Tobacco where you live : mapping techniques

The goal of Tobacco Where You Live is to empower tobacco control program managers, staff, and partners to understand how commercial tobacco use varies within their communities, overcome challenges, and reduce disparities. Each Tobacco Where You Live brief will cover a topic important to reduce commercial tobacco use in communities with the highest prevalence.Mapping Techniques focuses on how to create, share, and use commercial tobacco prevention and control maps. Mapping allows programs to focus their efforts where they can have the greatest impact. Maps can help you:\u2022 Understand community trends and show disparities\u2022 Find gaps in program and policy implementation\u2022 Educate decision makers and the public\u2022 Model potential strategies\u2022 Evaluate interventionsThe Best Practices User Guides project is funded by CDC contract 75D30120C09195. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC. References to non-CDC sites and the use of advertisements and images do not constitute or imply endorsement of these organizations or their programs by CDC or the U.S. Department of Health and Human Services. CDC is not responsible for the content of pages found at external sites. URL addresses listed were current as of the date of publication.This brief was produced for the Centers for Disease Control and Prevention by the Center for Public Health Systems Science at the Brown School at Washington University in St. Louis.Suggested citation: Centers for Disease Control and Prevention. Tobacco Where You Live: Mapping Techniques. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, 2022.best-practices-mapping-techniques.pdfCDC contract 75D30120C0919

Developing Accessible Collection and Presentation Methods for Observational Data

The processes of collecting, cleaning, and presenting data are critical in ensuring the proper analysis of data at a later date. An opportunity exists to enhance the data collection and presentation process for those who are not data scientists – such as healthcare professionals and businesspeople interested in using data to help them make decisions. In this work, creating an observational data collection and presentation tool is investigated, with a focus on developing a tool prioritizing user-friendliness and context preservation of the data collected. This aim is achieved via the integration of three approaches to data collection and presentation.In the first approach, the collection of observational data is structured and carried out via a trichotomous, tailored, sub-branching scoring (TTSS) system. The system allows for deep levels of data collection while enabling data to be summarized quickly by a user via collapsing details. The system is evaluated against the stated requirements of usability and extensibility, proving the latter by providing examples of various evaluations created using the TTSS framework.Next, this approach is integrated with automated data collection via mobile device sensors, to facilitate the efficient completion of the assessment. Results are presented from a system used to combine the capture of complex data from the built environment and compare the results of the data collection, including how the system uses quantitative measures specifically. This approach is evaluated against other solutions for obtaining data about the accessibility of a built environment, and several assessments taken in the field are compared to illustrate the system’s flexibility. The extension of the system for automated data capture is also discussed.Finally, the use of accessibility information for data context preservation is integrated. This approach is evaluated via investigation of how accessible media entries improve the quality of search for an archival website. Human-generated accessibility information is compared to computer-generated accessibility information, as well as simple reliance on titles/metadata. This is followed by a discussion of how improved accessibility can benefit the understanding of gathered observational data’s context

Big data analytics and processing for urban surveillance systems

Urban surveillance systems will be more demanding in the future towards smart city to improve the intelligence of cities. Big data analytics and processing for urban surveillance systems become increasingly important research areas because of infinite generation of massive data volumes all over the world. This thesis focused on solving several challenging big data issues in urban surveillance systems. First, we proposed several simple yet efficient video data recoding algorithms to be used in urban surveillance systems. The key idea is to record the important video frames when cutting the number of unimportant video frames. Second, since the DCT based JPEG standard encounters problems such as block artifacts, we proposed a very simple but effective method which results in better quality than widely used filters while consuming much less computer CPU resources. Third, we designed a novel filter to detect either the vehicle license plates or the vehicles from the images captured by the digital camera imaging sensors. We are the first to design this kind of filter to detect the vehicle/license plate objects. Fourth, we proposed novel grate filter to identify whether there are objects in these images captured by the cameras. In this way the background images can be updated from time to time when no object is detected. Finally, we combined image hash with our novel density scan method to solve the problem of retrieving similar duplicate images

5Ws model for big data analysis and visualization

Big Data, which contains image, video, text, audio and other forms of data, collected from multiple datasets, is difficult to process using traditional database management tools or applications. In this paper, we establish the 5Ws model by using 5Ws data dimension for Big Data analysis and visualization. 5Ws data dimension stands for, What the data content is, Why the data occurred, Where the data came from, When the data occurred, Who received the data and How the data was transferred. This framework not only classifies Big Data attributes and patterns, but also establishes density patterns that provide more analytical features. We use visual clustering to display data sending and receiving densities which demonstrate Big Data patterns. The model is tested by using the network security ISCX2012 dataset. The experiment shows that this new model with clustered visualization can be efficiently used for Big Data analysis and visualization. © 2013 IEEE