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Elizabeth Sayed Elizabeth Stoltzfus December 4, 2002 PowerPoint PPT Presentation


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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. Spatial Database Basics. Common applications.

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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


Spatial Database Basics

Common applications


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


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


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


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


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


Core Spatial Functionality

Taxonomy

Data types

Operations

Query language

Algorithms

Access methods

Spatial application

Interface to spatial application

Interface to DBMS

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

DBMS


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


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)


Spatial Data Entity Creation

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

    CREATE TABLE County(

    Name varchar(30),

    State varchar(30),

    PopInteger,

    ShapePolygon);

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

    CREATE TABLE River(

    Name varchar(30),

    Sourcevarchar(30),

    DistanceInteger,

    ShapeLineString);


Example Spatial Query

  • Find all the counties that border on Contra Costa county

    SELECT C1.Name

    FROMCounty C1, County C2

    WHERETouch(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

    FROMCounty C, River R

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

CREATE TABLE County(

Name varchar(30),

State varchar(30),

PopInteger,

ShapePolygon);

CREATE TABLE River(

Name varchar(30),

Sourcevarchar(30),

DistanceInteger,

ShapeLineString);


Geographic Information System (GIS) Basics

Common applications


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


GIS Data Format

  • Modeling

    • Vector – geometric objects such as points, lines and polygons

    • Raster – array of points

  • Analysis

    • Geomorphometric –slope values, gradients, aspects, convexity

    • Aggregation and expansion

    • Querying

  • Integration

    • Relationship and conversion among vector and raster data


GIS – Data Modeling using Objects & Fields

(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


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


Pictograms - Shapes

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

*

N

0, N

!


Extending the ER Diagram with Spatial Pictograms: State Park Example

Standard ER Diagram

Spatial ER Diagram

LineID

RName

RName

Supplies_to

River

River

PolygonID

Supplies_to

FoName

FoName

FacName

Touches

FacName

Facility

Forest

Facility

Forest

Belongs_to

Belongs_to

PointID

Within

Monitors

Monitors

Fire Station

Fire Station

FiName

FiName

PointID


Case Studies

Specific applications of spatial databases


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

Vegetation Durability

Distance to Open Water

Water Depth

Assumptions with Classical Data mining

Data is independently generated – no autocorrelation

Local vs. global trends

Spatial accuracy

Predictions vs. actual

Impact

Case Study: Wetlands

Location of Nests

Actual Pixel Locations

Case 1:

Possible Prediction

Case 2:

Possible Prediction

Source: What’s Spatial About Spatial Data Mining pg 490


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”


Case Study: Green House Gas Emission Estimations (cont)

  • Results:

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

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

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

      • 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


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


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


Problem 1 Solution

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

SELECT C2.Name

FROMCounty C1, City C2

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

b) Find any rivers that borders on Mendocino County.

SELECT R.Name

FROMCounty C, River R

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

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

SELECT C1.Name

FROMCounty C1, County C2

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


Problem 2 Solution

ClosetID

Length

Type

Closet

Hallway

RoomID

Accesses

HallID

Belongs_To

Room

Belongs_To

FurnID

Belongs_To

Furniture

Name


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