Mapcube

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Mapcube

• Shashi Shekhar
• Computer Science Department, AHPCRC
• University of Minnesota
• shekhar_at_cs.umn.edu
• [phone]
• http//www.cs.umn.edu/shekhar
• http//www.cs.umn.edu/research/shashi-group/

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Biography Highlights

• 7/01-now Professor, Dept. of CS, U. of MN
• 12/89-6/01 Asst./Asso. Prof. of CS, U of MN
• Ph.D. (CS), M.B.A., U of California, Berkeley
  (1989)
• Member CTS(since 1990),Army Center, CURA
• Author A Tour of Spatial Database (Prentice
  Hall, 2002) and 100 papers in Journals,
  Conferences
• Editor Geo-Information(2002-onwards), IEEE
  Transactions on Knowledge and Data Eng.(96-00)
• Program chair ACM Intl Conf. on GIS (1996)
• Tech. Advisor UNDP(1997-98), ESRI(1995), MNDOT
  GuideStar(1993-95 on Genesis Travlink)
• Grants FHWA, MNDOT, NASA, ARMY, NSF, ...
• Supervised 7 Ph.D Thesis (placed at Oracle, IBM
  TJ Watson Research Center etc.), 30 MS. Thesis

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Research Interests

• Knowledge and Data Engineering
• Spatial Database Management
• Spatial Data Mining(SDM) and Visualization
• Geographic Information System
• Application Domains Transportation,
  Climatology, Defence Computations

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Spatial Data Mining, SDBMS

• Historical Examples
• London Cholera (1854)
• Dental health in Colorado
• Current Examples
• Environmental justice
• Crime mapping - hot spots (NIJ)
• Cancer clusters (CDC)
• Habitat location prediction (Ecology)
• Site selection, assest tracking, spatial outliers

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Project Traffic Database System

• Sponsor and time-period MNDOT, 1998-1999
• Students Xinhong Tan, Anuradha Thota
• Contributions to Transportation Domain
• Reduce response of queries from hours to minutes
• Performance tuning (table design, index
  selection)
• Contributions to Computer Science
• GUI design for extracting relevant summaries
• Evaluate technologies with large dataset

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Map of Station in Mpls
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Gui Design

• http//www.cs.umn.edu/research/shashi-group/TMC/ht
  ml/gui.html

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Existing Table
Fivemin
Detector ReadDate Time Dayofweek Volume Occupancy
Validity Speed
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Benchmark Queries

• 1. Get 5-min Volume, occupancy for detector ID
  10 on Oct. 1st, 1997 from 7am to 8am
• 2. Get 5-min volume, Occupancy for detector 5
  on Aug1 1997.
• 3. Get 5-min volume, Occupancy for detector 5
  on Aug1 1997 from 6.30am to 7.30am.
• 4. Get average 5-min volume, occupancy, for
  Monday in Aug1997 between 8.00 - 8.05,8.05-8.10
  9.00
• 5. Get maximum volume, Occupancy for detector 5
  on Aug1 1997 from 6am to 7am
• 6. Get the average of AM rushhour hourly volume
  for a set of stations on highway I35W-NB with
  milepoint between 0.0 and 4.0 from Oct. 1st, 1997
  to Oct. 5th , 1997

Conclusion
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Examples of the Query

• Example1
• Query description
• Get 5-min Volume, occupancy for detector ID 10
  on Oct. 1st, 1997 from 7am to 8am
• SQL statement
• SELECT readdate, time, xtan.fivemin.detector,
  occupancy, volume
• FROM xtan.fivemin, xtan.datetime
• WHERE ReadDate to_date('01-OCT-97',
  'DD-MON-YYYY')
• AND time BETWEEN '0705' AND '0800'
• AND xtan.fivemin.Detector '10'
• AND xtan.fivemin.

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Examples of the Query

• Query result 1

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Examples of the Query

• Example2
• Query description
• Get the average of AM rushhour hourly volume for
  a set of stations on highway I35W-NB with
  milepoint between 0.0 and 4.0 from Oct. 1st, 1997
  to Oct. 5th , 1997
• SQL statement
• SELECT hour, xtan.v_stat_hour.station,
  avg(volume)
• FROM tan.v_stat_hour, xtan.statrdwy
• WHERE ReadDate BETWEEN to_date('01-OCT-97','DD-MON
  -YYYY') AND to_date('05-OCT-97','DD-MON-YYYY')
  
• AND hour BETWEEN '06' AND '09'
• AND statrdwy.route 'I35W-I'
• AND statrdwy.mp gt 0.0
• AND statrdwy.mp lt 4.0
• AND xtan.v_stat_hour.station statrdwy.station
• GROUP BY xtan.v_stat_hour.station, hour

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Examples of the Query

• Query result 2

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Project Traffic Data Visualization

• Sponsor and time-period USDOT/ITS Inst.,
  2000-2001
• Students Alan Liu, CT Lu
• Contributions to Transportation Domain
• Allow intuitive browsing of loop detector data
• Highlight patterns in data for further study
• Contributions to Computer Science
• Mapcube - Organize visualization using a
  dimension lattice
• Visual data mining, e.g. for clustering

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Motivation for Traffic Visualization

• Transportation Manager
• How the freeway system performed yesterday?
• Which locations are worst performers?
• Traffic Engineering
• Where are the congestion (in time and space)?
• Which of these recurrent congestion?
• Which loop detection are not working properly?
• How congestion start and spread?
• Traveler, Commuter
• What is the travel time on a route?
• Will I make to destination in time for a meeting?
• Where are the incident and events?
• Planner and Research
• How much can information technique to reduce
  congestion?
• What is an appropriate ramp meter strategy given
  specific evolution of congestion phenomenon?

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Dimensions

• Available
• TTD Time of Day
• TDW Day of Week
• TMY Month of Year
• S Station, Highway, All Stations
• Others
• Scale, Weather, Seasons, Event types,

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Comparison with IWEDA

• Summary of IWEDA Weather Visualizations
• Dimension system of components, time slot,
  space
• User chooses a system component and a timeslot
• A cell in the matrix of systems x timeslots
• Select a component from the system
• User gets a weather map
• It is querying a time slice
• Possibilities with mapcube
• Other visualizations are facilitated
• Changes in weather for a day for a location
• Changes in weather for a day for a given route
• Possibilities with Spatial Data Mining
• Co-location of micro phenomena with terrain types
• Spatial outliers or discontinuities
• Hotspots, e.g., tornado alley

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Mapcube Which Subset of Dimensions ?
TTDTDWTMYS
TTDTDWS
TTDTDW
TDWS
STTD
S
TTD
TDW
Next Project
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Data Fusion levels and Mapcube

• Different Sub-cubes help with different data
  fusion levels
• Level 0 Single Sensor
• Local weather as a function of time
• Level 1 Correlating Multiple Sensors
• Map of spatial variation in weather
• Space-time plot for a route for a day
• Level 2 Interpret, Aggregate
• Detect spatial discontinuities, spatial outliers
• Group sensors with similar weather measurements
• Group timeslots with similar weather measurements

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Singleton Subset TTD
Configuration

• X-axis time of day Y-axis Volume
• For station sid 138, sid 139, sid 140, on
  1/12/1997

Trends

• Station sid 139 rush hour all day long
• Station sid 139 is an S-outlier

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Singleton Subset TDW

• Configuration

• X axis Day of week Y axis Avg. volume.
• For stations 4, 8, 577
• Avg. volume for Jan 1997

• Friday is the busiest day of week
• Tuesday is the second busiest day of week

Trends
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Singleton Subset S
Configuration

• X-axis I-35W South Y-axis Avg. traffic
  volume
• Avg. traffic volume for January 1997

Trends?

• High avg. traffic volume from Franklin Ave to
  Nicollet Ave
• Two outliers 35W/26S(sid 576) and 35W/TH55S(sid
  585)

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Dimension Pair TTD-TDW
Configuration

• X-axis time of date Y-axis day of Week
• f(x,y) Avg. volume over all stations for Jan
  1997, except Jan 1, 1997

Trends

• Evening rush hour broader than morning rush hour
• Rush hour starts early on Friday.
• Wednesday - narrower evening rush hour

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Dimension Pair S-TTD

• Configuration
• X-axis Time of Day
• Y-axis Route
• f(x,y) Avg. volume over all stations for 1/15,
  1997
• Trends
• 3-Cluster
• North sectionEvening rush hour
• Downtown area All day rush hour
• South sectionMorning rush hour
• S-Outliers
• station ranked 9th
• Time 235pm
• Missing Data

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Dimension Pair TDW-S

• X-axis stations Y-axis day of week
• f(x,y) Avg. volume over all stations for
  Jan-Mar 1997

Configuration

• Busiest segment of I-35 SW is b/w Downtown MPLS
  I-62
• Saturday has more traffic than Sunday
• Outliers Route branch

Trends
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Post Processing of cluster patterns

• Clustering Based Classification
• Class 1 Stations with Morning Rush Hour
• Class 2 Stations Evening Rush Hour
• Class 3 Stations with Morning Evening Rush
  Hour

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Triplet TTDTDWS Compare Traffic Videos
Configuration Traffic volume on Jan 9 (Th) and
10 (F), 1997

• Evening rush hour starts earlier on Friday
• Congested segments I-35W (downtown Mpls
  I-62)
• I-94 (Mpls St. Paul) I-494 ( intersection
  I-35W)

Trends
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Size 4 Subset TTDTDWTMYS(Album)

• Outer X-axis (month of year) Y-axis (route)
• Inner X-axis (time of day) Y-axis (day of
  week)

Configuration
Trends

• Morning rush hour I-94 East longer than I-35 W
  North
• Evening rush hour I-35W North longer than I-94
  East
• Evening rush hour on I-94 East Jan longer than
  Feb