Spatial Clustering Research Papers - Academia.edu (original) (raw)

2025, Remote Sensing of Environment

SAR interferometry based on Permanent Scatterers (PS-InSAR™) is used here to study the present crustal mobility of a large area of NW Italy, in the Piemonte region. Thirty-eight satellite scenes (ERS SAR), taken from May 1992 to January 2001, were analysed for detecting more than 2 million PS on the study area. Continuous velocity surface maps (Iso-Kinematic Maps: IKM) were obtained from geo-statistical and spatial cluster techniques (Hot Spot analysis) of PS "short-period" data, to identify relative ground motions and to compare them with "long-period" tectonic mobility trends, i.e. those inferred at regional scale over geological times (some million years). The comparison was made by individuation of homogeneous kinematic areas, represented in the IKM, and characterization of the boundaries between them (Iso-Kinematic Boundaries: IKB). The IKB were used as tools to asses if the PS-InSAR data on present-day crustal mobility could fit with the distribution of real tectonic structures or field geological elements. IKM were drawn for uplifting geological sectors of Piemonte (Maritime Alps, Gran Paradiso, Langhe) where moderate to very low seismicity is recorded, and gravitational instabilities of rock mass on mountain slopes are widespread. The land sectors have been chosen in order to test the suitability of IKM in very different geomorphological conditions. Different types of correspondence between the IKM and the geological kinematic trend were found: -a first type in which the kinematic trend of short-period (a decade of years, i.e. the PS-InSAR detection time span) is in agreement with a long-period tectonic trend (some million years) and seem to be driven by well known faults subparallel to the IKB. These kinematic trends can be hidden by the slope movement due to gravitational instabilities; -a second type in which the kinematic trend of short-period does not strictly correspond to the longperiod trend, but can be considered as minor-order, uplifting-subsidence cycles, even if in contrast with the long-period kinematic trend. Alternatively, the short-period kinematic trends could reflect the action of deep-seated geological forces or structures, not yet known or inferable (at least with the recorded PS-InSAR velocities) on the basis of the available geological data and models.

2025, Memórias do Instituto Oswaldo Cruz

The aim of this study was to describe spatial patterns of the distribution of leprosy and to investigate spatial clustering of incidence rates in the state of Ceará, Northeast Brazil. The average incidence rate of leprosy for the period of 1991 to 1999 was calculated for each municipality of Ceará. Maps were used to describe the spatial distribution of the disease, and spatial statistics were applied to explore large-and small-scale variations of incidence rates. Three regions were identified in which the incidence of leprosy was particularly high. A spatial gradient in the incidence rates was identified, with a tendency of high rates to be concentrated on the North-South axis in the middle region of the state. Moran's I statistic indicated that a significant spatial autocorrelation also existed. The spatial distribution of leprosy in Ceará is heterogeneous. The reasons for spatial clustering of disease rates are not known, but might be related to an heterogeneous distribution of other factors such as crowding, social inequality, and environmental characteristics which by themselves determine the transmission of Mycobacterium leprae.

2025

Die Entwicklung von Clustern ist ein komplexer Prozess, in dem sowohl zentral und formal initiierte als auch dezentrale und selbst organisierte Aktivitaten der Cluster-Teilnehmer eine Rolle spielen. Untersuchungen zur Clusterentwicklung, die beides betrachten, sind selten. An dieser Stelle setzt der vorliegende Beitrag an, der auf eine strukturationstheoretische Fundierung von Clustern rekurriert und diese mit dem Konstrukt der Netzwerkidentitat kombiniert. Auf Basis von Beobachtungen und rezipierten Narrationen im Berlin-Brandenburger Energietechnik-Cluster wird ein dreistufiges Clusterentwicklungs-Modell aus Cluster-Potenzialen, Cluster-Initiativen und Cluster-Projekten entworfen, das im Kern einen identitatsbasierten Strukturationsprozess darstellt.

2025

Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all 3 terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images. Morphological features extracted from these segmentations were found to able to discriminate different Gleason grade patterns with a classification accuracy of 84% via a Support Vector Machine classifier. On average the AdACM model provided 100% savings in computational times compared to a non-optimized hybrid AC model involving a shape prior.

2025, Oryx

The population declines affecting Asian Gyps vultures are among the most rapid and geographically widespread recorded for any species. This paper describes the rates and patterns of mortality and population change over 4 years at three Oriental white-backed vulture Gyps bengalensis colonies in Pakistan: Dholewala (initially 421 pairs), Toawala (initially 445 pairs) and Changa Manga (initially 758 pairs). Vulture mortality led to the extirpation of two of these colonies (Changa Manga and Dholewala) in 3 years, and a decline of 54.3% in the third. Visceral gout, indicative of diclofenac poisoning, was the largest single cause of death in vultures examined. Annual adult mortality from diclofenac poisoning was significantly positively correlated with annual population declines at each colony indicating a direct causal relationship. Visceral gout occurred in temporal and spatial clusters suggesting multiple point sources of diclofenac exposure. The spatial and temporal distribution of de...

2025, Journal of Geographical Systems

This paper analyzes the spatial patterns of households' distribution in clusters of cities and the effects on regional growth using spatial exploratory techniques and a model of growth that incorporates spatial location. Our empirical analysis shows that, over the 1980-1990 period, in Southern New England, patterns of spatial clustering of households did create heterogeneous growth rates in the region. Also, there is evidence that clusters of cities and isolated cities created spillover growth effects in bordering towns.

2025, Wetlands

Forested wetlands are important habitat for many bird species, but data about area and habitat relationships of birds in depressional (i.e., non-riverine) deciduous forested wetlands are scarce. Depressional forested wetlands are often surrounded by larger, continuous patches of upland forest, and it is not clear whether this surrounding forest should be considered part of the forested wetland. To contribute regional data to this question, we sampled birds and vegetation in depressional forested wetlands in southern Michigan, USA. Results indicated that the wetland per se should not be considered separate from the surrounding forest because forest area and forest characteristics were the most important predictors of richness and abundance of wetland-associated birds. Conversely, wetland area and wetland characteristics were important for some upland species. Because spatial clustering and model selection uncertainty are often encountered by wetland scientists, we describe analytical methods used to deal with these problems.

2025

Cluster analysis in data processing could be a main application of business. This investigation describes to gift DBSCALE algorithmic rule that extends enlargement seed choice into a DBSCAN algorithm rule. And also describes the density primarily based cluster conception and also describes its hierarchical extra space OPTICS has been planeed recently, and one among inthe main triumphant approaches to cluster. Aim of this analysis work is to manoeuvre on the advances cluster. During this work the planned procedure focuses on decrease the quantity of seeds points and additionally reduce the execution time cost of looking out neighbourhood information. And hierarchical cluster procedure are often helpful to those attention-grabbing subspaces so as to calculate a latitude for north and south cities and additional calculate line of longitude of various cities and also clustered them. Keywords— Data mining, Data Clustering Analysis, Density Based Clustering, Optics Algorithm, DBSCAN, IDBS...

2025, Kandylis, G., Papatzani, E. and Polyzou, I., "Between coexistence and marginality: migrants and socio-spatial change", in Th. Maloutas (ed) Athens A Rapidly Changing Metropolis in the European South, London and New York, Routledge

Routledge is an imprint of the Taylor & Francis Group, an informa business © 2025 selection and editorial matter, Thomas Maloutas; individual chapters, the contributors The right of Thomas Maloutas to be identified as the author of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988.

2025, Journal of Structural Biology

Cryogenic electron tomography (cryo-ET) has gained increasing interest in recent years due to its ability to image whole cells and subcellular structures in 3D at nanometer resolution in their native environment. However, due to dose restrictions and the inability to acquire high tilt angle images, the reconstructed volumes are noisy and have missing information. Thus, features are unreliable, and precision extraction of the cell boundary is difficult, manual and time intensive. This paper presents an efficient recursive algorithm called BLASTED (Boundary Localization using Adaptive Shape and Texture Discovery) to automatically extract the cell boundary using a conditional random field (CRF) framework in which boundary points and shape are jointly inferred. The algorithm learns the texture of the boundary region progressively, and uses a global shape model and shape-dependent features to propose candidate boundary points on a slice of the membrane. It then updates the shape of that slice by accepting the appropriate candidate points using local spatial clustering, the global shape model, and trained boosted texture classifiers. The BLASTED algorithm segmented the cell membrane over an average of 93% of the length of the cell in 19 difficult cryo-ET datasets.

2025, Astronomy and Astrophysics Supplement Series

2025, Algorithms for Molecular Biology

Background: Many k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds. Results: We introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome. Conclusions: WordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes.

2025, Empirical Economics

In this paper we aim at identifying stylized facts in order to suggest adequate models of spatial co-agglomeration of industries. We describe a class of spatial statistical methods to be used in the empirical analysis of spatial clusters. Compared to previous contributions using point pattern methods, the main innovation of the present paper is to consider clustering for bivariate (rather than univariate) distributions, which allows uncovering co-agglomeration and repulsion phenomena between the different industrial sectors. Furthermore we present the results of an empirical application of such methods to a set of European Patent Office (EPO) data and we produce a series of empirical evidences referred to the the pair-wise intra-sectoral spatial distribution of patents in Italy in the nineties. In this analysis we are able to identify some distinctive joint patterns of location between patents of different sectors and to propose some possible economic interpretations.

2025, Lecture Notes in Computer Science

2025

In order to detect potential disease clusters where a putative source cannot be specified, classical procedures scan the geographical area with circular windows through a specified grid imposed to the map. However, the choice of the windows' shapes, sizes and centers is critical and different choices may not provide exactly the same results. The aim of our work was to use an Oblique Decision Tree model (ODT) which provides potential clusters without pre-specifying shapes, sizes or centers. For this purpose, we have developed an ODT-algorithm to find an oblique partition of the space defined by the geographic coordinates. ODT is based on the classification and regression tree (CART). As CART finds out rectangular partitions of the covariate space, ODT provides oblique partitions maximizing the interclass variance of the independent variable. Since it is a NP-Hard problem in R N , classical ODT-algorithms use evolutionary procedures or heuristics. We have developed an optimal ODT-algorithm in R 2 , based on the directions defined by each couple of point locations. This partition provided potential clusters which can be tested with Monte-Carlo inference. We applied the ODT-model to a dataset in order to identify potential high risk clusters of malaria in a village in Western Africa during the dry season. The ODT results were compared with those of the Kulldorff' s SaTScan™. The ODT procedure provided four classes of risk of infection. In the first high risk class 60%, 95% confidence interval (CI95%) [52.22-67.55], of the children was infected. Monte-Carlo inference showed that the spatial pattern issued from the ODT-model was significant (p < 0.0001). Satscan results yielded one significant cluster where the risk of disease was high with an infectious rate of 54.21%, ]. Obviously, his center was located within the first high risk ODT class. Both procedures provided similar results identifying a high risk cluster in the western part of the village where a mosquito breeding point was located. ODT-models improve the classical scanning procedures by detecting potential disease clusters independently of any specification of the shapes, sizes or centers of the clusters.

2025, Journal of Financial and Quantitative Analysis

An underlying assumption in the executive compensation literature is that there is a national labor market for CEOs. The urban economics literature, however, documents higher ability among workers in large metropolitans, which results in a real and stable urban wage premium. In this paper, we investigate the link between the spatial clustering of firms in big, central cities (i.e., urban agglomeration) and the level and structure of CEO compensation. Using CEO compensation data for the period 1992-2004, we document a positive relation between the size and centrality of the city in which the firm is headquartered and the total, as well as the equity based portion of CEO pay. Our results are robust to a host of control variables, sensitivity and endogeneity tests, indicating that urban agglomeration may reflect positive externalities, such as knowledge spillovers, business connections and improved access to private information that have a positive effect on CEO pay and incentive driven compensation for good performance. We document gradual human capital gains acquired from big city work experience that are transferable to the rural area, and rewarded for, once the CEO relocates into a smaller, less central community. Our tests provide novel evidence of information spillovers and networking opportunities in big cities that can directly affect how CEOs are compensated. Such sources of information and influence represent something for which firms are willing to pay higher and more incentive driven pay, evidence in favor of a market-based explanation for CEO compensation.

2024, Jurnal Litbang Kota Pekalongan

This study analyzes infrastructure accessibility patterns in Pekalongan City using a grid-based approach and machine learning methods. By integrating data from BPS, OpenStreetMap, and ESRI 2023, the research employs 100m × 100m grid analysis units to measure accessibility to public facilities such as education, healthcare, and commerce. Analysis using three clustering methods (K-Means, Bisecting K-Means, and Agglomerative) identifies three distinctive accessibility patterns. The first cluster (40.29%) demonstrates optimal accessibility with high road density, predominantly in the city center. The second cluster (31.64%) exhibits moderate accessibility, characterizing transitional areas. The third cluster (32.90%) shows the lowest accessibility, particularly in southern and coastal regions. Machine learning modeling using Catboost achieves the highest accuracy with a logloss value of 0.0091, confirming distance to healthcare and commercial facilities as key determinants of accessibility. These findings provide empirical foundations for more targeted infrastructure development, with policy recommendations tailored to each cluster's characteristics. The developed methodology offers a novel approach to urban accessibility analysis that can be replicated in other cities with similar characteristics.

2024, TSINGHUA SCIENCE AND TECHNOLOGY

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services. Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices. However, when users utilize these services, they inevitably expose personal information such as their ID and sensitive location to the servers. Due to untrustworthy
servers and malicious attackers with colossal background knowledge, users’ personal information is at risk on these servers. Unfortunately, many privacy-preserving solutions for protecting trajectories have significantly decreased
utility after deployment. We have come up with a new trajectory privacy protection solution that contraposes the area
of interest for users. Firstly, Staying Points Detection Method based on Temporal-Spatial Restrictions (SPDM-TSR) is
an interest area mining method based on temporal-spatial restrictions, which can clearly distinguish between staying
and moving points. Additionally, our privacy protection mechanism focuses on the user’s areas of interest rather than
the entire trajectory. Furthermore, our proposed mechanism does not rely on third-party service providers and the
attackers’ background knowledge settings. We test our models on real datasets, and the results indicate that our
proposed algorithm can provide a high standard privacy guarantee as well as data availability.

2024, Advances in Neural …

Assume a uniform, multidimensional grid of bivariate data, where each cell of the grid has a count ci and a baseline bi. Our goal is to find spatial regions (d-dimensional rectangles) where the ci are significantly higher than expected... more

2024, Mathematics and Statistics

Spatial econometrics is pivotal in
understanding spatial dependencies across diverse fields
like urban economics, environmental economics, and
disease spread. This study highlights the significance of
spatial grouping for data management and pattern detection,
particularly in epidemiological analysis and policy
planning. The Spatial Autoregressive random effect (SARRE)
model is a classical model for analysing datasets with
repeated observations across units over time, particularly
when these units are situated in a spatial context. The
mixture effect models account for the presence of different
sub-groups within the overall population, each of which
has a unique response pattern. In this paper, the proposed
methodology integrates the SAR-RE model into a mixture
framework, allowing for the consideration of diverse
spatial patterns and class-specific coefficients. By
incorporating class-specific coefficients, the model
accommodates heterogeneous spatial structures within the
data, providing a more nuanced understanding of spatial
dependencies. The Spatial autoregressive model along with
the assumption that the random effect follows a mixture of
Gaussian distributions is developed to analyse panel data
with spatial dependency and unobserved heterogeneity.
The parameters of the model are estimated using the
Limited-Memory BFGS (L-BFGS) quasi-Newton methodbased
EM algorithm for good convergence of the estimated.
The classification of subjects into different latent classes is
carried out based on their posterior probabilities. The
model is applied to state-wise COVID-19 confirmed rates,
revealing insightful patterns. The analysis employs the
estimated model for the interpretation and comprehensive
understanding of spatially dependent panel data with
unobserved heterogeneity. The results of the empirical
study show that the proposed model outperforms the
existing model based on performance metrics criteria.

2024

In Data minting, clustering plays a very important role. The process of grouping a set of physical or abstract objects into classes of similar objects is called clustering. Density Based Clustering is a well-known clustering algorithm which having advantages for finding out the clusters of different shapes and size from a large amount of data that contains noise and outliers. In this paper, I have presented a study of DBSCAN algorithm and its further enhancement based on the varied densities.

2024, Biometrics

Spatial cluster detection is an important methodology for identifying regions with excessive numbers of adverse health events without making strong model assumptions on the underlying spatial dependence structure. Previous work has focused on point or individual-level outcome data and few advances have been made when the outcome data are reported at an aggregated level, for example, at the county-or census-tract level. This article proposes a new class of spatial cluster detection methods for point or aggregate data, comprising of continuous, binary, and count data. Compared with the existing spatial cluster detection methods it has the following advantages. First, it readily incorporates region-specific weights, for example, based on a region's population or a region's outcome variance, which is the key for aggregate data. Second, the established general framework allows for area-level and individual-level covariate adjustment. A simulation study is conducted to evaluate the performance of the method. The proposed method is then applied to assess spatial clustering of high Body Mass Index in a health maintenance organization population in the Seattle, Washington, USA area.

2024, Environmental Management

Analysis tools that combine large spatial and temporal scales are necessary for efficient management of wildlife species, such as the burrowing owl (Athene cunicularia). We assessed the ability of Ripley's K-function analysis integrated into a geographic information system (GIS) to determine changes in burrowing owl nest clustering over two years at NASA Ames Research Center. Specifically, we used these tools to detect changes in spatial and temporal nest clustering before, during and after conducting management by mowing to maintain low vegetation height at nest burrows. We found that the scale and timing of owl nest clustering matched the scale and timing of our conservation management actions over a short timeframe. While this study could not determine a causal link between mowing and nest clustering, we did find that Ripley's K and GIS were effective in detecting owl nest clustering and shows promise for future conservation uses.

2024, Applied Geography

Understanding the spatial distributions of multiple ecosystem services (ESs), their associations, and their underlying socio-ecological contributing factors is critical for ES management. Using the city belt along the Yellow River in Ningxia, northwestern China, as a case study, this study quantified the spatial distribution of six ESs (food production, carbon sequestration, carbon storage, nutrient retention, sand fixation and recreational opportunity), analyzed the synergy and trade-off relations among them through correlation analysis, classified ES bundles through a self-organizing map method (SOM), explored the impacts of socio-ecological factors on the ESs through Ordinary Least Square regression (OLS) and Geo-detector analysis, delineated socio-ecological clusters using the SOM, and characterized the relationship between ES bundles and driver clusters through overlap analysis. The results suggest that spatial associations among ESs can be predicted by their driving mechanisms. Synergy relations existed among crop production, carbon sequestration, carbon storage and nutrient retention, and these were impacted by similar driving mechanisms. Synergy also existed between sand fixation and recreational opportunity, but significant differences existed in their driving mechanisms. Trade-off relations were shown between ESs in these two groups at the whole region scale. Three bundles were detected among the six ESs: bundle 1, characterized by recreational opportunity of high supply and other services of limited supply, was located in the transitional region between the central plain and the fringe mountains, and mainly driven by climate and proximity factors; bundle 2, characterized by high sand fixation, medium carbon storage and limited other services, was located in the northwestern and southern mountains and driven by climate and geography factors; bundle 3, characterized by high food production, carbon sequestration, carbon storage and nutrient retention of medium supply and other two services of limited supply, was located in the central plain and driven by vegetation coverage and proximity factors. Human activities can partly overcome the limitations of ecological conditions, thus specific strategies for different regions are proposed to maintain and improve ESs under global climate change.

2024, International journal of health geographics

Spatial analyses and ecological studies are essential for epidemiology and public health. The present study combining these two methods was performed to identify spatial clusters of selected types of cancer in Japan and to determine their societal characteristics focusing on homogeneity among clusters. Spatial clusters of high mortality rates of male colon and lung cancer and of female breast cancer were identified by the spatial scan statistic using Japanese municipal data (N = 3360) from 1993 to 1998 and also municipalities were divided into four societal clusters based on socioeconomic indicators and population density (urban-rich, suburban, rural-poor, and clutter). Five, seven, and four mortality clusters were identified for lung, colon and breast cancer, respectively. For colon and breast cancer, most municipalities of all except one cluster were included in a single societal cluster (urban-rich). The municipalities associated with mortality clusters for lung cancer belonged t...

2024

The Lulea Image Processing System is presented in this paper. Some fundamental system desigh criteria are discussed tog~the~ w~th some aspects of the actual implementation. The sys~em is structured in two levels in form of a pseudo-overlay. The layout of the Image Data File is recognized as an integral part of system architecture. A number of major processing tasks discussed in detail.

2024, Social Science Research Network

Peer-to-Peer (P2P) FinTech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models.

2024, Cadernos de Saúde Pública

A análise espacial de indicadores de saúde tem sido um importante instrumento na detecção de diferenciais intra-urbanos. O estudo objetivou traçar um perfil dos nascimentos em Belo Horizonte, Minas Gerais, Brasil, em 2001, analisando a presença de conglomerados espaciais de indicadores de saúde do recém-nascido e suas mães, a partir de dados do Sistema de Informações sobre Nascidos Vivos. Para cada área de abrangência das Unidades Básicas de Saúde, foram calculadas as proporções desses indicadores, utilizando-se o método Bayesiano empírico. Para análise espacial, foram utilizados os índices de Moran global e local (LISA). Áreas com índices de autocorrelação espacial significantes (p < 0,05) foram visualizadas através de mapas. Foram encontrados conglomerados de áreas com índices de autocorrelação espacial significativos para os indicadores adolescentes, menos de oito anos de estudo, filhos mortos em gestações anteriores, cesárea e menos de quatro consultas no pré-natal, guardando...

2024

Maps with average recurrence time of horizontal acceleration based on the SIL catalogue from 1991 through 2000 have been made in the Hengill area and its surroundings. The dataset, which covers approximately 10 years period, is complete down to local moment magnitude 1. Comparision with similar maps based on catalogues covering 104 years and and earthquakes with local magnitude above 4 is excellent.

2024, 2008 Tenth IEEE International Symposium on Multimedia

Nowadays the photo-capturing devices are no longer limited to digital cameras but include mobile phones, PDAs and others. This is leading to a new problem: a very large number of digital photos captured and chaotically stored in multiple locations without being annotated. This paper presents a new system, called PhotoGeo, for self-organization of georeferenced photos. The proposed system uses metadata and external sources to organize the photos. These external sources include a map-based social network and user's calendar. Furthermore, spatial clustering and temporal segmentation techniques are used to join the photos into clusters with similar features.

2024, Integrated series on information systems

In the control of infectious diseases, epidemiologic information and useful clustering algorithms can be integrated to garner key indicators from huge amounts of daily surveillance information for the need of early intervention. This chapter first introduces the temporal, spatial and spatiotemporal clustering algorithms commonly used in surveillance systems-the key concepts behind the algorithms and the criteria for appropriate use. This description is followed by an introduction to different statistical methods that can be used to analyze the clustering patterns which occur in different epidemics and epidemic stages. Research methods such as flexible analysis of irregular spatial and temporal clusters, adjustment of personal risk factors, and Bayesian approaches to disease mapping and better prediction all will be needed to understand the epidemiologic characteristics of infectious diseases in the future.

2024, Integrated Series in Information Systems

2024, Global Ecology and Biogeography

Aim Distribution maps of species based on a grid are useful for investigating relationships between scale and the number or area of occupied grid cells. A species is scaled up simply by merging occupied grid cells on the observation grid to successively coarser cells. Scale-occupancy relationships (SORs) obtained in this way can be used to extrapolate species down, in other words to compute occupancies at finer scales than the observation scale. In this paper we demonstrate that the SOR is not unique but depends on where one positions the origin of the grid map. Innovation The effect of grid origin on SORs was explored with the aid of the Dutch national data base FLORBASE, which contains the observation records of all 1410 wild vascular plants in the Netherlands on a 1-km square basis. For each species, we generated 2500 unique SORs by scaling up from 1 km, in steps of 1 km, to squares of 50 km. We computed the sensitivity of the SOR to the grid origin for each species, and subsequently analysed the factors that determined this sensitivity. The effect of grid origin on downscaling was demonstrated by means of a simple power function that we used to extrapolate down from both a 2-km and a 5-km grid, to the original 1-km grid. It appeared that the position of grid origin could have a substantial effect on SORs. The sensitivity of SORs to the position of the grid origin depended on three characteristics of a species' spatial distribution: rarity, degree of spatial clustering and the position of the distribution relative to the border of the investigated area. Rare species with a clustered distribution near the border were particularly highly sensitive. The dependence of SOR on grid origin caused unpredictable and non-random errors in downscaled occupancies. Main conclusions In future, the whole bandwidth of scaled occupancies should be considered when testing and interpreting mathematical relationships between scale and occupancy. Moreover, downscaled occupancies should be interpreted cautiously.

2024, RePEc: Research Papers in Economics

Geographical proximity between firms is often believed to favour cooperation, mutual learning, knowledge creation as well as innovation. From Marshall onwards, study after study has demonstrated that both cooperation and successful innovations arise from geographically proximate clusters. Geographers have therefore argued that 'space matters'. However, all too often, a direct line was drawn from geographical proximity to the assumption of cooperation, or from the existence of cooperation to the assumption of innovation without specifying the links between space and innovation. This inevitably lead to a series of papers pointing out that proximity was not enough, or as Torre and Rallett put it "what does 'being near' someone mean" (2005: 48). Therefore space may be a necessary condition but it is not sufficient, 'something else' also must play a role (Gilly and Wallet 2001). This was further followed by a series of papers highlighting that it may not be even a necessary condition and other factors count as much as spatial proximity. We seem to have gone full circle, from ignoring the spatial element in economic development, through arguing that it is crucial, to arguing that it is not so important. A recent issue of Regional Studies (January 2005) was devoted to exploring this issue and the role proximity that plays in the cooperation between firms and their subsequent innovation performance. Expanding on the French school, which holds that geographical proximity is only one form of proximity, and that organisational and institutional proximity are important, this issue reopens the proximity/innovation debate. Indeed Boschma (2005) argues that both cognitive and social proximity should be added to the analysis of cooperation as well as innovation. We seek to add to this debate by exploring a geographically proximate cluster which does not cooperate. The cluster is the contract cleaning industry in Dublin which not only fails to innovate but acts in ways that are damaging to their individual and collective interests. Following Boschma (2005) we look at five dimensions of proximity: cognitive, organisational, social, institutional and geographical, to understand why a geographical cluster 'does not cluster'. The paper draws on an EU funded research project 'CRITICAL' and through the IIIS.

2024, Mobile Networks and Applications

This paper presents an investigation of spectrum coexistence between IEEE 802.11b and 802.16a networks in the same shared frequency band using cognitive radio techniques with different levels of complexity. Simple reactive interference avoidance algorithms as well as proactive spectrum coordination policies based on etiquette protocols are proposed and compared in terms of achievable spectrum efficiency in a shared Wi-Fi/Wi-Max scenario. In reactive interference avoidance methods, radio nodes coordinate spectrum usage without exchange of explicit control information-this is done by adaptively adjusting transmit PHY parameters such as frequency, power and time occupancy based on local observations of the radio band. Because local observations provide information only about transmitters, they may not be sufficient for resolving spectrum contention in scenarios with "hidden receivers". Proactive coordination techniques solve the hidden-receiver problem by utilizing a common spectrum coordination channel (CSCC) for exchange of transmitter and receiver parameters. Radio nodes can cooperatively select key PHY-layer variables such as frequency and power by broadcasting messages in the CSCC channel and then following specified spectrum eti

2024, Identities

Greece, a key European migration crossroads, has witnessed significant societal shifts due to successive waves of incoming and outgoing migrations. By using the concept of 'migratory stratification', this paper analyses the reality of a specific urban space, located in Athens, namely Victoria Square. Over the years this square and the surrounding neighbourhood have seen numerous transformations, transits, and settlements and over the years it has become a hub for migrants, their shops, their informal networks, and their interactions. Starting from ethnographic research, in this paper I analyse this particular context, by highlighting the interactions and interweaving triggered in such plural, layered and heterogeneous situations. As it emerges from the analysis, such spaces can be viewed as generative, as a fertile foundation for creating alternative senses of belonging, fostering practices of solidarity, and strengthening diverse networks and relations.

2024

We present spectroscopic redshifts of a large sample of galaxies with I AB < 22.5 in the COSMOS field, measured from spectra of 10,644 objects that have been obtained in the first two years of observations in the zCOSMOSbright redshift survey. These include a statistically complete subset of 10,109 objects. The average accuracy of individual redshifts is 110 km s −1 , independent of redshift. The reliability of individual redshifts is described by a Confidence Class that has been empirically calibrated through repeat spectroscopic observations of over 600 galaxies. There is very good agreement between spectroscopic and photometric redshifts for the most secure Confidence Classes. For the less secure Confidence Classes, there is a good correspondence between the fraction of objects with a consistent photometric redshift and the spectroscopic repeatability, suggesting that the photometric redshifts can be used to indicate which of the less secure spectroscopic redshifts are likely right and which are probably wrong, and to give an indication of the nature of objects for which we failed to determine a redshift. Using this approach, we can construct a spectroscopic sample that is 99% reliable and which is 88% complete in the sample as a whole, and 95% complete in the redshift range 0.5 < z < 0.8. The luminosity and mass completeness levels of the zCOSMOS-bright sample of galaxies is also discussed.

2024, RePEc: Research Papers in Economics

2024, The Astrophysical Journal

We use a high resolution collisionless simulation of a Virgo-like cluster in a ΛCDM cosmology to determine the velocity and clustering properties of the diffuse stellar component in the intracluster region at the present epoch. The simulated cluster builds up hierarchically and tidal interactions between member galaxies and the cluster potential produce a diffuse stellar component free-flying

2024, Pattern Recognition Letters

Spatial clustering, which groups similar spatial objects into classes, is an important component of spatial data mining [Han and Kamber, Data Mining: Concepts and Techniques, 2000]. Due to its immense applications in various areas, spatial clustering has been highly active topic in data mining researches, with fruitful, scalable clustering methods developed recently. These spatial clustering methods can be classified into four categories: partitioning method, hierarchical method, density-based method and grid-based method. Clustering large data sets of high dimensionality has always been a serious challenge for clustering algorithms. Many recently developed clustering algorithms have attempted to address either handling data with very large number of records or data sets with very high number of dimensions. This new clustering method GCHL (a Grid-Clustering algorithm for High-dimensional very Large spatial databases) combines a novel density-grid based clustering with axis-parallel partitioning strategy to identify areas of high density in the input data space. The algorithm work as well in the feature space of any data set. The method operates on a limited memory buffer and requires at most a single scan through the data. We demonstrate the high quality of the obtained clustering solutions, capability of discovering concave/deeper and convex/higher regions, their robustness to outlier and noise, and GCHL excellent scalability.

2024, regional-studies-assoc.ac.uk

0 ABSTRACT This paper is concerned with theories of territorial economic development in the knowledge based economy, and in particular those concerned with understanding the dynamics of innovation as an economic development process. There has recently been a growing disquiet about the various models used to explain these processes, the so-called Territorial Innovation models, focusing on their narrow regional focus, their orientation towards political rather than economic territories, their uncritical adoption to explain many ...

2024

This paper proposes a technique to describe the water quality quantitatively for the coastal water from multiple measured water quality parameters through an index called Water Quality Index (WQI) using different multivariate statistical techniques. Numerical classification and discriminant analysis were used to derive the WQI. Cluster analysis (CA) technique is carried out in the Euclidean space to classify the groups. Three groups of water quality i.e. good, average and poor have been identified based on the nutrient load. Discriminant analysis was used to generate a discriminant function for developing WQI. A suitable discriminant function has been generated to describe the maximum variance in the data set. Group centroids are calculated using the discriminant function for each group. The Water Quality Index (WQI) has been generated using these centroid values and found within the range -3.2 to +2.1. The negative value representing the centroid for potentially good and positive value representing the poor water quality. The interval of this range establishes the average water quality of coastal water. The application of the WQI is suggested as a very helpful tool that enables the public and decision makers to evaluate the coastal water quality.

2024, Journal of Geophysical Research

Being one of the most outstanding hydrodynamic processes at ocean margins, upwelling is not only a key factor controlling bioproduction but also acts as a driving mechanism for sediment transport. In order to quantify its capability to erode and transport sedimentary particles without being masked by other oceanographic processes, we present a numerical model only forced by surface wind drag. Thereby, transport of particles is not only controlled by upwelling circulation, but also by their physical properties as well as time and location of release into the water column. The study combines a hydrodynamic finite difference model and Lagrangian particle tracing technique. Model geometry mimics a two-dimensional profile from the passive margin offshore Walvis Bay, Namibia. Model runs describe a 5-day wind-forcing event and a subsequent 20-day period of relaxation. As our work is also motivated by paleoceanographic questions, a lowered sea level geometry is used simulating Last Glacial Maximum (LGM) conditions. Results suggest the establishment of a long-lasting circulation comprising an offshore-directed surface layer and an onshore-directed bottom current. Shelf currents are vigorous but short-lasting, allowing transport of particles up to sand size. In contrast, transport at the upper slope is more persistent but restricted to smaller grain sizes. Sea level changes cause a shift of upwelling front in cross-shelf direction and of sedimentary deposition centers along the slope. Transport paths of surface source tracers show systematic variations, which can be evaluated in terms of grain-size fractionation as well as temporal and spatial clusters.

2024, Australas. J Comb.

Let G be a digraph. A set S ⊆ V (G) is called an efficient total dominating set if the set of open out-neighborhoods N − (v) ∈ S is a partition of V (G). We say that G is efficiently open-dominated if both G and its reverse digraph G − have an efficient total dominating set. Some properties of efficiently open dominated digraphs are presented. Special attention is given to tournaments and directed tori.

2024

This paper provides a critical examination of the widely disseminated view that innovation in all or most activities is favoured by certain common characteristics in the local 'milieu', involving a cluster of many small firms benefiting from flexible inter-firm alliances, supported by mutual information exchanges of both an informal and formal nature. The general applicability of this model, and the localness of crucial linkages, is questioned on the basis of a theoretical analysis of the innovation processes, and relations between actors and their environments, leading to the identification of a range of different hypotheses about the geography of innovation. Examination of new survey evidence from a large number of firms in the London conurbation suggests that the importance of informal information spillovers enabled by spatial proximity for successful innovation is much more limited than has been suggested, both in relation to wider agglomeration economies and to non-local business linkages.

2024, Biostatistics

The paper demonstrates how existing theory to assess spatial clustering based on second-moment properties of a labelled point process can be adapted to matched case-control studies. The null hypothesis that cases are a random sample from the superposition of cases and controls is replaced by the hypothesis that each case is a random sample from the set consisting of itself and its k matched controls. We compare the proposed test with other tests of spatial clustering, and describe an application to data on childhood diabetes in Yorkshire, England.