Academic literature on the topic 'Spatial Data Focusing'
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Journal articles on the topic "Spatial Data Focusing"
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Guaragnella, Cataldo, and Tiziana D’Orazio. "A Data-Driven Approach to SAR Data-Focusing." Sensors 19, no. 7 (April 6, 2019): 1649. http://dx.doi.org/10.3390/s19071649.
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Synthetic Aperture RADAR (SAR) is a radar imaging technique in which the relative motion of the sensor is used to synthesize a very long antenna and obtain high spatial resolution. Several algorithms for SAR data-focusing are well established and used by space agencies. Such algorithms are model-based, i.e., the radiometric and geometric information about the specific sensor must be well known, together with the ancillary data information acquired on board the platform. In the development of low-cost and lightweight SAR sensors, to be used in several application fields, the precise mission parameters and the knowledge of all the specific geometric and radiometric information about the sensor might complicate the hardware and software requirements. Despite SAR data processing being a well-established imaging technique, the proposed algorithm aims to exploit the SAR coherent illumination, demonstrating the possibility of extracting the reference functions, both in range and azimuth directions, when a strong point scatterer (either natural or manmade) is present in the scene. The Singular Value Decomposition is used to exploit the inherent redundancy present in the raw data matrix, and phase unwrapping and polynomial fitting are used to reconstruct clean versions of the reference functions. Fairly focused images on both synthetic and real raw data matrices without the knowledge of mission parameters and ancillary data information can be obtained; as a byproduct, azimuth beam pattern and estimates of a few other parameters have been extracted from the raw data itself. In a previous paper, authors introduced a preliminary work dealing with this problem and able to obtain good-quality images, if compared to the standard processing techniques. In this work, the proposed technique is described, and performance parameters are extracted to compare the proposed approach to RD, showing good adherence of the focused images and pulse responses.
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TAKAHASHI, KAZUKO, and TAKAO SUMITOMO. "THE QUALITATIVE TREATMENT OF SPATIAL DATA." International Journal on Artificial Intelligence Tools 16, no. 04 (August 2007): 661–82. http://dx.doi.org/10.1142/s0218213007003497.
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This paper aims at an efficient treatment of spatial data using qualitative representation. We propose a new framework called PLCA, which provides a symbolic representation for the figure in a two-dimensional plane, focusing on the connections between regions. It is based on four simple objects: points(P), lines(L), circuits(C) and areas(A). The entire figure is represented as a combination of these objects. Pairs of areas, circuits or lines never cross. The simple, clear data structure based on objects makes the system feasible and easy to implement. A PLCA expression can be subject to topological reasoning such as judging the connection patterns of areas. We define the operations of area integration and area division on a PLCA expression. These operations preserve the consistency of the expression, and correspond to real actions on the figures. We can add attributes to each object, such as the properties that hold on an area or that an object represents, and make an attributed PLCA. The operations of area integration/division on an attributed PLCA correspond to the alteration of the classification level of objects, and semantic spatial reasoning can be performed on an attributed PLCA.
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Koh, Keumseok, Ayaz Hyder, Yogita Karale, and Maged N. Kamel Boulos. "Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health." Remote Sensing 14, no. 13 (June 23, 2022): 2996. http://dx.doi.org/10.3390/rs14132996.
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Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms ‘big’ geospatial data and geospatial ‘big data’ have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies.
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Csomós, György. "On the challenges ahead of spatial scientometrics focusing on the city level." Aslib Journal of Information Management 72, no. 1 (November 20, 2019): 67–87. http://dx.doi.org/10.1108/ajim-06-2019-0152.
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Purpose Spatial bibliometrics and scientometrics have traditionally focused on examining both country and regional levels; however, in recent years, numerous spatial analyses on the city level have been carried out. While city-level scientometric analyses have gained popularity among policymakers and statistical/economic research organizations, researchers in the field of bibliometrics are divided regarding whether it is possible to observe the spatial unit “city” through bibliometric and scientometric tools. The purpose of this paper is to reveal the most significant challenges ahead of spatial scientometrics focusing on the city level by examining relevant scientometric studies. Design/methodology/approach This analysis involves the most significant spatial scientometric studies focusing on the city level and carefully examines how they collect bibliometric and/or scientometric data, what methodologies they employ to process bibliometric data and most importantly, how they approach the spatial unit “city”. Findings After systematically scrutinizing relevant studies in the field, three major problems have been identified: there is no standardized method of how cities should be defined and how metropolitan areas should be delineated; there is no standardized method of how bibliometric and scientometric data on the city level should be collected and processed; and it is not clearly defined how cities can profit from the results of bibliometric and scientometric analysis focusing on them. Originality/value This is the first study that compiles a “database” of scientometric studies focusing on the city level. The paper not only reveals major challenges ahead of city level spatial analysis but recommends some possible solution as well.
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Furman, Alex, Ty P. Ferré, and Gail L. Heath. "Spatial focusing of electrical resistivity surveys considering geologic and hydrologic layering." GEOPHYSICS 72, no. 2 (March 2007): F65—F73. http://dx.doi.org/10.1190/1.2433737.
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Electrical resistivity tomography (ERT) has shown great promise for monitoring transient hydrologic processes. One advantage of ERT under those conditions is the ability of a user to tailor the spatial sensitivity of an ERT survey through selection of electrode locations and electrode combinations. Recent research has shown that quadripoles can be selected in a manner that improves the independent inversion of ERT data. Our ultimate interest lies in using ERT data along with measurements from other sensors, which typically can provide high-quality data from shallow regions of the subsurface, in a joint inversion. As a result, we do not consider the selection of quadripoles specifically for inde-pendent ERT inversion. Rather, we present an approach to focusthe spatial sensitivity of ERT surveys in specificsubsurface regions with the assumption that those data, when interpreted along with other measurements that are sensitive to those regions, will lead to more complete hydrologic characterization. Because we are interested in monitoring rapid processes, our approach is designed to efficiently identify optimal quadripoles. This is achieved by separating the optimization from the inversion grid, significantly reducing computational effort. We extend our previous work to consider the use of both surface and borehole ERT electrodes and to consider the impacts of horizontally layered electrical conductivity conditions. Results confirm the ability of the method to focus survey sensitivity while showing the importance of incorporation of prior knowledge of the subsurface electric conductivity structure in designing optimal ERT surveys.
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Feuillet, Thierry, Julien Coquin, Denis Mercier, Etienne Cossart, Armelle Decaulne, Helgi Páll Jónsson, and þorsteinn Sæmundsson. "Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland." Progress in Physical Geography: Earth and Environment 38, no. 3 (April 16, 2014): 354–77. http://dx.doi.org/10.1177/0309133314528944.
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Most studies focusing on landslide spatial analysis have considered the relationships between predictors and landslide occurrence as fixed effects. Yet spatially varying relationships, i.e. non-stationarity, often occur in any spatial data set and should be theoretically considered in statistical models for a better fit. In Skagafjörður, a landslide-rich north–south oriented area located in northern Iceland, we investigated whether spatial non-stationarity in the relationships between paraglacial variables (glacio-isostatic rebound and post-glacial debuttressing, both captured in this area by latitude) and landslide locations is detectable. To explore the non-stationarity of factors that predispose landslide occurrence, we performed two logistic regression models, one global (GLR) and the other enabling the regression parameters to vary locally (geographically weighted logistic regression, GWLR). Each model was computed with two types of outcome, one based on the entire masses of landslides and the other only on the scarps of landslides. GLR results reveal that increasing latitude is associated with increasing probability of landslide occurrence, confirming that post-glacial rebound is of prime importance at the regional scale. Nevertheless, GWLR indicates that this relationship is absent or reversed at some locations, meaning that the influence of paraglacial and other predisposing factors of landsliding (slope, valley depth and curvature) vary at the local scale. This result sheds light on the spatial clustering of three subzones where landsliding drivers are homogeneous. We conclude that a GWR-based approach provides some significant inputs for spatial analysis of mass movement processes, by identifying multi-scale process control zones and by highlighting local drivers, indecipherable in global models.
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Murakami, Daisuke, Mami Kajita, and Seiji Kajita. "Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis." ISPRS International Journal of Geo-Information 9, no. 10 (September 30, 2020): 577. http://dx.doi.org/10.3390/ijgi9100577.
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A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction, dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects a model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran.
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Ryan, Kendra, Andy Danylchuk, and Adrian Jordaan. "Is Marine Spatial Planning Enough to Overcome Biological Data Deficiencies?" Journal of Environmental Assessment Policy and Management 20, no. 04 (December 2018): 1850012. http://dx.doi.org/10.1142/s1464333218500126.
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The United States only accounts for 0.2% of the global offshore wind installed capacity despite a potential technical resource four orders of magnitude greater. A cumbersome permitting process is one of the challenges in implementing new projects. Part of this process requires biological data in order to inform assessments of environmental impacts; yet these data may be lacking for particular taxa at the required scale. Marine spatial planning (MSP) is a process that often includes data identification, collection, collation and analyses components. In this paper, we conduct a collective case study of three areas with offshore wind projects located in waters managed by marine spatial plans, focusing on how data efforts inform MSP and offshore wind development. Our study finds that MSP can facilitate data efforts during the permitting phase of offshore wind projects, but that other initiatives, particularly renewable energy policies and zoning, appear critical towards establishing offshore wind.
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Yeo, JungYoon, JooBong Jeong, and JongKyu Kim. "Spatial Distribution Characteristics of Seagrass Habitat Based on Remote Sensing Data: Focusing on Wan Island." GEO DATA 4, no. 2 (June 30, 2022): 23–36. http://dx.doi.org/10.22761/dj2022.4.2.003.
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In May 2019, UAV photogrammetry using drones (unmanned aircraft) was conducted to investigate the spatial distribution characteristics of the seagrass habitat in Wan Island. Wan Is. sea was divided into 3 geographical areas (Site A, B, C) by referring to the seagrass distribution identified by the National Coastal seagrass forest precision survey (Ministry of Oceans and Fisheries, 2015), and contour lines were extracted from grid depth data. In addition, a 3-D benthic topographic map using DTM (Digital Terrain Model) was created to understand the characteristics and slope of the benthic topographic map. Through the analysis of the seagrass distribution results and the water depth results, it was confirmed that the boundary between the seagrass distribution in the outer and coastal areas converges to different water depth limits (within 10 m, and within 5 m), which is estimated to be based on the characteristics of the outer sea area. As a result of the analysis of the benthic topography, it was confirmed that the slope of the well-covered distribution was relatively gentle below 0.2~0.8 degrees, and the well-covered distribution was limited to the vicinity of the boundary where the slope changed rapidly. As a result of comparing the area of the seagrass distribution survey in 2015 and 2019, it was confirmed that the area was 238.4 ha and 1,070.9 ha, respectively, which was 4.5 times more widerly when performing Drone survey than diving survey. In order to understand the surrounding environment of the seagrass distribution site, satellite image data and license fishing ground information map at the time of drone shooting (May 2019) were checked. As a result, seaweed (Kelp, Sea mustard seaweed, Seaweed, Seaweed fulvescens, etc.) and shellfish (Abalone) were densely distributed, and most of the facilities except for Seaweed fulvescens farms were separated from their habitat. Drone survey confirmed Zostera marina, Zostera caulescens, and Zostera japonica in Wan Is., but no Phyllospadix iwatensis and Halophila nipponica were identified in 2015. It was confirmed that there was a limit to classifying the types of vegetation due to the characteristics of Phyllospadix iwatensis attached to the rock substrate, and in the case of Halophila nipponica with short leaves of 2 to 3 cm, they were not exposed to the water even at low tide, so there was a limit to detection using drones. These research results are expected to be useful data for grasping the characteristics of a wide range of seagrass habitats in other seas in the future.
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Tian, Siquan, Yong Chen, Xinjun Chen, Liuxiong Xu, and Xiaojie Dai. "Impacts of spatial scales of fisheries and environmental data on catch per unit effort standardisation." Marine and Freshwater Research 60, no. 12 (2009): 1273. http://dx.doi.org/10.1071/mf09087.
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Spatial scale is an important factor that needs to be considered in data collection and analysis in ecological studies. Studies focusing on the quantitative evaluation of impacts of spatial scales are, however, limited in fisheries. Using the Chinese squid-jigging fishery in the north-western Pacific Ocean as an example, we evaluated impacts of spatial scale used in grouping fisheries and environmental data on the standardisation of fisheries catch per unit effort (CPUE). We developed 18 scenarios of different spatial scales with a combination of three latitudinal levels (0.5°, 1° and 2°) and six longitudinal levels (0.5°, 1°, 2°, 3°, 4° and 5°) to aggregate the data. We then applied generalised additive models to analyse the 18 scenarios of data for the CPUE standardisation, and quantified differences among the scenarios. This study shows that longitudinal and latitudinal spatial scale and size of the spatial area for data aggregation can greatly influence the standardisation of CPUE. We recommend that similar studies be undertaken whenever possible to evaluate the roles of spatial scales and to identify the optimal spatial scale for data aggregations in the standardisation of CPUE and fisheries stock assessment.
Dissertations / Theses on the topic "Spatial Data Focusing"
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Odhiambo, Michael Derrick. "Spatial data focusing using direct sequence spread spectrum modulation." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS060.
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Ce travail propose la mise en œuvre de la focalisation spatiale de données (FSD) en utilisant des techniques de spectre étalé. La FSD a récemment été proposée comme alternative aux schémas classiques de focalisation de puissance dans des scenarios de geocasting (diffusion géolocalisée des données). Dans la FSD, ce sont de données à transmettre qui sont traitées de telle qu'elles ne peuvent être décodées qu'à un endroit prédéfini. Ce travail exploite la double orthogonalité due aux IQ et aux séquences orthogonales d'étalement Gold pour concevoir le schéma de focalisation spatiale de données basé sur le IQ et le spectre étalé (DSSS-SDF-IQ). Il est démontré que la FSD permet d’obtenir des faisceaux plus étroits par rapport aux scénarios de focalisation de puissance. La robustesse du schéma proposé est ensuite démontrée par sa mise en œuvre sur un modèle de canal à trajets multiples. Il est démontré que la FSD atteint une largeur de faisceau allant jusqu'à 1 degré avec un réseau de 4 antennes seulement. Le chapitre 1 introduit le concept de géocasting. Le chapitre 2 passe en revue les différentes techniques qui permettent d'obtenir des capacités directionnelles sur les stations de base. Le chapitre 3 présente les principes de l'étalement du spectre par séquence directe basé sur la FSD. Le chapitre 4 étudie l'influence du canal à trajets multiples sur le schéma proposé. Pour tous les cas étudiés ci-dessus, des simulations pertinentes sont mises en œuvre pour valider les discussions. Le chapitre 5 résume le travail avec une conclusion et une perspective sur les orientations possibles de la recherche futureThis work proposes the implementation of Spatial Data Focusing (SDF) using spread spectrum techniques. SDF was recently proposed as a candidate alternative to classical power focusing schemes in wireless geocasting applications. Unlike power focusing approaches where radiated power is directed to a defined direction, in SDF, it is the data to be transmitted that is processed in such a manner that it can only be decoded at a predefined location. This work exploits the dual orthogonality due to classical quadrature components and orthogonal Gold spreading sequences to design the IQ and spread spectrum based spatial data focusing (DSSS-SDF-IQ) scheme. It is demonstrated that SDF attains better spatial selectivity than classical power focusing for a given antenna array size. The robustness of the proposed scheme is subsequently demonstrated by implementing it over a classical Urban Canyon 6-ray multipath channel model, where it is shown that the scheme can exhibit beamwidth as narrow as 1 degree with only a 4-antenna array. In SDF, the beamwidth is defined as the area within which data can be decoded as opposed to classical half power beamwidth. Chapter 1 introduces the concept of geocasting. Chapter 2 reviews the different techniques that enable directional capabilities on base stations. Chapter 3 introduces the principles of direct sequence spread spectrum based SDF. Chapter 4 investigates the influence of multipath channel on DSSS-SDF scheme. For all the cases studied above, relevant simulations are implemeneted to validate the discussions. Chapter 5 summarizes the work with a conclusion and perspective on possible future research directions
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Molineaux, Guylian. "Spatial Data Focusing for High-Precision Wireless Geocasting : Theoretical System Design and Practical Proof of Concept." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS491.
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Cette thèse étudie la focalisation spatiale de données (FSD) comme moyen d'effectuer la diffusion géolocalisée de données sans fil sur la couche physique, c'est-à-dire la multidiffusion basée sur la position ou la diffusion géographiquement limitée. Cette nouvelle approche peut aider à fournir des services et des messages basés sur la position à de grands groupes d'appareils mobiles qui existent dans les cadres émergents de l'internet des objets pour les villes intelligentes, les industries, les soins de santé, etc. Il permet de résoudre et d'éviter les problèmes de confidentialité qui existent dans les services de géodiffusion classiques, où les utilisateurs sont tenus de révéler leur position. En outre, il surmonte les exigences d'auto-localisation des nœuds et l'équilibre difficile entre la complexité, l'évolutivité et le taux de livraison qui existent dans les algorithmes de routage de géodiffusion de la couche réseau. Plus important encore, elle réussit à augmenter la précision, à réduire la taille du réseau d'antennes et à minimiser la complexité - les conditions les plus cruciales pour faire de la géodiffusion sur la couche physique un système attrayant - par rapport aux approches conventionnelles de focalisation de la puissance basées sur la formation de faisceaux. Dans le cadre du FSD, il remédie en outre à deux lacunes fondamentales. Il s'agit (i) d'une limitation à la focalisation dans le domaine angulaire uniquement ou, de manière équivalente, de l'incapacité de focaliser dans le domaine de la distance et (ii) d'une forte sensibilité à la propagation par trajets multiples qui compromet le fonctionnement correct en dehors des canaux hypothétiques de l'espace libre. Ces problèmes sont résolus par la conception de deux nouvelles architectures FSD qui exploitent les ressources de transmission multifréquence dans un cadre de multiplexage par répartition orthogonale de la fréquence (orthogonal frequency-division multiplexing, OFDM) et de réseaux de fréquences diverses (frequency diverse array, FDA). En outre, une architecture SDF expérimentale a été mise au point pour démontrer sa faisabilité pratique en tant que nouvelle technique de géocastingThis thesis investigates spatial data focusing (SDF) as a means of performing wireless physical-layer geocasting, i.e. location-based multicasting or geographically-confined broadcasting. This novel approach can aid in providing location-based services and messaging to large groups of mobile devices that exist in emerging internet-of-things frameworks for smart cities, industries, healthcare, etc., providing users with information that is related or contextualized to their geographical location. It addresses and avoids privacy concerns that exist in conventional location-based services, where users are required to disclose their location. In addition, it overcomes node self-localization requirements and the challenging balance between overhead, scalability, and delivery rate that exist in network-layer geocast routing algorithms. Most importantly, it succeeds in increasing precision, reducing array size, and minimizing complexity - the most crucial conditions in making physical-layer geocasting an attractive scheme - compared to conventional beamforming-based power focusing approaches. Within the SDF framework, it additionally addresses two fundamental shortcomings. That is, (i) a limitation to focusing in the angular domain only or, equivalently, the inability for range-domain focusing and (ii) a severe sensitivity to multipath propagation that jeopardizes correct operation outside hypothetical free space channels. They are overcome by designing two novel SDF architectures that exploit multi-frequency transmission resources in an orthogonal frequency-division multiplexing (OFDM) and frequency diverse array (FDA) framework. Additionally, an experimental proof-of-concept SDF architecture is developed that demonstrates its practical achievability as a novel geocasting technique
Books on the topic "Spatial Data Focusing"
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Majumdar, Satya N. Random growth models. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.38.
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This article discusses the connection between a particular class of growth processes and random matrices. It first provides an overview of growth model, focusing on the TASEP (totally asymmetric simple exclusion process) with parallel updating, before explaining how random matrices appear. It then describes multi-matrix models and line ensembles, noting that for curved initial data the spatial statistics for large time t is identical to the family of largest eigenvalues in a Gaussian Unitary Ensemble (GUE multi-matrix model. It also considers the link between the line ensemble and Brownian motion, and whether this persists on Gaussian Orthogonal Ensemble (GOE) matrices by comparing the line ensembles at fixed position for the flat polynuclear growth model (PNG) and at fixed time for GOE Brownian motions. Finally, it examines (directed) last passage percolation and random tiling in relation to growth models.
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Gao, Yanhong, and Deliang Chen. Modeling of Regional Climate over the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.591.
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The modeling of climate over the Tibetan Plateau (TP) started with the introduction of Global Climate Models (GCMs) in the 1950s. Since then, GCMs have been developed to simulate atmospheric dynamics and eventually the climate system. As the highest and widest international plateau, the strong orographic forcing caused by the TP and its impact on general circulation rather than regional climate was initially the focus. Later, with growing awareness of the incapability of GCMs to depict regional or local-scale atmospheric processes over the heterogeneous ground, coupled with the importance of this information for local decision-making, regional climate models (RCMs) were established in the 1970s. Dynamic and thermodynamic influences of the TP on the East and South Asia summer monsoon have since been widely investigated by model. Besides the heterogeneity in topography, impacts of land cover heterogeneity and change on regional climate were widely modeled through sensitivity experiments.In recent decades, the TP has experienced a greater warming than the global average and those for similar latitudes. GCMs project a global pattern where the wet gets wetter and the dry gets drier. The climate regime over the TP covers the extreme arid regions from the northwest to the semi-humid region in the southeast. The increased warming over the TP compared to the global average raises a number of questions. What are the regional dryness/wetness changes over the TP? What is the mechanism of the responses of regional changes to global warming? To answer these questions, several dynamical downscaling models (DDMs) using RCMs focusing on the TP have recently been conducted and high-resolution data sets generated. All DDM studies demonstrated that this process-based approach, despite its limitations, can improve understandings of the processes that lead to precipitation on the TP. Observation and global land data assimilation systems both present more wetting in the northwestern arid/semi-arid regions than the southeastern humid/semi-humid regions. The DDM was found to better capture the observed elevation dependent warming over the TP. In addition, the long-term high-resolution climate simulation was found to better capture the spatial pattern of precipitation and P-E (precipitation minus evapotranspiration) changes than the best available global reanalysis. This facilitates new and substantial findings regarding the role of dynamical, thermodynamics, and transient eddies in P-E changes reflected in observed changes in major river basins fed by runoff from the TP. The DDM was found to add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation.Although these advantages in the DDM over the TP are evidenced, there are unavoidable facts to be aware of. Firstly, there are still many discrepancies that exist in the up-to-date models. Any uncertainty in the model’s physics or in the land information from remote sensing and the forcing could result in uncertainties in simulation results. Secondly, the question remains of what is the appropriate resolution for resolving the TP’s heterogeneity. Thirdly, it is a challenge to include human activities in the climate models, although this is deemed necessary for future earth science. All-embracing further efforts are expected to improve regional climate models over the TP.
Book chapters on the topic "Spatial Data Focusing"
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van Nes, Akkelies, and Claudia Yamu. "Empirical Data Collection and Analysis, and Connecting Data with Space Syntax." In Introduction to Space Syntax in Urban Studies, 133–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59140-3_5.
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AbstractInprevious chapters, we demonstrated various analytic techniques focusing on the spatial aspects of the built environment. In this chapter, we discuss various methods and techniques for collecting qualitative andquantitative data dealing with human behaviour and how to connect such data to the results from various space syntax analyses. This chapter provides a brief introduction to these methods to stimulate ideas for connecting an array of spatial and socio-economicdata to space syntax. At the end of this chapter, we provide an exercise, references, and further readings.
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García-Álvarez, David, Javier Lara Hinojosa, Francisco José Jurado Pérez, and Jaime Quintero Villaraso. "Global General Land Use Cover Datasets with a Time Series of Maps." In Land Use Cover Datasets and Validation Tools, 287–311. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_15.
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AbstractGeneral Land Use Cover (LUC) datasets provide a holistic picture of all the land uses and covers on Earth, without focusing specifically on any individual land use category. As opposed to the LUC maps which are only available for one date or year, reviewed in Chap. “Global General Land Use Cover Datasets with a Single Date”, the maps with time series allow users to study LUC change over time. Time series of general LUC datasets at a global scale is useful for understanding global patterns of LUC change and their relation with global processes such as climate change or the loss of biodiversity. MCD12Q1, also known as MODIS Land Cover, was the first time series of LUC maps to be produced on a global scale. When it was first launched in 2002, there were already many organizations and researchers working on accurate, detailed global LUC maps, although these were all one-off editions for single years. The MCD12Q1 dataset continues to be updated today, providing a series of maps for the period 2001–2018. Since the launch of MCD12Q1, many other historical series of LUC maps have been produced, especially in the last decade. This has resulted in the LUC map series covering a longer time period at higher spatial resolution. Recent efforts have focused on producing consistent time series of maps that can track LUC changes over time with low levels of uncertainty. GLCNMO (500 m), GlobCover (300 m) and GLC250 (250 m) provide time series of LUC maps at similar spatial resolutions to MCD12Q1 (500 m), although for fewer reference years. GLCNMO provides information for the years 2003, 2008 and 2013, GlobCover for 2005 and 2009 and GLC250 for 2001 and 2010. GLASS-GLC is the dataset with the coarsest spatial resolution of all those reviewed in this chapter (5 km), even though it was released very recently, in 2020. Map producers have focused on this dataset’s long timespan (1982–2015) rather than on its spatial detail. LC-CCI and CGLS-LC100 are the recently launched datasets providing a consistent series of LUC maps, which show LUC changes over time with lower levels of uncertainty. LC-CCI provides LUC information for one of the longest timespans reviewed here (1992–2018) at a spatial resolution of 300 m. CGLS-LC100 provides LUC information for a shorter period (2015–2019) but at a higher spatial resolution (100 m). In both cases, updates are scheduled. The datasets with the highest levels of spatial detail are FROM-GLC and GLC30. These were produced using highly detailed Landsat imagery, delivering time series of maps at 30 m. The FROM-GLC project even has a test LUC map at a spatial resolution of 10 m from Sentinel-2 imagery for the year 2017, making it the global dataset with the greatest spatial detail of all those reviewed in this book. Both FROM-GLC and GLC30 provide data for three different dates: the former for 2010, 2015 and 2017 and the latter for 2000, 2010 and 2020.
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Smith, Janet L., Zafer Sonmez, and Nicholas Zettel. "Growing Income Inequality and Socioeconomic Segregation in the Chicago Region." In The Urban Book Series, 349–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64569-4_18.
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AbstractIncome inequality in the United States has been growing since the 1980s and is particularly noticeable in large urban areas like the Chicago metro region. While not as high as New York or Los Angeles, the Gini Coefficient for the Chicago metro area (.48) was the same as the United States in 2015 but rising at a faster rate, suggesting it will surpass the US national level in 2020. This chapter examines the Chicago region’s growing income inequality since 1980 using US Census data collected in 1990, 2000, 2010, and 2015, focusing on where people live based on occupation as well as income. When mapped out, the data shows a city and region that is becoming more segregated by occupation and income as it becomes both richer and poorer. A result is a shrinking number of middle-class and mixed neighbourhoods. The resulting patterns of socioeconomic spatial segregation also align with patterns of racial/ethnic segregation attributed to historical housing development and market segmentation, as well as recent efforts to advance Chicago as a global city through tourism and real estate development.
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García-Álvarez, David, Javier Lara Hinojosa, and Francisco José Jurado Pérez. "Global Thematic Land Use Cover Datasets Characterizing Artificial Covers." In Land Use Cover Datasets and Validation Tools, 419–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_21.
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AbstractThe mapping of artificial covers at a global scale has received increasing attention in recent years. Numerous thematic global Land Use Cover (LUC) datasets focusing on artificial surfaces have been produced at increasingly high spatial resolutions and using methods that ensure improved levels of accuracy. In fact, there are several long time series of maps showing the evolution of artificial surfaces from the 1980s to the present. Most of them allow for change detection over time, which is possible, thanks to the high level of accuracy at which artificial surfaces can be mapped and because transitions from artificial to non-artificial covers are very rare. Global thematic LUC datasets characterizing artificial covers usually map the extent or percentage of artificial or urban areas across the world. They do not provide thematic detail on the different uses or covers that make up artificial or urban surfaces. Unlike other general or thematic LUC datasets, those focusing on artificial covers make extensive use of radar data. In several cases, optical and radar imagery have been used together, as each source provides complementary information. Global Urban Expansion 1992–2016 and ISA, which were produced at a spatial resolution of 1 km, are the coarsest of the nine datasets reviewed in this chapter. ISA provides information on the percentage of impervious surface area per pixel. The GHSL edition of 2014 and the GMIS at 30 m also provide sub-pixel information, whereas all the other datasets reviewed here only map the extent of artificial/impervious/urban areas. Most of the datasets reviewed in this chapter were produced at a spatial resolution of 30 m. This is due to the extensive use of Landsat imagery in the production of these datasets. Landsat provides a long, high-resolution series of satellite imagery that enables effective mapping of the evolution of impervious surfaces at detailed scales. Of the datasets produced at 30 m, Global Urban Land maps artificial covers for seven different dates between 1980 and 2015, while GHSL does the same for five different dates between 1987 and 2016, although the map for the last date was produced at 20 m. GUB maps the extent of urban land for seven dates between 1990 and 2018 and was produced together with GAIA, which provides an annual series of maps for the period 1985–2018. HBASE, GMIS and GISM, also at 30 m, are only available for one reference year. The same is true of GUF and WSF, which were produced as part of the same effort to map global artificial surfaces as accurately as possible. They provide the most detailed datasets up to date, with spatial resolutions of 12 m (GUF) and 10 m (WSF). Future updates of WSF will produce a consistent time series of global LC maps of artificial areas from the 1980s to the present. It aims to be the longest, most detailed, most accurate dataset ever produced on this subject.
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García-Álvarez, David, Javier Lara Hinojosa, and Jaime Quintero Villaraso. "Global General Land Use Cover Datasets with a Single Date." In Land Use Cover Datasets and Validation Tools, 269–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_14.
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AbstractGlobal general Land Use and Land Cover (LUC) datasets map all land uses and covers across the globe, without focusing on any specific use or cover. This chapter only reviews those datasets available for one single date, which have not been updated over time. Seven different datasets are described in detail. Two other ones were identified, but are not included in this review, because of its coarsens, which limits their utility: Mathews Global Vegetation/Land Use and GMRCA LULC. The first experiences in global LUC mapping date back to the 1990s, when leading research groups in the field produced the first global LUC maps at fine scales of 1 km spatial resolution: the UMD LC Classification and the Global Land Cover Characterization. Not long afterwards, in an attempt to build on these experiences and take them a stage further, an international partnership produced GLC2000 for the reference year 2000. These initial LUC mapping projects produced maps for just one reference year and were not continued or updated over time. Subsequent projects have mostly focused on the production of timeseries of global LUC maps, which allow us to study LUC change over time (see Chapter “Global General Land Use Cover Datasets with a Time Series of Maps”). As a result, there are relatively few single-date global LUC maps for recent years of reference. The latest projects and initiatives producing global LUC maps for single dates have focused on improving the accuracy of global LUC mapping and the use of crowdsourcing production strategies. The Geo-Wiki Hybrid and GLC-SHARE datasets built on the previous research in a bid to obtain more accurate global LUC maps by merging the data from existing datasets. OSM LULC is an ongoing test project that is trying to produce a global LUC map cheaply, using crowdsourced information provided by the Open Street Maps community. The other dataset reviewed here is the LADA LUC Map, which was developed for a specific thematic project (Land Degradation Assessment in Dryland). This dataset is not comparable to the others reviewed in this chapter in terms of its purpose and nature, as is clear from its coarse spatial resolution (5 arc minutes). We therefore believe that this dataset should not be considered part of initiatives to produce more accurate, more detailed land use maps at a global level.
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Mas, Jean-François, David García-Álvarez, Martin Paegelow, Roberto Domínguez-Vera, and Miguel Ángel Castillo-Santiago. "Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps." In Land Use Cover Datasets and Validation Tools, 127–51. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_8.
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AbstractThe overlaying of two map layers is a standard GIS procedure. As we saw in the previous chapter, it enables us to compute the intersection between two feature classes and cross-tabulate either the area or the pixel count of the intersecting features depending on whether raster or vector data are being used. Cross-tabulation can be used to evaluate different topics depending on the nature of the input data. In this chapter, cross-tabulation is used to assess land cover changes, the spatial agreement between maps and map accuracy. In Sect. 1, Land use/cover changes (LUCC) are quantified by comparing two LUC maps, computing different indices of change and creating a change matrix. In Sect. 2, we used various metrics to evaluate the spatial agreement between two maps. This procedure was applied to compare a LUC map with a reference map, a simulated LUC map with a reference map and a simulated LUCC map with a reference map of changes. Section 3 introduces the Kappa indices, which allow us to assess the agreement between two datasets, given the agreement expected by random coincidence. We used the indices to compare observed or simulated maps with a reference map. In Sect. 4 we evaluate the agreement between maps at a global level (the entire map) by focusing on a specific feature such as a smaller area or a particular category (stratum level). Finally, in Sect. 5, the cross-tabulation between a map and reference sample data is used to assess the thematic accuracy of the map by calculating various different accuracy indices. We present examples of analyses based on cross-tabulation for four different cases: To validate a series of maps with two or more time points, to validate a map against a reference map, to validate a simulation against a reference map and to validate simulated changes against a reference map of changes. In the example exercises, we use CORINE and SIOSE maps from the Asturias Central Area and Ariège Valley datasets and maps of the Marqués de Comillas region of south-eastern Mexico (MarquesLUC dataset). The cross-tabulation techniques proposed by Robert Gilmore Pontius Jr. are applied in Chapter “Pontius Jr. Methods Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps”.
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Jia, Menghao, Fanyi Zhang, Xinyi Lyu, Yuncheng Wen, and Hua Xu. "Three-Dimensional Hydrodynamic Analysis and Early Warning of Ω-Collapse in the Lower Reaches of the Yangtze River Based on Experimental Study on Generalized Model." In Lecture Notes in Civil Engineering, 1589–603. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6138-0_140.
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AbstractUnder the background of the construction of cascade reservoir group in the main stream of the Yangtze River, the lower reaches of the Yangtze River are faced with new water and sediment situation, which leads to the increased risk of bank collapse in the lower reaches of the Yangtze River. Therefore, the purpose of this study is to have a certain supporting significance for the prevention and control of riverbank collapse disasters. The study takes the Jiangsu section of the lower reaches of the Yangtze River as the research object. Firstly, according to the measured data over the years, the macroscopic characteristics of the collapsed bank in the Jiangsu section are analyzed, and the temporal and spatial distribution characteristics are mainly analyzed. The research results show that the collapse of the Jiangsu section of the Yangtze River is dominated by Ω-collapse, which mostly occurs in the flood season and the post-flood receding water period. The frequency of bank collapse in the Yangzhong Reach is relatively the highest among all river segments, and there are more bank collapses on the north bank than the south bank. According to statistics, the average collapse width of the collapse in the Jiangsu section can reach 130 m, the depth of the collapse can reach 60 m, and the ratio of the average bank collapse to the depth can reach 2.15. Then, aiming at the characteristics of the main bank collapse type in the Jiangsu section is the Ω-collapse, the experimental investigation and numerical calculation are used to conduct in-depth research, combined with the measured data, probability and statistical analysis and theoretical analysis. Three-dimensional hydrodynamic analysis is carried out on the mainstream area near the collapse area and outside the collapse area, focusing on the analysis of its nearshore velocity and shear stress and other factors. Combined with the water tank test, the water flow in the inner surface, middle and bottom layers of the Ω-type nest was studied under different flow levels, and it was concluded that the water flow in the nest had a counterclockwise backflow, and the backflow intensity gradually weakened from the side wall to the center. And the phenomenon that the surface layer and the bottom water flow are separated, and this phenomenon becomes more prominent with the increase of the flow rate.
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Owen, Gwilym, Yu Chen, Gwilym Pryce, Tim Birabi, Hui Song, and Bifeng Wang. "Deprivation Indices in China: Establishing Principles for Application and Interpretation." In The Urban Book Series, 305–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74544-8_14.
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AbstractIndicesofMultiple Deprivation(IMDs) aim to measure living standards at the small area level. These indices were originally developed in the United Kingdom, but there is a growing interest in adapting them for use in China. However, due to data limitations, Chinese deprivation indices sometimes diverge considerably in approaches and are not always connected with the underlying concepts within UK analysis. In this paper, we seek to bring direction and conceptual rigour to this nascent literature by establishing a set of core principles for IMD estimation that are relevant and feasible in the Chinese context. These principles are based on specifying deprivation domains from theory, selecting the most appropriate measurements for these domains, and then applying rigorous statistical techniques to combine them into an IMD. We apply these principles to create an IMD for Shijiazhuang, the capital city of Hebei Province. We use this to investigate the spatial patterns of deprivation in Shijiazhuang, focussing on clusteringand centralisationof deprivation as well as exploring different deprivation typologies. We highlight two distinct types of deprived areas. One is clustered in industrial areas on the edge of the city, while the second is found more centrally and contains high proportions of low-skilled service workers.
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Dibble, Catherine. "Beyond Data: Handling Spatial and Analytical Contexts with Genetics-Based Machine Learning." In Spatial Evolutionary Modeling. Oxford University Press, 2001. http://dx.doi.org/10.1093/oso/9780195135688.003.0012.
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Geographic information systems (GISs) are fairly good at handling three types of data: locational, attribute, and topological. Recent work holds promise for adding temporal data to this list as well (e.g., see Langran, 1992). Yet the unprecedentedly vast resources of geographically referenced data continue to outstrip our ability to derive meaningful information from such databases, despite dramatic improvements in computer processing power, algorithm efficiency, and parallel processing. In part this is because such research has emphasized improvements in processing efficiency rather than effectiveness. We humans are slow-minded compared with our silicon inventions; yet our analytical capabilities remain far more powerful, primarily because we have evolved elaborate cognitive infrastructures devoted to ensuring that we leverage our limited processing power by focusing our attention on the events and information most likely to be relevant. In GIS use, so far only human perception provides the requisite integration of spatial context, and human attention directs the determination of relevance and the selection of geographic features and related analyses. Understanding of spatial context and analytical purpose exists only in the minds of humans working with the GIS or viewing the displays and maps created by such operations. We still extract information from our geographic data systems primarily through long series of relatively tedious and complex spatial operations, performed—or at least explicitly preprogrammed—by a human, in order to derive each answer. Human integration of analytical purpose and spatial and attribute contexts is perhaps the most essential and yet the most invisible component of any geographic analysis, yet it is also perhaps the most fundamental missing link in any GIS. Only humans can glance at a map of a toxic waste dumps next to school yards, or oil spills upstream from fisheries, and recognize the potential threat of such proximity; human cartographers understand the importance of emphasizing either road or stream networks depending on the purpose of a map; humans understand that “near” operates at different scales for corner stores versus cities, or tropical jungle habitat versus open savannah. Given a GIS with the capability to deluge any inquiry with myriad layers of extraneous data, this natural human ability to filter data and manipulate only the meaningful elements is essential.
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Monsia, Symphorien, and Sami Faiz. "High-Level Languages for Geospatial Analysis of Big Data." In Interdisciplinary Approaches to Spatial Optimization Issues, 62–81. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-1954-7.ch004.
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In recent years, big data has become a major concern for many organizations. An essential component of big data is the spatio-temporal data dimension known as geospatial big data, which designates the application of big data issues to geographic data. One of the major aspects of the (geospatial) big data systems is the data query language (i.e., high-level language) that allows non-technical users to easily interact with these systems. In this chapter, the researchers explore high-level languages focusing in particular on the spatial extensions of Hadoop for geospatial big data queries. Their main objective is to examine three open source and popular implementations of SQL on Hadoop intended for the interrogation of geospatial big data: (1) Pigeon of SpatialHadoop, (2) QLSP of Hadoop-GIS, and (3) ESRI Hive of GIS Tools for Hadoop. Along the same line, the authors present their current research work toward the analysis of geospatial big data.
Conference papers on the topic "Spatial Data Focusing"
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Sarrazin, Julien, Michael Odhiambo, Sidney Golstein, Philippe De Doncker, and Francois Horlin. "Spatial Data Focusing: An Alternative to Beamforming for Geocasting Scenarios." In 2018 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium). IEEE, 2018. http://dx.doi.org/10.1109/usnc-ursi.2018.8602761.
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Molineaux, Guylian, Sidney Golstein, Michael Odhiambo, Francois Horlin, Philippe De Doncker, and Julien Sarrazin. "Spatial Data Focusing Using Time and IQ Resources for Wireless Geocasting." In GLOBECOM 2019 - 2019 IEEE Global Communications Conference. IEEE, 2019. http://dx.doi.org/10.1109/globecom38437.2019.9013948.
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Molineaux, Guylian, Michael Odhiambo, Francois Horlin, Philippe De Doncker, and Julien Sarrazin. "OFDM-based Spatial Data Focusing for High Resolution 2-Dimensional Wireless Geocasting." In 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2020. http://dx.doi.org/10.1109/pimrc48278.2020.9217222.
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Molineaux, Guylian, François Horlin, Muriel Darces, Philippe De Doncker, and Julien Sarrazin. "Frequency Diverse Array Spatial Data Focusing: Free Space and Multipath Experimental Validation." In GLOBECOM 2023 - 2023 IEEE Global Communications Conference. IEEE, 2023. http://dx.doi.org/10.1109/globecom54140.2023.10436781.
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Bocquet, Michael, Atika Rivenq, Christophe Loyez, and Nathalie Rolland. "A focusing technique based on a data spatial diversity at millimetre-wave frequency." In 2014 44th European Microwave Conference (EuMC). IEEE, 2014. http://dx.doi.org/10.1109/eumc.2014.6986584.
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Molineaux, Guylian, Francois Horlin, Philippe De Doncker, and Julien Sarrazin. "Frequency Diverse Array Spatial Data Focusing for High Precision Range-angle-based Geocasting." In GLOBECOM 2022 - 2022 IEEE Global Communications Conference. IEEE, 2022. http://dx.doi.org/10.1109/globecom48099.2022.10001165.
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Muenchausen, R. E., A. R. Garcia, R. A. Keller, and N. S. Nogar. "Studies of Gasdynamic Focusing in a Near Critically Choked Expansion." In Laser Applications to Chemical Analysis. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/laca.1987.wa7.
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We have developed a technique well suited for gaseous flow field visualization using laser induced fluorescence (LIF) coupled with an array detector which allows us to obtain real-time spatial profiles of fluorescence from iodine seeded gas jets. Furthermore, array detection provides signals that are linear in fluorescence intensity as well as producing data ammenable to digital signal processing.
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Constantinides, Yiannis, and Owen H. Oakley. "Numerical Simulations of Cylinder VIV Focusing on High Harmonics." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-80002.
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The discovery of high frequency contributions in flexible cylinder VIV during the DeepStar experiments revealed a significant omission and raised concerns among riser designers due to the high fatigue contribution. Researchers are now focusing on this area by analyzing the sparse experimental data or performing simplified experiments. Building on past success with CFD simulations, this numerical study focuses on understanding the origin and cause of the 3rd harmonic in particular. An array of basic spring supported rigid cylinder and various configurations of flexible cylinder computations are performed to validate CFD and to gain a qualitative understanding. A relationship between the hydrodynamic force causing the 3rd harmonic and cylinder motion as well its spatial characteristics is established.
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Shin, Junseob, Jean-Luc Robert, Can Meral, Iason Apostolakis, Man Nguyen, and Jason Yu. "K-space domain spatial filtering for retrospective transmit beam focusing/shaping and per-element data estimation from arrays with microbeamforming." In 2022 IEEE International Ultrasonics Symposium (IUS). IEEE, 2022. http://dx.doi.org/10.1109/ius54386.2022.9957563.
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Imitazione, G., F. Zolezzi, A. Murianni, F. Giovacchini, and M. Miola. "3D Ground Model: An Alternative Approach for the Treatment of Heterogeneously Distributed Spatial Data." In Offshore Technology Conference. OTC, 2024. http://dx.doi.org/10.4043/35265-ms.
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Abstract This paper emphasizes the significance of adopting a robust approach for effectively handling spatially heterogeneous distributed data. We present a dedicated workflow focusing on enhancing the processes involved from acoustic reflectors data to surface and mesh definitions. Results derived from the robust approach are compared to the ones generated following a "reference approach". These distinct approaches are then employed as inputs for separate 3D subsurface models for a reciprocal comparison. The data on which this study is based pertains to the interpretation of specific acoustic reflectors from a geophysical survey typically related for subaqueous and sub-seabed engineering assessment. Generally, these data exhibit a significantly uneven distribution, characterized by abundant data along the acquisition lines and data absence in the space between lines. This distinctive distribution emphasizes the need to carefully consider the appropriate strategy during the surface creation phases. The workflow commences with robust data simplification applied along seismic lines, followed by mesh refinement to improve surface definition. The resulting mesh exhibits a non-uniform resolution, avoiding uncontrolled sub-sampling, accommodating higher data density in areas with greater spatial variability and vice versa. Comparing proposed approaches output reveals a strong level of correspondence, underscoring the reliability and efficacy of the proposed methodology. The benefits of employing proposed ad-hoc meshing criteria are evident in more robust sub-sampling of original interpreted reflector data, resulting in lighter mesh files and reduced computational time during mesh editing and successively 3D modelling phases, all without sacrificing crucial original information.
Reports on the topic "Spatial Data Focusing"
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Aguilar, G., H. Waqa-Sakiti, and L. Winder. Using Predicted Locations and an Ensemble Approach to Address Sparse Data Sets for Species Distribution Modelling: Long-horned Beetles (Cerambycidae) of the Fiji Islands. Unitec ePress, December 2016. http://dx.doi.org/10.34074/book.008.
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In response to unique species in Fiji which are threatened or endangered, and in critical need of effective conservation measures to ensure their survival, author Glenn Aguilar has produced an eMedia publication and learning research tool, called GIS For Conservation.The eMedia website hosts tutorial material, videos and modelling results for conservation management and planning purposes. Users will learn spatial analytical skills, species distribution modelling and other relevant GIS tools, as well as enhance ArcMap skills and the species distribution modelling tool Maxent. Accompanying the GIS For Conservation website is a peer-reviewed research report. The report details the case study and research methods that have informed the eMedia publication, focusing on the development of maps predicting the suitability of the Fiji Islands for longhorned beetles (Cerambycidae) that include endemic and endangered species such as the Giant Fijian Beetle Xixuthrus heros.
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Walker, Samantha, Tomoko McGaughey, and Paul Peters. Spatial models of access to health and care services in rural and remote Canada: a scoping review protocol. Spatial Determinants of Health Lab, 2023. http://dx.doi.org/10.22215/rrep/2023.sdhl.606.
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Objective: The objective of this review is to determine the scope of spatial modelling approaches used to evaluate geographic access to health and care services in rural Canada. Introduction: Canada’s health and social policy agenda has made the requirement for equal access to primary and secondary health services for rural populations a key priority. Most rural health research in Canada has focused on measuring patterns of health outcomes or modelling geographic access to a narrow range of services, health conditions, or within specific regions. This scoping review will provide an in depth look at the spatial modelling currently being used to evaluate the barriers and facilitators for access to health and care services and will provide direction for further research. Inclusion criteria: This review will consider studies that include any person accessing health and care services in Canada, focusing on those who reside in rural or remote communities, or access health services in those areas. Methods: Published primary studies, reviews, opinion papers, reports, theses, and dissertations published in English or French across all dates will be searched in databases including CINAHL via EBSCO, PubMed, ProQuest, Scopus, Web of Science and Dissertations and Theses Global. Following the search, all titles and abstracts will then be assessed against the inclusion criteria for the review. Potentially relevant papers will be assessed in detail against the inclusion criteria. The data extracted will include geographic location, service under study, analytic methodology, data included, and specifics of the spatial models employed.
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Boyle, Maxwell, and Elizabeth Rico. Terrestrial vegetation monitoring at Fort Pulaski National Monument: 2019 data summary. National Park Service, December 2021. http://dx.doi.org/10.36967/nrds-2288716.
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The Southeast Coast Network (SECN) conducts long-term terrestrial vegetation monitoring as part of the nationwide Inventory and Monitoring Program of the National Park Service (NPS). The vegetation community vital sign is one of the primary-tier resources identified by SECN park managers, and monitoring is currently conducted at 15 network parks (DeVivo et al. 2008). Monitoring plants and their associated communities over time allows for targeted understanding of ecosystems within the SECN geography, which provides managers information about the degree of change within their parks’ natural vegetation. 2019 marks the first year of conducting this monitoring effort on four SECN parks, including Fort Pulaski National Monument (FOPU). Twelve vegetation plots were established at Fort Pulaski National Monument in August. Data collected in each plot included species richness across multiple spatial scales, species-specific cover and constancy, species-specific woody stem seedling/sapling counts and adult tree (greater than 10 centimeters [3.9 inches {in}]) diameter at breast height (DBH), overall tree health, landform, soil, observed disturbance, and woody biomass (i.e., fuel load) estimates. This report summarizes the baseline (year 1) terrestrial vegetation data collected at Fort Pulaski National Monument in 2019. Data were stratified across two dominant broadly defined habitats within the park (Maritime Tidal Wetlands and Maritime Upland Forests and Shrublands). Noteworthy findings include: Sixty-six vascular plant taxa were observed across 12 vegetation plots, including six taxa not previously known from the park. Plots were located on both Cockspur and McQueen’s Island. The most frequently encountered species in each broadly defined habitat included: Maritime Tidal Wetlands: smooth cordgrass (Spartina alterniflora), perennial saltmarsh aster(Symphyotrichum enuifolium), and groundsel tree (Baccharis halimifolia) Maritime Upland Forests and Shrublands: yaupon (Ilex vomitoria), southern/eastern red cedar (Juniperus silicicola + virginiana), and cabbage palmetto (Sabal palmetto). Four non-native species identified as invasive by the Georgia Exotic Pest Plant Council (GA-EPPC 2018) were found during this monitoring effort. These species (and their overall frequency of occurrence within all plots) included: Japanese honeysuckle (Lonicera japonica; 17%), bahiagrass (Paspalum notatum; 8%), Vasey’s grass (Paspalum urvillei; 8%), and European common reed (Phragmites australis; 8%). Two rare plants tracked by the Georgia Department of Natural Resources (GADNR 2013) were found during this monitoring effort. These include Florida wild privet (Forestiera segregata) and Bosc’s bluet (Oldenlandia boscii). Southern/eastern red cedar and cabbage palmetto were the most dominant species within the tree stratum of the maritime Upland Forest and Shrubland habitat type. Species that dominated the sapling and seedling strata of this type included yaupon, cabbage palmetto, groundsel tree, and Carolina laurel cherry (Prunus caroliniana). The health status of sugarberry (Celtis laevigata)—a typical canopy species in maritime forests of the South Atlantic Coastal Plain--observed on park plots appeared to be in decline, with most stems experiencing elevated levels of dieback and low vigor. Over the past decade, this species has been experiencing unexplained high rates of dieback and mortality throughout its range in the Southeastern United States; current research is focusing on what may be causing these alarming die-off patterns. Duff and litter made up the majority of downed woody biomass (fuel loads) across FOPU vegetation plots.
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Mushongera, Darlington, Prudence Kwenda, and Miracle Ntuli. An analysis of well-being in Gauteng province using the capability approach. Gauteng City-Region Observatory, 2020. http://dx.doi.org/10.36634/2020.op.1.
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As countries across the globe pursue economic development, the improvement of individual and societal well-being has increasingly become an overarching goal. In the global South, in particular, high levels of poverty, inequality and deteriorating social fabrics remain significant challenges. Programmes and projects for addressing these challenges have had some, but limited, impact. This occasional paper analyses well-being in Gauteng province from a capability perspective, using a standard ‘capability approach’ consistent with Amartya Sen’s first conceptualisation, which was then operationalised by Martha Nussbaum. Earlier research on poverty and inequality in the Gauteng City-Region was mainly based on objective characteristics of well-being such as income, employment, housing and schooling. Using data from the Gauteng City-Region Observatory’s Quality of Life Survey IV for 2015/16, our capability approach provides a more holistic view of well-being by focusing on both objective and subjective aspects simultaneously. The results confirm the well-known heterogeneity in human conditions among South African demographic groups, namely that capability achievements vary across race, age, gender, income level and location. However, we observe broader (in both subjective and objective dimensions) levels of deprivation that are otherwise masked in the earlier studies. In light of these findings, the paper recommends that policies are directly targeted towards improving those capability indicators where historically disadvantaged and vulnerable groups show marked deprivation. In addition, given the spatial heterogeneities in capability achievements, we recommend localised interventions in capabilities that are lagging in certain areas of the province.
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Land Conflicts in India: An Interim Analysis. Rights and Resources Initiative, November 2016. http://dx.doi.org/10.53892/dogb3075.
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Land and resource conflicts in India have deep implications for the well-being of the country’s people, institutions, investments, and long-term development. These conflicts reveal deep structural flaws in the country’s social, agrarian, and institutional structures, including ambiguities in property rights regimes and institutions. In 2014, a study focusing primarily on reports in the national media reflected the gravity of these conflicts. There was great interest in this narrow exercise, which prompted a more rigorous and detailed data collection on conflicts and creation of an interactive web portal on land and resource conflicts in India. While the exercise is ongoing, this brief provides an interim analysis of approximately 289 conflicts for which data was collected thus far, covering the period between January-September, 2016. This analysis provides a powerful instrument to understand land resource conflicts in India. The emerging patterns from the analysis of the 289 conflicts provide inferences about sectoral and spatial distribution of conflicts. Based on these patterns, our assessment is that this brief has captured roughly 25- 40 percent of active and substantive land conflicts in the country.