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COVID-19: Challenges to GIS with Big Data
#MMPMIDC7156159
Zhou C
; Su F
; Pei T
; Zhang A
; Du Y
; Luo B
; Cao Z
; Wang J
; Yuan W
; Zhu Y
; Song C
; Chen J
; Xu J
; Li F
; Ma T
; Jiang L
; Yan F
; Yi J
; Hu Y
; Liao Y
; Xiao H
ä-/-ä 2020[Mar]; 1
(1
): 77-87
PMIDC7156159
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The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more
than 100,000 people infected and thousands of deaths. Currently, the number of
infections and deaths is still increasing rapidly. COVID-19 seriously threatens
human health, production, life, social functioning and international relations.
In the fight against COVID-19, Geographic Information Systems (GIS) and big data
technologies have played an important role in many aspects, including the rapid
aggregation of multi-source big data, rapid visualization of epidemic
information, spatial tracking of confirmed cases, prediction of regional
transmission, spatial segmentation of the epidemic risk and prevention level,
balancing and management of the supply and demand of material resources, and
social-emotional guidance and panic elimination, which provided solid spatial
information support for decision-making, measures formulation, and effectiveness
assessment of COVID-19 prevention and control. GIS has developed and matured
relatively quickly and has a complete technological route for data preparation,
platform construction, model construction, and map production. However, for the
struggle against the widespread epidemic, the main challenge is finding
strategies to adjust traditional technical methods and improve speed and accuracy
of information provision for social management. At the data level, in the era of
big data, data no longer come mainly from the government but are gathered from
more diverse enterprises. As a result, the use of GIS faces difficulties in data
acquisition and the integration of heterogeneous data, which requires
governments, businesses, and academic institutions to jointly promote the
formulation of relevant policies. At the technical level, spatial analysis
methods for big data are in the ascendancy. Currently and for a long time in the
future, the development of GIS should be strengthened to form a data-driven
system for rapid knowledge acquisition, which signifies that GIS should be used
to reinforce the social operation parameterization of models and methods,
especially when providing support for social management.
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