Elizabeth Sayed Elizabeth Stoltzfus December 4, 2002

Elizabeth SayedElizabeth StoltzfusDecember 4, 2002

Project 2 Presentation

Spatial DatabasesGIS Case Studies

UC Berkeley: IEOR 215

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Agenda

Spatial Database Basics

Geographic Information Systems (GIS) Basics

Case Studies

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Spatial Database Basics

Common applications

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Spatial Databases Background

Spatial databases provide structures for storage and analysis of spatial data

Spatial data is comprised of objects in multi-dimensional space

Storing spatial data in a standard database would require excessive amounts of space

Queries to retrieve and analyze spatial data from a standard database would be long and cumbersome leaving a lot of room for error

Spatial databases provide much more efficient storage, retrieval, and analysis of spatial data

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Types of Data Stored in Spatial Databases

Two-dimensional data examples

– Geographical

– Cartesian coordinates (2-D)

– Networks

– Direction

Three-dimensional data examples

– Weather

– Cartesian coordinates (3-D)

– Topological

– Satellite images

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Spatial Databases Uses and Users

Three types of uses

– Manage spatial data

– Analyze spatial data

– High level utilization

A few examples of users

– Transportation agency tracking projects

– Insurance risk manager considering location risk profiles

– Doctor comparing Magnetic Resonance Images (MRIs)

– Emergency response determining quickest route to victim

– Mobile phone companies tracking phone usage

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Spatial Databases Uses and Users

Three types of uses

– Manage spatial data

– Analyze spatial data

– High level utilization

A few examples of users

– Transportation agency tracking projects

– Insurance risk manager considering location risk profiles

– Doctor comparing Magnetic Resonance Images (MRIs)

– Emergency response determining quickest route to victim

– Mobile phone user determining current relative location of businesses

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Spatial Database Management System

Spatial Database Management System (SDBMS) provides the capabilities of a traditional database management system (DBMS) while allowing special storage and handling of spatial data.

SDBMS:

– Works with an underlying DBMS

– Allows spatial data models and types

– Supports querying language specific to spatial data types

– Provides handling of spatial data and operations

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SDBMS Three-layer Structure

SDBMS works with a spatial application at the front end and a DBMS at the back end

SDBMS has three layers:

– Interface to spatial application

– Core spatial functionality

– Interface to DBMS

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Core Spatial Functionality

Taxonomy

Data types

Operations

Query language

Algorithms

Access methods

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Spatial Query Language

Number of specialized adaptations of SQL

– Spatial query language

– Temporal query language (TSQL2)

– Object query language (OQL)

– Object oriented structured query language (O2SQL)

Spatial query language provides tools and structures specifically for working with spatial data

SQL3 provides 2D geospatial types and functions

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Spatial Query Language Operations

Three types of queries:

– Basic operations on all data types (e.g. IsEmpty, Envelope, Boundary)

– Topological/set operators (e.g. Disjoint, Touch, Contains)

– Spatial analysis (e.g. Distance, Intersection, SymmDiff)

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Spatial Data Entity Creation

Form an entity to hold county names, states, populations, and geographies

CREATE TABLE County(

Name varchar(30),

State varchar(30),

Pop Integer,

Shape Polygon);

Form an entity to hold river names, sources, lengths, and geographies

CREATE TABLE River(

Name varchar(30),

Source varchar(30),

Distance Integer,

Shape LineString);

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Example Spatial Query

Find all the counties that border on Contra Costa county

SELECT C1.Name

FROM County C1, County C2

WHERE Touch(C1.Shape, C2.Shape) = 1 AND C2.Name = ‘Contra Costa’;

Find all the counties through which the Merced river runs

SELECT C.Name, R.Name

FROM County C, River R

WHERE Intersect(C.Shape, R.Shape) = 1 AND R.Name = ‘Merced’;

CREATE TABLE County(

Name varchar(30),

State varchar(30),

Pop Integer,

Shape Polygon);

CREATE TABLE River(

Name varchar(30),

Source varchar(30),

Distance Integer,

Shape LineString);

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Geographic Information System (GIS) Basics

Common applications

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GIS Applications 1. Cartographic

– Irrigation– Land evaluation– Crop Analysis– Air Quality– Traffic patterns– Planning and facilities management

2. Digital Terrain Modeling– Earth science resources– Civil Engineering & Military Evaluation– Soil Surveys– Pollution Studies– Flood Control

3. Geographic objects– Car navigation systems– Utility distribution and consumption– Consumer product and services

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GIS Data Format

Modeling

1. Vector – geometric objects such as points, lines and polygons

2. Raster – array of points

Analysis

1. Geomorphometric –slope values, gradients, aspects, convexity

2. Aggregation and expansion

3. Querying

Integration

1. Relationship and conversion among vector and raster data

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GIS – Data Modeling using Objects & Fields

Name Shape

Pine [(0,2), (4,2), (4,4), (0,4)]

Fir [(0,0), (2,0), (2,2), (0,2)]

Oak [(2,0), (4,0), (4,2), (2,2)

Pine

Fir Oak

(0,4)

(0,2)

(0,0) (2,0) (4,0)

Object Viewpoint Field Viewpoint

Pine: 0<x<4; 2<y<4

Fir: 0<x<2; 0<y<2

Oak: 2<x<4; 0<y<2

Source: “Spatial Pictogram Enhanced Data Models pg 79

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Conceptual Data Modeling

Relational Databases: ER diagram

Limitations for ER with respect to Spatial databases: – Can not capture semantics

– No notion of key attributes and unique OID’s in a field model

– ER Relationship between entities derived from application under consideration

– Spatial Relationships are inherent between objects

Solution: Pictograms for Spatial Conceptual Data-Modeling

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Pictograms - Shapes

Types: Basic Shapes, Multi-Shapes, Derived Shapes, Alternate Shapes, Any possible Shape, User-Defined Shapes

Basic Shapes Alternate Shapes

Multi-Shapes Any Possible Shape

Derived Shapes User Defined Shape

N 0, N

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Extending the ER Diagram with Spatial Pictograms: State Park Example

ForestFacilityBelongs_to

River

Standard ER Diagram

Supplies_to

Fire Station

Monitors

LineID

PointID

PointID Within

Touches

FiName

FacName

RName

FoName

ForestFacility

Belongs_to

RiverSupplies_to

Fire Station Monitors

FiName

FacName

RName

FoName

Spatial ER Diagram

PolygonID

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Case Studies

Specific applications of spatial databases

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Case Study: Wetlands Objective: To predict the spatial distribution of the

location of bird nests in the wetlands

Location: Darr and Stubble on the shores of lake Erie in Ohio

Focus

1. Vegetation Durability

2. Distance to Open Water

3. Water Depth

Assumptions with Classical Data mining

1. Data is independently generated – no autocorrelation

2. Local vs. global trends

Spatial accuracy

1. Predictions vs. actual

2. Impact

P A

P P

A A

A

A

A

P

P P A

A A

Location of Nests

Actual Pixel Locations

Case 1:

Possible Prediction

Case 2:

Possible PredictionSource: What’s Spatial About Spatial Data Mining pg 490

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Case Study: Green House Gas Emission Estimations

Objective:

– To assess the impact of land-use and land cover changes on ground carbon stock and soil surface flux of CO2, N2O and CH4 in Jambi Province, Indonesia

Methodology:

– Initiated by development of land-use/land cover maps and followed by field measurements

– Spatial database construction development based on 1986 and 1992 land-use/land cover maps that developed from Landsat MSSR and SPOT

– Weight of sample components of the tree and streams, branches, twigs, etc were estimated from equations and literature

– Emission rates were developed by plotting and analyzing collected air samples

– Field data measurements and GIS spatial data were combined using a Look Up Table of Arc/Info.

Source: “Spatial Database Development for green house gas emission Estimation using remote sensing and GIS”

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Case Study: Green House Gas Emission Estimations (cont)

Results:

– Able to quantitatively compare emission changes between 1986 to 1992:

o Determined that there was a loss of 8.3 million tons of Carbon

o Proportion of primary forest decreased from 19.3% to 12.5%

o Showed 24% of primary forest was converted into logged forest, shrub, cash crops

– Greenhouse gas emission varied depending on the site condition and season.

– Process gave impacts of greenhouse gas on the soil surface

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Case Study: Pantanal Area, Brazil

Objective: To assess the drastic land use changes in the Pantanal region since 1985

Data Source:

– 3 Landsat TM images of the Pantal study area from 1985, 1990, 1996

– A land-use survey from 1997

Assessment Methodology:

– Normalized Difference Vegetation Index (NDVI) was computed for each year

– NDVI maps of the three years combined and submitted to multi-dimensional image segmentation

– Classified vegetation

– Produced a color composite by year that identified the density of vegetation

Source: Integrated Spatial Databases pg 116

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Conclusion

Many varied applications of spatial databases

Stores spatial data in various formats specific to use

Captures spatial data more concisely

Enables more thorough understanding of data

Retrieves and manipulates spatial data more efficiently and effectively

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Problem 1 Solution

a) Find all cities that are located within Marin County.

SELECT C2.Name

FROM County C1, City C2

WHERE Within(C1.Shape, C2.Shape) = 1 AND C1.Name = ‘Marin’;

b) Find any rivers that borders on Mendocino County.

SELECT R.Name

FROM County C, River R

WHERE Touch(C.Shape, R.Shape) = 1 AND C.Name = ‘Mendocino’;

c) Find the counties that do not touch on Orange County.

SELECT C1.Name

FROM County C1, County C2

WHERE Disjoint(C1.Shape, C2.Shape) = 1 AND C2.Name = ‘Orange’;

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Problem 2 Solution

Room

HallwayCloset

Furniture

Length

Name

RoomID

FurnID

HallID

Type

ClosetID

Belongs_To

Belongs_To

Belongs_To

Accesses

Elizabeth Sayed Elizabeth Stoltzfus December 4, 2002

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