VTechWorks Repository :: Browsing by Author "Sforza, Peter M."

Browsing by Author "Sforza, Peter M."

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Advancing the Global Land Grant Institution: Creating a Virtual Environment to Re-envision Extension and Advance GSS-related Research, Education, and Collaboration
Hall, Ralph P.; Polys, Nicholas F.; Sforza, Peter M.; Eubank, Stephen D.; Lewis, Bryan L.; Krometis, Leigh-Anne H.; Pollyea, Ryan M.; Schoenholtz, Stephen H.; Sridhar, Venkataramana; Crowder, Van; Lipsey, John; Christie, Maria Elisa; Glasson, George E.; Scherer, Hannah H.; Davis, A. Jack; Dunay, Robert J.; King, Nathan T.; Muelenaer, Andre A.; Muelenaer, Penelope; Rist, Cassidy; Wenzel, Sophie (Virginia Tech, 2017-05-15)
The vision for this project has emerged from several years of research, teaching, and service in Africa and holds the potential to internationalize education at Virginia Tech and in our partner institutions in Malawi. The vision is simple, to develop a state-of-the-art, data rich, virtual decision-support and learning environment that enables local-, regional-, and national-level actors in developed and developing regions to make decisions that improve resilience and sustainability. Achieving these objectives will require a system that can combine biogeophysical and sociocultural data in a way that enables actors to understand and leverage these data to enhance decision-making at various levels. The project will begin by focusing on water, agricultural, and health systems in Malawi, and can be expanded over time to include any sector or system in any country. The core ideas are inherently scalable...
Calibration of an Artificial Neural Network for Predicting Development in Montgomery County, Virginia: 1992-2001
Thekkudan, Travis Francis (Virginia Tech, 2008-06-11)
This study evaluates the effectiveness of an artificial neural network (ANN) to predict locations of urban change at a countywide level by testing various calibrations of the Land Transformation Model (LTM). It utilizes the Stuttgart Neural Network Simulator (SNNS), a common medium through which ANNs run a back-propagation algorithm, to execute neural net training. This research explores the dynamics of socioeconomic and biophysical variables (derived from the 1990 Comprehensive Plan) and how they affect model calibration for Montgomery County, Virginia. Using NLCD Retrofit Land Use data for 1992 and 2001 as base layers for urban change, we assess the sensitivity of the model with policy-influenced variables from data layers representing road accessibility, proximity to urban lands, distance from urban expansion areas, slopes, and soils. Aerial imagery from 1991 and 2002 was used to visually assess changes at site-specific locations. Results show a percent correct metric (PCM) of 32.843% and a Kappa value of 0.319. A relative operating characteristic (ROC) value of 0.660 showed that the model predicted locations of change better than chance (0.50). It performs consistently when compared to PCMs from a logistic regression model, 31.752%, and LTMs run in the absence of each driving variable ranging 27.971% – 33.494%. These figures are similar to results from other land use and land cover change (LUCC) studies sharing comparable landscape characteristics. Prediction maps resulting from LTM forecasts driven by the six variables tested provide a satisfactory means for forecasting change inside of dense urban areas and urban fringes for countywide urban planning.
Data Management Bootcamp 2016: Peter Sforza
Sforza, Peter M. (Virginia Tech. University Libraries, 2016-01-15)
Enhancing the Indoor-Outdoor Visual Relationship: Framework for Developing and Integrating a 3D-Geospatial-Based Inside-Out Design Approach to the Design Process
Obeidat, Laith Mohammad (Virginia Tech, 2020-04-16)
This research study aims to enhance the effectiveness of the architectural design process regarding the exploration and framing of the best visual connections to the outside environment within built environments. Specifically, it aims to develop a framework for developing and integrating an inside-out design approach augmented and informed by digital 3D geospatial data as a way to enhance the explorative ability and decision-making process for designers regarding the visual connection to the outside environment. To do so, the strategy of logical argumentation is used to analyze and study the phenomenon of making visual connections to a surrounding context. The initial recommendation of this stage is to integrate an inside-out design approach that operates within the digital immersion within 3D digital representations of the surrounding context. This strategy will help to identify the basic logical steps of the proposed inside-out design process. Then, the method of immersive case study is used to test and further develop a proposed process by designing a specific building, specifically, an Art Museum building on the campus of Virginia Tech. Finally, the Delphi method is used in order to evaluate the necessity and importance of the proposed approach to the design process and its ability to achieve this goal. A multi-round survey was distributed to measure the consensus among a number of experts regarding the proposed design approach and its developed design tool. Overall, findings refer to a total agreement among the participating experts regarding the proposed design approach with some different concerns regarding the proposed design tool.
Evaluating visual channels for multivariate map visualization
Mohammed, Ayat; Polys, Nicholas F.; Sforza, Peter M. (EuroGraphics, 2018-06-04)
Visual differencing, or visual discrimination, is the ability to differentiate between two or more objects in a scene depending on the values of certain attributes. Focusing on multivariate maps visualization, this work examined human’s predictable bias in interpreting visual-spatial information and inference making. Moreover, this study seeks to develop and evaluate new techniques to mitigate the trade-off between proximity and occlusion and to enable analysts to explore multivariate maps. Therefore, we developed a multi-criteria decision-making technique for land suitability using multivariate maps, and we carried out a user study where users are tasked to choose the most suitable piece of land to plant grapes. We designed the user study to evaluate mapping a map’s layers (variables) to visual channels (Transparency, Hue, Saturation and Brightness/Lightness); two color spaces were used Hue Saturation Value (HSV)and Hue Saturation Lightness (HSL). The categorical variables were mapped to the Hue channel and the quantitative/ordinal variables were mapped to either Saturation, Brightness/lightness, or Transparency channels. Our online user study was taken by 85 participants to test the users’ perception of different map visualizations. The statistical analysis of survey responses showed that mapping quantitative layers to the Transparency channel outperformed the other channels, and the use of HSV color space showed a more efficient mapping than HSL, especially for the extreme values in the dataset.
Evaluation of Rainwater Harvesting on Residential Housing on Virginia Tech Campus
McCloskey, Tara (Virginia Tech, 2010-04-29)
Rainwater harvesting (RWH) refers to the collection of rainwater for subsequent on-site use. Rainwater is most often used for non-potable purposes including toilet flushing, laundering, landscape and commercial crop irrigation, industry, fire fighting, air-conditioning, and vehicle-washing. This study evaluates the potential impacts of RWH on residential housing on Virginia Tech campus in southwestern Virginia in regards to potable water offset, energy conservation, stormwater mitigation, carbon emission reduction, and financial savings. Potential rainwater collection was estimated from three simulations used to approximate the maximum, average, and minimum range of annual precipitation. Collected rainwater estimates were used to calculate the impacts on the areas of interest. Cumulatively, the sample buildings can collect 3.4 to 5.3 millions of gallons of rainwater — offsetting potable water use and reducing stormwater by an equivalent amount, save 320 to 1842 kWh of energy, and reduce carbon emissions by 650 to 3650 pounds annually. Cumulative savings for the nine buildings from combined water and energy offsets range between $5751 and $9005 USD, not substantial enough to serve as the sole basis of RWH implementation on campus. A significant advantage of RWH relates to the management and improvement of the Stroubles Creek watershed in which the majority of the campus sits. Additionally, RWH implementation would benefit sustainable initiatives and provide Virginia Tech additional opportunities for conservation incentives and environmental stewardship funding.
Framework for Integrated Multi-Scale CFD Simulations in Architectural Design
Kalua, Amos (Virginia Tech, 2021-09-17)
An important aspect in the process of architectural design is the testing of solution alternatives in order to evaluate them on their appropriateness within the context of the design problem. Computational Fluid Dynamics (CFD) analysis is one of the approaches that have gained popularity in the testing of architectural design solutions especially for purposes of evaluating the performance of natural ventilation strategies in buildings. Natural ventilation strategies can reduce the energy consumption in buildings while ensuring the good health and wellbeing of the occupants. In order for natural ventilation strategies to perform as intended, a number of factors interact and these factors must be carefully analysed. CFD simulations provide an affordable platform for such analyses to be undertaken. Traditionally, these simulations have largely followed the direction of Best Practice Guidelines (BPGs) for quality control. These guidelines are built around certain simplifications due to the high computational cost of CFD modelling. However, while the computational cost has increasingly fallen and is predicted to continue to drop, the BPGs have largely remained without significant updates. The need to develop a CFD simulation framework that leverages the contemporary and anticipates the future computational cost and capacity can, therefore, not be overemphasised. When conducting CFD simulations during the process of architectural design, the variability of the wind flow field including the wind direction and its velocity constitute an important input parameter. Presently, however, in many simulations, the wind direction is largely used in a steady state manner. It is assumed that the direction of flow downwind of a meteorological station remains constant. This assumption may potentially compromise the integrity of CFD modelling as in reality, the wind flow field is bound to be dynamic from place to place. In order to improve the accuracy of the CFD simulations for architectural design, it is therefore necessary to adequately account for this variability. This study was a two-pronged investigation with the ultimate objective of improving the accuracy of the CFD simulations that are used in the architectural design process, particularly for the design and analysis of natural ventilation strategies. Firstly, a framework for integrated meso-scale and building scale CFD simulations was developed. Secondly, the newly developed framework was then implemented by deploying it to study the variability of the wind flow field between a reference meteorological station, the Virginia Tech Airport, and a selected localized building scale site on the Virginia Tech campus. The findings confirmed that the wind flow field varies from place to place and showed that the newly developed framework was able to capture this variation, ultimately, generating a wind flow field characterization representative of the conditions prevalent at the localized building site. This framework can be particularly useful when undertaking de-coupled CFD simulations to design and analyse natural ventilation strategies in the building design process.
Geo-Locating Tweets with Latent Location Information
Lee, Sunshin (Virginia Tech, 2017-02-13)
As part of our work on the NSF funded Integrated Digital Event Archiving and Library (IDEAL) project and the Global Event and Trend Archive Research (GETAR) project, we collected over 1.4 billion tweets using over 1,000 keywords, key phrases, mentions, or hashtags, starting from 2009. Since many tweets talk about events (with useful location information), such as natural disasters, emergencies, and accidents, it is important to geo-locate those tweets whenever possible. Due to possible location ambiguity, finding a tweet's location often is challenging. Many distinct places have the same geoname, e.g., "Greenville" matches 50 different locations in the U.S.A. Frequently, in tweets, explicit location information, like geonames mentioned, is insufficient, because tweets are often brief and incomplete. They have a small fraction of the full location information of an event due to the 140 character limitation. Location indicative words (LIWs) may include latent location information, for example, "Water main break near White House" does not have any geonames but it is related to a location "1600 Pennsylvania Ave NW, Washington, DC 20500 USA" indicated by the key phrase 'White House'. To disambiguate tweet locations, we first extracted geospatial named entities (geonames) and predicted implicit state (e.g., Virginia or California) information from entities using machine learning algorithms including Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF). Implicit state information helps reduce ambiguity. We also studied how location information of events is expressed in tweets and how latent location indicative information can help to geo-locate tweets. We then used a machine learning (ML) approach to predict the implicit state using geonames and LIWs. We conducted experiments with tweets (e.g., about potholes), and found significant improvement in disambiguating tweet locations using a ML algorithm along with the Stanford NER. Adding state information predicted by our classifiers increased the possibility to find the state-level geo-location unambiguously by up to 80%. We also studied over 6 million tweets (3 mid-size and 2 big-size collections about water main breaks, sinkholes, potholes, car crashes, and car accidents), covering 17 months. We found that up to 91.1% of tweets have at least one type of location information (geo-coordinates or geonames), or LIWs. We also demonstrated that in most cases adding LIWs helps geo-locate tweets with less ambiguity using a geo-coding API. Finally, we conducted additional experiments with the five different tweet collections, and found significant improvement in disambiguating tweet locations using a ML approach with geonames and all LIWs that are present in tweet texts as features.
Geographical Information System (GIS) web applications for data visualization of Drinking Water pipelines
Shekhawat, Pururajsingh (Virginia Tech, 2018-08-08)
Robust decision support tools that aid water utilities to make informed, swift and precise decisions are becoming the need of the hour. Application of sophisticated models to aid the process of condition assessment and risk analysis on water pipelines have been limited owing to the lack of scalability, inability to incorporate external open source datasets and mathematically complicated output results. Interactive visualization of resultant model output is the key element in extracting valuable information to support decision making. This thesis presents a framework for visualization of data related to drinking water pipelines. Critical components of strategic, tactical and operational level decision making are explored in context with data presentation and information depiction. This thesis depicts various aspects of developing GIS web applications and their important functionalities for query and visualization of data. Multiple facets of data storage, standardization and application development are highlighted in this document. Publishing of application geo processing services in web environment is done through Virginia Tech enterprise geodatabase. Risk assessment and Performance models developed by a utility are projected in the application environment through help of widgets. Applications are coded into links on a Drupal website (www.pipeid.org) for model dissemination and utility engagement purposes.
Incorporating User Opinion into a New Wine Tourism Map for Southwest Virginia
Pritchard, Katherine (Virginia Tech, 2008-12-10)
Thematic tourist maps provide users with a tangible geographic route to their travel destinations and also may contain a wide variety of additional information to enhance traveler experiences. Unlike other types of maps that focus on accurate topographic representation of an area or on depiction of spatial data, tourist maps should be specifically constructed to appeal directly to the end-user. Toward that end, this research developed and implemented a model to incorporate user opinion on content, levels of detail, and labeling conventions during the process of designing and creating a wine tourism map for southwest Virginia. Over 700 (total) wine tourists completed brief questionnaires during five distinct phases of data collection and map modeling. At each point, we incorporated user input into map design for the preceding phase, and a final assessment surveyed tourist attitude of the finished product. Interestingly, surveys indicated a propensity for users to highly rank the idea of more and more detailed content data, as well as high levels of spatial detail, but when presented with the corresponding maps, they tended to favor a cleaner more simplified display. This finding underscores our conclusion that while user input is critical for developing successful tourist maps, cartographic training and skill is still required to achieve a quality product. Overall, the final map incorporating user input received overwhelmingly positive user reviews when compared to existing regional maps indicating that our iterative method of seeking user input at various stages of map development was successful, and facilitated creation of an improved product.
Investigation of Biotic and Abiotic Factors Affecting Double-Cropped Corn (Zea mays L.) Production in Virginia
Sforza, Peter M. (Virginia Tech, 2004-04-30)
Double-cropping of corn (Zea mays L.) for grain following the harvest of a small grain crop has been under evaluation in Virginia as an alternative cropping strategy (Brann and Pitman, 1997). To assess the potential constraints on late planted corn imposed by insects and diseases, double-cropped corn was evaluated in field experiments in Montgomery County, Virginia from 1998 to 2000. Factors included two near-isoline hybrids (NK4640 and NK4640Bt), insecticides at planting (tefluthrin in all years, 1998-2000; and imidacloprid in 1999 and 2000), and fungicide treatments (azoxystrobin or propiconazole). Response variables included yield, moisture at harvest, grain test weight, damage by European corn borer (Ostrinia nubilalis), damage by corn earworm (Heliothis zea), disease progress curves for gray leaf spot Cercospora zeae-maydis), and number of plants exhibiting virus symptoms. The Bt hybrid performed significantly better than the non-Bt hybrid for yield and test weight in double-cropped corn in 1998 and 2000, but not in 1999. A spatially referenced site suitability analysis was performed for full season and double-cropped corn in Virginia using weighted abiotic factors and constraints. Thornthwaite potential evapotranspiration (PET) and PET minus precipitation were used to identify areas of the state having a lower average moisture deficit during the silking months for double-cropped corn compared to full-season corn. It is concluded that double-cropped corn production is a viable option in Virginia where abiotic factors are not constraining, particularly growing season length and moisture availability during the sensitive stages of development.
New Opportunities in Crowd-Sourced Monitoring and Non-government Data Mining for Developing Urban Air Quality Models in the US
Lu, Tianjun (Virginia Tech, 2020-05-15)
Ambient air pollution is among the top 10 health risk factors in the US. With increasing concerns about adverse health effects of ambient air pollution among stakeholders including environmental scientists, health professionals, urban planners and community residents, improving air quality is a crucial goal for developing healthy communities. The US Environmental Protection Agency (EPA) aims to reduce air pollution by regulating emissions and continuously monitoring air pollution levels. Local communities also benefit from crowd-sourced monitoring to measure air pollution, particularly with the help of rapidly developed low-cost sampling technologies. The shift from relying only on government-based regulatory monitoring to crowd-sourced effort has provided new opportunities for air quality data. In addition, the fast-growing data sciences (e.g., data mining) allow for leveraging open data from different sources to improve air pollution exposure assessment. My dissertation investigates how new data sources of air quality (e.g., community-based monitoring, low-cost sensor platform) and model predictor variables (e.g., non-government open data) based on emerging modeling approaches (e.g., machine learning [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring data based on neighboring land use and geographic variables. I explore the use of crowd-sourced low-cost monitoring data, new/open dataset from government and non-government sponsored platforms, and emerging modeling techniques to develop LUR models in the US. I focus on testing whether: (1) air quality data from community-based monitoring is feasible for developing LUR models, (2) air quality data from non-government crowd-sourced low-cost sensor platforms could supplement regulatory monitors for LUR development, and (3) new/open data extracted from non-government sponsored platforms could serve as alternative datasets to traditional predictor variable sources (e.g., land use and geographic features) in LUR models. In Chapter 3, I developed LUR models using community-based sampling (n = 50) for 60 volatile organic compounds (VOC) in the city of Minneapolis, US. I assessed whether adding area source-related features improves LUR model performance and compared model performance using variables featuring area sources from government vs. non-government sponsored platforms. I developed three sets of models: (1) base-case models with land use and transportation variables, (2) base-case models adding area source variables from local business permit data (government sponsored platform), and (3) base-case models adding Google point of interest (POI) data for area sources. Models with Google POI data performed the best; for example, the total VOC (TVOC) model had better goodness-of-fit (adj-R2: 0.56; Root Mean Square Error [RMSE]: 0.32 µg/m3) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). This work suggests that VOC LUR models can be developed using community-based samples and adding Google POI could improve model performance as compared to using local business permit data. In Chapter 4, I evaluated a national LUR model using annual average PM2.5 concentrations from low-cost sensors (i.e., PurpleAir platform) in 6 US urban areas (n = 149) and tested the feasibility of using low-cost sensor data for developing LUR models. I compared LUR models using only the PurpleAir sensors vs. hybrid LUR models (combining both the EPA regulatory monitors and the PurpleAir sensors). I found that the low-cost sensor network could serve as a promising alternative to fill the gaps of existing regulatory networks. For example, the national regulatory monitor-based LUR (i.e., CACES LUR developed as part of the Center for Air, Climate, and Energy Solutions) may fail to capture locations with high PM2.5 concentrations and the within-city spatial variability. Developing LUR models using the PurpleAir sensors was reasonable (PurpleAir sensors only: 10-fold CV R2 = 0.66, MAE = 2.01 µg/m3; PurpleAir and regulatory monitors: R2 = 0.85, MAE = 1.02 µg/m3). I also observed that incorporating PurpleAir sensor data into LUR models could help capture within-city variability and merit further investigation on areas of disagreement with the regulatory monitors. This work suggests that the use of crowd-sourced low-cost sensor networks for LUR models could potentially help exposure assessment and inform environmental and health policies, particularly for places (e.g., developing countries) where regulatory monitoring network is limited. In Chapter 5, I developed national LUR models to predict annual average concentrations of 6 criteria pollutants (NO2, PM2.5, O3, CO, SO2 and PM10) in the US to compare models using new data (Google POI, Google Street View [GSV] and Local Climate Zone [LCZ]) vs. traditional geographic variables (e.g., road lengths, area of built land) based on different modeling approaches (partial least square [PLS], stepwise regression and machine learning [ML] with and without Kriging effect). Model performance was similar for both variable scenarios (e.g., random 10-fold CV R2 of ML-kriging models for NO2, new vs. traditional: 0.89 vs. 0.91); whereas adding the new variables to the traditional LUR models didn't necessarily improve model performance. Models with kriging effect outperformed those without (e.g., CV R2 for PM2.5 using the new variables, ML-kriging vs. ML: 0.83 vs. 0.67). The importance of the new variables to LUR models highlights the potential of substituting traditional variables, thus enabling LUR models for areas with limited or no data (e.g., developing countries) and across cities. The dissertation presents the integration of new/open data from non-government sponsored platform and crowd-sourced low-cost sensor networks in LUR models based on different modeling approaches for predicting ambient air pollution. The analyses provide evidence that using new data sources of both air quality and predictor variables could serve as promising strategies to improve LUR models for tracking exposures more accurately. The results could inform environment scientists, health policy makers, as well as urban planners interested in promoting healthy communities.
A Novel Level-of-Detail Technique for Virtual City Environments: Design and Evaluation
Singh, Ankit (Virginia Tech, 2012-04-26)
Virtual City Environments (VCEs) and Mirror Worlds can be a useful resource for communities such as the local government, researchers and the general public to collaborate on tasks like town planning, threat assessment, commerce and research. There are open standards like Extensible 3D (X3D, which represents 3D graphics) and CityGML (a Geography Markup Language to manage 3D building data). These standards are royalty-free and used to create, manage, share and portray such environments. However, there are critical challenges to delivering such complex and detailed Mirror Worlds in real-time. In this work, we focus on runtime data structures and performance for Level-of-Detail (LOD) management and real-time portrayal. We begin with a VCE defined in existing semantic models such as the CityGML specification. We implement and evaluate a novel X3D-based Level-of-Detail technique called ProxyPrismLOD, which leverages the CityGML standard of a 4-step LOD hierarchy. For switching between different models of the same object at near ranges, our LOD technique uses a custom shape we call a ProxyPrism to optimally encapsulate irregularly and asymmetrically shaped building models. First, we ran a user study to understand the visual dynamics of range-based LOD switching. Specifically, we evaluated several scaling factors for an exponential range cutoff function. The function is based on the model's size as well as the environment density. In this experiment, participants rated "visual granularity" and "distraction" levels of the LOD technique over two Software Field-of-View (sFOV) conditions. A scaling factor of Beta = 3 was determined. Second, we ran a series of simulations to study the performance benefits of ProxyPrismLOD technique over the basic range-based LOD. We observed performance benefits up to 7.46% in terms of overall Frames-per-Seconds (FPS) on the models we tested.
Operationalizing Scale in Watershed-based Stormwater Management
Adams, Erica Elaine (Virginia Tech, 2011-04-29)
Watershed-based stormwater management (WSM) has been proposed as more effective for stormwater management than traditional methods of controlling stormwater, which are carried out based on jurisdictional lines at the parcel-scale. Because WSM considers the watershed as a total unit, this method is considered to be more effective in reducing problems associated with stormwater management including environmental degradation and flooding. However, larger watersheds encompass smaller watersheds, and therefore WSM can be implemented at a wide range of scales. There has been little research on what scale is most appropriate, and more specifically, only a modest amount of work has taken stakeholder opinion into account. The specific objectives of this study are to determine: 1) if watershed scale is an important factor in WSM, 2) whether stakeholder opinion has an effect on the appropriate scale used in WSM, and 3) what scale is most appropriate for WSM, if scale is an important factor. To meet these objectives, we delineated sub-watersheds within a watershed in southwestern Virginia, surveyed stakeholders within the watershed on their opinions of stormwater management methods, and compared the results at both watershed scales using statistical tests and decisions support software. The results of this study have important implications for geographic scale in WSM as well as the use of qualitative data in determining appropriate geographic scale in matters of implementation in the field of planning.
The Plant Disease Clinic and Weed Identification Laboratory 1999 Annual Report
(Virginia Tech. Plant Pathology, Physiology, and Weed Science Department, 1999)
This is the 1999 annual report for the Plant Disease Clinic at Virginia Tech. The clinic provides plant disease diagnostic services to Virginia Cooperative Extension agents.
The Plant Disease Clinic and Weed Identification Laboratory 2001 Annual Report
(Virginia Tech. Plant Pathology, Physiology, and Weed Science Department, 2001)
This is the 2001 annual report for the Plant Disease Clinic at Virginia Tech. The clinic provides plant disease diagnostic services to Virginia Cooperative Extension agents.
Report to the Virginia Department of Veterans Services Virginia Wounded Warrior Program: Assessing the Experiences, Supportive Service Needs and Service Gaps of Veterans in the Commonwealth of Virginia Final Report
Still, George; Dickerson, Thomas; White, Nancy; Sforza, Peter M.; Schroeder, Aaron; Willis-Walton, Susan M. (Virginia Tech Institute for Policy & Governance, 2010-08-05)
The Commonwealth of Virginia is the home to over 800,000 veterans who have served in conflicts ranging from World War II to the current engagements in the gulf region, Operation Iraqi Freedom (OIF/Iraq) and Operation Enduring Freedom (OEF/Afghanistan). The Virginia Wounded Warrior Program has been charged with coordinating and facilitating the services that are needed by Virginia’s veterans who have served in the United States military. In order to evaluate how to best serve and facilitate services for these veterans, the VWWP has commissioned a needs assessment of Virginia’s veterans that is summarized in this report.
The Scalability of X3D4 PointProperties: Benchmarks on WWW Performance
Sun, Yanshen (Virginia Tech, 2020-09-29)
With the development of remote sensing devices, it becomes more and more convenient for individual researchers to acquire high-resolution point cloud data by themselves. There have been plenty of online tools for researchers to exhibit their work. However, the drawback of existing tools is that they are not flexible enough for the users to create 3D scenes of a mixture of point-based and triangle-based models. X3DOM is a WebGL-based library built on Extensible 3D (X3D) standard, which enables users to create 3D scenes with only a little computer graphics knowledge. Before X3D 4.0 Specification, little attention has been paid to point cloud rendering in X3DOM. PointProperties, an appearance node newly added in X3D 4.0, provides point size attenuation and texture-color mixing effects to point geometries. In this work, we propose an X3DOM implementation of PointProperties. This implementation fulfills not only the features specified in X3D 4.0 documentation, but other shading effects comparable to the effects of triangle-based geometries in X3DOM, as well as other state-of-the-art point cloud visualization tools. We also evaluate the performances of some of these effects. The result shows that a general laptop is able to handle most of the examined conditions in real-time.
Understanding Attitudes and Perceptions For Civil War Battlefield, Interpretive Images
Abu Bakar, Shamsul (Virginia Tech, 2013-05-08)
Civil War images are important visual records that captured and depict the realities endured by the American people during the Civil War. These images are a powerful visual platform that depicts the vivid representation of past history. Images of Civil War are frequently used as interpretive media, particularly at historic battlefields to enhance the visitor experience and understanding. However, empirical studies of the characteristics of Civil War images that can influence visitors\' experience are limited. Using historic images of Civil War landscapes, this study identified visitors\' preferences and attitudes for Civil War images at five different American Civil War battlefields: Chickamauga and Chattanooga Chickamauga and Chattanooga National Military Park, Shiloh National Military Park, Manassas National Battlefield Park, Antietam National Battlefield, and Gettysburg National Military Park. For this study, the Content Identifying Method (CIM) and eye-tracking technology were used in understanding visitors\' preferences. The results indicate that visitors at historic battlefields prefer images that have a strong sense of active military activities and battle-related action. In addition visitors also preferred images that exhibit a high sense of mystery and are visually complex. The study also revealed that visitor background variables such as gender, age group, and ancestors who fought in the Civil War significantly influence visitor preference for Civil War images. Motivation variables such as interest in learning about "the people," "the military elements," "physical artifacts of the Civil War," and distance travelled to historic battlefields also significantly influence visitor preferences. In term of visitor attitudes towards gruesome images, the majority of the participants believe that these types of images are important visual media that can influence their visitation experience at historic battlefields. Eye-tracking technology was useful in revealing the content that attracted participant attention in some of the images, but not in other images. This study provides information that will be useful to park managers and interpretive designers regarding the characteristics of Civil War images that are important in developing interpretive media for the public and factors that may help in the process of customizing the visitor experience at historic battlefields.
Use of GIS in Radio Frequency and Positioning Applications
Jewell, Victoria Rose (Virginia Tech, 2014-09-12)
GIS are geoprocessing programs that are commonly used to store and perform calculations on terrain data, maps, and other geospatial data. GIS offers the latest terrain and building data as well as tools to process this data. This thesis considers three applications of GIS data and software: a Large Scale Radio Frequency (RF) Model, a Medium Scale RF Model, and Indoor Positioning. The Large Scale RF Model estimates RF propagation using the latest terrain data supplied in GIS for frequencies ranging from 500 MHz to 5 GHz. The Medium Scale RF Model incorporates GIS building data to model WiFi systems at 2.4 GHz for a range of up to 300m. Both Models can be used by city planners and government offcials, who commonly use GIS for other geospatial and geostatistical information, to plan wireless broadband systems using GIS. An Indoor Positioning Experiment is also conducted to see if apriori knowledge of a building size, location, shape, and number of floors can aid in the RF geolocation of a target indoors. The experiment shows that correction of a target to within a building's boundaries reduces the location error of the target, and the vertical error is reduced by nearly half.

Browsing by Author "Sforza, Peter M."

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