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International Journal of Computer Engineering and Applications, Volume VII, Issue I, Part II,
July 14
A REVIEW ON SPATIAL DATA MINING METHODS AND
APPLICATIONS
Aakunuri Manjula1, Dr.G.Narsimha2
1
Research Scholor, CSE, JNTUH, Telangana
Associate Professor, CSE, JNTUHCEJ, Telangana
2
ABSTRACT:
Spatial Data Mining (SDM) is a complex phenomenon as it deals with data that represents both
spatial and non-spatial correlations in spatial databases. SDM extracts latent and implicit trends
in spatial data to acquire business intelligence which support expert decision making. Spatial
database is very vast as it can hold the spatial objects spread across the globe. Mining such
databases have plethora of real world utilities such as discovering cancer clusters, crime hotspots,
warming of oceans, traffic risk analysis, agriculture land grading, analyzing forest extent changes
to mention few. In order to achieve this various techniques are used. This paper focuses on
reviewing various spatial data mining techniques and their applications. The essence of SDM is to
have applications pertaining to pattern families such as location prediction, spatial interactions,
and hotspots.
Keywords – Spatial data mining (SDM), SDM techniques, SDM applications
[1] INTRODUCTION
A spatial database is the database which has been specially optimized to store data
pertaining to objects in the real world. In other words spatial data is the data which represents
objects in geometric space. The objects are stored in database in the form of lines, points and
polygons. A Relational Database Management System (RDBMS) with additional features can
support spatial databases which are extensively used in environmental studies, Global Positioning
System (GPS), and Geographic Information System (GIS). Spatial Data Mining (SDM) is a process
of discovering trends or patterns from large spatial databases that hold geographical data. Objects
in space such as roads, rivers, forests, deserts, buildings, cities etc., are stored in spatial database.
Spatial databases are so complex and make the SDM more difficult when compared with traditional
databases. The major applications of SDM are related to co-location mining, spatial outlier
detection and location prediction. PostGIS, Microsoft SQL Server, Oracle Spatial, SpatiaLite etc.,
are the products available for building spatial databases. Object relational models can support
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spatial data. Therefore all Object Relational Database Management Systems (ORDBMS) can
support spatial data provided primitives such as point, line and region.
Spatial objects are uniquely identified by latitude and longitude. Generally a GIS is used to
store, retrieve and manipulate spatial data. Spatial database can also include data from CAD and
CAM systems that represents smaller scale objects on printed circuit boards, automobile engines
and so on. While modeling spatial database geometric types are used to store spatial data. These
types include point, line string, polygon, arc line string, arc polygon, compound polygon,
compound line string, circle and rectangle as shown in [Figure-1].
Figure: 1. Illustrates geometric types [1]
He et al. [2] presented uncertainty issues in SDM. Peng et al. [3] made SDM experiments
of POI databases. Thirumurugan and Suresh [4] focused on statistical spatial clustering. Spatial
data mining is explored on big data in [5] and [6]. Geo SDM was focused in [7]. Sheng wu [8]
focused on the problem of “data rich and knowledge lack” with respect to SDM. Moraes and
Bastos [9] throw light into pattern recognition with SDM. The idea of smart earth with SPD is
explored in [10]. Bi et al. [11] focused on settlement archeology through SDM. Wang et al. [12]
contributed to cluster analysis in SDM by introducing two methods.
Spatial data mining has plenty of real world applications such as traffic risk analysis, fire
accident analysis, analysis of forest extent changes, grading of agriculture land, analysis of railways
etc. In fact it can be integrated GIS systems and existing MIS of organizations. More details on
SDM can be found in the rest of the sections of this paper. Our contributions in this paper include
the study and review of various spatial data mining techniques and real world applications. The
remainder of the paper is structured as follows. Section 2 focuses on the real world applications of
SDM. Section 3 throws light into various spatial data mining techniques. Section 4 shows the
importance of SDM with GIS. Section 5 describes CUBE usage with SDM for effectiveness.
Section 6 bestows importance of visualization of SDM results while section concludes besides
providing directions for future work.
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[2] APPLICATIONS OF SPATIAL DATA MINING
There are many real world applications that need spatial data mining. The following sub
sections provide such applications in some detail.
[2.1] IDENTIFYING FIRE HOT SPOTS IN FORESTS
Tay et al. [13] studied on the problem of knowledge derivation from spatial data. They
focused on fire hot spots in forests with the help of satellite images. The fire hotspots can have
false alarms. Their approach is based on the assumption that fires do not spread in straight line.
The fire hot spots are clustered using region growing algorithm while pattern recognition is used
to identify possible false alarms. They used 30 datasets of SPOT and NOVAA for experiments.
Their prototype application demonstrates the detection of false alarms. Yu and Bian [14] did their
research on fire cases that occur in the geographical space. They employed frequency theory and
incremental spatial data mining in order to find out the relation between fire related factors
available and the surrounding environment. They mined association rules so as to achieve this.
The important steps involved in the solution include data preparation, datasets joint which
combines multiple datasets, mining association rules and concluding results besides building an
application known as “Forest Fireproof system” which is based on GIS. This research results can
be used in real time reaction systems for efficient decision support services.
[2.2] RAILWAY GEOGRAPHIC INFORMATION SYSTEM
Spatial data mining can be used in GIS pertaining to railways. Xu et al. [15] studied various
techniques that can be used in making a Railway Geographic Information System (RGIS) which
can be used for spatial data presentation and statistical analysis besides helping in making well
informed decisions. Some of the techniques useful for GRIS include spatial analysis, induction,
classification and clustering, trend or spatial characteristic analysis, pattern recognition and digital
map image analysis. Other approaches that can also be applied to spatial data mining are
visualization approaches, rough set and fuzzy set approaches, genetic algorithms, and artificial
neural networks. Thus the intelligent RGIS has high utility in the real world when it is used along
with intelligent transportation systems. Moreover the RGIS can be integrated with existing
management information systems (MIS) pertaining to railways. Such integrated application
brings about more intuitiveness to the application.
[2.3] EVALUATION OF FOREST EXTENT CHANGES
Forests play an important role in eco system which has telling effect on living creatures.
The extent of forest has its impact on the eco system dynamics. Interestingly SDM can be used to
know these dynamics that will help nations to make well informed decisions. Jayasinghe and
Yoshida [16] made significant research on evaluating the extent changes in forest. Their research
was based on satellite images that are multi-temporal in nature. They used supervised and
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unsupervised classification methods to derive thematic maps that are integrated with a GIS. For
actual spatial data mining back propagation algorithm was used. The results revealed that there
was decrease in extent of forests between 1992 and 2006.
[2.4] ANALYZING DISTRIBUTION OF REGIONAL ECONOMY
SDM can be used to analyze many social aspects through geographical data. For instance,
Lian et al. [17] studied regional economic differences. They could find an interesting fact that
regional economy has strong spatial correlation. With respect to rural regional economy, global
spatial autocorrelation with respect to the variables such as per capita, agriculture total and output
value classification technique pertaining to spatial data mining is used. This is done to discover
knowledge pertaining to rural regional economy. Other approaches used for the analysis of
regional economy are local spatial autocorrelation and global spatial autocorrelation. The results
revealed that considering both global and local spatial autocorrelations yield better output.
[2.5] GRADING OF AGRICULTURE LAND THROUGH SDM
Grading of agriculture land grading can have important utility in real world. Jian [18]
proposed spatial data mining approach for agriculture land grading. An empirical study made by
Jia has methodology that covers problems pertaining to land grading. The application can produce
missing land information as well besides identifying difficulties that are associated with factors of
quantitative analysis. The analysis includes economic, social and natural factors. These results are
visualized with annotations. The grading of agriculture land thus helps in understanding the
importance of land in given area socially, economically and naturally.
[2.6] CLUSTERING AREA GEOGRAPHICAL ENTITIES
Guang-xue et al. [19] studied Clustering Area Geographical entities by using clustering
algorithms. The algorithms work on the concept of geometric space similarity. Towards it
similarity criteria is used. The experiments were made on line segments where space similarity
criteria are adapted. Another similarity criterion considered is area geographical similarity. Table
1 summarizes real world spatial data mining applications. However, it needs to be noted that the
list is not exhaustive.
[2.7] AGRICULTURE CROP YIELD PREDICTION
Crop yield prediction has important utility towards precison agriculture. Towards this many
researchers proposed techniques. According to Prasad et al. [29] many techniques came into
existence using remote sensing data that are linked to crop yield prediction directly or indirectly.
The techniques include Temperature Condition Index (TCI), Vegetation Condition Index (VCI)
and Normalized Vegetation Index (NDVI). In [30] a methodology was discussed for crop yield
prediction. Stathakisa et al. [30] explored neural networks for prediction of wheat crop. This is
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done using remote sensing data. They used a system known as Adaptive Neuro-Fuzzy Inference
System (ANFIS) which takes many parameters as input and generates forecast pertaining to wheat
yield.
Table 1 - Summary of Real World Applications of SDM
Author (s)
Year
Application
Identifying real forest fire
events and disclosing false
alarms
Railway graphic
information system that
support visual presentation,
query and expert decision
making.
Remarks
Experiments are
made on satellite
images
Tay et al. [13]
2003
Xu, Qin, and
Huang [15]
2003
Xingxing et al.
[20]
2005
Integrated GIS with mining
features
Lian et al. [17]
2008
Distribution of regional
economy analysis
Jinlin et al. [21]
2008
Accident analysis
Jia [18]
2009
Grading of agriculture land
Wang and Chen
[22]
2011
Study of land use
Agriculture land
dataset
Jayasinghe and
Yoshida [16]
2013
Analyzing forest extent
changes which will help in
taking steps to bring
balance in eco system
Experiments are
made using
satellite images
Ravikumar &
Gnanabhaskaran
[23]
-
Traffic risk analysis
Data collected
from government
Data from railway
MIS
Data is used from
existing GIS
Spatial data
collected from
agriculture lands
Data collected
from GIS
Agriculture land
dataset
[3] SPATIAL DATA MINING TECHNIQUES
This section focuses on methods or techniques used for spatial data mining by the
researchers earlier. Tay et al. [13] used region growing method and Hough transform for
identifying forest fire hotspots besides disclosing false alarms. Xu, Qin, and Huang [15] applied
association rule mining, classification and forecast, trend analysis and planning for railway GIS.
Xingxing et al. [20] used SPMML based method for integrating SDM and GIS so as to make the
GIS more effective in serving spatial information and making expert decisions. Zhang et al. [24]
Identified problems with current techniques that deal with data present in data warehouse and
developed a new method of data representation namely “Spatial Data Cube” which improves the
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quality of SDM. Yu and Bian [14] employed frequency based method for making GIS based
fireproof system which helps in analyzing fire accidents in forests. Jayasinghe and Yoshida [16]
used back propagation algorithm to know the changes in forest extent. Ravikumar &
Gnanabhaskaran [23] used ant colony optimization technique for traffic risk analysis. Table 2
summarizes SDM techniques used in some of the data mining applications. However, it needs to be
noted that the list is not exhaustive.
Table 2- Summary of SDM Techniques
Author (s)
Tay et al. [13]
Year
2003
Xu, Qin, and
Huang [15]
2003
Xingxing et al.
[20]
2005
Zhang et al.
[24]
2005
Yu and Bian
[14]
2007
Jayasinghe and
Yoshida [16]
2013
Research
Area
SDM to
identify
false alarms
in forest fire
hotspots
SDM
integrated
with railway
MIS to
improve the
effectiveness
of railway
operations
like
monitoring
railway
tracks,
material
besides
analyzing its
operations
and
geographical
spread
Integration
of SDM
with GIS
Improving
spatial data
mining
effectiveness
GIS based
fireproof
system
Spatial Data
Mining to
evaluate
forest extent
changes
Technique
Remarks
Region
growing
method,
Hough
transform
Satellite
images are
used for
experiments
Association
rule mining,
classification
and forecast,
trend
analysis and
planning
Data is taken
from railway
MIS
SPMML
based
method
A new data
model called
spatial data
cube
Frequency
theory based
method
Back
propagation
algorithm
Data is taken
from the
existing GIS
Spatial data
from
warehouse
Sample spatial
dataset
Satellite
images are
taken as
dataset
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Ravikumar &
Gnanabhaskaran
[23]
-
Traffic risk
analysis
Ant colony
optimization
Data collected
from
government.
[4] SPATIAL DATA MINING WITH GIS
Geographical Information System (GIS) is required in order to leverage the benefits of
spatial data mining. Xu, Qin, and Huang [15] built an intelligent railway GIS that allows users to
perform various data mining tasks pertaining to railways. Since there is a need for integrating both
GIS and SDM Xingxing et al. [20] proposed a novel method that could integrate the both. The
solution was based on XML and its related markup languages. Thus the integration works
seamlessly with any future enhancements in heterogeneous environment as well. The two aspects
work in tandem with each other. For instance spatial data mining brings about locations where
breast cancer is spread across the globe while the GIS present it nicely to help users make decisions
faster. Therefore the division of labor brings about modularity and quality in work and presentation.
The prototype built was tested using the spatial data mining on spatial database built in postgreSQL
(postGIS extension). The prototype application has components like task manager, data adapter,
data mining algorithms, and user-friendly interface.
GIS can be used for various domains. Yu and Bian [14] built decision support services with
a GIS that will work as fire proof system with respect to forests. It shows the relation between the
fire events and their environment. Association rule mining has been made on spatial data in order to
achieve this. The application is able to locate fire points and analyze the environment which caused
that. This will help authorities to have preliminary estimation of the fire incidents in forests. This is
especially useful in places where forests are spread and they need to be monitored to resolve fire
problems. Decision support services integrated with forest fire proof system are very useful in
making expert decisions. The forecast made by the GIS enabled system might be weak or strong.
However, it makes significant progress towards making a comprehensive solution for fire proof
mechanisms.
Governments of various states or countries can make use of GIS. However, such GIS need
to deal with huge amount of geographical data. This is where SDM can come into picture for
efficient GIS that can render quality services. Li et al. [25] studied uncertainty of spatial data of
governments that affects the quality of SDM. There might be much number of uncertainties such as
immaturity uncertainty, inconsonance uncertainty, topology uncertainty, attribute uncertainty,
location uncertainty, and error uncertainty. Li et al. emphasized on uncertainty pertaining to
attributes and introduced control methods. The control methods include improving attribute
definitions, choosing correct data sources, and improving accuracy of the data model which is used
in SDM.
Traffic accidents can also be analyzed using spatial data mining. Jinlin et al. [21] proposed
spatial data mining approach with GIS for traffic analysis. Accident analysis is done using spatial
association rule mining which will discover knowledge pertaining to traffic accident distribution in
spatial domain. The mining process is carried out as described here. GIS data inquiry is made in
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order to obtain the user-interested fact to be mined. After obtaining user interested facts, spatial
association rule mining algorithm is applied in order to generate association rules. The analysis
results in identifying accident prone areas that can help in future to take necessary steps to prevent
accidents.
The power of grid computing can also be leveraged for SDM. Fan and Luo [26] proposed a
model which exploits the power of grid based servers in order to achieve SDM. The application
proposed is a GIS that makes use of server associated with grid. Therefore the mining process is
decentralized so as to make it faster and also have other features like fault tolerance, reliability,
availability and scalability. [Figure-2] shows the architecture for Grid Spatial Data Mining.
Figure: 2. Illustrates GIS Based Grid Spatial Data Mining [26]
As can be seen in [Figure-2], it is evident that grid is used to discover information
based on the client request. Data server provides data services while spatial data mining server
does information discovery which is invoked by data mining middleware.
[5] SPATIAL DATA MINING WITH CUBE
Spatial data mining needs innovative data models as they are complex in nature. Zhang et
al. [24] proposed a new data model known as “Spatial Data Cube” which leverages the data
modeling in data warehousing. As the current techniques could not handle spatial data mining well
in data warehouses, the new data model resolves this issue by providing improved support for
SDM. The spatial data cube is very flexible as it supports both spatial and non spatial data
seamlessly. Spatial data cube makes use of selective materialization concept in order to process
queries efficiently. Not only the data, but the measures and dimensions also can be represented both
spatially and non-spatially using spatial data cube. Global climate data explored in [27] is used to
build spatial data cube which works with the underlying application.
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[6] SPATIAL DATA MINING AND VISUALIZATION
Visualization has got high importance in SDM since it brings about quality and
comprehensiveness in presentation. Ferrucci, Laurini, and Polese [28] built a tool that can make
visual presentation of spatial relationships, perform mining activities on both spatial and nonspatial data with intuitive user interface. The tool named “VisMiner” also guides users to perform
various activities pertaining to SDM. The tool has provision for organization of user work by
exploiting Windows OS concepts like folder, file, etc. Various characteristics of the tool can be
configured. They include modularity, structured organization, interactivity, standardization, besides
extending its capabilities. The tool is extendable and its existing algorithms can be improved and
new algorithms can be adapted to make it more flexible and intuitive. Tay et al. [13] also visualized
false alarms in an application that classifies fire hot spots in forests.
[7] CONCLUSION AND FUTURE WORK
This paper reviews spatial data mining, its real world applications and mining techniques. It
studies the essence of data mining in terms of applications and techniques. Some of the applications
of spatial data mining include hop spot classification of fire in forests, identifying false alarms of
fire hotspots, building an effective GIS, GIS for railways, evaluation of forest extent changes,
agriculture land grading, analysis of distribution of rural regional economy, clustering area
geographic entities, GIS and SDM integration, SDM with spatial data cube, SDM and visualization.
The techniques used in literature for SDM include region growing method, Hough transform,
association rule mining, classification and forecast, planning, trend analysis, SPMML based
method, frequency theory based method, back propagation algorithm and ant colony optimization.
Traditional data mining techniques cannot be directly used for spatial data mining. As a future
direction we are going to make empirical study of SDM on location prediction, spatial interactions,
and hotspots.
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