Spatial Multidimensional Association Rules Mining in Forest Fire Data

doi:10.4236/jdaip.2013.14010

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Journal of Data Analysis and Information Processing, 2013, 1, 90-96

Published Online November 2013 (http://www.scirp.org/journal/jdaip)

http://dx.doi.org/10.4236/jdaip.2013.14010

Open Access JDAIP

Spatial Multidimensional Association Rules Mining in

Forest Fire Data

Imas Sukaesih Sitanggang

Department of Computer Science, Bogor Agricultural University, Bogor, Indonesia

Email: imas.sitanggang@ipb.ac.id

Received September 20, 2013; revised October 25, 2013; accepted November 8, 2013

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occur-

rence is essential so that fire events can be predicted based on characteristics of a certain area. This study discovers the

possible influence factors on the occurrence of fire events using the association rule algorithm namely Apriori in the

study area of Rokan Hilir Riau Province Indonesia. The Apriori algorithm was applied on a forest fire dataset which

contained data on physical environment (land cover, river, road and city center), socio-economic (income source, popu-

lation, and number of school), weather (precipitation, wind speed, and screen temperature), and peatlands. The experi-

ment results revealed 324 multidimensional association rules indicating relationships between hotspots occurrence and

other factors. The association among hotspots occurrence with other geographical objects was discovered for the mini-

mum support of 10% and the minimum confidence of 80%. The results show that strong relations between hotspots

occurrence and influence factors are found for the support about 12.42%, the confidence of 1, and the lift of 2.26. These

factors are precipitation greater than or equal to 3 mm/day, wind speed in [1 m/s, 2 m/s), non peatland area, screen tem-

perature in [297K, 298K), the number of school in 1 km2 less than or equal to 0.1, and the distance of each hotspot to

the nearest road less than or equal to 2.5 km.

Keywords: Data Mining; Spatial Association Rule; Hotspot Occurrence; Apriori Algorithm

1. Introduction

Forest fires are considered to be a potential hazard that

causes enormous physical, biological and environmental

losses. Hotspots are image pixels that may represent fires.

A study on the spatial relationships between the location

of hotspots occurrence and specific geographical objects

near the hotspots is essential. Therefore, the possible in-

fluence factors for fires can be determined to predict the

future hotspots occurrence.

Spatial data mining is a growing research area in ana-

lyzing large spatial data. It is a process to extract knowl-

edge, spatial relationships, or the other interesting pat-

terns not explicitly stored in spatial databases [1]. In a

spatial data mining system, attributes of neighbors of an

object may have a significant influence on the object

itself. Therefore, the discovery process for spatial data is

more complex than those for non-spatial data, because

spatial data mining algorithms have to consider the

neighbors of objects in order to extract useful knowledge

[2].

In this study, a technique in data mining, namely asso-

ciation rule mining, is applied to a case study. The pur-

pose of the case study is to discover relations between hot-

spots occurrence and the characteristics of neighboring

objects of hotspots. Pre-processing steps for spatial data

were performed to prepare a dataset as the input of well-

known association rule algorithm, i.e. Apriori. The results

are spatial association rules describing frequent co-oc-

currences between variables in the spatial database.

Some works related to mining spatial association rules

are discussed in [3-6]. Moreover, Berardi, et al. [7] dis-

covered spatial association rules from a particular kind of

images, namely document images. This work studied six

papers, published in the IEEE Transactions on Pattern

Analysis and Machine Intelligence, in the January and

February 1996 issues. The rules discovery is based on the

processes of layout structure extraction (layout analysis)

and logical structure extraction (document image under-

standing). This work uses SPADA (Spatial Pattern Dis-

covery Algorithm) [8] to generate association rules, for

example [7]: is_a(A,running_head)  on_top(A,B), is_a

I. S. SITANGGANG 91

(B,content), type_text(A), support: 90.9%; confidence:

90.9%

This rule means that if a logical component (A) is a

running head, then it is textual and it is on top of another

layout component (B) which is a component of type

content. This rule has a high support and a high confi-

dence i.e. 90.9%.

A case study by [9] determines the existing spatial re-

lationships between the location of incidents and specific

geographical objects near the center of Helsinki. This

work performed the transformation of spatial data to the

transaction format such that the classic association rules

algorithms can be applied to the data. Each object in the

transactional file is identified by only its unique ID, geo-

graphical coordinates and the specification of object type

(point, line, or polygon). The algorithm based on the Ap-

riori algorithm was utilized to extract association rules

from the transaction file. One of the rules is as follows:

bars and restaurants  incidents (1.7%; 40.0%).

This rule states that an incident has occurred in a

neighbourhood of 40% of all bars and restaurants within

the Helsinki city center during the studied time period.

Spatial association rules of land use were extracted in

the study by [10] from the land use data of Yi city in

Hubei Province in China. This work used the fuzzy con-

cept lattice method to obtain land use spatial association

rules, which can offer decision supports in land suitabil-

ity evaluation, classification and grading and land use

planning [10].

2. Material and Methods

2.1. Study Area and Forest Fires Data

The study area is Rokan Hilir district in Riau Province in

Indonesia. Rokan Hilir spans an area of 8881.59 Km2 [11]

or approximately 10% of Riau’s total land area. Rokan

Hilir is located in the western part of the north Sumatera,

the southern part of Bengkalis district and Rokan Hulu

district, the eastern of Dumai and the northern part of the

north Sumatera and Melaka strait. The district is divided

into 13 subdistricts with the total of population is

552,400 based on Population Census 2010 of the Riau

Province [12].

The data used in this study are as follows:

1) Spread and coordinates of MODIS hotspots 2008.

The data are provided by Fire Information for Resource

Management System (FIRMS), University of Maryland,

NASA, Conservation International.

2) Digital maps for road, rivers, city centers, land cov-

er, and the administrative border from National Coordi-

nating Agency for Survey and Mapping (BAKOSUR-

TANAL), Indonesia.

3) Socio-economic data from BPS-Statistics Indonesia

including inhabitant’s income source, population density,

and number of school per km2.

4) Weather data 2008 (in the NetCDF format) includ-

ing screen temperature, precipitation, 10 m wind speed,

and surface height. The data were collected from Mete-

orological Climatological and Geophysical Agency (BMKG),

Indonesia.

5) Digital maps for peatland depth and peatland types

provided by the Wetland International.

A MODIS hotspot/active fire is a vegetation fire, but

sometimes it is a volcanic eruption or the flare from a gas

well. It is detected using the MODIS (or Moderate Reso-

lution Imaging Spectroradiometer) instrument, on board

NASA’s Aqua and Terra satellites [13]. A MODIS hot-

spot represents the center of a 1 km (approximately)

pixel flagged as containing one or more actively burning

hotspots/fires (Figure 1).

In the study area, as many 517 MODIS hotspots were

found in 2008. We create a buffer of each hotspot and

then 513 points were randomly generated outside buffers

as non-hotspot points. The radius of buffer is 0.90737 km

as the result of Landsat TM image processing.

The spatial data as influencing factors for hotspots

occurrence are stored in layers in the spatial database.

There are three types of spatial features in the layers i.e.

point, line, and polygon. The spatial reference system

UTM 47N and datum WGS84 were assigned to all layers

in the spatial database.

2.2. Data Transformation

Association rules mining requires a dataset in the trans-

action format which contains transaction id and item sets.

Several steps were performed to create the transaction

dataset from the set of layers of influencing factors for

hotspots occurrence. The tools utilized in data transfor-

mation are PostgreSQL 9.1 (http://www.postgresql.org)

to manage the spatial database, PostGIS 1.5

(http://www.postgis.org) to perform spatial operations,

and Quantum GIS 1.7.2 (http://www.qgis.org) to analyze

and to visualize spatial data.

This work applied topological and distance relation-

ships to relate spatial objects in two different layers. The

topological operation ST_Within that is available in Post

GIS defines whether a point feature is located inside a

polygon feature. For example, for each hotspot and non-

hotspot point, we determine whether the points are inside

a land cover type in which land cover objects are repre-

sented in polygon (Figure 2). The operation ST_Within

was also used to relate the hotspot occurrence layer to

other layers i.e. income source, precipitation, screen

temperature, 10 m wind speed, peatland type and peat-

land depth.

Moreover, the distance function is used to calculate

distance from a point (or line) to another point (or line).

Open Access JDAIP

I. S. SITANGGANG

Figure 1. The MODIS hotspot represents the center of a 1

km (approximately) pixel [13].

Figure 2. Hotspot locations overlaid with the land cover la-

yer.

This work computed distance from hotspots and non-

hotspots (point features) to the nearest river (line fea-

tures), to the nearest road (line features), and to the near-

est city centers (point features). For example, Figure 3

shows hotspot locations overlaid with road and city cen-

ters. Figure 4 shows how distance from a hotspot to

every river segment is calculated and the minimum value

is considered as the distance from a hotspot to the nearest

river. To perform this task, the spatial operation ST_

Distance in PostGIS 1.5 was applied to calculate distance

of objects to the nearest river, road, and city center. Be-

cause the Apriori algorithm requires categorical values in

a dataset, the minimum distance were converted to cate-

gorical values based on the classes provided in Table 1.

Table 2 provides the number of spatial features in all

layers in the forest fire database. The layers contain spa-

tial objects that may influence hotspots occurrence. In

order to discover associations between spatial objects and

hotspots occurrence using the Apriori algorithm, each

layer is related to the hotspot layer.

Relating the hotspot layer to other layers using the

spatial operation ST_Within and ST_Distance results

several new layers. For example, Figure 5 shows the

relations as the representation of layers. Each relation has

the attribute the_geom which stores the geometry type of

spatial features. The new layer (c) is obtained by apply-

ing the spatial operation ST_Within to define whether

points in the hotspot layer (a) are inside polygons in the

land cover layer (b) or not.

Figure 3. Hotspot locations overlaid with road and city cen-

ters.

Figure 4. Hotspot locations overlaid with the river layer.

Table 1. Classes for distance from target objects to nearest

city centers , ri vers, and roads.

Class

Distance target

object to nearest

city center (x)

in km

Distance target

object to river

(y) in km

Distance target

object to road

(z) in km

Low x ≤ 7 y ≤ 1.5 z ≤ 2.5

Medium 7 < x ≤ 14 1.5 < y ≤ 3 2.5 < z ≤ 5

High x > 14 y > 3 z > 5

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I. S. SITANGGANG 93

Table 2. Layers in the database.

Layer Number of features

Distance to nearest river (dist_river) 1030 points

Distance to nearest road (dist_road) 1030 points

Distance to nearest city center (dist_city) 1030 points

Land cover (land_cover) 3058 polygons

Income source (income_source) 117 polygons

Population density (population) 117 polygons

Number of school per km2 (school) 117 polygons

Precipitation in mm/day (precipitation) 7 polygons

Screen temperature in K (screen_temp) 7 polygons

10m wind speed in m/s (wind_speed) 7 polygons

Peatland type (peatland_type) 58 polygons

Peatland depth (peatland_depth) 68 polygons

(a)

(b)

(c)

Figure 5. A new layer (c) as the result of relating the hotspot

layer (a) and the land cover layer (b).

All new layers were integrated into a single layer by

matching identifiers of objects in the hotspot layer and

those in other layers. This step produced a relation that is

considered as a transactional dataset for the Apriori algo-

rithm.

2.3. Spatial Association Rules

The basic idea of mining association rules from spatial

databases is similar to those from non-spatial databases

(transactional or relational databases). A spatial associa-

tion rule has the form A  B (s%, c%), where A and B

are sets of spatial or non-spatial predicates, s% is the

support of the rule, and c% is the confidence of the rule

[1]. Spatial association rules differ with non-spatial asso-

ciation rules because it may include spatial predicates

such as distance information (for instance, close_to, and

far_away), topological relations (for example, touch,

overlap, and intersect), and spatial orientation (such as

right_of, and east_of). An example of spatial association

rule is as follows: x is a shopping centre  x close to a

bus station  x close to a settlement area (0.5%, 75%).

The rule says that 75% of shopping centers that are

close to bus stations are also close to settlement areas,

and 0.5% of the data belong to such a rule.

2.4. Apriori Algorithm

The Apriori algorithm was introduced by [14] to discover

frequent itemsets and association rules in a transactional

dataset that have support and confidence greater than the

user-specified minimum support (minsup) and minimum

confidence (minconf) respectively. An association rule

has the form X  Y, where X and Y are a subset I, I is a

set of items, and X  Y = . The Apriori algorithm is as

follows [14]:

Lk is a set of large k-itemsets. This set contains k-

itemsets that have minimum support. Ck is a set of can-

didate k-itemsets. Itemsets in this set are potentially large

itemsets. In the Apriori algorithm, the apriori-gen func-

tion has the argument Lk1 i.e. the set of all large (k1)-

itemsets. The output of this function is a superset of the

set of all large k-itemsets [14].

1) L1 = {large 1-itemsets};

2) for (k = 2; Lk  1 ≠ ; k++) do begin

3) Ck = apriori-gen (Lk  1); //New candidates

4) forall transactions t  D do begin

5) Ct = subset(Ck, t); //Candidates contained in t

6) forall candidates c  Ct do

7) c.count ++;

8) end

9) Lk = {c  Ck | c.count  minsup}

10) end

11) Answer = kLk;

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There are three most widely-used measures for select-

ing interesting rules i.e. support, confidence and lift.

Support and confidence of the rule A  B are defined as

follows [1]:



support



BPAB  (1)



confidence |



BPBA (2)



support



B is the percentage of transaction in a

transactional dataset D that contain both A and B whereas

confidence(AB) is the percentage of transactions in D

containing A that also contain B [1]. Equation (2) is also

stated as follows:

 



support

confidence upport

AB sA



 (3)

In order to measure the correlation between A and B in

the rule AB, the correlation measure Lift may be used

which is computed as follows [1]:

 

lift ,PA B

AB PA PB



 (4)

Based on the value of





lift ,

B in Equation (4), the

relation of occurrence of A and B is described as follows.



lift ,



B is greater than 1, then A and B are posi-

tively correlated meaning that the occurrence of A im-

plies the occurrence of B. If





lif t

B is less than 1,

then A and B are negatively correlated. A and B are inde-

pendent if



t ,



lif

B is equal to 1. It means that there is

no correlation between A and B [1].

2.5. Multiple Dimensional Association Rule

Mining

In multiple dimensional association rule mining, associa-

tion rules are discovered from a dataset which contains

more than one attribute (called as a dimension). For ex-

ample, in single dimension mining, we can generate a

rule: buys (X, “pc tablet”)  buys (X, “earphones”),

whereas in a multidimensional mining, we can generate a

rule: Occupation (X,” IT staff”) and Salary (X, “10-20K”)

 buys (X, “smartphone”).

In this rule, occupation, salary and buys are dimen-

sions that may have different types such as boolean, cate-

gorical and numerical.

Srikant and Agrawal [15] introduced an approach to

map the quantitative association rules problem to the

boolean association rules problem. The quantitative val-

ues are partitioned into intervals and then the pair < at-

tribute, interval > is mapped to a boolean attribute [15].

For example, the attribute Age can be partitioned into

two intervals: 20 - 29 and 30 - 39. The categorical attrib-

ute correspond to <attribute, value>. For example, the

attribute Married that has two values: yes and no, is re-

placed to the pair < Married: Yes > and < Married: No>.

Figure 6 shows an example of a dataset before and af-

ter mapping to boolean association rules problem. We

can apply the algorithms for mining single dimension

association rule to the new dataset (Figure 6(b)).

3. Result and Discussion

Pre-processing steps on the spatial forest fires data result

a dataset consisting of 490 records. Variables in the

dataset are hotspots occurrence, distance to nearest river

(dist_river), distance to nearest road (dist_road), distance

to nearest city, center (dist_city), land cover (land_cover),

income source (income_source), population density (popu-

lation), number of school per km2 (school), precipitation

in mm/day (precipitation), screen temperature in k (screen

_temp), 10m wind speed in m/s (wind_speed), peatland

type (peatland_type), and peatland depth (peatland_

depth). The Apriori algorithm which is available in the

statistical computing tool R (http://www.r-project.org/)

was executed on the dataset and it generated 2981 asso-

ciation rules. The purpose of this study is to find possible

factors that strongly influence hotspots occurrence.

Therefore for further analysis, we only study association

rules that include hotspots occurrence. There are 324

rules or about 10.87% containing hotspots occurrence

generated from the dataset with the minimum support of

10% and the minimum confidence of 80%.

For the support value greater than or equal to 25%,

weather variables and socio-economic variables occur

with hotspots in the study area. The support of 25%

means that 123 transactions out of 490 transactions sup-

port the association rules. The weather variables included

in the rules are precipitation  3 mm/day and screen

temperature = [297˚K, 298˚K) whereas the socio-eco-

nomic variables appeared in the rules are population den-

sity ≤ 50 and number of school in 1 km2 ≤ 0.1.

The physical environmental factors including dist_city

= (7 km, 14 km], dist_river ≤ 1.5 km, dist_road ≤ 2.5 km,

and land_cover = Plantation occur together with hot

(a)

(b)

Figure 6. A dataset before (a) and after (b) mapping to boo-

lean association rules problem (Srikant and Agrawal, 1996).

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I. S. SITANGGANG 95

spots in the rules that have the support less than 25%.

Moreover, hotspots appear in non-peatland and in the

area where inhabitant’s income source is plantation.

Several rules extracted from the forest fire transaction

dataset are as follows:

1) {hotspot_occurrence = Yes} => {precipitation  3

mm/day} (44.49%, 100%, 1.03)

2) {hotspot_occurrence = Yes} => {school ≤ 0.1}

(36.94%, 83.03%, 1.00)

3) {population ≤ 50, hotspot_occurrence = Yes} =>

{screen_temp = [297K,298K]] (25.31%, 80%, 1.05)

4) {income_source = Plantation, hotspot_occurrence =

Yes} => {population ≤ 50} (18.57%, 85.85%, 1.27)

5) {dist_city = (7 km, 14 km), hotspot_occurrence =

Yes) => {precipitation  3 mm/day} (20.20%, 100%,

1.03)

6) {peatland_type = non_peatland, hotspot_occurrence

= Yes} => {wind_speed = [1 m/s, 2 m/s)} (13.27%,

83.33%, 1.20)

For each rule, the first number between brackets repre-

sents the support, the second is the confidence of the rule,

and the third is the lift of the rule. The rule 1 is the

strongest rule among all rules generated from the forest

fires dataset. This rule has the support of 44.49%, the

confidence of 100%, and the lift of 1.03. It means that

44.49% of the transactions contain at least the factor

hotspot_occurrence = Yes and precipitation  3 mm/day

and all transactions that contains hotspot_occurrence =

Yes also contain precipitation  3 mm/day. The lift of

rule 1 is greater than 1 meaning that hotspot_occurrence

= Yes and precipitation  3 mm/day are positively corre-

lated. This rule means that there is a high probability that

the hotspots occurred in the area which has precipitation

is greater than or equal to 3 mm/day. The rule 2 states

that about 36.94% of the transactions in the dataset that

contain hotspot_occurrence = Yes also contain school ≤

0.1. This means hotspots are probably occurred in less

populated regions in which the number of school in the

area of 1 km2 is less than or equal to 0.1. Rules 3 and 4

show the associations between hotspots occurrence and

two socio-economic factors i.e. population ≤ 50 and in-

come_source = Plantation as well as the weather factor

i.e. screen_temp = [297K, 298K).

According to the rule 5, hotspots occur in locations in

which the precipitation is greater than or equal to 3 mm/

day. The distance between the locations to nearest city

centers is greater than 7 km and less than 14 km. As

many 99 transactions out of 490 transactions (20.20%)

support this association. The rule 6 means that hotspots

were found in non-peatlands with the range of 10 m wind

speed is [1 m/s, 2 m/s).

Figure 7 shows the scatter plot for 324 association

rules containing the item hotspot_occurrence = Yes. Ea-

ch point in the plot represents a rule. Support and lift are

Figure 7. Scatter plot for 324 association rules containing

the item hotspot_occurrence = Yes.

used for the x-axis and y-axis respectively while the

color of the points is used to indicate the confidence level

of the rules.

The rule in the bottom right in Figure 7 has the high-

est support i.e. 44.4898%. There are 24 rules in the top

left corner with the highest lift of 2.258065 and the high-

est confidence of 1. In the average, these rules are sup-

ported by 12.4149667% of records in the dataset. In ad-

dition to hotspots occurrence, the rules include other

factors that are considered as influencing factors for fire

events. These factors are precipitation greater than or

equal to 3 mm/day, wind speed in [1 m/s, 2 m/s), non

peatland area, screen temperature in [297K, 298K), the

number of school in 1 km2 less than or equal to 0.1, and

the distance of each hotspot to the nearest road less than

or equal to 2.5 km.

4. Summary and Future Work

This paper discusses the application of the association

rule algorithm to discover strong relationships among

hotspots occurrence and other geographical objects for

forest fires. Pre-processing steps were conducted on the

spatial forest fire dataset in order to prepare a task rele-

vant dataset for the Apriori algorithm. Two types of spa-

tial relationships namely topological and metric were

applied to relate a spatial feature to other spatial features.

Our analysis with the minimum support of 25% and

the minimum confidence of 80% shows strong relations

among hotspot occurrence, weather variables, and socio-

economic. Hotspots mostly occur in less-populated areas

with population density being less than or equal to 50

and number of schools per km2 being less than or equal

to 0.1. The precipitation when the hotspots occur is

greater than or equal to 3 mm/day and the interval for

screen temperature is [297˚K, 298˚K).

The association among hotspots occurrence and phy-

sical environmental factors was discovered for the sup-

port greater than 10% and less than 25%, and the mini-

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I. S. SITANGGANG

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mum confidence of 80%. Hotspots were found not far

from rivers and roads where the distance of the hotspots

to the nearest river and road was less than or equal to 1.5

km and 2.5 km, respectively. Areas where hotspots found

are covered by plantation and thus inhabitant’s income

source is plantation.

In future work, we intend to investigate how negative

association rules algorithms may be applied on the forest

fire dataset to discover strong relations between geo-

graphical objects and locations where hotspots are not

probably occurred.

REFERENCES

[1] J. Han and M. Kamber, “Data Mining: Concepts and

Techniques,” 2nd Edition, Morgan Kaufmann, 2006.

[2] M. Ester, H. Kriegel, and J. Sander, “Spatial Data Mining:

A Database Approach,” Proceedings of the Symposium on

Large Spatial Databases, Berlin, 15-18 July 1997, pp. 47-

66. http://dx.doi.org/10.1007/3-540-63238-7_24

[3] K. Koperski and J. Han, “Discovery of Spatial Associa-

tion Rules in Geographic Information Databases,” Pro-

ceedings of the 4th International Symposium on Advances

in Spatial Databases, Springer-Verlag, London, 1995, pp.

47-66. http://dx.doi.org/10.1007/3-540-60159-7_4

[4] A. Appice, M. Ceci, A. Lanza, F. A. Lisi and D. Malerba,

“Discovery of Spatial Association Rules in Geo-Refer-

enced Census Data: A Relational Mining Approach,” In-

telligent Data Analysis, Vol. 7, No. 6, 2003, pp. 541-566.

[5] L. Wang, K. Xieb, T. Chena and X. Mab, “Efficient Dis-

covery of Multilevel Spatial Association Rules Using Par-

titions,” Journal of Information and Software Technology,

Vol. 47, No. 13, 2005, pp. 829-840.

http://dx.doi.org/10.1016/j.infsof.2004.03.007

[6] J. Mennis and J. W. Liu, “Mining Association Rules in

Spatio-Temporal Data: An Analysis of Urban Socioeco-

nomic and Land Cover Change,” Transactions in GIS,

Vol. 9, No. 1, 2005, pp. 5-17.

http://dx.doi.org/10.1111/j.1467-9671.2005.00202.x

[7] M. Berardi, M. Ceci and D. Malerba, “Mining Spatial

Association Rules from Document Layout Structures,”

Proceedings of the 3rd Workshop on Document Layout

Interpretation and its Application, Edinburgh, 2 August

2003, pp. 9-13.

[8] D. Malerba and F. A. Lisi, “Discovering Associations

between Spatial Objects: An ILP Application,” Proceed-

ings of the 11th International Conference on Inductive

Logic Programming, Springer-Verlag, London, 2001, pp.

156-163.

[9] V. Karasová, J. M. Krisp and K. Virrantaus, “Application

of Spatial Association Rules for Improvement of a Risk

Model for Fire and Rescue Services,” Proceedings on the

10th Scandinavian Research Conference on Geographi-

cal Information Science (ScanGIS), Stockholm, 13-15

June 2005, pp. 183-194.

[10] J. Niu, Y. Zhang, W. Feng and L. Ren, “Spatial Associa-

tion Rules Mining for Land Use Based on Fuzzy Concept

Lattice,” Proceedings of the 19th International Confer-

ence on Geoinformatics, Shanghai, 24-26 June 2011, pp.

1-6.

[11] P. K. R. Hilir, “Gambaran Umum Kabupaten,” 2010.

http://www.rohilkab.go.id/?tampil=link&act=profil&id=4

[12] B. P. S. K. R. Hilir, “Hasil Sensus Penduduk 2010, Ka-

bupaten Rokan Hilir, Data Agregat per Kabupaten/Kota,”

2010. http://sp2010.bps.go.id/files/ebook/1409.pdf

[13] Fire Information for Resource Management System

(FIRMS), “Frequently Asked Questions,” 2013.

https://earthdata.nasa.gov/data/near-real-time-data/faq/fir

[14] R. Agrawal and R. Srikant, “Fast Algorithms for Mining

Association Rules,” Proceedings of 20th International

Conference on Very Large Data Bases, VLDB, 1994, pp.

487-499.

[15] R. Srikant and R. Agrawal, “Mining Quantitative Asso-

ciation Rules in Large Relational Tables,” Proceedings of

the 1996 ACM SIGMOD International Conference on

Management of Data, Montreal, 4-6 June 1996, pp. 1-12.

http://dx.doi.org/10.1145/233269.233311