MyShake: Using Human-Centered Design Methods to Promote Engagement in a Smartphone-Based Global Seismic Network

MyShake is a global seismic network that uses private citizens’ smartphones to detect earthquakes and record both ground shaking and users’ experiences. The goal is to reduce earthquake risk and provide users with a resource for earthquake science and information. It is powered by the participation of users, therefore, its success as a global network and its utility for the users themselves is reliant on their engagement and continued involvement. This paper discusses the citizen scientist participation that enables MyShake, with specific attention to the human-centered design process that was used to overhaul the mobile application’s user interface. After the successful initial launch of the application in February of 2016, we had the opportunity to revisit the user interface based on user feedback and needs. The process began with an assessment of the demographics and distribution of the original user base through surveys and Google Play Store analytics. Subsequently, through systematic examination of the motivations and needs of community members in the San Francisco Bay Area and iterative evaluations of design decisions, MyShake was redesigned to appeal as a resource to a wider range of users in earthquake-prone regions. The efficacy of the new user interface was evaluated through interviews, surveys, and meetups with potential users. We highlight the human-centered methodology we employed, as well as the roadblocks we faced, in the hopes that our experience will be valuable to other citizen science projects in the future.

[1]  S. Hough Spatial Variability of “Did You Feel It?” Intensity Data: Insights into Sampling Biases in Historical Earthquake Intensity Distributions , 2013 .

[2]  Qingkai Kong,et al.  Smartphone-based networks for earthquake detection , 2015, 2015 15th International Conference on Innovations for Community Services (I4CS).

[3]  J. Clinton,et al.  Evidence for universal earthquake rupture initiation behavior , 2016 .

[4]  M. Page,et al.  Nonparametric Aftershock Forecasts Based on Similar Sequences in the Past , 2018 .

[5]  Luca Greco,et al.  Monitoring Earthquake through MEMS Sensors (MEMS project) in the town of Acireale (Italy) , 2018, 2018 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL).

[6]  Qingkai Kong,et al.  MyShake: Building a global smartphone earthquake early warning system , 2018 .

[7]  Elizabeth S. Cochran,et al.  The Quake-Catcher Network: Citizen Science Expanding Seismic Horizons , 2009 .

[8]  Alessandro Fassò,et al.  A statistical approach to crowdsourced smartphone-based earthquake early warning systems , 2015, Stochastic Environmental Research and Risk Assessment.

[9]  Rémy Bossu,et al.  Felt Reports for Rapid Mapping of Global Earthquake Damage: The Doughnut Effect? , 2018 .

[10]  Arno Puder,et al.  Machine Learning Aspects of the MyShake Global Smartphone Seismic Network , 2018, Seismological Research Letters.

[11]  Qingkai Kong,et al.  MyShake: A smartphone seismic network for earthquake early warning and beyond , 2016, Science Advances.

[12]  Keiiti Aki,et al.  Magnitude‐frequency relation for small earthquakes: A clue to the origin of ƒmax of large earthquakes , 1987 .

[13]  A. Fasso,et al.  The Earthquake Network Project: Toward a Crowdsourced Smartphone‐Based Earthquake Early Warning System , 2015, 1512.01026.

[14]  Laure Fallou,et al.  LastQuake: From rapid information to global seismic risk reduction , 2018 .

[15]  Kim Goodwin,et al.  Designing for the Digital Age: How to Create Human-Centered Products and Services , 2009 .

[16]  Peter Gerstoft,et al.  Machine Learning in Seismology: Turning Data into Insights , 2018, Seismological Research Letters.

[17]  Louis Schreier,et al.  MyShake: Initial observations from a global smartphone seismic network , 2016 .

[18]  Philip J. Maechling,et al.  SCEC Broadband Platform: System Architecture and Software Implementation , 2015 .

[19]  Giuseppe D'Anna,et al.  Suitability of Low‐Cost Three‐Axis MEMS Accelerometers in Strong‐Motion Seismology: Tests on the LIS331DLH (iPhone) Accelerometer , 2013 .

[21]  Minoru Yoshida,et al.  Development and Testing of a Mobile Application for Recording and Analyzing Seismic Data , 2013 .

[22]  Thomas H. Heaton,et al.  Structural Health Monitoring of Buildings Using Smartphone Sensors , 2018 .

[23]  G. Atkinson,et al.  “Did You Feel It?” Intensity Data: A Surprisingly Good Measure of Earthquake Ground Motion , 2007 .

[24]  Ronald W. Perry,et al.  Behavioral foundations of community emergency planning , 1992 .

[25]  T. Lay,et al.  Regional and stress drop effects on aftershock productivity of large megathrust earthquakes , 2016 .

[26]  Michael K Lindell,et al.  The Protective Action Decision Model: Theoretical Modifications and Additional Evidence , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[27]  Keiiti Aki,et al.  Local site effects on weak and strong ground motion , 1993 .

[28]  C. Christensen,et al.  On the Reliability of Quake‐Catcher Network Earthquake Detections , 2015 .

[29]  Andreas Krause,et al.  Community Seismic Network , 2012 .

[30]  R. Allen,et al.  The value of real‐time GNSS to earthquake early warning , 2017 .