Spatial Data Model 2

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Transcript of Spatial Data Model 2

  • Lecture 4, Wednesday 27th August 2014



  • Most popular DBMS model for GIS

    Based on a set of mathematical principals called relational algebra

    More of a concept than a data structure

    Internal architecture varies substantially from one RDBMS to another

    Link the complex spatial relationships between objects

    Type of relation:

    1. One to one

    2. One to many

    3. Many to many

    4. Many to one

  • Example of Geo-relational data model

  • Advantage

    1. There is no data redundancy

    - type of building of an owner can be changed without destroying the relation between type and rate

    - a new type of building can be inserted such as clay

    2. The most flexible data model


    1. Most RDBMS data manipulation languages require the user to know the contents of relations

  • In this concept, each individual piece of data can be linked directly anywhere in the database

    This is developed in mid 1960s as part of work of CODASYL which proposed programming language COBOL (1966) and then network model (1971)


    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.

    We 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

  • Advantage

    1. Can handle many to many relations

    2. Much greater flexibility of search

    3. Reduce redundancy of data


    1. Links between records of the same type are not allowed

    2. While a record can be owned by several record of different

    types, it cannot be owned by more than one record of the

    same type (patient can have only one doctor, only one word)

    3. Need more storage in the computer

  • 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

    - 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

    In geographic database, quadtree can be an example of hierarchical data model

  • Advantage

    1. High speed of access to large datasets and eases of updating

    2. The model is based on one to one and many to one relationships


    1. Linkages are only possible vertically but not horizontally or diagonally, that means there is no relation between different trees at the same level unless they share the same parent

    1. Restricted to branch to network itself such as many to many relationship

  • Uses functions to model spatial and non-spatial relationships of geographic objects and the attributes

    An object is an encapsulated unit which is characterized by attributes, a set of orientations and rules

    Includes four basic elements:

    1. Object oriented user interfac

    2. Object oriented programming languages

    3. Object oriented analysis and design methodologies

    4. Object oriented database management

  • Generic properties: there should be an inheritance relationship

    Abstraction: objects, classes and super classes are to be generated by classification, generalization, association and aggregation

    Adhoc queries: user can order spatial operations to obtain spatial relationships of geographic objects using a special language

  • Refers to the fitness for use of data for intended application

    Qualitative criteria for high quality data:

    1. Data must be accurate and reliable

    2. Current and up to date

    3. Complete and precise

    4. Concise and intelligible

    5. Conventionally handled (maintained, transmitted, distributed, classified, resampled, retrieved and updated)

    Other factors:

    a. Must be projected to the real world

    b. Must be captured at a scale using a classification scheme

    c. Cartographic properties

    d. Transfer format

  • Accuracy

    Degree to which data agree with the values or descriptions of the real-world features that they represent.

    Measure of how close data match the true values or descriptions.

    Accuracy is related to cost of data acquisition.

  • Data accuracy is often grouped into three


    1. thematic accuracy

    2. positional accuracy

    3. temporal accuracy

  • How exact data are measured and stored

    In mathematics, the exactness of representation is the number

    of significant digits used to record data. But for digital

    geographic data, this is the number of bits and the form

    (long integer; floating point etc.) used for data capture and


  • Comparison of the precision of storing data by the three storage formats in PC

    Format Bits of

    storage Significant digits of precision

    True floating point


    Long integer 16 9 No

    Single precision floating point

    32 7 Yes

    Double precision floating point

    64 13 Yes

  • The deviation between two values-

    1. measured value

    2. value of the real world feature

    Three types of error that may occure in measurement and


    1. gross error: blunders and mistakes

    2. systematic error

    3. random error: (normal distribution and least square


  • Certain degree of doubt

    Lack of confidence in the use of the data

  • Can be divided into three basic groups:

    1. Original source maps

    - Map projection

    - Map scale

    - Cartographic generalisations

    - Cartographic revision

    - Feature classification/ coding

    - Field survey measurements

    - Photogrammetric measurements

    - Image analysis

    - Sampling design

    - Aging maps

  • 2. Data automation and compilation

    - digitizing

    - attribute data inpute

    - format translation

    - map projection transformation

    - vectorization of raster data

  • 3. data processing and analysis

    - numerical rounding in computing

    - Overlay analysis

    - Classification and re-classification

    - Generalization and aggregation

    - Interpolation

    - Inappropriate use of algorithm

    On top of the above, Vitek et al. (1984) grouped them into two categories:

    1. Inherent errors

    2. operational errors

  • Components of spatial data quality

    Lineage of spatial data

    Positional accuracy

    Attribute accuracy

    Error matrix/ confusion matrix

    Kappa coefficient

    Temporal accuracy

    Semantic accuracy