DOI: 10.5061/dryad.1c59zw3zg
title: GPS data from cellphones for Mechanical Analog

description: Motivated by the increasing need to develop a quantitative, science-based,
 predictive understanding of the dynamics and response of cities when
 subjected to hazards, in this paper we apply concepts from statistical
 mechanics and microrheology to develop mechanical analogs for cities with
 predictive capabilities. We envision a city to be a matrix where people
 (cell-phone users) are driven by the city’s economy and other associated
 incentives while using the collection of its infrastructure networks in a
 similar way that thermally driven Brownian probe particles are moving
 within a complex viscoelastic material. Mean-square displacements
 (ensemble averages) of thousands of cell-phone users are computed from GPS
 location data to establish the creep compliance and the resulting impulse
 response function of a city. The derivation of these time-response
 functions allows the synthesis of simple mechanical analogs that model
 satisfactorily the city’s behavior under normal conditions. Our study
 concentrates on predicting the response of cities to acute shocks (natural
 hazards that stress the entire urban area) that are approximated with a
 rectangular pulse with finite duration, and we show that the solid-like
 mechanical analogs for cities that we derived predict that cities revert
 immediately to their pre-event response suggesting that they are
 inherently resilient. Our findings are in remarkably good agreement with
 the recorded response of the Dallas metroplex following the February 2021
 North American winter storm, which happened at a time for which we have
 dependable GPS location data.
description: <strong>Data Acquisition</strong>
 For this preliminary study, we limit our data search to 2
 metropolitan cities: Dallas Fort Worth metroplex and San Francisco Bay
 area. To acquire the raw data, we purchased the
 individual cell phone location data from IRYS (https://irys.us/) and NEAR
 (https://near.com)
 organizations for the study period of 2 months from Feb
 1<sup>st</sup> 2021–March 31<sup>st</sup> 2021.
 This time frame covers the snowmageddon that invaded DFW during a week in
 Feb 2021 and resulted in more than 500K claims and $10B total
 loss. The raw data received in MATLAB data files and
 TSV (tab-separated-value) format for every ping in seconds for the area of
 (97.98W -95.94W, 32.17N -33.58N) for DFW and (122.56W -121.74W, 37.21N
 -37.92N) for San Francisco Bay area, both inland and on water surface.
 Each individual’s cellphone is given a particular ID and is unique
 throughout the study period. <strong>Data
 Cleaning</strong> The raw data was given in the
 tabular format of Device ID, Unix time, Long, Lat of that current
 visit We use Dask distributed computing library to
 process the data in ManeFrame II HPC in order to utilize the multiple
 processors to accelerate the computation process,
 First, we convert the Unix time to local date and local
 time. For each of the metroplexes examined in this
 work, we processed longitude and latitude data (in degrees from the
 Greenwich meridian and from the equator) at various times for thousands of
 anonymous cellphone users (IDs). We retain only the IDs
 that appear on top of the hour (for DFW) and top of the 2-hour (for SF)
 during the study period. Finally, we shortlisted 13k
 IDs from DFW and ~4k IDs from the San Francisco area for Feb and Mar 2021.
 We checked and ensured that NO missing data are in the final processed
 dataset submitted to Dryad.
description: <strong>Usage Notes</strong> The
 data is in tabular CSV format Columns are the
 anonymous/unique IDs Rows are the top of the hour/2hr
 during the study period The corresponding values are
 Long/Lat for each ID at a particular hour To open the
 file, you can use Microsoft Excel, R, Python, or any other
 program