we have to consider how to represent/synthesize the all area/macroarea • Possible choices: • average rank; • relative rank. • Kendall W: • To measure concordance/discordance between classifications; • It is a relative index (easy to read); • We computed it into areas and macroareas.

17. First results We applied the rank transformation to all data in each macroarea. Average and relative ranks revealed to be not suitable. We had to exclude several macroareas for lack of data etc. The need for further investigation emerged.

18. Some considerations • Response indicators have to be treated separately. • We add a further macroarea: Administrative response. • Data lack (reduced dimensionality of the problem) and the high level of discordance inside several macroareas led us to analyze most indicators together without the distinction between macroareas.

19. Wroclaw method (taxonomy) (I) Widely used in social sciences, it allows us to measure and compare the development dynamic of a phenomenon. In this setting indicators have a “delaying” or “accelerating” role that have to be established. We obtain a final ranking based on the units distance from an “ideal”/reference observation. Indicators have to be standardized.

20. Wroclaw method (taxonomy)(II) Critical points: • Definition of indicators role (delaying or accelerating) with respect to a development model; => indicator direction • Choice of the “ideal” observation. Here we build it using min. or max. observed values; • Choice of a distance; here we adopt the Canberra distance (Canberra); We use this method for both synthesis of a macroarea and analysis of indicators.

21. Results All indicators W = 0,95

22. Consensus Ranking (I) • We adopt an operational research approach. More precisely we imagine to have more then one decision maker and one target. - p indicators (decision makers) - n towns (units-candidates). • For each indicator we build a ranking with total order. • if rik is the rank of town i according to the kth indicator let R=[rik] be the “rank” matrix (i=1,…,103 e k=1,…,7).

23. Consensus Ranking (II) The main issue is to find a total ordering of the n towns (a permutation P) that can “agree” with the single indicators ranking. Target: to minimize where ri (P) is the rank of town i in permutation P, rk is the kth column of the rank matrix R = [ rik ] .

24. Consensus Ranking (III) Any permutation P (i.e. and arbitrary ranking based on indicator g) can be represented by a permutation matrix [xij] with elements: there is only a 1 in each column and row. if candidate i occupies position j in permutation P otherwise

25. Consensus Ranking (IV) Given a distance  = r, 1, 2,  this problem becomes a linear allocation problem: where cij depend on metric . In our study the metric is based on the Canberra distance:

26. Consensus Ranking (V) Critical points: • total ordering (we have to find an absolute minimum); • choice of the metric (Canberra);

27. Results consensus W = 0,83 Each list is obtained with Wroclaw

28. Comparison W = 0,76 W = 0,69

29. Concluding Remarks • Especialywhen a largenumberof indicators isavailableitisprefarabletouse the consensusmethodtobulid the final ranking. • Theseapproaches do notallowto account for uncertainty in the data and/or in the position assumed. • We are goingtodevelop a Bayesianmixtureclassifiertobeapplied (see Jona Lasinio et al. 2005) toranks and buildgroupsoftownstobeidentified in termsofenvironmentalrisk.