Matching Items (16)

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Web-Scale Multidimensional Visualization of Big Spatial Data to Support Earth Sciences—A Case Study with Visualizing Climate Simulation Data

Description

The world is undergoing rapid changes in its climate, environment, and ecosystems due to increasing population growth, urbanization, and industrialization. Numerical simulation is becoming an important vehicle to enhance the

The world is undergoing rapid changes in its climate, environment, and ecosystems due to increasing population growth, urbanization, and industrialization. Numerical simulation is becoming an important vehicle to enhance the understanding of these changes and their impacts, with regional and global simulation models producing vast amounts of data. Comprehending these multidimensional data and fostering collaborative scientific discovery requires the development of new visualization techniques. In this paper, we present a cyberinfrastructure solution—PolarGlobe—that enables comprehensive analysis and collaboration. PolarGlobe is implemented upon an emerging web graphics library, WebGL, and an open source virtual globe system Cesium, which has the ability to map spatial data onto a virtual Earth. We have also integrated volume rendering techniques, value and spatial filters, and vertical profile visualization to improve rendered images and support a comprehensive exploration of multi-dimensional spatial data. In this study, the climate simulation dataset produced by the extended polar version of the well-known Weather Research and Forecasting Model (WRF) is used to test the proposed techniques. PolarGlobe is also easily extendable to enable data visualization for other Earth Science domains, such as oceanography, weather, or geology.

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Created

Date Created
  • 2017-06-26

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An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

Description

Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data.

Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.

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Created

Date Created
  • 2016-09-30

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Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System

Description

Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions.

Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions. Identifying polar cyclones proves to be a task of greater complexity. To tackle this challenge, a new method which utilizes pressure level data and velocity field is proposed to improve the identification accuracy. In addition, the dynamic, simulative cyclone visualized with a 4D (four-dimensional) wind field further validated the identification result. A knowledge-driven system is eventually constructed for visualizing and analyzing an atmospheric phenomenon (cyclone) in the North Pole. The cyclone is simulated with WebGL on in a web environment using particle tracing. To achieve interactive frame rates, the graphics processing unit (GPU) is used to accelerate the process of particle advection. It is concluded with the experimental results that: (1) the cyclone identification accuracy of the proposed method is 95.6% when compared with the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data; (2) the integrated knowledge-driven visualization system allows for streaming and rendering of millions of particles with an interactive frame rate to support knowledge discovery in the complex climate system of the Arctic region.

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Created

Date Created
  • 2016-09-05

Remote Sensing of the Surface Urban Heat Island and Land Architecture in Phoenix, Arizona: Combined Effects of Land Composition and Configuration and Cadastral/Demographic/Economic Factors

Description

This study seeks to determine the role of land architecture—the composition and configuration of land cover—as well as cadastral/demographic/economic factors on land surface temperature (LST) and the surface urban heat

This study seeks to determine the role of land architecture—the composition and configuration of land cover—as well as cadastral/demographic/economic factors on land surface temperature (LST) and the surface urban heat island effect of Phoenix, Arizona. It employs 1 m National Agricultural Imagery Program data of land-cover with 120mLandsat-derived land surface temperature, decomposed to 30 m, a new measure of configuration, the normalized moment of inertia, and U.S. Census data to address the question for two randomly selected samples comprising 523 and 545 residential neighborhoods (census blocks) in the city. The results indicate that, contrary to most other studies, land configuration has a stronger influence on LST than land composition. In addition, both land configuration and architecture combined with cadastral, demographic, and economic variables, capture a significant amount of explained variance in LST. The results indicate that attention to land architecture in the development of or reshaping of neighborhoods may ameliorate the summer extremes in LST.

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Created

Date Created
  • 2015-12-29

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GIS Visualization of New York City Photography Studios, ca. 1830-1880

Description

The humanities as a discipline have typically not used a rigid or technical method of assessment in the process of analysis. GIScience offers numerous benefits to this discipline by applying

The humanities as a discipline have typically not used a rigid or technical method of assessment in the process of analysis. GIScience offers numerous benefits to this discipline by applying spatial analysis to rigorously understand it. Photography studios developed in the mid-19th Century as a highly popular business and emerging technology. This project was initiated by Dr. Jeremy Rowe with support from the ASU Emeritus College Research and Creative Activity and Undergraduate Research Initiative grants, and seeks to use GIS tools to understand the explosive growth of photography studios in the New York City area, specifically Manhattan and Brooklyn. Demonstrated in this project are several capabilities of the ESRI online GIS, including queries for year information, a tool showing growth over time, and a generated density map of photography studios.

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Created

Date Created
  • 2015-05

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A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Description

Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack

Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.

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Created

Date Created
  • 2013-05-21

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Open Geospatial Analytics with PySAL

Description

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.

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Created

Date Created
  • 2015-06-01

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An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems

Description

A measure of shape compactness is a numerical quantity representing the degree to which a shape is compact. Ways to provide an accurate measure have been given great attention due

A measure of shape compactness is a numerical quantity representing the degree to which a shape is compact. Ways to provide an accurate measure have been given great attention due to its application in a broad range of GIS problems, such as detecting clustering patterns from remote-sensing images, understanding urban sprawl, and redrawing electoral districts to avoid gerrymandering. In this article, we propose an effective and efficient approach to computing shape compactness based on the moment of inertia (MI), a well-known concept in physics. The mathematical framework and the computer implementation for both raster and vector models are discussed in detail. In addition to computing compactness for a single shape, we propose a computational method that is capable of calculating the variations in compactness as a shape grows or shrinks, which is a typical application found in regionalization problems. We conducted a number of experiments that demonstrate the superiority of the MI over the popular isoperimetric quotient approach in terms of (1) computational efficiency; (2) tolerance of positional uncertainty and irregular boundaries; (3) ability to handle shapes with holes and multiple parts; and (4) applicability and efficacy in districting/zonation/regionalization problems.

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Created

Date Created
  • 2013-08-15

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A taxonomy of parallel vector spatial analysis algorithms

Description

Nearly 25 years ago, parallel computing techniques were first applied to vector spatial analysis methods. This initial research was driven by the desire to reduce computing times in order to

Nearly 25 years ago, parallel computing techniques were first applied to vector spatial analysis methods. This initial research was driven by the desire to reduce computing times in order to support scaling to larger problem sets. Since this initial work, rapid technological advancement has driven the availability of High Performance Computing (HPC) resources, in the form of multi-core desktop computers, distributed geographic information processing systems, e.g. computational grids, and single site HPC clusters. In step with increases in computational resources, significant advancement in the capabilities to capture and store large quantities of spatially enabled data have been realized. A key component to utilizing vast data quantities in HPC environments, scalable algorithms, have failed to keep pace. The National Science Foundation has identified the lack of scalable algorithms in codified frameworks as an essential research product. Fulfillment of this goal is challenging given the lack of a codified theoretical framework mapping atomic numeric operations from the spatial analysis stack to parallel programming paradigms, the diversity in vernacular utilized by research groups, the propensity for implementations to tightly couple to under- lying hardware, and the general difficulty in realizing scalable parallel algorithms. This dissertation develops a taxonomy of parallel vector spatial analysis algorithms with classification being defined by root mathematical operation and communication pattern, a computational dwarf. Six computational dwarfs are identified, three being drawn directly from an existing parallel computing taxonomy and three being created to capture characteristics unique to spatial analysis algorithms. The taxonomy provides a high-level classification decoupled from low-level implementation details such as hardware, communication protocols, implementation language, decomposition method, or file input and output. By taking a high-level approach implementation specifics are broadly proposed, breadth of coverage is achieved, and extensibility is ensured. The taxonomy is both informed and informed by five case studies im- plemented across multiple, divergent hardware environments. A major contribution of this dissertation is a theoretical framework to support the future development of concrete parallel vector spatial analysis frameworks through the identification of computational dwarfs and, by extension, successful implementation strategies.

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Created

Date Created
  • 2015

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Multiscale Geographically Weighted Regression: Computation, Inference, and Application

Description

Geographically Weighted Regression (GWR) has been broadly used in various fields to

model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter

Geographically Weighted Regression (GWR) has been broadly used in various fields to

model spatially non-stationary relationships. Classic GWR is considered as a single-scale model that is based on one bandwidth parameter which controls the amount of distance-decay in weighting neighboring data around each location. The single bandwidth in GWR assumes that processes (relationships between the response variable and the predictor variables) all operate at the same scale. However, this posits a limitation in modeling potentially multi-scale processes which are more often seen in the real world. For example, the measured ambient temperature of a location is affected by the built environment, regional weather and global warming, all of which operate at different scales. A recent advancement to GWR termed Multiscale GWR (MGWR) removes the single bandwidth assumption and allows the bandwidths for each covariate to vary. This results in each parameter surface being allowed to have a different degree of spatial variation, reflecting variation across covariate-specific processes. In this way, MGWR has the capability to differentiate local, regional and global processes by using varying bandwidths for covariates. Additionally, bandwidths in MGWR become explicit indicators of the scale at various processes operate. The proposed dissertation covers three perspectives centering on MGWR: Computation; Inference; and Application. The first component focuses on addressing computational issues in MGWR to allow MGWR models to be calibrated more efficiently and to be applied on large datasets. The second component aims to statistically differentiate the spatial scales at which different processes operate by quantifying the uncertainty associated with each bandwidth obtained from MGWR. In the third component, an empirical study will be conducted to model the changing relationships between county-level socio-economic factors and voter preferences in the 2008-2016 United States presidential elections using MGWR.

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Created

Date Created
  • 2020