# Chapter 2: Spatial Concepts and Data Models

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1 Chapter 2: Spatial Concepts and Data Models 2.1 Introduction 2.2 Models of Spatial Information 2.3 Three-Step Database Design 2.4 Extending ER with Spatial Concepts 2.5 Summary

2 Learning Objectives Learning Objectives (LO) LO1: Understand concept of data models LO2 : Understand the models of spatial information LO3: Understand the 3-step design of databases Conceptual - ER model Logical - Relational model Physical Translation from Conceptual to Logical LO4: Learn about the trends in spatial data models Mapping chapter sections to learning objectives LO2-2.1 LO3-2.2 LO4-2.3, 2.4 2

3 2.2 Three-Step Database Design Database applications are modeled using a three-step design process Conceptual data types, relationships and constraints (ER model) Logical-mapping to a Relational model and associated query language(relational Algebra) Physical-file structures, indexing 3

4 Example Application Domain Spatial application domain A state-park consists of forests. A forest is a collection of forest-stands of different species Forests are accessed by roads Each forest has a manager Forests have facilities Rivers runs through forests and supplies water to the facilities Forests are monitored by fire-stations 4

5 2.2.1 Conceptual DM: The ER Model 3 basic concepts Entities have an independent conceptual or physical existence. Examples: Forest, Road, Manager,... Entities are characterized by Attributes Example: Forest has attributes of name, elevation, etc. An Entity interacts with another Entity through relationships. Road allow access to Forest interiors. This relationship may be name Accesses 5

6 Relationship Types Relationships can be categorized by cardinality constraints other properties, e.g. number of participating entities Binary relationship: two entities participate Types of Cardinality constraints for binary relationships One-One: An instance of an entity relates to a unique instance of other entity. Many-One: Many instances of an entity relate to an instance of an other. Many-Many: Many instances of one entity relate to multiple instances of another. 6

7 ER Diagrams Graphical Notation ER Diagrams are graphic representation of ER models Several different graphic notation are used We use a simple notation summarized below Example ER Diagram for Forest exampl in next slide Q? Compare and contrast Atributes and Multi-valued attributes. Concept Symbol Entities Attributes Multi-valued Attributes Relationships Cardinality of Relationship 1:1, M:1, M:N 7

8 Example Create the ER diagram for the State- Park example Entities and characteristic attributes Relationships and constraints Both spatial and non spatial 8

9 ER Diagram for State-Park M Pointid 9

10 2.2.2 Logical Data Model: The Relational Model Relational model is based on set theory Main concepts Domain: a set of values for a simple attribute Relation: cross-product of a set of domains Represents a table, i.e. homogeneous collection of rows (tuples) The set of columns (i.e. attributes) are same for each row Comparison to concepts in conceptual data model Relations are similar to but not identical to entities Domains are similar to attributes Translation rules establishing exact correspondence 10

11 2.2.3 Mapping ER to Relational Highlights of translation rules (section 2.2.3) Entity becomes Relation Attributes become columns in the relation Relationships (1:1, 1:N) become foreign keys M:N Relationships become a relation containing foreign keys or relations from participating entities Multi-valued attributes become a new relation includes foreign key to link to relation for the entity 11

12 Example Create the relational model of the example ER Relations Keys Spatial types 12

13 Relational Schema Example Crosses RivName RoadName (varchar) (varchar) Similar for Accesses 13

14 Relational Schema for Point, Line, Polygon and Elevation Relational model restricts attribute domains simple atomic values, e.g. a number Disallows complex values (e.g. polygons) for columns Complex values need to be decomposed into simpler domains Elevation applies to points 14

15 Learning Objectives Learning Objectives (LO) LO1: Understand concept of data models LO2 : Understand the models of spatial information LO3: Understand the 3-step design of databases LO4: Learn about the trends in spatial data models Pictograms in conceptual models UML class diagrams Mapping Sections to learning objectives LO2-2.1 LO3-2.2 LO4-2.3,

16 2.3 Extending ER with Spatial Concepts Motivation ER Model is based on discrete sets with no implicit relationships Spatial data comes from a continuous set with implicit relationships Any pair of spatial entities has relationships like distance, direction, Explicitly drawing all spatial relationship clutters ER diagram generates additional tables in relational schema Misses implicit constraints in spatial relationships (e.g. partition) 16

17 Pictograms Label spatial entities along with their spatial data types Allows inference of spatial relationships and constraints Reduces clutter in ER diagram and relational schema 17

18 ER Diagram with Pictograms: An Example M For simplicity no multishape pictograms 18

19 2.5 Summary Spatial Information modeling can be classed into Field based and Object based Field based for modeling smoothly varying entities, like rainfall Object based for modeling discrete entities, like country 19

20 Summary A data model is a high level description of the data it can help in early analysis of storage cost, data quality There are two popular models of spatial information Field based and Object based Database are designed in 3-steps Conceptual, Logical and Physical Pictograms can simplify Conceptual data models 20

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