The Earthquake Network Project: Toward a Crowdsourced Smartphone‐Based Earthquake Early Warning System

Finazzi, Francesco; Fassò, Alessandro

doi:10.1785/0120150354

Cited by 65 publications

(44 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we consider location data collected by the app of the Earthquake Network (EQN) research project (http://www.earthquakenetwork.it) that implements a worldwide earthquake early warning system based on networks of smartphones (Finazzi, ; Finazzi and Fassò, ). EQN requires knowing the location of the smartphones with the app installed for detecting earthquakes in real time.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Personal Exposure to Air Pollution from Smartphone-Based Location Data

Finazzi

Paci

2019

Biometrics

Self Cite

View full text Add to dashboard Cite

Personal exposure assessment is a challenging task that requires both measurements of the state of the environment as well as the individual's movements. In this paper, we show how location data collected by smartphone applications can be exploited to quantify the personal exposure of a large group of people to air pollution. A Bayesian approach that blends air quality monitoring data with individual location data is proposed to assess the individual exposure over time, under uncertainty of both the pollutant level and the individual location. A comparison with personal exposure obtained assuming fixed locations for the individuals is also provided. Location data collected by the Earthquake Network research project are employed to quantify the dynamic personal exposure to fine particulate matter of around 2500 people living in Santiago (Chile) over a 4-month period. For around 30% of individuals, the personal exposure based on people movements emerges significantly different over the static exposure. On the basis of this result and thanks to a simulation study, we claim that even when the individual location is known with nonnegligible error, this helps to better assess personal exposure to air pollution. The approach is flexible and can be adopted to quantify the personal exposure based on any location-aware smartphone application. K E Y W O R D SDynamic models, Markov chain Monte Carlo, particulate matter, space-time modeling

show abstract

Section: Introductionmentioning

confidence: 99%

Quantifying Personal Exposure to Air Pollution from Smartphone-Based Location Data

Finazzi

Paci

2019

Biometrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Multiple efforts make use of the accelerometer. In some cases, a "trigger" message is generated and sent to a central server when a phone moves Olson et al, 2011;Finazzi, 2016]; in others phones are used in a dedicated way to record earthquake shaking by attaching them to walls or other structures and recording continuously [Naito et al, 2013]. The GPS/Global Navigation Satellite Systems sensor on the phone can also be used to detect ground motion when the motion is sufficiently large [Minson et al, 2015].…”

Section: Introductionmentioning

confidence: 99%

MyShake: Initial observations from a global smartphone seismic network

Kong

Allen

Schreier³

2016

Geophysical Research Letters

View full text Add to dashboard Cite

MyShake is a global smartphone seismic network that harnesses the power of crowdsourcing. In the first 6 months since the release of the MyShake app, there were almost 200,000 downloads. On a typical day about 8000 phones provide acceleration waveform data to the MyShake archive. The on‐phone app can detect and trigger on P waves and is capable of recording magnitude 2.5 and larger events. More than 200 seismic events have been recorded so far, including events in Chile, Argentina, Mexico, Morocco, Nepal, New Zealand, Taiwan, Japan, and across North America. The largest number of waveforms from a single earthquake to date comes from the M5.2 Borrego Springs earthquake in Southern California, for which MyShake collected 103 useful three‐component waveforms. The network continues to grow with new downloads from the Google Play store everyday and expands rapidly when public interest in earthquakes peaks such as during an earthquake sequence.

show abstract

“…talked about a smartphone EEW system, but there was no public deployment. [8] also aims to build a global smartphone early warning system, but it lacks the capability of MyShake to separate earthquake signals from other human activities. There is also an app that detects earthquakes by monitoring when users launch the app and collecting users' reports [3], but it is much slower in terms of detection due to the added human reaction time.…”

Section: Related Workmentioning

confidence: 99%

Earthquake Early Warning and Beyond

Kong

Allen

2019

Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications

View full text Add to dashboard Cite

Earthquake Early Warning (EEW) systems can effectively reduce fatalities, injuries, and damages caused by earthquakes. Current EEW systems are mostly based on traditional seismic and geodetic networks, and exist only in a few countries due to the high cost of installing and maintaining such systems. The MyShake system takes a different approach and turns people's smartphones into portable seismic sensors to detect earthquake-like motions. However, to issue EEW messages with high accuracy and low latency in the real world, we need to address a number of challenges related to mobile computing. In this paper, we first summarize our experience building and deploying the MyShake system, then focus on two key challenges for smartphone-based EEW (sensing heterogeneity and user/system dynamics) and some preliminary exploration. We also discuss other challenges and new research directions associated with smartphone-based seismic network. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools; • Applied computing → Earth and atmospheric sciences.

show abstract

The Earthquake Network Project: Toward a Crowdsourced Smartphone‐Based Earthquake Early Warning System

Cited by 65 publications

References 35 publications

Quantifying Personal Exposure to Air Pollution from Smartphone-Based Location Data

Quantifying Personal Exposure to Air Pollution from Smartphone-Based Location Data

MyShake: Initial observations from a global smartphone seismic network

Earthquake Early Warning and Beyond

Contact Info

Product

Resources

About