ifp ifp 3.7 Thematic Modeling The Object-Oriented Approach Universität Stuttgart Universität Stuttgart hydrography transport

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1 transport hydrography fed_hwy state_hwy cnty_hwy river stream lake primary secondary hwy_line hwy_pt hwy_area hdy_line hdy_pt hdy_area Beispiel: GIS-Produkt INTERGRAPH 3.7 Thematic Modeling The Object-Oriented Approach a) layer model geometric data layer 1 layer 2 layer n b) object class model classes object id attributes attribute values or hyper classes classes object id hyper class attributes hyper class attribute values attributes attribute values

2 3.8 Data Structures Geometric, topologic and thematic data are represented and stored in the GIS with data structures Typical data structures: unstructured vector data: simple list topologic structured vector data: node and edge lists topology: incidence and adjacency matrices raster data: matrix, run length coding, quad trees thematic data: tables

3 3.8 Data Structures - Unstructured Vector Data simple line lists (spaghetti): collection of unstructured line strings advantage: very efficient for plotting problem: points of adjacent polygons have to be digitized twice, which leads to gaps (slivers) no neighborhood information - topology is only implicit pt P2 3 P1... object (polygon) position (x,y),(x,y),... line 10 data structure: Feature # Position Point 1 x,y Line 10 (x1,y1),(x2,y2),..., (xn,yn) Polygon P1 (x1,y1),(x2,y2),..., (x1,y1) Polygon P2 (x1,y1),(x2,y2),..., (x1,y1) 3.8 Data Structures - Topologically Structured Data storage without topology: P1 L1 P2 L2 P3 faces line from to L1 X1, Y1 X2, Y2 L2 X3, Y3 X4, Y4 L3 X5, Y5 X6, Y6 L4 X7, Y7 X8, Y L3 P4 L8 F1 L6 F3 L4 P5 L9 F2 L7 F4 L5 P6 L10 1:n edges n : m nodes 1:1 storage with topology faces edges F1 L1, L3, L4, L6 F2 L2, L4, L5, L7 F3 L6, L8, L9, L11 F4 L7, L9, L10, L12 P7 edges L1 L2 L3... L11 nodes P1, P2 P2, P3 P1, P4 P8 L12 P9 nodes coordinates P1 X1, Y1 P2 X2, Y2 P3 X3, Y coordinates

4 3.8 Data Structures - Raster Data (Grids) matrix: n rows, m columns: storage needed: n x m x dimension (gray values) dimension (gray values) is usually 8 bit runlength-encoding: suitable for images with only few objects on homogeneous background (e.g. scanned maps) objects (not background) are stored in the following way: p,n,g: (2,3), 3,blue, (3,3),2,blue, (4,3),1,blue p = position of new gray value n = number of same gray values in that row g = gray value chain-codes, e.g. Freeman-Code: efficient storage of lines (3,2) 1,3,1,2,8,6,

5 3.8 Data Structures - Raster Data and Quadtree hierarchical subdivision of space into 4 equal parts (areas) division stops, when only homogeneous elements in area organization in tree-like structure: every node has 4 successors; the value of an area is stored in leaf node

6 3.8 Data Structures - Quadtree 3.8 Data Structures - Thematic Data and Tables Country Language Capital France french Paris Germany german B e rlin Problem: tables have a fixed number of columns sets of attributes where the number of attributes is not fixed cannot be stored in one fixed table (example: hobbies - person 1: no hobbies; person 2: hiking; person 3: reading, sports, walking;...) Solution: two tables ID first name last name age 1 John Smith 34 2 Mary Miller 23 3 Peter Johnsen ID hobbies 1 no 2 hiking 3 reading 3 sports 3 walking......

7 3.8 Geospatial Data Storage - Relational Database Model Institute employees number_empl. group project em_nr Name project group no GIS ATKIS 1 Schneider DHM GIS 7 GIS traffic 2 Haala Interpretation SENSOR 4 SENSOR GPS 3 Kilian GPS CV 11 CV CV DHM Interpretation 4 5 Walter Anders traffic ATKIS 4. Geospatial Data Storage Databases vs. file systems Transaction concept Relational data model SQL

8 4. Geospatial Data Storage - Four-layer-model of a GIS external view conceptual view logical view internal view 4.1 Geospatial Data Storage - File Systems If several user work on the same data: every user creates his own data directories and uses them for the solution of his problem -> data are stored parallel in file systems very often user A doesn t know anything about the data of user B -> multi user access (and use) of the data is not possible problems arise if one user changes (updates) his data

9 4.1 Geospatial Data Storage - Characteristics of File Systems Disadvantages parallel data storage data changes of one user are not visible for all other users no multi user access on the data not flexible file systems have fixed structures which are very hard (sometime impossible) to change low data security user is responsible for data backup Advantages easy to realize 4.2 Geospatial Data Storage - Characteristics of Databases no redundant data storage no problems if a user changes his data multi user access several users can work with the same data at the same time without producing inconsistencies flexible data storage the data are stored into easy manageable structures (for example in tables) which are easy to modify data security data cannot be lost. Protection against hardware failures, software failures, user failures up to disasters like fire, earth quake, etc. high performance systems with parallel processing it is possible to realize systems with several million transactions per second

10 4.2 Geospatial Data Storage - Problems by Multi User Access Situation: green land forest user A wants to change the border between green land and forest user B wants to represent all objects in a map both users make their query at the same time! 4.2 Geospatial Data Storage - Problems with Multi User Access user A reads green land and forest user B reads green land user A changes border time user B reads forest??

11 4.2 Geospatial Data Storage - Transaction Concept (ACID-principle) every query, update, insert or delete is called a transaction A tomicity C onsistency I solation D urability transaction is not separable transaction transforms the database from one consistent status into another consistent status transaction is isolated from any other transaction an update in the database is durable and cannot be lost 4.2 Geospatial Data Storage - Concurrency Control employee SELECT SUM(salary) FROM employee UPDATE employee SET salary = salary*1.1 Multi user access is only possible if the system is able to lock parts of the database. Setting and unsetting of locks is transparent to the user.

12 4.2 Geospatial Data Storage - Data Security The data are the most expensive part in a GIS (also in most of others application) and must be stored in a secure way Protection against not authorized access every element in the database can be protected with access rights (read, write, change, delete) Protection against hardware and software failures every change in the database is stored into a protocol so that the database can be reconstructed after a failure Disaster protection several hard discs store exactly the same data. If one disc crashes the database can access the data from the other disc. Sometimes different hard discs are distributed on different continents (for example money transaction systems) 4.2 Geospatial Data Storage - Protocol Backup of the database + protocol after the backup = no loss of data logical protocol TIMESTAMP INSERT... DELETE... UPDATE... TIMESTAMP INSERT... DELETE... UPDATE...

13 4.2 Geospatial Data Storage - Evolution of Database Systems first generation: hierarchical and network database systems (Codasyl) partly still used today second generation: relational systems today most successful systems third generation: extended relational systems: object relational systems, object oriented systems increasing use, systems of the future fourth generation: deductive systems automatic inference procedures: derivation of new information from database hardly used in commercial area 4.2 Geospatial Data Storage - Relational Database Systems Advantages: descriptive query language -> ad hoc queries relational algebra is based on a sound theoretical model easy to learn ANSI-Standard -> easily portable data independent model

14 4.2 Geospatial Data Storage - Relational Database Model Institute employees number_empl. group project em_nr Name project group no GIS ATKIS 1 Schneider DHM GIS 7 GIS traffic 2 Haala Interpretation SENSOR 4 SENSOR GPS 3 Kilian GPS CV 11 CV CV DHM Interpretation 4 5 Walter Anders traffic ATKIS 4.3 Geospatial Data Storage - SQL (Structured Query Language) Non procedural query language: One only has to specify WHICH information has to be retrieved and not HOW the information has to be retrieved (declarative language) SQL-Query result table: SELECT Name, Project FROM employees WHERE em_no < 4 Give me the projects and names of all employees whose employee number is less than four Schneider Haala Kilian DHM Interpretation GPS

15 4.3 Geospatial Data Storage - Language Elements of SQL SQL DDL DML DCL Data Definition Language Data Manipulation Language Data Control Language CREATE ALTER DROP... SELECT UPDATE DELETE INSERT... GRANT REVOKE Geospatial Data Storage - Example Database a database shall store parcels, owners and tax offices parcels have a unique number, size and a use owner own parcels, have a first name and last name, a date of birth and a responsible tax office tax offices have a number and a tax rate

16 4.3 Geospatial Data Storage - Tables in the Database table: parcel number size use GEW GEW SONDER WOHN 5 78 WOHN SONDER table: tax_office number tax rate table: owner first name last name date of birth to_no pa_no Charly Anders Martin Kada Steffen Volz Antje Quednau Esther Hinz Heike Meyer Geospatial Data Storage - Training Examples 1) Select all owners 2) Select all owners which belong to tax office 3 3) Print all tax offices sorted by the tax rate 4) Change the tax office of Heike Meyer to number 1 5) Print the size of the parcel of Steffen Volz 6) Delete parcel number 5 7) Print the tax rate of Charly Anders

17 1) Select all owners SELECT * FROM owner 2) Select all owners which belong to tax office 3 SELECT * FROM owner WHERE to_no = 3 3) Print all tax offices sorted by the tax rate SELECT * FROM tax_office ORDER BY tax_rate 4) Change the tax office of Heike Meyer to number 1 UPDATE owner SET to_no = 1 WHERE first_name = Heike AND last_name = Meyer

18 5) Print the size of the parcel of Steffen Volz SELECT size FROM parcel WHERE number IN SELECT pa_no FROM owner WHERE first_name = Steffen AND last_name = Volz 6) Delete parcel number 5 DELETE FROM parcel WHERE number = 5 7) Print the tax rate of Charly Anders SELECT tax_rate FROM tax_office WHERE number IN SELECT to_no FROM owner WHERE first_name = Charly AND last_name = Anders

19 5. Geospatial Data Structures Databases vs. file systems Transaction concept Relational data model SQL Selected References, GIS-Monographs, Books D. Ian Heywood, Sarah Cornelius, Steve Carver, Ian Heywood: An Introduction to Geographical Information Systems, 2nd Edition 2002, Prentice Hall. Christopher Jones: Geographical Information Systems and Computer Cartography, Addison Wesley Longman Ltd., Harlow, Michael Worboys: GIS A Computing Perspective, Taylor and Francis, S. Aronoff: Geographic Information Systems: A Management Perspective, WDL Publications, Ottawa, Canada, D. Fritsch: Signal Processing Lecture Notes. GEOENGINE, Univ Stuttgart,

20 Contents Data Structures (Chapter 1) sequential files, direct access, Hash tables, inverse file systems, list structures, raster data, data compression Access Methods for Spatial Data (Chapter 2) Hierarchichally (statistical methods): sequentially, KD tree, Quadtree..., dynamic methods: Gridfile,R tree, cell tree... Methods of Data Analysis (Chapter 3) geometrische Operatoren, Verschneidungen, topologische Basisoperatoren, Dreiecksvermaschung, Linienglättung, Raster/Vektor- und Vektor/Raster-Konvertierungen Topological Relations (Chapter 4)