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Comparing the Suitability of Strava and Endomondo Gps Tracking Data for Bicycle Travel Pattern Analysis

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Comparing the Suitability of Strava and Endomondo Gps Tracking Data for Bicycle Travel Pattern Analysis COMPARING THE SUITABILITY OF STRAVA AND ENDOMONDO GPS TRACKING DATA FOR BICYCLE TRAVEL PATTERN ANALYSIS by Dariia Strelnikova BACHELOR THESIS 2 in fulfillment of the requirements for the degree of Bachelor of Science Carinthia University of Applied Sciences Geoinformation BSc Program Supervisors FH-Prof. Dr. Gernot Paulus, MSc. MAS, Carinthia University Of Applied Sciences Hartwig Hochmair, Ph.D., University of Florida Villach, February 2017 Comparing the Suitability of Strava and Endomondo GPS Tracking Data for Bicycle Travel Pattern Analysis DECLARATION I declare that this thesis has been composed solely by myself and that it has not been submitted, in whole or in part, in any previous application for a degree. Wherever contributions of others are involved, every effort is made to indicate this clearly, with due reference to the literature, and acknowledgement of collaborative research and discussions. Davie, FL, 28.05.2017 _____________________ Dariia Strelnikova CUAS, Geoinformation and Environmental Technologies Villach 2017 2 Comparing the Suitability of Strava and Endomondo GPS Tracking Data for Bicycle Travel Pattern Analysis ACKNOWLEDGEMENTS I would like to thank my little son who managed to play on his own during the long hours that were invested in the creation of this work. I would like to thank my supervisors Dr. Hochmair and Dr. Paulus for their wisdom, support and patience. I would like to thank G-d for everything including the benefits of the modern-day world that made this work possible. CUAS, Geoinformation and Environmental Technologies Villach 2017 3 Comparing the Suitability of Strava and Endomondo GPS Tracking Data for Bicycle Travel Pattern Analysis ABSTRACT Over the past few years crowd-sourced data collected through GPS based bicycle tracking apps on mobile devices have become a widely used data source for the analysis of spatio-temporal travel patterns of cyclists around the world. These large-volume GPS data collections supplement travel information obtained from traditional travel household surveys and data from permanent count stations. Due to their high spatial and temporal resolution particularly in urban areas, GPS tracking data provide sufficient coverage to better understand spatio-temporal bicycle travel patterns. This research project compared coverage and segment based activity counts between two prominent bicycle tracking apps, namely Endomondo and Strava. The first goal was to determine the pros and cons of using the data from these two platforms for travel analysis, based on coverage, spatial and temporal resolution, and other aspects of spatial data quality. The second goal was to compare spatio-temporal usage patterns between the two platforms. Some of the research findings are that Endomondo is less used in South Florida than Strava for the analyzed time frame of three months, and hence provides less detail about travel patterns in South Florida. However, Endomondo tracks are observed on many more small roads and off-road tracks (e.g. in parks) than Strava, complementing Strava data in a meaningful way. There were also some differences in the temporal distribution of trips over the day between both platforms, indicating that the platforms are to some extent used for different trip purposes. This research is unique in the sense that it is the first work that compares Strava and Endomondo data at this level of detail. ABBREVIATIONS API Application Programming Interface UAV Unmanned Flying Vehicle CUAS Carinthia University of Applied Sciences UF The University of Florida, USA ER Entity-Relationship <diagram> URL Universal Resource Locator GI Geographic Information UTC Universal Coordinate Time GIS Geoinformation System VGI Volunteer Geographic Information GNSS Global Navigation Satellite System XHR XML HTTP Requests GPS Global Positioning System GUI Graphical User Interface IDW Inverse Distance Weighting OSM OpenStreetMap RBF Radial Basis Function CUAS, Geoinformation and Environmental Technologies Villach 2017 4 Comparing the Suitability of Strava and Endomondo GPS Tracking Data for Bicycle Travel Pattern Analysis Contents DECLARATION ............................................................................................................................................. 2 ACKNOWLEDGEMENTS .................................................................................................................................. 3 ABSTRACT ................................................................................................................................................... 4 ABBREVIATIONS ........................................................................................................................................... 4 1. INTRODUCTION .................................................................................................................................... 7 1.1. Motivation ............................................................................................................................... 7 1.2. Problem Definition and Research Objectives .......................................................................... 8 1.3. Expected Results .................................................................................................................... 11 1.4. Thesis Structure ..................................................................................................................... 12 2. THEORETICAL BACKGROUND ................................................................................................................ 12 2.1. Nature and Application of Bicycle Tracking Volunteer Geographic Information .................. 12 2.2. Map-Matching ....................................................................................................................... 14 3. METHODOLOGY AND DATA.................................................................................................................. 14 3.1. Methodology ......................................................................................................................... 14 3.2. Conceptual Model ................................................................................................................. 17 3.3. Study Area ............................................................................................................................. 19 3.4. Overview of Used Data Sources ............................................................................................ 19 3.4.1. Strava and Endomondo Bicycle Tracking Data .............................................................. 19 3.4.2. Here Navstreets and OpenStreetMap Road Network Data ........................................... 26 4. DATA EXTRACTION AND PREPARATION .................................................................................................. 27 4.1. Extraction and Storage of Endomondo Data ......................................................................... 27 4.1.1. Data Retrieval ................................................................................................................ 27 4.1.2. Database Management ................................................................................................. 33 4.2. Road Network Preparation: Integration of Navstreets and OpenStreetMap Data ............... 36 4.3. Map-Matching: Transformation of GPS Points into Road Segments .................................... 41 4.4. Calculation of Segment Based Usage Statistics ..................................................................... 43 4.5. Calculation of Area-Based Statistics ...................................................................................... 44 5. RESULTS ........................................................................................................................................... 47 5.1. Comparison of Endomondo and Strava Data ........................................................................ 47 5.1.1. Data Coverage ............................................................................................................... 47 5.1.2. Bicycle Travel Patterns across Different Sub-Regions and Different Road Classes ....... 51 5.1.3. Temporal Usage Patterns .............................................................................................. 56 5.2. Endomondo: Selected Usage Patterns .................................................................................. 59 CUAS, Geoinformation and Environmental Technologies Villach 2017 5 Comparing the Suitability of Strava and Endomondo GPS Tracking Data for Bicycle Travel Pattern Analysis 6. DISCUSSION ...................................................................................................................................... 64 6.1. Summary of Identified Similarities and Differences in Travel Patterns ................................ 64 6.2. Differences in Application of Strava and Endomondo Data for Bicycle Travel Pattern Analysis .............................................................................................................................................. 67 7. DATA QUALITY AND LIMITATIONS OF STUDY RESULTS .............................................................................. 73 8. CONCLUSIONS AND FUTURE WORK ....................................................................................................... 74 9. REFERENCES .....................................................................................................................................
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