The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy and scaled to the entire population of each county and state. The research team are making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public in order to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.
The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.
To analyze data such as the US Federal Budget or characteristics of the student population of a University it is common to look for changes over time. This task can be made easier and more fruitful if the analysis is performed by grouping by attributes, such as by Agencies, Bureaus and Accounts for the Budget, or Ethnicity, Gender and Major in a University. We present TreeVersity2, a web based interactive data visualization tool that allows users to analyze change in datasets by creating dynamic hierarchies based on the data attributes. TreeVersity2 introduces a novel space filling visualization (StemView) to represent change in trees at multiple levels--not just at the leaf level. With this visualization users can explore absolute and relative changes, created and removed nodes, and each node's actual values, while maintaining the context of the tree. In addition, TreeVersity2 provides overviews of change over the entire time period, and a reporting tool that lists outliers in textual form, which helps users identify the major changes in the data without having to manually setup filters. We validated TreeVersity2 with 12 case studies with organizations as diverse as the National Cancer Institute, Federal Drug Administration, Department of Transportation, Office of the Bursar of the University of Maryland, or eBay. Our case studies demonstrated that TreeVersity2 is flexible enough to be used in different domains and provide useful insights for the data owners. A TreeVersity2 demo can be found at https://treeversity.cattlab.umd.edu.
Transportation is the backbone of the civilization and the reason for the economic prosperity. There's serious money in our transportation infrastructure, research, policy, data collection, and, yes, software and other IT systems. The paper presents a highlevel introduction to current visualization research for transportation, discuss research opportunities, and encourage the CG community to get involved. It briefly covers transportation data visualization, wide-area real-time simulation, visualizing and mining archived data, massively multiplayer online games (MMOGs), and even virtual design and construction.
Transportation systems are being monitored at an unprecedented scope, which is resulting in tremendously detailed traffic and incident databases. Although the transportation community emphasizes developing standards for storing these incident data, little effort has been made to design appropriate visual analytics tools to explore the data, extract meaningful knowledge, and represent results. Analyzing these large multivariate geospatial data sets is a nontrivial task. A novel, web-based, visual analytics tool called Fervor is proposed as an application that affords sophisticated, yet user-friendly, analysis of transportation incident data sets. Interactive maps, histograms, two-dimensional plots, and parallel coordinates plots are four featured visualizations that are integrated to allow users to interact simultaneously with and see relationships among multiple visualizations. Using a rich set of filters, users can create custom conditions to filter data and focus on a smaller data set. However, because of the multivariate nature of the data, finding interesting relationships can be a time-consuming task. Therefore, a rank-by-feature framework has been adopted and further expanded to quantify the strength of relationships among the different fields describing the data. In this paper, transportation incident data collected by the Maryland State Highway Administration's CHART program are used; however, the tool can be easily modified to accept other transportation data sets.
Traffic congestion and overall performance monitoring of roadways continues to be a major initiative of departments of transportation and planning boards nationwide. Information from traffic monitoring programs has direct relevance to both analysts and the general public. Traditional data analysis tools and methods in this area fail to connect congestion data with incident and event data and thus make the task of determining the causes of congestion difficult in many cases. As more traffic data are collected, the need for tools that can facilitate effective visualization of this data, both archived and real time, is becoming increasingly apparent. The proposed congestion and incident scanner tool is a web-based application that affords dynamic and interactive analysis of traffic congestion conditions. The tool provides an intuitive, minimalistic interface for generating congestion performance visualizations for specific date ranges and locations. These visualizations improve on existing tools by allowing users to interact with and manipulate the visualizations to better highlight specific areas of interest. The integration of incident and traffic event data with visualization allows users to easily correlate congestion abnormalities with possible causes as well as evaluate the full effects of events, such as roadwork and major incidents, on traffic conditions.
Transportation agencies have invested significantly in extensive closed-circuit television (CCTV) systems to monitor freeways in urban areas. While thes systems have proven to be very effective in supporting incident management, they do not support the collection of quantitative measures of traffic conditions. Instead, they simply provide images that must be interpreted by trained operators. While there are several video image vehicle detection systems (VIVDS) on the market that have the capability to automatically derive traffic measures fro video imagery, these systems require the installation of fixed-position cameras. Thus, they have not been integrated with the existing moveable CCTV cameras. VIVDS camera positioning and calibration challenges were addressed and a prototype machine-vision system was developed that successfully integrated existing moveable CCTV cameras with VIVDS. Results of testing the prototype are presentedindicating that when the camera’s initial zoom level was kept between ×1 and ×1.5, the camera consistently could be returned to its original position with a repositioning accuracy of less than 0.03 to 0.1 regardless of the camera’s displaced pan, tilt, or zoom settings at the time of repositioning. This level of positional accuracy when combined with a VIVDS resulted in vehicle count errors of less than 1%.
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