Elizabeth Sayed Elizabeth Stoltzfus December 4, 2002

1. Project 2 Presentation Spatial Databases GIS Case Studies Elizabeth Sayed Elizabeth Stoltzfus December 4, 2002 UC Berkeley: IEOR 215

2. Agenda Spatial Database Basics Geographic Information Systems (GIS) Basics Case Studies

3. Spatial Database Basics Common applications

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. 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. 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. 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. 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

9. 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

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. 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. 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. 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. Geographic Information System (GIS) Basics Common applications

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. 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

17. 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

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 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

19. Pictograms - Shapes • Types: Basic Shapes, Multi-Shapes, Derived Shapes, Alternate Shapes, Any possible Shape, User-Defined Shapes * N 0, N !

20. 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

21. Case Studies Specific applications of spatial databases

22. 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

23. 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”

24. 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

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 image segmentation • Classified vegetation • Produced a color composite by year that identified the density of vegetation Source: Integrated Spatial Databases pg 116

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

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. Problem 2 Solution ClosetID Length Type Closet Hallway RoomID Accesses HallID Belongs_To Room Belongs_To FurnID Belongs_To Furniture Name