Spatial Data Mining Research Articles

Clustering Customer Shopping Trips with Network Structure

Moving objects can be tracked with sensors such as RFID tags or GPS devices. Their movement can be represented as sequences of time-stamped locations. Studying such spatio-temporal movement sequences to discover spatial sequential patterns holds promises in many real-world settings. A few interesting applications are customer shopping traverse pattern discovery, vehicle traveling pattern discovery, and route prediction. Traditional spatial data mining algorithms suitable for the Euclidean space are not directly applicable in these settings. We propose a new algorithm to cluster movement paths such as shopping trips for pattern discovery. In our work, we represent the spatio-temporal series as sequences of discrete locations following a pre-defined network. We incorporate a modified version of the Longest Common Subsequence (LCS) algorithm with the network structure to measure the similarity of movement paths. With such spatial networks we implicitly address the existence of spatial obstructs as well. Experiments were performed on both hand-collected real-life trips and simulated trips in grocery shopping. The initial evaluation results show that our proposed approach, called Net-LCSS, can be used to support effective and efficient clustering for shopping trip pattern discovery.

An Intelligent Approach For Mining Frequent Spatial Objects In Geographic Information System

Spatial Data Mining is based on correlation of spatial objects in space. Mining frequent pattern from spatial databases systems has always remained a challenge for researchers. In the light of the first law of geography “everything is related to everything else but nearby things is more related than distant things” suggests that values taken from samples of spatial data near to each other tend to be more similar than those taken farther apart. This tendency is termed spatial autocorrelation or spatial dependence. It’s natural that most spatial data are not independent, they have high autocorrelation. In this paper, we propose an enhancement of existing mining algorithm for efficiently mining frequent patterns for spatial objects occurring in space such as a city is located near a river. The frequency of each spatial object in relation to other object tends to determine multiple occurrence of the same object. We further enhance the proposed approach by using a numerical method. This method uses a tree structure based methodology for mining frequent patterns considering the frequency of each object stored at each node of the tree. Experimental results suggest significant improvement in finding valid frequent patterns over existing methods.

Data Clustering Method for Discovering Clusters in Spatial Cancer Databases

vast amount of hidden data in huge databases has created tremendous interests in the field of data mining. This paper discusses the data analytical tools and data mining techniques to analyze the medical data as well as spatial data. Spatial data mining includes discovery of interesting and useful patterns from spatial databases by grouping the objects into clusters. This study focuses on discrete and continuous spatial medical databases on which clustering techniques are applied and the efficient clusters were formed. The clusters of arbitrary shapes are formed if the data is continuous in nature. Furthermore, this application investigated data mining techniques such as classical clustering and hierarchical clustering on the spatial data set to generate the efficient clusters. The experimental results showed that there are certain facts that are evolved and can not be superficially retrieved from raw data.

Approaches to understanding adaptations of skin color variation by detecting gene–environment interactions

Genetic and environmental factors are both part of an elaborate feedback mechanism whereby the human adaptive form reacts to environmental stimuli via internal adjustments. Human survival may ultimately depend on understanding two important components of future environmental adaptation. First, we must elucidate the dynamics of the human genome underpinning the complex human phenotype. Second, we must understand how the environment pressures and affects the genome, helping to determine human traits. This article reviews current approaches to detecting the natural selection of skin color variation in human populations. We include statistical methods for clarifying gene–environment interactions applicable to the interactions with UV radiation levels. We recommend spatial data mining as an efficient approach that applies environmental association rules, extending our knowledge of adaptation to the environment.

공간 데이터마이닝을 이용한 고객 관리시스템

최근 마케팅이나 기업전략 분야에서 고객관리 및 점포관리 등의 업무를 위하여 GIS 기법을 적용한 다양한 응용시스템이 개발되고 있다. 그러나 기존의 시스템들은 대부분 개별점포나 고객 담당자의 경험치를 이용하여 이루어져 왔으며, 특정업종이나 특정 고객들에 대한 객관적인 분석시스템이 제시되지 않았다. 따라서 본 연구에서는 GIS 기법뿐만 아니라 시공간 데이터마이닝 기법을 적용한 gCRMs을 개발하였다. 본 시스템은 상권추출을 위한 새로운 시공간 데이터마이닝 기법을 개발하여 다양한 GIS 응용S/W의 개발이 가능하며, 상권에서 추출된 특성정보와 상권에서 발생하는 매출 등을 정성적, 정량적으로 평가할 수 있으며, 더 많은 다양한 지역에 적용하기 위한 일반화 기술의 원천기술을 획득하여 향후 기술을 이용한 각종 마케팅이 가능하다. 또한 도지시역의 변화를 예측하는 것과 같은 시계열분석 등의 모델링 툴을 개발하는 기초적인 기술을 제공할 수 있다. Spatial data mining has been developed to support spatial association knowledge between spatial features or its non-spatial attributes for an application areas. At the present time, a number of researchers attempt to the data mining techniques apply to the several analysis areas, for examples, civil engineering, environmental, agricultural areas. Despite the efforts that, until such time as not existed practical systems for the gCRMDMs. gCRMDMs is merged with very large spatial database and CRM information system. Also, it is discovery the association rule for the predictions of customer's shopping pattern informations in a huge database consisted with spatial and non-spatial dataset. For this goal, gCRMDMs need spatial data mining techniques. But, nowadays, in a most case not exist utilizable model for the gCRMDMs. Therefore, in this paper, we proposed a practical gCRMDMs model to support a customer, store, street, building and geographical suited to the trade area.

Spatial Decision Support System for Urban Planning: Case Study of Harbin City in China

Urban land structure, layout, and land-use mode influence regional sustainable development greatly. However, planners usually cannot work perfectly because of insufficient environmental information for decision making. In recent years, with urban geographic information system (GIS) development many cities have already built up integrated urban information platform to support regional digitalization process. By using spatial data mining technology urban planning related knowledge can be discovered form GIS database to help planners and decision makers in their work. Through analyzing of Chinese cities’ construction mode and developing trend, this paper illustrates how to build up an urban planning spatial decision support system (UPSDSS) based on exploratory spatial data analysis technology support. By spatial data mining form GIS database, UPSDSS can provide related knowledge for urban planning decision making with inference engines (rule inference and case inference) support. As an important part of the ...

Preliminary establishment of core collection in <I>Houttuynia cordata</I> Thunb of Huaihua

In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine-tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross-validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.

Research on Spatial Knowledge Reasoning about Jiangzhai Settlement in First Period

According to the analysis of settlement configuration of Jiangzhai in its first period, this paper places emphasis on the spatial knowledge reasoning about the characteristics of vector graphs of relics. With the special designed spatial rule knowledge base put into practice, this paper adopts the precondition base and the consequent base to respectively store the precondition and consequent of spatial knowledge rules. In addition, with the help of rules of the spatial data mining and extraction, it makes a case study on the spatial knowledge reasoning about two house relics in the prehistoric Jiangzhai settlement through three methods of reasoning, ordinal reasoning, converse reasoning, and mixed reasoning that combines ordinal and converse reasoning together. What’s more, with a desired effect, the research successfully introduces ideas and methods into the study about the interior structure of site in the single settlement site.

Spatial neighborhood based anomaly detection in sensor datasets

Success of anomaly detection, similar to other spatial data mining techniques, relies on neighborhood definition. In this paper, we argue that the anomalous behavior of spatial objects in a neighbo...

Integrating Spatial Data Mining Technique to Identify Potential Areas for Forest Production Using GIS and Remote Sensing

The purpose of the present study was to find potential areas for forest production using Spatial Data Mining (SDM) technique, GIS, and remote sensing. Artificial Neural Network (ANN) was the data mining method used. Current land use, average annual rainfall, soil erosion, slope, and fire hazards were selected as the criteria for the forest production. For this study, an ASTER-VNIR (Level 1B) satellite image acquired on 11 July 2006 was used. The image was classified into eight land use classes. Digital Elevation Model (DEM) was used to derive the slope map (20 m). The normalized Difference Vegetation Index (NDVI) was applied to detect areas of vegetation cover. A model was developed to evaluate areas vulnerable to soil erosion. Thematic paper maps (rainfall and soil) of the study area were screen digitized and converted to the raster format. The Alyuda NeuroIntelligence 2.2 application was used to implement the standard Back-Propagation (BP) algorithm for ANN modeling. Training stage of ANN helped to identify the optimal neural network structure. The final result was saved in spreadsheet format and converted to the GIS format for the output process, which was evaluated to produce the final map. This map was used to separate the suitable areas for the forest production into three suitability classes. The results showed that 19.65%, 28.43%, and 51.93% land areas were most suitable, suitable and less suitable, respectively, for the forest production. The SDM is more powerful than conventional cartography modeling technique for land suitability analysis.

A Novel Spatial Clustering Algorithm Based on Delaunay Triangulation

Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques which is the discovery of interesting rela-tionships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applications such as pattern recognition, data analysis, and image processing and so forth. However most of the well-known clustering algorithms have some drawbacks which will be presented later when ap-plied in large spatial databases. To overcome these limitations, in this paper we propose a robust spatial clustering algorithm named NSCABDT (Novel Spatial Clustering Algorithm Based on Delaunay Triangulation). Delaunay dia-gram is used for determining neighborhoods based on the neighborhood notion, spatial association rules and colloca-tions being defined. NSCABDT demonstrates several important advantages over the previous works. Firstly, it even discovers arbitrary shape of cluster distribution. Secondly, in order to execute NSCABDT, we do not need to know any priori nature of distribution. Third, like DBSCAN, Experiments show that NSCABDT does not require so much CPU processing time. Finally it handles efficiently outliers.

Spatial data mining and geographic knowledge discovery—An introduction

Voluminous geographic data have been, and continue to be, collected with modern data acquisition techniques such as global positioning systems (GPS), high-resolution remote sensing, location-aware services and surveys, and internet-based volunteered geographic information. There is an urgent need for effective and efficient methods to extract unknown and unexpected information from spatial data sets of unprecedentedly large size, high dimensionality, and complexity. To address these challenges, spatial data mining and geographic knowledge discovery has emerged as an active research field, focusing on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This paper highlights recent theoretical and applied research in spatial data mining and knowledge discovery. We first briefly review the literature on several common spatial data-mining tasks, including spatial classification and prediction; spatial association rule mining; spatial cluster analysis; and geovisualization. The articles included in this special issue contribute to spatial data mining research by developing new techniques for point pattern analysis, prediction in space–time data, and analysis of moving object data, as well as by demonstrating applications of genetic algorithms for optimization in the context of image classification and spatial interpolation. The papers concludes with some thoughts on the contribution of spatial data mining and geographic knowledge discovery to geographic information sciences.

Multi-scale digital terrain analysis and feature selection for digital soil mapping

Terrain attributes are the most widely used predictors in digital soil mapping. Nevertheless, discussion of techniques for addressing scale issues and feature selection has been limited. Therefore, we provide a framework for incorporating multi-scale concepts into digital soil mapping and for evaluating these scale effects. Furthermore, soil formation and soil-forming factors vary and respond at different scales. The spatial data mining approach presented here helps to identify both the scale which is important for mapping soil classes and the predictive power of different terrain attributes at different scales. The multi-scale digital terrain analysis approach is based on multiple local average filters with filter sizes ranging from 3 × 3 up to 31 × 31 pixels. We used a 20-m DEM and a 1:50 000 soil map for this study. The feature space is extended to include the terrain conditions measured at different scales, which results in highly correlated features (terrain attributes). Techniques to condense the feature space are therefore used in order to extract the relevant soil forming features and scales. The prediction results, which are based on a robust classification tree (CRUISE) show that the spatial pattern of particular soil classes varies at characteristic scales in response to particular terrain attributes. It is shown that some soil classes are more prevalent at one scale than at other scales and more related to some terrain attributes than to others. Furthermore, the most computationally efficient ANOVA-based feature selection approach is competitive in terms of prediction accuracy and the interpretation of the condensed datasets. Finally, we conclude that multi-scale as well as feature selection approaches deserve more research so that digital soil mapping techniques are applied in a proper spatial context and better prediction accuracy can be achieved.

A Customization of the Arc Marine Data Model to Support Whale Tracking via Satellite Telemetry

AbstractThe Arc Marine data model is a generalized template to guide the implementation of geographic information systems (GIS) projects in the marine environment. It developed out of a collaborative process involving research and industry shareholders in coastal and marine research. This template models and attempts to standardize common ocean and coastal data types to facilitate data sharing and analytical tool development. In this study, Arc Marine is extended from its core model to fit the research goals of the whale satellite‐telemetry‐tagging program of the Oregon State University Marine Mammal Institute (MMI). The study sought the best customization of the generic Arc Marine data model to enhance the key advantages of satellite telemetry for mapping the distribution and movement of endangered marine mammal species. It was found that three new groups of object classes were needed (Animal, Telemetry, and Operations). Further customization involved the development of a comprehensive framework for animal tracking with Argos satellite telemetry data. A new multidimensional data cube model was also devised, showing how this extension of Arc Marine serves as an appropriate target schema for the application of on‐line analytical processing (OLAP) tools and spatial data mining of satellite telemetry tracking datasets.

FARICS: a method of mining spatial association rules and collocations using clustering and Delaunay diagrams

The paper presents problems pertaining to spatial data mining. Based on the existing solutions a new method of knowledge extraction in the form of spatial association rules and collocations has been worked out and is proposed herein. Delaunay diagram is used for determining neighborhoods. Based on the neighborhood notion, spatial association rules and collocations are defined. A novel algorithm for finding spatial rules and collocations has been presented. The approach allows eliminating the parameters defining neighborhood of objects, thus avoiding multiple "test and trial" repetitions of the process of mining for various parameter values. The presented method has been implemented and tested. The results of the experiments have been discussed.

Multi-relational spatial data mining method based on S-CART decision tree

Regarding the complicated relationship existing in the spatial data,a kind of improved arithmetic based on the S-CART was presented.In this arithmetic,firstly,the relational atoms propositions from different thematic layers were drawn by the Spatial Jion Index Table(SJIT).And then,based on the predicates,a multi-relational binary decision tree was established and the spatial association rules were drawn out.The efficiency of this arithmetic is validated based on the soil pollution data of some areas of Daye,Hubei.

EFFECTIVENESS OF SUPPORT VECTOR MACHINE FOR CRIME HOT-SPOTS PREDICTION

Crime hot-spot location prediction is important for public safety. The output from the prediction can provide useful information to improve the activities aimed at detecting and preventing safety and security problems. Location prediction is a special case of spatial data mining classification. For instance, in the public safety domain, it may be interesting to predict location(s) of crime hot spots. In this study, we present a support vector machine (SVM)-based approach to predict the location as an alternative to existing modeling approaches. Support vector machine forms the new generation of machine-learning techniques used to find optimal separability between classes within datasets. We compare the performance of two types of SVMs techniques: two-class SVMs and one-class SVMs. We also compared SVM with a neural network-based approach and spatial auto-regression-based approach. Experiments on two different spatial datasets demonstrate that the former approach performs slightly better and the latter one gives reasonable results. Furthermore, in this study, we provide a general framework to customize the spatial data classification task for other spatial domains that have datasets similar to the analyzed crime datasets.

A hybrid spatial data clustering method for site selection: The data driven approach of GIS mining

This article applies customer service to be the research background. Spatial data mining method is proposed to solve site selection of the service center. Firstly, a new data model for recording all the information of customer management is given, which transforms the traditional model-driven strategy to data-oriented method. Secondly, a hybrid spatial clustering method named OETTC–MEANS–CLASA algorithm is proposed. It has the advantages of applying k-means algorithm to reduce the result space and using simulated annealing method (CLASA) as result-searching strategy to find more qualified solutions. On the basis of GIS functions, we design deeper analytical function to take spatial obstacle factors, spatial environmental factors, spatial terrain factors, spatial traffic factors and cost factors into account. The result of the experiment declares that the algorithm does better at the both aspects of perform efficiency and result quality.

A Scale-Space Clustering Method: Mitigating the Effect of Scale in the Analysis of Zone-Based Data

Scale-space clustering methods have a variety of applications in image processing, spatial imagery data mining, classification of land uses, identification of seismic belts, pattern recognition, and more. The full potential of these methods, particularly in a socioeconomic context, has not been realized due to its cumbersome mathematical formulations and lack of implementation in a ready-to-use module for wide distribution. The objectives of this article are threefold: to develop a modified scale-space clustering method (MSSC) that accounts for both attribute homogeneity and spatial contiguity, to implement the method in a geographic information system (GIS) program for wide distribution, and to demonstrate its values in a case study. This is coherent with the intent of developing frame-independent and scale-invariant methods, and has important implications in several spatial analysis issues. For instance, it can be used (1) to construct geographic areas with sufficiently large base population to mitigate the small population problem, (2) to mitigate the modifiable areal unit problem (MAUP) in analyzing zone-based data, and (3) to risk less model-building error in using ordinary least squares (OLS) regression as the clustered zones exhibit less spatial autocorrelation. The case study reveals a converging effect of the clustering process, characterized by an exponential function. A metric, level of convergence, is developed to measure the closeness of area objects in terms of both attribute homogeneity and spatial proximity.