ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Geography and Sustainability xxx (xxxx) xxx
Contents lists available at ScienceDirect
Geography and Sustainability
journal homepage: www.elsevier.com/locate/geosus
COVID-19: Challenges to GIS with Big Data
Chenghu Zhoua,c,†,∗, Fenzhen Sua,c,e,†,∗, Tao Peia,c, An Zhanga, Yunyan Dua,c, Bin Luoa,
Zhidong Caob,d, Juanle Wanga,c, Wen Yuana, Yunqiang Zhua,c, Ci Songa, Jie Chena, Jun Xua,e,
Fujia Lia, Ting Maa,c, Lili Jianga, Fengqin Yana, Jiawei Yia, Yunfeng Hua, Yilan Liaoa,
Han Xiaoa,c
a
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing, 100101, China
b
Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
c
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
d
School of Artiﬁcial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
e
Commission on Geographical Information Science, International Geographic Union, Beijing, 100101, China
a r t i c l e i n f o
Key words:
COVID-19
Big data
GIS
Spatial transmission
Social management
a b s t r a c t
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 socialemotional
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.
1. Background
The outbreak of 2019 novel coronavirus disease (COVID-19) is a
public health emergency of international concern that had caused more
than 100,000 infections and 3,830 deaths in more than 100 countries by
March 8, 2020 (NHC, 2020; WHO, 2020), seriously aﬀecting economic
∗
Corresponding author; Tel.: +86 010-64888956
E-mail addresses: zhouch@lreis.ac.cn, sufz@lreis.ac.cn (C. Zhou), sufz@lreis.ac.cn (F. Su), peit@lreis.ac.cn (T. Pei), zhangan@igsnrr.ac.cn (A. Zhang),
duyy@lreis.ac.cn (Y. Du), luob@igsnrr.ac.cn (B. Luo), zhidong.cao@ia.ac.cn (Z. Cao), wangjl@igsnrr.ac.cn (J. Wang), yuanw@lreis.ac.cn (W. Yuan),
zhuyq@lreis.ac.cn (Y. Zhu), songc@lreis.ac.cn (C. Song), chenj@lreis.ac.cn (J. Chen), xujun@lreis.ac.cn (J. Xu), lifujia2003@163.com (F. Li), mting@lreis.ac.cn
(T. Ma), jiangll@lreis.ac.cn (L. Jiang), yanfq@lreis.ac.cn (F. Yan), yijw@lreis.ac.cn (J. Yi), huyf@lreis.ac.cn (Y. Hu), liaoyl@lreis.ac.cn (Y. Liao), xiaoh@lreis.ac.cn
(H. Xiao).
†
These authors contributed equally to this work.
and social development. On February 28, UN Secretary-General Guterres
called on governments to take action to do everything possible to
control the COVID-19 epidemic (New.cn, 2020).
The United Nations Sustainable Development Goals (SDGs) aim to
address social, economic, and environmental issues from 2015 to 2030
and move towards sustainable development (SDG, 2015). The United
https://doi.org/10.1016/j.geosus.2020.03.005
Available online 20 March 2020
2666-6839/© 2020 Published by Elsevier B.V. on behalf of Beijing Normal University. This is an open access article under the CC BY-NC-ND license.
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Please cite this article as: C. Zhou, F. Su and T. Pei et al., COVID-19: Challenges to GIS with Big Data, Geography and Sustainability, https:
//doi.org/10.1016/j.geosus.2020.03.005
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Nations SDGs contain 17 goals and 169 targets. SDG 3 aims to ensure
healthy lives and promote well-being for all at all ages including the
speciﬁc in SDG 3.3, which aims to end epidemics of AIDS, tuberculosis,
malaria and neglected tropical diseases and to combat hepatitis, waterborne
diseases and other communicable diseases by 2030 (SDG, 2015).
The COVID-19 epidemic directly threatens the achievement of the above
goals for health, and also aﬀects the realization of goals for economic
and social development. In the context of global environmental changes,
the transmission characteristics of the COVID-19 epidemic have not yet
been suﬃciently recognized (CDC, 2020). Additionally, the acceleration
of global urbanization, increased concentration of population, more frequent
and complex interactions, and shortage of medical protection in
developing countries all increase the diﬃculties of the prevention and
control of COVID-19.
1.1. China’s response to COVID-19
At the beginning of the epidemic, the medical and research communities
responded quickly. They quickly isolated the new coronavirus,
conducted gene sequencing to determine the intermediate host, actively
shared data with the international community, and sent three successive
expert teams to Wuhan. On January 23, 2020, the Chinese government
took decisive measures to lock down the city of Wuhan and to close
the external routes to all cities in Hubei Province (State council, 2020).
Each province has successively launched a ﬁrst-level public health response,
which has eﬀectively curbed the spread of the epidemic. China
has undertaken enormous personal and socioeconomic losses and has
won valuable time for the Chinese and for global prevention and control
of the epidemic. On February 3, only 10 days after construction, Huoshenshan
Hospital, which is a 1000-bed hospital in Wuhan, Hubei, was
put into use (China Daily, 2020a). On February 8, Leishenshan Hospital,
which has 1600 beds, was completed and put into use (China Daily,
2020b). In the interim, medical staﬀ from all over the country rushed to
Hubei to ﬁght the epidemic. On February 12, the local government took
the measure of receiving and curing all patients that should be treated,
following which the epidemic in Hubei Province reached a turning point
and began to decline. During this period, we utilized GIS and spatial big
data technology, which have a high degree of scientiﬁc and technological
display (Zhou et al., 2016), to provide important scientiﬁc and technical
support to allow the government to judge the epidemic situation
and formulate prevention and control measures (Health Commission of
Hubei Province, 2020).
1.2. Spatiotemporal development of COVID-19 in China
During December 2019, a cluster of unexplained viral pneumonia
cases was detected in Wuhan city, Hubei Province, China. The COVID-
19 epidemic subsequently spread to the rest of Hubei, and to other parts
of China. The number of newly conﬁrmed cases increased rapidly from
January 10 to January 24, 2020, and the reported cases reached a peak
and ﬂattened from January 31 to February 7. The number of conﬁrmed
cases increased on February 12 because of a change in how cases were
diagnosed and reported in Hubei Province that began on the same date.
As of March 8, China has cumulatively conﬁrmed 80,735 cases (National
Health Commission of the PRC, 2020). The daily conﬁrmed cases from
2020/01/10 to 2020/03/04 are summarized in Figure 1.
In China, the epidemic outbroke in Wuhan and then spread throughout
Hubei Province. Since January 18, because of the large-scale migration
associated with the Chinese Lunar New Year, the epidemic has
spread rapidly across the country. By January 29, conﬁrmed cases were
recorded in all provinces and regions in China. After February 14, the
number of newly conﬁrmed cases in the areas outside Hubei Province
gradually decreased. By February 21, the number of newly conﬁrmed
cases in Hubei Province had increased by one hundred people per day.
The newly conﬁrmed cases outside Hubei Province fell to single digits,
Figure 1. Daily Changes in new conﬁrmed cases of COVID-19 in China
(2020/01/10 - 2020/03/04).
(Data source: National Health Commission of the People’s Republic
of China. Daily brieﬁng on novel coronavirus cases in China,
http://en.nhc.gov.cn/DailyBrieﬁng.html)
and the national epidemic situation was eﬀectively controlled. The national
distribution maps of newly conﬁrmed cases at the province-level
are shown in Figure 2.
2. Ten challenges in using GIS with spatiotemporal big data
The characteristics of strong infectivity, a long incubation period
and uncertain detection of COVID-19, combined with the background of
large-scale population ﬂow and other factors, led to the urgent need for
scientiﬁc and technological support to control and prevent the spread of
the epidemic. During the struggle against epidemic, GIS and spatial big
data technology have played an important role in identifying the spatial
transmission of the epidemic, in spatial prevention and control of the
epidemic, in spatial allocation of resources, and in spatial detection of
social sentiment, among other things. Here, we discuss ten of these challenges
and responses, viz., 1) rapid construction of a big data information
system for the epidemic; 2) rapid problem-oriented big data acquisition
and integration; 3) convenient multi-scale dynamic mapping for epidemics;
4) comparison between spatial tracking and the spatiotemporal
trajectory of big data; 5) spatiotemporal prediction of the transmission
speed and scale of the epidemic; 6) spatial segmentation of the epidemic
risk and prevention level; 7) spatial dynamic balancing of supply and
demand for medical resources; 8) assessment of the supply of materials
and transportation risk; 9) rapid estimation of the population ﬂow and
distribution; and 10) monitoring the spatial spread of social sentiment
and detection.
2.1. Rapid construction of a big data information system for epidemics
With the development of GIS technology, an information system for
a relevant subject can be constructed rapidly, especially in terms of
database management, spatial analysis tools, and mapping. However,
the constructed information system is commonly limited by the basic
functions of the commercial software. In response to the epidemic,
many institutions and research groups have built a number of information
systems, such as "epidemic map displays", "fever clinic queries" and
"passenger information queries", based on existing commercial software,
which has made an important contribution to epidemic prevention and
control (CAICT: China Academy of Information and Communications
Technology 2020). Considering that decision-making in regard to epidemic
prevention and control requires rapid analysis of the spatiotemporal
dynamics and comprehensive consideration of multiple geographical
scales, the systems development teams have 1) connected health
departments and the internet to build a virtual perception network of
multi-source spatiotemporal big data about the epidemic, and developed
GIS for the epidemic from a daily time scale, which is a relatively
static information system, into real-time dynamic GIS on hourly or even
minute-by-minute timescales; 2) built a spatiotemporal cube model of
big data about the epidemic, and realized the normalized modeling of
2
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Figure 2. Changes in new conﬁrmed cases of COVID-19 in China at province-level (2020/01/17 - 2020/02/21).
(Data source: National Health Commission of the People’s Republic of China. Daily brieﬁng on novel coronavirus cases in China,
http://en.nhc.gov.cn/DailyBrieﬁng.html)
multi-source heterogeneous data with diﬀerent spatial references, different
times, diﬀerent scales and diﬀerent semantics, as well as creating
a uniﬁed storage system and management of mixed polymorphic
data; 3) set up a computing engine for epidemic description, diagnosis,
prediction and decision-making, and developed the traditional online
GIS for epidemics with the "visualizing query" function to an integrated
stage with "visualization query analysis"; 4) developed a multi-scale integrated
spatiotemporal dynamic visualization technology to visualize
the epidemic at the “country, province, city, county, community, and individual”
scales in order to manage visualization analysis on “one map”
of multidimensional epidemic data under a uniﬁed spatiotemporal datum;
5) adopted the new generation of native cloud architecture technology
to develop a three-tier structure consisting of the infrastructure as
the background, a platform for spatiotemporal big data management as
the middle ground, and application for the epidemic as the foreground,
which solved problems caused by traditional information systems, such
as overfull links in development mode, complex processing and long
lead time, and satisﬁed the demand for rapid construction of GIS for an
epidemic in an emergency. The system interfaces at diﬀerent scales are
shown in Figure 3.
2.2. Rapid problem-oriented big data acquisition and integration
The decisions and actions of large-scale epidemic prevention and
control depend on data support. The development and application of big
data will undoubtedly contribute to quickly identifying the spatiotemporal
process of epidemic development, prevention and control measures,
and the resulting eﬀectiveness. Strategies for gathering and integrating
massive geographic and social-spatial information in the face of prevention
and control of an epidemic emergency is the most basic problem
for subsequent temporal and spatial mining and analysis. Based on the
uniﬁed geographical framework, this research quickly absorbed and integrated
geographical big data, including the WHO data released internationally,
daily domestic health and disease control data, professional
population health platform data, Tencent location request data, Baidu
migration data, microblog text data, patient spatiotemporal trajectory
data, international airline data, census data, education enrollment data,
land cover data, remote sensing imagery and other multi-source data
through statistical data collection, network data mining, API interface
connection, and international and domestic data platform exchange.
With the support of uniﬁed spatial registration, raster-vector transformation,
statistical normalization, format transformation, etc., these data
were rapidly merged and applied to the spatiotemporal analysis and
visualization systems for the COVID-19 epidemic. In this process, due
to the weakness of current research on the classiﬁcation and security
management of spatiotemporal big data, it is clear that GIS needs to be
developed in the aspects of automatic correlation aggregation of data,
historical data tracing, adaptive conversion of heterogeneous data, and
standardized storage of multi-source data.
2.3. Convenient multi-scale dynamic mapping for epidemics
Since the outbreak of COVID-19, in addition to the news published
by the government, epidemic information has also been widely disseminated
through internet platforms such as Weibo, WeChat, and other
channels. This massive and multi-source information has created considerable
challenges for epidemic mapping. ESRI’s expert Kenneth Field
appealed that coronavirus mapping should be responsible (Field, 2020).
He called attention to misunderstandings in the current COVID-19 epidemic
maps, including the incorrect use of map projections, choropleth
maps, classiﬁcation schemes, color schemes, point density maps, graduated
symbols, heat maps, and three-dimensional maps.
In this study, a data-driven multi-scale mapping template was designed.
By rapidly processing real-time public outbreak data in 34
provinces and more than 300 prefecture-level cities, we implemented
a multi-scale map matching database template. By making the thematic
map templates in advance, the rapid production and release of largescale
epidemic maps were realized. Several color schemes were designed
to not only reﬂect thematic knowledge with the use of color but also to
provide more emotively intuitive information, such as a red-purple color
scheme for the COVID-19 cases, a blue-black color scheme for the cases
with mortality, and a yellow-green color scheme for cured cases. Daily
animation maps were applied to express the spatiotemporal characteristics
of the spread of the epidemic. The knowledge mapping method com-
3
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Figure 3. Dynamic information query system for diﬀerent scales: a) City level; b) County level; c) Community level.
Figure 4. Rapid mapping based on multi-scale templates.
bined with the epidemiological and emergency response-related professional
knowledge were also utilized further. We designed maps for the
multidimensional dynamic expression of the epidemic situation, such as
the cumulative distribution map of conﬁrmed cases per 100,000 people
and the distribution map of places with zero new recorded cases. We
also launched daily reports called the Epidemic Map Story starting on
February 1, 2020, via the oﬃcial WeChat platform. More than ten daily
update maps for the public were published and updated, including those
for the global COVID-19 epidemic situation, the spatial distribution of
the COVID-19 epidemic situation in China, and the development and
change of the epidemic situation as well as the spatial distribution and
change information of the epidemic situation in critical provinces; there
were more than 50,000 page views in total up to March 4, 2020. Two
template application examples are shown in Figure 4.
2.4. Comparison between spatial tracking and spatiotemporal trajectory of
big data
The comparison of the spatial track of the patient’s activities is a
critical technical task for virus tracking and transmission chain reconstruction.
The comparison of activity tracks between patients and populations
provides an important scientiﬁc basis for delimiting the potentially
infected population. Methods to quickly and automatically extract
the patient’s spatiotemporal trajectory from text data, establish the spatiotemporal
comparison method, ﬁnd the potential spatiotemporal exposure
link of patients, support epidemiological investigation and rapid
analysis, and realize automatic detection of the cross-regional epidemic
infection path are major challenges for GIS. In this research, we 1) developed
a reconstruction technology for the progress of the spatiotemporal
events in a patient’s text track data that can automatically convert
the track text into quantitative spatiotemporal events; 2) established a
spatiotemporal events database with more than 70000 pieces of patient
track text covering the whole country; 3) constructed an exposure calculation
model and a patient-node-patient association model that integrates
time, space and text similarity, which showed the exposure assessment
and site risk assessment of each individual (the exposure analysis
of a patient’s spatial trajectory is shown in Figure 5.). Based on
the above work, the key epidemic transmission centers were located,
including Baodi Mall in Tianjin; Toulong Mall in Harbin, Heilongjiang;
and Yintaishimao Mall in Wenzhou, Zhejiang.
2.5. Spatiotemporal prediction of transmission speed and magnitude
The spatiotemporal spread of infectious diseases in large populations
is a very large and complex system that poses great challenges to mathematical
modeling (Grassly and Fraser, 2008; Riley, 2007). This research
conducted a spatial simulation from the perspective of the geographical
environment and social space. A spatiotemporal diﬀusion model (Multi
susceptible-exposed-infectious-removed-died-cumulative model, multiSEIRDC
model) centered on Wuhan was established, which takes into
4
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Figure 5. Exposure analysis of a patient’s spatial trajectory.
account factors including the eﬀects of spatial barriers from human intervention,
the impact of large-scale population migration during the
Chinese New Year and the spatial heterogeneity of diﬀerent epidemic
areas. The multi-SEIRDC model was used to track, derive and predict
the COVID-19 epidemic situation in diﬀerent regions of China. Research
has shown that the earliest time when COVID-19 began to spread from
person to person was in late November or early December. The expected
basic reproduction number was 4.08, with a range of 3.37-4.77. The day
before the Wuhan lockdown, there were approximately 20,000 people
infected with COVID-19 nationwide. As of March 5, the number of potentially
infected people in Wuhan exceeds 100,000. Due to the serious
lack of detection capacity, there may have been a large number of neglected
mild and asymptomatic infections before February 13, 2020. The
eﬀective reproduction number outside Hubei fell below the threshold of
1 on February 2, and reached an inﬂection point and entered a steady
decline. The number of new cases per day will ﬂuctuate under 10 cases
for approximately one month before the end of the COVID-19 epidemic.
If there is no import of overseas infection cases, the end of the COVID-19
epidemic outside Hubei is expected to be at mid-March.
2.6. Spatial segmentation of the epidemic risk and prevention level
Assessing the risk of epidemics and transmission in diﬀerent regions
is of great signiﬁcance for decision-making and the adjustment of prevention
and control eﬀorts. Considering that the epidemic center of
China was in Wuhan, the correlation between the number of conﬁrmed
cases in each province and the population ﬂow from Wuhan to each
province was examined ﬁrst. The research indicated that until 24:00 on
February 2, 2020, the severity of the epidemic in each province was
highly correlated with the population who travelled there from Wuhan
before the city of Wuhan was locked down, with a correlation coeﬃcient
of 0.77. A risk assessment model was constructed with the spatial distribution
of the number of conﬁrmed cases and the population migration,
and three risk level areas were outlined on the regional scale and on the
urban scale for the cities with high risks of epidemics, including Beijing,
Shenzhen, Guangzhou, Shanghai, Chongqing, Wenzhou, Zhuhai, Changsha
and Harbin. However, after the Spring Festival, the epidemic risk
had a high probability of increasing due to the return ﬂow. The three
variables, viz., case number, population migration, and transportation
network, were incorporated into the prediction. The results (not including
those for Hubei) suggested that, after the Spring Festival Holiday,
Beijing, Shenzhen, Guangzhou, and Shanghai would have the highest
levels of risk, followed by Chongqing, Changsha, Hangzhou, Zhengzhou,
Nanjing, Xi’an, and Chengdu. See Figure 6 for details.
2.7. Spatial dynamic balancing of supply and demand for medical
resources
The spatial distribution of medical resources is generally balanced
according to factors such as population density, but the spatially uneven
outbreak of the epidemic and their rapid development result in
a spatiotemporal imbalance of the supply and demand for medical resources.
In this case, the key to epidemic prevention and control is knowing
the spatiotemporal dynamics of the supply and demand for medical
resources to optimize the allocation of materials. Based on the factors of
online hospital help information, local cases and forecasts, and existing
resource data, we analyzed the current dynamic situation of medical
protective equipment across the country through cross-validation and
sampling veriﬁcation (phone inquiry and web inquiry) and attained the
following: 1) the prompt identiﬁcation of a shortage of medical protective
equipments in 462 hospitals, including 336 in Hubei Province
and the rest in Sichuan Province, Anhui Province, Guangdong Province,
Jiangsu Province and Hunan Province, among others; and 2) based on
the number of conﬁrmed cases, the number of hospitals that lacked sufﬁcient
medical protective equipments and the urban population, the
shortage of medical supplies was divided into four levels on a regional
scale and three levels on a city scale (the result for Hubei Province is
shown in Figure 7, and the national result is shown in Figure 8). The
above achievements provided an important scientiﬁc basis for the national
allocation of medical care resources for the prevention and control
of the epidemic.
5
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Figure 6. National spatial segmentation of the
COVID-19 epidemic risk.
Figure 7. Relationship between conﬁrmed
case number and hospital shortage levels at
the city-scale. Mi is cumulative cases number
of city i; Pi is estimation number of population
of city i;Ni is number of hospitals
in shortage of city i; (Statistics until 19:00
2020/02/02).
2.8. Assessment of material supply and transportation risk
A stable and eﬃcient national material supply and transportation
system provides important support for successful epidemic prevention
and control. We have integrated multiple datasets, such as provincial
epidemic data, online consumption data and postal service data, to analyze
the supply-demand situation and price changes in necessities and
food, including vegetables and meat, for every province during the epidemic
prevention period, as well as the changing volume and trends
of postal and express delivery businesses in each region, to identify the
area, type, and the transportation support capacity of the material shortage
risks. Through this, we provided scientiﬁc data for the social management
departments to obtain the material supply-demand dynamic
information in real time. Meanwhile, by tracking the transportation of
materials, we identiﬁed the highly sensitive nodes that may invoke virus
transmission during the transportation process and provided advance
warning and decision support for the prevention and control of the regional
spread of the epidemic (the national distribution of risk index is
shown in Figure 9). Due to the involvement of company business information
in the data, there were many diﬃculties in the acquisition
process, which revealed that knowledge of how businesses constrain
data sharing will become an important research direction. At present,
6
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Figure 8. National distribution of hospitals in shortage of medical protective materials.
Figure 9. Risk index of new coronavirus transmission associated with the logistics process in China on January 31, 2020.
7
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Figure 10. The spatial distribution of county-level
total population changes during the 2018 Spring Festival
(Lunar New Year’s Day- the fourth day after Lunar
New Year’s Day) in Hubei Province.
JD.COM, SF Express, and other large domestic online shopping and logistics
companies have begun to establish GIS-based logistics monitoring
systems. In the future, with the support of Internet of Things technology,
a national classiﬁed material transportation monitoring system and
national data integration and analysis platform will be gradually established,
which will provide more accurate and timely information on the
material supply and transportation capacity during emergency policymaking
for the whole society.
2.9. Rapid estimation of population ﬂow and distribution
The magnitude and scale of population mobility are essential information
for spatial transmission prediction, risk area division, and control
measure decision-making for infectious diseases (Wang et al., 2019).
Research has shown a strong relationship between the number of train
journeys and the number of COVID-19 cases (Zhao et al., 2020). Before
the lockdown of Wuhan on January 23, 2020, more than ﬁve million
people had left Wuhan to other regions in China (News China, 2020).
A better understanding of the destinations of those people can assist
in decision-making and prevention of coronavirus spread. Therefore, in
this research, by using multi-source spatiotemporal big data, including
Tencent location request data, Baidu migration data, and land cover
data, we have developed a dynamic estimation model of the multilevel
spatial distribution of the interregional migrant population and further
characterized the spatial distribution of the population migrating from
Wuhan to other regions of Hubei Province. The results show that 1) during
the Spring Festival, the average ratio between the increases in rural
populations to the total population was 124.7% in the prefecture-level
cities in Hubei Province, and at least 51.3% of the population moving
from Wuhan to prefecture-level cities ﬂowed into rural areas, and 2)
the spatial distribution of migrants among cities and counties in Hubei
Province exhibited a three-ringed structure. The ﬁrst ring was the core
area of disease, which included Wuhan and its surrounding areas, which
mainly experienced population outﬂows. The second ring was the primary
focus area, including Huanggang, Huangshi, Xiantao, Tianmen,
Qianjiang, Suizhou, Xiangyang, and parts of Xiaogan, Jingzhou, Jingmen,
and Xianning, where the total population and the population in
rural areas increased signiﬁcantly during the Spring Festival. The third
ring was the secondary focus area, which included Yichang, Enshi, Shennongjia,
and parts of Jingzhou and Jingmen, which were located in the
western part of Hubei Province and were mainly characterized by a
small population inﬂow (shown in Figure 10). The above results were
shared with government oﬃcials, and the development of the epidemic
conﬁrmed the objectivity of this research, which strongly supported government
decision-making.
Population mobility in the Spring Festival of 2020 was signiﬁcantly
lower than that during the Spring Festival of 2019 (shown in Figure 11)
due to the quarantine measure, which prevented the wider spread of
the epidemic (Hu, 2019; Wei et al., 2018). However, Figure 11 shows
that population mobility has increased since February 17 (the 24th of the
ﬁrst lunar month of 2020). The return of people to work will possibly increase
the spread of COVID-19. Baidu migration data were utilized here
to predict the risk (Xu et al., 2017). We calculated the speed of the population
mobility recovery in each city (shown in Figure 12-a.) based on
the population migration from February 17-23, 2020. The result showed
that the speed of the return to normal levels of population mobility was
rapid in economically developed cities in South China. The community
division of the population mobility network showed that the population
ﬂows were mostly from the cities in Henan, Anhui, and Jiangsu to the
Yangtze River Delta and from cities in Hunan, Guizhou, and Jiangxi to
the Pearl River Delta. Both the departure cities and the destination cities
are places with large outbreak of COVID-19. Based on the above analysis,
taking strict measures on these moving populations was suggested
in the case of the second surge of COVID-19 cases.
2.10. Spatial spread and detection of social sentiment
When a major epidemic occurs, the negative impact of uncertainty
and panic on social operations may exceed that of the viral diseases.
Therefore, this research applied massive social media data to track and
evaluate the spatial spread of public sentiment (Miller and Goodchild,
2015). Considering that public behavior has the characteristics of irrationality,
strong infectivity and conformity, we needed to build a knowledge
base of epidemic sentiment association by mining the dynamic
evolution process of public opinion in time and space and using the
semantics from social media (Goodchild and Glennon, 2010; Xiubao et
al., 2017). The Sina Weibo, which is similar to Twitter, is the social
media platform on which Chinese people typically share their opinions.
With the goal of modelling and visualizing the semantic and spatiotemporal
evolution of public opinion on COVID-19, a topic extraction and
classiﬁcation framework for extracting topics of a COVID-19-related microblogs
was designed and implemented. Based on the complex network,
a user semantic behavior evolution model was introduced to measure
and analyze the change in public opinion by tracking topics and
sentiments that appeared in microblog users’ timelines in response to
COVID-19. The results indicated that from January 9 to February 10,
2020, more than 60% of posts related to science popularization of disease
prevention, government announcements, and responses suggested
that public sentiment was positive and stable. The posts with the topics
of “help-seeking” were concentrated in the key epidemic area of Wuhan,
and the posts related to “donation information” were widely distributed
8
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
Figure 11. National population ﬂow trend in
the same period of Spring Festival in 2019
and 2020.
Figure 12. a) Recovery of urban population ﬂow; b) Rework population ﬂow network and community Division.
Figure 13. Spatial distribution of help and donation information of COVID-19 during the epidemic period (2020/01/09 - 2020/02/10).
throughout the country (shown in Figure 13). The results reﬂected the
characteristics of China’s emergency disaster relief, which was characterized
by the adage "Trouble on one side, help from all sides".
3. Conclusion
COVID-19 is characterized by a long incubation period, strong infectivity
and diﬃculty of detection, which has led to the sudden outbreak
and the rapid development of an epidemic. This situation requires GIS
and big data technology to allow rapid responses and analyses, a quick
supply of information about the epidemic dynamics and an understanding
of the epidemic development rules to provide timely support for the
prevention and control decisions and actions.
In this study, we analyzed the spatial representation of the disease,
material, population and social psychology at three scales: individual,
9
C. Zhou, F. Su and T. Pei et al. Geography and Sustainability xxx (xxxx) xxx
ARTICLE IN PRESSJID: GEOSUS [m5GeSdc;March 28, 2020;14:39]
group and regional. At the individual scale, the comparison between
spatial epidemic tracking and the spatiotemporal trajectories of patients
was carried out. At the group scale, the estimation of population ﬂow
and the spatial distribution was carried out. At the regional scale, the
segmentation of spatial risk, the analysis of balance between the supply
and demand of medical resources, and the spatial diﬀerentiation analysis
of material transportation capacity and social sentiment were carried
out.
From the perspective of GIS technology, this study revolutionized the
data acquisition methods through big data technology, which achieved
rapid data acquisition and integration from traditional data to big data
of various organizations. The analysis platform was quickly constructed
through an innovative construction technology system, which provided
the technical platform for timely epidemic analysis. The production
of epidemic maps was quickly completed through multi-scale dynamic
template technology, which allowed timely dissemination of epidemic
dynamic information. From the perspective of spatial simulation and
analysis, this study simulated the spatial transmission process of the epidemic
scientiﬁcally by increasing detailed regional variables and changing
the population ﬂow and R0 (basic reproduction number) according
to the prevention and control measures. The calculation of the overlap
between the spatial tracking of the virus and that of patient trajectories
was realized based on the exposure index of fused spatial and text
information via text spatialization technology. The estimation of population
ﬂow and spatial distribution was realized via the combination of
big data and traditional geographic data, which identiﬁed the problems
in the key risk areas and the spatial mismatching of medical resources
in a timely manner, to bring about a rapid supply of information about
the delimitation of prevention areas and resource deployment in epidemic
control. The monitoring of social sentiment from social media
was realized through the construction of a knowledge database on public
opinion, which provided important foundational information on public
opinion to guide the government.
In assessing the contribution of GIS and spatial big data technology
to the containment of the COVID-19 epidemic, it is clear that many
challenges remain to be studied. For example, the status of big data
source restrictions in commercial enterprises may restrict the data supply
needed for social management, result in the lack of a mature scheme
for big data aggregation, and cause diﬃculty of rapid online application
of deep integration, which are ongoing issues. Regarding data-driven
knowledge acquisition, the uncertainty of social operations, especially
with the high spatial heterogeneity of responses to epidemic development
throughout the country, may lead to spatial deviations from the
model simulation. Strategies for a technical system of knowledge acquisition
based on big spatial data focused on social operations are existing
challenges. From the aspect of result presentation, the status presentation
can be fully reﬂected, while the multi-scale dynamic presentation
driven by big data is urgently needed and promising.
Declaration of Competing Interest
The authors declare no conﬂict of interest.
Acknowledgment
This work was funded by the National Natural Science Foundation
of China (41421001). We thank the reviewers who provided valuable
comments on this paper. We are very grateful to those who participated
in the data preparation, processing and analysis for this study. We ackonwledge
Professor Michael E Meadows and colleagues in International
Geographic Union and in its Commission on Geographical information
Science. We also acknowledge all the support from the State Key Laboratory
of Resources and Environmental Information System, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy
of Sciences.
References
CAICT: China Academy of Information and Communications Technology, Research
Report on Data and Intelligent Application in Epidemic Prevention and Control (1.0).
http://www.caict.ac.cn/kxyj/qwfb/ztbg/202003/P020200305495005485729.pdf.
2020(accessed 01 March 2020). (In Chinese).
CDC: Centers for Disease Control and Prevention, 2020. Healthcare Professionals:
Frequently Asked Questions and Answers https://www.cdc.gov/coronavirus/2019-
ncov/hcp/faq.html/.
China Daily, 2020b. Wuhan: Leishenshan Hospital Undergoing Quality Check Before
Phased Delivery. China Daily https://www.chinadaily.com.cn/a/202002/08/
WS5e3e7810a310128217275fb3.html/.
China Daily, 2020a. Superfast hospital in Wuhan ready. China Daily https://epaper.
chinadaily.com.cn/a/202002/03/WS5e37713ca310a2fabb7a1dc7.html/.
Field, K, 2020. responsibly. Mapping coronavirus https://www.esri.com/arcgis-blog/
products/product/mapping/mapping-coronavirus-responsibly/.
Goodchild, M., Glennon, J., 2010. Crowdsourcing geographic information for disaster response:
a research frontier. International Journal of Digital Earth 3 (3), 231–241.
Grassly, N., Fraser, C., 2008. Mathematical models of infectious disease transmission. Nature
Reviews Microbiology 6 (6), 477–487.
Health Commission of Hubei Province. http://wjw.hubei.gov.cn/bmdt/ztzl/
fkxxgzbdgrfyyq/xxfb/202002/t20200213_2025580.shtml/. 2020. (In Chinese)
Hu, M., 2019. Visualizing the largest annual human migration during the Spring Festival
travel season in China. Environment and Planning A: Economy and Space 51 (8),
1618–1621.
Miller, H., Goodchild, M., 2015. Data-driven geography. Geojournal 80 (4), 449–461.
National Health Commission of the PRC, 2020. COVID-19 epidemic situation
up to 24:00 on March 8th http://www.nhc.gov.cn/xcs/yqtb/202003/
f2c83db9f73d4be5be0dc96af731813c.shtml/.
New.cn, 2020. COVID-19 is urged by the UN Secretary General to do everything possible to
contain the outbreak http://www.xinhuanet.com/2020-02/29/c_1125642849.htm/.
News China, 2020. Mayor of Wuhan: more than 5 million people left Wuhan
http://news.china.com.cn/2020-01/26/content_75650784.htm/.
Riley, S., 2007. Large-Scale Spatial-Transmission Models of Infectious Disease. Science
316 (5829), 1298–1301.
SDG: Sustainable Development Goals, What are the Sustainable Development Goals.
https://www.undp.org/content/undp/en/home/sustainable-development-goals.
html/. 2015.
State council, http://www.gov.cn/xinwen/2020-01/23/content_5471751.htm/. 2020
(accessed 23 January 2020). (In Chinese)
Wang, Y., Dong, L., Liu, Y., Huang, Z., Liu, Y., 2019. Migration patterns in China extracted
from mobile positioning data. Habitat International 86, 71–80.
Wei, Y., Song, W., Xiu, C., Zhao, Z., 2018. The rich-club phenomenon of China’s population
ﬂow network during the country’s spring festival. Applied Geography 96, 77–85.
WHO: World Health Organization, Coronavirus disease (COVID-2019) situation Reports.
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situationreports/,
2020 (accessed 21 January 2020).
Xiubao, Shi, Lei, Ma, Bin., Li, 2017. Study on disaster information management system
compatible with VGI and crowdsourcing. Science of Surveying and Mapping 42 (3),
191–195 (In Chinese).
Xu, J., Li, A., Li, D., Liu, Y., Du, Y., Pei, T., et al., 2017. Diﬀerence of urban development
in China from the perspective of passenger transport around Spring Festival. Applied
Geography 87, 85–96.
Zhao, S., Zhuang, Z., Ran, J., Lin, J., Yang, G., Yang, L., He, D., 2020. The association
between domestic train transportation and novel coronavirus (2019-nCoV) outbreak
in China from 2019 to 2020: A data-driven correlational report. Travel Medicine and
Infectious Disease 33, 101568.
Zhou, C., Su, F., Harvey, F., Xu, J., 2016. Spatial Data Handling in Big Data Era. Springer,
Beijing.
10