Environmental Urban Indicators: Synthesis and

InterpretationMara Cammarrota, Natalia Golini, Giovanna Jona Lasinio

Workshop GRASPA Siena 27-28 March 2008

AIMTo 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).

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.

General aspects (II)

In the 6th Framework Programme (2002-2006) (n. 1600/2002/CE) 4 environmental areas were considered:

Climate changes;Climate changes;

Ecosystems and biodiversityEcosystems and biodiversity

Health, environment and life quality;Health, environment and life quality;

Natural resources and waste.Natural resources and waste.

The attention to the urban environment touches at least 3 out this 4 area.

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.

Urban environmental risk

Urban environment = town with at least 10.000 inhabitants

We have to take into account all system components:

Multidisciplinary approach:

• Chemistry • Biology• Epidemiology• Meteorology• Geology• Statistics• Demography

Environment

Population

Economy

WaterAir

Energy….

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).

Steps

Define macroareas (environmental dimensions).

Define ideal urban environmental indicators.

Collect available data (proxy).

Macroarea Area Indicators DPSIRDescription and

measure unit

Macroarea

Area AIndicator A1 P …………………..

Indicator A2 R …………………..

Area BIndicator B1 S …………………..

Indicator B2 D …………………..

Macroareas:1. Water

2. Air

3. Electromagnetic Fields

4. Energy

5. Population

6. Waste

7. Noise

8. Soil

9. Transports

10. Green Areas

Ideal Indicators: how to choose them?

Current literature

Experts of specific topics

National and European regulations

Critical analysis

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.

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.

Macroarea Area Available indicators DPSIR

Air

Emissions

Economical activities emissions

P

Family emissions P

Concentrations

Number of monitored pollutants per town

R

Number of monitoring stations per Km2 of town surface

R

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.

Macroarea Area Ideal Indicator DPSIR

Air

EmissionsRegional inventory of emitting sources

P

Concentrations

Number of over threshold registrations

S

Central tendency by pollutant

S

Extreme events by pollutant

S

Exposed population S

Density of monitoring stations per surface unit

R

Percentage of traffic block days due to over threshold

R

Percentage of traffic block days due to preventive actions

R

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

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.

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.

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.

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.

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.

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.

ResultsTown Water Air Energy Waste

Transport

Green Areas

Response Pos.

Pos. no “response

”

Palermo 19 19 5 45 97 86 5 1 9

Parma 11 10 60 9 70 39 12 2 6

Milano 21 3 30 13 30 23 26 3 2

Varese 17 62 27 23 17 67 21 4 4

………… ………… ………… ………… ………… ………… ………… ………… ……… …………

………… ………… ………… ………… ………… ………… ………… ………… ……… …………

Imperia 102 11 83 43 11 30 63 100 100

Arezzo 15 98 77 103 4 25 97 101 101

Gorizia 59 25 103 30 75 71 72 102 102

Venezia 103 74 61 93 31 33 56 103 103

W = 0,95All

indicators

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).

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 ] .

f P d r P ; r k k 1

p

r P

r P . . .

r P . . .

r P

1

i

n

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.

,0

,1ijx

if candidate i occupies position j in permutation Potherwise

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:

Consensus Ranking (IV)

mini

n

ijj

n

ijc x

1 1

p

j ik

ikij rj

rjc

1 ||

||

Consensus Ranking (V)

Critical points:

total ordering (we have to find an absolute minimum);

choice of the metric (Canberra);

ResultsTown Water Air Energy

Waste

Transport

Green Areas

Response Pos.

Pos. no “response

”

Como 60 52 90 1 35 69 1 1 61

Teramo 32 27 3 97 50 2 91 2 3

Grosseto

3 64 85 50 41 1 31 3 1

Bologna 5 90 91 61 29 47 4 4 90

……….. ……….. ……….. ……….. ……….. ……….. ……….. ……….. ……….. ………..

……….. ……….. ……….. ……….. ……….. ……….. ……….. ……….. ……….. ………..

Vibo Valentia

8 30 100 100 67 80 25 100 100

Trieste 85 4 1 81 62 100 29 101 101

Nuoro 63 2 20 40 102 101 45 102 102

Arezzo 15 98 77 103 4 25 97 103 4

W = 0,83 Each list is obtained with Wroclaw

consensus

Comparison

TownWroclaw Consensus Ranking

Pos.Pos. no

“response”Pos. Pos. no “response”

Palermo 1 9 5 19

Parma 2 6 10 9

Milano 3 2 21 21

Varese 4 4 17 17

……….. ………… ………… ……….. ………..

……….. ………… ………… ……….. ………..

Imperia 100 100 11 11

Arezzo 101 101 103 4

Gorizia 102 102 72 71

Venezia 103 103 61 95

W = 0,76 W = 0,69

Concluding Remarks

Especialy when a large number of indicators is available it is prefarable to use the consensus method to bulid the final ranking.

These approaches do not allow to account for uncertainty in the data and/or in the position assumed.

We are going to develop a Bayesian mixture classifier to be applied (see Jona Lasinio et al. 2005) to ranks and build groups of towns to be identified in terms of environmental risk.