Prediction and control of the spread of infectious diseases such as COVID-19 benefits greatly from our growing computing capacity to quantify fine-scale human movement (Hancock et al., 2014; Kraemer et al., 2020). In response to the soaring needs of human mobility data during the COVID-19 pandemic, we extracted the worldwide daily population flows from billions of geotagged tweets and SafeGraph data, and developed an interactive geospatial web portal, called ODT (Origin-Destination-Time) Flow Explorer (http://gis.cas.sc.edu/GeoAnalytics/od.html, Figure 1), that allows researchers to query, aggregate, visualize, and download daily human movement data at various geographic scales (from local to global). This article briefly explains how we extracted of population movement from Twitter and SafeGraph data, demonstrates how the ODT Flow Explorer can be used to query, visualize, and download human mobility data, and discusses the limitations of each dataset.
Extracting Human Mobility
We derived the human movement data in the format of origin-destination (OD) flows from two data sources:
- Worldwide geotagged tweets collected using the Twitter public API (https://developer.twitter.com/en/docs/twitter-api)
- U.S. mobile device-based Social Distancing Metrics provided by SafeGraph (https://docs.safegraph.com/docs/social-distancing-metrics).
Extracting Daily OD Flows from Geotagged Tweets
The daily OD flows derived from geotagged tweets are the combination of Twitter users’ single-day movement and cross-day movement. The concept of single-day and cross-day movements was introduced in Huang et al. (2020). In general, the single-day movement represents the users’ daily maximum travel distance of all locations relative to the initial location, and cross-day movement measures the mean center shift between two consecutive days. Following Martin et al. (2020), we removed the non-human tweets (tweets posted by bots, such as weather reports and job offers) by checking the tweet source. For example, tweets automatically posted for job offers from the source TweetMyJOBS were removed. We also excluded the tweets that are geotagged with spatial resolution coarser than the city level. After the data cleaning, we derived 2.1 billion (2,148,780,155) geotagged tweets posted by over 21 million (21,777,336) Twitter users from 01/01/2019 to 10/31/2020. Following the single-day and cross-day approach, we further extracted over 591 million (591,417,926) user-level daily OD flows covering the whole world. The process was performed using Apache Hive (https://hive.apache.org) coupled with Esri GIS tools for Hadoop (http://esri.github.io/gis-tools-for-hadoop) on our Hadoop computing environment. Note that the Twitter-derived OD flows do not consider users’ home location. The movements were directly derived from the locations of geotagged tweets at the individual (Twitter user) level on a daily basis.
Extracting Daily OD Flows from SafeGraph Data
We extracted the daily OD flows in the U.S. using Social Distancing Metrics (SDM) data downloaded from SafeGraph. There are 23 fields in the SDM table, and we used 3 of them to derive the population movement, including origin_census_block_group, destination_cbgs, and date_range_start. The origin_census_block_group is the unique 12-digit FIPS code for the Census Block Group. destination_cbgs contains a list of key-value pairs with key indicating the a destination census block group (from the origin census block group) and “value is the number of devices with a home in census_block_group that stopped in the given destination census block group for >1 minute during the time period” (https://docs.safegraph.com/docs/social-distancing-metrics). The date_range_start was used to extract the date information. Based on the three fields, we generated an OD table with each row showing the number of devices from an original block group to destination block group on a specific day. The new OD table contains over 6 billion (6,144,802,397) block group level daily OD flows for 2019 and over 3.7 billion (3,770,910,837) daily OD flows for 2020 (updated to Sep. 30) covering the U.S. The process was performed using Apache Hive environment (the HiveQL used to generate the OD table is provided at the end of the document). Note that the SafeGraph-derived OD flows consider users’ home location (the movements are originated from home). For example, an OD flow of 100 devices (users) from county A to county B indicating that the home location of the 100 devices is county A.
Aggregated Daily OD Flows at various geographic levels
We further aggregated the billions of daily OD flows (Twitter-derived flows at the user level for the whole world and SafeGraph-derived flows at the census block group level for the U.S.) to various geographic scales, including Countries (Twitter only), Worldwide first-level country subdivisions (Twitter only), US States (Twitter and SafeGraph), US Counties (Twitter and SafeGraph), and US Census Tracts (SafeGraph). The spatially aggregated daily OD flows are available in the tool for exploration and download.
Origin-Destination-Time (ODT) Data Cube
To efficiently manage, query, and aggregate billions of OD flows at different spatial and temporal scales, we developed an Origin-Destination-Time data cube (ODT cube) as a conceptual data model for the ODT Flow Explorer (Figure 2). In the ODT data cube, origin (O) and destination (D) is a set of places or locations (e.g., administrative boundaries such as county, state, and country, or grids) that can be displayed with a map. Each cell in the data cube has a value that indicates the number of flows from the origin location to the destination location during a specific time period (e.g., in an hour, a day, or a month). Three types of matrices can be derived from the ODT data cube: origin-destination (OD) matrix quantifies the population flows between all the origin and destination locations during a time period. Destination-time (DT) matrix captures the number of incoming flows to all destination locations from a specific origin location over a series of times. Similarly, an origin-time (OT) matrix captures the number of outgoing flows from all origins to a specific destination over a series of times. The ODT Flow Explorer aims to provide an interactive interface for on-the-fly querying, slicing, aggregating, and visualizing the ODT data cube. Backed by a high-performance computing cluster, the queries generally take less than 15 seconds in our computing environment.
The ODT Flow Explorer allows researchers to explore daily population mobility aggregated at various geographic levels and scales with a few clicks. Most of the components and buttons of the Explorer are self-explanatory (tooltips are also available). Below are the general steps to use this tool: (1) Users start by choosing a movement dataset they are interested in. Currently, the available selections are Twitter derived flows and SafeGraph derived flows; (2) choose a geographic level from the following list: US County (available for both Twitter and SafeGraph), US State (Twitter and SafeGraph), Worldwide Country/Region (Twitter). US Census Tract (SafeGraph) and worldwide first-level country subdivisions from Twitter will be added in the future; (3) the next step is to choose a time period. Currently, only 2020 data are included. Twitter-derived mobility data are updated to October 31, 2020 and SafeGraph-derived mobility data are updated to September 30, 2020; (4) once the dataset, geographic level, and time period are selected, users choose what to do with the selected data (a subset of the ODT data cube). Three options are available: Choropleth Map, Flow Map, Daily Cross-unit Movements,and Download.
For the Choropleth Map option, users click on the map to select a geographic unit such as a county or a state to display its aggregated movement between other units as a choropleth map. Flow directions (Inflow, Outflow, and In & Out) can be configured. Inflow refers to the number of users/devices from other units moving to the selected unit during the selected time period. Outflow refers to the number of users/devices moving from the selected unit to other units. In & Out contains the movements from both directions. Figure 3 left shows SafeGraph-derived county population flows to New York County (Manhattan) from 03/06/2020 to 03/14/2020. The right map shows the flows to New York County for the following week (03/15/2020 to 03/21/2020). Figure 4 left shows state level population flows from/to South Carolina on 01/01/2020, and the right map shows the country level movement.
For the Flow Map option, origin-destination flow lines are directly drawn on the map based on the selected dataset, geographic level, and time period. Users can choose the area of interest (AOI) by drawing a bounding box on the map or use the full spatial coverage of the data. Flow direction (Inflow, Outflow, and In & Out) and the flow color can also be configured. Figure 5 shows county level population movement from 01/01/2020 to 01/05/2020 based on Twitter (left) and SafeGraph (right). Note that for SafeGraph-derived mobility, only flows with aggregated device number great than 20 within the selected time period are displayed to make the number of returned flows manageable for the mapping. WebGL-enabled mapping component such as kepler.gl will be integrated in later versions to overcome such limitations.
For the Daily Cross-unit Movements option, daily number of movements for a selected geographic unit (e.g., county or country) is computed and displayed as a time series chart. The direction option has four selections: Inflow, Outflow, In&Out, and Intraflow. Inflow refers to the number of daily users/devices from all the other units moving to the selected unit. Outflow refers to the number of daily users/devices moving from the selected unit to all the other units. In & Out contains the daily movements from both directions. Intraflow refers to the number of daily movements within the selected unit (flows with a movement distance greater than zero but not crossing the unit boundary).. Figure 6 shows the county level daily outflow movements for New York county and Los Angeles county in the U.S. from 01/01/2020 to 09/30/2020 (based on SafeGraph-derived OD data). Figure 7 shows the country level daily intraflow movements for France and Spain from 01/01/2020 to 10/31/2020 (based on Twitter-derived OD data). The impact of the COVID-19 pandemic on the human mobility is well reflected from the charts at different geographic scales.
Figure 8 shows the daily number of intraflow movement (left figure) and inflow/outflow movement (right figure) for Japan from 01/01/2020 to 10/31/2020 based on Twitter-derived flows. The intraflow movement reveals human mobility dynamics within the country in responding to the pandemic. The in&out flow movement, on the other hand, shows the international travels for Japan started to decrease in early March, 2020 and stay on a low level since then.
Lastly but most importantly, users can download mobility data (mobility matrix) as CSV (comma-separated values) files for further analysis or integrating with predictive models. To request the data download link, users need to select a mobility dataset, a geographic level, a time period, and draw a bounding box to define the interested area. Figure 9 shows over 2.7 million county-level daily flows were extracted and downloaded for the selected area (flows that are from/to the bbox). Each row in the CSV file contains origin county (o_fips), destination county (d_fips), date (year, month, day), number of devices/users moved from origin to destination (cnt), and mean center of all flow origins (o_lat, o_lon) and flow destinations (d_lat, d_lon). If the Aggregated option is selected, the data file becomes a mobility matrix with the summed number of devices/users moved from origins to destinations during the selected time period.
Twitter-derived population flows: Twitter data have intrinsic limitations which have been examined by a number of studies (e.g., Li et al., 2013; Malik et al., 2015; Jiang et al., 2019). Twitter is not proportionally used by different population groups and thus shows demographic and socioeconomic biases. In addition, geotagged tweets collected from the free public Twitter API (about 1% of the whole Twitter stream) are sparse in less populated areas and not enough to capture the temporal patterns at the daily level. This is particularly the case when deriving county level daily population flows because a Twitter user was included only when the user posted at least two tweets on a day or posted tweets on at least two consecutive days. Another limitation is that the dynamics of people’s Twitting behavior (e.g., people tend to tweet more during big events) as well as the changing of Twitter’s internal API affect the daily number of tweets being collected. Studies using the Twitter-derived flow data should be aware of these limitations when interpreting the results.
SafeGraph-derived population flows: SafeGraph data have a high penetration rate (~10% of mobile devices in the U.S.) and a very good representation of U.S. population groups according to SafeGraph (2019). So the flows derived from SafeGraph are much denser than Twitter-derived flows, which overcomes the Twitter data limitations. One downside of SafeGraph data (Social Distancing Metrics dataset), comparing to Twitter data, is that the data is only freely available in the U.S. dating back to 2019.
By enabling comparison of the two mobility datasets in the ODT Flow Explorer, this work highlights the importance and necessity of sharing and fusing multiple data sources for human mobility studies.
The ODT Flow Explore is still in its early stage. As the next step, we will add mobility data aggregated for other geographic levels, including the U.S. census tracts (SafeGraph) and worldwide first-level country subdivisions (Twitter). We will also periodically update the mobility datasets as new data (Twitter and SafeGraph) become available. From the function perspective, we plan to add WebGL support (such as kepler.gl) to the system so that it can handle large datasets visualization more efficiently. Currently, the flow visualization functions are very basic and become slow when a large number of records are returned from the query.
Funding: The study and system development are supported by National Science Foundation (NSF) under grant 2028791, the National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH) under grant R01AI127203-4S1, and the University of South Carolina COVID-19 Internal Funding Initiative under grant 135400-20-54176. The funders had no role in study design, data collection and analysis, or preparation of this article/system.
Appendix: HiveQL for extracting the census block group level OD flows from SafeGraph Social Distancing Metrics dataset.
create view destination_list as
select origin_census_block_group, year, month, day,
split(translate(substr(destination_cbgs, 2, length(destination_cbgs) – 2),”\””,””), “,”) as destinations
create table sg_od as
select origin_census_block_group as origin_bg, split(blck,”:”) as destination_bg, cast(split(blck,”:”) as int) as device_count, year, month, day from destination_list
LATERAL VIEW explode(destinations) dest_table as blck;
Hancock PA, Rehman Y, Hall IM, Edeghere O, Danon L, House TA, Keeling MJ. (2014). Strategies for controlling non-transmissible infection outbreaks using a large human movement data set. PLOS Comput Biol Public Library of Science; 2014;10(9):e1003809.
Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., … & Brownstein, J. S. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497.
Huang X., Li Z., Jiang Y., Li X., Porter D., (2020) Twitter reveals human mobility dynamics during the COVID-19 pandemic, PloS One, https://doi.org/10.1371/journal.pone.0241957
Martín, Y., Cutter, S. L., Li, Z., Emrich, C. T., & Mitchell, J. T. (2020). Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria. Population and Environment, 1-24.
Li, L., Goodchild, M. F., & Xu, B. (2013). Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartography and geographic information science, 40(2), 61-77.
Malik, M. M., Lamba, H., Nakos, C., & Pfeffer, J. (2015). Population bias in geotagged tweets. People, 1(3,759.710), 3-759.
Jiang, Y., Li, Z., & Ye, X. (2019). Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level. Cartography and geographic information science, 46(3), 228-242.
SafeGraph, What about bias in the SafeGraph dataset? (2019), https://www.safegraph.com/blog/what-about-bias-in-the-safegraph-dataset, last accessed on November 8, 2020