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Project 2 Presentation
Spatial Databases
GIS Case Studies
Elizabeth Sayed
Elizabeth Stoltzfus
December 4, 2002
UC Berkeley: IEOR 215
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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– Core spatial functionality
– Interface to DBMS
Taxonomy
Data types
Operations
Query language
Algorithms
DBMS
– Interface to spatial application
Core Spatial
Functionality
Interface to DBMS
 SDBMS has three layers:
Spatial application
 SDBMS works with a spatial application at the front
end and a DBMS at the back end
Interface to spatial application
SDBMS Three-layer Structure
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(
CREATE TABLE River(
Name
varchar(30),
Name
varchar(30),
State
varchar(30),
Source
varchar(30),
Pop
Integer,
Distance Integer,
Shape
Polygon);
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
(0,4)
Pine
(0,2)
Fir
(0,0)
Object Viewpoint
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)
Oak
(2,0)
(4,0)
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
N
Derived Shapes
0, N
*
User Defined Shape
!
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Extending the ER Diagram with Spatial
Pictograms: State Park Example
Standard ER Diagram
Spatial ER Diagram
LineID
RName
Supplies_to
RName
River
PolygonID
River
Supplies_to
FoName
FoName
FacName
Touches
FacName
Facility
Facility
Forest
Forest
Belongs_to
Belongs_to
PointID
Within
Monitors
Fire Station
Monitors
Fire Station
FiName
FiName
PointID
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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 of Nests
 Location: Darr and Stubble on the shores of lake
Erie in Ohio
A
 Focus
1. Vegetation Durability
A
Actual Pixel Locations
A
2. Distance to Open Water
3. Water Depth
 Assumptions with Classical Data mining
1. Data is independently generated – no
autocorrelation
P
P
P
A
Case 1:
A
A
Possible Prediction
2. Local vs. global trends
 Spatial accuracy
P
1. Predictions vs. actual
2. Impact
P
A
A
P
Case 2:
A
Possible Prediction
Source: 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
ClosetID
Length
Type
Hallway
Closet
RoomID
Accesses
HallI
D
Belongs_T
o
Room
Belongs_
FurnID
To
Belongs_To
Furniture
Nam
e
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