spatial databases: data collection spring, 2015 ki-joune li
TRANSCRIPT
Spatial Databases:Data Collection
Spring, 2015
Ki-Joune Li
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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
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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 System
New DBExisting DB Copy fromLegacy DB
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Geo-Data Clearinghouse
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/)
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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
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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)
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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
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Elimination
Eliminate spatial objects not satisfying given conditions Example
Eliminate *From BuildingsWhere area < 100 m2
Propagation of Elimination Elimination may destroy cardinality condition Example: What to do in this case ?
District District_Office1..1
1..1
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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
AB
C ApartmentComplex
Aggregation
- With Boundary - Without Boundary
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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
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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
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Topological Issues in Generalization
Another Example
A
B
B contains AA contains B
A is equal to B at least
R(B, A) RG(B, A)
A
Collapse to pointB
What is the correct topology after the collapse ?
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Topological Issues in Generalization
Another Example
Buildings
Road
R(B, A)= Disjoint
B1 B2
B3 B4
Buildings
AggregationB
Road
AA
R(B, A)= Overlap
R(B, A) RG(B, A)
Is it correct ?
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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.
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Example: Topographic Map and Cadastral Map
Example
Topographic Map
Cadastral Map
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Example
Building DBA Manhole DBB Pole DBC
Find the nearest manhole or pole to Building P
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Example
Building DB Manhole DB Pole DB
Find the nearest manhole or pole to Building PA
B
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Example
Building DB Manhole DB Pole DB
Find the nearest manhole or pole to Building P
result of the query
A
B
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Example
Building DB Manhole DB Pole DB
adjust the building position
the correct answer
result of the query
A
B
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Example
Building DB Manhole DB Pole DB
Find the nearest manhole or pole to Building P
result of the query
the correct answer
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Elastic Map Transformation: Rubber Sheeting
Elastic transformation of objects in each spatial database.
Building DB
Manhole DB Pole DB
Transformed Manhole DB
Transformed Pole DB
Reference Spatial DB
or Base Map
Consistent Spatial Databases
Spatial Query
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Elastic Map Transformation by Delaunay Triangulation
Procedure
1. Select sets of Control Point Pair on MapRef and another MapA, respectively.
2. Delaunay Triangulation with Control Points on MapRef.
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
MapRef MapA
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Triangular Transformation
p1(x1,y2 )
p2(x2, y2 ) p3(x3, y3 )
q1(u1, v2 )
q2(u2, v2 )q3(u3, v3 )
Triangle on MapRef
q (u, v )
p (x, y )
(u,v) = (a1u1+a2u2+a3u3, a1v1+a2v2+a3v3)
where a1 = b {(y2 – y3)x + (x3 – x2)y + x2 y3 – x3 y2 }
a2 = b {(y3 – y1)x + (x1 – x3)y + x3 y1 – x1 y3 }
a3 = b {(y1 – y2)x + (x2 – x1)y + x1 y2 – x2 y1 }
b = (x1 y2 + x2 y3 + x3 y1 – (x2 y1 + x3 y2 + x1 y3 ))-1
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Elastic Map Transformation : Example
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Elastic Map Transformation : Example
Control Points
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Mismatched between Adjacent Maps
Examples
Discontinuity
Disappearance
Shift
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Edge Matching
Rules Priority on More Recent Data Respect of Pivot Objects Respect of Predefined Constraints
e.g. Building should be rectangular
Pivot Object