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UC Berkeley: IEOR 215
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<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](https://image.slidesharecdn.com/215-spatialdb-250520224249-15c23ae3/75/215-SpatialDB-ppt-case-about-space-for-study-17-2048.jpg)
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215-SpatialDB.ppt case about space for study
- 1. Elizabeth Sayed Elizabeth Stoltzfus December4, 2002 Project 2 Presentation Spatial Databases GIS Case Studies UC Berkeley: IEOR 215
- 2. 2 UC Berkeley: IEOR215 Agenda Spatial Database Basics Geographic Information Systems (GIS) Basics Case Studies
- 3. 3 UC Berkeley: IEOR215 Spatial Database Basics Common applications
- 4. 4 UC Berkeley: IEOR215 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. 5 UC Berkeley: IEOR215 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. 6 UC Berkeley: IEOR215 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. 7 UC Berkeley: IEOR215 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. 8 UC Berkeley: IEOR215 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. 9 UC Berkeley: IEOR215 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 Spatial application DBMS Interface to DBMS Interface to spatial application Core Spatial Functionality Taxonomy Data types Operations Query language Algorithms Access methods
- 10. 10 UC Berkeley: IEOR215 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. 11 UC Berkeley: IEOR215 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. 12 UC Berkeley: IEOR215 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. 13 UC Berkeley: IEOR215 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. 14 UC Berkeley: IEOR215 Geographic Information System (GIS) Basics Common applications
- 15. 15 UC Berkeley: IEOR215 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. 16 UC Berkeley: IEOR215 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. 17 UC Berkeley: IEOR215 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<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. 18 UC Berkeley: IEOR215 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. 19 UC Berkeley: IEOR215 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. 20 UC Berkeley: IEOR215 Extending the ER Diagram with Spatial Pictograms: State Park Example Forest Facility Belongs_to River Standard ER Diagram Supplies_to Fire Station Monitors LineID PointID PointID Within Touches FiName FacName RName FoName Forest Facility Belongs_to River Supplies_to Fire Station Monitors FiName FacName RName FoName Spatial ER Diagram PolygonID
- 21. 21 UC Berkeley: IEOR215 Case Studies Specific applications of spatial databases
- 22. 22 UC Berkeley: IEOR215 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 P P P A A A 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. 23 UC Berkeley: IEOR215 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. 24 UC Berkeley: IEOR215 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. 25 UC Berkeley: IEOR215 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. 26 UC Berkeley: IEOR215 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.
- 28. 28 UC Berkeley: IEOR215 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 UC Berkeley: IEOR215 Problem 2 Solution Room Hallway Closet Furniture Length Name RoomID FurnID HallI D Type ClosetID Belongs_T o Belongs_To Belongs_T o Accesses
Editor's Notes
- #15 GIS systems are used to collect, model, store and analyze information describing physical properties describing the geographic world. It’s possible to divide GIS into these three main categorize. In geographic object applications, objects of interest are identified from a physical domain. For example power plants, electroral districts, product distribution districts and city landmarks. In cartographic and terrain modeling applications, variations in spatial attributes ar captured. For example, soil charactertis, crop density and air quality. Both cartographic and terrain based applications require a field-based representation whereas geographic applications require object based.
- #16 GIS data can be represented in two formats: vector and raster. Vector data represents geometric objects such as points, lines and polygons. For example if you were modeling a lake, you could use represent it as a polygon or a river by a series of line segments. Raster data is characterized as an array of points, where each point represents the value of an attribute for a real-world location. Raster images are n-dimensional arrays where each entry is a unit of the image and represents attributes. Two dimensional units are called pizels, while three dimensional units are called voxels. Raster data is typically used with maps of land cover classes such as temperature, rainfall, pasture, urban areas, and standing water. GIS data undergoes various types of analysis. For example an application such as soil erosion studies, environmental impact studies or hydrological runoff simulations, data undergoes various types of geomorphic analysis – which is the measurement of slope values, gradients, aspect (a complex description of the gradient), convexity (the change of the gradient) When used for decision support applications it may undergo aggregation and expansion operations using data warehousing as well as querying. GIS data must integrate both vector and raster data from a variety of sources. Sometimes edges and regions are inferred from a raster image to form a vector model or conversely, raster images are used to update vector models.
- #17 GIS data models are usually grouped into broad categories: object and field. So imagine a forest consisting of clusters of pine, fir and oak trees. What would be the best way to model the forest and capture the aggregate information? Consider a function that maps the underlying geographic space of the forest onto a set consisting of three values (fir, oak and pine). This function would be a field whose varying spatial distribution captures the diversity of the forest. The function itself would be constant over the areas where the tree types were alike and sharply jump into different values when the tree species change. For an object on the other hand, this function will be composed of a series of polygon that correspond to the different areas with trees.
- #18 As we know from this class, ER diagrams are typically used to initial model databases. However for spatial databases ER diagrams are unable at least intuitively to capture some important semantics inherent in spatial modeling. As a result a field model cannot be naturally mapped using the ER model. For example ther is no notion of key attributes and unique OIDS’s in a field model. Although in traditional ER modeling, the relationship between entitites are derived from the application under consideration, in spatial modeling there are always inherent relationships between spatial objects. So if ER are the best solution for best conceptual spatial data modeling, what is? The answer is using pictograms.
- #19 Pictograms are a seriees of shapes that can be used to capture concepts related to spatial geometry. Basic Shapes: In a vector model the basic elements are point, line and polygon. Multi-Shapes: To deal with objects which cannot be represented by the basic shapes, this set of aggregate shapes were defined. Derived Shapes: If the shape of an object is derived form the shapes of other objects it’s pictogram is italicized. User Defined Shapes: Apart from the basic shapes of point, line and polygon, user-defined shapes are possible. Any possible Shape: A combination of shapes is represented by a wild card * symbol inside a box, implying that any geometry is possible. Alternative Shapes: Alternative shapes can be used for the same object depending on certain conditions, for example objects of size less than x units are represented as points while those greater than x units are represented as polygons. They are represented as the concatenation of possible pictograms. Similarly, multiple shapes are needed to represent objects at different scales: for example at higher scales lakes may be presented as points, and at lower scales as polygons.
- #22 There are two wetlands – Darr and Stubble on the shores of Lake Erie. Using data collected from April lto June in 1995 and 1996 we want to predict the spatial distribution of a marsh-breed bird, the red-winged blackbird. A uniform grid was imposed on the 2 wetlands with different types of measurements recorded at pixel. In total, values of 7 attributes were recorded at each pixel. Understanding how the birds interacted with their environment was key to creating the parameters of this studyinig. Forr example vegetation durability was chosen over vegetation species because specialized knowledge about the bird-nest habits suggested that the choice of next location is more dependent on plant structure, plant resistance to wind and wave action than on the plant species. So three attributes selected to use in this study: vegetation durability, distance to opn water and water depth. In this study determining the spatial accuracy – how far the prediction are from the actual nests was critical because of dramatic change in the m eaning of the information if was A or B in this slide.
- #23 Landsat MSSR – remote sensing satelittles MSSR – Multispectrum Scanner Raster SPOT – System Pour le’Observation de la Terre