Spatial Clustering to Uncluttering Map Visualization in SOLAP
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Transcript of Spatial Clustering to Uncluttering Map Visualization in SOLAP
Authors:
Ricardo Silva (1)
João Moura-Pires (1)
Maribel Yasmina Santos (2)
(1) Universidade Nova de Lisboa - Faculdade de Ciências e Tecnologias
Departamento de Informática
(2) Universidade do Minho
Departamento de Sistemas de Informação
Spatial Clustering To Uncluttering Map Visualization in SOLAP
Content
1. Context and Motivation
2. Approach Overview
3. Details
4. Conclusions and Future Work
Spatial OLAP (SOLAP)
• Business Intelligence (BI)
• Different display modes (synchronized):
Cartographic Tabular Statistical Diagrams
• New and better way to assimilate knowledge
new display for OLAP users • Better and faster perception of the query results
• Analysis of huge amount of data
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
SOLAP: High interactivity
• Dynamic queries:• The user can change on-the-fly:
• Dimensions
• Measures
• The level of granularity
• The user can:Cartographic
• Perform uni, bi, multivariate analysis
• Slices, spatial slices
• View contextual information
No!Can we always ensure that the maps offer a better and faster perception of query re-sults?
Why?
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Example of a cluttered map
Context:
• Lot of data
• Analysis at a lower level of granularity
• Point data
• One spatial attribute, one numerical measure
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Example of a cluttered map
Context:
• Few data
• Analysis at a lower level of granularity
• Point data
• One spatial attribute • One numerical measure• One semantic attribute
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Example of a cluttered map
Context:
• Not so much data
• Not at a such lower level of granularity
• Polygon data
• One spatial attribute • One numerical measure• One semantic attribute
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
How to approach this problem?
To avoid the represen-tations overlapping
Spatial clustering
We need to summa-rize the data from the query
How identify the overlapping groups?
How to approach this problem?
through
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Examples
Not too complex problem
SOLAP context
More complex
But… there other contexts that already use spatial clustering
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Characteristics of the solution
• Allow automatic detection for the need to summarize more the data
• Maintain the synchronization between the displays (tables, maps)
• Able to handle with point data and polygon data
• DBSCAN• P-DBSCAN
• Clusters represented on the map (depending on type of spatial objects)
• Data at a multi-granularity (roll-up creation to each cluster)
• The user is able to:
• enable or disable the summarization process
• to constraint the clusters by a spatial hierarchy level
• control the intensity of summarization
• change the cluster representation
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
1. Synchronization between map andtabular display
Stores Total ProfitA 5
B 5
C 5
D 25E 25F 15
AB
C
D
F
E51525
Stores Total ProfitA 5B 5
A
BG1515>25
Group1 70
Spatial cluster-ing algorithm
Synchronization between map and tabular display
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Data aggre-
gated
New representa-tion
Store Type Total ProfitA X 5B Y 5C X 5D W 25E W 25F W 15
Cluster 1 X 10
Store Type Total Profit
Cluster 2 W 65
B Y 5
The semantic attribute (Type attribute) is at a same or at a higher level than the spatial attribute (Store attribute )
Each cluster must share the semantic attribute value
Close objects
Close objects
Synchronization between map and tabular display
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Cluster 1 X,Y 15
Store Type Total Profit
Cluster 2 W 65
County Total Profit Total ProfitA 15 5B 5 10C 25 2
X Y
Cluster 1 45 17Close
objects
Straight summarization
Synchronization between map and tabular display
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
The semantic attribute comes from other dimension
Type
County Total Profit Total Profit
X Y
Type
The semantic attribute (Type attribute) is at a incomparable or at a lower level than the spatial attribute (County attribute )
Synchronization between map and tabular display
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Example
Synchronization between map and tabular display
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Example
ConcaveHull
2. Control the intensity of summarization
Control the intensity of summarization
Spatial clustering algo-rithm G1
mmm… i want more
clusters
Interact with the systemin an easy way
G1G2
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Control the intensity of summarization
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Example
Moving to the right or to the left
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
DBSCAN
Eps = radius
MinPts = 3
Sorted 3.distance neighborhood
Mapping each object to the distancefrom its 3-th nearest neighbor
DBSCAN/P-DBSCAN: Novel Heuristic
Aims to find more than one value for Eps
• Looks for gaps in 3.distance function
• It is created a 3.distance function
How?
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Conclusions
• A mechanism to control the map visualization in SOLAP context
• Rely on spatial clustering technique
• Takes into account the spatial information to be displayed
• The possible overlapping between representations
• Query-aware
• Novel heuristic to estimate eps DBSCAN/P-DBSCAN algorithm
• Adhoc/Region-based clustering
Help to maintain the benefits from map visualization in a SOLAP environment
• The user has the ability to control the existence, or not, of the post-processing stage
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details
Future Work
• Proper evaluation of this work:
• Comparative analysis between several spatial clustering algorithms
• DBSCAN authors heuristic versus our novel heuristic
• Heuristic to detect the need to summarize the data
• Spatial clustering applied to the map representations instead real coordinate space
• The level of users’ satisfaction
• To consider datasets with the line as a spatial object
ApproachOverview
Conclusionsand Future Work
Context and Motivation
Details