SPATIAL DATA MINING. Ms. S. Malathi, Lecturer in Computer Applications, KGiSL - IIM

Size: px
Start display at page:

Download "SPATIAL DATA MINING. Ms. S. Malathi, Lecturer in Computer Applications, KGiSL - IIM"

Transcription

1 SPATIAL DATA MINING Ms. S. Malathi, Lecturer in Computer Applications, KGiSL - IIM INTRODUCTION The main difference between data mining in relational DBS and in spatial DBS is that attributes of the neighbors of some object of interest may have an influence on the object and therefore have to be considered as well. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations), which are used by spatial data mining algorithms. Therefore, new techniques are required for effective and efficient data mining. SPATIAL DATA Spatial data are data that have a spatial or location component. Spatial data can be viewed as data about objects that they are located in a physical space. This may be implemented with a specific location attributed such as address or latitude/longitude or may be more implicitly included such as by a portioning of the database based on location. In addition, spatial data me be accessed using queries containing spatial operators such as near, north and south adjacent and contained in. Spatial data are stored in spatial databases that contain spatial and non-spatial data about objects, because of the inherent distance information associated with spatial data, spatial databases are often stored using special data structures or indices built using distance or topological information. Spatial data are required for many current information technology systems, Geographic Information Systems (GIS) are used to store information related to geographic locations on the surface of the earth. This includes applications related to weather, community infrastructure needs, disaster management, and hazardous waste.

2 Data mining activities include prediction of environmental catastrophes, biomedical applications including imaging and illness diagnosis, also require spatial systems. SPATIAL DATA MINING Spatial data mining i.e., mining knowledge from large amounts of spatial data, is a highly demanding field because huge amounts of spatial data have been collected in various applications, ranging from remote sensing to Geographical Information Systems (GIS), Computer Cartography, Environmental Assessment and planning, etc. The collected data far exceeded human s ability to analyze. Recent studies on data mining have extended the scope of data mining from relational and transactional databases to spatial databases. This paper summarizes recent works on spatial data mining from spatial data generalization, to spatial data clustering, mining spatial association rules etc. It shows that spatial data mining is a promising field, with fruitful research results and many challenging issues. Spatial Data Mining Background Statistical spatial analysis has been the most common approach for analyzing spatial data. Statistical analysis is a well-studied area and therefore there exist a large number of algorithms including various optimization techniques. It handles very well numerical data and usually comes up with realistic models of spatial phenomena. The major disadvantage of this approach is the assumption of statistical independence among the spatially distributed data. This causes problems as many spatial data are in fact interrelated, i.e. spatial objects are influenced by their neighboring objects. Furthermore, the statistical approach cannot model non-linear rules very well and symbolic values like names are handled poorly. Statistical methods also do not work well with incomplete or inconclusive data. Another problem related to statistical spatial analysis is the expensive computation of the results.

3 With the advent of data mining researchers proposed various methods for discovering knowledge from large databases. Most of them concentrate on relational or transaction databases. These methods strived to combine the already mature areas like machine learning, databases and statistics. Studies laid a foundation for spatial data mining. Machine learning techniques learning from examples and generalization and specialization are widely used in spatial data mining. It did not take long before the statistical cluster analysis technique was modified for the use in spatial data mining. Also other methods were extended toward knowledge discovery in spatial databases. In the next section we define some commonly used terms in spatial data mining. PRIMITIVES OF SPATIAL DATA MINING We have developed a set of database primitives for mining in spatial databases which are sufficient to express most of the algorithms for spatial data mining and which can be effectively supported by a Database Management Systems (DBMS). We believe that the use of these database primitives will enable the integration of spatial data mining with existing Database Management Systems and will speed-up the development of new spatial data mining algorithms. The database primitives are based on the concepts of neighborhood graphs and neighborhood paths. RULES Various kinds of rules can be discovered from databases in general. For example, characteristic rules, discriminant rules, association rules, or deviation and evolution rules can be mined. A spatial characteristic rule is a general description of spatial data. For example, a rule describing the general price range of houses in various geographic regions in a city is a spatial characteristic rule.

4 A spatial discriminant rule is a general description of the features discriminating or contrasting a class of spatial data from other classes like the comparison of price ranges of houses in different geographical regions. Finally, a spatial association rule is a rule, which describes the implication of one or a set of features by another set of features in spatial databases. For example, a rule associating the price range of the houses with nearby spatial features, like beaches, is a spatial feature, like beaches is a spatial association rule. THEMATIC MAPS Thematic maps present the spatial distribution of a single or a few attributes. This differs from general or reference maps where the main objective is to present the position of objects in relation to other spatial objects. Thematic maps may be used for discovering different rules. For example, we may want to look at temperature thematic map while analyzing the general weather pattern of a geographic region. There are two ways to represent thematic maps: raster and vector. In the raster image from thematic maps have pixels associated with the attribute values. For example, a map may have the altitude of the spatial objects coded as the intensity of the pixel (or the color). In the vector representation a spatial object is represented by its geometry most commonly being the boundary representation along with thematic attributes. IMAGE DATABASES There is special kind of spatial databases where data almost entirely consists of images or pictures. Image databases are used in remote sensing, medical imaging, etc.

5 They are usually stored in form of grid arrays representing the image intensity in one or more spectral ranges. TOPOLOGICAL RELATIONSHIPS These relationships are based on the ways in which two objects are placed in a geographic domain. Disjoint: A is disjoint from B if there are no points in A that are contained in B. Overlaps or Intersects: A overlaps with B if there is at least one point in A that is also in B. Equals: A equals B if all points in the two objects are in common. Covered By or Inside or Contained In: A is contained in B if all points in A are in B. There may be points in B that are not in A. Covers or Contains: A contains B if B is contained in A. Spatial Data Structures, Computations and Queries Algorithms for spatial data mining involve the use of spatial operations like spatial data joins, map overlays, nearest neighbor queries and others. Therefore, efficient Spatial Access Methods (SAM) and the data structures for such computation is also a concern in spatial data mining. SPATIAL DATA STRUCTURES Spatial data structures consist of points, lines, rectangles etc. In order to build indices for these data, multidimensional trees have been proposed. These include quad trees, k-d trees, R-trees, R*-trees etc. one of the prominent SAMs which was much discussed in the literature recently is R-tree and its modification R*-tree.

6 R-Tree One approach to indexing spatial data represented as MBRs is an R-tree. Each successive layer in the tree identifies smaller rectangles. In an R-tree, cells may actually overlap. An object is represented by an MBR that is located within one cell. Basically, a cell is the MBR that contains the related set of objects at a lower level of decomposition. Objects stored in R-trees are approximated by Minimum Bounding Rectangles (MBR). R-tree in every node stores as a set of rectangles. At the leaves there are stored pointers to representation of polygon s boundaries and polygon s MBRs. At the internal nodes each rectangle is associated with a pointer to a child and represents minimum bounding rectangle of all rectangles stored in the child. The illustration of the use of MBRs by looking at a lake is shown in figure1. Figure 2 illustrates the use of R-Tree: Figure 1 : Minimum Bounding Rectangles (MBR) Figure 2 : R-Tree

7 Quad Tree One of the original data structures proposed for spatial data is that of a quad tree. A quad tree represents a spatial object by a hierarchical decomposition of the space into quadrants (cells). The spatial area has been divided into two layers of quadrant divisions. The number of layers needed depends on the precision desired. Obviously, the more layers the more overhead is required for the data structure. Each level in the quad tree corresponds to one of the hierarchical layers. The quad tree example is shown in figure 3. Figure 3 : Quad Tree Spatial Computations Spatial Join is one of the most expensive spatial operations. In order to make spatial queries efficient spatial join has to be efficient as well. Brinkhoff et al proposed an efficient multilevel processing of spatial joins using R*-Trees and various approximation of spatial objects. The first step - filter - finds possible pairs of intersecting objects using their MBRs and later other approximations. In the second step - refinement - detailed geometric procedure is performed to check for intersection. Another important spatial operation, map overlay, is especially important in Geographic Information Systems.

8 Spatial Query Processing The complexity of spatial operations, much work has been performed to examine spatial query processing and its optimization. A traditional selection query accessing non-spatial data uses the standard comparison operations: <, >, <=, >=,!=. A spatial selection is a selection on spatial data that may use other selection comparison operations. The types of spatial comparators that could be used to include near, north, south, west and east contained in and overlap or intersect. The following are examples of several spatial selection queries. * Find all houses near to Big Temple of Tanjore *. The architecture for spatial database called SAND (spatial and non-spatial data) architecture, which is a model of the extended relational database with spatial operations. This architecture provides both spatial and non-spatial components of spatial database to participate in query processing and optimization. SPATIAL DATA MINING ARCHITECTURE Various architectures (models) have been proposed for data mining. They include Han s architecture for general data mining prototype DBLEARN/DBMINER, Holsheimer et al s parallel architecture, and Matheus et al s multi component architecture. Almost all of those architectures have been used or extended to handle spatial data mining. Matheus et al s architecture seems to be very general and has been used by other researchers in spatial data mining, including Ester et al. This architecture comparable to others is presented in the below figure 4. In this architecture, the user may control every step of the mining process. Background knowledge like spatial and non-spatial concept hierarchies or information about database is stored in a knowledge base. Data is fetched from the storage using the DB Interface which enables optimization of queries.

9 User Controller DBM S DB Interface Focus Pattern Extraction Evaluatio n Discoveries Domain Knowledge Knowledge base Figure 4 : Spatial Data Mining Architecture Spatial data index structures like R-Trees may be used for efficient processing. The Focusing Component decides which parts of data are useful for pattern recognition. For example, it may decide that only some attributes are relevant to the knowledge discovery task, or it may extract objects whose usage promises good results. Rules and patterns are discovered by the Pattern Extraction module. This module may use statistical, machine learning, and data mining techniques on conjunction with computational geometry algorithms to perform the task of finding rules and relations. The interestingness and significance of these patterns is then processed by the Evaluation module to possibly eliminate obvious and redundant knowledge. The four last components may interact between themselves through the Controller part. ARCHITECTURE: KDD DESIGN Methods for Knowledge Discovery in Spatial Databases Geographic data consist of spatial objects and non-spatial description of these objects. Non-spatial description of spatial objects can be stored in a traditional relational database where one attribute is a pointer to spatial description of the object. Spatial data

10 can be described using two different properties, geometric and topological. For example, geometric properties can be spatial location, area, perimeter etc., whereas topological properties can be adjacency (object A is neighbor of object B), inclusion (object A is inside in object B), and others. Thus, the methods for discovering knowledge can be focused on the non-spatial and/or spatial properties of spatial objects. The algorithms for spatial data mining include generalization-based methods for mining special characteristic and discriminate rules, two-step spatial computation technique for mining spatial association rules, aggregate proximity technique for finding characteristics of spatial clusters etc., In the following sections, we categorize and describe a number of these algorithms. Generalization-Based Knowledge Discovery One of the widely used techniques in machine learning is learning from examples. This method is often combined with generalization. This approach cannot be directly adopted for large spatial databases because: 1. The algorithms are exponential in the number of examples and 2. It does not handle noise and in consistent data very well. The generalization-based knowledge discovery requires the existence of background in the form of concept hierarchies. In case of spatial databases, there can be two kinds of concept hierarchies, non-spatial databases; there can be two kinds of concept hierarchies, non-spatial and spatial. Concept hierarchies can be explicitly given by experts, or in some cases they can be generated automatically by data analysis. It can be done on non-spatial data by (a) Climbing the concept hierarchy when attribute values in a tuple are changed to generalized values, (b) Removing attributes when further generalization is impossible and there are too many different values for an

11 attribute, and (c) Merging identical tuples. Induction is continued until values for every attribute are generalized to the desired level. The desired level is reached when the number of different values for the attribute in the generalized table is no greater than the generalization threshold for this attribute. During the process of merging of identical tuples the number of merged tuples is stored in additional attribute count to enable quantitative presentation of acquired knowledge. Other aggregate values for merge tuples may be stored well. The two generalization based algorithms are spatial-datadominant and non-spatial-data-dominant generalizations. Both algorithms assume that the rules to be mined are general data characteristics and that the discovery process is initiated by the user who provides a learning request (query) explicitly, in syntax similar to SQL. Spatial-Data-Dominant Generalization In the first step all data described in the query are collected. Given the spatial data hierarchy, generalization can be performed first on the spatial data by merging the spatial regions according to the description stored in the concept hierarchy. Generalization of the spatial objects continues until the spatial generalization threshold is reached. Non-Spatial-Data-Dominant Generalization This method also starts with collecting all data relevant to the user query. In the example presented in Figure 4 the DB interface extracts the perception data relevant to the province and time period specified in the query. In the second step the algorithm performs attribute-oriented induction on the non-spatial attribute, generalizing them to a higher (more general) concept level. For example, the precipitation value in the large (10 in., 15 in.) Can be generalized to the concept wet. The generalization threshold is used to determine whether to continue or stop the generalization process. In this step the pointers to spatial object are collected as a set and put with the generalized non-spatial data.

12 In the third and last step of the step of the algorithm, neighboring areas with the same generalized attribute are merged together based on the spatial function adjacent to For example, if in one area the precipitation value was 17 in., and in neighboring area it was 18 in. Both precipitation values are generalized to the concept very wet and both areas are merged. Approximation can used to ignore all the small regions with different non-spatial description. For example, if the majority of area land can be described as industrial, but a few gas stations exist in this area the whole area can be described as industrial one. The query may be presented in the form of a map with a small number of regions with high level description. Methods Using Clustering Cluster analysis is a branch of statistic that has been studied extensively for many years. The many advantage of using this technique is that has been studied extensively for many years. The main advantage of using these techniques is that interesting structures or clusters can be found directly from the data without using any background knowledge, like concept hierarchies. A similar approach in machine learning is known as unsupervised learning. We can exploit the results of research on clustering techniques in the spatial data mining process as proposed in. Clustering algorithms used in statistic, like PAM or CLARA, are reported to be inefficient from the computational complexity point of view. As for the efficiency concern, a new algorithm, called CLARANS (Clustering Large Application based upon RANdomized Search), was developed for cluster analysis. Experimental evidence showed that CLARANS out performs the two existing cluster analysis algorithm, PAM (Partitioning Around Mediods) and CLARA (Clustering Large Application). Ng and Han used CLARANS in spatial data mining algorithms, SD (CLARANS). First, we will briefly describe the three cluster analysis algorithms.

13 The difference between the PAM and CLARA algorithms is that the latter one is based upon sampling. Only a small portion of the real data is chosen as a representative of the data and Mediods are chosen from this sample using PAM. The idea is that if the sample is selected in a fairly random manner, then it correctly represents the whole data set and therefore, the representative objects (Mediods) chosen, will be similar as if chosen from the whole data set. CLARA draws multiple samples and outputs the best clustering out of these samples. As expected, CLARA can deal with larger data sets than PAM. Algorithm SD (CLARANS) In this spatial dominant approach, spatial component(s) of the relevant data item are collected and collected and clustered using CLARANS. Then, the algorithm performs an attribute oriented induction on non-spatial description of the object in each cluster. The result of the query presents high-level non-spatial description of objects in every cluster. For example, one can find that in Vancouver expensive housing units are clustered in 3 clusters. In the downtown cluster there are mainly expensive condominiums; in the waterfront cluster mansions and single house are located; and the third cluster consists mainly of single houses. Algorithm NSD (CLARANS) This non-spatial dominant approach first applies non-spatial generalizations. Attribute oriented generalization is performed on the non spatial generalization is performed on the non-spatial generalized tuples. For example, the description of expensive housing units can be generalized to a single houses, mansions and condo minimums. For each such generalized tuples, all spatial components are collected and clustered using CLARANS to find clusters. In the final step, the clusters obtained that way are checked to see if they overlap with clusters describing other types of objects. If

14 so, then the clusters are merged, and the corresponding generalized non-spatial description of tuples is merged as well. Depending upon the rules or the form of knowledge that user wants to discover, it may be better to choose one or the other of the other of the above two algorithms. Usually SD (CLARANS) is more efficient than NSD (CLARANS). But, when the distribution of points is mainly determined by their non-spatial attributes NSD (CLARANS) may have an edge. CLARANS in Large Spatial Databases Focusing methods pointed some of the drawbacks of the drawbacks of the CLARANS clustering algorithm. First of all, CLARANS assumes that the objects to be clustered are all stored in the main memory. This assumption may not be valid for large database and that is why disk-based methods could be required. Secondly, the efficiency of the algorithm can be substantially improved by modifying the focusing component of the algorithm. The other technique to reduce the computations is to restrict the access to certain object that does not actually contribute the computation. The two different focusing techniques which try to exploit this approach is focus on the relevant clusters, and focus on a cluster. MINING IN IMAGE AND RASTER DATABASES Knowledge mining from Image and Raster Databases can be viewed as a case of spatial data mining. Data mining in image database may be seen as similar to image processing. However, In the case of data mining studies large data was processed, while image processing usually concentrate on analysis of single or a few images.

15 The system is composed of three basic components: Data Focusing, Feature extraction, classification learning. Like all other data focusing techniques, the first component increase the overall efficiency of the system by first identifying the portion of the image being analyzed that is most likely to contain a volcano. This is achieved by comparing the intensity of the central pixel of a region to the region to the estimated mean background intensity of its neighborhood pixels. The second component of the system extracts interesting features from the data. Standard method used in pattern recognition like edge detection or Hough transform, deal poorly with the variability and noise presented in the case of natural data. Since it is difficult to find attribute describing volcanoes exactly, matrices containing volcanoes images were decomposed into eigenvectors. Eigenvalues were treated as attribute describing volcanoes. Then the final task, which is performed by the rest of the system, is to discriminate between volcanoes and other objects looking like volcanoes. APPLICATIONS Spatial Trend Detection in GIS Spatial trend described a regular change of non-spatial attribute when moving away from certain start object. Global and local trends can be distinguished. To detect and explain such spatial trends, e.g. with respect to the economic power, is an important issue in economic geography. Spatial Characterization of Interesting Regions Another important task of economic geography is to characterize certain target region such as areas with a high percentage of retirees. Spatial characterization does not only consider the attribute of the target regions but also neighboring regions and their properties.

Spatial Data Mining: Progress and Challenges. Survey paper. Krzysztof Koperski Junas Adhikary Jiawei Han. fkoperski, adhikary,

Spatial Data Mining: Progress and Challenges. Survey paper. Krzysztof Koperski Junas Adhikary Jiawei Han. fkoperski, adhikary, Spatial Data Mining: Progress and Challenges Survey paper Krzysztof Koperski Junas Adhikary Jiawei Han fkoperski, adhikary, hang@cs.sfu.ca School of Computing Science Simon Fraser University Burnaby, B.C.,

More information

Mining Knowledge in Geographical Data. Krzysztof Koperski Jiawei Han Junas Adhikary. spatial access methods.

Mining Knowledge in Geographical Data. Krzysztof Koperski Jiawei Han Junas Adhikary. spatial access methods. Mining Knowledge in Geographical Data Krzysztof Koperski Jiawei Han Junas Adhikary Huge amounts of data have been stored in databases, data warehouses, geographic information systems, and other information

More information

Algorithms for Characterization and Trend Detection in Spatial Databases

Algorithms for Characterization and Trend Detection in Spatial Databases Published in Proceedings of 4th International Conference on Knowledge Discovery and Data Mining (KDD-98) Algorithms for Characterization and Trend Detection in Spatial Databases Martin Ester, Alexander

More information

Exploring Spatial Relationships for Knowledge Discovery in Spatial Data

Exploring Spatial Relationships for Knowledge Discovery in Spatial Data 2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) (2011) IACSIT Press, Singapore Exploring Spatial Relationships for Knowledge Discovery in Spatial Norazwin Buang

More information

GIS Data Structure: Raster vs. Vector RS & GIS XXIII

GIS Data Structure: Raster vs. Vector RS & GIS XXIII Subject Paper No and Title Module No and Title Module Tag Geology Remote Sensing and GIS GIS Data Structure: Raster vs. Vector RS & GIS XXIII Principal Investigator Co-Principal Investigator Co-Principal

More information

5 Spatial Access Methods

5 Spatial Access Methods 5 Spatial Access Methods 5.1 Quadtree 5.2 R-tree 5.3 K-d tree 5.4 BSP tree 3 27 A 17 5 G E 4 7 13 28 B 26 11 J 29 18 K 5.5 Grid file 9 31 8 17 24 28 5.6 Summary D C 21 22 F 15 23 H Spatial Databases and

More information

INTRODUCTION TO GIS. Dr. Ori Gudes

INTRODUCTION TO GIS. Dr. Ori Gudes INTRODUCTION TO GIS Dr. Ori Gudes Outline of the Presentation What is GIS? What s the rational for using GIS, and how GIS can be used to solve problems? Explore a GIS map and get information about map

More information

Overview key concepts and terms (based on the textbook Chang 2006 and the practical manual)

Overview key concepts and terms (based on the textbook Chang 2006 and the practical manual) Introduction Geo-information Science (GRS-10306) Overview key concepts and terms (based on the textbook 2006 and the practical manual) Introduction Chapter 1 Geographic information system (GIS) Geographically

More information

Representation of Geographic Data

Representation of Geographic Data GIS 5210 Week 2 The Nature of Spatial Variation Three principles of the nature of spatial variation: proximity effects are key to understanding spatial variation issues of geographic scale and level of

More information

Basics of GIS. by Basudeb Bhatta. Computer Aided Design Centre Department of Computer Science and Engineering Jadavpur University

Basics of GIS. by Basudeb Bhatta. Computer Aided Design Centre Department of Computer Science and Engineering Jadavpur University Basics of GIS by Basudeb Bhatta Computer Aided Design Centre Department of Computer Science and Engineering Jadavpur University e-governance Training Programme Conducted by National Institute of Electronics

More information

Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig

Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig Multimedia Databases Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de 13 Indexes for Multimedia Data 13 Indexes for Multimedia

More information

Soft Spatial Query Processing in Spatial Databases-A Case Study

Soft Spatial Query Processing in Spatial Databases-A Case Study International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 10, Issue 4 (April 2014), PP.47-52 Soft Spatial Query Processing in Spatial Databases-A

More information

Distributed Clustering and Local Regression for Knowledge Discovery in Multiple Spatial Databases

Distributed Clustering and Local Regression for Knowledge Discovery in Multiple Spatial Databases Distributed Clustering and Local Regression for Knowledge Discovery in Multiple Spatial Databases Aleksandar Lazarevic, Dragoljub Pokrajac, Zoran Obradovic School of Electrical Engineering and Computer

More information

Geographic Information Systems (GIS) in Environmental Studies ENVS Winter 2003 Session III

Geographic Information Systems (GIS) in Environmental Studies ENVS Winter 2003 Session III Geographic Information Systems (GIS) in Environmental Studies ENVS 6189 3.0 Winter 2003 Session III John Sorrell York University sorrell@yorku.ca Session Purpose: To discuss the various concepts of space,

More information

5 Spatial Access Methods

5 Spatial Access Methods 5 Spatial Access Methods 5.1 Quadtree 5.2 R-tree 5.3 K-d tree 5.4 BSP tree 3 27 A 17 5 G E 4 7 13 28 B 26 11 J 29 18 K 5.5 Grid file 9 31 8 17 24 28 5.6 Summary D C 21 22 F 15 23 H Spatial Databases and

More information

Multimedia Databases 1/29/ Indexes for Multimedia Data Indexes for Multimedia Data Indexes for Multimedia Data

Multimedia Databases 1/29/ Indexes for Multimedia Data Indexes for Multimedia Data Indexes for Multimedia Data 1/29/2010 13 Indexes for Multimedia Data 13 Indexes for Multimedia Data 13.1 R-Trees Multimedia Databases Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig

More information

2.6 Complexity Theory for Map-Reduce. Star Joins 2.6. COMPLEXITY THEORY FOR MAP-REDUCE 51

2.6 Complexity Theory for Map-Reduce. Star Joins 2.6. COMPLEXITY THEORY FOR MAP-REDUCE 51 2.6. COMPLEXITY THEORY FOR MAP-REDUCE 51 Star Joins A common structure for data mining of commercial data is the star join. For example, a chain store like Walmart keeps a fact table whose tuples each

More information

Canadian Board of Examiners for Professional Surveyors Core Syllabus Item C 5: GEOSPATIAL INFORMATION SYSTEMS

Canadian Board of Examiners for Professional Surveyors Core Syllabus Item C 5: GEOSPATIAL INFORMATION SYSTEMS Study Guide: Canadian Board of Examiners for Professional Surveyors Core Syllabus Item C 5: GEOSPATIAL INFORMATION SYSTEMS This guide presents some study questions with specific referral to the essential

More information

RESEARCH ON THE DISTRIBUTED PARALLEL SPATIAL INDEXING SCHEMA BASED ON R-TREE

RESEARCH ON THE DISTRIBUTED PARALLEL SPATIAL INDEXING SCHEMA BASED ON R-TREE RESEARCH ON THE DISTRIBUTED PARALLEL SPATIAL INDEXING SCHEMA BASED ON R-TREE Yuan-chun Zhao a, b, Cheng-ming Li b a. Shandong University of Science and Technology, Qingdao 266510 b. Chinese Academy of

More information

SPATIAL INDEXING. Vaibhav Bajpai

SPATIAL INDEXING. Vaibhav Bajpai SPATIAL INDEXING Vaibhav Bajpai Contents Overview Problem with B+ Trees in Spatial Domain Requirements from a Spatial Indexing Structure Approaches SQL/MM Standard Current Issues Overview What is a Spatial

More information

SEARCHING THROUGH SPATIAL RELATIONSHIPS USING THE 2DR-TREE

SEARCHING THROUGH SPATIAL RELATIONSHIPS USING THE 2DR-TREE SEARCHING THROUGH SPATIAL RELATIONSHIPS USING THE DR-TREE Wendy Osborn Department of Mathematics and Computer Science University of Lethbridge 0 University Drive W Lethbridge, Alberta, Canada email: wendy.osborn@uleth.ca

More information

Geography 38/42:376 GIS II. Topic 1: Spatial Data Representation and an Introduction to Geodatabases. The Nature of Geographic Data

Geography 38/42:376 GIS II. Topic 1: Spatial Data Representation and an Introduction to Geodatabases. The Nature of Geographic Data Geography 38/42:376 GIS II Topic 1: Spatial Data Representation and an Introduction to Geodatabases Chapters 3 & 4: Chang (Chapter 4: DeMers) The Nature of Geographic Data Features or phenomena occur as

More information

GIS = Geographic Information Systems;

GIS = Geographic Information Systems; What is GIS GIS = Geographic Information Systems; What Information are we talking about? Information about anything that has a place (e.g. locations of features, address of people) on Earth s surface,

More information

An Introduction to Geographic Information System

An Introduction to Geographic Information System An Introduction to Geographic Information System PROF. Dr. Yuji MURAYAMA Khun Kyaw Aung Hein 1 July 21,2010 GIS: A Formal Definition A system for capturing, storing, checking, Integrating, manipulating,

More information

Spatial Database. Ahmad Alhilal, Dimitris Tsaras

Spatial Database. Ahmad Alhilal, Dimitris Tsaras Spatial Database Ahmad Alhilal, Dimitris Tsaras Content What is Spatial DB Modeling Spatial DB Spatial Queries The R-tree Range Query NN Query Aggregation Query RNN Query NN Queries with Validity Information

More information

Mining Geo-Referenced Databases: A Way to Improve Decision-Making

Mining Geo-Referenced Databases: A Way to Improve Decision-Making Mining Geo-Referenced Databases 113 Chapter VI Mining Geo-Referenced Databases: A Way to Improve Decision-Making Maribel Yasmina Santos, University of Minho, Portugal Luís Alfredo Amaral, University of

More information

Carnegie Mellon Univ. Dept. of Computer Science Database Applications. SAMs - Detailed outline. Spatial Access Methods - problem

Carnegie Mellon Univ. Dept. of Computer Science Database Applications. SAMs - Detailed outline. Spatial Access Methods - problem Carnegie Mellon Univ. Dept. of Computer Science 15-415 - Database Applications Lecture #26: Spatial Databases (R&G ch. 28) SAMs - Detailed outline spatial access methods problem dfn R-trees Faloutsos 2

More information

GIS CONCEPTS ARCGIS METHODS AND. 2 nd Edition, July David M. Theobald, Ph.D. Natural Resource Ecology Laboratory Colorado State University

GIS CONCEPTS ARCGIS METHODS AND. 2 nd Edition, July David M. Theobald, Ph.D. Natural Resource Ecology Laboratory Colorado State University GIS CONCEPTS AND ARCGIS METHODS 2 nd Edition, July 2005 David M. Theobald, Ph.D. Natural Resource Ecology Laboratory Colorado State University Copyright Copyright 2005 by David M. Theobald. All rights

More information

Classification Based on Logical Concept Analysis

Classification Based on Logical Concept Analysis Classification Based on Logical Concept Analysis Yan Zhao and Yiyu Yao Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada S4S 0A2 E-mail: {yanzhao, yyao}@cs.uregina.ca Abstract.

More information

Welcome to NR502 GIS Applications in Natural Resources. You can take this course for 1 or 2 credits. There is also an option for 3 credits.

Welcome to NR502 GIS Applications in Natural Resources. You can take this course for 1 or 2 credits. There is also an option for 3 credits. Welcome to NR502 GIS Applications in Natural Resources. You can take this course for 1 or 2 credits. There is also an option for 3 credits. The 1st credit consists of a series of readings, demonstration,

More information

GIS CONCEPTS ARCGIS METHODS AND. 3 rd Edition, July David M. Theobald, Ph.D. Warner College of Natural Resources Colorado State University

GIS CONCEPTS ARCGIS METHODS AND. 3 rd Edition, July David M. Theobald, Ph.D. Warner College of Natural Resources Colorado State University GIS CONCEPTS AND ARCGIS METHODS 3 rd Edition, July 2007 David M. Theobald, Ph.D. Warner College of Natural Resources Colorado State University Copyright Copyright 2007 by David M. Theobald. All rights

More information

NR402 GIS Applications in Natural Resources

NR402 GIS Applications in Natural Resources NR402 GIS Applications in Natural Resources Lesson 1 Introduction to GIS Eva Strand, University of Idaho Map of the Pacific Northwest from http://www.or.blm.gov/gis/ Welcome to NR402 GIS Applications in

More information

Cell-based Model For GIS Generalization

Cell-based Model For GIS Generalization Cell-based Model For GIS Generalization Bo Li, Graeme G. Wilkinson & Souheil Khaddaj School of Computing & Information Systems Kingston University Penrhyn Road, Kingston upon Thames Surrey, KT1 2EE UK

More information

Applied Cartography and Introduction to GIS GEOG 2017 EL. Lecture-2 Chapters 3 and 4

Applied Cartography and Introduction to GIS GEOG 2017 EL. Lecture-2 Chapters 3 and 4 Applied Cartography and Introduction to GIS GEOG 2017 EL Lecture-2 Chapters 3 and 4 Vector Data Modeling To prepare spatial data for computer processing: Use x,y coordinates to represent spatial features

More information

Research on Object-Oriented Geographical Data Model in GIS

Research on Object-Oriented Geographical Data Model in GIS Research on Object-Oriented Geographical Data Model in GIS Wang Qingshan, Wang Jiayao, Zhou Haiyan, Li Bin Institute of Information Engineering University 66 Longhai Road, ZhengZhou 450052, P.R.China Abstract

More information

43400 Serdang Selangor, Malaysia Serdang Selangor, Malaysia 4

43400 Serdang Selangor, Malaysia Serdang Selangor, Malaysia 4 An Extended ID3 Decision Tree Algorithm for Spatial Data Imas Sukaesih Sitanggang # 1, Razali Yaakob #2, Norwati Mustapha #3, Ahmad Ainuddin B Nuruddin *4 # Faculty of Computer Science and Information

More information

Multiple-Site Distributed Spatial Query Optimization using Spatial Semijoins

Multiple-Site Distributed Spatial Query Optimization using Spatial Semijoins 11 Multiple-Site Distributed Spatial Query Optimization using Spatial Semijoins Wendy OSBORN a, 1 and Saad ZAAMOUT a a Department of Mathematics and Computer Science, University of Lethbridge, Lethbridge,

More information

Introduction-Overview. Why use a GIS? What can a GIS do? Spatial (coordinate) data model Relational (tabular) data model

Introduction-Overview. Why use a GIS? What can a GIS do? Spatial (coordinate) data model Relational (tabular) data model Introduction-Overview Why use a GIS? What can a GIS do? How does a GIS work? GIS definitions Spatial (coordinate) data model Relational (tabular) data model intro_gis.ppt 1 Why use a GIS? An extension

More information

USING GIS CARTOGRAPHIC MODELING TO ANALYSIS SPATIAL DISTRIBUTION OF LANDSLIDE SENSITIVE AREAS IN YANGMINGSHAN NATIONAL PARK, TAIWAN

USING GIS CARTOGRAPHIC MODELING TO ANALYSIS SPATIAL DISTRIBUTION OF LANDSLIDE SENSITIVE AREAS IN YANGMINGSHAN NATIONAL PARK, TAIWAN CO-145 USING GIS CARTOGRAPHIC MODELING TO ANALYSIS SPATIAL DISTRIBUTION OF LANDSLIDE SENSITIVE AREAS IN YANGMINGSHAN NATIONAL PARK, TAIWAN DING Y.C. Chinese Culture University., TAIPEI, TAIWAN, PROVINCE

More information

Quality Assessment and Uncertainty Handling in Uncertainty-Based Spatial Data Mining Framework

Quality Assessment and Uncertainty Handling in Uncertainty-Based Spatial Data Mining Framework Quality Assessment and Uncertainty Handling in Uncertainty-Based Spatial Data Mining Framework Sk. Rafi, Sd. Rizwana Abstract: Spatial data mining is to extract the unknown knowledge from a large-amount

More information

Spatial Concepts: Data Models 2

Spatial Concepts: Data Models 2 Spatial Concepts: Data Models 2 2009/2010 CGI GIRS 2/31 Data modeling in 4 steps 1. Geographical perception Continuous phenomenon Discrete phenomena Virtual boundaries Tangible boundaries altitude, EM

More information

Global Grids and the Future of Geospatial Computing. Kevin M. Sahr Department of Computer Science Southern Oregon University

Global Grids and the Future of Geospatial Computing. Kevin M. Sahr Department of Computer Science Southern Oregon University Global Grids and the Future of Geospatial Computing Kevin M. Sahr Department of Computer Science Southern Oregon University 1 Kevin Sahr - November 11, 2013 The Situation Geospatial computing has achieved

More information

Visualizing Big Data on Maps: Emerging Tools and Techniques. Ilir Bejleri, Sanjay Ranka

Visualizing Big Data on Maps: Emerging Tools and Techniques. Ilir Bejleri, Sanjay Ranka Visualizing Big Data on Maps: Emerging Tools and Techniques Ilir Bejleri, Sanjay Ranka Topics Web GIS Visualization Big Data GIS Performance Maps in Data Visualization Platforms Next: Web GIS Visualization

More information

Nearest Neighbor Search with Keywords in Spatial Databases

Nearest Neighbor Search with Keywords in Spatial Databases 776 Nearest Neighbor Search with Keywords in Spatial Databases 1 Sphurti S. Sao, 2 Dr. Rahila Sheikh 1 M. Tech Student IV Sem, Dept of CSE, RCERT Chandrapur, MH, India 2 Head of Department, Dept of CSE,

More information

Discovery of General Knowledge in Large Spatial Databases

Discovery of General Knowledge in Large Spatial Databases Discovery of General Knowledge in Large Spatial Databases Wei Lu & Jiawei Han School of Computing Science Simon Fraser University Burnaby, British Columbia, Canada V5A 1S6 and Beng Chin Ooi Department

More information

A Basic Introduction to Geographic Information Systems (GIS) ~~~~~~~~~~

A Basic Introduction to Geographic Information Systems (GIS) ~~~~~~~~~~ A Basic Introduction to Geographic Information Systems (GIS) ~~~~~~~~~~ Rev. Ronald J. Wasowski, C.S.C. Associate Professor of Environmental Science University of Portland Portland, Oregon 3 September

More information

LAYERED R-TREE: AN INDEX STRUCTURE FOR THREE DIMENSIONAL POINTS AND LINES

LAYERED R-TREE: AN INDEX STRUCTURE FOR THREE DIMENSIONAL POINTS AND LINES LAYERED R-TREE: AN INDEX STRUCTURE FOR THREE DIMENSIONAL POINTS AND LINES Yong Zhang *, Lizhu Zhou *, Jun Chen ** * Department of Computer Science and Technology, Tsinghua University Beijing, China, 100084

More information

SRJC Applied Technology 54A Introduction to GIS

SRJC Applied Technology 54A Introduction to GIS SRJC Applied Technology 54A Introduction to GIS Overview Lecture of Geographic Information Systems Fall 2004 Santa Rosa Junior College Presented By: Tim Pudoff, GIS Coordinator, County of Sonoma, Information

More information

Geometric Algorithms in GIS

Geometric Algorithms in GIS Geometric Algorithms in GIS GIS Software Dr. M. Gavrilova GIS System What is a GIS system? A system containing spatially referenced data that can be analyzed and converted to new information for a specific

More information

GEOGRAPHY 350/550 Final Exam Fall 2005 NAME:

GEOGRAPHY 350/550 Final Exam Fall 2005 NAME: 1) A GIS data model using an array of cells to store spatial data is termed: a) Topology b) Vector c) Object d) Raster 2) Metadata a) Usually includes map projection, scale, data types and origin, resolution

More information

GIS Visualization Support to the C4.5 Classification Algorithm of KDD

GIS Visualization Support to the C4.5 Classification Algorithm of KDD GIS Visualization Support to the C4.5 Classification Algorithm of KDD Gennady L. Andrienko and Natalia V. Andrienko GMD - German National Research Center for Information Technology Schloss Birlinghoven,

More information

Michael Harrigan Office hours: Fridays 2:00-4:00pm Holden Hall

Michael Harrigan Office hours: Fridays 2:00-4:00pm Holden Hall Announcement New Teaching Assistant Michael Harrigan Office hours: Fridays 2:00-4:00pm Holden Hall 209 Email: michael.harrigan@ttu.edu Guofeng Cao, Texas Tech GIST4302/5302, Lecture 2: Review of Map Projection

More information

Spatial Process VS. Non-spatial Process. Landscape Process

Spatial Process VS. Non-spatial Process. Landscape Process Spatial Process VS. Non-spatial Process A process is non-spatial if it is NOT a function of spatial pattern = A process is spatial if it is a function of spatial pattern Landscape Process If there is no

More information

Geographical Information System GIS

Geographical Information System GIS Geographical Information System GIS LOOM.02.331 anto.aasa@ut.ee Scale GIS and spatial planning National Regional Local Strategic (National Dev. Plan) National Goals and development policy Tactical (Regional

More information

Introduction to GIS I

Introduction to GIS I Introduction to GIS Introduction How to answer geographical questions such as follows: What is the population of a particular city? What are the characteristics of the soils in a particular land parcel?

More information

Geographers Perspectives on the World

Geographers Perspectives on the World What is Geography? Geography is not just about city and country names Geography is not just about population and growth Geography is not just about rivers and mountains Geography is a broad field that

More information

OBEUS. (Object-Based Environment for Urban Simulation) Shareware Version. Itzhak Benenson 1,2, Slava Birfur 1, Vlad Kharbash 1

OBEUS. (Object-Based Environment for Urban Simulation) Shareware Version. Itzhak Benenson 1,2, Slava Birfur 1, Vlad Kharbash 1 OBEUS (Object-Based Environment for Urban Simulation) Shareware Version Yaffo model is based on partition of the area into Voronoi polygons, which correspond to real-world houses; neighborhood relationship

More information

RESEARCH ON SPATIAL DATA MINING BASED ON UNCERTAINTY IN GOVERNMENT GIS

RESEARCH ON SPATIAL DATA MINING BASED ON UNCERTAINTY IN GOVERNMENT GIS Abstract RESEARCH ON SPATIAL DATA MINING BASED ON UNCERTAINTY IN GOVERNMENT GIS Bin Li 1,2, Jiping Liu 1, Lihong Shi 1 1 Chinese Academy of Surveying and Mapping 16 beitaiping Road, Beijing 10039, China

More information

Introducing GIS analysis

Introducing GIS analysis 1 Introducing GIS analysis GIS analysis lets you see patterns and relationships in your geographic data. The results of your analysis will give you insight into a place, help you focus your actions, or

More information

CS570 Introduction to Data Mining

CS570 Introduction to Data Mining CS570 Introduction to Data Mining Department of Mathematics and Computer Science Li Xiong Data Exploration and Data Preprocessing Data and Attributes Data exploration Data pre-processing Data cleaning

More information

Chapter 5. GIS The Global Information System

Chapter 5. GIS The Global Information System Chapter 5 GIS The Global Information System What is GIS? We have just discussed GPS a simple three letter acronym for a fairly sophisticated technique to locate a persons or objects position on the Earth

More information

Knowledge Discovery in Spatial Databases

Knowledge Discovery in Spatial Databases Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India Vol. 4, 101 Knowledge Discovery in Spatial Databases Jyoti Jadhav Shah & Anchor Kutchhi College of Engineering, Chembur,

More information

Using 3D Geographic Information System to Improve Sales Comparison Approach for Real Estate Valuation

Using 3D Geographic Information System to Improve Sales Comparison Approach for Real Estate Valuation XXV FIG Congress, Kuala Lumpur, Malaysia TS02E-3D Using 3D Geographic Information System to Improve Sales Comparison Approach for Real Estate Valuation Haicong Yu Center for Assessment and Development

More information

Spatial Co-location Patterns Mining

Spatial Co-location Patterns Mining Spatial Co-location Patterns Mining Ruhi Nehri Dept. of Computer Science and Engineering. Government College of Engineering, Aurangabad, Maharashtra, India. Meghana Nagori Dept. of Computer Science and

More information

Multimedia Databases. Previous Lecture. 4.1 Multiresolution Analysis. 4 Shape-based Features. 4.1 Multiresolution Analysis

Multimedia Databases. Previous Lecture. 4.1 Multiresolution Analysis. 4 Shape-based Features. 4.1 Multiresolution Analysis Previous Lecture Multimedia Databases Texture-Based Image Retrieval Low Level Features Tamura Measure, Random Field Model High-Level Features Fourier-Transform, Wavelets Wolf-Tilo Balke Silviu Homoceanu

More information

An Interactive Framework for Spatial Joins: A Statistical Approach to Data Analysis in GIS

An Interactive Framework for Spatial Joins: A Statistical Approach to Data Analysis in GIS An Interactive Framework for Spatial Joins: A Statistical Approach to Data Analysis in GIS Shayma Alkobaisi Faculty of Information Technology United Arab Emirates University shayma.alkobaisi@uaeu.ac.ae

More information

2.2 Geographic phenomena

2.2 Geographic phenomena 2.2. Geographic phenomena 66 2.2 Geographic phenomena 2.2. Geographic phenomena 67 2.2.1 Defining geographic phenomena A GIS operates under the assumption that the relevant spatial phenomena occur in a

More information

Spatial Decision Tree: A Novel Approach to Land-Cover Classification

Spatial Decision Tree: A Novel Approach to Land-Cover Classification Spatial Decision Tree: A Novel Approach to Land-Cover Classification Zhe Jiang 1, Shashi Shekhar 1, Xun Zhou 1, Joseph Knight 2, Jennifer Corcoran 2 1 Department of Computer Science & Engineering 2 Department

More information

software, just as word processors or databases are. GIS was originally developed and cartographic capabilities have been augmented by analysis tools.

software, just as word processors or databases are. GIS was originally developed and cartographic capabilities have been augmented by analysis tools. 1. INTRODUCTION 1.1Background A GIS is a Geographic Information System, a software package for creating, viewing, and analyzing geographic information or spatial data. GIS is a class of software, just

More information

Clustering analysis of vegetation data

Clustering analysis of vegetation data Clustering analysis of vegetation data Valentin Gjorgjioski 1, Sašo Dzeroski 1 and Matt White 2 1 Jožef Stefan Institute Jamova cesta 39, SI-1000 Ljubljana Slovenia 2 Arthur Rylah Institute for Environmental

More information

Multimedia Databases. Wolf-Tilo Balke Philipp Wille Institut für Informationssysteme Technische Universität Braunschweig

Multimedia Databases. Wolf-Tilo Balke Philipp Wille Institut für Informationssysteme Technische Universität Braunschweig Multimedia Databases Wolf-Tilo Balke Philipp Wille Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de 4 Previous Lecture Texture-Based Image Retrieval Low

More information

An Optimized Interestingness Hotspot Discovery Framework for Large Gridded Spatio-temporal Datasets

An Optimized Interestingness Hotspot Discovery Framework for Large Gridded Spatio-temporal Datasets IEEE Big Data 2015 Big Data in Geosciences Workshop An Optimized Interestingness Hotspot Discovery Framework for Large Gridded Spatio-temporal Datasets Fatih Akdag and Christoph F. Eick Department of Computer

More information

Generalized map production: Italian experiences

Generalized map production: Italian experiences Generalized map production: Italian experiences FIG Working Week 2012 Knowing to manage the territory, protect the environment, evaluate the cultural heritage Rome, Italy, 6-10 May 2012 Gabriele GARNERO,

More information

ENV208/ENV508 Applied GIS. Week 1: What is GIS?

ENV208/ENV508 Applied GIS. Week 1: What is GIS? ENV208/ENV508 Applied GIS Week 1: What is GIS? 1 WHAT IS GIS? A GIS integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information.

More information

Application of Topology to Complex Object Identification. Eliseo CLEMENTINI University of L Aquila

Application of Topology to Complex Object Identification. Eliseo CLEMENTINI University of L Aquila Application of Topology to Complex Object Identification Eliseo CLEMENTINI University of L Aquila Agenda Recognition of complex objects in ortophotos Some use cases Complex objects definition An ontology

More information

INTRODUCTION TO GEOGRAPHIC INFORMATION SYSTEM By Reshma H. Patil

INTRODUCTION TO GEOGRAPHIC INFORMATION SYSTEM By Reshma H. Patil INTRODUCTION TO GEOGRAPHIC INFORMATION SYSTEM By Reshma H. Patil ABSTRACT:- The geographical information system (GIS) is Computer system for capturing, storing, querying analyzing, and displaying geospatial

More information

Imago: open-source toolkit for 2D chemical structure image recognition

Imago: open-source toolkit for 2D chemical structure image recognition Imago: open-source toolkit for 2D chemical structure image recognition Viktor Smolov *, Fedor Zentsev and Mikhail Rybalkin GGA Software Services LLC Abstract Different chemical databases contain molecule

More information

A STUDY ON INCREMENTAL OBJECT-ORIENTED MODEL AND ITS SUPPORTING ENVIRONMENT FOR CARTOGRAPHIC GENERALIZATION IN MULTI-SCALE SPATIAL DATABASE

A STUDY ON INCREMENTAL OBJECT-ORIENTED MODEL AND ITS SUPPORTING ENVIRONMENT FOR CARTOGRAPHIC GENERALIZATION IN MULTI-SCALE SPATIAL DATABASE A STUDY ON INCREMENTAL OBJECT-ORIENTED MODEL AND ITS SUPPORTING ENVIRONMENT FOR CARTOGRAPHIC GENERALIZATION IN MULTI-SCALE SPATIAL DATABASE Fan Wu Lina Dang Xiuqin Lu Weiming Su School of Resource & Environment

More information

Mining Spatial Trends by a Colony of Cooperative Ant Agents

Mining Spatial Trends by a Colony of Cooperative Ant Agents Mining Spatial Trends by a Colony of Cooperative Ant Agents Ashan Zarnani Masoud Rahgozar Abstract Large amounts of spatially referenced data has been aggregated in various application domains such as

More information

Optimization Methods for Machine Learning (OMML)

Optimization Methods for Machine Learning (OMML) Optimization Methods for Machine Learning (OMML) 2nd lecture (2 slots) Prof. L. Palagi 16/10/2014 1 What is (not) Data Mining? By Namwar Rizvi - Ad Hoc Query: ad Hoc queries just examines the current data

More information

Introduction to GIS. Dr. M.S. Ganesh Prasad

Introduction to GIS. Dr. M.S. Ganesh Prasad Introduction to GIS Dr. M.S. Ganesh Prasad Department of Civil Engineering The National Institute of Engineering, MYSORE ganeshprasad.nie@gmail.com 9449153758 Geographic Information System (GIS) Information

More information

THE 3D SIMULATION INFORMATION SYSTEM FOR ASSESSING THE FLOODING LOST IN KEELUNG RIVER BASIN

THE 3D SIMULATION INFORMATION SYSTEM FOR ASSESSING THE FLOODING LOST IN KEELUNG RIVER BASIN THE 3D SIMULATION INFORMATION SYSTEM FOR ASSESSING THE FLOODING LOST IN KEELUNG RIVER BASIN Kuo-Chung Wen *, Tsung-Hsing Huang ** * Associate Professor, Chinese Culture University, Taipei **Master, Chinese

More information

A MULTISCALE APPROACH TO DETECT SPATIAL-TEMPORAL OUTLIERS

A MULTISCALE APPROACH TO DETECT SPATIAL-TEMPORAL OUTLIERS A MULTISCALE APPROACH TO DETECT SPATIAL-TEMPORAL OUTLIERS Tao Cheng Zhilin Li Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong Email: {lstc;

More information

Deriving Uncertainty of Area Estimates from Satellite Imagery using Fuzzy Land-cover Classification

Deriving Uncertainty of Area Estimates from Satellite Imagery using Fuzzy Land-cover Classification International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 10 (2013), pp. 1059-1066 International Research Publications House http://www. irphouse.com /ijict.htm Deriving

More information

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction Data Mining Part 5. Prediction 5.5. Spring 2010 Instructor: Dr. Masoud Yaghini Outline How the Brain Works Artificial Neural Networks Simple Computing Elements Feed-Forward Networks Perceptrons (Single-layer,

More information

CS 350 A Computing Perspective on GIS

CS 350 A Computing Perspective on GIS CS 350 A Computing Perspective on GIS What is GIS? Definitions A powerful set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world (Burrough,

More information

Spatial Analysis using Vector GIS THE GOAL: PREPARATION:

Spatial Analysis using Vector GIS THE GOAL: PREPARATION: PLAN 512 GIS FOR PLANNERS Department of Urban and Environmental Planning University of Virginia Fall 2006 Prof. David L. Phillips Spatial Analysis using Vector GIS THE GOAL: This tutorial explores some

More information

A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases

A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases L.K. Sharma 1, O. P. Vyas 1, U. S. Tiwary 2, R. Vyas 1 1 School of Studies in Computer Science Pt. Ravishankar

More information

Statistical Machine Learning

Statistical Machine Learning Statistical Machine Learning Christoph Lampert Spring Semester 2015/2016 // Lecture 12 1 / 36 Unsupervised Learning Dimensionality Reduction 2 / 36 Dimensionality Reduction Given: data X = {x 1,..., x

More information

Geog 469 GIS Workshop. Data Analysis

Geog 469 GIS Workshop. Data Analysis Geog 469 GIS Workshop Data Analysis Outline 1. What kinds of need-to-know questions can be addressed using GIS data analysis? 2. What is a typology of GIS operations? 3. What kinds of operations are useful

More information

Quality and Coverage of Data Sources

Quality and Coverage of Data Sources Quality and Coverage of Data Sources Objectives Selecting an appropriate source for each item of information to be stored in the GIS database is very important for GIS Data Capture. Selection of quality

More information

SPATIAL INFORMATION GRID AND ITS APPLICATION IN GEOLOGICAL SURVEY

SPATIAL INFORMATION GRID AND ITS APPLICATION IN GEOLOGICAL SURVEY SPATIAL INFORMATION GRID AND ITS APPLICATION IN GEOLOGICAL SURVEY K. T. He a, b, Y. Tang a, W. X. Yu a a School of Electronic Science and Engineering, National University of Defense Technology, Changsha,

More information

Least-Cost Transportation Corridor Analysis Using Raster Data.

Least-Cost Transportation Corridor Analysis Using Raster Data. Least-Cost Transportation Corridor Analysis Using Raster Data What is GeoMedia Grid: Key Grid Concepts Vector Model Vector based systems show data by means of a series of points, lines, and polygons. Each

More information

GIS: Definition, Software. IAI Summer Institute 19 July 2000

GIS: Definition, Software. IAI Summer Institute 19 July 2000 GIS: Definition, Software IAI Summer Institute 19 July 2000 What is a GIS? Geographic Information System Definitions DeMers (1997): Tools that allow for the processing of spatial data into information,

More information

Multimedia Databases. 4 Shape-based Features. 4.1 Multiresolution Analysis. 4.1 Multiresolution Analysis. 4.1 Multiresolution Analysis

Multimedia Databases. 4 Shape-based Features. 4.1 Multiresolution Analysis. 4.1 Multiresolution Analysis. 4.1 Multiresolution Analysis 4 Shape-based Features Multimedia Databases Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de 4 Multiresolution Analysis

More information

THE SPATIAL DATA WAREHOUSE OF SEOUL

THE SPATIAL DATA WAREHOUSE OF SEOUL THE SPATIAL DATA WAREHOUSE OF SEOUL Jae-Ho Han The Seoul Metropolitan Government Seoul City Hall, Taepyeongno 1(il)-ga, Jung-gu, Seoul 100-744, Korea djhjha@hanmail.net Impyeong Lee Dept. of Geoinformatics,

More information

Introduction to Geographic Information Science. Updates/News. Last Lecture 1/23/2017. Geography 4103 / Spatial Data Representations

Introduction to Geographic Information Science. Updates/News. Last Lecture 1/23/2017. Geography 4103 / Spatial Data Representations Geography 4103 / 5103 Introduction to Geographic Information Science Spatial Data Representations Updates/News Waitlisted students First graded lab this week: skills learning Instructional labs vs. independence

More information

ST-DBSCAN: An Algorithm for Clustering Spatial-Temporal Data

ST-DBSCAN: An Algorithm for Clustering Spatial-Temporal Data ST-DBSCAN: An Algorithm for Clustering Spatial-Temporal Data Title Di Qin Carolina Department First Steering of Statistics Committee and Operations Research October 9, 2010 Introduction Clustering: the

More information

Mining Spatial Trends by a Colony of Cooperative Ant Agents

Mining Spatial Trends by a Colony of Cooperative Ant Agents Mining Spatial Trends by a Colony of Cooperative Ant Agents Ashan Zarnani Masoud Rahgozar Abstract Large amounts of spatially referenced data have been aggregated in various application domains such as

More information

Technical Building Branches. Multidisciplinary Integration of Needs. Interdisciplinary Subject BUT

Technical Building Branches. Multidisciplinary Integration of Needs. Interdisciplinary Subject BUT Usage of a Multidisciplinary GIS Platform for the Design of Building Structures Dalibor Barton k, Ji í Bureš, Aleš Dráb, Miroslav Menšík Integration of Needs in Many Technical Building Branches Civil Engineering

More information