215-SpatialDB

8/6/2019 215-SpatialDB

1/29

Elizabeth SayedElizabeth Stoltzfus

December 4, 2002

Project 2 Presentation

Spatial DatabasesGIS Case Studies

UC Berkeley: IEOR 215


2/29

2


Agenda

Spatial Database Basics

Geographic Information Systems (GIS) Basics

Case Studies


3/29

3


Spatial Database Basics

Common applications


4/29

4


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


5/29

5


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


6/296


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


7/297


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


8/29 8


Spatial Database Management System

Spatial Database Management System (SDBMS) provides the capabilities of a traditionaldatabase 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


9/29 9


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

Spatialapp

lication

DBMS

Interfacet o

DBMS

Interfacet o

spatialapplication

Core Spatial

Functionality

Taxonomy

Data types

Operations

Query language

Algorithms

Access methods


10/29 10


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


11/29 11


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)


12/29 12


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);


13/2913


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);


14/29


15/29

15


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


16/29

16


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


17/29

17


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


18/29

18


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 OIDs 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


19/29

19


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 *

!


20/29

20


Extending the ER Diagram with SpatialPictograms: State Park Example

ForestFacilityBelongs_to

River

Standard ER Diagram

Supplies_to

Fire Station

Monitors

LineID

PointID

PointIDWithin

Touches

FiName

FacName

RName

FoName

ForestFacilityBelongs_to

RiverSupplies_to

Fire StationMonitors

FiName

FacName

RName

FoName

Spatial ER Diagram

PolygonID


21/29

21


Case Studies

Specific applications of spatial databases


22/29

22


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

PP P A

A A

Location of Nests

Actual Pixel Locations

Case 1:

Possible Prediction

Case 2:

Possible PredictionSource: Whats Spatial About Spatial Data Mining pg 490


23/29


24/29

24


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


25/29

25


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 imagesegmentation

Classified vegetation

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

Source: Integrated Spatial Databases pg 116


26/29

26


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


27/29

27



28/29

28


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;


29/29

29


Problem 2 Solution

Room

HallwayCloset

Furniture

Length

Name

RoomID

FurnID

HallI

D

Type

ClosetID

Belongs_T

o

Belongs_To

Belongs_T

o

Accesses

Documents

215-SpatialDB