GIS Data Models

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GIS Data Models
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Objective

• To understand basic GIS data types
• To understand how vector data models work in
  todays GIS

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Topics

• Brief history of GIS evolution
• Overview of GIS data models
• Explanation of databases

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RASTER AND VECTOR FORMATS
RASTER Grid-based, Simplify reality VECTOR
Analog map, Cartography
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DATA MODEL OF RASTER AND VECTOR
REAL WORLD
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GRID RASTER
VECTOR
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RASTER DATA MODEL

• Derive from formulation that real world - it has
  spatial elements and objects fills those elements
• Real world is represented with uniform cells
• List of cells is a rectangle
• Cell comprises of triangles, hexagon and higher
  complexities
• A cell reports its own true characteristics
• Per units cell does not represent an object
• An object is represented by a group of cells

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Lake
River
Pond
Reality - Hydrography
Lake
River
Pond
Reality overlaid with a grid
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Resulting raster
Creating a Raster
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Resolution

• Resolution is the size of the cell used in
  relation to scale
• Most are fundamentally aware of this concept
  through digital photography Megapixels
• In GIS, resolution is typically described as size
• 1m resolution aerial photography each pixel in
  the image represents 1 square meter of ground
  area
• As resolution decreases in units of size, the
  finer the detail of the image
• A fire hydrant cannot be seen in a 1m
  resolution image but likely will be discernable
  in a 0.1m image
• Resolution and scale are linked concepts

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VECTOR DATA MODEL

• Derived from the formulation of spatial concepts
  that emphasize on real world objects
• Geometry primitives of vector data model are
  point, line and polygon
• objects can be built from these primitives
• Object location determined by represented
  location point
• Uniqueness of vector data model lies in its
  management and storage of data geometry
  primitives
• spaghetti model
• topology model

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VECTOR CHARACTERISTICS
POINT X LINE POLYGON
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RASTER TO VECTOR
RIVER CHANGED FROM RASTER TO VECTOR FORMAT
RIVER THAT HAS BEEN
VECTORISED ORIGINAL RIVER
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PROS AND CONS OF RASTER MODEL

• Pro
• Raster data is more affordable
• Simple data structure
• Very efficient overlay operation
• Cons
• Topology relationship difficult to implement
• Raster data requires large storage
• Not all world phenomena related directly with
  raster representation
• Raster data mainly is obtained from satellite
  images and scanning

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PROS AND CONS OF VECTOR MODEL

• Pro
• More efficient data storage
• Topological encoding more efferent
• Suitable for most usage and compatible with data
• Good graphic presentation
• Cons
• Overlay operation not efficient
• Complex data structure

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Explanation of database types

• a database is a collection of non-redundant data
  which can be shared by different application
  systems
• implies separation of physical storage from use
  of the data by an application program, i.e.
  program/data independence
• changes can be made to data without affecting
  other components of the system.

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Database types

• Tabular ("flat file") - data in a single table
• Hierarchical
• Network
• Relational

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The ideal GIS database is one that maximizes the
uniqueness of every feature while minimizing
total data quantity
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Hierarchical databases

• Developed in the 1960s by International Business
  Machines (IBM)
• Somewhat resembles real-world filing systems
• Tree-structured, similar to folder arrangements
  in a computer directory
• The database keeps track of the different record
  types, their attributes, and the hierarchical
  relationships between them
• The attribute which assigns records to levels in
  the database structure is called the key (e.g. is
  record a department, part or supplier?)

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Features of a hierarchical model

• A set of record "types"
• E.g. supplier record type, department record
  type, part record type
• A set of links connecting all record types in one
  data structure diagram (tree)
• At most one link between two record types, hence
  links need not be named
• For every record, there is only one parent record
  at the next level up in the tree

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Features (contd)

• E.g. every county has exactly one state, every
  part has exactly one department
• No connections between occurrences of the same
  record type
• Cannot go between records at the same level
  unless they share the same parent

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http//dev.mysql.com/tech-resources/articles/hiera
rchical-data-1.png
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Pros and cons

• Data must possess a tree structure
• Tree structure is natural for geographical data
• Data access is easy via the key attribute, but
  difficult for other attributes
• In the business case, easy to find record given
  its type (department, part or supplier)
• In the geographical case, easy to find record
  given its geographical level (state, county,
  city, census tract), but difficult to find it
  given any other attribute

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Pros and cons (contd)

• E.g. find the records with population 5,000 or
  less
• Tree structure is inflexible
• Cannot define new linkages between records once
  the tree is established
• E.g. in the geographical case, new relationships
  between objects
• Cannot define linkages laterally or diagonally in
  the tree, only vertically

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Pros and cons (contd)

• The only geographical relationships which can be
  coded easily are "is contained in" or "belongs
  to"
• DBMSs based on the hierarchical model (e.g.
  System 2000) have often been used to store
  spatial data, but have not been very successful
  as bases for GIS

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Network data model

• Developed in mid 1960s by Charles Bachman as part
  of work of CODASYL (Conference on Data Systems
  Languages) which proposed programming language
  COBOL (1966) and then network model (1971)
• Other aspects of database systems also proposed
  at this time include database administrator, data
  security, audit trail
• Objective of network model is to separate data
  structure from physical storage, eliminate
  unnecessary duplication of data with associated
  errors and costs

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Networked model (contd)

• Uses concept of a data definition language, data
  manipulation language
• Uses concept of mn linkages or relationships
• An owner record can have many member records
• A member record can have several owners
• Hierarchical model allows only 1n

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Networked model (contd)

• Example of a network database
• A hospital database has three record types
• Patient name, date of admission, etc.
• Doctor name, etc.
• Ward number of beds, name of staff nurse, etc.
• Need to link patients to doctor, also to ward
• Doctor record can own many patient records
• Patient record can be owned by both doctor and
  ward records
• Network DBMSs include methods for building and
  redefining linkages, e.g. when patient is
  assigned to ward

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Problems with the networked model

• Links between records of the same type are not
  allowed
• While a record can be owned by several records of
  different types, it cannot be owned by more than
  one record of the same type (patient can have
  only one doctor, only one ward)

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http//en.wikipedia.org/wiki/FileNetwork_Model.jp
g
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Relational database model

• The most popular DBMS model for GIS
• Flexible approach to linkages between records
  comes closest to modeling the complexity of
  spatial relationships between objects
• Proposed by IBM researcher E.F. Codd in 1970
• More of a concept than a data structure
• Internal architecture varies substantially from
  one RDBMS to another

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Relational databases (contd)

• Each record has a set of attributes
• The range of possible values (domain) is defined
  for each attribute
• Records of each type form a table or relation
• Each row is a record or tuple
• Each column is an attribute
• Note the potential confusion - a "relation" is a
  table of records, not a linkage between records
• The degree of a relation is the number of
  attributes in the table

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Relational databases (contd)

• 1 attribute is a unary relation
• 2 attributes is a binary relation
• n attributes is an n-ary relation
• Examples
• unary COURSES(SUBJECT)
• binary PERSONS(NAME,ADDRESS) OWNER(PERSON
  NAME,HOUSE ADDRESS)
• ternary HOUSES(ADDRESS,PRICE,SIZE)

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How a relational database works

• A key of a relation is a subset of attributes
  with the following properties
• Unique identification
• The value of the key is unique for each tuple
• Non-redundancy
• No attribute in the key can be discarded without
  destroying the key's uniqueness
• A prime attribute of a relation is an attribute
  which participates in at least one key
• All other attributes are non-prime

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Relational database key example

• For example, a phone number is a unique key in a
  phone directory
• In the normal phone directory the key attributes
  are last name, first name, street address
• If street address is dropped from this key, the
  key is no longer unique (many Smith, Mary's)

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Pros and cons

• The most flexible of the database models
• No obvious match of implementation to model -
  model is the user's view, not the way the data is
  organized internally
• Is the basis of an area of formal mathematical
  theory

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Pros and cons (contd)

• Most RDBMS data manipulation languages require
  the user to know the contents of relations, but
  allow access from one relation to another through
  common attributes Example Given two relations
• PROPERTY(ADDRESS,VALUE,COUNTY_ID)
• COUNTY(COUNTY ID,NAME,TAX_RATE)
• To answer the query What are the taxes on
  property X" the user would

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Pros and cons (contd)

• PROPERTY(ADDRESS,VALUE,COUNTY_ID)
• COUNTY(COUNTY ID,NAME,TAX_RATE)
• Retrieve the property record
• Link the property and county records through the
  common attribute COUNTY_ID
• Compute the taxes by multiplying VALUE from the
  property tuple with TAX_RATE from the linked
  county tuple

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http//www.mbari.org/ssds/ReferenceDocuments/RDB_E
R.gif
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Evolution of GIS data models

• CAD
• Coverage
• Geodatabase

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CAD model

• Vector based mapping
• Maps created with computer aided design programs
  (CAD)
• Little or no attribute data

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Coverage data model

• Created in 1981 by ESRI as part of ArcInfo, the
  first commercially available GIS package
• Spatial data stored with attribute data using
  indexed binary files
• Allowed for storage of topological relationships

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Limitations of coverage model

• All features have a generic behavior
• For example, a highway running across a polygon
  split that polygon made defining behaviors
  extremely difficult!
• Topology (spatial relationship among objects) was
  explicitly defined
• Required use of macro code (ArcAML) to resolve
  complex features

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http//webhelp.esri.com/arcgisdesktop/9.2/publishe
d_images/Coverage20data20elements.GIF
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The geodatabase

• Created in 2000 by ESRI
• Allows for specific behaviors to be assigned to
  specific features without writing code
• Based upon a relational database
• Said to be object-oriented data model
• Topology built more easily

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The ArcGIS Environment

• ArcGIS is packaged similar to Microsoft Office.
  Whereas Office encompasses Excel, Word, and
  PowerPoint, ArcGIS comes with
• ArcMap
• ArcCatalog
• ArcToolbox

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Component Overview ArcCatalog

• ArcCatalog acts as the operating system for GIS
  its look and feel is similar to Windows Explorer
• ArcCatalog allows users to preview data in both a
  geographic (map) format and table format
  (attributes).
• ArcCatalog is the principal management tool for
  reading and writing metadata

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Windows Explorer vs. ArcCatalog
Windows Explorer
ArcCatalog
Table of Contents
Preview Pane
Table of Contents
Preview Pane
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A Quick Tour of ArcCatalog
Quick Launch Buttons
Navigation Buttons
Preview Selection Tabs
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ArcCatalog uses a unique symbol set to indicate
data formats
Raster
Geodatabase
Feature Dataset
Feature Classes
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Symbology is maintained among different data
formats
Shapefile formats appear green
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Preview tab allows user to view either geography
or table
Toggle views here
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Metadata Data about the data
Metadata toolbar accessed from VIEW toolbars Allo
ws users to edit all metadata and
select metadata format convention
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References

• National Center for Geographic Information
  Analysis (NCGIA), Core Curriculum at
• http//www.geog.ubc.ca/courses/klink/gis.notes/ncg
  ia/toc.html
• Goodchild, M.F., and K.K. Kemp,eds.  1990.  NCGIA
  Core Curriculum in GIS. National Center for
  Geographic Information and Analysis, University
  of California, Santa Barbara CA