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Living Land Use
Irene Celino (CEFRIEL)
Soheil Behnam (Politecnico di Milano)
Kourosh Sheykhvand (Politecnico di Milano)
Living Land Use - http://livinglanduse.cefriel.com
Motivations – support urban planning with digital traces
Urban planning is concerned with the (re)design
of the urban environment, exploiting land use
information
Collecting and classifying land use information
consists of census activities (e.g. the EU CORINE
Land Cover programme1) which are expensive to
carry out and to update
Human activities leave several traces in the
digital world that can be exploited to "elicit" and
predict the land use in urban environments2
1 Coordination of Information on the Environment (CORINE) of the European Environment Agency (EEA): http://en.wikipedia.org/wiki/CORINE
2 Cf. for example "Characterizing Urban Landscapes Using Geolocated Tweets": http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.19
From:CORINELandCoverMethodology
(source:http://www.eea.europa.eu/)
From:TelecomBigData
Challengeinfographics
Goal – detect the "living" land use
Example: Porta Nuova-Garibaldi area
Piazza Gae Aulenti, Milano (with the easily
recognizable "Podio" skyscraper)
It used to be a construction site, but today it is
a very lively business neighbourhood
Living Land Use - http://livinglanduse.cefriel.com
2009
2013
But what about less evident changes in land use?
Can we automatically detect the "living" land use
from the analysis of streaming activity data?
Idea – analyse the activity data to elicit land use footprints
The Living Land Use application aims at:
1. Deriving land use "footprints" of Milano by
analysing the "activity data" provided by
the Big Data Challenge 2013
2. Comparing the "elicited" land use
footprints with the land use classification
provided by CORINE in 2009
3. Identifying relevant deviations in land use
between 2009 and 2013
Living Land Use - http://livinglanduse.cefriel.com
Milano grid and in-calls footprint for
week days/weekend in cell 6060
CORINE land use classification (viz: QGIS, background map: OpenStreetMap)
2009
2013
Construction site
Main outcomes – the Living Land Use application
Living Land Use - http://livinglanduse.cefriel.com
Used datasets
• BDC datasets (Milano):
Milano Grid, Telecommunications,
Private Transportation – Cobra
Telematics*, Geo Tweets*
• Additional datasets: CORINE land
use classification 2009 from
Lombardy Open Data Portal
Technical details
• Data crunching: QGIS, R scripts
• Web application: HTML5, PHP,
JavaScript, Python, MySQL, Leaflet,
JS charts, Bootstrap…
* Incrementally being added to the application (work in progress)
Impact and evolutions – towards continuous urban planning
Concrete impact of Living Land Use:
- Better understanding of the citizens' actual use of the urban environment
- Methods and tools to monitor the urban evolution over time
- Strong cost reduction for land use classification activities compared to manual census
- Support to urban planning activities for all urban stakeholders (public bodies, utilities,
service providers, local communities)
Future evolutions of Living Land Use:
- Adding support for multiple comparisons of the elicited land use
- Enhancing land use footprints representation and computation
- Introducing a paradigm shift from batch processing to continuous analysis
Living Land Use - http://livinglanduse.cefriel.com

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Living Land Use - Telecom Big Data Challenge - Trento ICT Days 2014

  • 1. Living Land Use Irene Celino (CEFRIEL) Soheil Behnam (Politecnico di Milano) Kourosh Sheykhvand (Politecnico di Milano)
  • 2. Living Land Use - http://livinglanduse.cefriel.com Motivations – support urban planning with digital traces Urban planning is concerned with the (re)design of the urban environment, exploiting land use information Collecting and classifying land use information consists of census activities (e.g. the EU CORINE Land Cover programme1) which are expensive to carry out and to update Human activities leave several traces in the digital world that can be exploited to "elicit" and predict the land use in urban environments2 1 Coordination of Information on the Environment (CORINE) of the European Environment Agency (EEA): http://en.wikipedia.org/wiki/CORINE 2 Cf. for example "Characterizing Urban Landscapes Using Geolocated Tweets": http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.19 From:CORINELandCoverMethodology (source:http://www.eea.europa.eu/) From:TelecomBigData Challengeinfographics
  • 3. Goal – detect the "living" land use Example: Porta Nuova-Garibaldi area Piazza Gae Aulenti, Milano (with the easily recognizable "Podio" skyscraper) It used to be a construction site, but today it is a very lively business neighbourhood Living Land Use - http://livinglanduse.cefriel.com 2009 2013 But what about less evident changes in land use? Can we automatically detect the "living" land use from the analysis of streaming activity data?
  • 4. Idea – analyse the activity data to elicit land use footprints The Living Land Use application aims at: 1. Deriving land use "footprints" of Milano by analysing the "activity data" provided by the Big Data Challenge 2013 2. Comparing the "elicited" land use footprints with the land use classification provided by CORINE in 2009 3. Identifying relevant deviations in land use between 2009 and 2013 Living Land Use - http://livinglanduse.cefriel.com Milano grid and in-calls footprint for week days/weekend in cell 6060 CORINE land use classification (viz: QGIS, background map: OpenStreetMap) 2009 2013 Construction site
  • 5. Main outcomes – the Living Land Use application Living Land Use - http://livinglanduse.cefriel.com Used datasets • BDC datasets (Milano): Milano Grid, Telecommunications, Private Transportation – Cobra Telematics*, Geo Tweets* • Additional datasets: CORINE land use classification 2009 from Lombardy Open Data Portal Technical details • Data crunching: QGIS, R scripts • Web application: HTML5, PHP, JavaScript, Python, MySQL, Leaflet, JS charts, Bootstrap… * Incrementally being added to the application (work in progress)
  • 6. Impact and evolutions – towards continuous urban planning Concrete impact of Living Land Use: - Better understanding of the citizens' actual use of the urban environment - Methods and tools to monitor the urban evolution over time - Strong cost reduction for land use classification activities compared to manual census - Support to urban planning activities for all urban stakeholders (public bodies, utilities, service providers, local communities) Future evolutions of Living Land Use: - Adding support for multiple comparisons of the elicited land use - Enhancing land use footprints representation and computation - Introducing a paradigm shift from batch processing to continuous analysis Living Land Use - http://livinglanduse.cefriel.com