Mining Spatial Co-location Patterns Research Articles

Efficiently mining spatial co-location patterns utilizing fuzzy grid cliques

Spatial co-location pattern (SCP) mining discovers subsets of spatial feature types whose objects frequently co-locate in a geographic space. Many existing methods treat the space as homogeneous, use absolute Euclidean distance to measure the neighbor relationship between objects and use a participation index to measure the prevalence of SCPs. Several issues arise: (1) it may be that the distance between objects cannot be accurately defined since it is a relative and fuzzy concept; (2) the degree of neighborliness and sharing relationships between objects are neglected; (3) current methods for collecting participating objects by generating candidate table instances utilizing combined search techniques are computationally expensive. In this paper, we propose a method based on fuzzy grid cliques to find all prevalent SCPs. Specifically, fuzzy theory is introduced to define the proximity between objects. The fuzzy participating contribution index (FPCI) is defined to measure the prevalence of SCPs, and it considers both the neighbor degree and sharing relationship between objects. Based on the defined proximity, a basic mining framework based on fuzzy grid cliques is proposed. We first design a naive algorithm based on the participating objects’ filtering and verification called POFV, which uses a fuzzy grid clique search technology instead of combination search to collect participating objects and avoids enumerating all table instances. To solve a dilemma within POFV, we develop a maximal fuzzy grid cliques search based algorithm called MFGC, which can effectively reuse information. Experiments on both real and synthetic data sets verify the superiority of our proposed approaches, by showing that MFGC greatly outperforms the baseline algorithm and more efficiently captures SCPs.

THE IMPLEMENTATION OF HESITANT FUZZY SPATIAL CO-LOCATION PATTERN MINING ALGORITHM BASED ON PYTHON

Abstract. As one of the important research directions in the spatial data mining, spatial co-location pattern mining aimed at finding the spatial features whose the instances are frequent co-locate in neighbouring domain. With the introduction of fuzzy sets into traditional spatial co-location pattern mining, the research on fuzzy spatial co-location pattern mining has been deepened continuously, which extends traditional spatial co-location pattern mining to deal with fuzzy spatial objects and discover their laws of spatial symbiosis. In this paper, the operation principle of a classical join-based algorithm for mining spatial co-location patterns is briefly described firstly. Then, combining with the definition of classical participation rate and participation degree, a novel hesitant fuzzy spatial co-location pattern mining algorithm is proposed based on the establishment of the hesitant fuzzy participation rate and hesitant fuzzy participation formula according to the characteristics in fusion of hesitant fuzzy set theory, the score function and spatial co-location pattern mining. Finally, the proposed algorithm is written and implemented based on Python language, which uses a NumPy system to the expansion of the open source numerical calculation. The Python program of the proposed algorithm includes the method of computing hesitant fuzzy membership based on score function, the implementation of generating k-order candidate patterns, k-order frequent patterns and k-order table instances. A hesitant fuzzy spatial co-location pattern mining experiment is carried out and the experimental results show that the proposed and implemented algorithm is effective and feasible.

Mining Spatial Co-location Patterns from Vague Set

The target of mining spatial co-location patterns is discovering spatial association rule from spatial database, which plays an important role in many fields, investigate the distribution of plants for instance. Vague set extend the concept of fuzzy set, more flexible in terms of express and process fuzzy information. Some concepts such as vague spatial neighborhood relationship, vague participation ratio and vague participation index are defined in this paper first, after that, vague data filter rule and the algorithm of mining co-location patterns form vague set are proposed, the efficient prune strategy is presented in order to improve the efficiency of algorithm finally. The significance and efficiency of the techniques we propose are verified by experiments.

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 spatial colocation patterns: a different framework

Recently, there has been considerable interest in mining spatial colocation patterns from large spatial datasets. Spatial colocation patterns represent the subsets of spatial events whose instances are often located in close geographic proximity. Most studies of spatial colocation mining require the specification of two parameter constraints to find interesting colocation patterns. One is a minimum prevalent threshold of colocations, and the other is a distance threshold to define spatial neighborhood. However, it is difficult for users to decide appropriate threshold values without prior knowledge of their task-specific spatial data. In this paper, we propose a different framework for spatial colocation pattern mining. To remove the first constraint, we propose the problem of finding N-most prevalent colocated event sets, where N is the desired number of colocated event sets with the highest interest measure values per each pattern size. We developed two alternative algorithms for mining the N-most patterns. They reduce candidate events effectively and use a filter-and-refine strategy for efficiently finding colocation instances from a spatial dataset. We prove the algorithms are correct and complete in finding the N-most prevalent colocation patterns. For the second constraint, a distance threshold for spatial neighborhood determination, we present various methods to estimate appropriate distance bounds from user input data. The result can help an user to set a distance for a conceptualization of spatial neighborhood. Our experimental results with real and synthetic datasets show that our algorithmic design is computationally effective in finding the N-most prevalent colocation patterns. The discovered patterns were different depending on the distance threshold, which shows that it is important to select appropriate neighbor distances.

A Joinless Approach for Mining Spatial Colocation Patterns

Spatial colocations represent the subsets of features which are frequently located together in geographic space. Colocation pattern discovery presents challenges since spatial objects are embedded in a continuous space, whereas classical data is often discrete. A large fraction of the computation time is devoted to identifying the instances of colocation patterns. We propose a novel joinless approach for efficient colocation pattern mining. The joinless colocation mining algorithm uses an instance-lookup scheme instead of an expensive spatial or an instance join operation for identifying colocation instances. We prove the joinless algorithm is correct and complete in finding colocation rules. We also describe a partial join approach for a spatial data set often clustered in neighborhood areas. We provide the algebraic cost models to characterize the performance dominance zones of the joinless method and the partial join method with a current join-based colocation mining method, and compare their computational complexities. In the experimental evaluation, using synthetic and real-world data sets, our methods performed more efficiently than the join-based method and show more scalability in dense data.