The characterisation of “living” landscapes: the role of mixed descriptors and volunteering geographic information Ernesto Marcheggiani, Hubert Gulinck - Katholieke Universiteit Leuven Andrea Galli - Polytechnic University of Marche

1. The Characterisation of “Living” Landscapes: Mixed Descriptors and Volunteering Geographic Information Ernesto Marcheggiani Hubert Gulinck and Andrea Galli Dep. of Earth and Environmental Science – KULeuven - Division of Forest, Nature and Landscape and Dep. SAIFET – Univpm – Section of Agroingegneria e Territorio

3. 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

4. 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.

5. The Case 8 municipalities involved Case study area: 55’000 ha Synclinal – Anticlinal structure, N-S oriented unlike the general morphology of the Region

6. Prevalent baseline spatial “3 beans” model

7. Morphological units Land covers Regional Planning (PIT) Landscape and ecology

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

9. 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)

10. Census indexes spatialization Census tracts polygons 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 Demography Population aging rate Resident pop. > 65 year / Resident pop. < 15 ISTAT – Population census 2001 Population density Residents / Area ISTAT – Population census 2001 Agriculture employees Agriculture employees / Total employees ISTAT – Population census 2001 Economy Industry employees Industry employees / Total employees ISTAT – Population census 2001 Other sec. employees Other sec. employees / Total employees ISTAT – Population census 2001

11. testo

12. Landscape metrics (Mc Garigal, Fragstat…)

13. Maptool a Moving Windows tool MW height MW width Metric val. = x1 Metric val. = x2 Metric val. = x3 Metric val. = xn

14. Maptool Ascii file Ascii file

15. Principal Component Analysis PC-1 Economy and Society PC-2 Landscape PC-3 Interactions? % of variance in unrotated factors % of variance in rotated factors (Varimax) PC-1 32.7 25.9 PC-2 16.4 20.6 PC-3 11.8 14.1

16. Cluster Analysis (Nearest Neighborhood)

17. Scatter plot of co-occurrences pairs Landscape Metrics Society and economy Red Ageing people living in highly fragmented complex settings Light green Intermediate condition in simplified settings Orange Ageing people living in mild or simplified settings Blue Commuters or farmers living in highly fragmented complex settings Green Intermediate condition characterised by significant sprawl Dark blue Commuters or farmers living in mild or simplified settings

18. testo Red Ageing people living in highly fragmented complex settings Orange Ageing people living in mild or simplified settings Green Intermediate condition characterised by significant sprawl Light green Intermediate condition in simplified settings Blue Commuters or farmers living in highly fragmented complex settings Dark blue Commuters or farmers living in mild or simplified settings

19. What we have so far…

20. How to characterise the aggregates? What over the local administrators, practitioners, rural land managers, …?

21. 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)

24. Subjects despite 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.

25. Motivation of shots The “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.

27. Thank you for your attention [email_address] [email_address]