324 research outputs found
A collection of global geotagged tweets with COVID-19 related mentions since January 2020
No full textA dataset of over 1.97 million global geotagged tweets that contain COVID-19 related keywords or hashtags, updated weekly from January 15 to December 31, 2020, as well as the summary of totals by national and subnational levels. All individual tweet data are geotagged at national levels, and US tweets are geotagged at subnational levels. (2021-11-30
Estimation of Twitter user demographics in the USA, 2014
No full textThe dataset contains the estimated demographics of 3,775,014 Twitter users in the continental USA in 2014, including gender, age, race/ethnicity, and county of residence of each Twitter user. The codes for estimating Twitter user demographics were also enclosed; the codes were designed for analyzing raw Twitter data with user profile information including username, screen name, profile image, and geo-locations. Twitter users were anonymized to protect their privacy per the data user agreement of Twitter, Inc. Twitter users in the shared data set were anonymized
A collection of global geotagged tweets with COVID-19 related mentions in 2020.
No full textA dataset of over 1.75 million global geotagged tweets that contain COVID-19 related keywords or hashtags, updated weekly from January 15 to December 31, 2020, as well as the summary of totals by national and subnational levels. All individual tweet data are geotagged at national levels, and US tweets are geotagged at subnational levels
Outlier Detection and Comparison of Origin-Destination Flows Using Data Depth
No full textAdvances in location-aware technology have resulted in massive trajectory data. Origin-destination (OD) trajectories provide rich information on urban flow and transport demand. This study describes a new method for detecting OD flows outliers and conducting hypothesis testing between two OD flow datasets in terms of the variations of spatial extent, that is, spread. The proposed method is based on data depth, which measures the centrality and outlyingness of a point with respect to a given dataset in R^d. Based on the center-outward ordering property, the proposed method analyzes the underlying characteristics of OD flows, such as location, outlyingness, and spread. The ability of the method to detect OD anomalies is compared with that of the Mahalanobis distance approach, and an F-test is used to verify the difference in scale. Empirical evaluation has demonstrated that our method effectively identifies OD flows outliers in an interactive way. Furthermore, the method can provide new perspectives such as spatial extent by considering the overall structure of data when comparing two different OD flows in terms of scale
Supplemental Material, sj-pptx-4-gsj-10.1177_21925682211027833 - Effect of Zoledronic Acid on the Vertebral Body Bone Mineral Density After Instrumented Intervertebral Fusion in Postmenopausal Women With Osteoporosis
No full textSupplemental Material, sj-pptx-4-gsj-10.1177_21925682211027833 for Effect of Zoledronic Acid on the Vertebral Body Bone Mineral Density After Instrumented Intervertebral Fusion in Postmenopausal Women With Osteoporosis by Junjun Fan, Tao Liu, Xin Dong, Siguo Sun, Hongtao Zhang, Chunbao Yang, Xin Yin, Bo Liao and Xiaoxiang Li in Global Spine Journal</p
Exploring Chinese Consumers’ Perception and Potential Acceptance of Cell-Cultured Meat and Plant-Based Meat: A Focus Group Study and Content Analysis
No full text(1) Background: In recent years, meat alternatives, including plant-based and animal cell-cultured meat, have attracted substantial interest among Chinese food science researchers and consumers, prompting considerable debate; (2) Methods: This study utilizes qualitative research methods, specifically focus group interviews with 59 participants across five administrative regions and seven cities in China, to explore consumer knowledge, perceptions, and potential acceptance of meat substitutes; (3) Results: The findings reveal that Chinese consumers generally exhibit a low level of understanding of new meat substitutes, particularly animal cell-cultured meat. Although participants acknowledge the potential environmental, resource-saving, and animal welfare benefits associated with meat substitutes, they also express concerns about perceived risks, such as artificial taste, high costs, market monopolization, diminished consumer welfare, and adverse impacts on traditional animal husbandry and employment. Despite a willingness to try meat substitutes, the regular purchase and consumption of these remain limited. The acceptance of meat substitutes is influenced by factors including personal characteristics, price, safety, and the authenticity of taste; (4) Conclusions: The study concludes that legislative support, technological advancements in production and regulation, price reductions, and the establishment of a robust traceability system may enhance consumer confidence and acceptance of meat substitutes in China
Enhancing Inverse Modeling in Groundwater Systems through Machine Learning: A Comprehensive Comparative Study
No full text<p><span>1. Surrogate models</span></p>
<p><span>FCDNN.rar: codes and data for FCDNN surrogate model method</span></p>
<p><span>MSVR.rar: codes and data for surrogate model method</span></p>
<p><span>LeNet.rar: codes and data for LeNet surrogate model method</span></p>
<p><span>ResNet.rar: codes and data for ResNet surrogate model method</span></p>
<p><span>2. Optimization algorithms</span></p>
<p><span>Optimization algorithm comparison.rar: codes and data for optimization algorithm comparison</span></p>
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Assessing the validity of SafeGraph data for visitor monitoring in Yellowstone National Park
No full textYun Liang is a Ph.D. student in the Department of Recreation, Park and Tourism Management at the Pennsylvania State University. Her academic interest is to combine social media data analysis with conservation in national parks or protected areas.
Junjun Yin, Ph.D., is an assistant research professor at the Social Science Research Institute and Population Research Institute, Pennsylvania State University. His research interests center on computational GIScience with a focus on developing geospatial Big Data analytics for studying urban and population dynamics concerning urban mobility, accessibility, and sustainability.
Bing Pan, Ph.D., is an Associate Professor in the Department of Recreation, Park, and Tourism Management at Pennsylvania State University, University Park. His research interests include data analytics, tourism big data, destination marketing, and benefits of travel.
Michael Lin is a Ph.D. student in the School of Hospitality Management at the Pennsylvania State University. His academic interest is strategy and finance.
Guangqing Chi, Ph.D., is an associate professor of rural sociology and demography and director of the Computational and Spatial Analysis Core of the Pennsylvania State University. His research is focused on socio-environmental systems by developing spatial and Big Data analytic methods. He leads generalizing Twitter data for social science research.Monitoring visitor demographics and temporal visitation patterns will help park managers allocate resources, develop infrastructure, and predict the demands of visitors. Previous studies have only validated temporal visitation patterns with big data and traditional survey data, while research on validation national park visitor demographics using mobile data is scant. This study compares SafeGraph data (a type of mobile Location-based Service data) and survey/count data, assessing visitor demographics and temporal visitation patterns in Yellowstone National Park. The comparison between two data sources suggests that SafeGraph data can serve as an additional and complementary source of information to traditional visitor use study/count data. However, biases of SafeGraph data, such as data at an aggregation level and data only in the United States, resulting in differences compared to traditional survey/count data. This study contributes to understanding visitors in national parks by validating a new data source
Exploring the topological patterns of urban street networks from analytical and visual perspectives
No full textResearch interests in the studies of complex systems have been booming in many disciplines for the last decade. As the nature of geographic environment is a complex system, researches in this field are anticipated. In particular, the urban street networks in the Geographic Information System (GIS) as complex networks are brought forth for the thesis study. Meanwhile, identifying the scale-free property, which is represented as the power law distribution from a mathematical perspective, is a hot topic in the studies of complex systems. Many previous studies estimated the power law distributions with graphic method, which used linear regression method to identify the exponent value and estimate the quality that the power law fits to the empirical data. However, such strategy is considered to cause inaccurate results and lead to biased judgments. Whereas, the Maximum Likelihood Estimation (MLE) and the Goodness of fit test based on Kolmogorov-Smironv (KS) statistics will provide more solid and trustable results for the estimations. Therefore, this thesis addresses these updated methods exploring the topological patterns of urban street networks from an analytical perspective, which is estimating the power law distributions for the connectivity degree and length of the urban streets. Simultaneously, this thesis explores the street networks from a visual perspective as well. The visual perspective adopts the large network visualization tool (LaNet-vi), which is developed based on the k-core decomposition algorithm, to analyze the cores of the urban street networks. By retrieving the spatial information of the networks from GIS, it actually enables us to see how the urban street networks decomposed topologically and spatially. In particular, the 40 US urban street networks are reformed as natural street networks by using three "natural street" models. The results from analytical perspective show that the 80/20 principle still exists for both the street connectivity degree and length qualitatively, which means around 20% natural streets in each network have a connectivity degree or length value above the average level, while the 80% ones are below the average. Moreover, the quantitative analysis revealed the fact that most of the distributions from the street connectivity degree or length of the 40 natural street networks follow a power law distribution with an exponential cut-off. Some of the rest cases are verified to have power law distributions and some extreme cases are still unclear for identifying which distribution form to fit. The comparisons are made to the power law statement from previous study which used the linear regression method. Moreover, the visual perspective not only provides us the chance to see the inner structures about the hierarchies and cores of the natural street networks topologically and spatially, but also serves as a reflection for the analytical perspective. Such relationships are discussed and the possibility of combining these two aspects are pointed out. In addition, the future work is also proposed for making better studies in this field
Mobile 2D and 3D Spatial Query Techniques for the Geospatial Web
No full textThe increasing availability of abundant geographically referenced information in the Geospatial Web provides a variety of opportunities for developing value-added LBS applications. However, large data volumes of the Geospatial Web and small mobile device displays impose a data visualization problem, as the amount of searchable information overwhelms the display when too many query results are returned. Excessive returned results clutter the mobile display, making it harder for users to prioritize information and causes confusion and usability problems. Mobile Spatial Interaction (MSI) research into this “information overload” problem is ongoing where map personalization and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real-world to the processing/display limitations of mobile devices. In this thesis, we propose that another way to filter this information is to intelligently refine the search space. 3DQ (3-Dimensional Query) is our novel MSI prototype for information discovery on today’s location and orientation-aware smartphones within 3D Geospatial Web environments. Our application incorporates human interactions (interpreted from embedded sensors) in the geospatial query process by determining the shape of their actual visibility space as a query “window” in a spatial database, e.g. Isovist in 2D and Threat Dome in 3D. This effectively applies hidden query removal (HQR) functionality in 360º 3D that takes into account both the horizontal and vertical dimensions when calculating the 3D search space, significantly reducing display clutter and information overload on mobile devices. The effect is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s 3D field-of-view
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