Co-location Mining Research Articles

Methods for mining co–location patterns with extended spatial objects

Abstract The paper discusses various approaches to mining co-location patterns with extended spatial objects. We focus on the properties of transaction-free approaches EXCOM and DEOSP, and discuss the differences between the method using a buffer and that employing clustering and triangulation. These theoretical differences between the two methods are verified experimentally. In the performed tests three different implementations of EXCOMare compared with DEOSP, highlighting the advantages and downsides of both approaches.

Identifying and Analyzing the Prevalent Regions of a Co-Location Pattern Using Polygons Clustering Approach

Given a co-location pattern consisting of spatial features, the prevalent region mining process identifies local areas in which these features are co-located with a high probability. Many approaches have been proposed for co-location mining due to its key role in public safety, social-economic development and environmental management. However, traditionally, most of the solutions focus on itemsets mining and results outputting in a textual format, which fail to adequately treat all the spatial nature of the underlying entities and processes. In this paper, we propose a new co-location analysis approach to find the prevalent regions of a pattern. The approach combines kernel density estimation and polygons clustering techniques to specifically consider the correlation, heterogeneity and contextual information existing within complex spatial interactions. A kernel density estimation surface is created for each feature and subsequently the generated multiple surfaces are combined into a final surface with cell attribute representing the pattern prevalence measure value. Polygons consisting of cells are then extracted according to the predefined threshold. Through adding appended environmental data to the polygons, an outcome of similar groups is achieved using polygons clustering approach. The effectiveness of our approach is evaluated using Points-of-Interest datasets in Shenzhen, China.

Spatial Co-Location Pattern Discovery from Fuzzy Objects

A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region. The spatial co-location pattern mining problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem for fuzzy objects. Fuzzy objects play an important role in many areas, such as the geographical information system and the biomedical image database. In this paper, we propose two new kinds of co-location pattern mining for fuzzy objects, single co-location pattern mining (SCP) and range co-location pattern mining (RCP), to mining co-location patterns at a membership threshold or within a membership range. For efficient SCP mining, we optimize the basic mining algorithm to accelerate the co-location pattern generation. To improve the performance of RCP mining, effective pruning strategies are developed to significantly reduce the search space. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments.

An Improved Approximation Algorithm for Co-location Mining in Uncertain Data Sets using Probabilistic Approach

In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs). This method tries to find all colocations that are to be generated from a random world. For this we first apply an approximation error to find all the PPCs which reduce the computations. Next find all the possible worlds and split them into two different worlds and compute the prevalence probability. These worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant improvement in computational time in comparison to some of the existing methods used in colocation mining.

Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects

ABSTRACTThe aim of mining spatial co-location patterns is to find the corresponding subsets of spatial features that have strong spatial correlation in the real world. This is an important technology for the extraction and comprehension of implicit knowledge in large spatial databases. However, existing methods of co-location mining consider events as taking place in a homogeneous and isotropic context in Euclidean space, whereas the physical movement in an urban space is usually constrained by a road network. Furthermore, previous works do not take the ‘distance decay effect’ of spatial interactions into account, which may reduce the effectiveness of the result. Here we propose an improved spatial co-location pattern mining method, including the network-constrained neighborhood and addition of a distance-decay function, to find the spatial dependence between network phenomena (e.g. urban facilities). The underlying idea is to utilize a model function in the interest measure calculation to weight the contribution of a co-location to the overall interest measure instance inversely proportional to the separation distance. Our approach was evaluated through extensive experiments using facility points-of-interest data sets. The results show that the network-constrained approach is a more effective method than the traditional one in network-structured space. The proposed approach can also be applied to other human activities (e.g. traffic accidents) constrained by a street network.

MINING CO-LOCATION PATTERNS FROM SPATIAL DATA

Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

MINING CO-LOCATION PATTERNS FROM SPATIAL DATA

Abstract. Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

On discovering co-location patterns in datasets: a case study of pollutants and child cancers

We intend to identify relationships between cancer cases and pollutant emissions and attempt to understand whether cancer in children is typically located together with some specific chemical combinations or is independent. Co-location pattern analysis seems to be the appropriate investigation to perform. Co-location mining is one of the tasks of spatial data mining which focuses on the detection of co-location patterns, the sets of spatial features frequently located in close proximity of each other. Most previous works are based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location mining problem. The approach we propose is based on a grid "transactionization" of the geographic space and is designed to mine datasets with extended spatial objects. Uncertainty of the feature presence in transactions is taken into account in our model. The statistical test is used instead of global thresholds to detect significant co-location patterns and rules. We evaluate our approach on synthetic and real datasets. This approach can be used by researchers looking for spatial associations between environmental and health factors. In addition, we explain the data modelling framework which is used on real datasets of pollutants (PRTR/NPRI) and childhood cancer cases.

Top-k probabilistic prevalent co-location mining in spatially uncertain data sets

A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data sets and makes the following contributions: 1) the concept of the top-k probabilistic prevalent co-locations based on a possible world model is defined; 2) a framework for discovering the top-k probabilistic prevalent co-locations is set up; 3) a matrix method is proposed to improve the computation of the prevalence probability of a top-k candidate, and two pruning rules of the matrix block are given to accelerate the search for exact solutions; 4) a polynomial matrix is developed to further speed up the top-k candidate refinement process; 5) an approximate algorithm with compensation factor is introduced so that relatively large quantity of data can be processed quickly. The efficiency of our proposed algorithms as well as the accuracy of the approximation algorithms is evaluated with an extensive set of experiments using both synthetic and real uncertain data sets.

Multidimensional Circlet Lattice based Approach to Mine Spatially Co-Located Patterns

With immense extent of applications, one of the important tasks in data mining is the spatial pattern identification. Discovering spatial co-location patterns presents challenges as spatial objects are embedded in a continuous space. For co-location pattern mining, previous studies often emphasize on the association analysis of every spatial feature. As a result, interesting patterns involving events with different frequency cannot be captured. In this paper, we study the problem of efficiently mining co-location patterns with lattice structure called, Multi-dimensional Circlet Lattice-based Spatial Co-location Pattern Mining (MCL-SCPM), frequently located together in spatial proximity. MCL-SCPM initially identifies the candidate co-location using feature inclusive ratio which incorporates the spatial co-location patterns at minimum time interval with maximum coverage ratio. Next, MCL-SCPM discovers co-location patterns in a multi-dimensional spatial structure for different movements of an object by measuring the cohesion of a pattern. To extract maximal co-located patterns, MCL-SCPM method finally, uses Circlet Lattice-based structure to extract maximal co-located patterns. We conduct an extensive performance study to test and evaluate the effectiveness of MCL-SCPM method. Our experimental results show that MCL-SCPM method minimizes the execution time for mining co-location patterns without producing large computational overheads.

Spatial co-location pattern mining for location-based services in road networks

With the evolution of geographic information capture and the emergency of volunteered geographic information, it is getting more important to extract spatial knowledge automatically from large spatial datasets. Spatial co-location patterns represent the subsets of spatial features whose objects are often located in close geographic proximity. Such pattern is one of the most important concepts for geographic context awareness of location-based services (LBS). In the literature, most existing methods of co-location mining are used for events taking place in a homogeneous and isotropic space with distance expressed as Euclidean, while the physical movement in LBS is usually constrained by a road network. As a result, the interestingness value of co-location patterns involving network-constrained events cannot be accurately computed. In this paper, we propose a different method for co-location mining with network configurations of the geographical space considered. First, we define the network model with linear referencing and refine the neighborhood of traditional methods using network distances rather than Euclidean ones. Then, considering that the co-location mining in networks suffers from expensive spatial-join operation, we propose an efficient way to find all neighboring object pairs for generating clique instances. By comparison with the previous approaches based on Euclidean distance, this approach can be applied to accurately calculate the probability of occurrence of a spatial co-location on a network. Our experimental results from real and synthetic data sets show that the proposed approach is efficient and effective in identifying co-location patterns which actually rely on a network.

Domain-driven co-location mining

Co-location mining is a classical problem in spatial pattern mining. Considering a set of boolean spatial features, the goal is to find subsets of features frequently located together. It has wide applications in environmental management, public safety, transportation or tourism. These last years, many algorithms have been proposed to extract frequent co-locations. However, most solutions do a "data-centered knowledge discovery" instead of a "expert-centered knowledge discovery". Successfully providing useful and interpretable patterns to experts is still an open problem. In this setting, we propose a domain-driven co-location mining approach that combines constraint-based mining and cartographic visualization. Experts can push new domain constraints into the mining algorithm, resulting in more relevant patterns and more efficient extraction. Then, they can visualize solutions using a new concise and intuitive cartographic visualization of co-locations. Using this original visualization approach, they identify new interesting patterns, and use uninteresting ones to define new constraints and refine their analysis. These proposals have been integrated into a prototype based on PostGIS geographic information system. Experiments have been done using a real geological datasets studying soil erosion, and results have been validated by a domain expert.

Mining Statistically Significant Co-location and Segregation Patterns

In spatial domains, interaction between features gives rise to two types of interaction patterns: co-location and segregation patterns. Existing approaches to finding co-location patterns have several shortcomings: (1) They depend on user specified thresholds for prevalence measures; (2) they do not take spatial auto-correlation into account; and (3) they may report co-locations even if the features are randomly distributed. Segregation patterns have yet to receive much attention. In this paper, we propose a method for finding both types of interaction patterns, based on a statistical test. We introduce a new definition of co-location and segregation pattern, we propose a model for the null distribution of features so spatial auto-correlation is taken into account, and we design an algorithm for finding both co-location and segregation patterns. We also develop two strategies to reduce the computational cost compared to a naive approach based on simulations of the data distribution, and we propose an approach to reduce the runtime of our algorithm even further by using an approximation of the neighborhood of features. We evaluate our method empirically using synthetic and real data sets and demonstrate its advantages over a state-of-the-art co-location mining algorithm.

Multi-Resolution Pruning Based Co-Location Identification In Spatial Data

A co-location spatial pattern is a pattern of multiple groups which co-relates spatial features or events that are frequently located in same zone. Co-location pattern mining emphasizes overall analysis by manipulating the proportion of spatial features and other relevant information's. In this paper we are trying to remove the problem of co-location mining from the large data set. Data is generated from wide range of data sources that are available in geographical space. One method to mine the widely spread co-location is to compute the participation index in measuring the prevalence of the co-location. First option from the above said the measure is closely inter-related to cross function, which often used for measuring the statistical among various pair of spatial features. Second option focused on the property of anti-monotones which can be included for computational perspective efficiency. In this paper, we are trying to incorporate a novel multi-resolution pruning technique to address the problem of mining co-location data pattern with rare spatial features.

Finding Probabilistic Prevalent Colocations in Spatially Uncertain Data Sets

A spatial colocation pattern is a group of spatial features whose instances are frequently located together in geographic space. Discovering colocations has many useful applications. For example, colocated plant species discovered from plant distribution data sets can contribute to the analysis of plant geography, phytosociology studies, and plant protection recommendations. In this paper, we study the colocation mining problem in the context of uncertain data, as the data generated from a wide range of data sources are inherently uncertain. One straightforward method to mine the prevalent colocations in a spatially uncertain data set is to simply compute the expected participation index of a candidate and decide if it exceeds a minimum prevalence threshold. Although this definition has been widely adopted, it misses important information about the confidence which can be associated with the participation index of a colocation. We propose another definition, probabilistic prevalent colocations, trying to find all the colocations that are likely to be prevalent in a randomly generated possible world. Finding probabilistic prevalent colocations (PPCs) turn out to be difficult. First, we propose pruning strategies for candidates to reduce the amount of computation of the probabilistic participation index values. Next, we design an improved dynamic programming algorithm for identifying candidates. This algorithm is suitable for parallel computation, and approximate computation. Finally, the effectiveness and efficiency of the methods proposed as well as the pruning strategies and the optimization techniques are verified by extensive experiments with “real + synthetic” spatially uncertain data sets.

AN EFFICIENT ALGORITHM FOR MINING SPATIALLY CO-LOCATED MOVING OBJECTS

Mining co-location patterns from spatial databases may disclose the types of spatial features which ar e likely located as neighbors’ in space. Accordingly, we present an algorithm previously for mining spat ially co-located moving objects using spatial data mining techniques and Prim’s Algorithm. In the previous technique, the scanning of database to mine the spa tial co-location patterns took much computational c ost. In order to reduce the computation time, in this st udy, we make use of R-tree that is spatial data str ucture to mine the spatial co-location patterns. The importan t step presented in the approach is that the transf ormation of spatial data into the compact format that is wel l-suitable to mine the patterns. Here, we have adap ted the R-tree structure that converts the spatial data wit h the feature into the transactional data format. T hen, the prominent pattern mining algorithm, FP growth is us ed to mine the spatial co-location patterns from th e converted format of data. Finally, the performance of the proposed technique is compared with the prev ious technique in terms of time and memory usage. From the results, we can ensure that the proposed techniq ue outperformed of about more than 50% of previous algorithm in time and memory usage.

Mining Of Spatial Co-location Pattern from Spatial Datasets

Spatial data mining, or knowledge discovery in spatial database, refers to the extraction of implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases. Spatial data mining is the process of discovering interesting characteristics and patterns that may implicitly exist in spatial database. A huge amount of spatial data and newly emerging concept of Spatial Data Mining which includes the spatial distance made it an arduous task. Knowledge discovery in spatial databases is the extraction of implicit knowledge, spatial relations and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. Co-location pattern discovery is the process of finding the subsets of features that are frequently located together in the same region. Spatial co-location patterns associate the co-existence of non-spatial features in a spatial neighborhood. The Previous methods of mining co-location patterns, converts neighborhoods of feature instances to item sets and applies mining techniques for transactional data to discover the patterns, combines the discovery of spatial neighborhoods with the mining process. It is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. Previous works on discovering co-location patterns is based on participation index and participation ratio. In this paper we address the problem of mining co-location patterns with a novel method called Mediod participation index Our technique is an extension of maximal participation ratio and deploys the idea of K-mediods from clustering algorithms.. As demonstrated by experimentation, our method yields significant performance improvements compared to previous approaches.

Discovering Co-location Patterns from Spatial Domain using a Delaunay Approach

Spatial co-location patterns represent the subsets of boolean spatial features whose instances are often located in close geographic proximity. Spatial statistics and data mining approaches are used to identify co-location patterns from spatial data sets. Spatial proximity is the important concept to determine the co-location patterns from massive data sets. A Delaunay diagram based co-location mining approach is developed to mine co-location patterns from spatial data by using the concept of spatial proximity. Delaunay diagram is used to model the spatial proximity between the objects. This approach eliminates the parameters from the user to define neighborhood of objects and avoids multiple test and trail repetitions in the process of mining. An algorithm to discover co-location patterns are designed which generates candidate locations and their table instances. Finally the co-location rules are generated to identify the patterns. The results of the experiments have been discussed

Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation

This paper presents a new decision tree induction method, called co-location-based decision tree (CL-DT), to enhance the decision-making of pavement maintenance and rehabilitation strategies. The proposed algorithm utilizes the co-location characteristics of spatial attribute data in the pavement database. The paper first presented the co-location mining algorithm, including spatial attribute data selection, determination of rough candidate co-locations, determination of candidate co-locations, pruning the non-prevalent co-locations, and induction of co-location rules, and then focused on the development of the co-location decision tree (CL-DT) algorithm, which includes the non-spatial attribute data selection, co-location algorithm modeling, node merging criteria, and co-location decision tree induction. A pavement database covering four counties, which are provided by North Carolina Department of Transportation (NCDOT), is used to verify the proposed method. The experimental results demonstrated that (1) the proposed CL-DT algorithm can make a better decision, and has higher accuracy than the existing decision tree methods do; (2) the training data can be fully played roles in contribution to decision tree induction and the computational time taken for the tree growing, tree drawing and rule generation is largely decreased; (3) quantity and locations of six treatment strategies proposed by the ITRC and by CL-DT is much close for each treatment strategy.

Spatial Pattern Mining for Soil Erosion Characterization

The protection and the maintenance of the exceptional environment of New Caledonia are major goals for this territory. Among environmental problems, erosion has a strong impact on terrestrial and coastal ecosystems. However, due to the volume of data and its complexity, assessment of hazard at a regional scale is time-consuming, costly and rarely updated. Therefore, understanding and predicting environmental phenomenons need advanced techniques of analysis and modelization. In order to improve the understanding of the erosion phenomenon, this paper proposes a spatial approach based on co-location mining and GIS. Considering a set of Boolean spatial features, the goal of co-location mining is to find subsets of features often located together. This system provides useful and interpretable knowledge based on a new interestingness measure for co-locations and a new visualization of the discovered knowledge. The interestingness measure better reflects the importance of a co-location for the experts, and is completely integrated in the mining process. The visualization approach is a simple, concise and intuitive representation of the co-locations that takes into consideration the spatial nature of the underlying objects and the experts practice.