"GeoPlot": Spatial Data Mining on Video LibrariesDocument Transcript
In Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002), Mclean, VA, November 4-9, 2002
“GeoPlot”: Spatial Data Mining on Video Libraries∗
Jia-Yu Pan, Christos Faloutsos
Computer Science Department
Carnegie Mellon University
ABSTRACT intrinsic dimension, pair correlation, correlation integral
Are “tornado” touchdowns related to “earthquakes”? How
about to “ﬂoods”, or to “hurricanes”? In Informedia , 1. INTRODUCTION
using a gazetteer on news video clips, we map news onto
Events are not related only through their subjects, but
points on the globe and ﬁnd correlations between sets of
also through the locations and time they occur. Geospatial
points. In this paper we show how to ﬁnd answers to such
data mining exploits the geographic information associated
questions, and how to look for patterns on the geo-spatial
with spatial objects and ﬁnds interesting patterns, trends,
relationships of news events. The proposed tool is “Geo-
and relations among them.
Plot”, which is fast to compute and gives a lot of useful
The geographic association is even more prominent among
information which traditional text retrieval can not ﬁnd.
news stories. Incidences happen at one location or locations
We describe our experiments on 2-year worth of video
nearby usually have common or related subjects, or causal
data (≈ 20 Gbytes). There we found that GeoPlot can
relations, since the characteristics of an area aﬀect the way
ﬁnd unexpected correlations that text retrieval would never
of living and, as a result, the incidences occurred on it. On
ﬁnd, such as those between “earthquake” and “volcano”,
the other hand, related events will occur together at many
and “tourism” and “wine”.
In addition, GeoPlot provides a good visualization of a
Keyword-based methods have long been studied to ﬁnd
data set’s characteristics. Characteristics at all scales are
the relationship among news events. Keywords are assigned
shown in one plot and a wealth of information is given, for
to news events, either manually by human experts after un-
example, geo-spatial clusters, characteristic scales, and in-
derstanding the subject of a news event, or automatically
trinsic (fractal) dimensions of the events’ locations.
by computer programs which simply select words from the
transcripts reporting the event, assuming that words in the
Categories and Subject Descriptors transcripts reveal the subject of the event. However, two
H.2.8 [Database Applications]: Data mining—spatial data events occurred at nearby locations and have eﬀects on each
mining on video digital libraries; H.3.1 [Content Analysis other can not be found to have relationship, if they do not
and Indexing]: Abstracting methods; H.3.7 [Digital Li- have shared keywords. One way to incorporate the concept
braries]: Collection of “closeness” is to assign extra locational keywords (such
as adjacent cities or the country in which it happens) to
General Terms “connect” events that are close to each other. However, this
method does not scale when more and more locations needed
Algorithms to be considered, and these extra keywords also introduce
noise into the processing.
Keywords How do we ﬁnd patterns of global geographic phenomena?
Spatial data mining, video data mining, fractal dimension, Does one event often come with another? Or does it repel
another event? To ﬁnd global patterns like these, we can
∗This material is based upon work supported by the Na- not consider one place at a time , instead, all locations have
tional Science Foundation under Cooperative Agreement to be examined at the same time. Keyword-based methods,
No. IRI-9817496, IIS-9988876, IIS-0083148, IIS-0113089, which link locations to locations by keyword expansion, are
IIS-0209107 and by the Defense Advanced Research Projects
Agency under Contract No. N66001-00-1-8936. not suitable for this task. One problem is when relating
two faraway locations, many words will have to be included,
which may end up introducing too much noise into the sub-
sequent inference. In addition, it is diﬃcult to present the
Permission to make digital or hard copies of all or part of this work for notion of distance (degree of closeness) on terms.
personal or classroom use is granted without fee provided that copies are In this paper, we propose a tool, GeoPlot, for mining
not made or distributed for proﬁt or commercial advantage and that copies global geospatial pattern. In particular, rather than mea-
bear this notice and the full citation on the ﬁrst page. To copy otherwise, to suring the closeness of two events by counting shared loca-
republish, to post on servers or to redistribute to lists, requires prior speciﬁc tional terms, it examines the geographic information more
permission and/or a fee.
CIKM’02, November 4–9, 2002, McLean, Virginia, USA. directly, in the sense that the actual physical distance be-
Copyright 2002 ACM 1-58113-492-4/02/0011 ...$5.00. tween two places is computed and used as an indicator of
“closeness”. We ﬁnd that GeoPlot is eﬀective on spotting Geoplot: deg+45 and region2+45+75+45+75
global cross-event geospatial patterns. It detects the pat- A-A
terns that keyword-based methods can give, and also gives -2 A-B
novel patterns missed by the keyword-based methods. -4
The paper is organized as follows. In section 2, we present -6
some related works on geospatial data mining. In section 3, -8
we introduce GeoPlot, and show how it works and how to -10
interpret it. In section 4, our proposed method of using
GeoPlot on geospatial data mining is explained. Section 5 -6 -5 -4 -3 -2 -1 0 1
gives experimental results on real world data gathered from
a video digital library. Several discussions are given in sec-
tion 6. Section 7 concludes the paper.
Figure 1: An example GeoPlot Curves labelled A-A and
B-B are self-plots of the two data sets A and B, and
2. RELATED WORK A-B curve is the cross-plot. Set A has points along
Spatial data mining focuses on ﬁnding interesting pat- a 45o big circle on the surface of the globe (Figure
terns, rules and trends among spatial objects. Often, spatial 2(c)). Set B has points inside a rectangle region be-
objects are stored in spatial databases as tuples with spa- tween [45o , 75o ] longitude and [45o , 75o ] latitude (Fig-
tial attributes. Spatial attributes could be topological, such ure 3(d)).
as adjacency or inclusion information, or geometric, such as
position (longitude/latitude) or the boundary polygon. For
a general survey, see . point pairs that are not apart from each other by more than
Several approaches have been studied on spatial data min- r. More speciﬁcally:
ing , such as generalization as rule searching , cluster-
ing, and association rule. Attribute-oriented induction  Deﬁnition 1. Given two data sets A (with NA points)
learns rules by generalizing attribute values using spatial and B (with NB points), we deﬁne the cross-plot between
concept hierarchy (e.g., California is a generalization of Los the two datasets as the plot of
Angeles and San Francisco). The quality of the rules pro-
duced depends on that of the concept hierarchy used. Cross A,B (r) = log versus log(r),
NA · NB
Clustering techniques are used in spatial data mining to
cluster objects based on their spatial attributes. Similarity where NA,B (r) is the number of point pairs (each consists
between objects is often deﬁned based on the physical dis- of one point from A and B) within distance r.
tances among spatial objects, or their terrain types (hill or
riverside). Interesting information is then inferred from the Deﬁnition 2. The self-plot of a given data set A (with
clusters formed. One drawback of the clustering techniques NA points) is the plot of
is that they tend to focus on local characteristics and are
computationally expensive. NA,A (r)
Self A (r) = log ¢
NA ·(NA −1)
Association rule  has also been applied to spatial data 2
mining. Spatial association rules are rules such as “near(x,
coast) ∧ southeast(x, USA) ⇒ hurricane(x), (70%)”, which where NA,A (r) is the number of point pairs of set A within
says that if an object x is close to the coast and it is in distance r. Note that the self-plot is indeed the “correlation
southeast United States, then about 70% of the cases, x integral”  of the data set A.
has hurricanes. Spatial association rules could reveal global
relations among objects, not just the local ones. Deﬁnition 3. The GeoPlot of two data sets A and B,
Real-world data sets tend to have skewed distributions  is the graph which contains the cross-plot, Cross A,B (r), and
and are self-similar . Recent studies have found that the the self-plots for both data sets, Self A (r) and Self B (r). Fig-
intrinsic (fractal) dimension is a good representation of real- ure 1 shows an example of the GeoPlot .
world data  , where characteristics at both local and
global scales are considered at the same time. It also spots
non-linear correlations among objects. With these proper-
3.1 Characteristics of self-plots
ties, the idea of intrinsic (fractal) dimension could be a good We could observe interesting characteristics of a data set
tool on spotting global spatial patterns inside real-world from its self-plot. Figure 2(a) shows the self-plot of a 45o
data sets. GeoPlot is based on this idea and aims to dis- big circle on the globe (Figure 2(c)). Recall that self-plot is
cover correlations among spatial objects, at both local and indeed the correlation integral whose slope gives the intrinsic
global scales. Related work includes the so-called K func- (fractal) dimension of the corresponding data set. In this
tion  in spatial statistics , as well as the tri-plots . case, the slope is 1, since the circle is in fact an 1-dimensional
object. Figure 2(b) is the self-plot of a set of clusters (Figure
2(d)). We can see a ﬂat portion (plateau) of the curve in the
3. BACKGROUND self-plot which indicates the existence of clusters in the data
In this section, we describe the concept of GeoPlot and set. In this case, the slope of the non-ﬂat portion is 2, which
how to interpret a GeoPlot to understand the characteristics corresponds to the intrinsic dimension of the clusters, which
of the data sets from which the GeoPlot is constructed. are 2-dimensional regions.
A GeoPlot is deﬁned on two given sets of points and is In Figure 2(b), we label 4 meaningful characteristic scales,
a plot which, given a distance r, indicates the number of namely, rmin , rmax , rcdmax , and rsepc .
ˆ ˆ ˆ ˆ
Self-plot: 10 rectangular regions Geoplot: region1+15+75+15+75 and region2+15+75+15+75 Geoplot: region1+15+75+15+75 and region2+45+75+45+75
Self-plot: 45 degree big circle 0 0 0
0 -1 B-B -1 B-B
-3 -4 -3 -3
-4 -4 -4
-5 -5 -5
-6 Slope=1 -8
-7 Slope=2 -6 -6
-8 -10 -7 -7
-9 -8 -8
-10 -12 -5.5 -5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 -5.5 -5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5
-10 -8 -6 -4 -2 0 2 -8 -7 -6 -5 -4 -3 -2 -1 0 1
r log(dist) ^cdmax ^sepc
r r ^max
(a) Identical (b) Inclusion
(a) (Self-plot) 45o big circle (b) (Self-plot) 10 regions
(c) [15o , 75o ] longitude, [15o , 75o ] latitude
(c) 45o big circle and 10 rectangular regions on the globe
Figure 2: Self-plots (a),(b) self-plots of synthetic data
sets shown in (c).
• rmin (ˆmax ) denotes the minimum(maximum) distance
between two points of a given data set. In other words,
rmin is the smallest distance where the count of pairs
(d) [45o , 75o ] longitude, [45o , 75o ] latitude
is not zero, and rmax is the distance that for distances
bigger than rmax , the counts remain the same.
• rcdmax denotes the maximum diameter of the clusters.
ˆ Figure 3: GeoPlot : Identical and inclusion (a) GeoPlot of
2 statistically identically distributed data sets. Both
• rsepc denotes the inter-cluster distance.
ˆ of them have points in the region as shown in (c)
Observation 1. Characteristic scale A plateau in the ((c) is one of them). (b) GeoPlot of (c) and (d).
self-plot indicates the existence of clusters in the correspond- The coverage of the data set in (c) includes that in
ing data set. (d). Self-plot: A-A, B-B curves. Cross-plot: A-B
Observation 2. Local/Global distribution behavior
The behaviors of a distribution which appear at scale less
than (left to) the characteristic scale (plateau) can be con- 2. Inclusion: The distribution of one data set is included
sidered as local behaviors. Those appear at scale greater in that of the other data set (Figure 3(b)).
than (right to) the characteristic scale are the global behav- 3. Same dimension but not identical: The two data
iors. sets have the same intrinsic (fractal) dimension but are
Observation 3. Intrinsic dimension If the self-plot is not identical (Figure 4(a)).
linear, its slope reﬂects the intrinsic (fractal) dimension of
4. Dominating at diﬀerent scales: Distributions of
the corresponding data set.
two data sets may dominate each other at diﬀerent
3.2 Interpreting GeoPlot scales. Figure 4(b) is an example, where data set A is
The relation between two data sets of locations on the the set of points scattered along meridian −60o (Figure
globe (e.g. identical or disjoint) aﬀects the look of their 4(c)), and data set B is the set of points in a rectangu-
GeoPlot. A particular type of relation between two data lar region (Figure 4(c)).B dominates A at small (local)
sets causes a particular appearance of GeoPlot. In Observa- scale but is dominated by A at large scale. This is be-
tion 4, we list 5 possible relations and refer each case to its cause distribution of B is more compact (localized),
corresponding GeoPlot. while A has more pairs separated at larger distances
than B does, which makes A’s characteristics remain
Observation 4. GeoPlot Rules We catalog the rela- signiﬁcant at large scale.
tions between 2 data sets (A and B) into 5 cases and show
an example GeoPlot for each case. These are the rules for
ﬁnding hidden relations between the distributions of 2 data 4. PROPOSED METHOD
sets from their GeoPlot.
We would like to ﬁnd relations among events such as
1. Identical distributed: The two data sets are from “earthquake”, “hurricane”, “storm”, and “ﬂood”. We look
distributions statistically identical (Figure 3(a)). In at the locations where these events occur, and check how
other words, they have similar spatial distributions. one event is associated with another.
Geoplot: longitude+60 and longitude-60 Geoplot: longitude-60 and region-45-75-45-75 Event Locations Event Locations
beach 84 earthquake 38
ﬂood 131 hurricane 56
-6 -4 island 128 storm 215
tornado 51 volcano 19
-7 tourism 28 wine 33
-6 -5 -4 -3 -2 -1 0 1 -6 -5 -4 -3 -2 -1 0 1
log(dist) log(dist) Table 1: Experimental events and the number of
(a) A:Meridian+60 , B:−60 (b) 2 data sets in (c) their associated locations
Topic 1 # Doc. Topic 2 # Doc. # Shared
ﬂood 133 storm 236 29
ﬂood 133 hurricane 49 10
earthquake 46 volcano 23 3
beach 87 hurricane 49 3
storm 236 unemployment 87 2
tourism 24 wine 21 0
(c)−60o meridian, and the region between [−45o ,−75o ] lon- tornado 42 volcano 23 0
gitude and [−45o ,−75o ] latitude beach 87 earthquake 46 0
Table 2: Transcript sets of the selected events
Figure 4: GeoPlot : intrinsic dimension and local/global scale
(a) 2 data sets (+60o , +60o meridians) have the same
intrinsic dimension 1 (= self-plot slope), but are not 5.1 Data sets
statistically identical (do not overlap with the cross- The locations on which the events occurred are collected
plot). (b) 2 data sets (shown in (c)) dominate each from a news video digital library. A 2-year long collection
other at diﬀerent scales. Self-plot: A-A, B-B curves. (from January 1, 2000 to December 31, 2001) of 33, 632 tran-
Cross-plot: A-B curve. scripts are examined. We dropped segments of commercials
and kept 8, 499 transcripts. Table 1 listed some of the events
The locations where a speciﬁc event occurs are collected Table 2 shows the number of transcripts that the 2 events
from transcripts about that event. A transcript is consid- in an event pair share with each other. Note that the event
ered to be about an event if the words describing that event pairs we used in our study share only a small amount of
appear in the transcript. In the context of traditional infor- transcripts. This reduces the chances of falsely declaring
mation retrieval, the set of transcripts about an event could two events to be similar when in fact they are not.
be viewed as the result of query using the word of the event.
Speciﬁcally, the process contains 3 steps: 5.2 Textual similarity
We compare the GeoPlot to the traditional text retrieval
1. Transcripts from a large video digital library  are
technique to see if GeoPlot can give us interesting informa-
examined, and if the words describing an event are
tion which the traditional text retrieval misses.
mentioned in a transcript, the locations mentioned in
For a pair of events, we compare their GeoPlot with the
the same transcript are associated with that event.
cosine similarity value calculated from the 2 sets of tran-
For example, if words “Iran” and “earthquake” are
scripts associated with them. GeoPlot gives information
both mentioned in a transcript, then Iran is associ-
about how similar the two events (sets of locations) are dis-
ated with the event “earthquake”. In our experiments,
tributed. On the other hand, cosine similarity has been used
we consider only transcripts from news segments, and
to determine similarity between two documents, and can be
drop those of commercials. Several domain-speciﬁc
slightly modiﬁed to report similarity between two sets of
heuristics are also used to associate locations to events
(e.g., drop locations which are too common, like “At-
Table 3 lists the event pairs we compared and their cosine
lanta”, where the headquarter of the news agency is,
similarity scores. In our experiment, we used the dtb term
and “United States”).
weighting scheme  to compute the similarity between two
2. The collected locations are then mapped to 3-dimensional sets of documents. The dtb scheme is reported as one of the
points on the surface of a sphere (globe) of radius 0.5 well-performed term weighting scheme that gives high mean
(according to their longitudes and latitudes). average precision at TREC-7 . The original dtb scheme
is designed for comparing two documents. We slightly mod-
3. GeoPlots of event pairs are then constructed based on iﬁed the dtb scheme to compare two sets of transcripts1 . In
the geodesic distances among the locations associated the following, we call the cosine similarity score between the
with the events. These GeoPlots are then examined transcripts of event “A” and those of event “B” the dtb
(using the rules listed in Observation 4) to ﬁnd how 1
We change the d-factor to be the term frequency on the set
an event is related to another. of transcripts, i.e., the sum of the term frequency on tran-
scripts in the set. The b-factor is also changed, where the
length of document is replaced by average document length
5. EXPERIMENTS in the set. The t-factor, the idf factor, is not changed.
Event 1 Event 2 dtb score Event ˆ
rcdmax plateau width slope of plateau
ﬂood storm 0.509 beach -1.105 0.191 0.131
ﬂood hurricane 0.433 hurricane -0.723 0.385 0.039
earthquake volcano 0.298 tornado -1.200 0.477 0.015
beach hurricane 0.350 Event rcdmax (miles)
ˆ plateau width (miles)
storm unemployment 0.366 beach 2625.2 552.5
tourism wine 0.187 hurricane 3846.4 804.9
tornado volcano 0.166 tornado 2387.3 1459.2
beach earthquake 0.243
Table 4: Analysis of the plateaus in the self-plots Mea-
Table 3: Cosine similarity scores surements at the top are in the unit of scale, and
those at the bottom are in miles. Recall that rcdmax is
Self-plots: beach, hurricane, tornado the diameter of the cluster, which is also the scale at
which the plateau starts. The width of the plateau
is (ˆsepc -ˆcdmax ).
-4 Slope = 1.47
ing Alaska and Hawaii) spans about 3,000 miles from east
to west, and about 2,000 miles from north to south. From
-6 -5 -4 -3 -2
-1 0 1
Table 4, we found that “beach” and “tornado” have clusters
with diameter between 2,000 and 3,000. This suggests that
the clusters of “beach” and “tornado” are probably caused
Figure 5: Self-Plots The self-plots of events “beach”, by locations in the United States. We checked this result
“hurricane”, and “tornado”. The intrinsic dimen- with the map of “beach” (Figure 6(c)), and found that the
sion of their distributions at local scale is about 1.47. cluster is indeed on the United States. The map of “tor-
nado” (not shown) has the same result.
As for the event “hurricane” which has clusters of diame-
score of “A” and “B”. ter greater than 3,000, we checked the map of its distribution
Next, we present experimental results on (a) how useful (Figure 6(d)) and found that its cluster spans beyond the
are the self-plot and GeoPlot on revealing underlying rela- United States reaches the Caribbean Islands, which explains
tions, (b) when the analysis of the self-plot and GeoPlot why this cluster has diameter greater than the size of the
agrees with the cosine similarity value, and (c) when they United States (3,000 miles).
disagree. We summarize the results of the self-plot analysis in the
5.3 Case studies
Observation 5. Characteristic scale In our study, the
We are interested in questions such as “do earthquakes geographic distribution of an event tends to have one single
always happen at where volcanos are? ”, or “do hurricanes cluster of the size of the United States. In our implementa-
and storms always cause ﬂoods? ” To ﬁnd patterns giving tion, this characteristic scale (ˆcdmax ) is about −1.1 (2,638
additional information on these questions, we conduct case miles). This might due to the fact that our transcripts are
studies on selected events using the GeoPlots (self-plots and from a US-based news agency which focuses more on news
cross-plots). We investigate characteristics of an event from occurred in the United States.
the behavior of its self-plot. The relation between two events
is examined using their GeoPlot. The relations found from Observation 6. Intrinsic dimension The intrinsic di-
examining the GeoPlots are compared with the cosine simi- mension of the geographic distribution of an event is the
larity scores to see how the ﬁndings from GeoPlot compare slope of its self-plot. For example, the intrinsic dimension of
to those from the cosine similarity score. the three events “beach”, “hurricane” and “tornado” at local
scale is about 1.47 (Figure 5). In comparison, the intrinsic
5.3.1 Self-plots analysis dimension of the points scattered uniformly in a region is 2,
Self-plots of several events have clear plateaus. These in- and that of the points scattered along a line is 1. Therefore,
clude events such as “beach”, “hurricane”, and “tornado”. a dimension of 1.47 indicates a distribution not uniformed
Figure 5 shows the self-plots of these three events. Detailed on the surface of the globe.
statistics of these self-plots are shown in Table 4.
Table 4 shows the statistics about the plateaus in the Observation 7. Similarity determination Cosine sim-
self-plots of events “beach”, “hurricane” and “tornado”. To ilarity score provides a mean to rank the degree of similar-
make the result more understandable, we converted the dis- ity. However, sometimes it is hard to choose the cutting
tance scales into miles2 . The plateau in a self-plot indicates threshold of “similar” and “dissimilar”. On the other hand,
the existence of clusters, and rcdmax (the scale where the
ˆ visualizing the degree of similarity by displaying self-plots
plateau starts) on a self-plot is the (maximal) diameter of help tell the similar from the dissimilar quickly.
the clusters. We know that the United States (not includ- 5.3.2 GeoPlot analysis
The conversion is done as the following: for rcdmax =-1.105,
ˆ We are also interested in ﬁnding relations among events.
the distance in miles is exp(−1.105)∗ 7926 = 2625.2, where 1 Self-plot analysis in the previous section reveals interesting
is the diameter of the Earth we used in our implementation characteristics of an individual event, but for ﬁnding rela-
and 7926 is the true diameter of the Earth in miles. tions among events, we have to use the GeoPlot. We use the
GeoPlot of a pair of events to determine whether the two
events have similar spatial distributions.
In the following, we will show:
1. GeoPlot successfully detects event correlations which
are missed by the textual similarity function.
2. If the textual similarity function ﬁnds correlation be-
tween events, so will GeoPlot.
3. Even then, GeoPlot gives more information about how
two events are correlated at diﬀerent scales.
Result 1. GeoPlot can ﬁnd new correlations which the
textual similarity function misses.
Justiﬁcation 1.1 Figure 6 shows cases where GeoPlots
and the textual similarity function disagree. Here, curves in
the GeoPlots are overlapped. By the rules in Observation
4, this suggests relations exist between “earthquake” and (b) Volcano
“volcano”, and between “beach” and “hurricane”, while the
dtb scores are low (suggest dissimilarity) as 0.298 and 0.350
(Table 3), respectively. However, we know that there are
indeed spatial relations between earthquakes and volcanos
(both are closed to geological faults, e.g., the Philippines and
Italy), and hurricanes and beaches (hurricanes are formed
above oceans and get high news coverage when they hit the
land). In these cases, GeoPlot reveals hidden relations that
the textual similarity function misses.
Justiﬁcation 1.2 Figure 7 shows another interesting pair (c) Beach
of events which GeoPlot ﬁnds similar: “tourism” and “wine”.
They are missed by the textual similarity function with dtb
score 0.187. A plausible explanation is that tourists prefer
mild climate (Florida, California) where grapevines natu-
rally grow. This also demonstrates the usefulness of the
GeoPlot on spotting novel, hidden relations among events.
Result 2. GeoPlot seems more general than the textual
similarity function and can discover relations (similarly or
dissimilarly distributed) between events which are also pre-
Geoplot: earthquake and volcano Geoplot: beach and hurricane
dicted by the textual similarity function. 0 0
-1 B-B -1 B-B
Justiﬁcation 2 Figure 8(c) shows the GeoPlot of the -2
event pair “ﬂood” and “storm”. By the rules in Obser- -3 -3
vation 4, we found that the spatial distributions of “ﬂood” -4 -4
and “storm” are statistically similar, since the self-plots and -6 -6
cross-plot in the GeoPlot overlap. The dtb score of “ﬂood” -6 -5 -4 -3 -2 -1 0 1 -6 -5 -4 -3 -2 -1 0 1
and “storm” is 0.509 (Table 3), which is relatively higher
than those of other pairs. Therefore, “ﬂood” and “storm” (e) A:Earthquake, B:Volcano (f) A:Beach, B:Hurricane
are considered distributed similarly by both methods, i.e.,
they agree on this case. This ﬁnding suggests that the place
Figure 6: GeoPlot disagrees with cosine similarity Dis-
where a ﬂood occurs is likely to have had a storm.
tributions of (a) “earthquake”, (b) “volcano”, (c)
One possible explanation for Result 2 is as follows: for
“beach”, (d) “hurricane”. GeoPlot of (e) “earth-
textual similarity to report “similar”, the two sets of tran-
quake” and “volcano” (dtb score: 0.298), and (f )
scripts about the two events must have features such as high
“beach” and “hurricane” (dtb score: 0.350).
shared-term frequency. A location mentioned in the set of
transcripts about one event is then very likely to also ap-
pear in the set of transcripts about the other event. This Result 3. GeoPlot gives more information about how two
sharing of associated locations will cause the overlapping of data sets are correlated (how does the degree of correlation
self-plots and cross-plot in the GeoPlot of the two events, change as the scale changes), not just a single-valued indica-
which by our rules in Observation 4, is a sign of spatial tor of degree of correlation as the textual similarity function
distribution similarity. The explanation for that GeoPlot gives us.
generally agrees with textual similarity function on dissimi-
lar event pairs is similar to the explanation given above for Justiﬁcation 3 Figure 8(d) shows the GeoPlot of “ﬂood”
the “similar” case. and “hurricane”. The dtb score of “ﬂood” and “hurricane”
(a) Tourism (a) Flood
(b) Wine (b) Storm
Geoplot: tourism and wine Geoplot: flood and storm Geoplot: flood and hurricane
0 0 0
A-A A-A A-A
B-B -1 B-B -1 B-B
-2 A-B A-B A-B
-4 -3 -3
-5 -4 -4
-6 -5 -5
-7 -6 -6
-6 -5 -4 -3 -2 -1 0 1 -6 -5 -4 -3 -2 -1 0 1 -6 -5 -4 -3 -2 -1 0 1
log(dist) log(dist) log(dist)
(c) A:Tourism, B:Wine (c) A:Flood, B:Storm (d) A:Flood, B:Hurricane
Figure 7: Interesting relation Distributions of (a) Figure 8: GeoPlot agrees with cosine similarity Both meth-
“tourism”, and (b) “wine”. (c) GeoPlot of ods predict the events similar. Distributions of (a)
“tourism” and “wine” (dtb score: 0.187). “ﬂood”, (b) “storm”. GeoPlot of (c) “ﬂood” and
“storm” (dtb score: 0.509), and (d): “ﬂood” and
“hurricane” (dtb score: 0.433). Note the discrep-
is 0.433 (Table 3), which is relatively high and indicates ancy among the curves in (d) at large scale (at
that they are related. Their GeoPlot gives the same im- ≈ −1.3, i.e. 2,160 miles).
plication that the two events have similar geographic dis-
tributions and they are related. Furthermore, GeoPlot also
points out that this correlation only happens at local scale, GeoPlot is an eﬀective tool for ﬁnding hidden relations
speciﬁcally, ≤ 2, 160 miles, which corresponds to log(dist) between two events. To determine the relation between the
≤ -1.3. This is shown at the portion left to the plateaus geographic distributions of the two events, we examine the
in the GeoPlot, where the cross-plot (A-B curve) is over- closeness among the self-plot curves and the cross-plot curve
lapped with one of the self-plot (A-A curve). By the rules in their GeoPlot.
in Observation 4, this overlapping indicates the distribution Figure 9 shows the GeoPlots with the 20% error bar along
of event A (“ﬂood”) includes that of event B (“hurricane”) the cross-plots (A-B curves). The 20% error bar could be
at local scale. From the self-plot analysis in the previous used as a visual aid to determine the closeness of the curves.
section, which shown that the portion left to the plateaus We consider curves are close-by when they are inside the
corresponds to the cluster located at the United States and range covered by the error bar. In the ﬁgure, the error bar
the Caribbean Islands on the map, we concluded that this shows that all curves in (a) are close-by at small scale, but
GeoPlot provides a knowledge that ﬂoods and hurricanes in not at large scale. Also, the A-A (self-plot) curve is closed
the United States and the Caribbean Islands are correlated. to the A-B (cross-plot) curve at all scales. This result is the
We note that this is a more reasonable result than just say- same as our analysis in Justiﬁcation 3.
ing “ﬂood” and “hurricane” distributed similarly, since the As for the GeoPlot in Figure 9(b), the three curves are
correlation between “ﬂood” and “hurricane” is only true at far away from one another at all scales, which indicates dis-
west Atlantic Ocean. In fact, the word “hurricane” is not similarity of the geographic distributions of the two events:
even used for those happened outside west Atlantic, they “tornado” and “volcano”.
are called “typhoon” in west Paciﬁc, or “monsoon” in the
Indian Ocean. Hence, analysis of GeoPlot gives us more in-
formation about the local/global characteristics of the dis-
tributions of the two events. We have introduced a new tool, GeoPlot, to ﬁnd spatial
patterns within a single group of video clips and across two
groups of video clips, where video clips are grouped accord-
6. DISCUSSION ing to their common topic (or called events). The idea is
Geoplot: flood and hurricane Geoplot: tornado and volcano 8. REFERENCES
A-A A-A  R. Agrawal, T. Imielinski, and A. Swami. Mining
0 B-B 0 B-B
-1 A-B -1 A-B association rules between sets of items in large
databases. Proc. of ACM SIGMOD, May 1993.
-4 -4  A. Belussi and C. Faloutsos. Estimating the selectivity
of spatial queries using the ‘correlation’ fractal
-6 -5 -4 -3 -2 -1 0 1 -6 -5 -4 -3 -2 -1 0 1 dimension. Proc. of the VLDB Conference, 1995.
 N. A. C. Cressie. Statistics for Spatial Data. Wiley,
(a) A:Flood, B:Hurricane (b) A:Tornado, B:Volcano
 P. M. Dixon. Ripley’s k function. Technical report
Figure 9: GeoPlot with error bar The 20% error bar of 01-18, Department of Statistics, Iowa State University,
the cross-plot is shown with the GeoPlots. GeoPlot December 2001.
of (a) “ﬂood” and “hurricane” (dtb score: 0.433),  M. Faloutsos, P. Faloutsos, and C. Faloutsos. On
and (b) “tornado” and “volcano” (dtb score: 0.166). power-law relationships of the internet topology. Proc.
of SIGCOMM, 1999.
 C. T. Jr., A. Traina, L. Wu, and C. Faloutsos. Fast
to extract place names from the transcripts of video clips, feature selection using the fractal dimension. XV
put these places on the globe, and ﬁnd patterns across the Brazilian Symposium on Databases (SBBD), 2000.
spatial distributions of points. We showed that GeoPlots  K. Koperski, J. Adhikary, and J. Han. Spatial data
have signiﬁcant advantages over the textual (cosine) simi- mining: Progress and challenges. Proc. of SIGMOD
larity functions: Workshop on Research Issues in Data Mining and
Knowledge Discovery, 1996.
• Spot new patterns GeoPlots can detect geographic  W. Lu, J. Han, and B. C. Ooi. Discovery of general
closeness of two groups of video clips, that textual sim- knowledge in large spatial databases. Proc. of Fareast
ilarity function might miss. (Result 1) Workshop on Geographic Information Systems, 1993.
• Capture known patterns GeoPlot does not miss re-  T. M. Mitchell. Machine Learning.
lations that the textual similarity function gives. (Re- WCB/McGraw-Hill, 1997.
sult 2)  M. Schroeder. Fractals, Chaos, Power Laws: Minutes
From an Inﬁnite Paradise. W. H. Freeman and
• Full information GeoPlots give whole functions, as Company, 1991.
opposed to just single numbers. They can reveal char-  A. Singal, C. Buckley, and M. Mitra. Pivoted
acteristics at all scales, clusters (plateaus), and intrin- document length normalization. Proc. of the
sic dimensions (slopes), which are not captured in the Nineteenth ACM SIGIR Conference, August 1996.
textual similarity score. (Result 3)  A. Traina, C. Traina, S. Papadimitriou, and
C. Faloutsos. Tri-plots: Scalable tools for
We showed how to use GeoPlots to discover multidimensional data mining. Proc. of ACM KDD,
• Clusters and intrinsic dimension Using self-plot,
 E. M. Voorhees and D. K. Harman. Overview of the
we can determine whether a set of points (places) is
seventh Text REtrieval Conference (TREC-7). Proc.
clustered (plateaus in the self-plot), or self-similar (the
of the Seventh Text REtrieval Conference (TREC-7),
linearity of the self-plot). The slope of the self-plot is
also the intrinsic dimension of the geographic distribu-
tion of the places. (Figure 5)  H. Wactlar, M. Christel, Y. Gong, and
A. Hauptmann. Lessons learned from the creation and
• Event correlation Using GeoPlot and the charac- deployment of a terabyte digital video library. IEEE
terization rules (Observation 4), we could determine Computer, 32(2):66–73, February 1999.
whether two groups of video clips have similar (or dis-  G. K. Zipf. Human Behavior and Principle of Least
similar) geographic distributions (Figure 9). More- Eﬀort: an Introduction to Human Ecology. Addison
over, the relation between two groups of video clips Wesley, 1949.
can be identiﬁed as global (Figure 8(c)) or local (Fig-
ure 8(d)) features, which can help discover (clarify) the
source of the correlation.
In addition, the visualization of GeoPlot is user-friendly
that a viewer can quickly tell whether two sets of points are
similarly distributed (at local scale or global scale) or not.
Moreover, GeoPlots can be computed quickly in linear
time O(N ) on the number of points N , using the so-called
“box-counting plots” from the fractal theory  .
This is the ﬁrst step towards a new tool for video data
mining. Future work could include the time (date) dimen-
sion of the news events and explore the evolving patterns of
the news events.