CoronaViz is a research prototype that enables the dynamic
map visualization of COVID-19
related variables including the number of infections, active cases,
recoveries, and deaths all on a daily basis from the Johns Hopkins University web site
at https://coronavirus.jhu.edu/map.html, as well as
mentions in news articles and
tweets as they occur, by allowing the underlying spatial region and the
spanned time interval to vary. Any combination of the variables can be
viewed subject to a possibility of clutter which is avoided by the use
of concentric circles (termed geo-circles whose radii correspond to
ranges of variable values. The variable values are provided both on
cumulative and day-by-day bases. The visualization enables spatial,
temporal, and keyword variation (i.e., it can be used for names of
other disease names or entirely other concepts such as names of brands,
people, etc. with an appropriate set of variables and document
collections).
CoronaViz is being developed at the University of Maryland
at College Park under the leadership of Prof. Hanan Samet
https://www.cs.umd.edu/~hjs
with Mr. John Kastner and Mr. Hong Wei. It was motivated by the continuing
spread of COVID-19 which led to the desire to track its progress over
time to be better prepared to anticipate its emergence in new
regions. There exist numerous systems to monitor and map officially
released numbers of cases [1] which are the current established means
of keeping track of the progress of the virus. However, these systems
do not necessarily paint a complete picture. For example, they are
primarily mashups in that they do not support zooming in on the map in
the sense that they just increase the resolution of the map but do not
show the data for the additional units (e.g., states/provinces,
counties, etc.) that have become visible as a result of the zoom.
CoronaViz is designed to fill in gaps in the official
reports thereby providing a more complete picture. It incorporates the
NewsStand system [2,4]
http://newsstand.umiacs.umd.edu
(see also the related TwitterStand system [3]
http://twitterstand.umiacs.umd.edu)
which are example applications of a general framework being developed
at the University of Maryland at College Park under the direction of
Prof. Hanan Samet with his associates to enable searching for
information using a map query interface. When the information domain
is news, the underlying search domain results from monitoring the
output of over 10,000 RSS news sources and is available for retrieval
within minutes of publication. The advantage of doing so is that a
map, coupled with the ability to vary the zoom level at which it is
viewed, provides an inherent granularity to the search process that
facilitates an approximate search.
CoronaViz makes use of NewsStand to find all news articles
and tweets (identified by containing a pointer to a URL of an RSS
feed) that contain the keyword (COVID-19 or Coronavirus in our
case). It also identifies each toponym (geographic location) that is
mentioned in the article or tweet. Next, it takes the cross product of
these sets as the set of geocoded keywords. In other words, a pair
associating every keyword in the article or tweet with every location
mentioned in the article or tweet. Each of the keyword location pairs
is also associated with the time of publication of the article in
order to enable the temporal component of CoronaViz. The
result is the ability to explore the spread of the disease through
analysis of keyword prevalence in geotagged news article and tweets
over spatial and temporal ranges.
References:
-
E. Dong, H. Du, and L. Gardner. An interactive web-based
dashboard to track COVID-19 in real time. The Lancet Infectious
Diseases, 2020.
-
H. Samet, J. Sankaranarayanan, M. D. Lieberman, M. D. Adelfio,
B. C. Fruin, J. M. Lotkowski, D. Panozzo, J. Sperling,
B. E. Teitler. Reading news with maps by exploiting spatial
synonyms. Communications of the ACM, 57(10):64-77, October
2014. Cover article of the October CACM issue. Video specially
made by ACM at http://vimeo.com/106352925
-
J. Sankaranarayanan, H. Samet, B. Teitler, M. D. Lieberman,
J. Sperling. TwitterStand: News in tweets. Proceedings of the
17th ACM SIGSPATIAL International Conference on Advances in
Geographic Information Systems, pages 42-51, Seattle, WA, November
2009.
-
B. Teitler, M. D. Lieberman, D. Panozzo, J. Sankaranarayanan,
H. Samet, J. Sperling. NewsStand: A new view on news.
Proceedings of the 16th ACM SIGSPATIAL International Conference on
Advances in Geographic Information Systems, pages 144-153, Irvine,
CA, November 2008. (2008 ACM SIGSPATIAL (ACMGIS08) Best Paper
Award and 2018 SIGSPATIAL 10 Year Impact Award).
* This work was sponsored in part by the National Science Foundation under Grant IIS-1816889.