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



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



Details


Context:

• Few data

• Analysis at a lower level of granularity

• Point data

• One spatial attribute • One numerical measure• One semantic attribute

ApproachOverview



Details


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



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



Details

Examples

Not too complex problem

SOLAP context

More complex

But… there other contexts that already use spatial clustering

ApproachOverview



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



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



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


ApproachOverview



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


ApproachOverview



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 )


ApproachOverview



Details

Example


ApproachOverview



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



Details

Control the intensity of summarization

ApproachOverview



Details

Example

Moving to the right or to the left

ApproachOverview



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



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



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



Details

Spatial Clustering to Uncluttering Map Visualization in SOLAP

Education

Transcript of Spatial Clustering to Uncluttering Map Visualization in SOLAP