Увійти

Готові списки джерел за темами / Spatial Agglomerative Clustering

Добірка наукової літератури з теми "Spatial Agglomerative Clustering"

Автор: Grafiati

Опубліковано: 1 лютого 2023

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Spatial Agglomerative Clustering".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Зміст

Статті в журналах
Дисертації
Частини книг
Тези доповідей конференцій

Статті в журналах з теми "Spatial Agglomerative Clustering":

1

Lacasta, Javier, Francisco Javier Lopez-Pellicer, Javier Zarazaga-Soria, Rubén Béjar, and Javier Nogueras-Iso. "Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series." ISPRS International Journal of Geo-Information 11, no. 2 (January 26, 2022): 87. http://dx.doi.org/10.3390/ijgi11020087.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset series using only textual information from metadata elements. We assess the combination of different kinds of text cleaning approaches, word and sentence-embeddings representations (Word2Vec, GloVe, FastText, ELMo, Sentence BERT, and Universal Sentence Encoder), and clustering techniques (K-Means, DBSCAN, OPTICS, and agglomerative clustering) for the task. The results demonstrate that combining word-embeddings representations with an agglomerative-based clustering creates better quasi-spatial dataset series than the other approaches. In addition, we have found that the ELMo representation with agglomerative clustering produces good results without any preprocessing step for text cleaning.

2

Spierenburg, Lucas, Sander van Cranenburgh, and Oded Cats. "A regionalization method filtering out small-scale spatial fluctuations." AGILE: GIScience Series 3 (June 11, 2022): 1–6. http://dx.doi.org/10.5194/agile-giss-3-61-2022.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Abstract. Regionalization is the process of aggregating contiguous spatial units to form areas that are homogeneous with respect to one or a set of variables. It is useful when studying spatial phenomena or when designing region-based policies, as it allows to unravel the latent spatial structure of a dataset. However, this task is challenging when small-scale fluctuations in the data interfere with the phenomenon of interest. In such circumstances, regionalization techniques are prone to overfitting small-scale fluctuations, and producing erratic regions. This paper presents a regionalization method robust to small-scale variations that is particularly relevant when handling demographic data. Fluctuations are filtered out using a weighted spatial average before applying agglomerative clustering. The method is tested against a conventional agglomerative clustering approach on a fine-resolution demographic dataset, for a set of indicators quantifying: the ability to identify large-scale spatial patterns, the homogeneity of the regions produced, and the spatial regularity of these regions. These indicators have been computed for the two methods for a number of clusters ranging from 2 to 101, and results show that the proposed approach performs better than conventional agglomerative clustering more than 90% of the time at identifying large-scale patterns, and produces more regular regions 96% of the time.

3

Tie, Jun, Wenying Chen, Chong Sun, Tengyue Mao, and Guanglin Xing. "The application of agglomerative hierarchical spatial clustering algorithm in tea blending." Cluster Computing 22, S3 (January 30, 2018): 6059–68. http://dx.doi.org/10.1007/s10586-018-1813-z.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Kamer, Yavor, Guy Ouillon, and Didier Sornette. "Fault network reconstruction using agglomerative clustering: applications to southern Californian seismicity." Natural Hazards and Earth System Sciences 20, no. 12 (December 23, 2020): 3611–25. http://dx.doi.org/10.5194/nhess-20-3611-2020.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Abstract. In this paper we introduce a method for fault network reconstruction based on the 3D spatial distribution of seismicity. One of the major drawbacks of statistical earthquake models is their inability to account for the highly anisotropic distribution of seismicity. Fault reconstruction has been proposed as a pattern recognition method aiming to extract this structural information from seismicity catalogs. Current methods start from simple large-scale models and gradually increase the complexity trying to explain the small-scale features. In contrast the method introduced here uses a bottom-up approach that relies on initial sampling of the small-scale features and reduction of this complexity by optimal local merging of substructures. First, we describe the implementation of the method through illustrative synthetic examples. We then apply the method to the probabilistic absolute hypocenter catalog KaKiOS-16, which contains three decades of southern Californian seismicity. To reduce data size and increase computation efficiency, the new approach builds upon the previously introduced catalog condensation method that exploits the heterogeneity of the hypocenter uncertainties. We validate the obtained fault network through a pseudo prospective spatial forecast test and discuss possible improvements for future studies. The performance of the presented methodology attests to the importance of the non-linear techniques used to quantify location uncertainty information, which is a crucial input for the large-scale application of the method. We envision that the results of this study can be used to construct improved models for the spatiotemporal evolution of seismicity.

5

Ćulafić, Golub, Tatjana Popov, Slobodan Gnjato, Davorin Bajić, Goran Trbić, and Luka Mitrović. "Spatial and temporal patterns of precipitation in Montenegro." Időjárás 124, no. 4 (2020): 499–519. http://dx.doi.org/10.28974/idojaras.2020.4.5.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

The paper analyses, spatial and temporal patterns of precipitation over Montenegro. Data on mean monthly precipitation during the period 1961–2015 from 17 meteorological stations were used for the analysis. Four regions with different spatial precipitation regimes were identified by using the principal component analysis and the agglomerative hierarchical clustering method. A downward tendency in annual precipitation prevails over Montenegro. The most prominent reduction was present in the summer season. In contrast, precipitation increased during autumn. However, the majority of estimated trend values was low and statistically insignificant.

6

Bataineh, Bilal. "Fast Component Density Clustering in Spatial Databases: A Novel Algorithm." Information 13, no. 10 (October 2, 2022): 477. http://dx.doi.org/10.3390/info13100477.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Clustering analysis is a significant technique in various fields, including unsupervised machine learning, data mining, pattern recognition, and image analysis. Many clustering algorithms are currently used, but almost all of them encounter various challenges, such as low accuracy, required number of clusters, slow processing, inability to produce non-spherical shaped clusters, and unstable performance with respect to data characteristics and size. In this research, a novel clustering algorithm called the fast component density clustering in spatial databases (FCDCSD) is proposed by utilizing a density-based clustering technique to address the aforementioned existing challenges. First, from the smallest to the largest point in the spatial field, each point is labeled with a temporary value, and the adjacent values in one component are stored in a set. Then, all sets with shared values are merged and resolved to obtain a single value that is representative of the merged sets. These values represent final cluster values; that is, the temporary equivalents in the dataset are replaced to generate the final clusters. If some noise appears, then a post-process is performed, and values are assigned to the nearest cluster based on a set of rules. Various synthetic datasets were used in the experiments to evaluate the efficiency of the proposed method. Results indicate that FCDCSD is generally superior to affinity propagation, agglomerative hierarchical, k-means, mean-shift, spectral, and density-based spatial clustering of applications with noise, ordering points for identifying clustering structures, and Gaussian mixture clustering methods.

7

Ding, Linfang, Liqiu Meng, Jian Yang, and Jukka M. Krisp. "Interactive visual exploration and analysis of origin-destination data." Proceedings of the ICA 1 (May 16, 2018): 1–5. http://dx.doi.org/10.5194/ica-proc-1-29-2018.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel coordinates plot for visualizing the precomputed clusters and for further exploration of interesting clusters. Finally, we propose a gradient line rendering technique to show the spatial and directional distribution of origin-destination clusters on a map view. We implement the visual analytics approach in a web-based interactive environment and apply it to real-world floating car data from Shanghai. The experiment results show the origin/destination hotspots and their spatial interaction patterns. They also demonstrate the effectiveness of our proposed approach.

8

Lamb, David, Joni Downs, and Steven Reader. "Space-Time Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data." ISPRS International Journal of Geo-Information 9, no. 2 (February 1, 2020): 85. http://dx.doi.org/10.3390/ijgi9020085.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved space-time clustering approach that relies on agglomerative hierarchical clustering to identify groupings in movement data. The approach, i.e., space–time hierarchical clustering, incorporates location, time, and attribute information to identify the groups across a nested structure reflective of a hierarchical interpretation of scale. Simulations are used to understand the effects of different parameters, and to compare against existing clustering methodologies. The approach successfully improves on traditional approaches by allowing flexibility to understand both the spatial and temporal components when applied to data. The method is applied to animal tracking data to identify clusters, or hotspots, of activity within the animal’s home range.

9

Shivakumar, Abhishek, Thomas Alfstad, and Taco Niet. "A clustering approach to improve spatial representation in water-energy-food models." Environmental Research Letters 16, no. 11 (October 29, 2021): 114027. http://dx.doi.org/10.1088/1748-9326/ac2ce9.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Abstract Currently available water-energy-food (WEF) modelling frameworks to analyse cross-sectoral interactions often share one or more of the following gaps: (a) lack of integration between sectors, (b) coarse spatial representation, and (c) lack of reproducible methods of nexus assessment. In this paper, we present a novel clustering tool as an expansion to the Climate-Land-Energy-Water-Systems modelling framework used to quantify inter-sectoral linkages between water, energy, and food systems. The clustering tool uses Agglomerative Hierarchical clustering to aggregate spatial data related to the land and water sectors. Using clusters of aggregated data reconciles the need for a spatially resolved representation of the land-use and water sectors with the computational and data requirements to efficiently solve such a model. The aggregated clusters, combined together with energy system components, form an integrated resource planning structure. The modelling framework is underpinned by an open-source energy system modelling tool—OSeMOSYS—and uses publicly available data with global coverage. By doing so, the modelling framework allows for reproducible WEF nexus assessments. The approach is used to explore the inter-sectoral linkages between the energy, land-use, and water sectors of Viet Nam out to 2030. A validation of the clustering approach confirms that underlying trends actual crop yield data are preserved in the resultant clusters. Finally, changes in cultivated area of selected crops are observed and differences in levels of crop migration are identified.

10

Faroqi, Hamed, Mahmoud Mesbah, and Jiwon Kim. "Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data." Mathematical Problems in Engineering 2019 (April 14, 2019): 1–16. http://dx.doi.org/10.1155/2019/5070794.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

Більше джерел

Дисертації з теми "Spatial Agglomerative Clustering":

1

Chen, Fati. "Réduction de l'encombrement visuel : Application à la visualisation et à l'exploration de données prosopographiques." Thesis, Université de Montpellier (2022-….), 2022. http://www.theses.fr/2022UMONS023.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

La prosopographie est utilisée par les historiens pour désigner des notices biographiques afin d'étudier des caractéristiques communes d'un groupe d'acteurs de l'histoire au moyen d'une analyse collective de leur vie. La visualisation d'informations présente des perspectives intéressantes pour analyser les données prosopographiques. C'est dans ce contexte que se situe le travail présenté dans ce mémoire. Dans un premier temps, nous présentons la plateforme ProsoVis conçue pour analyser et naviguer dans des données prosopographiques. Nous décrivons les différents besoins exprimés et détaillons les choix de conception ainsi que les techniques de visualisation employées. Nous illustrons son utilisation avec la base Siprojuris qui contient les données sur la carrière des enseignants de droit de 1800 à 1950. La visualisation d'autant de données pose des problèmes d'encombrement visuel. Dans ce contexte, nous abordons la problématique des chevauchements des nœuds dans un graphe. Différentes approches existent mais il est difficile de les comparer car leurs évaluations ne sont pas basées sur les mêmes critères de qualité. Nous proposons donc une étude de l'état de l'art et comparons les résultats des algorithmes sur une liste homogène de critères. Enfin, nous abordons une autre problématique d'encombrement visuel au sein d'une carte et proposons une approche de regroupement spatial agglomératif, F-SAC, beaucoup plus rapide que les propositions de l'état de l'art tout en garantissant la même qualité de résultats
Prosopography is used by historians to designate biographical records in order to study common characteristics of a group of historical actors through a collective analysis of their lives. Information visualization presents interesting perspectives for analyzing prosopographic data. It is in this context that the work presented in this thesis is situated. First, we present the ProsoVis platform to analyze and navigate through prosopographic data. We describe the different needs expressed and detail the design choices as well as the different views. We illustrate its use with the Siprojuris database which contains data on the careers of law teachers from 1800 to 1950. Visualizing so much data induces visual cluttering problems. In this context, we address the problem of overlapping nodes in a graph. Even if approaches exist, it is difficult to compare them because their respective evaluations are not based on the same quality criteria. We therefore propose a study of the state-of-the-art algorithms by comparing their results on the same criteria. Finally, we address a similar problem of visual cluttering within a map and propose an agglomeration spatial clustering approach, F-SAC, which is much faster than the state-of-the-art proposals while guaranteeing the same quality of results

2

Haedo, Christian Martin <1971&gt. "Measure of Global Specialization and Spatial Clustering for the Identification of "Specialized" Agglomeration." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1735/.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

The intensity of regional specialization in specific activities, and conversely, the level of industrial concentration in specific locations, has been used as a complementary evidence for the existence and significance of externalities. Additionally, economists have mainly focused the debate on disentangling the sources of specialization and concentration processes according to three vectors: natural advantages, internal, and external scale economies. The arbitrariness of partitions plays a key role in capturing these effects, while the selection of the partition would have to reflect the actual characteristics of the economy. Thus, the identification of spatial boundaries to measure specialization becomes critical, since most likely the model will be adapted to different scales of distance, and be influenced by different types of externalities or economies of agglomeration, which are based on the mechanisms of interaction with particular requirements of spatial proximity. This work is based on the analysis of the spatial aspect of economic specialization supported by the manufacturing industry case. The main objective is to propose, for discrete and continuous space: i) a measure of global specialization; ii) a local disaggregation of the global measure; and iii) a spatial clustering method for the identification of specialized agglomerations.

Частини книг з теми "Spatial Agglomerative Clustering":

1

Castermans, Thom, Bettina Speckman, and Kevin Verbeek. "A Practical Algorithm for Spatial Agglomerative Clustering." In 2019 Proceedings of the Twenty-First Workshop on Algorithm Engineering and Experiments (ALENEX), 174–85. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2019. http://dx.doi.org/10.1137/1.9781611975499.14.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Senthilarasi, S., and S. Kamalakkannan. "Unsupervised Deep Learning on Spatial-Temporal Traffic Data Using Agglomerative Clustering." In Inventive Systems and Control, 757–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1395-1_56.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Shekhar, Shashi, and Hui Xiong. "Spatially Agglomerative Clustering." In Encyclopedia of GIS, 1113. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1308.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

"Spatially Agglomerative Clustering." In Encyclopedia of GIS, 2126. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_101299.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Mazongonda, Simbarashe Show, and Innocent Chirisa. "Spatiality, Clustering, and the Agglomeration Economies of Scale." In Advances in Electronic Government, Digital Divide, and Regional Development, 224–47. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4165-3.ch013.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

This chapter is based on a study that tests the realities of agglomeration economies of scale due to clustering of small-scale manufacturing firms of the informal type in Zimbabwe. Little has been studied on how the informal sector thrives on agglomeration economies of scale in developing countries. Despite this lack of research, this chapter acknowledges the existence of strong networks among small-scale manufacturers in urban Zimbabwe. These linkages, contrary to practices within large-scale manufacturers, are cemented by strong ties of entrepreneurialism. With big manufacturers, the ties are usually worker-based and less defined along entrepreneurial lines. Using spatial statistical approach, the test revealed that tool sharing, output-input relationship, employment creation, and sharing of knowledge economies of scale are also evident in developing countries.

6

Ferragina, Anna M., Giulia Nunziante, and Erol Taymaz. "Spatial agglomeration, innovation clustering and firm performances in Turkey 1." In Mediterranean Migration and the Labour Markets, 102–32. Routledge, 2019. http://dx.doi.org/10.4324/9781315150963-6.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Deutschmann, Emanuel. "Why Does Regionalism Occur in Transnational Human Activity?" In Mapping the Transnational World, 106–28. Princeton University Press, 2022. http://dx.doi.org/10.23943/princeton/9780691226491.003.0004.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

This chapter assesses why regionalism occurs in transnational human activity. Is it because countries within regions tend to be culturally similar, frequently sharing a common history, language, or religion? Is it because they have stronger economic bonds than countries situated in disparate regions? Is it because they often form part of the same supranational political community whose policies sometimes explicitly aim at increasing internal mobility and communication while enforcing external border controls? Or is it simply due to smaller geographic distances within regions? The answer may be a combination of all these factors—but in that case one might still want to know which factors are most influential in creating the intraregional agglomeration of human cross-border activity. Using a network-analytical modeling technique called multiple regression quadratic assignment procedure (MRQAP), the chapter establishes that while most of these factors play some role, spatial proximity is clearly the main explanation for the clustering of transnational activity within world regions. Its effect is particularly strong in Europe with its comparatively small geographic territory.

Тези доповідей конференцій з теми "Spatial Agglomerative Clustering":

1

Senthilarasi, S., and S. Kamalakkannan. "Implementation of Spatial-Temporal Road Traffic Data using Agglomerative Clustering." In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021. http://dx.doi.org/10.1109/iciccs51141.2021.9432370.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Goudarzi, Navid, and Dorsa Ziaei. "Wind Farm Clustering Methods for Power Forecasting." In ASME 2022 Power Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/power2022-86666.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Abstract This paper initiates the development of a hybrid model for wind farm output power forecasting based on spatiotemporal parameters of a studied site. The time-dependency of local wind patterns is addressed by developing three wind farm clustering, K-means, Agglomerative, and Density-Based Spatial Clustering of Applications with Noise. Clustered wind turbines obtain a more accurate representation for wind power forecasting. The results for this work will be later used to extract key features based on singular value decomposition (SVD) and build the forecast model. the emphasis in this paper is on the clustering method and not the forecasting algorithms. Hourly-wind data of an onshore wind farm in the US for one year are used for developing this model. The results will be further used in improving wind clustering algorithms, feature identification, and time-dependency analyses of short- to medium wind forecasting.

3

Liu, Yaobin, and Ling Liu. "Spatial Integration of Urban Agglomeration Based on DEA Dynamic Clustering." In 2010 International Conference on E-Business and E-Government (ICEE). IEEE, 2010. http://dx.doi.org/10.1109/icee.2010.22.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Pihui Huang, Tienying Chou, and Wentzu Lin. "Using an improved spatial clustering model for evaluation of industry agglomeration." In 2012 20th International Conference on Geoinformatics. IEEE, 2012. http://dx.doi.org/10.1109/geoinformatics.2012.6270258.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

See, John, and Chikkannan Eswaran. "Exemplar extraction using spatio-temporal hierarchical agglomerative clustering for face recognition in video." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126405.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.