A Survey on Geographical Information System, Spatial Data mining

A Survey on Geographical Information System, Spatial Data mining and Ontology P Mousi1, R Kalpana2, S Sangeetha3, Dr. V Bhuvaeswari4 1,3

Research scholar, Bharathiar University, Coimbatore, Tamil Nadu, India. 2 PG Student, Bharathiar University, Coimbatore, Tamil Nadu, India. 4 Assistant Professor, Bharathiar University, Coimbatore, Tamil Nadu, India.  Abstract-- GIS (Geographical Information System) is one of the most recent research areas for its various applications across human needs, Which benefits the society through its varied range of applications such as Remote Sensing, Engineering, Crime, History, Archaeology, Planning, Management, Transport/Logistics, Insurance, Telecommunications, and Business etc., GIS technologies are applied to diverse fields to assist experts and professionals in analyzing various types of geospatial data and dealing with complex situations. The objective of this paper is to provide with an Overview of GIS data, structure and applications. The inter connectivity of various component remain a great challenge. To overcome this complexity ontology concepts are used. The objective of this paper is to provide with a detailed overview of GIS, Ontology and spatial data mining model.

systems, data visualization, statistics, machine learning, and information theory it is an exploratory process aimed at discovering hidden features in the database, testing the hypothesis and building the model. Recent widespread use of spatial databases has lead to the studies of Spatial Data Mining (SDM), Spatial Knowledge Discovery (SKD), and the development of spatial data mining techniques. The GIS system consists of number of heterogeneous components connected, with each other such as sensors, laptop, mobile etc. Each component in the GIS frame work consists of heterogeneous data. Integrating the data generated from the components becomes a challenging task. Ontology is used in integrating these data. Ontology is a technique to use represent and share the knowledge about a particular entity by modeling object in the entity and the relationship between those object. These are describes the properties of those objects. In these objects are any one is the domain is being modeled. Ontology is a specification of conceptualization. It means simplified view of the world that to represent for some purpose. Ontological commitments are make and used for the specification only. The ontological commitment is used for knowledge sharing. In this paper we have done a detailed study about the various GIS components, data format, standards and Ontology tools for modularizing the semantic data representation is done.

Index Terms-- GIS, Spatial Data Mining, Association Rule Mining, Clustering, Remote Sensing, Ontology I. INTRODUCTION GIS is an upcoming research area used in various application domains for the benefit of mankind.GIS is a collection of Spatial and non spatial data. In recent years due to various technological development huge volumes of data is generated representing spatial information of water bodies, forest reserves, urbanization, etc., Analysis of GIS deals to match with non-spatial data mining. Data mining is a technique that helps in analysis of spatial data. In recent years of development spatial data mining has gained major importance. Increasing availability of large datasets from different agents creates the necessity of knowledge information discovery from data, which leads to an emerging field of data mining or knowledge discovery in databases. Data mining involves the fields of database

II. LITERATURE REVIEW In this section we review the GIS related spatial data mining and ontology. In [9], Elewa et.al detailed an approach integrating geographic information systems, remote sensing and water shed modeling to identify the suitable areas for implementing the runoff water harvesting constructions. In [19], Câmara proposes solution for the complexity and dimension of subject involved in the deforestation problem in Amazon case using plentiful

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image datasets to use detect & prevent the deforestation the application domain. In [15], Hwang have concerned the quality algorithm to developing a new algorithm for different domains and problems, they proposed a conceptual framework for spatial data mining system driven by formal ontology. In [20], Maniraj studied has been reported on the different features and issues of the ontology languages such as a rules, relations, state and process. In [3], Zhdanova analyzed the past changes and evolution of semantic web language then they presented current trends of development of semantic web languages they extended that to emerging ontology language. In [18], Mantha et al. proposes solution for Geo-Spatial Agricultural Census data using an ontology class to associate geographic data. In [16], Irena Spasic et al. focused on the relation between ontology and text mining, discussed how the ontology is used in some other applications i.e Information Retrieval and Information Extraction and also explained use the machine learning methods in Text Mining Applications. In [25], Tazin Malgundkar et al. proposes the developing a knowledge on urban traffic information based on ontology and the user detecting the current traffic information using GPS mobile device then the result will be sent to the user from the data center. In [5], Alaa et.al provided a method to optimize the cells towers distribution by using spatial mining with Geographic Information System applying the Digital Elevation Mode.

the name of the location, population of the area or name of the road. TABLE I: VECTOR FORMATS

B. Data Storage GIS stores location and data that are generated during digitization process. The attribute data of locations are created stored separately. Relational database model is suitable for storage and linkage and database query language can be used to retrieve data. C. Data Analysis and Modeling The spatial analysis functions use spatial and non-spatial data to answer questions about the real world. The primitive analytical functions that must be provided by any GIS are: Retrieval, Reclassification, and Generalization, Topological Overlay Techniques, Neighborhood Operations, Connectivity Functions.

III. GEOGRAPHIC INFORMATION SYSTEM GIS is a tool for geographical analysis of spatial information by collecting, storing, transforming, retrieving and displaying spatial data from the real GIS world. It provides electronic representation of information about the earth’s natural and man- made features. Creating GIS involves four steps,

D. Data Output and Presentation It conveys the results of analysis to the people who make decisions about resources in the form of graphic displays. E. GIS Applications GIS occurs in almost every industry. It is used for education, crime mapping, water management, land management, oceanography, environmental and aeronautical applications. The Table II lists out the various fields of GIS applications.

A. Data Input The data inputs are of two types: spatial data latitude/longitude for geo-referencing, the features on a map, ex: soil units, administrative districts. Attribute data descriptive data about the features, ex: soil properties, population of districts, etc.) GIS data input is the process of encoding analogue data maps, imageries or photographs into computer readable digitized form and writing data into GIS database. Spatial data can be in two formats: vector format based on discrete objects. It is represented as points, lines and areas. Raster format represented as grid of cells or pixels - each cell or pixel is represented by a unique reference coordinate (cell address). Each cell also has discrete attribute data assigned to it. Attribute data are descriptive data of point, line and area features. Descriptive data are shown in Table I. It may be

IV. SPATIAL DATA MINING Increasing availability of large datasets from different agents creates the necessity of knowledge information discovery from data, which leads to an emerging field of data mining or knowledge discovery in databases. Data mining involves the fields of database systems, data visualization, statistics, machine learning, and information theory it is an exploratory process aimed at discovering hidden features in the database, testing the hypothesis and building the model. Recent widespread use of spatial databases has lead to the studies of Spatial Data Mining

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(SDM), Spatial Knowledge Discovery (SKD), and the development of spatial data mining techniques. Traditional data mining methods assume independence among studied objects and lack the ability to handle the inter-relational nature of spatial data. Spatial data mining methods can be used to understand spatial data, discover relationships between spatial and non- spatial variables, detect the spatial distribution patterns of certain phenomena, and predict the trend of such patterns. Spatial data mining include spatial statistics and data mining. Spatial data mining tasks can be grouped into description, exploration, and prediction. To understand the data, spatial data and spatial phenomena have to be first described and analyzed; and hidden patterns and relationships among spatial or non-spatial variables have to be explored. Based on the current pattern of spatial distribution and the understanding of spatial relationships, future state and trend of the spatial pattern and spatial distribution can be predicted. Spatial data mining techniques include, Visual interpretation and analysis, spatial and attribute query and selection, characterization, generalization and classification, detection of spatial and non-spatial association rules, clustering analysis, and spatial regression. The Table III is the survey of Spatial Data Mining. Spatial data mining, or knowledge discovery in spatial database, refers to the extraction of implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases Previous works in machine learning database systems and statistics laid the foundation for research into knowledge discovery in databases. Also, advances in spatial databases, such as spatial data structures, spatial reasoning, computational geometry, etc., paved the way for the study of spatial data mining. Geographic Information Systems (GIS), remote sensing, image databases exploration, medical imaging, robot navigation, and other areas where spatial data are used. Knowledge discovered from spatial data can be of various forms, like characteristic and discriminated rules, extraction and description of prominent structures or clusters, spatial associations , and others. The purpose of this survey is to provide an overall picture of the methods of spatial data mining, their strengths and weaknesses, how and when to apply them, and to determine what was achieved so far and what are the challenges yet to be faced.

commitments are make and used for the specification only. The ontological commitment is used to a vocabulary for knowledge sharing. Ontologies has extensively used in data integration system because they provide an explicit and machine-understandable conceptualization of a domain. They have been used in one of the three ways, (1) Single ontology approaches: All source schemas are directly related to a shared global ontology that provides a uniform interface to the user. However, this approach requires that all sources have nearly the same view on a domain, with the same level of granularity. A typical example of a system using this approach is SIMS [25]. (2) Multiple ontology approaches: Each data source is described by its own (local) ontology separately. Instead of using a common ontology, local ontologies are mapped to each other. For this purpose, additional representation formalism is necessary for defining the inter-ontology mappings. The observer system [20] is an example of this approach. (3) Hybrid ontology approaches: A combination of the two preceding approaches is used. First, a local ontology is build for each source schema, which, however, is not mapped to other local ontologies, but to a global shared ontology. New sources can be easily added with no need for modifying existing mappings. The layered framework is an example of this approach. The single and hybrid approaches are appropriate for building central data integration systems. A hybrid peer-to-peer system, where a global ontology exists in a “super-peer” can also use the hybrid ontology approach [Peer-to-Peer Semantic Integration of XML and RDF Data Sources]. The multiple ontology approach can be best used to construct pure peer-to-peer data integration systems, where there are no super-peers.

V. ONTOLOGY

A. Ontology Editor Tools In ontology editor tools are used to design the application to assist in the creation or manipulate of ontology. The table IV will be explained the ontology editor tools.

Ontology is a technique to use represent and share the knowledge about a particular entity by modeling object in the entity and the relationship between those object. These are describes the properties of those objects. In these objects are any one is the domain is being modeled. Ontology is a specification of conceptualization. It means simplified view of the world that to represent for some purpose. Ontological

B. Ontology Langauges The ontology language is used to construct the ontology. The ontology classifies based on the structure or syntax. The TABLE V will be explained the ontology languages and their uses.

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TABLE II : GIS APPLICATIONS

TABLE III: SURVEY ON SPATIAL DATA MINING

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TABLE IV: ONTOLOGY EDITOR TOOLS

TABLE V: ONTOLOGY LANGUAGES

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[12] Hwang, S (2004), Using Formal Ontology for Integrated Spatial Data Mining, In: Laganà A et al (ed) Computational Science and Its Applications – ICCSA2004, LNCS Vol. 3044, Springer-Verlag, pp. 1026-1035 [13] Irena Spasic, Sophia Ananiadou, John McNaught and Anand Kumar,Text mining and ontologies in biomedicine: Making sense of raw text, HENRY STEWART PUBLICATIONS 1467-5463. BRIEFINGS IN BIOINFORMATICS. VOL 6. NO 3. 239–251. SEPTEMBER 2005 [14] Koperski, K., Han, J. (1995). Discovery of spatial association rules in geographic information databases. In The 4th int’l symp. on large spatial databases (SSD95) (pp. 47–66), Maine, USA. [15] Dr. S.S Mantha, Mrs. Madhuri Rao , Ms. Naina Saita Design and Implementation of Ontology based on Semantic analysis for GIS Application, CS & IT-CSCP 2011, 10.5121/csit.2011.1310 [16] Marcelino Pereira dos Santos Silva, Gilberto Câmara ,Remote Sensing Image Mining Using Ontologies [17] V. Maniraj, Dr.R. Sivakumar, Ontology Languages – A Review, International Journal of Computer Theory and Engineering, Vol.2, No.6, December, 2010. 1793-8201 [18] Mennis, J., & Liu, J. W. (2005). Mining association rules in spatiotemporal data: An analysis of urban socioeconomic and land cover change. Transactions in GIS, 9(1), 5–17. [19] Mena.E, Kashyap.V, Sheth.A.P and Illarramendi.A, OBSERVER An Approach for Query Processing in Global Information Systems based on Interoperation across Pre-existing Ontologies., In proceedings of the 1st IFCIS International Conference on Cooperative Information Systems(CoopIS 1996),pages 14-25, 1996. [20] Pace, R. K., Barry, R., Clapp, J. M., & Rodriquez, M. (1998). Spatiotemporal autoregressive models of neighborhood effects. Journal of Real Estate Finance and Economics, 17(1), 15–33. [21] Roy Ladner, Frederick E Petry, Maria : A CobbFuzzy Set Approaches to Spatial Data Mining of Association Rules. Transactions in GIS, 2003, 7(1): 123Ð138 [22] Tazin Malgundkar, Madhuri Rao and Dr. S.S. Mantha, GIS DRIVEN URBAN TRAFFIC ANALYSIS BASED ON ONTOLOGY, International Journal of Managing Information Technology (IJMIT) Vol.4, No.1, February 2012 [23] Thill, J.-C., & Wheelerm, A. (2000). Tree induction of spatial choice behavior. Transportation Research Record, 1719, 250–258. [24] Wache.H, Vogele.T, Visser.U, Stuckenschmidt.H, Schuster.G, Neumann.H and Hubner.S, Ontology-Based Integration of Information —A Survey of Existing Approaches., In proceedings of the IJCAI-01 Workshop on ontologies and Information Sharing,2001. [25] Yao, X., & Thill, J.-C. (2007). Neurofuzzy modeling of context– contingent proximity relations. Geographical Analysis, 39(2), 169– 194.

VI. RESULT AND DISCUSSIONS GIS benefits the human in a wide range of application. This study produces a detailed survey of GIS in various applications, data formats and data standards. In this study we have explored the application of spatial data mining tasks for GIS applications and knowledge extraction. As GIS components are heterogeneous in nature with various data formats and Ontology. Ontology is also presented to repeat the semantics of GIS applications and is compared. This paper provides survey of various applications in table format for easy exploration. The future work includes GIS semantic model, ontology for integrating data from GIS application with spatial data mining task for any specific domain. REFERENCES [1]

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