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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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Elizabeth sayed elizabeth stoltzfus december 4 2002

Project 2 Presentation

Spatial Databases

GIS Case Studies

Elizabeth Sayed

Elizabeth Stoltzfus

December 4, 2002

UC Berkeley: IEOR 215


Agenda

Agenda

Spatial Database Basics

Geographic Information Systems (GIS) Basics

Case Studies


Spatial database basics

Spatial Database Basics

Common applications


Spatial databases background

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

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

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 users1

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

  • 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


Sdbms three layer structure

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

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

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

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

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

Geographic Information System (GIS) Basics

Common applications


Gis 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

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

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

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

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

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

Case Studies

Specific applications of spatial databases


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

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

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

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

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

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

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

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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