Spatial Database & Spatial Data Mining Shashi Shekhar Dept. of Computer Sc. and Eng. University of...
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Transcript of Spatial Database & Spatial Data Mining Shashi Shekhar Dept. of Computer Sc. and Eng. University of...
Spatial Database &
Spatial Data Mining
Shashi ShekharDept. of Computer Sc. and Eng.
University of Minnesota
[email protected], www.cs.umn.edu/~shekhar
www.spatial.cs.umn.edu
Spatial Data
• Location-based Services– E.g.: MapPoint, MapQuest, Yahoo/Google Maps, …
Courtesy: Microsoft Live Search (http://maps.live.com)
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”
Geo-Spatial Databases: Management and Mining
Nest locations Distance to open water
Vegetation durabilityWater depth
1. Recent book from our group! 3. Shortest Path Queries 4. Storing roadmaps in disk blocks2. Parallelize Range Queries
6. Spatial outlier detect bad sensor (#9) on Highway I-355. Location prediction to characterize nesting grounds.
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
Nest locationsDistance to open water
Vegetation durability Water depth
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
Classical Linear Regression Low
Spatial Auto-Regression High
Name ModelClassification Accuracy
εx βy
εxβWyy ρ
framework spatialover matrix odneighborho -by- :
parameter n)correlatio-(auto regression-auto spatial the:
nnW
SSEnn
L 2
)ln(
2
)2ln(ln)ln(
2WI
Computational Challenge: Computing determinant of a very large matrix in the Maximum Likelihood Function:
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• 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
• 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
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 )
Florida, Lousiana (Andrew, 1992)
( www.washingtonpost.com)
( National Weather Services) ( National Weather Services)
( FEMA.gov)
I-45 out of Houston
Houston
(Rita, 2005)
Monticello Emergency Planning Zone
Monticello EPZSubarea Population2 4,675 5N 3,9945E 9,6455S 6,7495W 2,23610N 39110E 1,78510SE 1,39010S 4,616 10SW 3,40810W 2,35410NW 707Total 41,950
Estimate EPZ evacuation time: Summer/Winter (good weather): 3 hours, 30 minutesWinter (adverse weather): 5 hours, 40 minutes
Emergency Planning Zone (EPZ) is a 10-mile radius around the plant divided into sub areas.
Data source: Minnesota DPS & DHS Web site: http://www.dps.state.mn.us
http://www.dhs.state.mn.us
A Real World Testcase
Source cities
Destination
Monticello Power Plant
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
Experiment Result
Total evacuation time:
- Existing Plan: 268 min.
- New Plan: 162 min.
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)
Physical model
Representation
Directed Acyclic Graph, TAGs
Storage method
Connectivity-Clustered Access Method (CCAM)
Frequent Operations
Breadth First Search
Path Computation
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
Example Query
SELECT * FROM travelmodeWHERE ONT_RELATED (transport,
‘IS_A’,‘Road’,‘Transport_Ontology’,123) = 1;
Result: All walk and drive modes.
…
Drive Walk
Transport
Road Commuter Rail
Bus
ApplicationsHomeland Security, Life Sciences, Web Services
Resource Description Framework (RDF)Multimodal Transportation System
Commonwealth Ave. and Subway (Green Line), Boston[source: http://maps.google.com/]
Subway Stations
Road Intersections
Transition Edge
N1 N2 N3 N4 N5
R1 R2 R3
Graph Representation
(between BU Central and Blandford St)
Resource Description Framework (RDF)
: Street
: TrafficLight
: RailRoute
: RailRoute
: bus
:busTerminals
: busStops
crosscuts used_by
parallel
has Start/end
halts
Light Rail System
: Rail_line
: Streets
: Streets
start/end
has
serves
crosscuts
parallel
: Terminals
used_by
Road System
: TrafficLight
: Stations
: Trains
Transit Edges(*)
Multimodal Transportation System
: Streets
SELECT S.street, S.busStop, R.Stations, R.RailRoute,R.TerminalFROM TABLE(SDO_RDF_MATCH(
‘(?x : halts ?b)
SDO_RDF_Models(‘rail_line R’,’street S’)),
‘(?rr :serves ? z),
WHERE S.b hasTransitTo R.z and S.Street = ‘Commonwealth’
‘(?rr :start/end ?tr),
Find all routes from the Commonwealth Avenue to the Logan Airport using bus and subway systems.
*Note: A subset of possible transition edges is shown.
and R.terminal = ‘Logan airport’;
Geo-Spatial Databases: Management and Mining
Nest locations Distance to open water
Vegetation durabilityWater depth
1. Recent book from our group! 3. Shortest Path Queries 4. Storing roadmaps in disk blocks2. Parallelize Range Queries
6. Spatial outlier detect bad sensor (#9) on Highway I-355. Location prediction to characterize nesting grounds.