Presentazione di PowerPoint

Download Report

Transcript Presentazione di PowerPoint

Land Cover and Census integration
geographic datasets to realize a statistics
synthetic map
Raffaella Chiocchini – Stefano Mugnoli
Istat – Italian National Institute of Statistic
Krakow 22nd October 2014
1 of 16
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
2 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 22nd October 2014
3 of 16
Introduction
•
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.
Statistics Sinthetic Map
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
4 of 16
Data Integration
Road
grafo
Thematic
Cartography
Statistics Synthetic
Map
Statistical Public
Archives
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
Point of
Interest
Census
Datasets
5 of 16
Some examples: Riddle
What are they?
Motordrome
Cod. 1426 CUS Emilia Romagna 2008
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
6 of 16
Some examples: Collage
Veneto
Lombardy
Emilia-Romagna
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
7 of 16
Some examples: where does the sea start?
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
8 of 16
Some examples: When?
850.000 Sqm
280.000 Solar panels
70 MW
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
9 of 16
Land cover italian languages
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)
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
10 of 16
Our proposal: main residential zones
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
11 of 16
Our proposal: extra-urban areas
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
12 of 16
What we did until now
Six Regions:
Valle d’Aosta
Lombardy
Emilia-Romagna
Umbria
Latium
Tutorial (83 pages)
Basilicata
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
13 of 16
What we are going to do (probably next year)
Other five Regions:
Veneto
Friuli Venezia-Giulia
Marche
Molise
Sardinia
Control and update (Orthophoto 2012-2014)
of the six Regions realized
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
14 of 16
Conclusion
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.
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
15 of 16
Thank You
Raffaella Chiocchini ([email protected])
Stefano Mugnoli ([email protected])
Istat – Italian National Institute of Statistics
R. Chiocchini, S. Mugnoli – Krakow 22nd October 2014
16 of 16