Co-location Mining Research Articles

Revealing association rules within intricate ecosystems: A spatial co-location mining method based on Geo-Eco knowledge graph

The analysis of association rules within ecosystems is crucial for monitoring, managing, and conserving natural resources. As widely adopted approaches for this task, geospatial methods involving spatial co-location pattern mining can reveal distribution rules and inherent associations among diverse geographical elements. Rooted in Tobler’s first law of geography, these methods focus on the impact of spatial proximity. However, apart from proximity, heterogeneity of environmental attributes such as elevation, temperature and precipitation are also essential for the formation of associations. For environmental co-location (Eco-location) pattern detection, we propose a method based on the Geo-Eco Knowledge Graph (GEKG) to mine multi-impact association rules. Firstly, we introduce the Adaptive Threshold (AT) to constrain the Delaunay triangular network, dynamically regulating adjacency relationships to generate geo-eco knowledge graph’s skeleton. For comprehensive ecosystem representation, various environmental attributes are integrated as semantic information into GEKG. In the reasoning of Eco-location patterns, we innovate beyond the traditional co-location paradigm by considering both spatial proximity and semantic similarity. Under the impact of various environmental information, sub-sets of geographically proximate entities are extracted to detect Eco-location patterns. For effective management and efficient computation, we utilize the Neo4j graph database to manage large-scale GEKG and mine Eco-location patterns with its graph search function. Experiments conducted on simulated and real-world ecological datasets show that, compared to existing techniques, our GEKG-based method can detect Eco-location patterns with greater accuracy and efficiency.

Graph-based spatial co-location pattern mining: integrate geospatial analysis and logical reasoning

ABSTRACT Spatial co-location patterns reflect the inherent correlations among geographical elements. Mining co-location patterns of POIs can provide valuable insights for urban planning and resource management. Generally, co-location mining comprises two steps: proximity relationship determination (geospatial analysis) and frequent pattern recursion (logical reasoning). Previous methods often separate these two steps: serializing proximity relationships to enumerate frequent sequences. However, this approach suffers from limited flexibility and intuitiveness: as continuous spatial contexts are segmented into numerous small parts, it fails to adequately represent geographic correlations and hinders the effective visualization of logical reasoning. Facing these challenges, this study proposes a novel graph-based spatial co-location mining method (GSCM), which leverages graphs to integrate geospatial analysis and logical reasoning. Initially, to establish adjacency relationships, GSCM constructs the adaptive neighborhood graph, which dynamically adjusts proximity thresholds to accommodate geographic heterogeneity. Subsequently, the Apriori logical recursive process is realized on the graph structure. By leveraging graph searching, pruning, and growing, the potential growth directions of co-location patterns are identified, enhancing both the efficiency and intuition of frequent pattern recursion. Through experiments conducted on large-scale POI datasets from Wuhan, GSCM is compared with existing baseline methods, verifying its potential to uncover co-location patterns in complex spatial contexts.

A fast spatial high utility co-location pattern mining approach based on branch-and-depth-extension

Mining co-location patterns hidden in spatial data is crucial for spatial association discovery, and it has broad prospects in many applications. High Utility Co-location Pattern Mining (HUCPM) further takes the utility factor of spatial features into consideration, so it is more realistic compared with the traditional co-location pattern mining. However, HUCPM is more difficult, since the Apriori-like pruning technique does not apply. To address this problem, we firstly suggest two novel pruning strategies to trim the pattern search space. Then, a series of optimizing techniques are presented to speed up the pattern utility ratio calculation of each candidate. Based on above techniques, a fast HUCPM algorithm is proposed, which searches for high utility co-locations involved in each pattern branch via a depth-extending manner and equips with a heuristic strategy to enhance the effect of pruning techniques. Moreover, we theoretically prove the completeness and correctness of the proposed algorithm, and discuss its algorithmic complexity. On multiple spatial datasets, we conduct substantial experiments to reveal the superiority of our algorithm in efficiency and scalability, as well as the effectiveness of the proposed technique. Particularly, the proposed algorithm in this paper runs faster than other baselines for several times to several orders of magnitude.

A parallel algorithm for regional co-location mining based on fuzzy density peak clustering

区域同位模式挖掘（RCPM， Regional Co-location Pattern Mining）是为了发掘某个局部区域内存在的同位（co-location）模式，以发现在全局中无法发现的信息.传统的区域挖掘大多会采用明确界限的几何体框定同位模式产生的区域.但是现实中的各类区域可能是无明确边界的.另外，数据的分布情况作为区域的重要特征之一，也应该成为区域选择的因素.基于上述思考，本文引入密度峰值聚类（DPC, Density Peak based Clustering），提出新的密度度量函数，并结合模糊集理论与k近邻距离，设计了一个行之有效的并行区域同位模式挖掘算法.实验结果表明，利用本文方法挖掘到的结果更具有现实意义，并且并行化极大地提升了挖掘算法的效率.在真实数据上，2线程下的加速比达到了1.89.

Spatial co-location pattern mining over extended objects based on cell-relation operations

Spatial co-location pattern mining (SCPM) is intended to discover subsets of spatial features whose instances are frequently located together in geographic areas. Traditional SCPM methods are designed for point spatial instances. However, in reality, instances are mostly in the form of extended objects, e.g., lines, polygons. In addition, current SCPM methods with extended objects are less well researched and have two disadvantages: (1) Existing researches cannot effectively capture neighborhood relationships between extended objects and their mining results cannot properly reflect the distribution dependence of features; (2) These methods are not efficient enough with large datasets. This paper proposes a novel framework called cell-relation operations framework to overcome these issues. To eliminate the first shortcoming, the framework uses the area overlapping of buffers between objects to gain the neighbor relationships between extended objects and introduces participation index under buffer size k to identify prevalent co-location patterns. To address the second problem, our framework employs cell-relation operations rather than instance relation computing as the basic computing unit for co-location mining, which substantially speeds up the computation. The framework obtains spatial co-locations by counting the feature transactions of the cells and calculates the feature overlap ratio of the cells to generate co-locations. We implement experiments with real datasets to demonstrate that our framework’s mining results are more reasonable and the proposed framework’s runtime outperforms the baselines by 2 to 4 orders of magnitude.

Mining spatial high-average utility co-location patterns from spatial data sets

The spatial co-location pattern refers to a subset of non-empty spatial features whose instances are frequently located together in a spatial neighborhood. Traditional spatial co-location pattern mining is mainly based on the frequency of the pattern, and there is no difference in the importance or value of each spatial feature within the pattern. Although the spatial high utility co-location pattern mining solves this problem, it does not consider the effect of pattern length on the utility. Generally, the utility of the pattern also increases as the length of the pattern increases. Therefore, the evaluation criterion of the high utility co-location mining is unfair to the short patterns. In order to solve this problem, this paper first considers the utility and length of the co-location pattern comprehensively, and proposes a more reasonable High-Average Utility Co-location Pattern (HAUCP). Then, we propose a basic algorithm based on the extended average utility ratio of co-location patterns to mining all HAUCPs, which solves the problem that the average utility ratio of patterns does not satisfy the downward closure property. Next, an improved algorithm based on the local extended average utility ratio is developed which effectively reduces the search space of the basic algorithm and improves the mining efficiency. Finally, the practicability and robustness of the proposed method are verified based on real and synthetic data sets. Experimental results show that the proposed algorithm can effectively and efficiently find the HAUCPs from spatial data sets.

Spatial Negative Co-Location Pattern Directional Mining Algorithm with Join-Based Prevalence

It is usually difficult for prevalent negative co-location patterns to be mined and calculated. This paper proposes a join-based prevalent negative co-location mining algorithm, which can quickly and effectively mine all the prevalent negative co-location patterns in spatial data. Firstly, this paper verifies the monotonic nondecreasing property of the negative co-location participation index (PI) value as the size increases. Secondly, using this property, it is deduced that any prevalent negative co-location pattern with size n can be generated by connecting prevalent co-location with size 2 and with an n − 1 size candidate negative co-location pattern or an n − 1 size prevalent positive co-location pattern. Finally, the experiment results demonstrate that while other conditions are fixed, the proposed algorithm has an excellent efficiency level. The algorithm can eliminate the 90% useless negative co-location pattern maximumly and eliminate the useless 40% negative co-location pattern averagely.

Maximal Instance Algorithm for Fast Mining of Spatial Co-Location Patterns

The explosive growth of spatial data and the widespread use of spatial databases emphasize the need for spatial data mining. The subsets of features frequently located together in a geographic space are called spatial co-location patterns. It is difficult to discover co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to generating row instances and candidate co-location patterns. This paper makes three main contributions for mining co-location patterns. First, the definition of maximal instances is given and a row instance (RI)-tree is constructed to find maximal instances from a spatial data set. Second, a fast method for generating all row instances and candidate co-locations is proposed and the feasibility of this method is proved. Third, a maximal instance algorithm with no join operations for mining co-location patterns is proposed. Finally, experimental evaluations using synthetic data sets and a real data set show that maximal instance algorithm is feasible and has better performance.

Computing Co-Location Patterns in Spatial Data with Extended Objects: A Scalable Buffer-Based Approach

Spatial co-location patterns are subsets of spatial features usually located together in geographic space. Recent literature has provided different approaches to discover co-location patterns over point spatial data. However, most approaches consider the neighborhood relationship among spatial objects as binary and are mainly designed for point spatial features, thus are not appropriate for extended spatial features such as line strings and polygons, the neighborhood relationship among which is naturally continuous. This paper adopts a buffer-based model for measuring the spatial relationship of extended objects and mining co-location patterns. While the buffer-based model has several advantages for extended spatial features, it involves high computational complexity due to the expensive buffer-level overlay operation. To tackle this challenge, we introduce a coarse-level co-location mining framework, which follows a filter-and-refine paradigm. Within the framework, we develop a serious of rigorous upper bounds based on geometric property and progressively prune search space with these upper bounds. Moreover, we develop a join-less schema to further reduce computation cost of size-k( $k>2$ k > 2 ) co-location patterns. Finally, we conduct experiments with large-scale spatial data to validate the efficiency of the developed algorithms against several state-of-art methods. All experimental results demonstrate the superiority of our methods.

Efficiently mining maximal co-locations in a spatial continuous field under directed road networks

Extracting useful spatial co-location patterns from urban service facilities can help planners allocate limited resources effectively. These facilities are mostly distributed within man-made spatial fields with road-network constraints. To promote urban-space adaptivity, co-location algorithms for this network circumstance have been designed with distance decay effects and topological relationships of roads. However, these algorithms neglect the traffic direction, which affects the accuracy of the results. Moreover, the efficiency problem is more severe than with the traditional algorithm (i.e., no constraints). To address these problems, we propose an efficient maximal co-location mining algorithm with directed road-network constraints and spatial-continuity consideration (CMDS). To improve the accuracy, we design a network-based prevalence index, combined with both distance decay effects and road direction interference, to measure the significance of a pattern. To promote the execution speed, we use a key-node-separating approach and an improved shortest-path batch task for the co-location mining process. The experiments with both the synthetic and real datasets show that the CMDS algorithm is more efficient and accurate than the state-of-the-art network co-location when applied to problems in an urban space.

Parallel Grid-Based Colocation Mining Algorithms on GPUs for Big Spatial Event Data

Colocation patterns refer to subsets of spatial features whose instances are frequently located together. Mining colocation patterns is important in many applications such as identifying relationships between diseases and environmental factors, but is computationally challenging due to the large number of instances and candidate patterns. Existing algorithms are mostly sequential, and thus can be insufficient for big spatial event data. Recently, parallel colocation mining algorithms have been developed based on the Map-reduce framework, which is economically expensive. Another work proposed a GPU algorithm based on iCPI tree, but assumes that the number of neighbors for each instance is within a small constant, and thus cannot be used when instances are dense and unevenly distributed. To address these limitations, we recently proposed grid-based GPU colocation mining algorithms that include a novel cell-aggregate-based upper bound filter, and two refinement algorithms. In this paper, we provide theoretical analysis of running time. Furthermore using GPU profiling, we identify our recent GPU implementation, GPU-grid-join, as a memory bound problem and to address its bottlenecks, we proposes GPU-grid-join+, an optimized GPU algorithm. Our experimental results on real world data shows that GPU-grid-join+ achieves 4 to 12-fold speedup over GPU-grid-join both running on Nvidia P100 GPU as well as 56 to 126-fold speedup over OpenMP implementation over Intel(R) Xeon(R) CPU with 12 cores. Also for synthetic data, the speedup is in ranges 3 to 7-fold and 9 to 42-fold respectively.

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

complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor networksdata, 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 allcolocations that are to be generated from a random world. For this we first apply an approximation error to find allthe PPCs which reduce the computations. Next find all the possible worlds and split them into two different worldsand compute the prevalence probability. These worlds are used to compare with a minimum probability threshold todecide whether it is Probabilistic Prevalent colocation (PPCs) or not. The experimental results on the selected dataset show the significant improvement in computational time in comparison to some of the existing methods used incolocation mining.

A Visualization Method for Mining Colocation Patterns Constrained by a Road Network

Colocation mining is useful for understanding the interactions or dependencies that occur among geographic phenomena. Most colocation mining methods are based on planar space. However, in urban spaces, many human-related activities are constrained by a road network. Planar colocation mining methods are not suitable for studying the concerning geographic phenomena in an urban space. In this paper, we propose a visualization method to discover colocation patterns constrained by a road network. The method consists of two major components: network kernel density estimation and network colocation rule map construction. In the colocation rule map construction component, spatial interactions among spatial network geographic phenomena are modeled based on the idea of color mixing. We use simulated datasets with different spatial patterns, different sample sizes, and different maximum distances between road network events to test our method. The results show that our method is effective for mining colocation patterns in different situations. We also change the resolution of the network colocation rule maps, and the results show that the resolution has little influence on the results. In the case study, we apply our method to explore the spatial association between crimes and city facilities in the Loop and the Near North Side districts of Chicago.

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.

Parallel co-location mining with MapReduce and NoSQL systems

With the rapid growth of georeferenced data, large-scale data processing and analysis methods are needed for spatial big data. Spatial co-location pattern mining is an interesting and important issue in spatial data mining area which discovers the subsets of features whose objects are frequently located together in geographic proximity. There are several works for efficiently processing co-location pattern discovery; however, they may be insufficient for large dense spatial data because the mining task takes up a lot of processing time and memory. In this work, we leveraged the power of a modern distributed computing platform, Hadoop, and developed an algorithm (called ParColoc) for parallel co-location mining on the MapReduce framework. This study explored challenge issues in designing the parallel co-location mining algorithm and solved them with adopting a spatial declusteirng technique and a NoSQL system. We conducted an experimental evaluation with real-world data and synthetic data to examine the effectiveness of proposed methods. The experiment result shows that ParColoc is a promising method for parallel co-location mining in cloud computing environment.

Application of Space Co-location Mining in Urban Construction

with the continuous development of the world economy, people’s living quality has been improved significantly, but meanwhile a series of urban construction problems have occurred. Due to the increasing population, the number of residential areas, schools, hospitals and other facilities is increasing, and unreasonable distribution will inevitably take place. The main purpose of urban planning is to coordinate the specific arrangement of urban spatial layout and various concrete construction by rationally utilizing the urban land according to the determined nature, scale and development direction of the city. From this point of view, by planning the city to make the population of the city rationally distributed, and fully analyzing the factors of people’s gathering according to the existing layout of the city, these elements can be planned and constructed in a centralized manner so as to solve the problems of transportation, environment, etc. in the city. This paper analyzes the reasonable and advanced urban layout and extracts useful knowledge from the spatial data, thus helping the relevant agencies of the government to plan and develop the new urban area.

A visualization approach for discovering colocation patterns

ABSTRACTColocation mining is one of the major spatial data mining tasks. When discovering colocation patterns, spatial statistics or data mining approaches are commonly used. Colocation mining results are typically presented in a textual form and do not provide any spatial information; thus, the results lack an intuitive approach to obtain cognition of colocation rules. Here, we propose a visualization approach to discover colocation patterns for two independent point distributions and generate visual results. This approach makes use of the ability of human color perception. For two geographic features, our approach first generates density surfaces of the input features and then visualizes the density surfaces using a red or green light with different intensities. Then, based on the law of additive color mixing, our approach mixes the colors of the two density surfaces to generate a colocation rule map. The visualization approach can also provide local details of colocation and be used for local colocation analysis. Users can detect colocation patterns and their distribution from the colocation rule maps. We use both synthetic data and real data to test the performance of our approach.

Mining co-location patterns from distributed spatial data

Co-location patterns in spatial dataset are the interesting collection of dissimilar objects which are located in proximity. We keep similar objects in an entity set and maintain that no two objects in a co-location pattern belong to an entity set. Location proximity is based on Euclidean distance measure. However, algorithms for mining patterns in transactional datasets are not directly applicable to spatial datasets for mining co-location patterns. Conventional methods are not applicable to distributed temporal data and many applications generating spatial dataset are inherently distributive in nature. In this paper, a Map-Reduce based approach is proposed to find all co-location patterns from a spatial dataset distributed over nodes. This approach is modularized one and consists of four algorithms. With the first three algorithms in the first approach and by proposing an algorithm for dynamic datasets, this paper contains another approach for the co-location patterns set, that also updates in an incremental manner (not from scratch) whenever certain changes occur in the dataset. Experimental results on larger datasets are also presented.

A spatial co-location mining algorithm that includes adaptive proximity improvements and distant instance references

ABSTRACTSpatial co-location pattern mining is employed to identify a group of spatial types whose instances are frequently located in spatial proximity. Current co-location mining methods have two limitations: (1) it is difficult to set an appropriate proximity threshold to identify close instances in an unknown region, and (2) such methods neglect the effects of the distance values between instances and long-distance instance effects on pattern significance. This paper proposes a novel maximal co-location algorithm to address these problems. To remove the first constraint, the algorithm uses Voronoi diagrams to extract the most related instance pairs of different types and their normalized distances, from which two distance-separating parameters are adaptively extracted using a statistical method. To remove the second constraint, the algorithm employs a reward-based verification based on distance-separating parameters to identify the prevalent patterns. Our experiments with both synthetic data and real data from Beijing, China, demonstrate that the algorithm can identify many interesting patterns that are neglected by traditional co-location methods.

Redundancy Reduction for Prevalent Co-Location Patterns

Spatial co-location pattern mining is an interesting and important task in spatial data mining which discovers the subsets of spatial features frequently observed together in nearby geographic space. However, the traditional framework of mining prevalent co-location patterns produces numerous redundant co-location patterns, which makes it hard for users to understand or apply. To address this issue, in this paper, we study the problem of reducing redundancy in a collection of prevalent co-location patterns by utilizing the spatial distribution information of co-location instances. We first introduce the concept of semantic distance between a co-location pattern and its super-patterns, and then define redundant co-locations by introducing the concept of δ-covered , where $\delta \,(0\leq \delta \leq 1)$ is a coverage measure. We develop two algorithms RRclosed and RRnull to perform the redundancy reduction for prevalent co-location patterns. The former adopts the post-mining framework that is commonly used by existing redundancy reduction techniques, while the latter employs the mine-and-reduce framework that pushes redundancy reduction into the co-location mining process. Our performance studies on the synthetic and real-world data sets demonstrate that our method effectively reduces the size of the original collection of closed co-location patterns by about 50 percent. Furthermore, the RRnull method runs much faster than the related closed co-location pattern mining algorithm.