Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.

Processing Constrained K Closest Pairs Query in Spatial Databases

In this paper, constrained K closest pairs query is introduced, wbich retrieves the K closest pairs satisfying the given spatial constraint from two datasets. For data sets indexed by R trees in spatial databases, three algorithms are presented for answering this kind of query. Among of them, two-phase Range＋Join and Join＋Range algorithms adopt the strategy that changes the execution order of range and closest pairs queries, and constrained heap-based algorithm utilizes extended distance functions to prune search space and minimize the pruning distance. Experimental results show that constrained heap-base algorithm has better applicability and performance than two-phase algorithms.

Federated Spatial Databases and Interoperability

It is a period of information explosion. Especially for spatial information science, information can be acquired through many ways, such as man made planet, aeroplane, laser, digital photogrammetry and so on. Spatial data sources are usually distributed and heterogeneous. Federated database is the best resolution for the share and interoperation of spatial database. In this paper, the concepts of federated database and interoperability are introduced. Three heterogeneous kinds of spatial data, vector, image and DEM are used to create integrated database. A data model of federated spatial databases is given.

Construction of Spatial Database of Tourism Resources in the Lijiang River Basin Based on MapGIS

GIS and GPS technology was applied to construct spatial database of ecotourism resources in the Lijiang River Basin,the process of constructing database was elaborated,data collection and processing as well as specific schemes of building graphic database and attribute database were introduced,in order to provide data support for optimizing the development spaces of regional tourism resources,regulating development activities,and realizing sustainable development of ecology,environment,tourism economy and society.In addition,this paper also offered data support for eco-environment monitoring,building tourism resource management information system and "Digital Lijiang".

Parallelism of spatial data mining based on autocorrelation decision tree

Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tree is presented. And the new method reduces CPU- and I/O-time and improves the query efficiency of spatial data. For dynamic load balancing, there are better control and optimization. Experimental performance comparison shows that the improved algorithm can obtain a optimal accelerator with the same quantities of processors. There are more completely accesses on nodes. And an individual implement of intelligent information retrieval for spatial data mining is presented.

Reciprocal Cloaking Algorithm for Spatial K-Anonymity

Mobile devices with global positioning capabilities allow users to retrieve points of interest (POI) in their proximity. Due to the nature of spatial queries, location-based service (LBS) needs the user position in order to process requests. On the other hand, revealing exact user locations to LBS may pinpoint their identities and breach their privacy. Spatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. However, existing reciprocal methods rely on a specialized data structure. In contrast, a reciprocal algorithm was proposed using existing spatial index on the user locations. At the same time, an adjusted median splits algorithm was provided. Finally, according to effectiveness (i.e., anonymizing spatial region size) and efficiency (i.e., construction cost), the experimental results verify that the proposed methods have better performance. Moreover, since using employ general-purpose spatial indices, the proposed method supports conventional spatial queries as well.

Discussions about Construction of Agricultural Ecological Geochemical Survey Database

[Objectives]To realize proper,update and dynamic analysis of the results of ecological geochemical survey,and provide useful information for government macro-decisions,land resources planning,agricultural structure adjustment,ecological environment monitoring,governance and evaluation.[Methods]This paper conducted the analysis with the aid of the computer database technology and GIS technology,as well as the regional geochemical database information system(GeoMDIS)developed by the Development Research Center of the China Geological Survey.[Results]It established a database of agricultural ecological geochemical survey in the lower Yellow River Basin of Shandong Province,realized the collection,storage,update and statistical analysis management of various sample data.[Conclusions]Through the discussion on the construction of agricultural ecological geochemical survey database,it is expected to provide an effective information platform for the future use of these data to serve government macro-decisions,land and resource planning,agricultural structure adjustment,ecological environment improvement,monitoring,governance and evaluation.

Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card

With the deployment of modern infrastructure for public transportation, several studies have analyzed movement patterns of people using smart card data and have characterized different areas. In this paper, we propose the “movement purpose hypothesis” that each movement occurs from two causes: where the person is and what the person wants to do at a given moment. We formulate this hypothesis to a synthesis model in which two network graphs generate a movement network graph. Then we develop two novel-embedding models to assess the hypothesis, and demonstrate that the models obtain a vector representation of a geospatial area using movement patterns of people from large-scale smart card data. We conducted an experiment using smart card data for a large network of railroads in the Kansai region of Japan. We obtained a vector representation of each railroad station and each purpose using the developed embedding models. Results show that network embedding methods are suitable for a large-scale movement of data, and the developed models perform better than existing embedding methods in the task of multi-label classification for train stations on the purpose of use data set. Our proposed models can contribute to the prediction of people flows by discovering underlying representations of geospatial areas from mobility data.

Geospatial Assessment of the Contributions of the Major Stakeholders to Secondary Schools in Some Parts of Benue State, Nigeria

This study assessed the contributions of the major stakeholders to secondary school education in some parts of Benue State using geospatial techniques. It aimed at producing a queriable spatial database of secondary schools which could be used as a guide for resource distribution and management in the school system. The study adopted, survey, field observation and measurement using Global Positioning System (GPS) to obtain the needed data. Geographic Information System (GIS) and descriptive statistical techniques were employed to map, visualize and analyze the data. The result shows that the private sector has the highest contribution to secondary schools in the area. In the private sector, the private individuals have the highest contributions to secondary school in the area both by ownership, sponsorship and size (student population). The implication is that the system will suffer exploitation by the dominant private individuals in the system. It was recommended that further research should be conducted to assess the relationship between government efforts and other stakeholders on the spatial distribution of secondary schools in the study area.

Approaches for Scaling DBSCAN Algorithm to Large Spatial Databases

The huge amount of information stored in databases owned by corporations (e.g., retail, financial, telecom) has spurred a tremendous interest in the area of knowledge discovery and data mining. Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Although researchers have been working on clustering algorithms for decades, and a lot of algorithms for clustering have been developed, there is still no efficient algorithm for clustering very large databases and high dimensional data. As an outstanding representative of clustering algorithms, DBSCAN algorithm shows good performance in spatial data clustering. However, for large spatial databases, DBSCAN requires large volume of memory support and could incur substantial I/O costs because it operates directly on the entire database. In this paper, several approaches are proposed to scale DBSCAN algorithm to large spatial databases. To begin with, a fast DBSCAN algorithm is developed, which considerably speeds up the original DBSCAN algorithm. Then a sampling based DBSCAN algorithm, a partitioning-based DBSCAN algorithm, and a parallel DBSCAN algorithm are introduced consecutively. Following that, based on the above-proposed algorithms, a synthetic algorithm is also given. Finally, some experimental results are given to demonstrate the effectiveness and efficiency of these algorithms.

Fast Web - Based Data Transmission

Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.

An IFC-based database schema for mapping BIM data into a 3D spatially enabled land administration database

There are different processes in 3D urban land administration in which spatial analysis plays an underpinning role.Among 3D data models,Industry Foundation Class(IFC)provides the potential capabilities for modelling legal and physical dimensions of urban properties.However,performing spatial analysis using IFC files cannot address the on-demand spatial analysis requirements of 3D urban land administration.In response to this limitation,3D data needs to be stored in a spatial database to enable spatial analyses required by different stakeholders.Therefore,in this paper,by considering spatial analysis requirements in 3D-enabled urban land administration,an IFC-based database schema is designed.Moreover,a methodology for transforming Building Information Modelling(BIM)data into the proposed schema is provided.This methodology includes seven steps:designing the architectural model and adding legal data,georeferencing,IFC data validation and cleaning,mapping process,database data validation and cleaning,spatial analysis,and visualisation.To demonstrate the feasibility of the proposed database,three datasets are implemented in the database.Moreover,a new method for modelling legal spaces with oblique structures and two applications of spatial analysis in 3D urban land administration are provided.

GIS Platform-based Multi-source Information System for Evaluation of Cu, W and Au Resources in the Northern Qilian, China

Based on the information of geology, geochemistry, geophysics and remote sensing, the GIS of multi-source information is used to evaluate Cu, W and Au mineral resources in Northern Qilian, China. As the GIS evaluation system works out in the thinking of geological prospecting, its functions include file management, graph edition, database maintenance, information inquiry and comprehensive spatial analysis as well as prospecting target prognosis. Accordingly, the GIS evaluation system can be used directly and conveniently for inquiry and analysis of visual graphs or images.

Design and Implementation of WebGIS Based on SOAP

Most popular applications currently running on the Internet, the World Wide Web is of an exponential growth in size, which results in network congestion and server overloading. In this paper, we analyze the characteristics of SOAP (Simple Object Access Protocol) and the state-of-art techniques, which have been used in WebGIS. Then, the basic characteristics of DM3 database system were introduced; Furthermore, a novel N-tier WebGIS architecture based on SOAP and the framework supported both server-based and client\|based solutions are proposed. Finally, some experiments and implementations are shown.

Efficient Top/Bottom-k Fraction Estimation in Spatial Databases Using Bounded Main Memory

Spatial databases store objects with their locations and certain types of attached items.A variety of modern applications have been developed by leveraging the utilization of locations and items in spatial objects,such as searching points of interest,hot topics,or users’attitude in specified spatial regions.In many scenarios,the high and low-frequency items in a spatial region are worth noticing,considering they represent the majority’s interest or eccentric users’opinion.However,existing works have yet to identify such items in an interactive manner,despite the significance of the endeavor in decision-making systems.This study recognizes a novel type of analytical query,called top/bottom-k fraction query,to discover such items in spatial databases.To achieve fast query response,we propose a multilayered data summary that is spread out across the main memory and external memory.A memory-based estimation method for top/bottom-k fraction queries is proposed.To maximize the use of the main memory space,we design a data summary tuning method to dynamically allocate memory space among different spatial partitions.The proposed approach is evaluated with real-life datasets and synthetic datasets in terms of estimation accuracy.Evaluation results demonstrate the effectiveness of the proposed data summary and corresponding estimation and tuning algorithms.

Modelling and mapping third dimension in a spatial database

In this paper,an overview of phases for modelling and mapping third dimension of spatial objects in a database is presented based on a selected spatial database management system(DBMS).These phases include(1)defining a spatial reference system for representing three-dimensional(3D)objects with real-world coordinates,(2)geometric modelling of 3D objects in the database,(3)3D spatial indexing for fast accessing/querying the 3D data,3D spatial queries and representation of 3D data.Then,a case study is performed to assess needs,possibilities and potential limitations of 3D data modelling in the spatial database.

Monitoring Median Queries over Moving Objects

The k-median problem has attracted a number of researchers. However,few of them have considered both the dynamic environment and the issue of accuracy. In this paper,a new type of query is studied,called continuous median monitoring （CMM） query. It considers the k-median problem under dynamic environment with an accuracy guarantee. A continuous group nearest neighbor based （CGB） algorithm and an average distance medoid （ADM） algorithm are proposed to solve the CMM problem. ADM is a hill climbing schemed algorithm and achieves a rapid converging speed by checking only qualified candidates. Experiments show that ADM is more efficient than CGB and outperforms the classical PAM （partitioning around medoids） and CLARANS （clustering large applications based on randomized search） algorithms with various parameter settings.

Dynamic Shortest Path Monitoring in Spatial Networks

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowa- days and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks （DSPM query）. When a traveler aims to a des- tination, his/her shortest path to the destination may change due to two reasons： 1） the travel costs of some edges have been updated and 2） the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons： 1） how to maintain and reuse the existing computation results to accelerate the following computations, and 2） how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.