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Spatial Database & Spatial Data Mining. Shashi Shekhar Dept. of Computer Sc. and Eng. University of Minnesota. shekhar@cs.umn.edu, www.cs.umn.edu/~shekhar www.spatial.cs.umn.edu. Spatial Data. Location-based Services E.g.: MapPoint, MapQuest, Yahoo/Google Maps, ….

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spatial database spatial data mining
Spatial Database & Spatial Data Mining

Shashi Shekhar

Dept. of Computer Sc. and Eng.

University of Minnesota

shekhar@cs.umn.edu,

www.cs.umn.edu/~shekhar

www.spatial.cs.umn.edu

spatial data
Spatial Data
  • Location-based Services
    • E.g.: MapPoint, MapQuest, Yahoo/Google Maps, …

Courtesy: Microsoft Live Search (http://maps.live.com)

spatial data3
Spatial Data
  • In-car Navigation Device

Emerson In-Car Navigation System (Courtesy: Amazon.com)

slide4
Book

http://www.spatial.cs.umn.edu

outline
Outline
  • Spatial Databases
    • Conceptual Modeling
      • Pictograms enhanced Entity Relationship Model
    • Logical Data Model
      • Direction predicates and queries
    • Physical Data Model
      • Query Processing – Shortest Paths, Evacuation Routes,
        • Correlated time-series
      • Storage – Connectivity Clustered Access Method
  • Spatial Data Mining
    • Location Prediction – fast algorithms
    • Co-location patterns – definition, algorithms
    • Spatial outliers – algorithms
    • Hot-spots – new work on “mean streets”
slide6

Nest locations

Distance to open water

Vegetation durability

Water depth

Geo-Spatial Databases: Management and Mining

1. Recent book from our group!

3. Shortest Path Queries

4. Storing roadmaps in disk blocks

2. Parallelize Range Queries

5. Location prediction to characterize nesting grounds.

6. Spatial outlier detect bad sensor (#9) on Highway I-35

spatial data mining sdm
Spatial Data Mining (SDM)
  • The process of discovering
    • interesting, useful, non-trivial patterns
      • patterns: non-specialist
      • exception to patterns: specialist
    • from large spatial datasets
  • Spatial pattern families
    • Spatial outlier, discontinuities
    • Location prediction models
    • Spatial clusters
    • Co-location patterns
spatial data mining example
Spatial Data Mining - Example

Nest locations

Distance to open water

Vegetation durability

Water depth

spatial autocorrelation sa
Spatial Autocorrelation (SA)
  • First Law of Geography
    • “All things are related, but nearby things are more related than distant things. [Tobler, 1970]”
  • Spatial autocorrelation
    • Nearby things are more similar than distant things
    • Traditional i.i.d. assumption is not valid
    • Measures: K-function, Moran’s I, Variogram, …

Pixel property with independent identical

distribution

Vegetation Durability with SA

implication of auto correlation
Implication of Auto-correlation

Computational Challenge:

Computing determinant of a very large matrix

in the Maximum Likelihood Function:

outline11
Outline
  • Spatial Databases
    • Conceptual Modeling
      • Pictograms enhanced Entity Relationship Model
    • Logical Data Model
      • Direction predicates and queries
    • Physical Data Model
      • Query Processing – Shortest Paths, Evacuation Routes,
        • Correlated time-series
      • Storage – Connectivity Clustered Access Method
  • Spatial Data Mining
    • Location Prediction – fast algorithms
    • Co-location patterns – definition, algorithms
    • Spatial outliers – algorithms
    • Hot-spots – new work on “mean streets”
spatio temporal query processing
Spatio-temporal Query Processing
  • Teleconnection
    • Find (land location, ocean location) pairs with correlated climate changes
      • Ex. El Nino affects climate at many land locations

Global Influence of El Nino during

the Northern Hemisphere Winter

(D: Dry, W: Warm, R: Rainfall)

Average Monthly Temperature

(Courtsey: NASA, Prof. V. Kumar)

auto correlation saves computation cost
Auto-correlation saves computation cost
  • Challenge
    • high dimensional (e.g., 600) feature space
    • 67k land locations and 100k ocean locations (degree by degree grid)
    • 50-year monthly data
  • Computational Efficiency
    • Spatial autocorrelation
      • Reduce Computational Complexity
    • Spatial indexing to organize locations
      • Top-down tree traversal is a strong filter
      • Spatial join query: filter-and-refine
        • save 40% to 98% computational cost at θ = 0.3 to 0.9
slide14

Houston

(Rita, 2005)

Florida, Lousiana (Andrew, 1992)

( National Weather Services)

( National Weather Services)

( www.washingtonpost.com)

I-45 out of Houston

( FEMA.gov)

Evacuation Route Planning - Motivation

  • No coordination among local plans means
    • Traffic congestions on all highways
    • e.g. 60 mile congestion in Texas (2005)
  • Great confusions and chaos

"We packed up Morgan City residents to evacuate in the a.m. on the day that Andrew hit coastal Louisiana, but in early afternoon the majority came back home. The traffic was so bad that they couldn't get through Lafayette."

Mayor Tim Mott, Morgan City, Louisiana ( http://i49south.com/hurricane.htm )

a real scenario
A Real Scenario

Nuclear Power Plants in Minnesota

Twin Cities

monticello emergency planning zone
Monticello Emergency Planning Zone

Emergency Planning Zone (EPZ) is a 10-mile radius around the plant divided into sub areas.

Monticello EPZ

Subarea Population

2 4,675

5N 3,994

5E 9,645

5S 6,749

5W 2,236

10N 391

10E 1,785

10SE 1,390

10S 4,616

10SW 3,408

10W 2,354

10NW 707

Total 41,950

Estimate EPZ evacuation time:

Summer/Winter (good weather): 

3 hours, 30 minutesWinter (adverse weather):

5 hours, 40 minutes

Data source: Minnesota DPS & DHS Web site: http://www.dps.state.mn.us

http://www.dhs.state.mn.us

slide17

A Real World Testcase

Experiment Result

Total evacuation time:

- Existing Plan: 268 min.

- New Plan: 162 min.

Monticello Power Plant

Source cities

Destination

Routes used only by old plan

Routes used only by result plan of

capacity constrained routing

Routes used by both plans

Congestion is likely in old plan near evacuation destination due to capacity constraints. Our plan has richer routes near destination to reduce congestion and total evacuation time.

Twin Cities

outline18
Outline
  • Spatial Databases
    • Conceptual Modeling
      • Pictograms enhanced Entity Relationship Model
    • Logical Data Model
      • Direction predicates and queries
    • Physical Data Model
      • Query Processing – Shortest Paths, Evacuation Routes,
        • Correlated time-series
      • Storage – Connectivity Clustered Access Method
  • Spatial Data Mining
    • Location Prediction – fast algorithms
    • Co-location patterns – definition, algorithms
    • Spatial outliers – algorithms
    • Hot-spots – new work on “mean streets”
resource description framework rdf
Resource Description Framework (RDF)

Physical model

  • Representation
    • Directed Acyclic Graph, TAGs
  • Storage method
    • Connectivity-Clustered Access Method (CCAM)
  • Frequent Operations
    • Breadth First Search
    • Path Computation
semantics in databases
Semantics in Databases
  • Ontology

- Shared Conceptualization of knowledge in a specific domain.

  • Resource Description Framework (RDF)

- Representation of resource information in World Wide Web.

  • Patterns
ontology based semantic computing

Transport

SELECT * FROM travelmode

WHERE ONT_RELATED (transport,

‘IS_A’,

Road

Commuter Rail

‘Road’,

‘Transport_Ontology’,

123) = 1;

Walk

Bus

Drive

  • Applications

Homeland Security,Life Sciences, Web Services

Ontology based Semantic Computing
  • Example Query

Result: All walk and drive modes.

resource description framework rdf22
Resource Description Framework (RDF)

Multimodal Transportation System

(between BU Central and Blandford St)

Commonwealth Ave. and Subway (Green Line), Boston

[source: http://maps.google.com/]

N2

N3

N4

N5

N1

Road Intersections

Subway Stations

R3

R1

R2

Transition Edge

Graph Representation

resource description framework rdf23

: Rail_line

: busStops

: TrafficLight

: Street

: bus

Resource Description Framework (RDF)

Multimodal Transportation System

: TrafficLight

: Trains

: RailRoute

used_by

used_by

crosscuts

has

serves

parallel

parallel

halts

: Stations

: Streets

: RailRoute

Start/end

has

start/end

crosscuts

:busTerminals

: Terminals

: Streets

Light Rail System

Road System

Transit Edges(*)

*Note: A subset of possible transition edges is shown.

Find all routes from the Commonwealth Avenue to the Logan Airport using bus and subway systems.

SELECT S.street, S.busStop, R.Stations, R.RailRoute,R.Terminal

FROM TABLE(SDO_RDF_MATCH(

: Streets

‘(?x : halts ?b)

‘(?rr :serves ? z),

‘(?rr :start/end ?tr),

SDO_RDF_Models(‘rail_line R’,’street S’)),

WHERE S.b hasTransitTo R.z and S.Street = ‘Commonwealth’

and R.terminal = ‘Logan airport’;

slide24

Nest locations

Distance to open water

Vegetation durability

Water depth

Geo-Spatial Databases: Management and Mining

1. Recent book from our group!

3. Shortest Path Queries

4. Storing roadmaps in disk blocks

2. Parallelize Range Queries

5. Location prediction to characterize nesting grounds.

6. Spatial outlier detect bad sensor (#9) on Highway I-35