policentrometer - icspconference.files.wordpress.com file18/12/2016 · Problem definition (by...
Transcript of policentrometer - icspconference.files.wordpress.com file18/12/2016 · Problem definition (by...
policentrometer
Lorenzo GabrielliDaniele Fadda • Giulio RossettiMirco Nanni • Fosca Giannotti
Dino Pedreschi • Leonardo Piccini
A big data proposal
Example of individual mobility observation
Tuscany municipalitiesCoverage space
150KNumber of Vehicles
February March 2014Time
12 mlnTrips
Problem definition (by domain expert)
◉ Q1: How can we automatically identify groups of contiguous areas having inner mobility higher than the surrounding network?
◉ Q2: How many communities we are able to identify by looking at human vehicular Mobility?
◉ Q3: Which are the most relevant characteristics of such communities? Which are their sizes and internal densities?
Requirement: maximise self-contained traffic
i=1 i=10 i=20
Our proposal: Agglomerative process
Red Communities/Regions created in the last iterationGrey Communities/Regions created in previous iterations
Iteration n
Local quality score: self containment index
Evaluationn (t,z)= tUz/(t*+*z)
50%52%
48% 40%
aUb = 50% bUc = 52% cUd = 48% dUe = 40%
Iteration n
Local quality score: self containment index
Evaluationn (t,z)= tUz/(t*+*z)
Iteration n+1
50%52%
48% 40%
aUb = 50% bUc = 52% cUd = 48% dUe = 40%
Cut criteria: maximise global quality scoreOptimum iteration to end the algorithm is suggested by the first local maximum of S
Real Expected
Flows of vehicles by the municipality i to municipality j
Cut criteria: maximise global quality scoreOptimum iteration to end the algorithm is suggested by the first local maximum of S
Real Expected
Flows of vehicles by the municipality i to municipality j
Cut criteria: maximise global quality score
Total outgoing flows starting from Municipality i
Optimum iteration to end the algorithm is suggested by the first local maximum of S
Real Expected
Flows of vehicles by the municipality i to municipality j
Cut criteria: maximise global quality score
Total outgoing flows starting from Municipality i
incoming flows in Municipality j
Optimum iteration to end the algorithm is suggested by the first local maximum of S
Real Expected
Flows of vehicles by the municipality i to municipality j
Cut criteria: maximise global quality score
Total outgoing flows starting from Municipality i
incoming flows in Municipality j
Total flows in the network
Optimum iteration to end the algorithm is suggested by the first local maximum of S
Real Expected
State-of-the-art
◉ Clustering problem similarity using Distance Matrix
◉ Community detection problem Network built using trips [OD Matrix]
Competitors
Network Based
◉Louvain MODULARITY BASED
◉Demon EGO NETWORK BASED
◉Infohiermap CONDUCTANCE BASED
Cluster Based
◉DBSCAN
◉KMedoid
NETWORK approachIn order to compare our approach with the state of art we observe those measures: internal density conductance modularity
Measure min/max/avg/std
Louvain Policentrometer
Internal Edge Density 0.15/0.32/0.21/0.07 0.27/0.75/0.49/0.20
Conductance 0.014/0.58/0.38/0.27 0.014/0.97/0.88/0.19
Modularity 0.16 -0.06
Network approach LOUVAIN
Drawback too few and too big communities
Measure min/max/avg/std
Louvain Policentrometer
Internal Edge Density 0.15/0.32/0.21/0.07 0.27/0.75/0.49/0.20
Conductance 0.014/0.58/0.38/0.27 0.014/0.97/0.88/0.19
Modularity 0.16 -0.06
Network approach LOUVAIN
Measure min/max/avg/std
Demon Policentrometer
Internal Edge Density 0.12/0.50/0.28/0.18 0.27/0.75/0.49/0.20
Conductance 0.37/0.90/0.50/0.17 0.014/0.97/0.88/0.19
Modularity -0.38 -0.06
Network approach DEMON
Drawback too overlapping communities
Measure min/max/avg/std
Demon Policentrometer
Internal Edge Density 0.12/0.50/0.28/0.18 0.27/0.75/0.49/0.20
Conductance 0.37/0.90/0.50/0.17 0.014/0.97/0.88/0.19
Modularity -0.38 -0.06
Network approach DEMON
Measure min/max/avg/std
INFOHIERMAP Policentrometer
Internal Edge Density
0.09/0.50/0.18/0.10 0.27/0.75/0.49/0.20
Conductance 0.90/0.98/0.95/0.24 0.014/0.97/0.88/0.19
Modularity 0.006 -0.06
Network approach INFOHIERMAP
Drawback non contiguous communities
Measure min/max/avg/std
INFOHIERMAP Policentrometer
Internal Edge Density
0.09/0.50/0.18/0.10 0.27/0.75/0.49/0.20
Conductance 0.90/0.98/0.95/0.24 0.014/0.97/0.88/0.19
Modularity 0.006 -0.06
Network approach INFOHIERMAP
DBSCAN
optimal choice for the two methods
Drawback: Policentrometer get a higher global score than DBSCAN
dbscan communities
◉ Flow inclusion based problem definition
◉ Ad hoc algorithm that outperforms state-of-the-art methods
◉ Preliminary evaluation of results
Conclusion
ADDITIONS◉ Comparison with optimization methods◉ Seeding using municipalities with more than 50.000 people
INTEGRATIONS
NEW APPLICATIONS
◉ Public transport system data
◉ Domain indipendent◉ All Geo OD Matrix can be used