# Analyzing change in spatial data by utilizing polygon models.

**ABSTRACT** Analyzing change in spatial data is critical for many applications including developing early warning systems that monitor environmental conditions, epidemiology, crime monitoring, and automatic surveillance. In this paper, we present a framework for the detection and analysis of patterns of change; the framework analyzes change by comparing sets of polygons. A contour clustering algorithm is utilized to obtain polygon models from spatial datasets. A set of change predicates is introduced to analyze changes between different models which capture various types of changes, such as novel concepts, concept drift, and concept disappearance. We evaluate our framework in case studies that center on ozone pollution monitoring, and on diagnosing glaucoma from visual field analysis.

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- Proc. ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics (DMG). 01/2010;

Page 1

Analyzing Change in Spatial Data by Utilizing Polygon

Models

Vadeerat Rinsurongkawong1, Chun Sheng Chen1, Christoph F. Eick1, and Michael D. Twa2

1Department of Computer Science,

University of Houston

Houston TX, 77204-3010

{vadeerat, lyon19, ceick}@cs.uh.edu

ABSTRACT

Analyzing change in spatial data is critical for many applications

including developing early warning systems that monitor

environmental conditions, epidemiology, crime monitoring, and

automatic surveillance. In this paper, we present a framework for

the detection and analysis of patterns of change; the framework

analyzes change by comparing sets of polygons. A contour

clustering algorithm is utilized to obtain polygon models from

spatial datasets. A set of change predicates is introduced to

analyze changes between different models which capture various

types of changes, such as novel concepts, concept drift, and

concept disappearance. We evaluate our framework in case

studies that center on ozone pollution monitoring, and on

diagnosing glaucoma from visual field analysis.

Categories and Subject Descriptors

H.2.8 [Database Management]: Database Applications – data

mining, spatial databases and GIS.

2College of Optometry,

University of Houston

Houston TX, 77204-6052

mtwa@optometry.uh.edu

H.3.3 [Information Storage and Retrieval]: Information Search

and Retrieval – clustering.

I.2.1 [Artificial Intelligence]: Application and Expert Systems –

Medicine and science.

General Terms

Algorithms, Measurement, Experimentation

Keywords

Change Analysis, Polygon Models, Density-based Clustering,

Concept Drift, Novelty Detection, Spatial Data Mining.

1. INTRODUCTION

With advances in data acquisition technologies, huge amount of

spatial data are collected every day. Detecting and analyzing

changes in such data is of particular importance and has many

applications including developing early warning systems that

monitor environmental or weather conditions, epidemiology,

crime monitoring, automatic surveillance, emergency first

responders‘ coordination for natural disasters. Addressing this

need, we introduce a framework that includes methodologies and

tools to discover and analyze change patterns in spatial data.

Change patterns capture how the most recent data differ from the

data model established from the historical data.

The main challenges for developing systems that automatically

detect and analyze change in spatial datasets include:

1. The development of a formal framework that characterizes

different types of change patterns

2. The development of a methodology to detect change patterns

in spatial datasets

3. The capability to find change patterns in regions of arbitrary

shape and granularity

4. The development of scalable change pattern discovery

algorithms that are able to cope with large data sizes and

large numbers of patterns

5. Support for different perspectives with respect to which

change is analyzed

In this paper, a framework for the detection and analysis of

patterns of change is proposed. Our methodologies use spatial

clustering and change analysis algorithms that operate on polygon

models. In particular, clusters discovered by contour clustering

algorithms serve as the models for the currently observed spatial

data. These models have to be updated to reflect changes as new

data arrive. Change analysis algorithms are responsible for

detecting the change patterns of interest that capture discrepancies

between new data and the current model. Finally, change reports

are generated that describe newly emerging clusters, disappearing

old clusters, and movement of the existing clusters.

Our contributions include the following:

1. The development of tools based upon spatial clustering and

polygon operations to detect and analyze change patterns in

spatial data

2. A definition of generalized change predicates that are utilized

to detect and analyze a variety of specific change patterns of

interest including concept drift and the emergence of novel

concepts

3. A demonstration of these methods in two very different

applications—ozone pollution monitoring, and the diagnosis

of glaucoma progression from visual field analysis

The paper is organized as follows. Section 2 is an overview of our

framework. Section 3 presents change analysis methodologies and

tools. In section 4, our framework is evaluated in case studies.

Page 2

Related work is discussed in section 5. Section 6 is our conclusion

and future work.

2. OVERVIEW

In this paper, we address a problem of change analysis in spatial

data; a framework that detects and analyzes the patterns of change

is proposed. We introduce a change analysis method that operates

on cluster models generated by density and interpolation functions

and a contour clustering algorithm. A set of change predicates is

introduced that is capable of defining change of concepts in

spatial data such as concept drift, novel concept, concept

disappearance, and local rearrangement.

A proposed change analysis is conducted as follows. Assume that

a set of spatial datasets at different time frames, O1, O2,…, On are

given, and we are interested in finding what patterns emerged in

Ot compared to O1, O2,…, Ot-1. First, we generate a density map

on spatial data in dataset Ot then a set of contour clusters {c1,

c2,…, cn} that represent interesting regions is created by running a

contour clustering algorithm on the density map. Next, we create a

cluster model Mt which is a set of polygons {m1, m2,…, mn} that

is generalized from dataset Ot-1, Ot-2,…, O1. Finally, a knowledge

base of change predicates is provided that allows analyzing

various aspects of change in interesting regions based on changes

in the set of contour clusters {c1, c2,…, cn} from Ot with respect to

a set of contour clusters {m1, m2,…, mn} in the base polygon

model Mt. The change analysis processes are repeated for dataset

Ot+1.

2.1 System Architecture

Figure 2-1. System architecture of the proposed framework

In this section, the system architecture is discussed. The overview

of the system is illustrated in Figure 2-1. The system consists of

three main modules: Cluster Generation, Model Generation, and

Change Analysis.

The Cluster Generation module employs density or interpolation

functions and a contour clustering algorithm named DCONTOUR

[3] to generate interesting regions from the new arriving data. (We

discuss how DCONTOUR works in section 2.2.) The interesting

regions resulted from the Cluster Generation module are presented

as contour clusters. For some problems, generating density or

interpolation functions from attributes of interest may be non-

trivial. For example, in analyzing social network problems,

domain experts or may desire to find areas where people with

high and low income live in close proximity to each other. In such

case, a region discovery framework introduced in [6] can be

applied as a preprocessing step for discovering the regions which

have high variance in income. Then the regions thus found can be

inputs for the Cluster Generation module.

Next, the Model Generation module acquires the density or

interpolation functions and cluster contours generated by the

Cluster Generation module to create a new model.

Finally, the Change Analysis module analyzes change by applying

polygon operations to the current model. A set of change

predicates is introduced that identifies different types change,

such as concept drift, novel concept, concept disappearance, and

local rearrangement. Change reports that summarize such changes

in the current data are automatically generated.

2.2 DCONTOUR

DCONTOUR [3] is a clustering algorithm that uses contouring

algorithms to obtain spatial clusters and employs polygons as

cluster models. Its input is a density function or interpolation

function O that has been derived from a spatial dataset O and a

threshold d. Its output is a set of polygons describing contiguous

regions containing points v for which O(v) d. Figure 2-2 gives

a pseudo-code of the algorithm.

Input: Function , threshold d.

Output: Polygons for threshold d.

1. Subdivide the space into D grid cells.

2. Compute (g) for all grid intersection points g.

3. Compute contour intersection points b on grid cell edges

where (b) =d using binary search and interpolation.

4. Compute contour polygons from contour intersection points b.

Figure 2-2. Pseudo-code of the DCONTOUR algorithm

First, a grid structure for the space is created and then O(g) for

each grid intersection point g is computed. Next, the contour

intersection points are calculated using binary search and

interpolation. Finally, contour intersection points found on cell

edges are connected. The process is continued until a closed

polygon is formed. DCONTOUR uses an algorithm proposed by

Snyder [12] to derive contours from intersection points in the final

step.

2.3 Polygon Models

In this paper, polygons are used as cluster models to discover

change patterns. Basically, cluster boundaries in spatial space are

represented by polygons. For prototype-based algorithms such as

k-means, clusters can be modeled as Voronoi cells generated from

a set of cluster representatives. In our work, cluster models for

different time frames are generated by DCONTOUR as sets of

polygons. Change analysis is then performed by comparing

polygons by using basic polygon operations, such as union,

intersection, area (or size) of polygons, and by analyzing distances

between polygons.

2.4 Model Generation

To detect the changes of patterns in datasets at different time

frames, we rely on cluster analysis to capture the arrival of new

concepts, the disappearance of old concept, and concept drift. In

this section, we describe how polygon models are generated and

updated to reflect changes in current data. It is important for the

polygon model to be responsive to relevant changes.

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Assume dataset Ot is the dataset of time frame t (1≤t≤T) and

polygon models are a set of polygons Mt={m1,…,mn}. The initial

polygon model M1 is generated by using the DCONTOUR

clustering algorithm on dataset O1. Each subsequent polygon

model Mt+1 is obtained from data in dataset Ot+1, Ot,…, O1. It is

important to note that each model captures the patterns of data in a

series of time frames rather than from a snapshot of a single

timeframe. Therefore, the model generating strategy needs to

consider the impact of previous observations.

The goal of our proposed change analysis approach is to detect

new patterns in the dataset Ot+1 in the context of what happened in

the past as captured in model Mt (based on datasets O1, O2,…, Ot).

One approach directly derives Mt from X=O1 … Ot. However,

this is not a feasible approach because of the number of data

objects accumulated along the time will eventually become the

performance bottleneck for running a clustering algorithm. Two

possible strategies to deal with this issue include limiting the size

of X by the use of random deletions, or using a sliding window

approach that considers only the most recent k datasets.

Additionally, data could be inversely weighted by age. This paper

employs an un-weighted sliding window approach for model

generation. Mt is derived by computing contour polygons from the

instances of the last k datasets, where k is an input parameter.

3. A TOOL FOR CHANGE ANALYSIS

Change Analysis is conducted in the following steps:

1. A user selects a set of change predicates with respect to the

changes to be analyzed.

2. The user selects parameter settings for the change predicates.

3. The parameterized change predicates are matched against a

model M and a new dataset O to obtain sets, pairs, triples,…

of clusters that match the change predicates. We call the

objects that match a change predicate instantiations of the

change predicate.

4. Change reports are generated from the instantiations of the

selected change predicates.

This section introduces basic change predicates to analyze

changes between clusters in the dataset O and the model M.

Let c, c1, c2, be clusters in O and XO the set of all clusters in O; let

m, m1, m2 be a clusters in M and XM the set all clusters in M. The

operators ‗‘ and ‗‘denote polygon intersection and union; |c|

computes the size (area) of a polygon c. In this case, agreement

between c and m can be computed as follows:

????????? ?,? = ? ∩ ?

? ∪ ?

Agreement measures how similar two polygons c and m are. In

addition to agreement, containment between two clusters is

defined as follows:

??????????? ?,? = ? ∩ ?

?

Basically, containment measures the degree to which a cluster c is

contained in another cluster m.

Many change predicates involve distances between clusters. There

are many different ways to measure distances between clusters.

Our current work uses Average Link or Group Average [15] as a

metric to measure the distance between two clusters c and m.

Average Link is defined as follows:

???????? ?,? =

1

? ? ????????(?,?)

?∈? ?∈?

where o is an object belonging to cluster c and v denotes an object

that lies in the scope of m.

Agreement, containment and average link distance are utilized to

define more complex change predicates; below we list several

popular change predicates:

1) Stabile Concept (c,m) Agreement (c,m) ≥ very high ∧

Distance (c,m) ≤ distancemin

2) Concept Drift (c,m)

2.1) Moving (c,m) (distancemin < Distance(c,m) <

distancemax) ∧ (Agreement (shift(c, m),m) ≥ medium)

2.2) Growing (c,m) (Containment (c,m) < Containment

(m,c)) ∧ (Agreement (c,m) ≈ Containment (c,m)) ∧

(distancemin < Distance(c,m) < distancemax)

2.3) Shrinking (c,m) (Containment (c,m) >

Containment (m,c)) ∧ (Agreement (c,m) ≈ Containment

(m,c)) ∧ (distancemin < Distance(c,m) < distancemax)

3) Local Rearrangement (c1,c2,m1,m2)

3.1) Merging (c1,m1,m2) (Containment (c1,m1) <

Containment (m1,c1)) ∧ (Containment (c1,m2) <

Containment (m1,c2)) ∧ (Distance(c1,m1) ≤ distancemax)

∧ (Distance (c1,m2) ≤ distancemax) ∧ (Containment

(c1,m1) + Containment (c1,m2) ≥ medium)

3.2) Splitting (c1,c2,m1) (Containment (c1,m1) >

Containment (m1,c1)) ∧ (Containment (c2,m1) >

Containment (m1,c2)) ∧ (Distance (c1,m1) ≤ distancemax)

∧ (Distance (c2,m1) ≤ distancemax) ∧ (Containment

(m1,c1) + Containment (m1,c2) ≥ medium)

4) Novel Concept (c) ∀m ∈ XM (Agreement (c,m) ≈ 0 ∧

Distance (c,m) ≥ distancemax)

5) Disappearing Concept (m) ∀c ∈ XO (Agreement (c,m)

≈ 0 ∧ Distance (c,m) ≥ distancemax)

In the above definitions, very high, high, medium, low,

distancemax, and distancemin are parameters whose values are

selected based on application specific needs. xy is a predicate

that returns true if ―x and y have approximately the same value‖.

In the above definition, concept drift captures cases when a cluster

moves or changes in size. Local rearrangement occurs when a

cluster splits into two or more clusters, or when two or more

clusters are merged into a single cluster. In the case of concept

drift with moving type, (Agreement (shift(c, m),m) measures the

agreement of c and m assuming c is shifted back to the position of

m.

In general, the polygon clusters for O and M are matched against

all change predicates, and pairs of clusters are reported for each

change predicate obtaining instantiated change predicates. For

example, in the case of the ―Stabile Concept‖ change predicate,

the following set of pairs of clusters is computed:

Page 4

{(c,m) | cXO mXM Agreement (c,m) very high

distance (c,m) ≤ distancemin}

Next, the pairs of clusters that match the change predicate, namely

the instantiations of the ―Stabile Concept‖ change predicate are

sent to the report generator and a summary is generated. For

example,{(c1,m12), (c3,m17)} indicates that c1 corresponds to

m12, and c3 corresponds to m17 and that the two pairs of clusters

are highly stabile between X and M. In general, XM and XO are

matched against all change predicates and the instantiations of

each change predicate are reported as the results of change

analysis.

Moreover, change predicates can be easily written as SQL

queries, assuming the query language supports polygon

operations. For example, one could use ORACLE SPATIAL as an

implementation platform for change predicates. In general, a

knowledge base of change predicates can be created for a

particular application domain, and our change analysis framework

can be directly applied to automate change analysis. For a

different application domain, some change predicates may be

different and some parameters in change predicates may have to

be modified, but—most importantly—all other components of our

change analysis framework can be reused.

4. CASE STUDIES

We evaluate our framework in two case studies centering on

diagnosing glaucoma from visual field analysis and on ozone

pollution monitoring in Houston, Texas.

4.1 Case Study in Diagnosing Glaucoma from

Visual Field Analysis

The data used in this case study come from a longitudinal study of

vision loss in glaucoma, a leading cause of blindness worldwide.

One of the key tests that clinicians use to diagnose glaucoma and

monitor its progression is a test of central and peripheral vision

sensitivity. A typical visual field test evaluates a patient‘s ability

to detect faint spots of light at 54 discrete points distributed over

the central 30-degrees of field of vision. At each point, higher

value corresponds with better vision. In glaucoma, patients

experience gradual and progressive damage to the optic nerve, and

this corresponds with several characteristic patterns of visual field

loss [9]. A total of 2,848 records from 232 patients comprise the

dataset. The mean age was 61 ± 11 years with an average of 6

yearly examinations. The visual field data were preprocessed by

adjusting raw values to a common age corrected value [8]. A

spatial map of the total deviation from the average age corrected

value for each point were then classified into 6 levels: -9 dB

(worse vision), -6 dB, -3 dB, 3 dB, 6 dB, and 9 dB (better vision).

which are represented in visualizations by clusters in red, orange,

yellow, green, blue-green, and green color respectively.

This case study represents a special case of our more general

framework. In the cluster generation processes, an interpolation

function is used instead of a density estimation function because

the points in the visual field dataset are evenly distributed in a

Cartesian grid structure. We have applied a bicubic spline

interpolation function [10] to increase the number of sample

points in the grid structure by a factor of 6.

The bicubic spline – an extension of cubic spline on two

dimensional grid points – is defined as follows:

? ?,? = ??? ??−1

?=1

4

?=1

4

??−1

?? ?,? = (? − 1)??? ??−2 ??−1

?=1 ?=1

44

?? ?,? = ? − 1 ??? ??−1 ??−2

?=1?=1

44

???= ? − 1 ? − 1 ??? ??−2??−2

?=1?=1

44

where cij are constant values, and 0 ≤ u, t ≤ 1.

We have generated cluster models by using a sliding window

technique with a window size 2. For data at time t, a model Mt is

created by averaging data at time t-1 and t-2. Parameter settings of

change predicates are shown in Table 5-1a.

Table 5-1a. Parameter settings of change predicates for visual

field dataset.

Agreement

Containment

Distance

Low=0.2

Low=0.2

Min=5

Medium=0.5

Medium=0.5

Max=15

High=0.8

High=0.8

Figure 5-1a depicts the visual field of the right eye of Case 1. The

images from top to bottom indicate progressive visual field loss

due to glaucoma. On the 5th visit at -9 dB level (red), cluster id 0

and 1 in the model are merged and become data cluster id 0.

Containment(c0,m0) is 0.56 and is less than Containment(m0,c0)

which is 0.92. Containment(c0,m1) is 0.058 and is less than

Containment(m1,c0) which is 0.82. Distance(c0,m0) and

Distance(c0,m1) are 5.1 and 10.3 which are less than distancemax.

On the 7th visit at the same level, data cluster id 1 is obviously a

novel concept for this level. Its agreements with every red cluster

in its associated model are all 0s and its distance to the closest

cluster in the model is more than 23.7.

From Figure 5-1b. and Table 5-1b., high agreements and low

distances of data cluster id 0 at -6 dB level (orange) on all visits

indicate that it is a stabile concept with respect to its associated

clusters. It should be noted that the models are kept on updating

so the changes in the data are handled.

In Figure 5-1c., two green clusters with id 0 and 1 in the model of

the 2nd visit are disappearing since their agreements with clusters

of the 4th visit are zeroes. From Table 5-1c., data cluster 0 and 1 at

-9 dB level (red) in the 4th visit are growing clusters as indicated

by their Agreement(c,m) values which are close to the

Containment(c,m) values. Moreover, their distances from their

associated model clusters are less than distancemin, pointing

towards concept drift. Their sizes also show that they are growing;

however, without agreement, containment, and distance values,

we cannot know which clusters they are associated with in the

model.

In summary, the proposed approach for polygonal change analysis

allows one to quantify changes and to associate the obtained

statistics with disease progression. Consequently, our work

provides a valuable tool and a methodology for the development

of automatic, computerized tools for glaucoma disease staging.

Page 5

Table 5-1b. Change predicate values for cluster id 0 at -6 dB

level (orange) of visual field test of Case 2.

Visit

No.

3rd

5th

7th

9th

Size

c0

619

902

953

953

Size

m0

793

876

868

868

Agreement

(c0,m0)

0.66

0.84

0.88

0.88

Containment

(c0,m0)

0.92

0.90

0.90

0.89

Distance

(c0,m0)

1.6

3.1

2.7

2.6

(m0,c0)

0.72

0.93

0.98

0.98

Figure 5-1a. Visualizations of visual field test on right eye of

Case 1; images on the left column are test results on the 3rd,

5th, 7th, and 9th visits respectively; image on the right column

are images of the models for the data in the left column.

Table 5-1c. Change predicate values at -9 dB level (red) of

visual field test of Case 3 in the 4th visit.

Size

c0

105

Size

c1

84

Size

m0

12

Size

m2

23

Agreement

(c0,m0)

0.07

Agreement

(c1,m2)

0.14

Containment

(c0,m0)

0.07

Containment

(c1,m2)

0.16

Distance

(c0,m0)

9.2

Distance

(c1,m2)

5.6

(m0,c0)

0.63

(m1,c2)

0.57

Figure 5-1b. Visualizations of visual field test on right eye of

Case 2; images on the left column are test results on the 3rd,

5th, 7th, and 9th visit respectively; image on the right column

are images of the models for the data in the left column.

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Figure 5-1c. Visualizations of visual field test on the left eye of

Case 3; images on the left column are test results on the 2rd,

4th, 6th, and 8th visit respectively; image on the right column

are images of the models for the data in the left column.

4.2 Case Study in Ozone Pollution Monitoring

The Texas Commission on Environmental Quality (TCEQ) is a

state agency responsible for environmental issues including the

monitoring of environmental pollution in the Texas. As seen on its

website [16], the agency collects hourly ozone concentration data

for metropolitan areas across the state. TCEQ uses a network of

27 ozone-monitoring stations in the Houston-Galveston area. The

area covers the geographical region within [-95.8061, -94.8561]

longitude and [29.0436, 30.3436] latitude. High ozone

concentrations are normally observed in a day that has high UV

radiation and low wind speed. TCEQ issues ozone pollution

warnings once the 1-hour ozone concentration exceeds 75 parts

per billion (ppb) based on the EPA‘s ozone standard.

In this case study, we apply our methodology to analyze the

progression of high ozone concentrations during high-level ozone

days. We divide the Houston metropolitan area into a 20×27 grid

and use ordinary Kriging interpolation method [4] to estimate the

hourly ozone concentration on the 20×27=540 grid intersection

points. Using Kriging is motivated by the fact that the number of

ozone-monitoring stations are far less than the number of the

intersection points of the grid structure.

Kriging interpolation is a common method used by scientists in

the environmental research. Particularly, Kriging interpolation

deals with the uneven sampling issue in the sample space. The

objective of Kriging interpolation is to estimate the value of an

unknown function, f, at a point ? , given the values of the function

at some observed points, x1, …, xn, and weights w1,…,wn.

? ? = ?? ?(??)

?=1

?

??= 1

?=1

?

We are interested in analyzing the progression of ozone pollution

over time. Moreover, we are interested in general patterns of

ozone concentration progression, but not in identifying suddenly

occurring ozone concentrations

Consequently, our approach creates polygon models from

sequences of ozone concentration snapshots using a sliding

window approach; in this case study ozone a 2 hour sliding

window is used. Figure 5-2a visualizes the progression of ozone

hotspots from 11am to 3pm on August 26th, 2008. The orange

polygons capture areas having ozone level above 75 ppb. The red

polygons represent areas having 1-hour ozone concentration

above 100 ppb. Table 5-2a summarizes parameter settings of

change predicates for the dataset.

that disappear quickly.

Table 5-2a. Parameter settings of change predicates for ozone

concentration dataset

Agreement

Containment

Distance

Low=0.2

Low=0.2

Min=0.05

Medium=0.5

Medium=0.5

Max=0.5

High=0.8

High=0.8

From Figure 5-2a, at 11am, two new orange hotspots are visible.

At 12pm, the two orange clusters (orange polygons id 0 and 1) in

the model merged into a larger data cluster (orange polygon id 0),

Containment(c0,m0) is 0.137 and is less than Containment(m0,c0)

which is 1. Containment(c0,m1) is 0.06 and is less than

Containment(m1,c0) which is

Distance(c0,m1) are 0.332 and 0.263 which are less than the

distancemax. A higher ozone level hotspot represented by red

polygon id 0 also becomes visible at 12pm, the Agreement(c0,m0)

is 0 and Distance(c0,m0) is greater than distancemax. The red

polygon id 0 in the data grows to its largest size of the day at 1pm.

Although this red polygon seems to be a polygon that grows from

the red polygon id 0 at 12pm, but we declare that it is still a novel

concept according to the novel concept predicate (see its

agreement and distance in Table 5-2b). This is because the model

does not immediately recognize the small, red polygon id 0 as a

pattern but does so at 1pm after another red polygon occurs in the

same area. In general, creating a model based on multiple ozone

readings makes the model more fault tolerant, and reduces the

probability of false alarms.

1. Distance(c0,m0) and

Page 7

11am

12pm

1pm

2pm

3pm

Figure 5-2a. Visualizations of hourly Ozone data on August

26th, 2008; images on the left column are Ozone hotspots

discovered by DCONTOUR from 11:00am to 03:00pm; image

on the right column are images of the models for the data in

the left column.

Table 5-2b. Change predicate values for polygon (red) id 0 of

high ozone concentration (above 100 ppb) a from 11:00 to

15:00 on August 26th,2008 (NaN is not a number caused by a

number divided by 0)

Time Size

c0

Size

m0

Agreement

(c0,m0)

Containment Distance

(c0,m0)

(c0,m0) (m0,c0)

11:00

12:00

13:00

14:00

15:00

0.0

0.006

0.061

0.01

0.0

0.0

0.0

0.0

0.025

0.02

NaN

0.0

0.0

0.337

0.0

NaN

0.0

0.0

0.913

NaN

NaN

NaN

NaN

0.348

0.0

0.0

Inf.

Inf.

0.043

Inf.

The size of the orange polygon starts to shrink at 1pm with the

Containment(c0,m0) which is 0.7406 is less than the

Containment(m0,c0) which is 0.7181 and Agreement(c0,m0)

which is 0.5738 is close to Containment(m0,c0) which is 0.7181.

The Distance(c,m) which is 0.092 is between distancemax and

distancemin and points towards concept drift. The red polygon also

starts to shrink at 2pm and completely disappears at 3pm.

5. RELATED WORK

Recently, several techniques have been proposed to detect and

analyze change patterns in different types of data. In work by

Asur et al. [1], a technique for mining evolutionary behavior of

interaction graphs is proposed. Yang et al. [17] proposed a

technique to discover the evolution of spatial objects. Association

analysis is used to detect changes. In [7], Jiang and Worboys

propose a technique to detect topological changes in sensor

networks. The topological changes that can be discovered include

region appearance and disappearance, region merging and

splitting, and hole formation and elimination. A framework for

tracking cluster transition in data streams is proposed by

Spiliopoulou et al in [13]. The proposed framework can detect

external and internal transitions of clusters. Kifer et al. [11]

present a method for the detection and estimation of change in

data streams. The proposed algorithm reduces the problem of

analyzing continuously streaming data to analyzing two static

sample sets: a reference window and a sliding window containing

current points in the data streams. The algorithm compares

discrepancies between two windows by means of analyzing

probability distributions.

In summary, the work in [17] and [7] is similar to our work in that

they focus on spatial data. Unlike [17] which uses association

analysis, our work utilizes cluster analysis. Similar to our

technique, the techniques in [1] [17], [7], [13], and [6] analyze

changes in data but require that the identity of objects must be

known or restrict analysis to objects that are characterized by

nominal attributes. In contrast, our work is applicable for data

with unknown object identity and for datasets that contain

numerical attributes.

There are many papers that address the problems of concept drift

or novelty detection but the Spinosa et al. [14] are the first that

tackles the problem of novelty detection in the presence of

concept drift. The paper introduces a cluster-based technique that

can detect novel concepts as well as deal with concept drift in data

streams; cluster models are created by k-means clustering

algorithm. Our framework, on the other hand, relies on contour

clustering algorithm to create polygon models; moreover,

polygons in our approach do not need to be convex. An advantage

of our work over [14] is that our change predicates allow

Page 8

discovery of not only novelty and concept drift but also other

types of change patterns.

6. CONCLUSION AND FUTURE WORK

We introduce a framework for change analysis that utilizes

polygons as cluster models to capture changes in spatial datasets

between different time frames. The proposed framework have

been applied to two real-world datasets to analyze the changes in

the visual fields and the ozone concentrations. The framework

consists of three modules: Cluster Generation, Model Generation,

and Change Analysis. The Cluster Generation module creates

contour clusters from newly arriving data using density or

interpolation functions and a contour clustering algorithm that

operates on those functions. The polygon models are updated in

the Model Generation module to reflect changes in data that occur

over time. The Change Analysis module uses sets of change

predicates, which can be viewed as queries that detect and analyze

changes through polygon operations. In general, the change

analysis tool is highly generic and supports arbitrary sets of

change predicates as long as they operate on sets of polygons. Our

prototype implementation uses the Java-based Cougar^2

framework [2], [5]; however, we are beginning to re-implement

the change analysis tool as a subcomponent of a spatial database

system.

The case studies show that our framework can capture various

kinds of changes in spatial datasets. We plan to implement a tool

for optometrists to study various change patterns that are

associated with different stages in the progression of glaucoma.

The tool aims to assist the optometrists to better understand the

development and progression of the disease. Our ultimate vision

of this project is to use the tool to help diagnose glaucoma and to

provide an expert system to assist optometry students in learning

about the different stages of the disease.

Moreover, we plan to integrate our change analysis framework

and change report generators into early warning systems so that

alarms are raised and change reports are generated automatically

when critical events are discovered. To accomplish this goal,

efficient data-driven algorithms that integrate change analysis

tools into early warning systems have to be developed.

7. REFERENCES

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[16] The Texas Commission on Environmental Quality,

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