1. 1 of 16 Land Cover and Census integration geographic datasets to realize a statistics synthetic map Raffaella Chiocchini – Stefano Mugnoli Istat – Italian National Institute of Statistic Krakow 22 nd October 2014

2. 2 of 16 R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014

3. 3 of 16 Index 1.Introduction 2.Data Integration 3.Some Examples (Riddle, Collage, Where, When) 4.Land cover italian languages 5.Our proposal: main residential zones 6.Our proposal: extra-urban areas 7.What we’ve done until now 8.What we are going to do 9.Conclusion R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014

4. 4 of 16 ISTAT Census Enumeration Areas vector layer represents the base to analyze the Italian territory that concern statistical data All the data collected during Census survey are linked to each of the over 400.000 enumeration areas ‘drawn’ upon Italy Land cover/use data must be linked to each enumeration area The census geographical dataset are used to classify and characterized Italian Territory with land cover and land use information Statistical information level can be used to evaluate others important phenomena like soil consumption, accessibility to territory and to describe the demographic change of population distribution. Introduction Statistics Sinthetic Map R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014

5. 5 of 16 Data Integration Thematic Cartography Road grafo Point of Interest Statistical Public Archives Statistics Synthetic Map Census Datasets R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014

6. 6 of 16 Some examples: Riddle R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 What are they? Motordrome Cod. 1426 CUS Emilia Romagna 2008

7. 7 of 16 Some examples: Collage R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 Veneto Lombardy Emilia-Romagna

8. 8 of 16 Some examples: where does the sea start? R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014

9. 9 of 16 Some examples: When? R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 850.000 Sqm 280.000 Solar panels 70 MW

10. 10 of 16 1.4.1. Green urban areas 1.2.1. Industrial, commercial and public or private services units (Source: ISPRA Report n.131/2010) 1413 – Cemeteries (Piedmont 2010) 12124 – Cemeteries (Lombardy DUSAF 2007) 1294 – Cemeterial complex (CUS Trento Province 2009) 142 – Cemetery (CUS Bolzano Province 2000) 1213 – Public, military and private services areas (CUS Veneto 2009) 143 – Cemeterial areas (CUS Liguria 2009) 143 – Cemeteries (CUS Emilia-Romagna 2008) 1411 – Cemeteries (CUS Tuscany 2010) 143 – Cemeteries (CUS Latium 2005) 143 – Cemeteries (CUS Abruzzo 2000) 143 – Cemeteries (CUS Puglia 2009) 143 – Cemeteries (CUS Sicily 2012) 143 – Cemeteries (CUS Sardinia 2008) Land cover italian languages R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014

11. 11 of 16 R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 Our proposal: main residential zones

12. 12 of 16 R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 Our proposal: extra-urban areas

13. 13 of 16 What we did until now R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 Six Regions: Valle d’Aosta Lombardy Emilia-Romagna Umbria Latium Basilicata Tutorial (83 pages)

14. 14 of 16 What we are going to do (probably next year) R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 Other five Regions: Veneto Friuli Venezia-Giulia Marche Molise Sardinia Control and update (Orthophoto 2012-2014) of the six Regions realized

15. 15 of 16 Conclusion R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 In conclusion:  The Statistic Synthetic Map is quite similar to an atlas where we can find a lot of information about many geographic, statistical and administrative matters.  The experimentation is ‘in fieri’ and we hope to complete the entire Italian territory at the end of 2016.  Information can be extracted from database by very simply SQL query and using the best known database software.  The proposal is to make uniform very different geographic products for the entire Italian territory not only for land cover/use matters but overall for many other statistic ones.  Transfer all the data to regular grids in the best possible way.

16. 16 of 16 Thank You R. Chiocchini, S. Mugnoli – Krakow 22 nd October 2014 Raffaella Chiocchini (rachiocc@istat.it)rachiocc@istat.it Stefano Mugnoli (mugnoli@istat.it)mugnoli@istat.it Istat – Italian National Institute of Statistics