Spatial Data Model 2
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This lecture discusses different data models used in geographic information systems (GIS), including the relational database management system (RDBMS) model, network model, hierarchical data model, and object-oriented data model. It also covers topics like data quality, accuracy, precision, and sources of error in spatial data. The key aspects of different data models like relationships, flexibility, and advantages/disadvantages are explained through examples.
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Spatial Data Model 2
- 1. Lecture 4, Wednesday 27th August 2014 DEPARTMENT OF GEOGRAPHY AND ENVIRONMENT UNIVERSITY OF DHAKA
- 6. 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
- 8. Example of Geo-relational data model
- 9. 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 Disadvantage 1. Most RDBMS data manipulation languages require the user to know the contents of relations
- 10. 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) Example: 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
- 12. Advantage 1.Can handle many to many relations 2.Much greater flexibility of search 3.Reduce redundancy of data Disadvantage 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
- 14. 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
- 15. 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 Disadvantage 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
- 17. 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
- 18. 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
- 20. 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
- 21. 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.
- 22. Data accuracy is often grouped into three forms: 1. thematic accuracy 2. positional accuracy 3. temporal accuracy
- 23. 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 storage.
- 24. 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 decimals Long integer 16 9 No Single precision floating point 32 7 Yes Double precision floating point 64 13 Yes
- 25. 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 observation: 1. gross error: blunders and mistakes 2. systematic error 3. random error: (normal distribution and least square adjustment)
- 26. Certain degree of doubt Lack of confidence in the use of the data
- 27. 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
- 28. 2. Data automation and compilation - digitizing - attribute data inpute - format translation - map projection transformation - vectorization of raster data
- 29. 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
- 30. Components of spatial data quality Lineage of spatial data Positional accuracy Attribute accuracy Error matrix/ confusion matrix Kappa coefficient Temporal accuracy Semantic accuracy