GIS-05_Databases.ppt 1456KB Mar 29 2010 04:55:20 AM

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GIS DATABASES

an overview

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Contents

– the basics of data storage – overview of databases

• the database approach • types of databases

• databases in GIS

– design considerations

– development of an ARC/INFO database

– the basics of data storage – overview of databases

• the database approach • types of databases

• databases in GIS

– design considerations

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Conceptual, logical and physical ...

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A storage hierarchy ...

– files/tables

• records

• fields(types …)

– databases

– information systems

– decision support systems (DSS)

– approaches to storage

• application/file based • databases

– files/tables

• records

• fields(types …)

– databases

– information systems

– decision support systems (DSS)

– approaches to storage

• application/file based • databases

increasin g

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Application based approach

Permits

Permits

Tax/Rates Assessment

Tax/Rates

Assessment Assessment Data

Permit Data

Sewer Data

Sewer

Maintenance

Sewer

Maintenance

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Database approach

Permits Permits Tax/Rates Assessment Tax/Rates

Assessment Assessment DataAssessment Data

Permit Data Permit Data Sewer Data Sewer Data Sewer Maintenance Sewer Maintenance D a ta b a s e M a n a g e m e n t S y s te m

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Database … a definition

• A collection of interrelated

data stored

together with controlled redundancy to

serve one or more applications in an

optimal fashion.

• A common and controlled approach is used

in adding new data and modifying and

retrieving existing data within the data base

• A collection of interrelated data stored

together with controlled redundancy to

serve one or more applications in an

optimal fashion.

• A common and controlled approach is used

in adding new data and modifying and

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Databases… objectives/advantages

– centralised data storage and management … global view of data … data dictionary

• standardisation of all aspects of data management • reduced duplication

• multiple access / retrieval flexibility

• integrity constraints … validation enforced • ...

– data base management system (DBMS)

– centralised data storage and management … global view of data … data dictionary

• standardisation of all aspects of data management • reduced duplication

• multiple access / retrieval flexibility

• integrity constraints … validation enforced • ...

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Database/s… data dictionary

– the most critical (?) element of a database – data about data… metadata

– essential for system development – uses include

• design - entities and data relationships • data capture - entry/validation

• operations - program documentation

• maintenance (impact assessment of proposed changes , est. of effort, cost …)

– the most critical (?) element of a database – data about data… metadata

– essential for system development – uses include

• design - entities and data relationships • data capture - entry/validation

• operations - program documentation

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Data dictionary…

types of information (general)

Data dictionary…

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GIS Metadata

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DBMS … key modules

– a data description/definition module

• defines/creates/restructures • enforces rules

– a query module

• retrieval for queries, ad-hoc queries, simple reports

– a report writing program

– a high level language interface – ...

– a data description/definition module • defines/creates/restructures

• enforces rules – a query module

• retrieval for queries, ad-hoc queries, simple reports – a report writing program

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Database… stages of development

– information systems plan for organisation – system specification … user needs analysis – conceptual design … data modelling

• hardware and software independent

– physical design … database design – database implementation

– monitoring/audit

– information systems plan for organisation – system specification … user needs analysis – conceptual design … data modelling

• hardware and software independent – physical design … database design – database implementation

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Database… stages of development

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Organisational strategy and IT

Land Information System (LIS) (i)

– Problems/issues:

• rationalisation of land related information in government agencies

• the removal/reduction of duplication

• introduction of economies in data capture, maintenance and storage

• better (and wider) access to data

– Problems/issues:

• rationalisation of land related information in government agencies

• the removal/reduction of duplication

• introduction of economies in data capture, maintenance and storage

• better (and wider) access to data

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Organisational strategy and IT

Land Information System (LIS) (ii)

– Solutions:

• better data distribution mechanism (data format and location transparent to user)

• knowledge of data distribution built into the data dictionary

• reduction of data duplication • uniform query language (SQL)

• coding and data interchange standardisation ( … SDTS)

– Solutions:

• better data distribution mechanism (data format and location transparent to user)

• knowledge of data distribution built into the data dictionary

• reduction of data duplication • uniform query language (SQL)

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Database types -

a history

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Database types - hierarchical (i)

– lends itself to GIS use as data are often

hierarchical in structure e.g. municipality x province x country

– records divided into logically related fields … connected in a tree-like arrangement

– master field in each group of records … pointers … updates require pointers to be modified

– fast preset queries … ad hoc queries difficult or impossible

– lends itself to GIS use as data are often

hierarchical in structure e.g. municipality x province x country

– records divided into logically related fields … connected in a tree-like arrangement

– master field in each group of records … pointers … updates require pointers to be modified

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Database types

- hierarchical (ii)

Database types

- hierarchical (ii)

COUNTRY (USA)

States

Counties

Boundaries

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Hierarchical Structure for a

Cadastral database

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Hierarchical Structure for a

Cadastral database

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Database types - network (i)

– similar to hierarchical but have multiple connections between files to accommodate many to many (M:M) relationships

– access to a particular file without searching the entire hierarchy above that file

– linked records … quick preset searches … large overhead in pointer management

– modification after creation difficult

– similar to hierarchical but have multiple connections between files to accommodate many to many (M:M) relationships

– access to a particular file without searching the entire hierarchy above that file

– linked records … quick preset searches … large overhead in pointer management

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Database types - relational (i)

– model developed from mathematics

– records and fields in a 2-dimensional table

– no pointers etc … any field can be used to link one table to another

– normalisation … redundancy/stable structure – ad hoc queries SQL… modifications easy – not very efficient for GIS …SQL3

– model developed from mathematics

– records and fields in a 2-dimensional table

– no pointers etc … any field can be used to link one table to another

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Database types - relational (i)

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Hierarchical structure

Network structure

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Centralised vs distributed

– a database does not necessarily mean a

centralised arrangement i.e. all data in one physical place

– a database does not necessarily mean a

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GIS and distributed

databases ...

– trend towards open systems ...

• special hardware and software can be used widely … specific applications optimised

• system/network communications is easier

– modular implementation from an overall design … incremental change

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Approaches to GIS system design

– develop a proprietary system

– develop a hybrid system: proprietary graphics + commercial DBMS for attribute data (e.g.

ARC/INFO)

– use commercial DBMS and develop spatial functions and graphics display used in

geographic analysis (e.g. siroDBMS, System9) – develop a spatial DBMS from scratch

– develop a proprietary system

– develop a hybrid system: proprietary graphics + commercial DBMS for attribute data (e.g.

ARC/INFO)

– use commercial DBMS and develop spatial functions and graphics display used in

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Software linkages (1) Separate Spatial and attribute data

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GIS databases … some problems (i)

– centralised risk

• centralisation demands better quality control other higher potential for disaster

– cost

• large DBMSs are expensive to design, implement and operate • piecemeal design is difficult

– complexity

• need to keep track of complex hardware and software

• need to keep track of graphical as well as attribute data and the links

– centralised risk

• centralisation demands better quality control other higher potential for disaster

– cost

• large DBMSs are expensive to design, implement and operate • piecemeal design is difficult

– complexity

• need to keep track of complex hardware and software

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GIS databases … some problems (ii)

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Objectives of design

– a good design results in a database which:

• contains necessary data but no redundant data

• organises data so that different users access the same data

• accommodates different views of the data

• distinguishes applications which maintain data from those that use it

• appropriately represents, codes and organises geographic features

– a good design results in a database which: • contains necessary data but no redundant data

• organises data so that different users access the same data

• accommodates different views of the data

• distinguishes applications which maintain data from those that use it

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Design methodology (for ARC/INFO)

– conceptual model

• model the users’ view

• define entities and their relationships

– logical model

• identify representation of entities • match to ARC/INFO data model • organise into geographic data sets

– physical model

– conceptual model

• model the users’ view

• define entities and their relationships – logical model

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Design methodology (for ARC/INFO)

– 1. Model the users’ view

– 2. Define entities and their relationships – 3. Identify representation of entities

– 4. Match to ARC/INFO data model – 5. Organise into geographic data sets –

– 1. Model the users’ view

– 2. Define entities and their relationships – 3. Identify representation of entities

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1. Model the users’ view

– create a model of work performed by users for which ‘location’ is a factor

• identify organisational functions

• identify the data which supports the functions

– organise data into sets of geographic features

• data function matrix

– high level classification of data

– interdependence of data and function

– difference between users and creators of data

– create a model of work performed by users for which ‘location’ is a factor

• identify organisational functions

• identify the data which supports the functions

– organise data into sets of geographic features • data function matrix

– high level classification of data

– interdependence of data and function

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Data function matrix …an example

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2. Define entities and their relationships

– entities: distinguishable objects which have a common set of properties

• identify and describe entities

• identify and describe the relationship among these entities

• document the process

– diagrams

– data dictionary

• Normalise the data

– entities: distinguishable objects which have a common set of properties

• identify and describe entities

• identify and describe the relationship among these entities

• document the process

– diagrams

– data dictionary

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Entity/relationship definition

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Diagramming … entities

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Normalisation

– First Normal Form (1NF) – Second Normal Form (2NF) – Third Normal Form (3NF)

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Underlying entities...

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3. Identify representation of entities

– determine the most effective spatial representation for geographic features – consider whether:

• a feature might be represented on a map

• the shape of a feature might be significant in performing geographic analysis

• the feature will have different representations and different map scales

• textual attributes of the feature will be displayed on map products

• ...

– determine the most effective spatial representation for geographic features – consider whether:

• a feature might be represented on a map

• the shape of a feature might be significant in performing geographic analysis

• the feature will have different representations and different map scales

• textual attributes of the feature will be displayed on map products

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4. Match to ARC/INFO data model

– determine the appropriate ARC/INFO representation for entities

• points, lines, polygons

– ensure complex feature classes are supported

• route comprised of sections which in turn are based on arcs

• a region is composed of polygons

• event is a point or a line which occurs along a route

– others (e.g. GRID, TIN)

– determine the appropriate ARC/INFO representation for entities

• points, lines, polygons

– ensure complex feature classes are supported • route comprised of sections which in turn are based

on arcs

• a region is composed of polygons

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Matching to ARC/INFO data model

Entity Spatial type

ARC/ INFO

Related to

Coverage Attribu te files

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5. Organise into geographic data sets

– to identify and name the geographic data sets that will contain the various entities:

• define the contents of geographic data sets (coverages, grids etc)

• name workspaces, geographic data sets, entities and attributes

• complete entity definitions

• add cartographic text and lookup tables

– to identify and name the geographic data sets that will contain the various entities:

• define the contents of geographic data sets (coverages, grids etc)

• name workspaces, geographic data sets, entities and attributes

• complete entity definitions

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5(i) Define the content of geographic data sets

– Data sets supported : coverage, grid, tin, image and drawing

– coverages several entities can be grouped into a single coverage

– DBMS : stored in a separate database management system

– Data sets supported : coverage, grid, tin, image and drawing

– coverages several entities can be grouped into a single coverage

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5 (ii) Geographic datasets, entities and attributes

– coverage definitions

• high level summary of the data physically stored in the database

• required for defining the coverage structure

– file naming conventions in ARC/INFO

– coverage definitions

• high level summary of the data physically stored in the database

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5 (iii) Complete entity definitions

– background information: coverage name, data source, agency, number of records etc.

– attribute definition

• attribute name, type, field width • validation rules/ permitted values

– background information: coverage name, data source, agency, number of records etc.

– attribute definition

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5 (iv) Cartographic text & code tables

– annotation (text, placing rules etc) – look up tables

• pre defined set of values • description/ labels

• means of creating displays based on attribute values

– annotation (text, placing rules etc) – look up tables

• pre defined set of values • description/ labels

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Robinson (Ch 14): Scale and GIS databases

– (past) map’s scale greatly influenced map content and data resolution

– GIS data are ‘scaleless’ … scale is still a critical factor with digital databases - because of the ways in which we create digital databases

– scale and resolution (Tab 14.1)

– (past) map’s scale greatly influenced map content and data resolution

– GIS data are ‘scaleless’ … scale is still a critical factor with digital databases - because of the ways in which we create digital databases

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Robinson (Ch 14): Scale and resolution issues

– symbolisation and display problems – handling databases of different scales

• join problems (e.g. urban rural) • merge problems (different themes) • scale levels

– in general

– large scale data (AM/FM etc.)

– symbolisation and display problems – handling databases of different scales

• join problems (e.g. urban rural) • merge problems (different themes) • scale levels

– in general

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Robinson (Ch 15): Managing large GIS

– Data organisation

• partitioning • spatial indexes • metadata

– data compression

• run length encoding (RLE) • quadtree encoding

• others ...

– Data organisation • partitioning

• spatial indexes • metadata

– data compression

• run length encoding (RLE) • quadtree encoding