shan's spm ppt

7/29/2019 shan's spm ppt

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SPATIAL DATA MINING

Name: Shan Parvej Gayen

Roll No :-069123

Department Of MtechCSEHeritage Institute Of Technology

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LEARNING OBJECTIVES

Understand the concept of Spatial Data Mining

Describe the concepts of patterns and SDM

Describe the motivation for SDM

Learn about patterns explored by SDM Learn techniques on how to find spatial patterns

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EXAMPLESOF SPATIAL PATTERNS

Historic Examples (section 7.1.5, pp. 186)

1855 Asiatic Cholera in London : A water pump

identified as the source

Fluoride and healthy gums near Colorado river

Theory of Gondwanaland - continents fit like pieces of

a jigsaw puzzle

Modern ExamplesCancer clusters to investigate environment health

hazardsCrime hotspots for planning police patrol routes

Bald eagles nest on tall trees near open water

Nile virus spreading from north east USA to south andwest

Unusual warming of Pacific ocean (El Nino) affectsweather in USA

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WHATISA SPATIAL PATTERN?

What is not a pattern?

Random, haphazard, chance, stray, accidental,

unexpected.

Without definite direction, trend, rule, method,design, aim, purpose.

What is a Pattern?

A frequent arrangement, configuration,

composition, regularity. A rule, law, method, design, description.

A major direction, trend, prediction.

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DEFINING SPATIAL DATA MINING

Search for spatial patterns.

Non-trivial searchas automated aspossible. Large search space of plausible hypothesis

Ex. Asiatic cholera : causes water, food, air, insects. Interesting, useful, and unexpected spatial

patterns. Useful in certain application domain

Ex. Shutting off identified water pump => saved human

lives. May provide a new understanding of the world

Ex. Water pumpCholera connection lead to the germtheory.

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WHATIS NOT SPATIAL DATA MINING

Simple querying of Spatial Data

Finding neighbors of Canada given names and

boundaries of all countries (Search space not large)

Uninteresting or obvious patterns Heavy rainfall in Minneapolis is correlated with

heavy rainfall in St. Paul (10 miles apart).

Common knowledge, nearby places have similar

rainfall

Mining of non-spatial data

Diaper sales and beer sales are correlated in

evenings

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FAMILIESOF SPATIAL DATA MINING

PATTERNS

Location Prediction:

Where will a phenomenon occur?

Spatial Interactions

Which subset of spatial phenomena interact?

Hot spot

Which locations are unusual or share commonalities?

Note:

Other families of spatial patterns may be defined

SDM is a growing field, which should accommodate

new pattern families

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LOCATION PREDICTION

Where will a phenomenon occur?

Which spatial events are predictable?

How can a spatial event be predicted from

other spatial events? Examples

Where will an endangered bird nest?

Which areas are prone to fire given maps of

vegitation and drought? What should be recommended to a traveler in a

given location?

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SPATIAL INTERACTIONS

Which spatial events are related to each other?

Which spatial phenomena depend on otherphenomenon?

Examples Earth science:

climate and disturbance => {wild fires, hot, dry, lightning}

Epidemiology: Disease type and enviornmental events => {West Nile

disease, stagnant water source, dead birds, mosquitoes}

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HOTSPOTS

Is a phenomenonspatially clutered?

Which spatial entities

are unusual or sharecommoncharacteristics?

Examples

Crime hot spots to planpolice patrols

Cancer cluster to learn

investigations

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SPATIAL QUERIES

Spatial Range Queries Find all cities within 50 miles of Paris

Query has associated region (location,boundary)

Answer includes overlapping or contained dataregions

Nearest-Neighbor Queries Find the 10 cities nearest to Paris

Results must be ordered by proximity Spatial Join Queries

Find all cities near a lake

Join condition involves regions and proximity.

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UNIQUE PROPERTIESOF SPATIAL

PATTERNS

Items in a traditional data are independent of each

other, where as properties of location in a map are

often auto-correlated (patterns exist)

Traditional data deals with simple domains, e.g.

numbers and symbols where as spatial data types

are complex

Items in traditional data describe discrete objects

where as spatial data is continuous

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ASSOCIATION RULES

Support = the number of time a rule shows

up in a database

Confidence = Conditional probability of Y

given X Example

(Bedrock type = limestone), (soil depth < 50 ft)

=> (sink hole risk = high)

Support = 20 %, confidence = 0.8

Interpretation: Locations with limestone bedrock

and low soil depth have high risk of sink hole

formation.

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APRIORI ALGORITHMTOMINE

ASSOCIATIONRULES

Key challenge

Very large search space

Key assumption

Few associations are support above given threshold

Associations with low support are not interesting

Key insight

If an association item set has high support, then so do

all its subsets

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ASSOCIATIONRULES EXAMPLE 5A

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TECHNIQUESFOR ASSOCIATION

MINING

Classical method

Association rules given item types and transactions

Assumes spatial data can be decomposed into

transactions

Such decomposition may alter spatial patterns

New spatial method

Spatial association rule

Spatial co-location

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ASSOCIATIONS, SPATIAL

ASSOCIATIONS, CO-LOCATION 5A

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ASSOCIATIONS, SPATIALASSOCIATINS,

CO-LOCATION 5A

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CO-LOCATION RULES

For point data in space

Does not need transaction, works directly with

continuous space

Use neighborhood definition and spatial joins

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CO-LOCATIONRULES

Co location

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CLUSTERING

Process of discovering groups in large databases

Spatial view: rows in a database = points in a multi-

dimentional space.

Visualization may reveal interesting groups

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CLUSTERING

Hierarchical

All points in one cluster

Split and merge till a stop criterion is reached

Partitional Start with random central point

Assign points to nearest central point

Update the central points

Approach with statistical rigor Density

Find clusters based on density of regions

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OUTLIERS

Observations inconsistent with rest of the dataset

Observations inconsistent with their neighborhoods

A local instability or discontinuity

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VARIOGRAM CLOUD

Create a variogram by plotting attributedifference, distance for each pair of points

Select points common to many outlying pairs

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MORAN SCATTER PLOT

Plot normalized attribute values, weighted average in theneighborhood for each location

Select points in upper left and lower right quadrant

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SCATTERPLOT

Plot normalized attribute values, weighted average in theneighborhood for each location

Fit a liner regression line

Select points which are unusually far from the regressionline.

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CONCLUSION

Patterns are opposite of random

Common spatial patterns:

Location prediction

Feature interaction

Hot spot

Spatial patterns may be discovered using:

Techniques like associations, clustering and outlier

detection

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