Co-location Rules Research Articles

Spatial Characteristic of Tourism Sites on Neighborhood Support Facilities and Proximities in Cultural World Heritage Sites

Tourism is continuously developing as a new economic source in Indonesia. Tourism activities extend to the various services, products, and experiences provided in the tourism site’s surrounding area. Tourism development requires information on possible related activities with tourism. However, there was a lack of studies that examined the relationship between tourism sites and the simultaneous presence of multiple public facilities, which would reveal the value of proximity. This paper aims to investigate the proximity patterns of tourism sites and the support facilities, to develop a strategy for tourism sites. The average nearest-neighbor results verify that there are clustering tendencies for almost all datasets. The Kernel Density Estimation (KDE)-based raster’s were created to visualize the patterns of tourism sites and nearby public facilities, which located near three world cultural heritage sites in Indonesia. Co-location pattern mining was applied to examine the co-location behavior between tourism sites and tourism support facilities using the Participation Index (PI) as the measurement parameter. This study provides knowledge, specifically the existence of co-location rules between tourism sites and tourism support facilities, which consist of food services, accommodations, transportation, shopping, and other tourism support facilities. The network graph shows that the location of tourism support facilities can be affected by the types of tourism sites, providing practical implications for individuals, business owners, and policymakers. Government policies related to planning for tourism destination development that consider the characteristics of spatial interactions are expected to be able to support government targets for increasing lengths of stay and tourist expenditures.

Discovering spatial patterns of fast-food restaurants in Jakarta, Indonesia

ABSTRACT This study aims to discover the spatial patterns of fast-food restaurants in Jakarta, an Indonesian megacity. The location of fast-food restaurants tends to cluster with the Nearest Neighbor Index of 0.41. The average surface distribution (estimated by Kernel Density Estimation) shows at least 20 locations that have densities between 4-7 fast-food restaurants. The co-location rules, as the results of transaction-based co-location pattern mining, revealed six main activities that affect the location behavior of fast-food restaurants, namely leisure time/shopping, traveling, education, religious activities, health activities, work activities. These refer to 11 minor attributes of public facilities with the Participation Index more than 50% and at a distance less than 1 km. Foodservice providers can employ the co-location rules in determining locations for their brand expansion to other regions. The research framework to mine up information based on spatial data can be utilized both for business and academic needs for other purposes.

Life convenience analysis of Lanzhou residential area based on the colorimetric co-location rule excavation

Life convenience analysis of Lanzhou residential area based on the colorimetric co-location rule excavation

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.

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.

AN INTEGRATIVE AND COLLABORATIVE APPROACH TO ASSOCIATING ADVERSE BIRTH OUTCOMES AND INDUSTRIAL AIR POLLUTION

Abstract BACKGROUND The relationship of many hazardous pollutants present in ambient air with adverse birth outcomes (ABO) is still unknown, particularly from those released by industry. The knowledge gap is even greater when considering the impact of mixtures of hazardous pollutants on pregnancy outcomes. OBJECTIVES We aimed to collaboratively overcome methodological limitations to identify potential hazardous mixtures of industrial air pollutants spatially related to the occurrence of ABO in Alberta. DESIGN/METHODS We applied integrative data analyses on large existing databases (2006–2012) from (i) the National Pollutant Release Inventory on chemicals released into air by industry; (ii) the Alberta Perinatal Health Program on births, known maternal risk factors for ABO, and the maternal postal code at birth; (iii) an area-level socioeconomic status index (SES). We developed a novel spatial data mining (DM) algorithm to identify significant spatial colocation rules of combinations of emitted chemicals and ABO (i.e., preterm birth [PTB], small for gestational age [SGA], and low birth weight at term [LBWT]). We used a Geographic Information System (GIS) to assign exposures to residences within 10 km of emission sites. We used epidemiological analysis to calculate the corresponding odds ratios of exposures to chemical mixtures and the occurrence of ABO, adjusted by maternal covariates and SES. RESULTS DM distinguished eleven rules combining three chemicals - gases, particulate matter and organics - as associated with ABO. GIS assigned exposure and epidemiological analysis identified seven rules with increased odds for induced PTB, SGA, and LBWT. One exclusively increased the odds for PTB (15%). CONCLUSION The combined interdisciplinary use of data mining, GIS, and epidemiology strategies enabled us to identify the risks associated with mixtures of hazardous chemicals and ABO – findings that proved elusive when applying methodologies individually. These discoveries enhance our understanding of the role of chemical mixtures in ABO.

Discovering co-location patterns with aggregated spatial transactions and dependency rules

Co-location pattern mining focuses on finding associations among spatial features. Existing co-location pattern mining techniques mainly rely on frequency based thresholds which discard the rare patterns and find the noisy patterns. This could be avoided by evaluating co-location patterns based on their statistical significance. Recent studies focused on association rule mining have successfully adopted statistical tests to find significant rules. By transforming spatial data to transaction data, the co-location pattern mining problem can be reduced to an association rule mining problem and such methods can be used to find co-location patterns robustly. A transactionization mechanism has been recently proposed to achieve this. However, this method ignores the effect of general instances, with non-overlapping buffer regions, on the reference instances in their proximity. Addressing this, we propose a novel approach, AGT-Fisher, to robustly transform spatial data to transaction data and use statistically significant dependency rule searching methods to find co-location rules from them. Our work is motivated by an application in environmental health to investigate potential associations between air pollution and adverse birth outcomes in Canada. We used AGT-Fisher to find such associations from real datasets. The discovered co-location patterns were evaluated based on their statistical dependency and the empirical evidence, and results showed that our approach is more robust. Furthermore, we evaluated the resulting patterns to find spatial common and contrast sets, which are two special types of co-location patterns, to compare spatial regions and gain more insights.

Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification

Because traditional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, several researchers have presented a co-location decision tree (CL-DT) method that combines the co-location technique with the traditional DT method. However, the CL-DT method only considers the Euclidean distance of neighborhood events, which cannot truly reflect the co-location relationship between instances for which there is a nonlinear distribution in a high-dimensional space. For this reason, this paper develops the theory and method for a maximum variance unfolding (MVU)-based CL-DT method (known as MVU-based CL-DT), which includes unfolding input data, unfolded distance calculations, MVU-based co-location rule generation, and MVU-based CL-DT generation. The proposed method has been validated by classifying remotely sensed imagery and is compared with four other types of methods, i.e., CL-DT, classification and regression tree (CART), random forests (RFs), and stacked auto-encoders (SAE), whose classification results are taken as “true values.” The experimental results demonstrate that: (1) the relative classification accuracies of the proposed method in three test areas are higher than CL-DT and CART, and are at the same level compared to RFs; and (2) the total number of nodes, the number of leaf nodes, and the number of levels are significantly decreased by the proposed method. The time taken for the data processing, decision tree generation, drawing of the tree, and generation of the rules are also shortened by the proposed method compared to CL-DT, CART, and RFs.

Parallel Approach for Finding Co-location Pattern – A Map Reduce Framework

Spatial co-location pattern mining is a sub field of data mining which is used to discover interesting patterns which are expressed as co-location rules. The objects that are frequently located in certain region are expressed as spatial co-locations. It presents a challenge for finding co-location patterns as the traditional data is considered discrete whereas the spatial objects are embedded in a continuous space. For this a join-less approach is proposed, but as the data size increases, a large amount of computation time is devoted to find co-location rules as the approach is purely sequential. We propose a parallelized join-less approach which finds the spatial neighbor relationship in order to identify co-location instances and co-location rules. The proposed work decreases the computation time drastically as it uses a Map-Reduce framework. This paper presents precise and completeness of the new approach. Finally, an experimental evaluations using synthetic data sets show the algorithm is computationally more efficient.

A conceptual framework for geographic knowledge engineering

In many applications, the management of geographic knowledge is very important especially not only for urban and environmental planning, but also for any application in territorial intelligence. However there are several practical problems hindering the efficiency, some of them being technical and other being more conceptual. The goal of this paper is to present a tentative conceptual framework for managing practical geographic knowledge taking account of accuracy, rotundity of earth, the mobility of objects, multiple-representation, multi-scale, existence of sliver polygons, differences in classifying real features (ontologies), the many-to-many relationship of place names (gazetteers) and the necessity of interoperability. In other words, this framework must be robust against scaling, generalization and small measurement errors. Therefore, geographic objects must be distinguished into several classes of objects with different properties, namely geodetic objects, administrative objects, manmade objects and natural objects. Regarding spatial relations, in addition to conventional topological and projective relations, other relations including tessellations and ribbon topology relations are presented in order to help model geographic objects by integrating more practical semantics. Any conceptual framework is based on principles which are overall guidelines and rules; moreover, principles allow at making predictions and drawing implications and are finally the basic building blocks of theoretical models. But before identifying the principles, one needs some preliminary considerations named prolegomena. In our case, principles will be essentially rules for transforming geographic knowledge whereas prolegomena will be assertions regarding more the foundations of geographic science. Based on those considerations, 12 principles are given, preceded by 12 prolegomena. For instance, some principles deal with the transformation of spatial relationships based on visual acuity and granularity of interest, with the influence of neighboring information and cross-boundary interoperability. New categories of geographic knowledge types are presented, spatial facts, cluster of areas, flows of persons, goods, etc., topological constraints and co-location rules. To represent knowledge chunks, three styles are presented, based respectively on descriptive logics, XML and visual languages. To conclude this paper, after having defined contexts of interpretation, an example of visual language to manage geographic knowledge is proposed.

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.

Event Centric Modeling Approach in Colocation Pattern Analysis From Spatial Data

Spatial co-location patterns are the subsets of Boolean spatial features whose instances are often located in close geographic proximity. Co-location rules can be identified by spatial statistics or data mining approaches. In data mining method, Association rule-based approaches can be used which are further divided into transaction-based approaches and distance-based approaches. Transaction-based approaches focus on defining transactions over space so that an Apriori algorithm can be used. The natural notion of transactions is absent in spatial data sets which are embedded in continuous geographic space. A new distance -based approach is developed to mine co-location patterns from spatial data by using the concept of proximity neighborhood. A new interest measure, a participation index, is used for spatial co-location patterns as it possesses an anti-monotone property. 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.

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.

Discovering colocation patterns from spatial data sets: a general approach

Given a collection of Boolean spatial features, the colocation pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology data set may reveal symbiotic species. The spatial colocation rule problem is different from the association rule problem since there is no natural notion of transactions in spatial data sets which are embedded in continuous geographic space. We provide a transaction-free approach to mine colocation patterns by using the concept of proximity neighborhood. A new interest measure, a participation index, is also proposed for spatial colocation patterns. The participation index is used as the measure of prevalence of a colocation for two reasons. First, this measure is closely related to the cross-K function, which is often used as a statistical measure of interaction among pairs of spatial features. Second, it also possesses an antimonotone property which can be exploited for computational efficiency. Furthermore, we design an algorithm to discover colocation patterns. This algorithm includes a novel multiresolution pruning technique. Finally, experimental results are provided to show the strength of the algorithm and design decisions related to performance tuning.