Spatial Co-location Research Articles

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.

Brain substrates of perceived spatial separation between speech sources under simulated reverberant listening conditions in schizophrenia.

People with schizophrenia recognize speech poorly under multiple-people-talking (informational masking) conditions. In reverberant environments, direct-wave signals from a speech source are perceptually integrated with the source reflections (the precedence effect), forming perceived spatial separation (PSS) between different sources and consequently improving target-speech recognition against informational masking. However, the brain substrates underlying the schizophrenia-related vulnerability to informational masking and whether schizophrenia affects the unmasking effect of PSS are largely unknown. Using psychoacoustic testing and functional magnetic resonance imaging, respectively, the speech recognition under either the PSS or perceived spatial co-location (PSC) condition and the underlying brain substrates were examined in 20 patients with schizophrenia and 16 healthy controls. Speech recognition was worse in patients than controls. Under the PSS (but not PSC) condition, speech recognition was correlated with activation of the superior parietal lobule (SPL), and target speech-induced activation of the SPL, precuneus, middle cingulate cortex and caudate significantly declined in patients. Moreover, the separation (PSS)-against-co-location (PSC) contrast revealed (1) activation of the SPL, precuneus and anterior cingulate cortex in controls, (2) suppression of the SPL and precuneus in patients, (3) activation of the pars triangularis of the inferior frontal gyrus and middle frontal gyrus in both controls and patients, (4) activation of the medial superior frontal gyrus in patients, and (5) impaired functional connectivity of the SPL in patients. Introducing the PSS listening condition efficiently reveals both the brain substrates underlying schizophrenia-related speech-recognition deficits against informational masking and the schizophrenia-related neural compensatory strategy for impaired SPL functions.

Knowledge, Promotional Events, and the Contribution of Clustering to Innovation

This research provides insight on the mechanisms through which knowledge acquired through promotional events and through spatial co-location simultaneously sustains firm's innovation. Applying the concept of temporary clusters to promotional events, we simultaneously test how internal resources' mediating effect and promotional events' moderating role affect innovation in clusters. Regression analyses with nonparametric bootstrapping and a large sample of Spanish clusters confirm the synergies derived from the combination of internal resources, local relationships, and complementary foreign events. Although extra-cluster linkages increase this effect, synergies creation requires attendance to international exhibitions or conventions. Valuable implications for practitioners and policy makers are discussed.

Spatial Relationship and Colocation of Crimes in Jefferson County, Kentucky

Crime is a major social issue in U.S. cities. Understanding the spatial pattern of crimes is imperative for developing informed policy and effective policing strategies that will reduce crime in local communities. This article examines the spatial distribution of major crimes in Jefferson County, Kentucky. Using crime data spanning the period between 2009 and 2011, this study looked into the spatial relationships and colocation patterns among different types of crime across the county. Specifically we tried to uncover which crime types tend to colocate with each other and where the colocations exist in the county. More than thirty types of crime (excluding sex crimes) were first reclassified into ten categories, and aggregated into each of the 575 census block groups. Principal component analysis was used at the census block group level to extract components from reclassified crime data to identify the categories of crime that are heavily loaded together and therefore share similar location patterns. Four components were extracted and named contraband, violent, property, and theft. Component scores for each component were mapped and analyzed for census block groups, which helps reveal spatial colocation patterns of crimes in Jefferson County. Temporal changing in crime colocation patterns from 2009 to 2011 was also examined in this study.

A GIS modelling framework to evaluate marine spatial planning scenarios: Co-location of offshore wind farms and aquaculture in the German EEZ

The concept of co-location of marine areas receives an increased significance in the light of sustainable development in the already heavily used offshore marine realm. Within this study, different spatial co-location scenarios for the coupling of offshore aquacultures and wind farms are evaluated in order to support efficient and sustainable marine spatial management strategies. A Geographic Information System (GIS) and multi-criteria evaluation (MCE) techniques were combined to index suitable co-sites in the German exclusive economic zone of the North Sea. The MCE was based on criteria such as temperature, salinity or oxygen. In total, 13 possible aquaculture candidates (seaweed, bivalves, fish and crustaceans) were selected for the scenario configuration. The GIS modelling framework proved to be powerful in defining potential co-location sites. The aquaculture candidate oarweed (Laminaria digitata) revealed the highest suitability scores at 10–20m depth from April to June, followed by haddock (Melanogrammus aeglefinus) at 20–30m depth and dulse (Palmaria palmata) and Sea belt (Saccharina latissima) at 0–10m depth between April and June. In summary, results showed several wind farms were de facto suitable sites for aquaculture since they exhibited high suitability scores for Integrated Multi-Trophic Aquaculture (IMTA) systems combining fish species, bivalves and seaweeds. The present results illustrate how synergies may be realised between competing needs of both offshore wind energy and offshore IMTA in the German EEZ of the North Sea. This might offer guidance to stakeholders and assist decision-makers in determining the most suitable sites for pilot projects using IMTA techniques.

The neglected heterogeneity of spatial agglomeration and co-location patterns of creative employment: evidence from Portugal

Empirical literature on the geographical location of creative activities has been traditionally based on the spatial analysis of industries, often disregarding the creative employment that lies outside the necessarily limited boundaries of creative industries. As an extension to the most recent methodologies using industry and occupational data on industrial cluster analysis, this paper analyses agglomeration and co-location patterns of core creative activities, considering both ‘embedded’ (creative professionals working outside the creative sectors) and ‘specialized’ (creative professionals working in the creative sectors) creative employment. Using location quotients and principal component factor and cluster analyses, applied to all 308 Portuguese municipalities, we found that the geographical agglomeration and co-location patterns of core creative groups differ substantially. The typical arguments sustained by the literature—the tendency of creative industries/employment to agglomerate and co-locate in large metropolises—are only supported in the case of knowledge-intensive activities subjected to Intellectual Property Rights, most notably ‘Advertising/Marketing’, ‘Publishing’, ‘TV/Radio’, and ‘Software/Digital Media’, densely concentrated and co-located in highly developed, large urban centres, with high levels of human capital. These arguments do not hold for the traditional creative activities of ‘Architecture’, ‘Design/Visual Arts’ and ‘Crafts’, which, although co-located, appear mostly dispersed with small concentrations around intermediate urban centres. ‘Teaching/training/research’ present quite dispersed geographical patterns with some clusterization around municipalities with tertiary education institutions. ‘Film/video/photography’ and ‘Music/Performing Arts’ show some dispersion throughout the Portuguese territory with concentration around small urban centres and in rural areas. It is evident that, from agglomeration to co-location patterns, creative employment reveals heterogeneous characteristics across creative groups.

Spatial Co-location Patterns Mining

mining refers to a process of analyzing data from different perspectives and summarizing it into useful information that can be used in variety of data centric applications in real time. Geographical Information System (GIS) combined with Data Mining has long being an area of research. GIS is a system that provides information about spatial data. Spatial Data consist of data about real world such as a point on a map represent a place given its latitude and longitude information. Such information of real world constitutes spatial datasets. Data mining in GIS provides a way to analyze this spatial datasets to provide a desired result. Knowledge discovery is a process of extracting implicit knowledge or information from spatial data. Co-location patterns discovery refers to a process of finding subsets of spatial features that are located in close proximity. Spatial Co- location pattern discovery is finding coexistence of non- spatial features in a spatial neighborhood. In this paper we have mined co-location patterns with an approach based on participation index and participation ratio. This technique finds the maximal participation index and uses a clustering algorithm. We have used aprior algorithm method that yields better performance improvement to the algorithm used.

Knowledge management technologies for semantic multimedia services

In ubiquitous or pervasive computing environment, end users want their services to be smart. Such smart services need to understand the semantics and context of the situation where the services are provided. In order to enable the understanding of the semantics and context, the knowledge of the service domain needs to be properly managed. On the other hand, the same knowledge may have different meanings in different domains depending on its size and the way of representing and using the knowledge. This indicates the importance of selecting the right knowledge in the right domain. This special issue features high quality contributions addressing for knowledge management technologies adopted by semantic multimedia services in ubiquitous and pervasive environments. We have selected eight manuscripts for this special issue after the two rounds of reviews. Each selected manuscript was blindly reviewed by at least three reviewers consisting of guest editors and external reviewers. The first paper entitled “A Framework of Spatial Co-location Pattern Mining for Ubiquitous GIS”, by Seung Kwan Kim et al. proposes a framework for co-location pattern mining using the transaction-based approach, which employs maximal cliques as a transaction-type dataset. The proposed framework is a theoretical methodology of coMultimed Tools Appl (2014) 71:195–198 DOI 10.1007/s11042-013-1740-1

Mining regional co-location patterns with kNNG

Spatial co-location pattern mining discovers the subsets of features of which the events are frequently located together in geographic space. The current research on this topic adopts a distance th...

Improved Algorithm of Complex Spatial Co-Location Pattern Based on Grid Clique

Improved Algorithm of Complex Spatial Co-Location Pattern Based on Grid Clique

Improved Algorithm of Complex Spatial Co-Location Pattern Based on Grid Clique

In this paper, an algorithm is presented, which takes grid clique as a whole to find its all instance points neighbor lists in 9-neighbor area, and inserts them into the candidate extension clique list, then deletes the records that dont meet the criteria in the list, finally combines grid cliques and candidate extension clique list to generate maximal clique. The paper discusses the characteristics of spatial co-location pattern algorithm, and makes statistics of its maximal clique number generated in different distance threshold. The experiment shows that the improved algorithm can find out more maximal cliques.

Impact of aerosol and thin cirrus on retrieving and validating XCO2 from GOSAT shortwave infrared measurements

Inadequate treatment of aerosol scattering can be a significant source of error when retrieving column‐averaged dry‐air mole fractions of CO2 (XCO2) from space‐based measurements of backscattered solar shortwave radiation. We have developed a retrieval algorithm, RemoTeC, that retrieves three aerosol parameters (amount, size, and height) simultaneously with XCO2. Here we evaluate the ability of RemoTeC to account for light path modifications by clouds, subvisual cirrus, and aerosols when retrieving XCO2 from Greenhouse Gases Observing Satellite (GOSAT) Thermal and Near‐infrared Sensor for carbon Observation (TANSO)‐Fourier Transform Spectrometer (FTS) measurements. We first evaluate a cloud filter based on measurements from the Cloud and Aerosol Imager and a cirrus filter that uses radiances measured by TANSO‐FTS in the 2 micron spectral region, with strong water absorption. For the cloud‐screened scenes, we then evaluate errors due to aerosols. We find that RemoTeC is well capable of accounting for scattering by aerosols for values of aerosol optical thickness at 750 nm up to 0.25. While no significant correlation of errors is found with albedo, correlations are found with retrieved aerosol parameters. To further improve the XCO2 accuracy, we propose and evaluate a bias correction scheme.Measurements from 12 ground‐based stations of the Total Carbon Column Observing Network (TCCON) are used as a reference in this study. We show that spatial colocation criteria may be relaxed using additional constraints based on modeled XCO2 gradients, to increase the size and diversity of validation data and provide a more robust evaluation of GOSAT retrievals. Global‐scale validation of satellite data remains challenging and would be improved by increasing TCCON coverage.

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.

Geographically Weighted Colocation Quotients: Specification and Application

The spatial correlation, or colocation, of two or more variables is a fundamental issue in geographical analysis but has received much less attention than the spatial correlation of values within a single variable, or autocorrelation. A recent paper by Leslie and Kronenfeld (2011) contributes to spatial correlation analysis in its development of a colocation statistic for categorical data that is interpreted in the same way as a location quotient, a frequently used measure in human geography and other branches of regional analysis. Geographically weighted colocation measures for categorical data are further developed in this article by generalizing Leslie and Kronenfeld's global measure as well as specifying a local counterpart for each global statistic using two different types of spatial filters: fixed and adaptive. These geographically weighted colocation quotients are applied to the spatial distribution of housing types to demonstrate their utility and interpretation.

HDO/H<sub>2</sub>O ratio retrievals from GOSAT

Abstract. We report a new shortwave infrared (SWIR) retrieval of the column-averaged HDO/H2O ratio from the Japanese Greenhouse Gases Observing Satellite (GOSAT). From synthetic simulation studies, we have estimated that the inferred δD values will typically have random errors between 20‰ (desert surface and 30° solar zenith angle) and 120‰ (conifer surface and 60° solar zenith angle). We find that the retrieval will have a small but significant sensitivity to the presence of cirrus clouds, the HDO a priori profile shape and atmospheric temperature, which has the potential of introducing some regional-scale biases in the retrieval. From comparisons to ground-based column observations from the Total Carbon Column Observing Network (TCCON), we find differences between δD from GOSAT and TCCON of around −30‰ for northern hemispheric sites which increase up to −70‰ for Australian sites. The bias for the Australian sites significantly reduces when decreasing the spatial co-location criteria, which shows that spatial averaging contributes to the observed differences over Australia. The GOSAT retrievals allow mapping the global distribution of δD and its variations with season, and we find in our global GOSAT retrievals the expected strong latitudinal gradients with significant enhancements over the tropics. The comparisons to the ground-based TCCON network and the results of the global retrieval are very encouraging, and they show that δD retrieved from GOSAT should be a useful product that can be used to complement datasets from thermal-infrared sounder and ground-based networks and to extend the δD dataset from SWIR retrievals established from the recently ended SCIAMACHY mission.

Work-life 'balance' and gendered (im)mobilities of knowledge and learning in high-tech regional economies

Over the past three decades, economic geographers have explored how the spatial co-location of firms in regional industrial agglomerations helps foster learning, innovation and economic competitiveness. While recent work highlights the crucial role of labour mobility in promoting inter-firm ‘knowledge spillovers’, it pays little attention to how gendered responsibilities of care and personal-life interests beyond the workplace shape workers’ (non)participation in the relational networks and communities of practice widely theorized as enabling learning and innovation. This article presents new data from two regional economies: Dublin, Ireland, and Cambridge, UK. It documents the role of ‘work–life balance’ provision across IT employers in shaping the cross-firm mobility of workers and the tacit knowledge, skills and competencies which they embody. The article disrupts the powerful premise that ‘cross-firm labour mobility is always and everywhere good’ which informs much of the regional learning literature. It also contributes to emerging debates around ‘holistic’ regional development.

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.

Overlooked runaway feedback in the marine nitrogen cycle: the vicious cycle

Abstract. The marine nitrogen (N) inventory is thought to be stabilized by negative feedback mechanisms that reduce N inventory excursions relative to the more slowly overturning phosphorus inventory. Using a global biogeochemical ocean circulation model we show that negative feedbacks stabilizing the N inventory cannot persist if a close spatial association of N2 fixation and denitrification occurs. In our idealized model experiments, nitrogen deficient waters, generated by denitrification, stimulate local N2 fixation activity. But, because of stoichiometric constraints, the denitrification of newly fixed nitrogen leads to a net loss of N. This can enhance the N deficit, thereby triggering additional fixation in a vicious cycle, ultimately leading to a runaway N loss. To break this vicious cycle, and allow for stabilizing negative feedbacks to occur, inputs of new N need to be spatially decoupled from denitrification. Our idealized model experiments suggest that factors such as iron limitation or dissolved organic matter cycling can promote such decoupling and allow for negative feedbacks that stabilize the N inventory. Conversely, close spatial co-location of N2 fixation and denitrification could lead to net N loss.

Structure, functions and interguild relationships of the soil nematode assemblage in organic vegetable production

The abundance and metabolic footprints of soil nematodes were quantified during four of eight years of an intensive organic vegetable production system. Treatment variables included cover crop mixtures and compost application rates. The abundances of bacterivore and fungivore nematodes were enhanced by the annual use of winter cover crops but showed no relationship to the level of residual soil organic matter. Metabolic footprints, based on biomass and respiratory activity, were calculated for functional guilds and ecosystem services of the nematode assemblage. The enrichment footprint, representing the ecosystem service of nutrient mineralization, was related to the level of soil organic carbon. It was strongly related to the metabolic footprint of bacterivore nematodes and both were enhanced in treatments that were cover cropped annually. Cover-cropped treatments also had a slightly higher herbivore footprint, suggesting support of some taxa of plant-feeding nematodes. The structure footprint, reflecting the metabolic activity of higher trophic level nematodes, including the predators of opportunistic species, did not differ among cover crop and compost amendment treatments. However, enrichment footprints were correlated with bacterivore footprints, which represented the predominantly bacterivore resources available to predators. Abundance of predators increased as a function of the abundance of those nematode prey that can be amplified by organic inputs. The functional connectance, represented by spatial co-location, between predators and amplifiable prey was greater in treatments with a greater abundance of predators. The functional connectance between predators and herbivore prey, representing a management target, was strongly related to the functional connectance between predators and amplifiable prey. We conclude that cover crops not only affect organisms at the entry level of the web but that resources are also transferred to higher trophic links which increases top-down pressure on plant-parasitic nematodes.

Spatial co-location pattern discovery without thresholds

Spatial co-location pattern mining discovers the subsets of features whose events are frequently located together in geographic space. The current research on this topic adopts a threshold-based approach that requires users to specify in advance the thresholds of distance and prevalence. However, in practice, it is not easy to specify suitable thresholds. In this article, we propose a novel iterative mining framework that discovers spatial co-location patterns without predefined thresholds. With the absolute and relative prevalence of spatial co-locations, our method allows users to iteratively select informative edges to construct the neighborhood relationship graph until every significant co-location has enough confidence and eventually to discover all spatial co-location patterns. The experimental results on real world data sets indicate that our framework is effective for prevalent co-locations discovery.