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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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).
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Huh, Yong, Jung-Ok Kim, and Ki-Yun Yu. "Detection of M:N corresponding class group pairs between two spatial datasets with agglomerative hierarchical clustering." Korean Journal of Geomatics 30, no. 2 (April 30, 2012): 125–34. http://dx.doi.org/10.7848/ksgpc.2012.30.2.125.
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Kuschnerus, Mieke, Roderik Lindenbergh, and Sander Vos. "Coastal change patterns from time series clustering of permanent laser scan data." Earth Surface Dynamics 9, no. 1 (February 19, 2021): 89–103. http://dx.doi.org/10.5194/esurf-9-89-2021.
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Abstract. Sandy coasts are constantly changing environments governed by complex, interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and to support analysis of geomorphological deformation processes. This novel technique delivers 3-D representations of the coast at hourly temporal and centimetre spatial resolution and allows us to observe small-scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatiotemporal data set is needed. To enable automated data mining, we extract time series of surface elevation and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well-known clustering algorithms (k-means clustering, agglomerative clustering and density-based spatial clustering of applications with noise; DBSCAN), apply them on the set of time series and identify areas that undergo similar evolution during 1 month. We test if these algorithms fulfil our criteria for suitable clustering on our exemplary data set. The three clustering methods are applied to time series over 30 d extracted from a data set of daily scans covering about 2 km of coast in Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer, is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow us to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The level of detail found with these algorithms depends on the choice of the number of clusters k. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused, for example, by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatiotemporal data set for predominant deformation patterns with the associated regions where they occur.
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Annaki, Ihababdelbasset, Mohammed Rahmoune, Mohammed Bourhaleb, Jamal Berrich, Mohamed Zaoui, Alexandre Castilla, Alain Berthoz, and Bernard Cohen. "Clustering analysis of human navigation trajectories in a visuospatial memory locomotor task using K-Means and hierarchical agglomerative clustering." E3S Web of Conferences 351 (2022): 01042. http://dx.doi.org/10.1051/e3sconf/202235101042.
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Throughout this study, we employed unsupervised machine learning clustering algorithms, namely K-Means [1] and hierarchical agglomerative clustering (HAC) [2], to explore human locomotion and wayfinding using a VR Magic Carpet (VMC) [3], a table test version known as the Corsi Block Tapping task (CBT) [4]. This variation was carried out in the context of a virtual reality experimental setup. The participants were required to memorize a sequence of target positions projected on the rug and walk to each target figuring in the displayed sequence. the participant’s trajectory was collected and analyzed from a kinematic perspective. An earlier study [5] identified three different categories, but the classification remained ambiguous, implying that they include both kinds of individuals (normal and patients with cognitive spatial impairments). On this basis, we utilized K-Means and HAC to distinguish the navigation behavior of patients from normal individuals, emphasizing the most important discrepancies and then delving deeper to gain more insights.
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Khosravi Kazazi, Ali, Fariba Amiri, Yaser Rahmani, Raheleh Samouei, and Hamidreza Rabiei-Dastjerdi. "A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods." Sustainability 14, no. 21 (October 28, 2022): 14106. http://dx.doi.org/10.3390/su142114106.
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The unequal distribution of healthcare services is the main obstacle to achieving health equity and sustainable development goals. Spatial accessibility to healthcare services is an area of interest for health planners and policymakers. In this study, we focus on the spatial accessibility to four different types of healthcare services, including hospitals, pharmacies, clinics, and medical laboratories at Isfahan’s census blocks level, in a multivariate study. Regarding the nature of spatial accessibility, machine learning unsupervised clustering methods are utilized to analyze the spatial accessibility in the city. Initially, the study area was grouped into five clusters using three unsupervised clustering methods: K-Means, agglomerative, and bisecting K-Means. Then, the intersection of the results of the methods is considered to be conclusive evidence. Finally, using the conclusive evidence, a supervised clustering method, KNN, was applied to generate the map of the spatial accessibility situation in the study area. The findings of this study show that 47%, 22%, and 31% of city blocks in the study area have rich, medium, and poor spatial accessibility, respectively. Additionally, according to the study results, the healthcare services development is structured in a linear pattern along a historical avenue, Chaharbagh. Although the scope of this study was limited in terms of the supply and demand rates, this work gives more information and spatial insights for researchers, planners, and policymakers aiming to improve accessibility to healthcare and sustainable urban development. As a recommendation for further research work, it is suggested that other influencing factors, such as the demand and supply rates, should be integrated into the method.
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Märzinger, Thomas, Jan Kotík, and Christoph Pfeifer. "Application of Hierarchical Agglomerative Clustering (HAC) for Systemic Classification of Pop-Up Housing (PUH) Environments." Applied Sciences 11, no. 23 (November 24, 2021): 11122. http://dx.doi.org/10.3390/app112311122.
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This paper is the result of the first-phase, inter-disciplinary work of a multi-disciplinary research project (“Urban pop-up housing environments and their potential as local innovation systems”) consisting of energy engineers and waste managers, landscape architects and spatial planners, innovation researchers and technology assessors. The project is aiming at globally analyzing and describing existing pop-up housings (PUH), developing modeling and assessment tools for sustainable, energy-efficient and socially innovative temporary housing solutions (THS), especially for sustainable and resilient urban structures. The present paper presents an effective application of hierarchical agglomerative clustering (HAC) for analyses of large datasets typically derived from field studies. As can be shown, the method, although well-known and successfully established in (soft) computing science, can also be used very constructively as a potential urban planning tool. The main aim of the underlying multi-disciplinary research project was to deeply analyze and structure THS and PUE. Multiple aspects are to be considered when it comes to the characterization and classification of such environments. A thorough (global) web survey of PUH and analysis of scientific literature concerning descriptive work of PUH and THS has been performed. Moreover, out of several tested different approaches and methods for classifying PUH, hierarchical clustering algorithms functioned well when properly selected metrics and cut-off criteria were applied. To be specific, the ‘Minkowski’-metric and the ‘Calinski-Harabasz’-criteria, as clustering indices, have shown the best overall results in clustering the inhomogeneous data concerning PUH. Several additional algorithms/functions derived from the field of hierarchical clustering have also been tested to exploit their potential in interpreting and graphically analyzing particular structures and dependencies in the resulting clusters. Hereby, (math.) the significance ‘S’ and (math.) proportion ‘P’ have been concluded to yield the best interpretable and comprehensible results when it comes to analyzing the given set (objects n = 85) of researched PUH-objects together with their properties (n > 190). The resulting easily readable graphs clearly demonstrate the applicability and usability of hierarchical clustering- and their derivative algorithms for scientifically profound building classification tasks in Urban Planning by effectively managing huge inhomogeneous building datasets.
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Brooks, Leah, and Byron Lutz. "Vestiges of Transit: Urban Persistence at a Microscale." Review of Economics and Statistics 101, no. 3 (July 2019): 385–99. http://dx.doi.org/10.1162/rest_a_00817.
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We document intracity spatial persistence and its causes. Streetcars dominated urban transit in Los Angeles County from the 1890s to the early 1910s, and were off the road entirely by 1963. However, we find that streetcars' influence remains readily visible in the current pattern of urban density and that this influence has not dissipated in the sixty years since the streetcar's removal. We examine land use regulation as both a consequence of streetcars and a mechanism for the persistent effect of streetcars. Our evidence suggests that the streetcar influences modern behavior through the mutually reinforcing pathways of regulation and agglomerative clustering.
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Khandelwal, Monika, Sabha Sheikh, Ranjeet Kumar Rout, Saiyed Umer, Saurav Mallik, and Zhongming Zhao. "Unsupervised Learning for Feature Representation Using Spatial Distribution of Amino Acids in Aldehyde Dehydrogenase (ALDH2) Protein Sequences." Mathematics 10, no. 13 (June 25, 2022): 2228. http://dx.doi.org/10.3390/math10132228.
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Aldehyde dehydrogenase 2 (ALDH2) enzyme is required for alcohol detoxification. ALDH2 belongs to the aldehyde dehydrogenase family, the most important oxidative pathway of alcohol digestion. Two main liver isoforms of aldehyde dehydrogenase are cytosolic and mitochondrial. Approximately 50% of East Asians have ALDH2 deficiency (inactive mitochondrial isozyme), with lysine (K) for glutamate (E) substitution at position 487 (E487K). ALDH2 deficiency is also known as Alcohol Flushing Syndrome or Asian Glow. For people with an ALDH2 deficiency, their face turns red after drinking alcohol, and they are more susceptible to various diseases than ALDH2-normal people. This study performed a machine learning analysis of ALDH2 sequences of thirteen other species by comparing them with the human ALDH2 sequence. Based on the various quantitative metrics (physicochemical properties, secondary structure, Hurst exponent, Shannon entropy, and fractal dimension), these fourteen species were clustered into four clusters using the unsupervised machine learning (K-means clustering) algorithm. We also analyze these species using hierarchical clustering (agglomerative clustering) and draw the phylogenetic trees. The results show that Homo sapiens is more closely related to the Bos taurus and Sus scrofa species. Our experimental results suggest that the testing for discovering medicines may be done on these species before being tested in humans to alleviate the impacts of ALDH2 deficiency.
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Gnat, Sebastian. "Spatial weight matrix impact on real estate hierarchical clustering in the process of mass valuation." Oeconomia Copernicana 10, no. 1 (March 31, 2019): 131–51. http://dx.doi.org/10.24136/oc.2019.007.
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Research background: The value of the property can be determined on an individual or mass basis. There are a number of situations in which uniform and relatively fast results obtained by means of mass valuation undoubtedly outweigh the advantages of the individual approach. In literature and practice there are a number of different types of models of mass valuation of real estate. For some of them it is postulated or required to group the valued properties into homogeneous subset due to various criteria. One such model is Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA). When using this algorithm, the area to be valued should be divided into the so-called location attractiveness areas (LAZ). Such division can be made in many ways. Regardless of the method of clustering, its result should be assessed, depending on the degree of implementation of the adopted criterion of division quality. A better division of real estate will translate into more accurate valuation results.
Purpose of the article: The aim of the article is to present an application of hierarchical clustering with a spatial constraints algorithm for the creation of LAZ. This method requires the specification of spatial weight matrix to carry out the clustering process. Due to the fact that such a matrix can be specified in a number of ways, the impact of the proposed types of matrices on the clustering process will be described. A modified measure of information entropy will be used to assess the clustering results.
Methods: The article utilises the algorithm of agglomerative clustering, which takes into account spatial constraints, which is particularly important in the context of real estate valuation. Homogeneity of clusters will be determined with the means of information entropy.
Findings & Value added: The main achievements of the study will be to assess whether the type of the distance matrix has a significant impact on the clustering of properties under valuation.
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Selvi, Huseyin Zahit, and Burak Caglar. "Using cluster analysis methods for multivariate mapping of traffic accidents." Open Geosciences 10, no. 1 (December 20, 2018): 772–81. http://dx.doi.org/10.1515/geo-2018-0060.
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Abstract Many factors affect the occurrence of traffic accidents. The classification and mapping of the different attributes of the resulting accident are important for the prevention of accidents. Multivariate mapping is the visual exploration of multiple attributes using a map or data reduction technique. More than one attribute can be visually explored and symbolized using numerous statistical classification systems or data reduction techniques. In this sense, clustering analysis methods can be used for multivariate mapping. This study aims to compare the multivariate maps produced by the K-means method, K-medoids method, and Agglomerative and Divisive Hierarchical Clustering (AGNES) method, which among clustering analysis methods, with real data. The results from the study will suggest which clustering methods should be preferred in terms of multivariate mapping. The results show that the K-medoids method is more appropriate in terms of clustering success. Moreover, the aim is to reveal spatial similarities in traffic accidents according to the results of traffic accidents that occur in different years. For this aim, multivariate maps created from traffic accident data of two different years in Turkey are used. The methods are compared, and the use of the maps produced with these methods for risk management and planning is discussed. Analysis of the maps reveals significant similarities for both years.
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Lawes, Roger A., Helen T. Murphy, and Anthony C. Grice. "Comparing agglomerative clustering and three weed classification frameworks to assess the invasiveness of alien species across spatial scales." Diversity Distributions 12, no. 6 (November 2006): 633–44. http://dx.doi.org/10.1111/j.1472-4642.2006.00291.x.
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Schaefer, James A., and François Messier. "Composition and spatial structure of plant communities on southeastern Victoria Island, arctic Canada." Canadian Journal of Botany 72, no. 9 (September 1, 1994): 1264–72. http://dx.doi.org/10.1139/b94-154.
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We used multivariate methods to investigate the composition and spatial organisation of tundra plant communities in the Wellington Bay region of southeastern Victoria Island (69°N, 106°W). Ordination and classification of sites at an intermediate scale (≈ 1 ha) were conducted using principal components analysis and sums of squares agglomerative clustering on a matrix of standardized chord distances. The findings suggested eight vegetation classes. These communities are described floristically. At this spatial scale, the vegetation showed correspondence to elevation, slope, and thickness of soil, but not to aspect. The spatial patterns of multiple plant species and physical variables (i.e., slope of terrain and thickness of soil) were examined using multiscale ordination and double logarithmic regressions of variance on distance, respectively. Multiscale ordination revealed ever-increasing plant heterogeneity with distance (0.25 – 1600 m) and suggested only weak general patterns at scales ≤ 200 m. Similarly, variance in the physical factors tended to increase continually with distance. Both the vegetation and physical environment thus appeared to be organised on gradients. Key words: Arctic, classification, ordination, spatial scale, tundra.
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Dzwinel, W., D. A. Yuen, K. Boryczko, Y. Ben-Zion, S. Yoshioka, and T. Ito. "Nonlinear multidimensional scaling and visualization of earthquake clusters over space, time and feature space." Nonlinear Processes in Geophysics 12, no. 1 (January 28, 2005): 117–28. http://dx.doi.org/10.5194/npg-12-117-2005.
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Abstract. We present a novel technique based on a multi-resolutional clustering and nonlinear multi-dimensional scaling of earthquake patterns to investigate observed and synthetic seismic catalogs. The observed data represent seismic activities around the Japanese islands during 1997-2003. The synthetic data were generated by numerical simulations for various cases of a heterogeneous fault governed by 3-D elastic dislocation and power-law creep. At the highest resolution, we analyze the local cluster structures in the data space of seismic events for the two types of catalogs by using an agglomerative clustering algorithm. We demonstrate that small magnitude events produce local spatio-temporal patches delineating neighboring large events. Seismic events, quantized in space and time, generate the multi-dimensional feature space characterized by the earthquake parameters. Using a non-hierarchical clustering algorithm and nonlinear multi-dimensional scaling, we explore the multitudinous earthquakes by real-time 3-D visualization and inspection of the multivariate clusters. At the spatial resolutions characteristic of the earthquake parameters, all of the ongoing seismicity both before and after the largest events accumulates to a global structure consisting of a few separate clusters in the feature space. We show that by combining the results of clustering in both low and high resolution spaces, we can recognize precursory events more precisely and unravel vital information that cannot be discerned at a single resolution.
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Nagy, Eszter, Ildikó Benedek, Attila Zsolnai, Tibor Halász, Ágnes Csivincsik, Virág Ács, Gábor Nagy, and Tamás Tari. "Habitat Characteristics as Potential Drivers of the Angiostrongylus daskalovi Infection in European Badger (Meles meles) Populations." Pathogens 10, no. 6 (June 7, 2021): 715. http://dx.doi.org/10.3390/pathogens10060715.
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From 2016 to 2020, an investigation was carried out to identify the rate of Angiostrongylus spp. infections in European badgers in Hungary. During the study, the hearts and lungs of 50 animals were dissected in order to collect adult worms, the morphometrical characteristics of which were used for species identification. PCR amplification and an 18S rDNA-sequencing analysis were also carried out. Global and local spatial autocorrelation methods were used to detect high-rated and low-rated infected animal clusters. We conducted a binary logistic regression analysis along with hierarchical agglomerative clustering to determine the relation between selected biotic and abiotic variables, and the prevalence of an A. daskalovi infection. We found a high prevalence (72%) and moderate mean intensity (14.1) of Angiostrongylus sp. infection. Morphology and sequencing revealed that all animals were infected by A. daskalovi. The results of both spatial autocorrelations suggested that the spatial distribution of infected badgers was more spatially clustered than random. The results of an analysis of the correlation between habitat characteristics and infection showed that the infected animals could be associated with dry and open landscape habitats without extended and connected canopy. It is suggested that the territorial behaviour of badgers and the landscape-directed aggregation of potential intermediate hosts might be the drivers of an A. daskalovi infection.
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Fiddes, Sonya, Acacia Pepler, Kate Saunders, and Pandora Hope. "Redefining southern Australia’s climatic regions and seasons." Journal of Southern Hemisphere Earth Systems Science 71, no. 1 (2021): 92. http://dx.doi.org/10.1071/es20003.
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Climate scientists routinely rely on averaging over time or space to simplify complex information and to concisely communicate findings. Currently, no consistent definitions of ‘warm’ or ‘cool’ seasons for southern Australia exist, making comparisons across studies difficult. Similarly, numerous climate studies in Australia use either arbitrarily defined areas or the Natural Resource Management (NRM) clusters to perform spatial averaging. While the NRM regions were informed by temperature and rainfall information, they remain somewhat arbitrary. Here we use weather type influence on rainfall and clustering methods to quantitatively define climatic regions and seasons over southern Australia. Three methods are explored: k-means clustering and two agglomerative clustering methods, Ward linkage and average linkage. K-means was found to be preferred in temporal clustering, while the average linkage method was preferred for spatial clustering. For southern Australia as a whole, we define the cool season as April–September and warm season as October–March, though we note that a three-season split may provide more nuanced climate analysis. We also show that different regions across southern Australia experience different seasons and demonstrate the changing spatial influence of weather types with the seasons, which may aid regionally or seasonally specific climate analysis. Division of southern Australia into 15 climatic regions shows localised agreement with the NRM clusters where distinct differences in rainfall amounts exist. However, the climate regions defined here better represent the importance of topographical aspect on weather type influence and the inland extent of particular weather types. We suggest that the use of these regions would provide consistent climate analysis across studies if widely adopted. A key requirement for climate scientists is the simplification of data sets into both seasonally or regionally averaged subsets. This simplification, by grouping like regions or seasons, is done for a number of reasons both scientific and practical, including to help understand patterns of variability, underlying drivers and trends in climate and weather, to communicate large amounts of data concisely, to reduce the amount of data required for processing (which becomes increasingly important with higher resolution climate model output), or to more simply draw a physical boundary between regions for other purposes, such as flora and fauna habitat analysis, appropriate agricultural practices or water management.
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Adin, A., D. Lee, T. Goicoa, and María Dolores Ugarte. "A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters." Statistical Methods in Medical Research 28, no. 9 (April 13, 2018): 2595–613. http://dx.doi.org/10.1177/0962280218767975.
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Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.
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Bobrova, Tatyana, and Valeriy Vorobyev. "Linear structure taxonomy with the account of environmental polystructures impact." MATEC Web of Conferences 216 (2018): 01003. http://dx.doi.org/10.1051/matecconf/201821601003.
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One of the specific features of transportation facilities design is the necessity to account changing environmental conditions in considerable distances along the track. The purpose of the study is theoretical substantiation of structural decomposition of linear structures based on the degree of uniformity of environmental factors effect on the geotechnical system of an object. A new approach to linear road zoning is suggested on the basis of comprehensive assessment of multidimensional spatial data on the environment. Theory of pattern recognition is used as a classification tool. Linear structure taxonomy, as a part of this theory, is based on hierarchical agglomerative clustering algorithms which provide consequential merge of operating territorial units into uniform linear complexes. Application of taxonomy methods is considered for automated tasks solving on various levels of roads information modeling: when designing structures, organizing construction and operating a facility.
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Mendez Alva, Francisco, Rob De Boever, and Greet Van Eetvelde. "Hubs for Circularity: Geo-Based Industrial Clustering towards Urban Symbiosis in Europe." Sustainability 13, no. 24 (December 16, 2021): 13906. http://dx.doi.org/10.3390/su132413906.
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Since the Green Deal, ambitious climate and resource neutrality goals have been set in Europe. Here, process industries hold a unique position due to their energy and material transformation capabilities. They are encouraged to develop cross-sectorial hubs for achieving not only climate ambition, but also joining a circular economy through urban–industrial symbiosis with both business and community stakeholders. This research proposes a data-based approach to identify potential hub locations by means of cluster analysis. A total of three different algorithms are compared on a set of location and pollution data of European industrial facilities: K-means, hierarchical agglomerative and density-based spatial clustering. The DBSCAN algorithm gave the best indication of potential locations for hubs because of its capacity to tune the main parameters. It evidenced that predominately west European countries have a high potential for identifying hubs for circularity (H4Cs) due to their industrial density. In Eastern Europe, the industrial landscape is more scattered, suggesting that additional incentives might be needed to develop H4Cs. Furthermore, industrial activities such as the production of aluminium, cement, lime, plaster, or electricity are observed to have a relatively lower tendency to cluster compared with the petrochemical sector. Finally, further lines of research to identify and develop industrial H4Cs are suggested.
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Pontin, Francesca, Nik Lomax, Graham Clarke, and Michelle A. Morris. "Characterisation of Temporal Patterns in Step Count Behaviour from Smartphone App Data: An Unsupervised Machine Learning Approach." International Journal of Environmental Research and Public Health 18, no. 21 (October 31, 2021): 11476. http://dx.doi.org/10.3390/ijerph182111476.
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The increasing ubiquity of smartphone data, with greater spatial and temporal coverage than achieved by traditional study designs, have the potential to provide insight into habitual physical activity patterns. This study implements and evaluates the utility of both K-means clustering and agglomerative hierarchical clustering methods in identifying weekly and yearlong physical activity behaviour trends. Characterising the demographics and choice of activity type within the identified clusters of behaviour. Across all seven clusters of seasonal activity behaviour identified, daylight saving was shown to play a key role in influencing behaviour, with increased activity in summer months. Investigation into weekly behaviours identified six clusters with varied roles, of weekday versus weekend, on the likelihood of meeting physical activity guidelines. Preferred type of physical activity likewise varied between clusters, with gender and age strongly associated with cluster membership. Key relationships are identified between weekly clusters and seasonal activity behaviour clusters, demonstrating how short-term behaviours contribute to longer-term activity patterns. Utilising unsupervised machine learning, this study demonstrates how the volume and richness of secondary app data can allow us to move away from aggregate measures of physical activity to better understand temporal variations in habitual physical activity behaviour.
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Miłek, Dorota. "Spatial differentiation in the social and economic development level in Poland." Equilibrium 13, no. 3 (September 30, 2018): 487–507. http://dx.doi.org/10.24136/eq.2018.024.
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Research background: Social and economic development involves a broad spectrum of social, economic and spatial phenomena. The multi-faceted nature of regional development arises directly from the fact that it is shaped by multiple factors. Current discourse emphasises the role of endogenous factors, which indicate the specific nature and the distinctive features of the given territory. Mobilising the endogenous potential ensures stable regional development dynamics. At the moment, one of the fundamental economic problems are the increasing differences in the development of specific regions.
Purpose of the article: The purpose of this study is to assess the differentiation of the social and economic level development of Polish Voivodeships, applying the selected assessment methods for the years 2010 and 2015, draw up a rank list of regional units according to their development levels, and identify the groups of Voivodeships sharing similar development levels. The indicators used in this study, characterising the level of the social and economic development, have been systematised according to the following areas: demographics and labour market, regional entrepreneurship, local economy structure, innovation and research & development activities, technical infrastructure, social infrastructure, and the condition and protection of the natural environment.
Methods: The level of the social and economic development of Polish Voivodeships was assessed using Zdzisław Hellwig’s development pattern method, which made it possible to rank them according to the level of development of Polish Voivodeship. The methodology is supplemented by Ward’s agglomerative clustering method, which made it possible to distinguish other Voivodeships according to the analysed phenomenon. The Voivodeship clustering method used Jenks' natural breaks classification method.
Findings & Value added: Pursuing the research aims, the authors focused in particular on clear spatial differences. Through the analysis we were able to identify the changes in the social and economic development processes of the Polish regions. The Voivodeships were divided into groups according to their development level: the highest, high, low and the lowest.
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Friese, Philipp A., Wibke Michalk, Markus Fischer, Cornelius Hardt, and Klaus Bogenberger. "Charging Point Usage in Germany—Automated Retrieval, Analysis, and Usage Types Explained." Sustainability 13, no. 23 (November 25, 2021): 13046. http://dx.doi.org/10.3390/su132313046.
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This study presents an approach to collect and classify usage data of public charging infrastructure in order to predict usage based on socio-demographic data within a city. The approach comprises data acquisition and a two-step machine learning approach, classifying and predicting usage behavior. Data is acquired by gathering information on charging points from publicly available sources. The first machine learning step identifies four relevant usage patterns from the gathered data using an agglomerative clustering approach. The second step utilizes a Random Forest Classification to predict usage patterns from socio-demographic factors in a spatial context. This approach allows to predict usage behavior at locations for potential new charging points. Applying the presented approach to Munich, a large city in Germany, results confirm the adaptability in complex urban environments. Visualizing the spatial distribution of the predicted usage patterns shows the prevalence of different patterns throughout the city. The presented approach helps municipalities and charging infrastructure operators to identify areas with certain usage patterns and, hence different technical requirements, to optimize the charging infrastructure in order to help meeting the increasing demand of electric mobility.
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Boluwade. "Regionalization and Partitioning of Soil Health Indicators for Nigeria Using Spatially Contiguous Clustering for Economic and Social-Cultural Developments." ISPRS International Journal of Geo-Information 8, no. 10 (October 15, 2019): 458. http://dx.doi.org/10.3390/ijgi8100458.
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Understanding the spatial variability of soil health and identifying areas that share similar soil properties can help nations transition to sustainable agricultural practices. This information is particularly applicable to management decisions such as tillage, nutrient application, and soil and water conservation. This study evaluated the spatial variability and derived the optimal number of spatially contiguous regions of Nigeria’s 774 Local Government Areas (LGAs) using three soil health indicators, organic carbon (OC), bulk density (BD) and total nitrogen (TN) extracted from the Africa Soil Information Service database. Missing data were imputed using the random forest imputation method with topography and normalized difference vegetation index (NDVI) as auxiliary variables. Using an exponential covariance function, the spatial ranges for BD, SN, and OC were calculated as 18, 42, and 60 km, respectively. These were the maximum distances at which there was no correlation between the sample data points. This finding suggests that OC has high variability across Nigeria as compared with other tested indicators. The ordinary kriging (OK) technique revealed spatial dependency (positive correlation) among TN and OC on interpolated surfaces, with high values in the southern part of the county and low values in the north. The BD values were also high in the northern regions where the soils are sandy; correspondingly, TN and OC had low values. The “regionalization with dynamically constrained agglomerative clustering and partitioning” (REDCAP) technique was used to divide LGAs into a possible number of regions while optimizing a sum of squares deviation (SSD). Optimal division was not observed in the resulting number of regional partitions. Conducting the Markov Chain Monte Carlo (MCMC) method on within-zone heterogeneity (WZH) revealed three partitions (two, five, and 15 regions) as optimal, in other words, there would be no significant change in WZH after three partitions. Ensuring a proper understanding of soil spatial variability and heterogeneities (or homogeneities) could facilitate agricultural planning that combines or merges state and local governments that share the same soil health properties, rather than basing decisions on geopolitical, racial, or ethnoreligious factors. The findings of this study could be applied to understand the importance of soil heterogeneities in hydrologic modeling applications. In addition, the findings may aid decision-making bodies such as the United Nations’ Food and Agricultural Organization, the International Fund for Agricultural Development, or the World Bank in their efforts to alleviate poverty, meet future food needs, mitigate the impacts of climate change, and provide financial funding through sustainable agriculture and intervention in developing countries such as Nigeria.
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Lahoorpoor, Bahman, Hamed Faroqi, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. "Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem." Sustainability 11, no. 11 (June 8, 2019): 3205. http://dx.doi.org/10.3390/su11113205.
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Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on the trips between stations is defined to discover groups of correlated stations, using a hierarchical agglomerative clustering method. Third, two levels for rebalancing are assumed as intra-clusters and inter-clusters with the aim of keeping the balance of the network at the beginning of days. The intra-cluster level keeps the balance of bike distribution inside each cluster, and the inter-cluster level connects different clusters in order to keep the balance between the clusters. Finally, rebalancing tours are optimized according to the positive or negative balance at both levels of the intra-clusters and inter-clusters using a single objective genetic algorithm. The rebalancing problem is modeled as an optimization problem, which aims to minimize the tour length. The proposed model is implemented in one week of bike sharing trip data set in Chicago, USA. Outcomes of the model are validated for two subsequent weekdays. Analyses show that the proposed model can reduce the length of the rebalancing tour by 30%.
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PORRAS-RAMÃREZ, Elà Secundino, YazmÃn SOSA-MARCOS, Felipe PALMA-CRUZ, and Aroldo CISNEROS. "Phenotyping the Genetic Diversity of Wild Agave Species that Coexist in the Same Spatial Region." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 44, no. 2 (December 14, 2016): 640–48. http://dx.doi.org/10.15835/nbha44210586.
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Phenotypic characteristics are important to identify species and provide valuable information for the uses in plant breeding. The aim of this study was to characterize through morphological traits the genetic diversity of the Agave genus under wild and semi-wild culture conditions in Maguey Largo region in Oaxaca, Mexico. Through field trips, eleven morphological characteristics of the Agave species were recorded. Principal component analysis (PCA), phylogenetic trees, and correlation analyses, were performed. Seven wild species were identified: Agave potatorum Zucc., A. seemanniana, A. nussaviorum subsp. nussaviorum, A. angustifolia Haw., A. marmorata Roezl., A. karwinskii Zucc. and A. americana var. Americana. Also, a semi-wild unclassified specie Agave sp. was found. The values of the first four principal components in the PCA explain more than 89% of the total morphological variance. The dendrogram of the agglomerative hierarchical clustering (AHC) shown a high similarity between the species and divide them in two main cluster with one unassociated specie (A. karwinskii Miahuatlán shape). Following the different analyses done, we observed a very close relationship between A. potatorum and A. nussaviorum, and dissociated from A. seemanniana, which are belonging to the “Tobala†complex and never described before. The results obtained in this work suggest a great genetic diversity expressed in a wide morphological variety of agaves in Oaxaca; which can be used in futures molecular studies.
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Katsanevakis, Stelios, Christos D. Maravelias, and Laurie T. Kell. "Landings profiles and potential métiers in Greek set longliners." ICES Journal of Marine Science 67, no. 4 (December 20, 2009): 646–56. http://dx.doi.org/10.1093/icesjms/fsp279.
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Abstract Katsanevakis, S., Maravelias, C. D., and Kell, L. T. 2010. Landings profiles and potential métiers in Greek set longliners. – ICES Journal of Marine Science, 67: 646–656. A very large number (>14 000) of generally small vessels operate as longliners in Greek seas. The aim of this study was to identify potential set longline métiers, based on a large sample of landings records from all over Greece. Landings data from set longliners between 2002 and 2006, collected from several ports in the Aegean and East Ionian Sea, were used. The landings profiles were grouped using a two-step procedure, the first involving factorial analysis of the log-transformed landing profiles, and the second a classification of the factorial coordinates (hierarchical agglomerative clustering). In all, 13 métiers were identified in the Aegean Sea and 7 in the Ionian Sea. The most important métiers identified were those targeting white sea bream (Diplodus sargus), hake (Merluccius merluccius), common sea bream (Pagrus pagrus), and common pandora (Pagellus erythrinus), and mixed métiers. Varying spatial (within the Aegean and Ionian Seas) and seasonal patterns were evident for the métiers identified, indicating that fisher motivation to engage in a specific métier varies both spatially and temporally.
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Xia, Mengyao, Di Zeng, Qi Huang, and Xinjian Chen. "Coupling Coordination and Spatiotemporal Dynamic Evolution between Agricultural Carbon Emissions and Agricultural Modernization in China 2010–2020." Agriculture 12, no. 11 (October 30, 2022): 1809. http://dx.doi.org/10.3390/agriculture12111809.
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Modern agriculture contributes significantly to greenhouse gas emissions. How to reduce such emissions without sacrificing agricultural development is a common issue concerning most developing countries. In China, a rural revitalization strategy proposed in 2018 aims to achieve agricultural modernization by 2050, while reaching a carbon emission peak by 2030 and neutrality by 2060. However, China’s progress towards these goals is largely unknown. This study evaluates the coupling coordination and spatiotemporal dynamic evolution between agricultural carbon emissions and agricultural modernization in China from 2010 to 2020 through a joint employment of spatial autocorrelation and coupling coordination degree modeling. The results show that from 2010 to 2020, the agricultural modernization level increased from 0.155 to 0.272, and the agricultural carbon emission intensity decreased from 4.9 tons per CYN million to 2.43 tons. Agricultural carbon emissions and the agricultural modernization level manifest significant spatially agglomerative patterns with noticeable discrepancies across different regions. Moreover, the coupling coordination degree between agricultural carbon emissions and agricultural modernization has increased every year, but disparities among provinces continued to widen. Specifically, coupling coordination in northern China is significantly higher than that in the south, and its spatial distribution exhibits a positive correlation and increasing levels of clustering. These results point to the continued need for sustainable agricultural development efforts, such as strengthening rural infrastructure and diffusing green technologies in achieving China’s dual carbon emission and agricultural modernization goals. This study also examines the sustainable agricultural development issue from a new perspective, and the findings can provide policy references for sustainable agricultural development policies in China.
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Issa, Hayder, and Azad Alshatteri. "Source Identification, Ecological Risk and Spatial Analysis of Heavy Metals Contamination in Agricultural Soils of Tanjaro Area, Kurdistan Region, Iraq." UKH Journal of Science and Engineering 5, no. 2 (December 28, 2021): 18–27. http://dx.doi.org/10.25079/ukhjse.v5n2y2021.pp18-27.
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The current work accomplished a comprehensive evaluation of heavy metals pollution in soil of agricultural areas from Tanjaro sub-district, Sulaimaniyah province, Kurdistan Region, NE Iraq. Ninety soil samples were collected from thirty different locations. Concentrations of 16 heavy metals were measured by inductively coupled plasma optical emission spectrometry ICP-OES. The pollution index (PI), potential ecological risk index (Er), enrichment factor (EF), and ecological risk index (RI) were used to assess the pollution in soil samples. High levels of Li and Ni, and moderate Ba, Cd, Hg, and Pb according to the results of concentration analysis, pollution index (PI), and potential ecological risk (ERI). High levels of Cd and Hg according to the results of Er. Agglomerative hierarchical clustering (AHC) and principal component analysis (PCA) suggested that heavy metals were generated from different natural and anthropogenic sources like natural weathering, fertilizer application, and transportation. Origins of Hg, Cd, Ni, and Pb are probably from activities like overuse of pesticides and fertilizers, whereas Pb could be exhausted from vehicle exhausts as well. Furthermore, spatial distributions revealed nonpoint source pollution for the studied heavy metals. The obtained results help in the remediation techniques of contaminated soils such as dilution with decontaminated soil or extraction or separation of heavy metals.
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Konstantaras, Antonios. "Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone." Informatics 7, no. 4 (September 29, 2020): 39. http://dx.doi.org/10.3390/informatics7040039.
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This research work employs theoretical and empirical expert knowledge in constructing an agglomerative parallel processing algorithm that performs spatio-temporal clustering upon seismic data. This is made possible by exploiting the spatial and temporal sphere of influence of the main earthquakes solely, clustering seismic events into a number of fuzzy bordered, interactive and yet potentially distinct seismic zones. To evaluate whether the unveiled clusters indeed depict a distinct seismic zone, deep learning neural networks are deployed to map seismic energy release rates with time intervals between consecutive large earthquakes. Such a correlation fails should there be influence by neighboring seismic areas, hence casting the seismic region as non-distinct, or if the extent of the seismic zone has not been captured fully. For the deep learning neural network to depict such a correlation requires a steady seismic energy input flow. To address that the western area of the Hellenic seismic arc has been selected as a test case due to the nearly constant motion of the African plate that sinks beneath the Eurasian plate at a steady yearly rate. This causes a steady flow of strain energy stored in tectonic underground faults, i.e., the seismic energy storage elements; a partial release of which, when propagated all the way to the surface, casts as an earthquake. The results are complementary two-fold with the correlation between the energy release rates and the time interval amongst large earthquakes supporting the presence of a potential distinct seismic zone in the Ionian Sea and vice versa.
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Li, Jingmin, Johannes Hendricks, Mattia Righi, and Christof G. Beer. "An aerosol classification scheme for global simulations using the K-means machine learning method." Geoscientific Model Development 15, no. 2 (January 25, 2022): 509–33. http://dx.doi.org/10.5194/gmd-15-509-2022.
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Abstract. The K-means machine learning algorithm is applied to climatological data of seven aerosol properties from a global aerosol simulation using EMAC-MADE3. The aim is to partition the aerosol properties across the global atmosphere in specific aerosol regimes; this is done mainly for evaluation purposes. K-means is an unsupervised machine learning method with the advantage that an a priori definition of the aerosol classes is not required. Using K-means, we are able to quantitatively define global aerosol regimes, so-called aerosol clusters, and explain their internal properties and their location and extension. This analysis shows that aerosol regimes in the lower troposphere are strongly influenced by emissions. Key drivers of the clusters' internal properties and spatial distribution are, for instance, pollutants from biomass burning and biogenic sources, mineral dust, anthropogenic pollution, and corresponding mixtures. Several continental clusters propagate into oceanic regions as a result of long-range transport of air masses. The identified oceanic regimes show a higher degree of pollution in the Northern Hemisphere than over the southern oceans. With increasing altitude, the aerosol regimes propagate from emission-induced clusters in the lower troposphere to roughly zonally distributed regimes in the middle troposphere and in the tropopause region. Notably, three polluted clusters identified over Africa, India, and eastern China cover the whole atmospheric column from the lower troposphere to the tropopause region. The results of this analysis need to be interpreted taking the limitations and strengths of global aerosol models into consideration. On the one hand, global aerosol simulations cannot estimate small-scale and localized processes due to the coarse resolution. On the other hand, they capture the spatial pattern of aerosol properties on the global scale, implying that the clustering results could provide useful insights for aerosol research. To estimate the uncertainties inherent in the applied clustering method, two sensitivity tests have been conducted (i) to investigate how various data scaling procedures could affect the K-means classification and (ii) to compare K-means with another unsupervised classification algorithm (HAC, i.e. hierarchical agglomerative clustering). The results show that the standardization based on sample mean and standard deviation is the most appropriate standardization method for this study, as it keeps the underlying distribution of the raw data set and retains the information of outliers. The two clustering algorithms provide similar classification results, supporting the robustness of our conclusions. The classification procedures presented in this study have a markedly wide application potential for future model-based aerosol studies.
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Zhou, Y., and Z. Fang. "LABELING RESIDENTIAL COMMUNITY CHARACTERISTICS FROM COLLECTIVE ACTIVITY PATTERNS USING TAXI TRIP DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1481–86. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1481-2017.
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There existing a significant social and spatial differentiation in the residential communities in urban city. People live in different places have different socioeconomic background, resulting in various geographically activity patterns. This paper aims to label the characteristics of residential communities in a city using collective activity patterns derived from taxi trip data. Specifically, we first present a method to allocate the O/D (Origin/Destination) points of taxi trips to the land use parcels where the activities taken place in. Then several indices are employed to describe the collective activity patterns, including both activity intensity, travel distance, travel time, and activity space of residents by taking account of the geographical distribution of all O/Ds of the taxi trip related to that residential community. Followed by that, an agglomerative hierarchical clustering algorithm is introduced to cluster the residential communities with similar activity patterns. In the case study of Wuhan, the residential communities are clearly divided into eight clusters, which could be labelled as ordinary communities, privileged communities, old isolated communities, suburban communities, and so on. In this paper, we provide a new perspective to label the land use under same type from people’s mobility patterns with the support of big trajectory data.
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Devaraj, Saravanan. "Video data image retrieval using – BRICH." World Journal of Engineering 14, no. 4 (August 7, 2017): 318–23. http://dx.doi.org/10.1108/wje-09-2016-0093.
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Purpose Data mining is the process of detecting knowledge from a given huge data set. Among the data set, multimedia is the data which contains diverse data such as audio, video, image, text and motion. In this growing field of video data, mining the video data plays vital role in the field of video data mining. In video data mining, video data are grouped into frames. In this vast amount of video frames, the fast retrieval of needed information is important one. This paper aims to propose a Birch-based clustering method for content-based image retrieval. Design/methodology/approach In image retrieval system, image segmentation plays a very important role. A text file, normally, is divided into sections, that is, piece, sentences, word and character for this information which are organized and indexed effectively like in a video, the information is dynamic in nature and this information is converted to static for easy retrieval. For this, video files are divided into a number of frames or segments. After the segmentation process, images are trained for retrieval process, and from these, unwanted images are removed from the data set. The noise or unwanted image removal pseudo-code is shown below. In the code image, pixel value represents the value of the difference between the two adjacent image pixel values. By assuming a threshold for the image value, the duplicate images are found. After finding the duplicate image, it is removed from the data set. Clustering is used in many applications as a stand-alone tool to get insight into data distribution and as a pre-processing step for other algorithms (Ester et al., 1996). Specifically, it is used in pattern recognition, spatial data analysis, image processing, economic science document classification, etc. Hierarchical clustering algorithms are classified as agglomerative or divisive. BRICH uses clustering attribute (CA) and clustering feature hierarchy (CA_Hierarchy) for the formation of clusters. It perform multidimensional data objects. Every BRICH algorithm based on the memory-oriented information, that is, memory constrains, is involved in the processing of the data sets. This information is represented in Figures 6-10. For forming clusters, they use the amount of object in the cluster (A), the sum of all points in the data set (S) and need the square value of the all objects (P). Findings The proposed technique brings an effective result for cluster formation. Originality/value BRICH uses a novel approach to model the degree of inter-connectivity and closeness between each pair of clusters that takes into account the internal characteristics of the clusters themselves.
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Feizizadeh, Bakhtiar, Zahra Abdollahi, and Behzad Shokati. "A GIS-Based Spatiotemporal Impact Assessment of Droughts in the Hyper-Saline Urmia Lake Basin on the Hydro-Geochemical Quality of Nearby Aquifers." Remote Sensing 14, no. 11 (May 24, 2022): 2516. http://dx.doi.org/10.3390/rs14112516.
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Urmia Lake is a hyper-saline lake in northwestern Iran that has been drying up since 2005. The main objective of this study was to evaluate the water quality in aquifers that are the main source of fresh water for the eastern plains Urmia Lake, which has been drying up due to intensive land use/cover changes and climate change. We evaluated hydro-geochemical data and factors contributing to aquifer pollution and quality variation for nine aquifers in the vicinity of Urmia Lake during the dry and wet seasons from 2000–2020. Our methodology was based on the analysis of 10 years of data from 356 deep and semi-deep wells using GIS spatial analysis, multivariate statistical analysis, and agglomerative hierarchical clustering. We developed a Water Quality Index (WQI) for spatiotemporal assessment of the status of the aquifers. In doing so, we highlighted the value of combining Principal Component Analysis (PCA), WQI, and GIS to determine the hydro-geochemical attributes of the aquifers. We found that the groundwater in central parts of the study area was unsuitable for potable supplies. Anthropogenic sources of contamination, such as chemical fertilizers, industrial waste, and untreated sewage water, might be the key factors causing excessive concentrations of contaminants affecting the water quality. The PCA results showed that over 80% of the total variance could be attributed to two principal factors for most aquifers and three principal factors for two of the aquifers. We employed GIS-based spatial analysis to map groundwater quality in the study area. Based on the WQI values, approximately 48% of groundwater samples were identified as poor to unsuitable for drinking purposes. Results of this study provide a better hydro-geochemical understanding of the multiple aquifers that require preventive action against groundwater damage. We conclude that the combined approach of using a multivariate statistical technique and spatial analysis is effective for determining the factors controlling groundwater quality.
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Robinson, N. H., J. D. Allan, J. A. Huffman, P. H. Kaye, V. E. Foot, and M. Gallagher. "Cluster analysis of WIBS single particle bioaerosol data." Atmospheric Measurement Techniques Discussions 5, no. 5 (September 7, 2012): 6387–422. http://dx.doi.org/10.5194/amtd-5-6387-2012.
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Abstract. Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.
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Robinson, N. H., J. D. Allan, J. A. Huffman, P. H. Kaye, V. E. Foot, and M. Gallagher. "Cluster analysis of WIBS single-particle bioaerosol data." Atmospheric Measurement Techniques 6, no. 2 (February 13, 2013): 337–47. http://dx.doi.org/10.5194/amt-6-337-2013.
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Abstract. Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.
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Han, Chen, Li, Zhang, and Sun. "Customized Bus Network Design Based on Individual Reservation Demands." Sustainability 11, no. 19 (October 8, 2019): 5535. http://dx.doi.org/10.3390/su11195535.
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With the advantages of congestion alleviation, environmental friendliness, as well as a better travel experience, the customized bus (CB) system to reduce individual motorized travel is highly popular in increasing numbers of cities in China. The line planning problem is a key aspect of the CB system. This paper presents a detailed flow chart of a CB network planning methodology, including individual reservation travel demand data processing, CB line origin–destination (OD) area division considering quantity constraints of demand in areas and distance constraints based on agglomerative hierarchical clustering (AHC), an initial set of CB lines generating quantity constraints of the demand on each line and line length constraints, and line selection model building, striking a balance between operator interests, social benefits, and passengers’ interests. Finally, the impacts of the CB vehicle type, the fixed operation cost of online car-hailing (OCH), and the weights of each itemized cost are discussed. Serval operating schemes for the Beijing CB network were created. The results show that the combination of CB vehicles with 49 seats and 18 seats is the most cost-effective and that CBs with low capacity are more cost-effective than those with larger capacity. People receive the best service when decision-makers pay more attention to environmental pollution and congestion issues. The CB network’s service acceptance rate and the spatial coverage increase with the fixed operating cost per OCH vehicle per day c0C. The CB vehicle use decreases as c0C ccincreases. The results of this study can provide technical support for CB operators who design CB networks.
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Bhatnagar, Aman, Prem Vrat, and Ravi Shankar. "Multi-criteria clustering analytics for agro-based perishables in cold-chain." Journal of Advances in Management Research 16, no. 4 (October 23, 2019): 563–93. http://dx.doi.org/10.1108/jamr-10-2018-0093.
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Purpose The purpose of this paper is to determine compatibility groups of different fruits and vegetables that can be stored and transported together based upon their requirements for temperature, relative humidity, odour and ethylene production. Pre-cooling which is necessary to prepare the commodity for subsequent shipping and safe storage is also discussed. Design/methodology/approach The methodology used in this journal is an attempt to form clusters/groups of storing together 43 identified fruits and vegetables based on four important parameters, namely, temperature, relative humidity, odour and ethylene production. An agglomerative hierarchical clustering algorithm is used to build a cluster hierarchy that is commonly displayed as a tree diagram called dendrogram. The same is further analyzed using K-means clustering to find clusters of comparable spatial extent. The results obtained from the analytics are compared with the available data of grouping fruits and vegetables. Findings This study investigates the usefulness and efficacy of the proposed clustering approach for storage and transportation of different fruits and vegetables that will eventually save huge investment made in terms of developing infrastructure components and energy consumption. This will enable the investors to adopt it for using the space more effectively and also reducing food wastage. Research limitations/implications Due to limited research and development (R&D) data pertaining to storage parameters of different fruits and vegetables on the basis of temperature, relative humidity, ethylene production/sensitivity, odour and pre-cooling, information from different available sources have been utilized. India needs to develop its own crop specific R&D data, since the conditions for soil, water and environment vary when compared to other countries. Due to the limited availability of the research data, various multi-criteria approaches used in other areas have been applied to this paper. Future studies might be interested in considering other relevant variables depending upon R&D and data availability. Practical implications With the increase in population, the demand for food is also increasing. To meet such growing demand and provide quality and nutritional food, it is important to have a clear methodology in terms of compatibility grouping for utilizing the available storage space for multi-commodity produce and during transportation. The methodology used shall enable the practitioners to understand the importance of temperature, humidity, odour and ethylene sensitivity for storage and transportation of perishables. Social implications This approach shall be useful for decision making by farmers, Farmer Producer Organization, cold-storage owners, practicing managers, policy makers and researchers in the areas of cold-chain management and will provide an opportunity to use the available space in the cold storage for storing different fruits and vegetables, thereby facilitating optimum use of infrastructure and resources. This will enable the investors to utilize the space more effectively and also reduce food wastage. It shall also facilitate organizations to manage their logistic activities to gain competitive advantage. Originality/value The proposed model would help decision makers to resolve the issues related to the selection of storing different perishable commodities together. From the secondary research, not much research papers have been found where such a multi-criteria clustering approach has been applied for the storage of fruits and vegetables incorporating four important parameters relevant for storage and transportation.
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Hu, Qian, Xinguang He, Xi-An Lu, and Xinping Zhang. "Trend Analysis of Seasonal Precipitation (1960–2013) in Subregions of Hunan Province, Central South China Using Discrete Wavelet Transforms." Journal of Applied Meteorology and Climatology 58, no. 10 (October 2019): 2159–75. http://dx.doi.org/10.1175/jamc-d-19-0023.1.
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AbstractIn this study, trends of seasonal precipitation are investigated in subregions of Hunan Province in China over 1960–2013 by discrete wavelet transform (DWT) and Mann–Kendall (MK) tests. The main purpose of trend analysis is to detect the most dominant periodic components affecting the observed trends of seasonal precipitation in each subregion. For this purpose, the homogeneous precipitation regions are first delineated by combining rotated empirical orthogonal functions with agglomerative hierarchical clustering. Results suggest dividing the precipitation of Hunan Province into five subregions for winter and three subregions for spring, summer, and autumn. Delineated subregions for each season show strongly coherent spatial patterns, all of which are along the strip extending in the southwest–northeast direction. After regionalization, areal seasonal precipitation series in each subregion are decomposed into several detail and approximation component subseries, and then the decomposed subseries are analyzed by three types of MK tests. Results reveal that winter and summer precipitations experience an increasing trend in all of the divisions—winter precipitation in southeast-central and southern Hunan and summer precipitation in southwest-central Hunan especially exhibit a statistically significant tendency. However, spring and autumn precipitations show a nonsignificant decreasing trend in all of the divisions. Results from DWT and sequential MK analyses suggest that detail component 1 (plus approximation) of seasonal precipitations in all subregions is the most dominant periodic mode affecting their observed trends, except for summer precipitation in the subregion of southern Hunan, where detail component 2 is the most dominant one. Therefore, short-term periodicity (2–4 yr) is the most influential in affecting the observed trends of seasonal precipitation over Hunan Province.
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Aggag, Ahmed M., and Abdulaziz Alharbi. "Spatial Analysis of Soil Properties and Site-Specific Management Zone Delineation for the South Hail Region, Saudi Arabia." Sustainability 14, no. 23 (December 5, 2022): 16209. http://dx.doi.org/10.3390/su142316209.
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Sustainable soil management with the appropriate understanding of soil characteristics is vital in maintaining and improving agriculture soil management. The objectives of the present study are to characterize the spatial variability of soil using the GIS technique and used agglomerative hierarchical clustering (AHC) for the delineation of management zones (MZs) for precision agriculture. A total of 111 soil samples were collected from 37 soil profiles in systematic depths (0–50, 50–100, and 100–150 cm) from the South Hail region, KSA. Samples were analyzed for pH, ECe, CaCO3, available macro and micronutrients, and hydrological properties. The best fit models, using ArcGIS software, were J-Bessel for pH, Clay, bulk density (BD), and available water (AW); K-Bessel for EC and available N; Stable for CaCO3, P, K, Fe, Zn, Sand, field capacity (FC) and saturated hydraulic conductivity (Ks); Spherical for Mn and Cu; Gaussian for saturation percentage (SP); whereas exponential for permanent wilting point (PWP). The principal component analysis (PCA) resulted in six principal components (PCs) explaining 79.75% of the total variance of soil properties. The PC1 was strongly influenced by soil BD, FC, clay, PWP, Ks, and sand. PC2 was dominated by N, ECe, and CaCO3; PC3 was dominated by pH; PC4 was dominated primarily by K and P, PC5 was mainly dominated by Fe; Mn, and Cu, and PC6 was mainly dominated by SP and Zn. Based on AHC, four soil management zones (MZs) cover 77.94, 14.10, 7.11 and 0.85% of the studied area. Management zone 1 (MZ1) and Management zone 3 (MZ3) are classified as moderately saline while Management zone 2 (MZ2) is classified as highly saline soils, greater than the limiting critical value for the sensitive crops. The potential solutions to reduce salinization in the area include: reducing irrigation, moving to salt-tolerant crops or applying humic acids to fix anions and cations and eliminate them from the root zone of the plants. Treating the area with diluted sulfuric acid to remove salts and reduce ECe to less than 2 dSm−1, to get maximum productivity. This finding is diagnostic for determining the amount of fertilizer and irrigation water to be applied to soils in different management zones. Its emphasis’s the importance of site-specific management for long-term crop productivity and, as a result, reducing environmental hazards caused by uneven fertilizers and water applications.
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Zhao, Chong, Yu Li, and Min Weng. "A Fractal Approach to Urban Boundary Delineation Based on Raster Land Use Maps: A Case of Shanghai, China." Land 10, no. 9 (September 7, 2021): 941. http://dx.doi.org/10.3390/land10090941.
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Given the diverse socioecological consequences of rapid urban sprawl worldwide, the delineation and monitoring of urban boundaries have been widely used by local governments as a planning instrument for promoting sustainable development. This study demonstrates a fractal method to delineate urban boundaries based on raster land use maps. The basic logic is that the number of built-up land clusters and their size at each dilation step follows a power-law function. It is assumed that two spatial subsets with distinct fractal characteristics would be obtained when the deviation between the dilation curve and a straight line reaches the top point. The top point is regarded to be the optimum threshold for classifying the built-up land patches, because the fractality of built-up land would no longer exist beyond the threshold. After that, all the built-up land patches are buffered with the optimum threshold and the rank-size distribution of new clusters can be re-plotted. Instead of artificial judgement, hierarchical agglomerative clustering is utilized to automatically classify the urban and rural clusters. The approach was applied to the case of Shanghai, the most rapidly urbanizing megacity in China, and the dynamic changes of the urban boundaries from 1994 to 2016 were analyzed. On this basis, urban–rural differences were further explored through several fractal or nonfractal indices. The results show that the proposed fractal approach can accurately distinguish the urban boundary without subjective choice of thresholds. Extraordinarily different fractal dimensions, aggregation and density and similar average compactness were further identified between built-up land in urban and rural areas. The dynamic changes in the urban boundary indicated rapid urban sprawl within Shanghai during the study period. In view of the popularization and global availability of raster land use maps, this paper adds fuels to the cutting-edge topic of distinguishing the morphological criteria to universally describe urban boundaries.
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Perera, Sandun J., David G. Herbert, Şerban Procheş, and Syd Ramdhani. "Land snail biogeography and endemism in south-eastern Africa: Implications for the Maputaland-Pondoland-Albany biodiversity hotspot." PLOS ONE 16, no. 3 (March 4, 2021): e0248040. http://dx.doi.org/10.1371/journal.pone.0248040.
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Invertebrates in general have long been underrepresented in studies on biodiversity, biogeography and conservation. Boundaries of biodiversity hotspots are often delimited intuitively based on floristic endemism and have seldom been empirically tested using actual species distributions, and especially invertebrates. Here we analyse the zoogeography of terrestrial malacofauna from south-eastern Africa (SEA), proposing the first mollusc-based numerical regionalisation for the area. We also discuss patterns and centres of land snail endemism, thence assessing the importance and the delimitation of the Maputaland-Pondoland-Albany (MPA) biodiversity hotspot for their conservation. An incidence matrix compiled for relatively well-collected lineages of land snails and slugs (73 taxa in twelve genera) in 40 a priori operational geographic units was subjected to (a) phenetic agglomerative hierarchical clustering using unweighted pair-group method with arithmetic means (UPGMA), (b) parsimony analysis of endemicity (PAE) and biotic element analysis (BEA). Fulfilling the primary objective of our study, the UPGMA dendrogram provided a hierarchical regionalisation and identified five centres of molluscan endemism for SEA, while the PAE confirmed six areas of endemism, also supported by the BEA. The regionalisation recovers a zoogeographic province similar to the MPA hotspot, but with a conspicuous westward extension into Knysna (towards the Cape). The MPA province, centres and areas of endemism, biotic elements as well as the spatial patterns of species richness and endemism, support the MPA hotspot, but suggest further extensions resulting in a greater MPA region of land snail endemism (also with a northward extension into sky islands—Soutpansberg and Wolkberg), similar to that noted for vertebrates. The greater MPA region provides a more robustly defined region of conservation concern, with centres of endemism serving as local conservation priorities.
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Terassi, Paulo Miguel De Bodas, Hélio Silveira, and Carlos Henrique Da Graça. "Regiões pluviométricas homogêneas e a erosividade das chuvas na unidade hidrográfica Pirapó, Paranapanema III e IV-Paraná / Homogeneous rainfall regions and rainfall erosivity in the hydrographic unit Pirapó, Paranapanema III and IV hydrographic (...)." Caderno de Geografia 26, no. 46 (May 2, 2016): 507. http://dx.doi.org/10.5752/p.2318-2962.2016v26n46p507.
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<p>O presente trabalho objetiva definir regiões pluviométricas homogêneas e investigar a relação entre o regime pluviométrico e o potencial da erosividade para a unidade hidrográfica Pirapó, Paranapanema III e IV - Paraná. Foram obtidos os dados de pluviosidade de trinta e cinco postos pluviométricos do Instituto das Águas do Paraná e de cinco estações meteorológicas do Instituto Agronômico do Paraná (IAPAR), Instituto Nacional de Meteorologia (INMET) e Sistema Meteorológico do Paraná (SIMEPAR), trabalhados com o segmento temporal de 1976 a 2012. O índice de erosividade da chuva utilizado foi calculado a partir da equação apresentada por Rufino, Biscaia e Merten (1993) para o estado do Paraná. Definiu-se que o método de agrupamento mais adequado é o método aglomerativo de Ward, tendo como medida de proximidade a distância euclidiana. A área de estudo apresenta uma variação espacial da pluviosidade que mostra a influência da orografia principalmente para a distribuição espacial na escala anual, enquanto que a localização dos grupos demonstra uma maior associação à dinâmica atmosférica, conforme consultado pela literatura, para a compreensão da distribuição mensal das chuvas. Sobretudo, a delimitação dos grupos pluviométricos homogêneos permitiu compreender a relação entre o relevo, as alturas pluviométricas e o potencial erosivo das chuvas.</p><p><strong>Palavras-chave</strong>: agrupamento, pluviosidade, potencial erosivo, bacia hidrográfica.</p><p> </p><p>Abstract</p><p>This paper aims to define homogeneous rainfall regions and to investigate the relationship between rainfall and the potential erosivity for Pirapó, Paranapanema III and IV hydrographic unit - Paraná. The rainfall data was collected from thirty five rain gauges at Paraná Water Institute and from five weather stations at Paraná Agronomy Institute (IAPAR), National Weather Institute (INMET) and Paraná Meteorological System (SIMEPAR) and were processed within the temporal segmentation 1976 to 2012. The erosivity index rain used was calculated from the equation presented by Rufino, Biscaia and Merten (1993) for the Paraná State. It was defined that the most appropriate clustering method is the agglomerative method of Ward, with the proximity measure the Euclidean distance. The study area presents a spatial variation of rainfall that shows the orography influence mainly to the spatial distribution in the annual scale, while the location of groups shows a greater association with the atmospheric dynamics, as referred in the literature, for understanding the monthly distribution of rainfall. Above all, the delimitation of homogeneous rainfall groups allowed to understand the relationship between relief, the rain heights and the erosive potential of rainfall.</p><p><strong>Keywords</strong>: clustering, rainfall, erosive potential, watershed.</p>