1. Spatial Databases: Data Collection Spring, 2015 Ki-Joune Li

2. STEMPNU 2 Why Data Collection? The cheapest way to build spatial DB  Get existing databases, but  Check if the requirements be satisfied Metadata: Description of Data  Data Migration vs. Interoperability Legacy Problem  Building an Information System No more entirely new system Integration with Existing systems  Integration with existing DB

3. STEMPNU 3 Integration with Existing Databases Data Migration  Copy from existing DB  Procedure Survey on existing DB: Data Clearinghouse Metadata Conversion from existing databases Integration of several databases: Mismatch Problem Existing System Legacy System New System New DBExisting DB Copy from Legacy DB

4. STEMPNU 4 Geo-Data Clearinghouse  Clearinghouse: financial services company that provides clearing and settlement services for financial transactions. (in Wikipedia.com)  Collection of Geospatial Data Servers Collection of metadata Not data itself. Providing a single interface to browse metadata of several servers  Z39.50 protocol and client Example: FGDC in USA  (http://clearinghouse1.fgdc.gov/)http://clearinghouse1.fgdc.gov/

5. STEMPNU 5 Metadata for Geospatial Data: ISO 19115 Title, and Alternative title, Originator, Abstract, and Data Frequency of update Presentation type Access constraint, Use constraints Topic category Bounding coordinates, and extent Spatial reference system, and resolution Supply media, and data format Supplier and Additional information source Date of update of metadata Sample of the dataset Dataset reference date, and language Vertical extent information Spatial representation type Lineage Online resource Some items are mandatory and others are optional

6. STEMPNU 6 Generalization: Conversion of spatial data Conversion from  Large-Scale Spatial DB to Small-Scale Spatial DB  Cartographic Aspect vs. DB Aspect Cartographic viewpoint: To make maps more visible DB viewpoint: To reduce the size of data Six Generalization Operators  Simplification  Elimination  Translation (Cartographic Purpose)  Aggregation  Collapse  Exaggeration (Cartographic Purpose)

7. STEMPNU 7 Simplification Simplification (Line Simplification)  Elimination of internal nodes from a polyline  To minimize the loss of accuracy  Example: which point to remove Douglas-Peucker Algorithm  A greedy algorithm

8. STEMPNU 8 Elimination Eliminate spatial objects not satisfying given conditions  Example Eliminate * From Buildings Where area < 100 m 2 Propagation of Elimination  Elimination may destroy cardinality condition  Example: What to do in this case ? DistrictDistrict_Office 1..1

9. STEMPNU 9 Aggregation Aggregate a set of spatial objects into a large object  Definition of the boundary of aggregated object ? Aggregation of Non-Spatial Data  Example: Number of Habitants in the apartment complex  Sum, Max, or Average A B C Apartment Complex Aggregation - With Boundary - Without Boundary

10. STEMPNU 10 Collapse Reduction of Dimensionality  From 2-D to 1-D (from surface to line)  From 2-D to 0-D (from surface to point)  Very Rarely from 1-D to 0-D Example Computation of collapsed objects Surface in 1/1,000 Map Line in 1/50,000 Map

11. STEMPNU 11 Topological Issues in Generalization Example How to Correct it  No drop vertex if  Topological Inconsistency  Translation of the object with problem Left Side Right Side  Topologically Incorrect

12. STEMPNU 12 Topological Issues in Generalization Another Example A B B contains A A contains B  A is equal to B at least R(B, A)  R G (B, A ) A Collapse to point B What is the correct topology after the collapse ?

13. STEMPNU 13 Topological Issues in Generalization Another Example Buildings Road R(B, A)= Disjoint B1B1 B2B2 B3B3 B4B4 Buildings Aggregation B Road A A R(B, A)= Overlap R(B, A)  R G (B, A ) Is it correct ?

14. STEMPNU 14 Mismatch Problems Mismatches  Integration of several databases from different sources  Adjacent Maps Different Accuracy Different Dates of Creation  Different Maps Different Ground Control Points Different Accuracy Etc.

15. STEMPNU 15 Example: Topographic Map and Cadastral Map Example Topographic Map Cadastral Map

16. STEMPNU 16 Example Building DB A Manhole DB B Pole DB C Find the nearest manhole or pole to Building P

17. STEMPNU 17 Example Building DBManhole DBPole DB Find the nearest manhole or pole to Building P A B

18. STEMPNU 18 Example Building DBManhole DBPole DB Find the nearest manhole or pole to Building P result of the query A B

19. STEMPNU 19 Example Building DBManhole DBPole DB adjust the building position the correct answer result of the query A B

20. STEMPNU 20 Example Building DBManhole DBPole DB Find the nearest manhole or pole to Building P result of the query the correct answer

21. STEMPNU 21 Elastic Map Transformation: Rubber Sheeting Elastic transformation of objects in each spatial database. Building DB Manhole DBPole DB Transformed Manhole DB Transformed Pole DB Reference Spatial DB or Base Map Consistent Spatial Databases Spatial Query

22. STEMPNU 22 Elastic Map Transformation by Delaunay Triangulation Procedure 1. Select sets of Control Point Pair on Map Ref and another Map A, respectively. 2. Delaunay Triangulation with Control Points on Map Ref. 3. Triangular Transformation, for each Point p on MapA. Control Points on the Reference Map Corresponding Control Points on Other Maps Corresponding Pair of Triangles Map Ref Map A

23. STEMPNU 23 Triangular Transformation p 1 (x 1,y 2 ) p 2 (x 2, y 2 )p 3 (x 3, y 3 ) q 1 (u 1, v 2 ) q 2 (u 2, v 2 ) q 3 (u 3, v 3 ) Triangle on Map Ref q (u, v ) p (x, y ) (u,v) = (a 1 u 1 +a 2 u 2 +a 3 u 3, a 1 v 1 +a 2 v 2 +a 3 v 3 ) wherea 1 = b {(y 2 – y 3 )x + (x 3 – x 2 )y + x 2 y 3 – x 3 y 2 } a 2 = b {(y 3 – y 1 )x + (x 1 – x 3 )y + x 3 y 1 – x 1 y 3 } a 3 = b {(y 1 – y 2 )x + (x 2 – x 1 )y + x 1 y 2 – x 2 y 1 } b = (x 1 y 2 + x 2 y 3 + x 3 y 1 – (x 2 y 1 + x 3 y 2 + x 1 y 3 )) -1

24. STEMPNU 24 Elastic Map Transformation : Example

25. STEMPNU 25 Elastic Map Transformation : Example Control Points

26. STEMPNU 26 Mismatched between Adjacent Maps Examples Discontinuity Disappearance Shift

27. STEMPNU 27 Edge Matching Rules  Priority on More Recent Data  Respect of Pivot Objects  Respect of Predefined Constraints e.g. Building should be rectangular Pivot Object