DOCSLIB.ORG

Center for Advanced Multimodal Mobility Solutions and Education EVALUATING the POTENTIAL USE of CROWDSOURCED BICYCLE DATA IN

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Center for Advanced Multimodal Mobility Solutions and Education EVALUATING the POTENTIAL USE of CROWDSOURCED BICYCLE DATA IN Center for Advanced Multimodal Mobility Solutions and Education Project ID: 2018 Project 03 EVALUATING THE POTENTIAL USE OF CROWDSOURCED BICYCLE DATA IN NORTH CAROLINA Final Report by Wei Fan (ORCID ID: https://orcid.org/0000-0001-9815-710X) Zijing Lin (ORCID ID: https://orcid.org/0000-0001-6529-5725) Wei Fan, Ph.D., P.E. Director, USDOT CAMMSE University Transportation Center Professor, Department of Civil and Environmental Engineering The University of North Carolina at Charlotte EPIC Building, Room 3261, 9201 University City Blvd, Charlotte, NC 28223 Phone: 1-704-687-1222; Email: [email protected] for Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE @ UNC Charlotte) The University of North Carolina at Charlotte 9201 University City Blvd Charlotte, NC 28223 September 2019 i ii ACKNOWLEDGEMENTS This project was funded by the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE @ UNC Charlotte), one of the Tier I University Transportation Centers that were selected in this nationwide competition, by the Office of the Assistant Secretary for Research and Technology (OST-R), U.S. Department of Transportation (US DOT), under the FAST Act. The authors are also very grateful for all of the time and effort spent by DOT and industry professionals to provide project information that was critical for the successful completion of this study. DISCLAIMER The contents of this report reflect the views of the authors, who are solely responsible for the facts and the accuracy of the material and information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation University Transportation Centers Program in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. The contents do not necessarily reflect the official views of the U.S. Government. This report does not constitute a standard, specification, or regulation. iii iv Table of Contents EXECUTIVE SUMMARY ......................................................................................................... xi Chapter 1. Introduction.................................................................................................................1 1.1 Problem Statement .................................................................................................................1 1.2 Objectives ..............................................................................................................................3 1.3 Expected Contributions ..........................................................................................................3 1.4 Report Overview ....................................................................................................................3 Chapter 2. Literature Review .......................................................................................................5 2.1 Introduction ............................................................................................................................5 2.2 Data Collection Methods .......................................................................................................5 2.2.1 Crowdsourcing ............................................................................................................................ 5 2.2.2 Open Data .................................................................................................................................... 6 2.2.3 Big Data ...................................................................................................................................... 6 2.2.4 Stated Preference Survey and Revealed Preference Survey ........................................................ 7 2.2.5 Traditional Survey Methods ........................................................................................................ 8 2.3 Smartphone Crowdsourcing Applications .............................................................................8 2.3.1 CycleTracks ................................................................................................................................. 8 2.3.2 AggieTrack .................................................................................................................................. 8 2.3.3 Cycle Atlanta ............................................................................................................................... 9 2.3.4 RenoTracks.................................................................................................................................. 9 2.3.5 Mon RésoVélo ............................................................................................................................. 9 2.3.6 MapMyRide ................................................................................................................................ 9 2.3.7 Strava ......................................................................................................................................... 10 2.3.8 MyTracks .................................................................................................................................. 10 2.3.9 ORcycle ..................................................................................................................................... 10 2.4 Potential Use of Crowdsourced Data ...................................................................................12 2.4.1 Crowdsourced data for route choice analysis ............................................................................ 12 2.4.2 Crowdsourced data for bicycle volume estimation ................................................................... 16 2.4.3 Crowdsourced data for other research purposes ........................................................................ 17 2.5 Summary ..............................................................................................................................17 Chapter 3. Collecting Crowdsourced Data and Other Supporting Data ...............................19 3.1 Introduction ..........................................................................................................................19 3.2 Introduction to Strava ..........................................................................................................19 3.3 Strava Metro Delivery ..........................................................................................................20 3.3.1 Core Data .................................................................................................................................. 20 3.3.2 Roll-ups ..................................................................................................................................... 20 3.3.3 Reports ...................................................................................................................................... 21 3.4 Data View ............................................................................................................................21 3.4.1 Street ......................................................................................................................................... 21 3.4.2 Intersections .............................................................................................................................. 22 3.4.3 Origin & Destination ................................................................................................................. 23 v 3.4.4 Heat Map ................................................................................................................................... 24 3.5 Other supporting data ...........................................................................................................24 3.5.1 Manual Count Data ................................................................................................................... 24 3.5.2 Bicycle facilities ........................................................................................................................ 25 3.5.3 Population ................................................................................................................................. 26 3.5.4 Slope .......................................................................................................................................... 26 3.6 Summary ..............................................................................................................................27 Chapter 4. Data Descriptive Analyses ........................................................................................29 4.1 Introduction ..........................................................................................................................29 4.2 Strava Data Analysis ............................................................................................................29 4.2.1 Demographics ............................................................................................................................ 29 4.2.2 Trip purpose .............................................................................................................................. 30 4.2.3 Strava Count .............................................................................................................................. 31 4.3 Data Comparison .................................................................................................................37
Load more

Recommended publications
  • Crowd Cycling: Understanding Cyclist Behaviour Using the Mobile Tracking App Strava
    Crowd Cycling: Understanding cyclist behaviour using the mobile tracking app Strava Mark Dunleavy A research paper submitted to the University of Dublin, in partial fulfilment of the requirements for the degree of Master of Science Interactive Digital Media 2015 Declaration I declare that the work described in this research paper is, except where otherwise stated, entirely my own work and has not been submitted as an exercise for a degree at this or any other university. Signed: ____________________________ Mark Dunleavy 14 May 2015 Permission to lend and/or copy I agree that Trinity College Library may lend or copy this research paper upon request. Signed: ____________________________ Mark Dunleavy 14 May 2015 Acknowledgement I would like to thank my research supervisor Professor Glenn Strong for his guidance and encouragement during this research. I would also like to thank my wonderful wife Jennifer for her support and amazing MS Word skills. Summary Concerns over traffic congestion, climate change and increased morbidity and mortality due to the rising rate of obesity have led to the promotion of cycling as a means to address all these issues simultaneously. Governments and city planners have created policies, and reserved significant funds, to promote cycling as a mode of transport. Many cities, however, have evolved for motor vehicles with little consideration for cyclists. In order to ensure that the cycle- friendly policies are implemented as efficiently and effectively as possible, planners need to understand how cyclists behave, both as individuals and as a group. Current information on cyclist behaviour is derived mainly from surveys and bicycle counting studies, providing a very limited snapshot of cyclist behaviour in cities.
    [Show full text]
  • Alejandra Garcia Project Coordinator
    City Council Report City Council Meeting: November 1, 2016 Agenda Item: 3.D To: Mayor and City Council From: David Martin, Director, Planning and Community Development, Strategic & Transportation Planning Subject: Safe Routes to School Program Encouragement & Education Contract Award Recommended Action Staff recommends that the City Council: 1. Award RFP #88 to Alta Planning + Design, Inc., a Los Angeles based company for grant-funded Safe Routes to School Program Encouragement and Education; and 2. Authorize the City Manager to negotiate and execute an agreement with Alta Planning + Design, Inc. in an amount not to exceed $450,000 over a 2-year period, with future year funding contingent on Council budget approval. Executive Summary The City of Santa Monica aims to expand the reach of the Safe Routes to School (SRTS) program by providing a series of encouragement and education activities to five elementary schools: John Muir Elementary School, Santa Monica Alternative Schoolhouse (SMASH), Franklin Elementary School, McKinley Elementary School, and Edison Language Academy. These schools were not included in the City’s initial 2012- 2013 SRTS Pilot program. Encouragement and Education programs catalyze desired outcomes around walking and biking behavior, and complement physical changes in the public right-of-way. This project is designed to provide “shelf ready” products and tools that can be easily used to educate students and inform parents about walking and biking to school. It will continue successful encouragement programs like Kidical Mass and Bike It! Walk It!, and complete walking and biking assessments at each school campus. Efforts include strengthening the relationship with student families to help ensure program sustainability.
    [Show full text]
  • Connect Compatible Apps and Devices to Go365®
    EARN REWARDS Connect compatible apps and devices to Go365® Go365.com GCHJHTFEN 0621 Table of contents Compatible fitness devices and apps 2 Earn Points by type of tracker 3 • Earn Points using your device 3 • Earn Points using your mobile app 4 • Earn Points for virtual coaching apps 5 How to connect to Go365 6 Important details after you’ve connected 7 How to earn Points 8 • Verified workouts and bonus Points 8 • Virtual well-being coaching activities 9 • Additional Go365 activities 10 1 Compatible fitness devices and apps Manufacturer Device Expresso All Expresso devices are compatible Fitbit All Fitbit wearable devices are compatible Garmin All Garmin wearable devices are compatible All Garmin Edge devices are compatible iHealth Edge Misfit All Misfit wearable devices are compatible Withings All Withings wearable, scale and blood (formerly Nokia Health) pressure devices are compatible Polar All Polar devices are compatible Qardio All Qardio scale and blood pressure devices are compatible Compatible apps Apple Health Health IQ Samsung Health Craving to Quit Life Fitness MyLife Daily Burn MyFitnessPal (formerly Stop, Breathe & Eat Right Now Noom Think) Fitbit RunDouble C25k Strava Fitbit Premium RunKeeper Unwinding Anxiety References to products and equipment in this material are not an endorsement or warranty by Humana or Go365 of the products or equipment. The manufacturer of the products or equipment is solely responsible for defects with or problems arising out of the use of the products or equipment. Such references to products and equipment are used as examples of products and equipment that are compatible with Go365, of which are subject to change at any time without notice.
    [Show full text]
  • How to Sync Your Device to Staywell the Following Slides Include a Step-By-Step Guide to Get You Started with Tracking Your Fitness in My Staywell Platform
    How to Sync your Device to StayWell The following slides include a step-by-step guide to get you started with tracking your fitness in My StayWell Platform. Choose from the following options: Available connections: Connect a Device • Apple Health Kit • Fat Secret (iPhone or Apple Watch) • Fitbit Connect the Apple Health App • Garmin • Google Fit Connect the Google Fit App (Android) • HealthSpective • IHealth • MapMyFitness • MyFitnessPal • Misfit • Runkeeper • Strava • Vitadock • Withings How to Sync your Device 1. Click the sync icon in the top right-hand corner of the dashboard. 2. Choose source (e.g. Fitbit) 3. Enter login credentials and share all activity How to Sync the Apple Health App (iPhone or Apple Watch) It’s simple to sync your iPhone or Apple watch with the My StayWell app! Review the following step by step guide to get started with tracking your fitness in the My StayWell Platform! A few notes before getting started: - The StayWell Platform app is called “My StayWell” in the App Store. - These instructions are how to sync your Apple Health app with the My StayWell Platform app, so be sure to find the My StayWell app in the App store on your phone, download it, and log in before continuing with this guide. - Once your Apple Health app is synced with the My StayWell app, just log in to the My StayWell app regularly to see your Apple Health data automatically shared to your StayWell account. How to Sync the Apple Health App (iPhone or Apple Watch) Before getting started, make sure that you have downloaded the My StayWell app from the App store and have logged in.
    [Show full text]
  • Attachment 2 ATP FY 2021Q2
    ATTACHMENT 2 Active Transportation Program - Non-SB1 Project List PAED PS&E RW CON CON-NI PAED PS&E RW CON CON-NI CON CON-NI PPNO Cycle County District Agency Project Name Project Description Total Project Cost Programmed Programmed Programmed Programmed Programmed Allocated Allocated Allocated Allocated Allocated Award Award The proposed Active Transportation Plan ATP and SRTS Plan within the City 4901 1 LA 7 Inglewood, City of Active Transportation Plan and Safe Routes to School Plan boundaries, and will incorporate bicycle, pedestrian, ADA considerations and $ 486 $ - $ - $ - $ - $ 486 $ - $ - $ - $ - $ 486 09/29/2015 SRTS analysis of 8 of the 17 Inglewood Unified School District school sites. In the City of Indio - Surrounding Andrew Jackson ES - Pedestrian Infrastructure Improvements - Installation of new sidewalks including curb ramps and driveways 1144 1 RIV 8 Indio, City of Andrew Jackson Elementary Pedestrian Improvements $ 2,581 $ 21 $ 186 $ - $ 2,374 $ - $ 21 $ 186 $ - $ 2,374 $ - 05/02/2018 approaches, installation of enhanced crosswalks with bulb-outs, and speed feedback signs. Oak Parkway Trail Under Crossing and Johnny Cash Trail Construct a grade separated crossing of Natoma Street and connecting the Oak 1683 1 SAC 3 Folsom, City of $ 1,121 $ 35 $ 75 $ - $ 882 $ - $ 35 $ 75 $ - $ 882 $ - 07/24/2018 Connection Parkway Trail with both the Johnny Cash and Historic Powerhouse Canal Trails. A road diet and improve existing asphalt pathway. Replace approximately 4,000 Foster Road Side Panel Safe Routes to School Improvement 4935 1 LA 7 Norwalk, City of linear feet of 60 year old uneven, cracked and root buckled side panels, along $ 2,208 $ - $ 100 $ - $ 2,078 $ 30 $ - $ 100 $ - $ 2,078 $ 30 10/03/2017 10/03/2017 Project with 94 large bottle brush trees.
    [Show full text]
  • Exploring the Use of Mobile and Wearable Technology Among University Student Athletes in Lebanon: a Cross-Sectional Study
    sensors Article Exploring the Use of Mobile and Wearable Technology among University Student Athletes in Lebanon: A Cross-Sectional Study Marco Bardus 1, Cecile Borgi 2, Marwa El-Harakeh 2 , Tarek Gherbal 3,4, Samer Kharroubi 2 and Elie-Jacques Fares 2,* 1 Department of Health Promotion and Community Health, Faculty of Health Sciences, American University of Beirut, Beirut 1107 2020, Lebanon; [email protected] 2 Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences, American University of Beirut, Beirut 1107 2020, Lebanon; [email protected] (C.B.); [email protected] (M.E.-H.); [email protected] (S.K.) 3 University Sports, Office of Student Affairs, American University of Beirut, Beirut 1107 2020, Lebanon; [email protected] 4 Aman Hospital, Doha, Qatar * Correspondence: [email protected] Abstract: The markets of commercial wearables and health and fitness apps are constantly growing globally, especially among young adults and athletes, to track physical activity, energy expenditure and health. Despite their wide availability, evidence on use comes predominantly from the United States or Global North, with none targeting college student-athletes in low- and middle-income Citation: Bardus, M.; Borgi, C.; countries. This study was aimed to explore the use of these technologies among student-athletes at El-Harakeh, M.; Gherbal, T.; the American University of Beirut (AUB). We conducted a cross-sectional survey of 482 participants Kharroubi, S.; Fares, E.-J. Exploring (average age 20 years) enrolled in 24 teams during Fall 2018; 230 students successfully completed the Use of Mobile and Wearable the web-based survey, and 200 provided valid data.
    [Show full text]
  • 2012 San Francisco State of Cycling Report
    2012 San Francisco State of Cycling Report SFMTA | Municipal Transportation Agency Page 1 Edwin M. Lee | Mayor San Francisco Municipal Transportation Agency City and County of San Francisco Board of Supervisors The San Francisco Municipal Transportation Agency (SFMTA) is responsible for the planning, implementation, regulation, John Avalos | District 11 maintenance and operation of the multimodal transportation system in the City and County of San Francisco. The city’s David Campos | District 9 transportation system includes public transit, paratransit, streets, bicycle and pedestrian facilities, parking, signals, traffic controls, David Chiu | District 3 and taxi services. Carmen Chu | District 4 Malia Cohen | District 10 Board of Directors Tom Nolan | Chairman Sean Elsbernd | District 7 Jerry Lee | Vice-Chairman Mark Farrell | District 2 Leona Bridges Jane Kim | District 6 Cheryl Brinkman Eric Mar | District 1 Malcolm Heinicke Christina Olague | District 5 Joél Ramos Scott Wiener | District 8 Cristina Rubke Director of Transportation Edward Reiskin SFMTA Sustainable Streets Staff Peter Brown, State of Cycling Project Manager Matt Lasky, Co-Author Bond Yee Timothy Papandreou Darton Ito Anne Fritzler Bridget Smith Seleta Reynolds Craig Raphael Survey Consultant Corey, Canapary & Galanis This report is made possible by the San Francisco County Transportation Authority through a grant of Proposition K Local Transportation Sales Tax Funds. Page 2 Executive Summary The City and County of San Francisco is a national leader in The SFMTA’s 2012 State of Cycling Report benchmarks bicycle bicycle ridership, innovative cycling infrastructure, greenhouse gas volumes, bicycle safety and opinions about bicycle riding in San reductions and active living. Cities around the world are investing Francisco.
    [Show full text]
  • Cycles of Investment: Bicycle Infrastructure, Gentrification, and the Restructuring of the San Francisco Bay Area
    Environment and Planning A 2015, volume 47, pages 121 – 137 doi:10.1068/a130098p Cycles of investment: bicycle infrastructure, gentrification, and the restructuring of the San Francisco Bay Area John Stehlin Department of Geography, University of California, Berkeley, 507 McCone Hall, Berkeley, CA 94720, USA; e-mail [email protected] Received 16 July 2013; in revised form 11 July 2014 Abstract. Bicycling for transportation in American cities has grown dramatically in the past twenty years, symbolizing the return of capital investment and commercial vitality to formerly disinvested urban cores. The cycling ‘renaissance’ taking place to the greatest extent in gentrifying neighborhoods has been noted, but the processes relating cycling and gentrification have gone largely unexplored. This paper examines the early role that bicycle advocacy organizations in San Francisco played in articulating the specifically economic value of bicycle infrastructure investment. This narrative is now commonplace, and widely applied both to neighborhood revitalization and urban competition for ‘talent’. This alliance of bicycle advocacy with the ‘livable’ turn of gentrification raises serious questions for those who would pursue a more democratic and socially just politics of the bicycle. Keywords: cycling, gentrification, livability, infrastructure, San Francisco Introduction In recent years the bicycle has become an important symbol of the revitalization and ‘greening’ of American cities. San Francisco, birthplace of Critical Mass, enjoys a leadership role in this movement. While cycling in San Francisco remains in no sense dominant, 3.5% of residents commute to work by bike, and the city is steadily expanding and integrating its bicycle network in a push to reach a goal of 20% of all trips (work and nonwork) by bicycle by 2020.
    [Show full text]
  • The Effect of Physical Activity Apps on Physical Activity Behavior and Users’ Evaluation of Physical Activity Apps
    Wageningen University – Department of Social Sciences Chair Group Strategic Communication The Effect of Physical Activity Apps on Physical Activity Behavior and Users’ Evaluation of Physical Activity Apps 31 March 2016 MSc Thesis Strategic Communication (CPT-81333) MSc Applied Communication Science Laura Ploumen 1st Supervisor: Jorinde Spook 2nd Supervisor: Edith Feskens Abstract Background: A new development in the promotion of health and physical activity (PA) is the use of PA apps. This development brings along a new field of research. Despite the broad range of research in the previous years, there are still research gaps with regard to the effect of PA apps on behavior determinants and with regard to the users’ evaluation of PA apps. Objective: Determine what the effect is of the use of PA apps on PA and its’ determinants self-efficacy, outcome expectations, socio-structural factors, and self-regulation. In addition, an objective is to find out how Dutch adults evaluate PA apps and why they use it or do not use it. Methods: The Social Cognitive Theory (SCT) was the theoretical framework for this study. A cross-sectional study design was used, with 251 participants. Differences in determinants, PA and PA enjoyment between app users (N=63) and non-users (N=188) were measured using ANCOVA’s, adjusting for the covariates age and education. As exploratory research, mediation analyses were performed to get insight into the underlying mechanisms of the SCT model. Several apps were evaluated using a system usability score, an evaluation of behavior change techniques and open questions. Results: App users scored significantly higher than non-users on self-efficacy, outcome expectations, and self- regulation.
    [Show full text]
  • Securerun: Cheat-Proof and Private Summaries for Location-Based Activities Anh Pham, Kévin Huguenin, Igor Bilogrevic, Italo Dacosta, Jean-Pierre Hubaux
    SecureRun: Cheat-Proof and Private Summaries for Location-Based Activities Anh Pham, Kévin Huguenin, Igor Bilogrevic, Italo Dacosta, Jean-Pierre Hubaux To cite this version: Anh Pham, Kévin Huguenin, Igor Bilogrevic, Italo Dacosta, Jean-Pierre Hubaux. SecureRun: Cheat-Proof and Private Summaries for Location-Based Activities. IEEE Transactions on Mo- bile Computing, Institute of Electrical and Electronics Engineers, 2016, 15 (8), pp.2109-2123. 10.1109/TMC.2015.2483498. hal-01198596 HAL Id: hal-01198596 https://hal.archives-ouvertes.fr/hal-01198596 Submitted on 18 Sep 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. IEEE TRANSACTIONS ON MOBILE COMPUTING 1 SecureRun: Cheat-Proof and Private Summaries for Location-Based Activities Anh Pham, Student Member, IEEE, Kevin´ Huguenin, Member, IEEE, Igor Bilogrevic, Member, IEEE, Italo Dacosta, Member, IEEE, Jean-Pierre Hubaux, Fellow, IEEE Abstract—Activity-tracking applications, where people record and upload information about their location-based activities (e.g., the routes of their activities), are increasingly popular. Such applications enable users to share information and compete with their friends on activity-based social networks but also, in some cases, to obtain discounts on their health insurance premiums by proving they conduct regular fitness activities.
    [Show full text]
  • Sport Trackers and Big Data: Studying User Traces to Identify Opportunities and Challenges Rudyar Cortés, Xavier Bonnaire, Olivier Marin, Pierre Sens
    Sport Trackers and Big Data: Studying user traces to identify opportunities and challenges Rudyar Cortés, Xavier Bonnaire, Olivier Marin, Pierre Sens To cite this version: Rudyar Cortés, Xavier Bonnaire, Olivier Marin, Pierre Sens. Sport Trackers and Big Data: Studying user traces to identify opportunities and challenges. [Research Report] RR-8636, INRIA Paris. 2014. hal-01092242 HAL Id: hal-01092242 https://hal.inria.fr/hal-01092242 Submitted on 8 Dec 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Sport Trackers and Big Data: Studying user traces to identify opportunities and challenges Rudyar Cortés, Xavier Bonnaire, Olivier Marin, Pierre Sens RESEARCH REPORT N° 8636 November 2014 Project-Teams ARMADA ISSN 0249-6399 ISRN INRIA/RR--8636--FR+ENG Sport Trackers and Big Data: Studying user traces to identify opportunities and challenges Rudyar Cortés, Xavier Bonnaire, Olivier Marin, Pierre Sens Project-Teams ARMADA Research Report n° 8636 — November 2014 — 15 pages Abstract: Personal location data is a rich source of big data. For instance, fitness-oriented sports tracker applications are increasingly popular and generate huge amounts of location data gathered from sensors such as GPS and accelerometers.
    [Show full text]
  • A Digital Forensic Study of Mobile Health and Fitness Applications Courtney Hassenfeldt University of New Haven
    University of New Haven Digital Commons @ New Haven Electrical & Computer Engineering and Computer Electrical & Computer Engineering and Computer Science Faculty Publications Science 8-2019 Map My Murder: A Digital Forensic Study of Mobile Health and Fitness Applications Courtney Hassenfeldt University of New Haven Shabana Baig University of New Haven Ibrahim Baggili University of New Haven, [email protected] Xiaolu Zhang University of Texas at San Antonio Follow this and additional works at: https://digitalcommons.newhaven.edu/ electricalcomputerengineering-facpubs Part of the Computer Engineering Commons, Electrical and Computer Engineering Commons, Forensic Science and Technology Commons, and the Information Security Commons Publisher Citation Hassenfeldt, C., Baig, S., Baggili, I., & Zhang, X. (2019, August). Map My Murder: A Digital Forensic Study of Mobile Health and Fitness Applications. In Proceedings of the 14th International Conference on Availability, Reliability and Security, ARES '19 (Article No. 42). ACM. Comments Dr. Baggili was appointed to the University of New Haven's Elder Family Endowed Chair in 2015. This is the authors' version of the paper published in ARES '19 Proceedings of the 14th International Conference on Availability, Reliability and Security by ACM. ISBN: 978-1-4503-7164-3 The ap per of record can be found at http://dx.doi.org/10.1145/3339252.3340515 Map My Murder! A Digital Forensic Study of Mobile Health and Fitness Applications Courtney Hassenfeldt Shabana Baig University of New Haven University of New Haven [email protected] [email protected] Ibrahim Baggili Xiaolu Zhang University of New Haven University of Texas at San Antonio [email protected] [email protected] ABSTRACT professionals.
    [Show full text]