Spatial Search Research Articles

The Swiss joint information platform for natural hazards

Abstract. The Swiss Joint Information Platform for Natural Hazards (GIN) has been realized from 2008 to 2010 as part of the Swiss federal government’s OWARNA project, which aimed at optimizing warning and alerting procedures against natural hazard. The first online-version of the platform went productive in 2011 with the primary goal of providing measured and forecast natural hazard data in form of processed cartographic, graphic and other multimedia products to professional users – before, during and after natural hazard events. In Switzerland water-, weather-, snow- and earthquake-related hazards are the most relevant ones.In 2013, an online survey showed that the platform does not fully meet user expectations, particularly as to user experience and usability of its cartographic, web-based user interface. Revaluation and redesign of the overall platform were necessary in order to improve map legibility, caused by the complexity of data, large data amounts, and high spatial density of online, real-time measurement data locations. A new web design and user interaction concept have been developed in 2014 and eventually put online in June 2017. User acceptance testing by means of surveys and direct user feedback sessions were key factors in this perennial redesign process. The GIN platform now features important novel technical and graphical elements: The starting page is based on a dashboard containing virtual dossiers (Fig. 1), with which users configure their desired information, data, and map bundles individually, or use predefined adaptable views on various existing data sets. In addition, there is a new overall spatial search function to query data parameters. A responsive approach further improves the usability of the platform. The focus of these new features is on multi-views involving maps, diagrams, tables, text products, as well as selected geographical areas on maps, and fast data queries (Fig. 2). Current user feedback suggests that the new GIN platform design is well received, and that it is moving closer to its very goal: online monitoring and management of natural hazard events by enhanced usability, more targeted and higher personalization.Several Swiss Cantons (i.e., the political entities in Switzerland below the federation) actively participated, and still participate, in the conceptual GIN platform development process through advisory board meetings and consultations. On the operational level, Cantons actively provide and contribute further natural hazard information and measurement data from their own natural hazard monitoring networks. These additional Cantonal regional-scale data sets help to fill spatial data gaps, where no Federal data is available. GIN thusly integrates natural hazard data from Federal and Cantonal levels (and partly even private level), which adds value to all stakeholders on various political levels involved in natural hazard management (Federal, Cantonal, Regional, Communal crisis committees). Stakeholders not only use GIN’s ample database and cartographic product portfolio to accomplish their early warning and crisis management tasks, but also benefit from seamless, secure and reliable IT-services, provided by the Swiss Federal Government. With the new GIN platform, Switzerland has a powerful, integrative, and comprehensive tool for monitoring and responding to natural hazard events.

S2R-tree: a pivot-based indexing structure for semantic-aware spatial keyword search

Semantic-aware spatial keyword search is an important technique for digital map services. However, existing indexing and search methods have limited pruning effect due to the high dimensionality in semantic space, causing query efficiency to be a serious issue. To handle this problem, this paper proposes a novel pivot-based hierarchical indexing structure S2R-tree to integrate spatial and semantic information in a seamless way. Instead of indexing objects in the original semantic space, we carefully design a space mechanism to transform the high dimensional semantic vectors to a low dimensional space, so that more effective pruning effect can be achieved. On top of the S2R-tree, an efficient query processing algorithm is further designed, which not only ensures efficient query processing by a set of theoretical bounds, but also returns accurate results despite of the indexing in the low dimensional space. Furthermore, we conduct extensive experiments to evaluate and compare our proposed and baseline methods.

Spatial keyword search: a survey

Spatial keyword search has been playing an indispensable role in personalized route recommendation and geo-textual information retrieval. In this light, we conduct a survey on existing studies of spatial keyword search. We categorize existing works of spatial keyword search based on the types of their input data, output results, and methodologies. For each category, we summarize their common features in terms of input data, output result, indexing scheme, and search algorithms. In addition, we provide detailed description regarding each study of spatial keyword search. This survey summarizes the findings of existing spatial keyword search studies, thus uncovering new insights that may guide software engineers as well as further research.

Massive Point Cloud Space Management Method Based on Octree-Like Encoding

Based on the mass point cloud data, this paper proposes a hybrid octree mixing point cloud index structure which combines the KD-tree spatial segmentation idea to realize the efficient management of mass point cloud. In this paper, the space of the point cloud is firstly divided by the KD-tree idea. On this basis, the octree is used for further segmentation to establish an octree-like index structure. Then the point cloud dataset is spatially encoded using the improved encoding to achieve better spatial management and neighborhood search. Finally, using five groups of incremented point cloud set as test data, the experimental results and comparison analysis show that the octree-like space can make the overall structure of the data organization more reasonable, effectively improve the access efficiency and reduce the occupancy of memory space. The index structure not only improves the speed of the traditional KD-tree construction index but also improves the problem that the traditional octree is too large for space occupation and the neighborhood search takes too long. It achieves reasonable management of massive point cloud space.

Going Molecular: Sequence-based spatiotemporal biodiversity evidence in GBIF

The Global Biodiversity Information Facility (GBIF) was established by governments in 2001, largely through the initiative and leadership of the natural history collections community, following the 1999 recommendation by a working group under the Megascience Forum (predecessor of the Global Science Forum) of the Organization for Economic Cooperation and Development (OECD). Over 20 years, GBIF has helped develop standards and convened a global community of data-publishing institutions, aggregrating over one billion specimen occurrence records freely and openly available for use in research and policy making. These GBIF mediated data range from vouchered museum specimens to observation records generated by humans and machines. New data are being generated from integrated remote sensing, ecological sampling, and molecular sequencing that have strong geospatial components but lack traditional vouchers. GBIF is working with partners to develop best practices of bringing this data into the GBIF architecture. Following discussions during the second Global Biodiversity Information Conference in 2018, GBIF and the European Bioinformatics Institute (EMBL-EBI), supported by ELIXIR, have extended collaboration to share species occurrence records known only from their genetic material. When these data providers contribute data coordinates along with the sequences to the European Nucleotide Archive (ENA), the records will appear on GBIF maps and in spatial searches. This collaboration enables significant new molecular data streams to become discoverable through GBIF.org: by mid-March 2019, over 7.8m individual occurrence records via the ENA, and over 13.2m records as standardized Darwin Core sampling-event datasets via MGnify, a resource that provides taxonomic and functional annotations on sequences derived from environmental sequencing projects. Sequence-based occurrence records published by ENA and MGnify boost representation of microbial diversity which was underrepresented at GBIF. The ELIXIR-ENA-MGnify-GBIF partnership is working on further refinement of the dynamic data linkages, frequency of updates and other improvements. The API-based tool that connects GBIF data infrastructures is open to new data contributors and for indexes of molecular occurrences. Indexing of these data streams is dependent on the presence of a name (any rank) with the sequence. Under the current Codes of nomenclature, animals, fungi, plants, and algae cannot be described based on exclusively sequence data. Yet, a significant volume of biodiversity data has only been represented by DNA sequences. Barcoding and sequence clustering procedures vary among taxa and research communities, but clusters can be related to a taxon with a Latin name. Many DNA similarity clusters do not contain a sequence from a formally described taxon; however these sequence clusters provide provisional molecular names for nomenclatural communication. In the best cases, curated libraries of reference sequences, their metadata, clusters, alignments, and links to individuals and physical material become de facto naming conventions for certain taxonomic groups, and co-exist with Latin names. Integration of molecular names into the taxonomic backbone of GBIF started with Fungi and UNITE, a data management and identification environment for fungal ITS barcodes with 87,000+ fungal species hypotheses demarcating 800,000+ sequence specimens as of March 2019. Checklist publication of all names in UNITE through GBIF.org including Linnaean names and stable, DOI-trackable molecular sequence based ‘species hypotheses’, enables indexing of fungal metabarcoding data worldwide, such as BIOWIDE. As names are currently essential to indexing the world’s occurrence data, GBIF will develop similar linkages with names in the Barcode of Life data system (BOLD) and in SILVA - a resource for high-quality ribosomal RNA sequence data and taxonomy, and welcomes other reference systems to this development. Expanding the molecular data streams (Fig. 1) allows GBIF to address spatial, temporal and taxonomic gaps and biases, and to support large-scale data-intensive research openly and worldwide.

Improving quantum spatial search in two dimensions

The question of whether quantum spatial search in two dimensions can be made optimal has long been an open problem. We report progress towards its resolution by showing that the oracle complexity for target location can be made optimal, by increasing the number of calls to the walk operator that incorporates the graph structure by a logarithmic factor. Our algorithm does not require amplitude amplification. An important ingredient of our algorithm is the implementation of multi-step quantum walks by graph powering, using a coin space of walk-length dependent dimension, which may be of independent interest. Finally, we demonstrate how to implement quantum walks arising from powers of symmetric Markov chains using our methods.

Continuous-time quantum walks on strongly regular graphs with loops and its application to spatial search for multiple marked vertices

The continuous-time quantum walk (CTQW) on the strongly regular graph is studied in this paper, and the exact transition probability distribution between any two vertices of the graph is provided by using the method of counting the walks between these two vertices. The CTQW is also considered on the perturbed strongly regular graph (SRG) by adding multiple loops on given vertices. Spatial search using CTQW can be regarded as a special case of CTQW on the perturbed SRG by adding loops. Combined with the approach of walk counting, the proper parameter settings and search time of spatial search for both a single marked vertex and two marked vertices are provided. The results show that both kinds of spatial search can be undertaken in $$O(\sqrt{N})$$ time in a degree k SRG with N vertices, by setting jump rate of CTQW to be 1 / k.

Joint Space and Time Processing for Unknown Mutual Coupling Blind Calibration and Mixed Sources Identification Using Uniform Circular Array

In classification and localization of mixed far-field and near-field sources, the unknown mutual coupling degrades the performance of most high-resolution algorithms. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. This paper proposes an effective algorithm of mixed sources identification using uniform circular array under unknown mutual coupling. Firstly, according to rank reduction and joint space–time processing, the directions of arrival of far-field sources is estimated directly without mutual coupling elimination. Addition, the joint space–time processing can improve the estimation results in the case of low signal noise ratio of incoming signal sources and small number of snapshots. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through spatial search after mutual coupling effects and far-field components elimination. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.

Robust abnormality detection methods for spatial search of radioactive materials

AbstractRadiological dirty bombs and improvised nuclear devices pose a significant threat to both public health and national security. Growing networks of radiation sensors have been deployed by a number of offices within the U.S. and international agencies. Detecting such threats while minimizing false alarm rates presents a considerable challenge to homeland security and public health. This research aims to achieve a higher probability of detection with a lower probability of false alarms. It focuses on the local spatial instability of radiation levels in order to detect radioactive materials based on robust outlier detection methods. Our approach includes a three‐step abnormality detection method consisting of one‐dimensional robust outlier detection for all gamma‐ray counts, a density‐based clustering analysis, and a two‐dimensional robust outlier detection method using a bagplot, based on spatial associations. The effectiveness of the method proposed is demonstrated through a case study, wherein radioactive materials are detected in urban environments, and its performance is compared with alternative methods employing a k‐sigma approach, local Getis–Ord () statistic, and the goodness of fit of the Poisson distribution.

Peritoneal mesothelioma mortality in Italy: Spatial analysis and search for asbestos exposure sources

BackgroundThis study is part of a national plan of epidemiological surveillance of malignant mesothelioma (MM) mortality in Italy. The paper shows the results of malignant peritoneal mesothelioma (MPeM) mortality study in Italian Regions and municipalities. MethodsNational Bureau of Statistics data for MPeM municipal mortality (ICD-10, Code C45.1) were analyzed in the time-window 2003–2014: mortality standardized rates (reference Italian population, census 2011), temporal trends of the annual national rates, Standardized Mortality Ratios and a municipal clustering analysis were performed. Results747 deaths for MPeM were recorded (0.10/100,000): 464 in men (0.14/100,000) and in 283 women (0.07/100,000). No significant MPeM mortality temporal trend was found.Seventeen municipalities showed excesses of mortality for MPeM in at least one gender and/or overall population.Four clusters in male population, and one in women were identified. ConclusionsThe study identifies some areas where remediation activities and/or health care actions may be warranted.

English

English

On workflow aware location-based service composition for personal trip planning

Trip planning is one of the most important techniques in supporting digital map services. It aims to find a set of location based services (LBSs) that can cover all user intended activities specified in query, such that the services have reasonable spatial distribution. In the temporal domain, the planned trip is supposed to satisfy all the workflow temporal dependencies among intended activities required by users. However, existing service composition solutions do not consider the complex spatio-temporal characteristics. To address this problem, this paper studies the problem of workflow aware location based service composition, which targets to return a rational trip formed by a set of location-based services that not only are rationally distributed in spatial, but also ensure all intended activities and their temporal workflow constraints to be met. Particularly, we propose a collective spatial keyword search based method to speed up the query processing. Experimental study on two real datasets demonstrates the effectiveness and efficiency of our proposed algorithms.

Low Carbon, Low Risk, Low Density: Resolving choices about sustainable development in cities

Urban areas face a conundrum, they need to reduce their greenhouse gas emissions and consumption of resources, whilst also increasing their resilience to climate change and extreme weather, and improving wellbeing. However, it is widely recognized that well intended intervention to address one of these sustainability objectives in isolation can undermine other objectives. This paper presents a framework to efficiently identify spatial development strategies that provide the best outcomes against multiple objectives. The framework has been applied to London (UK) to identify strategies that can simultaneously: (i) minimize exposure to future heat wave events; (ii) minimize the risk from flood events; (iii) minimize transport emissions; (iv) minimize urban sprawl; (v) maximize brownfield development; and, (vi) prevent development of greenspace that is recognized as important to wellbeing. Prioritizing each objective in isolation leads to considerably different spatial planning structures, exposing conflicts between many objectives. These include tradeoffs between urban heat risk and transport emissions; and also previously undocumented conflicts between minimizing flood and heat risks. Allowing greater flexibility in development density is shown to provide benefits in terms of heat risk reduction, whilst not significantly affecting mitigation objectives. The framework is shown to significantly improve upon the London Spatial Development Strategy for the objectives analyzed. Further analysis identifies optimal spatial strategies to achieve a Low Carbon, Low Risk or Low Density city - however, these cannot be simultaneously maximized. This work shows there are difficult, and often irreconcilable, choices to be made in the spatial planning of sustainable cities. Spatial search and optimization tools strengthen the evidence-base for planning. Rapid identification of development strategies that satisfy, and minimize conflicts between, multiple objectives helps planners to develop strategies that simultaneously improve urban sustainability and reduce the risks from natural hazards.

Septal GABA and Glutamate Neurons Express RXFP3 mRNA and Depletion of Septal RXFP3 Impaired Spatial Search Strategy and Long-Term Reference Memory in Adult Mice.

Relaxin-3 is a highly conserved neuropeptide abundantly expressed in neurons of the nucleus incertus (NI), which project to nodes of the septohippocampal system (SHS) including the medial septum/diagonal band of Broca (MS/DB) and dorsal hippocampus, as well as to limbic circuits. High densities of the Gi/o-protein-coupled receptor for relaxin-3, known as relaxin-family peptide-3 receptor (RXFP3) are expressed throughout the SHS, further suggesting a role for relaxin-3/RXFP3 signaling in modulating learning and memory processes that occur within these networks. Therefore, this study sought to gain further anatomical and functional insights into relaxin-3/RXFP3 signaling in the mouse MS/DB. Using Cre/LoxP recombination methods, we assessed locomotion, exploratory behavior, and spatial learning and long-term reference memory in adult C57BL/6J Rxfp3loxP/loxP mice with targeted depletion of Rxfp3 in the MS/DB. Following prior injection of an AAV(1/2)-Cre-IRES-eGFP vector into the MS/DB to delete/deplete Rxfp3 mRNA/RXFP3 protein, mice tested in a Morris water maze (MWM) displayed an impairment in allocentric spatial learning during acquisition, as well as an impairment in long-term reference memory on probe day. However, RXFP3-depleted and control mice displayed similar motor activity in a locomotor cell and exploratory behavior in a large open-field (LOF) test. A quantitative characterization using multiplex, fluorescent in situ hybridization (ISH) identified a high level of co-localization of Rxfp3 mRNA and vesicular GABA transporter (vGAT) mRNA in MS and DB neurons (~87% and ~95% co-expression, respectively). Rxfp3 mRNA was also detected, to a correspondingly lesser extent, in vesicular glutamate transporter 2 (vGlut2) mRNA-containing neurons in MS and DB (~13% and ~5% co-expression, respectively). Similarly, a qualitative assessment of the MS/DB region, identified Rxfp3 mRNA in neurons that expressed parvalbumin (PV) mRNA (reflecting hippocampally-projecting GABA neurons), whereas choline acetyltransferase mRNA-positive (acetylcholine) neurons lacked Rxfp3 mRNA. These data are consistent with a qualitative immunohistochemical analysis that revealed relaxin-3-immunoreactive nerve fibers in close apposition with PV-immunoreactive neurons in the MS/DB. Together these studies suggest relaxin-3/RXFP3 signaling in the MS/DB plays a role in modulating specific learning and long-term memory associated behaviors in adult mice via effects on GABAergic neuron populations known for their involvement in modulating hippocampal theta rhythm and associated cognitive processes.

Effects of sun compass error on spatial search by Clark's nutcrackers

Animals employ compasses during navigation, but little attention has been paid to how accuracy is maintained in the face of compass error, which is inevitable in biological systems. The use of multiple landmarks may minimize the effect of compass error. We allowed Clark's nutcrackers to cache seeds in an outdoor aviary with either one or four landmarks present, and subsequently subjected them to small clock-shifts mimicking the effects of compass error. As predicted, the results showed a significant decrease in search accuracy following the clock-shift when one landmark was present but not when four landmarks were present. These results support that nutcrackers encode information from the sun as well as terrestrial landmarks, and these spatial cues are used in a flexible manner. Overall, our results are important as they support the hypothesis that multiple landmarks may be used during situations where the sun compass has even a small amount of error.

Brain-wide cellular resolution imaging of Cre transgenic zebrafish lines for functional circuit-mapping.

Decoding the functional connectivity of the nervous system is facilitated by transgenic methods that express a genetically encoded reporter or effector in specific neurons; however, most transgenic lines show broad spatiotemporal and cell-type expression. Increased specificity can be achieved using intersectional genetic methods which restrict reporter expression to cells that co-express multiple drivers, such as Gal4 and Cre. To facilitate intersectional targeting in zebrafish, we have generated more than 50 new Cre lines, and co-registered brain expression images with the Zebrafish Brain Browser, a cellular resolution atlas of 264 transgenic lines. Lines labeling neurons of interest can be identified using a web-browser to perform a 3D spatial search (zbbrowser.com). This resource facilitates the design of intersectional genetic experiments and will advance a wide range of precision circuit-mapping studies.

Location‐specific knowledge in spatial job search and its outcomes: An empirical investigation

AbstractThis paper investigates how working location influences jobseekers' subsequent spatial job search. Further, it is assessed to what extent mobility between working regions is associated with wage growth. The results show that the working region functions as a prominent geographical anchor around which the new job search is focused. The jobseekers that do find a job far away from their old working region receive a small wage premium, but this premium disappears if selectivity is taken into account. It is concluded that employees demonstrate substantial stickiness to their working locations, and that this is motivated by asymmetry in search costs. No evidence was found that mobility between working regions in itself affects wages.

Distributional Effects of Local Minimum Wage Hikes: A Spatial Job Search Approach

This paper develops and estimates a spatial general equilibrium job search model to study the effects of local and universal (federal) minimum wage policies. In the model, firms post vacancies in multiple locations. Workers, who are heterogeneous in terms of location and education types, engage in random search and can migrate or commute in response to job offers. I estimate the model by combining multiple databases including the American Community Survey (ACS) and Quarterly Workforce Indicators (QWI). The estimated model is used to analyze how minimum wage policies affect employment, wages, job postings, vacancies, migration/ commuting, and welfare. Empirical results show that minimum wage increases in local county lead to an exit of low type (education

Discrete-time quantum walk search on Johnson graphs

The Johnson graph J (n, k) is defined by n symbols, where vertices are k-element subsets of the symbols, and vertices are adjacent if they differ in exactly one symbol. In particular, both J (n, 1), the complete graph Kn, and J (n, 2), the strongly regular triangular graph Tn, support fast quantum spatial search. Wong showed that continuous-time quantum walk search on J (n, 3) also supports fast search. The problem is reconsidered in the language of scattering quantum walk, a type of discrete-time quantum walk. Here the search space is confined to a low-dimensional subspace corresponding to the collapsed graph. Using matrix perturbation theory, we show that discrete-time quantum walk search on J (n, 3) also achieves full quantum speedup. The analytical method can also be applied to general Johnson graphs J (n, k) with fixed k.

Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

The purpose of this paper is to propose a new hybrid metaheuristic to solve the problem of feature selection. Feature selection problem is the process of finding the most relevant subset based on some criteria. A hybrid metaheuristic is a new trend in the development of optimization algorithms. In this paper, two different hybrid models are designed based on spotted hyena optimization (SHO) for feature selection problem. The SHO algorithm can find the optimal or nearly optimal feature subset in the feature space to minimize the given fitness function. In the first model, the simulated annealing algorithm (SA) is embedded in the SHO algorithm (called SHOSA-1) to enhance the optimal solution found by the SHO algorithm after each iteration. In the second model, SA enhances the final solution obtained by the SHO algorithm (called SHOSA-2). The performance of these methods is evaluated in 20 datasets in the UCI repository. The experiments show that SHOSA-1 performs better than the native algorithm and SHOSA-2. And then, SHOSA-1 is compared with six state-of-the-art optimization algorithms. The experimental results confirm that SHOSA-1 improves the classification accuracy and reduces the number of selected features compared with other wrapper-based optimization algorithms. That proves the excellent performance of SHOSA-1 in spatial search and feature attribute selection.