The characterisation of “living” landscapes: the role of mixed descriptors and volunteering...

LIVING LANDSCAPE The European Landscape Convention in research perspective 18-19 October 2010 Florence (Italy)

The Characterisation of “Living” Landscapes: Mixed Descriptors and Volunteering Geographic

Information

Ernesto MarcheggianiHubert Gulinck and Andrea Galli

Dep. of Earth and Environmental Science – KULeuven - Division of Forest, Nature and Landscapeand

Dep. SAIFET – Univpm – Section of Agroingegneria e Territorio


Outline

Introduction (Why) General problems setting Case

The Framework (What)

Mixed descriptors in GIS environment (How) PCA Clustering

Volunteering Information (How) SHP to KMZ First results

Conclusions and way forward


General problems setting

Over the last decade the need for public bodies to characterise the vitality and degree of sustainability of their territories is well acknowledged

This is particularly true in rural settings…

“rural land managers in Europe are faced with an increasingly complex decision making environment where production has to be achieved within narrowing environmental and social limits”

“…mix of financial, social and environmental goals has to be achieved….” Mattews (1999)

“how” to analyse, and asses each characteristic and value, from both the spatial (structural organization of their spatial patterns) and functional (goods and services provided, ecological, social, cultural, etc.) points of

view


The Italian Law on RQD (DL 228/2001)

The Italian law on agricultural orientation (DL 228/2001) “to identify its most suitable Rural Quality Districts

Regional bodies have to compulsorily identify RDQ boundaries and made explicit in maps

there are no clear guidelines that explicit criteria to define such RQD, leaving this burden on the shoulders of local regional authorities.


The Case

8 municipalities involved

Case study area: 55’000 ha

Synclinal – Anticlinal structure,N-S oriented unlike the

general morphology of theRegion


Prevalent baseline spatial “3 beans” model


Morphologicalunits

Land covers

Regional Planning (PIT)

Landscape and ecology


Is this the only potential reading that we can do about this area or are there other potential

patterns?


The baseline framework: volunteering information Spatialisation of socio-

economic descriptors

(Lattice 1)

Characterisation of underneath territories

Building of a multivariate vector by overlaying lattices 1 and 2

Principal Component Analysis

Clustering and similarity analysis

(according to average loads)

Clusters characterization

(according to spatial patterns)

Export of layers in Google Earth® format (Kmz)

Random selection of available volunteered information layers: Photos e Gallery

(Lattice 3)

Qualitative “panel test” analysis of records

Landscape metrics calculation by means of

moving windows (Lattice 2)


Census indexes spatialization

Census tractspolygons

Distribution of centroids

500 meters width lattice, Inverse Distance Weighted

Subject Index Meanings Source Year

Generational change ratio

Pop 55-65 year / pop. 15-25 year

ISTAT – Population census

2001

DemographyPopulation aging

rate

Resident pop. > 65 year / Resident pop.

< 15


2001

Population density Residents / AreaISTAT – Population

census2001

Agriculture employees

Agriculture employees / Total

employees


2001

Economy Industry employeesIndustry

employees / Total employees


2001

Other sec. employees

Other sec. employees / Total

employees


2001


testo


Landscape metrics (Mc Garigal, Fragstat…)


Maptoola Moving Windows tool

3 0 31.5 Kilometers.

Legend

Land Covers year 1980Aa - ARTIFICIAL BASINS

Ac - RIVERS AND CHANNELS

B - WOODS

Cf - SHRUBS

Co - OLIVE TREES

Cs - GREEN HOUSES

Cv - WINEYARD

Ea - TURISTIC AND SPORT FACILITES

Ei - INFRASTRUCTURE AND BUILDINGS

Eia - ROADS

Eib - SECONDARY ROADS

Eif - RAYLWAYS

Ep - INDUSTRIAL SETTLEMENTS

Er - BUILDINGS

Et - GROWTH AREAS

Ia - UNCULTIVATED WITH TREES

Iaa - ROADSIDE UNCULTIVATED

Iai - RIVERSIDE UNCULTIVATED

Ic - UNCULTIVATED WITH BUSHES

Im - INCOLTO MISTO

Ir - UNCULTIVATED WITH ROCKS

Lp - POPLARS

Nc - MINES

Ne - EROSION AREAS

Nr - ROCKS AND SCREES

P - PASTURES AND MEADOWS

Sa - ARABLE LAND WITH TREES

Sn - ARABLE LAND

So - HORTICULTURAL CROPS

Xp - UNCLASSIFIABLE

MW

hei

ght

MW width

Metric val. = x1

Metric val. = x2

Metric val. = x3

Metric val. = xn


Maptool

Ascii file

Ascii file



% of variance in unrotated

factors

% of variance in rotatedfactors

(Varimax)

PC-1 32.7 25.9

PC-2 16.4 20.6

PC-3 11.8 14.1

PC-1 Economy and Society PC-2 Landscape

PC-3 Interactions?


Cluster Analysis (Nearest Neighborhood)


LandscapeMetrics

Society and economy

RedAgeing people living in highly fragmented complex settings

Light greenIntermediate condition in simplified settings

OrangeAgeing people living in mild or simplified settings

BlueCommuters or farmers living in highly fragmented complex settings

GreenIntermediate condition characterised by significant sprawl

Dark blueCommuters or farmers living in mild or simplified settings

Scatter plot ofco-occurrences pairs


testo

RedAgeing people living in highly fragmented complex settings

Orange Ageing people living in mild or simplified settings

GreenIntermediate condition characterised by significant sprawl

Light green Intermediate condition in simplified settings

BlueCommuters or farmers living in highly fragmented complex settings

Dark blueCommuters or farmers living in mild or simplified settings


What we have so far…


How to characterise the aggregates?

What over the local administrators, practitioners, rural land managers, …?


The baseline framework: volunteering information Spatialisation of socio-

economic descriptors

(Lattice 1)

Characterisation of underneath territories

Building of a multivariate vector by overlaying lattices 1 and 2


Clustering and similarity analysis

(according to average loads)

Clusters characterization

(according to spatial patterns)

Export of layers in Google Earth® format (Kmz)

Random selection of available volunteered information layers: Photos e Gallery

(Lattice 3)

Qualitative “panel test” analysis of records

Landscape metrics calculation by means of

moving windows (Lattice 2)


Subjectsdespite local rural land managers depicts area as typically dominated by rural landscapes…only the 15% of the sharing has been dedicated to that…

cultural and architectural heritage (32%), biodiversity (25%), 27% miscellaneous.


Motivation of shotsThe “motivation” shows an even distribution between the willingness to communicate the beauties of places and neutral expressions, whilst the burden of shots to expose negative aspects represents not more than 7% of the selected records.


Conclusions

1) to contribute to the actual debate by proposing novel approaches to the characterisation of complex rural open spaces our purpose opens to challenge the analysis of the relationships of landscape metrics with indices or indexes referred to social aspects, as advocate by Uuemaa, Antrop et al. (2009)

3) thanks to a meaningful description of landscapes characteristics the proposed classification can best serve as an effective tool to support decision makers on the identification of the best suitable RQD areas, if compared with the classification currently in force for the rural areas in Marche region

5) the overly with volunteered information has shown how despite local rural land managers depicts the territories comprise in the study area as typically dominated by rural landscapes, only the 15% of the sharing has been dedicated to that subject

7) Several limits imposes to deeply review the method: descriptors, algorithms, …

9) The validation of results remains one of the most challenging task


Thank you for your attention

[email protected]@univpm.it

mailto:[email protected]

mailto:[email protected]

The characterisation of “living” landscapes: the role of mixed descriptors and volunteering...

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