Breaking news events, policy decisions, and their announcements induce behavioral responses in impacted citizens. The analysis attempts to measure and identify correlations between timings of announcements and activities/behaviors in localities preceding and following such a news event to attempt to identify possible unintended behavior incentives that may arise due to the timing of the event or announcement.
Our team was inspired to choose this challenge due to our combined interest in determining how the COVID-19 pandemic triggered events that changed mobility patterns around the world. We all had personal and prior knowledge of national border closures, cancelled large-scale events, and disruptive changes in migration patterns that fueled our desire to participate in this challenge. Our team was also intrigued by the fact that accessible night light data could be used to indicate human movement and add further evidence towards mobility trends. We were further motivated by how this project could aid in equal distribution of vaccines across cities, the prevention of future outbreaks, and public health-related decision-making for policymakers.
Since we were all interested in looking at event-driven mobility, our team split up in researching four different areas: space agency data, COVID-19 statistics, mobility data, and key dates for COVID-19 related events that may have triggered changes in mobility. Once we gathered enough relevant and meaningful data in those areas, we started to align dates across each area and look for patterns. For instance, after California’s state of emergency was declared on March 5th, we looked for trends indicating that human movement decreased after that date via the mobility and space agency data.
The space agency data we used was the Sentinel-5P NO2 data and VIIRS/NPP Day Night Band 500m imagery. We obtained VIIRS night light data from NASA’s Worldview Tool, useing OpenCV and Python to analyze video data from a time frame encompassing before and after lockdown policies. We used matplotlib to plot the values over time and find trends in the data. For the Sentinel-5P NO2 data, we used the EO Browser to query dates and find satellite imagery to be downloaded as an animation. We also used Google’s Mobility reports to track mobility behavior during the pandemic. An ipywidgets library allowed us to create interactive graphs tracking various activities such as Retail & Reaction, Grocery & Pharmacy, and Parks. We also used Adobe Xd to create mockups of our envisioned dashboard which would take in this data and display it for policy-makers and the general public.
Although we initially struggled to access and read the data, we were eventually able to download it and draw meaningful conclusions. We achieved our goal of using space agency and mobility data to understand human behaviors related to policy announcements in various cities. Our case study of four cities, Atlanta, Washington DC, Seattle, and Los Angeles, gave insight into how metropolitan areas across the country with different policy timelines reacted to COVID-19. This analysis can be used to inform efficacy of policies and help policy-makers with future decisions about the pandemic.
Google mobility Dataset - https://www.google.com/covid19/mobility/
https://coronadatascraper.com/#home
https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series
City Populations + Health Department Data
California: https://data.chhs.ca.gov/dataset/california-covid-19-hospital-data-and-case-statistics
Georgia: https://dph.georgia.gov/covid-19-daily-status-report
DC: https://coronavirus.dc.gov/page/coronavirus-data
Washington: https://www.doh.wa.gov/Emergencies/Coronavirus
https://apps.sentinel-hub.com/eo-browser/?lat=41.9000&lng=12.5000&zoom=10
https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP46A1
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