Sustainability 2020, 12, 4727; doi:10.3390/su12114727 www.mdpi.com/journal/sustainability
Analysis of Flood-Vulnerable Areas for Disaster
Planning Considering Demographic Changes in
Hye-Kyoung Lee, Young-Hoon Bae, Jong-Yeong Son and Won-Hwa Hong *
School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University,
Daegu 41566, Korea; email@example.com (H.-K.L.); firstname.lastname@example.org (Y.-H.B.);
* Correspondence: email@example.com; Tel.: +82-53-950-5597
Received: 15 April 2020; Accepted: 4 June 2020; Published: 9 June 2020
Abstract: Regional demographic changes are important regional characteristics that need to be
considered for the establishment of disaster prevention policies against climate change worldwide.
In this study, we propose urban disaster prevention plans based on the classification and
characterization of flood vulnerable areas reflecting demographic changes. Data on the property
damage, casualties, and flooded area between 2009 and 2018 in 229 municipalities in South Korea
were collected and analyzed, and 74 flood vulnerable areas were selected. The demographic change
in the selected areas from 2000 to 2018 was examined through comparative analyses of the
population size, rate of population change, and population change proportion by age group and
gender. Flood vulnerable areas were categorized into three types through K-mean cluster analysis.
Based on the analysis results, a strategic plan was proposed to provide information necessary for
establishing regional flood-countermeasure policies.
Keywords: urban disaster prevention plan; flood vulnerability; climate change; demographic
change; cluster analysis
Floods are one of the most dangerous and destructive natural hazards that can cause human loss
and economic damages [1–3]. Climate change is expected to increase the frequency of flooding and
the extent of damage caused by it [4–6].
To minimize the damage caused by natural hazards, policies, treaties, and conventions,
including the Nations Framework Convention on Climate Change (1992), Kyoto Protocol (1997),
Hyogo Framework (2005), Sendai Framework (2015) and Paris Agreement (2015), have been
established. There is increasing awareness of the important role of policies that consider regional
characteristics on the mitigation of damages, adaptation to climate change, and sustainable
development in response to climate change [7–11].
Although there are several studies on regional flood risk management globally, policies,
practices, and approaches relevant to and effective in some countries may not be applicable to the
rest. Indeed, differences in governance structures and processes, topography, weather patterns, and
vulnerabilities will lead to difficulties in developing effective community flood risk management
Various approaches for establishing a flood disaster prevention plan considering regional
characteristics have been proposed, including the development of flood vulnerability assessment
indicators and their application for the selection of vulnerable regions.
Sustainability 2020, 12, 4727 2 of 17
Cardona et al.  calculated the local disaster index using flood damage indicators, such as the
number of casualties and victims and the monetary value of the flood damage. Kubal et al. 
proposed three types of area vulnerability indicators: Social indicators, e.g., transport, housing, and
commerce; economic indicators, e.g., schools, hospitals, children, and elderly people; and ecological
indicators, e.g., forest, biodiversity, and potential pollution. They used these indicators to evaluate
the flood vulnerability of a city in Germany by a multicriteria approach. Balica et al.  proposed
the flood vulnerability index (FVI) with four types of vulnerability indicators: Social, economic,
environmental, and physical. Planners and policymakers can use the FVI as a tool to prioritize flood
risk hotspots. Balica et al.  developed the coastal city FVI using hydro-geological, socio-
economical, and politico-administration indicators and applied it to nine coastal cities around the
world to evaluate the most vulnerable ones.
In South Korea, various studies on flood damage mitigation have been carried out. The potential
flood damage indicator was proposed to evaluate the flood potential by evaluating the possibility of
flooding and the flood control capability considering potential damage factors, such as the
population, types of property, urbanization rate, and infrastructures, and risk factors, such as flood
damage extent, rainfall probability, river improvement rate, and flood control capacity . Regional
safety assessment techniques have been proposed to identify the risk of disaster related to natural
hazards in specific regions . The flood damage index was developed considering the main causes
of flood damage subdivided into natural, social, policy, and facility factors. This index considers 11
causes representing each factor . In areas were divided into risk and vulnerability groups through
the excess flood vulnerability index (EFVI) .
The results of the flood vulnerability analyses depend on the type, characteristics, and duration
of analysis of the selected evaluation indicators, as well as the region under investigation [22–24].
Several studies on flood vulnerability analyses considered flood damage indicators (e.g., casualties
and amount of damage), socio-environmental indicators (e.g., urbanization rate, traffic, and
residential ratio), regional indicators (coastal area, urban area, and rural area), and population
indicators (e.g., the number of people living in the disaster-prone areas, such as coastal areas and
areas with a history of multiple floods, disaster-vulnerable population, such as children and the
elderly, and the population affected by the disaster).
Demographic change is one of the most important topics discussed globally, along with the issue
of climate change [25,26]. Demographic change is an important criterion for policymaking because it
cannot be reversed in a short time, and it is necessary to analyze its characteristics to use them for
policymaking in a pre-emptive manner [27,28]. In its Fifth Assessment Report, the Intergovernmental
Panel on Climate Change (IPCC, 2015) highlighted demographic characteristics such as age, gender,
income, and health status as parameters to shape the resilience capacities of urban areas .
South Korea is the country with the fastest decrease in the birth rate and the fastest growth in an
aging population in the world [30,31]. Therefore, research has been conducted in various fields on
problems and prevention based on demographic changes, and many related national policies have
been established. When establishing a disaster prevention policy that prevents human losses and
property damage during disasters, it is essential to reflect demographic changes. However, studies
discussing and investigating demographic change are scarce in the flood disaster prevention plan. In
this study, we propose a strategy for establishing flood disaster preventive urban policies by
classifying flood vulnerable areas based on flood characteristics and demographic changes in each
region in South Korea.
2. Materials and Methods
2.1. Study Area: Spatial Division and Regional Characteristics of Floods
South Korea is characterized by its meteorological features: Rainfall during the rainy season
between June and September—when air masses from the northern Pacific and tropical cyclones head
north—accounts for 55% of the annual precipitation . In addition, more than two-thirds of the
entire land consists of mountainous terrain with weathered rocks, and most of the heavy rainfall is
Sustainability 2020, 12, 4727 3 of 17
concentrated in the west and south river basin because of the tilted landform (high altitudes in the
east and low altitudes in the west). Therefore, streams occur frequently in the western region. Further,
coastal floods occur repeatedly every year because the three sides of the peninsula are in contact with
the sea . However, the recent industrialization and urbanization have resulted in a larger
population concentration in metropolitan city areas and increased land use. This has led to an
increase in impervious surfaces. Therefore, the frequency and scale of damage caused by urban floods
South Korea is divided into 17 administrative regions: Three special cities, six metropolitan
cities, and eight provinces (Table 1). There are a total of 69 autonomous districts in nine special cities
and metropolitan cities, 78 autonomous cities and 82 counties in nine special cities and metropolitan
cities, and eight provinces, making up a total of 229 municipalities, hereinafter referred to as city
(si)/county (gun)/district (gu). In this study, we analyzed the flood damage characteristics and the
demographic change in the 229 municipalities of si/gun/gu in South Korea.
Table 1. Seventeen administrative regions of South Korea.
Regions Area (km2) Si
1 Seoul-si 605.24 - - 25
2 Busan-si 769.94 - 1 15
3 Deagu-si 883.52 - 1
4 Incheon-si 1063.27 - 2 8
5 Gwanju-si 501.18 - - 5
6 Deajeon-si 539.53 - - 5
7 Ulsan-si 1061.54 - 1 4
8 Sejong-si 464.91 1 - -
9 Gyeonggi-do 10,187.79 28
10 Gangwon-do 16,827.91 7 11 -
11 Chungcheongbuk-do 7407.85 3 8 -
12 Chungcheongnam-do 8229.20 8 7 -
13 Jeollabuk-do 8069.07 6 8 -
15 Gyeongsangbuk-do 19,032.87 10
16 Gyeongsangnam-do 10,540.12 8 10 -
17 Jeju-si 1850.16 2 - -
The purpose of this study is to provide the basis and implications for the establishment of urban
disaster preventive policies in response to climate change. To this end, we considered two aspects
that were not considered in previous studies. First, in relation to the establishment of a flood disaster
prevention policy considering regional characteristics, we conducted regional flood disaster
vulnerability assessments of the 229 municipalities in South Korea. In this study, the term “regional
flood disaster vulnerability” represents the degree to which flood disaster will occur again based on
past flood damage. Second, we categorized the flood vulnerable areas in South Korea by considering
the demographic change indicators. In this study, the “Demographic change” implies any change in
the population, for example, a change in population size, the rate of population change, and the
population proportion change by age groups and gender. The research flow chart applying the two
aspects considered is explained below (Figure 1).
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Figure 1. Flow chart of the procedure followed in this study.
First, we analyzed the three types of flood damage characteristics, i.e., the property damage,
casualties, and flooded area between 2009 and 2018. We rated the extent of flood damage among the
229 municipalities of si/gun/gu and selected the 30 most flooded areas for each category as the flood
vulnerable areas. Second, for the selected flood vulnerable areas, we compared the population size,
rate of population change, and population proportion change between 2000 and 2018 and analyzed
them by age groups and gender. Third, to generalize the demographic changes in the flood
vulnerable areas for comparison, we performed typing through cluster analysis.
In addition, through a comparison of the characteristics by flood vulnerable area type derived
from cluster analysis and spatial distribution analysis, this study aims to provide the information
necessary to establish flood countermeasure policies for each type of region in the future.
2.3. Flood Vulnerable Area Selection Data
Past flood damage data have been widely used for the selection of flood vulnerable areas
because they reflect the characteristics and status of the flood damage in each region [34–36]. In
particular, flood damage data comprehensively reflect socio-economic and topographical factors. In
this study, property damage (one million South Korean won), casualties (persons), and flooded area
(km2) data were selected as the flood vulnerable area characterization variables.
The South Korean Ministry of the Interior and Safety publishes major statistics on natural hazard
damage and recovery status in the annual disasters related to the natural hazard report in Statistics
Korea . Statistical data on annual disaster related to the natural hazard report provide statistics
for each natural hazard period by facility (public or private facility) and cause (e.g., typhoon, heavy
rain, heavy snow, storm, and earthquake). In terms of spatial distribution, they provide the statistics
for each major city and province in the 17 administrative regions up to the level of gun/gu. However,
the statistics by province are provided according to each natural cause (e.g., heavy rain, typhoon,
heavy snow, strong wind, and earthquake), while the statistics in the unit of gun/gu provide only the
total of the natural hazard and no classification according to the causes.
In this study, statistical damage data from the flooding of 229 municipalities are extracted by
comparing the total number of the natural hazards in the 229 municipalities with the flood damage
in the 17 administrative regions and the natural hazard for each period.
The damage status derived for comparative analysis by si/gun/gu was divided into the total area
by si/gun/gu to determine the amount of damage per km2, and through comparison, the 30 most
damaged regions in each category, i.e., property damage (one million South Korean won/km2),
casualties (persons/km2), and flooded area (km2/km2) were selected as the flood vulnerable areas.
2.4. Demographic Change Analysis Data
Characteristics, such as the population size, distribution, and structure, provide the basic data
for policymaking, planning, research, and evaluation. Statistics Korea periodically conducts a
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population census for all persons in the country. Statistical data for the population census are
prepared up to the eup-myeon-dong unit (the sub-district unit of si/gun/gu) and published every five
years. However, from 2015, the survey method changed to a registered census method using national
administrative data from the total number of surveys, and the data are now published every year .
For the analysis of the demographic change in flood vulnerable areas, the total population,
population by age, and gender between 2000 and 2018 of the 229 municipalities were extracted from
Statistics Korea's population census data. Unlike most demographic analysis studies and the data
provided by the National Statistical Office, which categorizes demographic age groups into youth
(0–14 years old), working-age (15–64 years old), and aged (+65 years old) [27,39,40], this study further
divided the youth group and aged group because they are more vulnerable to the flood damage. In
total, five age groups were categorized: Infants (0–9 years old), school-age (10–19 years old), working-
age (20–64 years old), aged (65–74 years old), and super-aged (+75). Then, a comparative analysis by
si/gun/gu was performed on the population size, rate of population change, population proportion,
and proportion change by age group and gender in the last 18 years.
2.5. Cluster Analysis
Cluster analysis is a statistical analysis whereby data values are converted into distances based
on the similarity between variables, and nearby variables are classified into clusters. This method can
be broadly divided into hierarchical clustering and non-hierarchical clustering [41,42]. For the
analysis by typing of flood vulnerable areas corresponding to demographic change, this study
utilized the K-mean method, which is a non-hierarchical method.
We selected the flood damage characteristics as indicators, i.e., the property damage (million
South Korean won/km2), casualties (person/km2), and flooded area (km2/km2), and the demographic
change characteristics, i.e., the population by age group and gender (population in 2018), population
change (population in 2018–population in 2000), rate of population change ((population in 2018–
population in 2000)/population in 2018 × 100), proportion (population by age group or gender/total
population in 2018 × 100) and proportion change (proportion in 2018–proportion in 2000) (Table 2).
Table 2. Variables considered for the cluster analysis.
Variable Calculation Method
Vulnerable area characteristics
Property damage (million South Korean won/km2) Flood property damage between
2009 and 2018
Casualties (person/km2) Flood casualties 2009 and 2018
Flooded areas (km2/km2) Flooded areas between 2009 and 2018
Population (person) 0–9
years old Male
Population in 2018
Population change (person) 10–19
years old Female
Population in 2018–Population in 2000
Rate of population change (%) 20–64
(Population in 2018–Population in
2000)/Population in 2018 × 100
Population proportion (%) 65–74
Population by age group or
gender/population in 2018 × 100
Rate of population proportion
Population proportion in 2018–Population
proportion in 2000
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3.1. Flood Vulnerable Area Selection
3.1.1. Selection and Characteristics of Vulnerable Areas by Major Administrative Region
Prior to the selection of the detailed flood vulnerable areas among the 229 municipalities of
si/gun/gu, based on the annual disaster report of the Ministry of the Interior and Safety, the flood
damage of 17 administrative regions nationwide in the last 10 years was recalculated as the ratio of
the area, and the results were compared and analyzed (Figure 2).
Figure 2. Comparison of flood damage for the 17 administrative regions (2009 to 2018).
As shown in Figure 2, Busan-si was the most damaged with the amount of damage reaching
5077 million won/km2 (36.45%), followed by Seoul-si with 2085 million won/km2 (14.97%), and
Gyeonggi-do with 1767 million won/km2 (12.68%). The casualties were 4632 persons/km2 (63.12%),
1061 persons/km2 (14.46%), and 801 persons/km2 (10.91%) in Seoul-si, Incheon-si, and Gyeonggi-do,
respectively. Seoul-si, Jeollabuk-do, and Busan-si had flooded areas of 23.68 km2/km2 (77.60%), 0.38
km2/km2 (11.07%), and 0.14 km2/km2 (4.13%), respectively. Overall, the flood damage in the western
provinces was greater than that in the eastern provinces because of the topographical features of the
tilted landform (higher altitudes in the east and low altitudes in the west) of South Korea.
In addition, more than half of the property damages, casualties, and flooded areas were
observed in two to three administrative regions. A large variation in flood damage was obtained in
different regions. These results indicate that flood disaster prevention plans need to be established
such that the most damaged regions are prioritized in terms of the implementation of plans. Further,
the most damaged areas need to be analyzed with more precise units such as si/gun/gu.
3.1.2. Selection and Characteristics of Vulnerable Areas (si/gun/gu)
Considering the last 10 years, the regions were ranked according to the property, casualty, and
flood area damages caused by floods in 229 municipalities of si/gun/gu, and the 30 most damaged
towns and districts for each category were selected as flood vulnerable areas (Figure 3 and Table 3).
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Figure 3. Spatial analysis of flood vulnerable regions (a) property damage, (b) casualty losses, (c)
flooded areas, (d) overlap.
Table 3. Characteristics of regions most vulnerable to floods.; Regions in the two categories of
property and casualty(yellow), property and flood area(red), casualty and flood area(green).regions
in the three categories of property, casualty, and flood area(gray).
Property Damage Casualties Flooded Areas
m2 % Municipalities
Total 7337.55 100.0
0 Total 3.46 100.0
Sub-Total : Top 30
Sub-Total : Top 30
Sub-Total: Top 30 3.46 99.94
Busan Suyeong 843.49
6.06 Seoul Yangcheon
Seoul Gangseo 2.683 77.59
Busan Seo 763.30
5.48 Seoul Dongjak 497.73 6.78
uk Jeonju 0.368 10.36
Busan Yeongdo 702.68
5.04 Seoul Gwanak 496.70 6.77
am Seocheon 0.080 2.31
Busan Saha 555.82
n Michuhol 441.16 6.01
Busan Yeonje 0.076 2.20
Busan Nam 438.00
3.14 Seoul Gangdong
Busan Haeundea 0.066 1.91
Seoul Seocho 436.77
3.14 Seoul Guro 352.38 4.80
m Sacheon 0.028 0.82
Busan Yeonje 332.96
2.39 Seoul Geumcheo
n 339.93 4.63
Busan Gijang 316.96
2.28 Seoul Gwangjin 314.18 4.28
Jeollanam Suncheon 0.024 0.68
n Bupyeong 313.88 4.28
Gyeonggi Hwaseong 0.017 0.50
Busan Buk 235.70
1.69 Seoul Seocho 267.71 3.65
uk Gimje 0.017 0.49
Jeollanam Wando 228.65
1.64 Seoul Gangseo 240.85 3.28
m Miryang 0.011 0.31
Seoul Gwanak 228.59
ggi Bucheon 239.78 3.27
am Taean 0.010 0.29
Gwanju Gwangsan 0.009 0.29
1.63 Busan Dongnae 167.68 2.29
k Pohang 0.007 0.20
1.53 Seoul Gangnam 162.22 2.21
uk Iksan 0.007 0.19
Jeollanam Mokpo 191.51
1.26 Seoul Songpa 119.21 1.62
m Jinju 0.006 0.18
Gwanju Nam 149.98
1.08 Seoul Mapo 115.68 1.58
am Buyeo 0.005 0.16
1.05 Busan Yeonje 112.49 1.53
Jeollanam Naju 0.005 0.14
Ulsan Buk 138.71
1.00 Seoul Eunpyeong
Jeollanam Boseong 0.002 0.06
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n Namdong 102.09 1.39
am Yesan 0.002 0.06
0.90 Busan Nam 89.21 1.22
Incheon Jung 0.002 0.06
Gyeonggi Yangju 119.23
0.86 Busan Dong 87.41 1.19
Jeollanam Gurye 0.002 0.05
Seoul Dongjak 114.73
n Gyeyang 87.37 1.19
Incheon Bupyeong 0.001 0.04
Ulsan Jung 114.17
uk Imsil 0.001 0.02
Gyeonggi Uiwang 111.31
0.80 Seoul Seodaemun
Incheon Ongjin 0.001 0.02
0.72 Seoul Gangbuk 72.96 0.99
Deagu Dalseong 0.001 0.01
Jeollanam Yeosu 98.03
ggi Anyang 70.73 0.96
Busan Gangseo 0.000 0.01
Seoul Songpa 97.71
0.70 Busan Yeongdo 68.80 0.94
am Hongseong 0.000 0.01
Jeollanam Shinan 94.65
n Seo 64.47 0.88
Seoul Seocho 0.000 0.01
Property damage to the 30 most damaged si/gun/gu accounted for 57.18% of the total damage.
Busan-si Suyeong-gu was the most damaged, accounting for 6.06% of the total damage. Among the
administrative regions analyzed, 12 gu/gun in Busan-si, 6 gu in Seoul-si, 5 si in Gyeonggi-do, and 4
si/gun in Jeollanam-do were the 30 most damaged. Among the administrative regions not categorized
into flood vulnerable areas in the analysis, Gwangju-si Nam-gu, Ulsan-si Buk-gu, Ulsan-si Jung-gu,
and Gyeongsangnam-do Tongyeong-si were among the 30 most damaged.
The 30 most damaged municipalities accounted for 87.77% of the total casualties. Yangcheon-gu
in Seoul-si had the largest number of casualties, accounting for 8.97% of the total. The si/gun/gu
distribution showed that these 30 areas were in Seoul-si (17 gu), Incheon-si (5 gu/gun), Gyeonggi-do
(3 cities), and Busan-si (4 districts).
Gangseo-gu in Seoul-si had the largest damaged area, accounting for 77.59% of the total
damaged area. The flooded areas of the 30 most damaged municipalities accounted for 99.94% of the
total damage. This result indicates that all the regions with flooded areas were ranked in the top 30.
With the analysis of the overlap between the 30 most damaged regions in the three categories of
property, casualty, and flood area, two si/gun/gu (i.e., Seocho-gu in Seoul-si and Yeonje-gu in Busan-
si) overlapped across all three categories of damage, whereas 12 si/gun/gu (i.e., Seodaemun-gu,
Yangcheon-gu, Gangseo-gu, Dongja-gu, Gwanak-gu and Songpa-gu in Seoul-si; Yeongdo-gu,
Dongrae-gu, Nam-gu and Haeundae-gu in Busan-si; Bupyeong-gu in Incheon-si; and
Gwangmyeong-si in Gyeonggi-do) overlapped across two categories. Considering these, 74
si/gun/gu were selected as the final flood vulnerable areas.
3.2. Demographic Change Analysis
3.2.1. Demographic Change Across South Korea
As of 2018, Korea's total population was 51,630,000, which is an increase of 12% (5,644,000) from
that in 2000.). However, the population decreased in 136 out of 229 municipalities (59.39%) and
increased in only 93 regions (40.61%) (Figure 4). This indicates that, like flood damage, population
change has large variations among regions, and this must be considered in disaster preventive urban
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Figure 4. Spatial analysis of Korea's population change (2000–2018). (a) 2018 population, (b) 2000
population, (c) population change (2018-2000).
Table 4a shows the demographic change for all age groups in South Korea between 2000 and
2018. The super-aged (+75) and infants (0–9) were the age group with the highest increase (3.96%)
and decrease (−6.01%), respectively. The proportion of infants, school-age (10–19), and working-age
(20–64) decreased, while that of aged (65–74) and super-aged (+75) increased. Similar to the results of
many previous studies, the above findings show that South Korea is the country with the fastest
decrease in the birth rate and the fastest aging population in the world. In addition, the low birth rate
and fast population aging are expected to accelerate the demographic change in the long run, and the
most flood vulnerable group is also changing from the younger groups to the older ones. Based on
several analyses, this change is expected to be more severe in the future.
Examining the demographic change by age group and by gender, the proportion of women in
the super-aged group (+75) showed the highest increase of 3.97% in 2018, which represents an
increase of 2.38% compared to the 1.59% increase in 2000. The proportion of men in the infants (0–9)
group showed the largest decrease to 4.26% in 2018, which represents a decrease of 3.30% over to the
7.55% increase in 2000. These results indicated that the proportion of women in the flood
vulnerability group increased.
3.2.2. Demographic Change in Flood Vulnerable Areas
The 74 flood vulnerable areas selected in Section 3.1 account for 17.18% (17,247 km2) of the total
land area in Korea and include 40.29% (20,799 thousand people) of the total population in 2018.
Therefore, most of the vulnerable areas are densely populated.
Table 4b also shows the demographic change for all age groups in flood vulnerable areas
between 2000 and 2018. Similar to the overall pattern in South Korea, the proportion of aged (65–74)
and super-aged (+75) people increased, and the proportion of infants (0–9) and school-age (10–19)
people decreased in flood vulnerable areas, indicating a low birth rate and fast population aging. The
demographic change according to gender in flood vulnerable areas also showed the largest increase
in the proportion of women in the super-aged (+75) groups (+2.26%) and the largest decrease in the
proportion of men in the infants (0–9) groups (−3.03%), similar to the overall pattern in South Korea.
Furthermore, the population decreased in 42 regions and increased in 32 regions. The demographic
changes by region according to age group are as follows. For infants (0–9) and school-age children
(10–19), the population decreased in 67 and 63 regions, respectively, and increased in 7 and 11
regions, respectively, which indicates many regions witnessed a decrease in the population of these
age groups. For the working (20–64) and aged (65–74) groups, the population increased in 43 and 63
regions, respectively, and decreased in 31 and 11 regions, respectively. This indicates that many
regions demonstrated an increase in the population of these age groups. The super-aged (+75) group
demonstrated a population increase in all 74 vulnerable areas.
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Table 4. Demographic changes between 2000 and 2018 in South Korea and in flood vulnerable areas in South Korea.
(a) South Korea (b) Flood Vulnerable Areas in South Korea
Proportion (%) Population
2000 2018 2018–2000 2000 2018 2018–2000 2000 2018 2018–2000 2000 2018 2018–2000
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3.3. Characteristics of Flood Vulnerable Area Type Reflecting Demographic Change
3.3.1. Type Classification through Cluster Analysis
For the analysis of flood vulnerable area type reflecting demographic changes, a K-mean cluster
analysis was conducted by selecting flood vulnerability and demographic change characteristics as
variables. Unlike hierarchical cluster analyses, the K-mean cluster analysis requires the number of
clusters to be specified in advance. To select the appropriate number of clusters, the number of
clusters was varied, and the number of cases for each cluster was determined through ten iterations
(Table 5). In this process, it was confirmed that each case (18, 25, 30, 1) showed relatively uniform
distribution when classified into four clusters, and, excluding one region with exceptional
characteristics, 73 si/gun/gu were categorized into three types.
Table 5. Comparison of the number of cases by clusters.
Cluster Number of Cases in Each Cluster
1 2 3 4 5 6
2 30 44
3 35 38 1
4 18 25 30 1
5 11 12 21 29 1
6 18 2 18 21 22 1
3.3.2. Analysis of Characteristics by Type
The characteristics of three types of flood vulnerable areas derived through cluster analysis were
examined by comparing the mean values of the flood vulnerable area characteristic variables and
demographic change characteristic variables (Table 6).
Table 6. Characteristics of clusters and comparison of the variables’ mean values.
Variable Cluster 1 Cluster 2 Cluster 3
Property damage 94.50 million won/
Casualties 241.09 person/km2 10.24 person/km2 71.83 person/km2
Flooded area 0.17 km2/km2 0.01 km2/km2 0.00 km2/km2
Male Female Male Female Male Female
253,366 259,782 40,145 39,388 145,500 147,723
Infants 20,284 19,209 2998 2847 12,420 11,775
School-age 24,313 22,593 3454 3166 14,590 13,706
Working-age 178,617 179,774 26,298 22,947 100,926 99,145
Aged 19,866 21,822 4305 4849 11,280 12,717
Super-aged 10,285 16,385 3088 5578 6283 10,380
2088 10,025 13,011
Working-age 14,109 17,266 693
1974 12,670 11,683
Aged 12,584 11,532 1,250 402 6643 6139
Super-aged 7803 10,680 1953 3230 4773 6879
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Rate of population change
Working-age +6.71% +8.63%
18.70% +11.29% +11.24%
Aged +63.06% +52.87% +25.03% +2.80% +59.51% +48.62%
Super-aged +75.79% +64.71% +62.93% +57.80% +76.28% +66.91%
Population proportion 49.37% 50.63% 50.48% 49.52% 49.62% 50.38%
Infants 3.95% 3.74% 3.77% 3.58% 4.24% 4.02%
School-age 4.74% 4.40% 4.34% 3.98% 4.98% 4.67%
Working-age 34.81% 35.03% 33.07% 28.85% 34.42% 33.81%
Aged 3.87% 4.25% 5.41% 6.10% 3.85% 4.34%
Super-aged 2.00% 3.19% 3.88% 7.01% 2.14% 3.54%
Rate of population
Working-age +1.58% +2.21% +1.88%
1.50% +1.75% +1.44%
Aged +2.40% +2.17% +1.69% +0.68% +2.13% +1.90%
Super-aged +1.50% +2.04% +2.50% +4.15% +1.58% +2.24%
Regions in Cluster 1 (Type 1) have the smallest property damage due to floods but the largest
casualties and flooded areas, and includes 18 si/gun/gu, with Yangcheon-gu, Seoul-si, which has the
largest number of casualties, and Dongjak-gu, Gwanak-gu in Seoul-si, Haeundae-gu in Busan-si,
Anyang-si in Gyeonggi-do, and Pohang-si in Gyeongsangbuk-do(Table 7). In addition, Type 1
regions have the largest mean population and relatively large population growth (smaller than that
of Type 3 regions). Although the male population in Type 1 regions increased by 4273 thousand
people on average, the female population increased by 13,851 thousand people, indicating a
significant increase in the female population. The decrease in the infants (0–9) and school-age (10–19)
population and the increase in the working-age (20–64), aged (65–74), super-aged (+75) population is
characteristic of the pattern of South Korea, i.e., low birth rate and fast population aging.
Type 2 regions cover 25 si/gun/gu, including Seo-gu and Dong-gu in Busan-si, Jung-gu and
Ongjin-gun in Incheon-si, Dongducheon-si, and Uiwang-si in Gyeonggi-do, Buyeo-gun and
Seocheon-si in Chungcheongnam-do Kim Jae-gun, Insil-gun in Chungcheongbuk-do, Naju-si, and
Gurye-gun in Jeollanam-do and Tongyeong-si and Sacheon-si in Gyeongsangnam-do(Table 7).
Excluding four gu in Busan-si and two gu/gun in Incheon-si, the majority of the Type 2 regions are
regional small- or medium-sized cities with the smallest population sizes and are the only ones where
the populations decreased. In the Type 2 regions, the population decreased across a wide range of
ages from infants (0–9) to working-age (20–64), and the rate of decrease was greater than that in Type
1 and three regions. The working-age (20–64) female population (
18.70%) and the infants (0–9) male
101.40%) decreased sharply. The proportion of infants and school-age people was the
smallest, and that of aged and super-aged was the highest, which had the greatest contribution to the
low birth rate and fast population aging. In addition, the casualties due to floods were the fewest
among the three types, and the property damages and flooded areas were the second largest, which
is attributed to the larger average land area of Type 2 flooded areas.
Type 3 regions have the largest property damage owing to floods and include 30 si/gun/gu.
Among these, Suyeong-gu in Busan-si, Gwangjin-gu, and Dongdaemun-gu in Seoul-si, Dalseong-
gun in Daegu-si, Gyeyang-gu in Incheon-si, Nam-gu in Gwangju-si, and Jung-gu in Ulsan-si have the
greatest property damage(Table 7). The population size in Type 3 regions is smaller than that in Type
1 regions, but the population growth is the largest. The number of men and women increased
Sustainability 2020, 12, 4727 13 of 17
uniformly, and the growth rate of working-age (20–64) (male: 11.29%, female: 11.24%) is large. In
addition, the reduction rate of infants (0–9) is the smallest, and the population proportion is the
Table 7. Regional classification by type.
(number) Characteristics Regions (Si/Do/Gun/Gu)
Large number of
large flooded areas;
Seoul: Eunpyeong, Yangcheon, Gangseo, Guro, Yeongdeungpo, Dongjak,
Gwanak, Gangnam, Songpa, and Gangdong; Busan: Haeundea; Incheon:
Michuhol, Namdong, and Bupyeong; Gyeonggi: Anyang and Bucheon;
Chungcheongbuk: Jeonju; Gyeongsangbuk: Pohang
Small and medium
low birth rate; fast
Busan: Seo, Dong, Yeongdo, and Gangseo; Incheon: Jung and Ongjin;
Gyeonggi: Dongducheon and Uiwang; Chungcheongnam: Buyeo, Seocheon,
Cheongyang, Hongseong, Yesan, and Taean; Chungcheongbuk: Gimje and
Imsil; Jeollanam: Naju, Gurye, Boseong, Wando, and Shinan;
Gyeongsangnam: Tongyeong, Sacheon, Miryang, and Changnyeong
high proportion of
Seoul: Gwangjin, Dongdaemun, Gangbuk, Seodaemun, Mapo, Geumcheon,
and Seocho; Busan: Dongnae, Nam, Buk, Saha, Geumjeong, Yeonje,
Suyeong, and Gijang; Deagu: Dalseong;, Incheon: Gyeyang and Seo;
Gwanju: Nam and Gwangsan; Ulsan: Jung and Buk; Gyeonggi:
Gwangmyeong, Gwangju, and Yangju; Chungcheongbuk: Iksan; Jeollanam:
Mokpo, Yeosu, and Suncheon; Gyeongsangnam: Jinju
Flood disaster risk reduction management is a continuous process that involves identifying
issues, defining objectives, assessing risks, appraising strategies, implementation, monitoring, and
review . Flood damage and demographic characteristics are constantly changing, and there is a
need to monitor the changing trends and re-evaluate flood vulnerable areas. Further, the effectiveness
of the proposed strategy for each region needs to be monitored based on type, and the goals and
strategies need to be adjusted according to the monitoring results. Within this monitoring system,
evaluating flood vulnerable areas is considered a cyclical process involving the design and evaluation
of alternative strategies. The proposed monitoring system is illustrated in Figure 5 and explained
1. Data monitoring: Identify trends of change through flood damage and population census data
2. Flood vulnerable area monitoring: The flood vulnerable areas are re-selected and re-categorized
according to the changing trend.
3. Strategic planning monitoring: The regional type is divided into “existing” and “new” to
monitor the strategic plan by region type.
4. Strategic effectiveness monitoring: The strategy is adjusted by monitoring the effectiveness of
the strategy in a region, or by reviewing the application of other strategies in other regions where
they worked well.
Sustainability 2020, 12, 4727 14 of 17
Figure 5. Monitoring flood risk prevention strategies.
4. Discussions and Conclusions
In this study, we designed a methodology for categorizing flood-vulnerable areas through cross
and cluster analyses of flood damage and demographic changes. The methodology was applied to
229 local governments in South Korea to derive three types of flood-vulnerable areas.
Type 1 regions comprise of 18 municipalities (si/gun/gu) with metropolitan cities having a large
average population (4865 people/km2). The total population has increased since 2000. In particular,
the female population in these areas has increased significantly compared to the male population,
and floods have caused substantial human casualties.
Type 2 areas typically include small- and medium-sized regional cities with a small population
over a large area (209 people/km2). Korea has 25 Type 2 municipalities (si/gun/gu). The flood damage
in these regions is not as extensive as that in other areas, however, they are the only regions
characterized by population decrease, low birth rate, and aging population.
Type 3 areas include 30 Korean municipalities (si/gun/gu) with a population size (1716
people/km2) larger than the overall average population size of Korea (514 people/km2), but smaller
than the metropolitan Type 1. This is a new city type with large population growth and the largest
increase in the youth population.
The vulnerable areas of categorized floods should be handled at the community level through
various strategies, such as installing structures that increase height (e.g., levees and sandbags [44,45]),
acquiring open spaces and conserving wetlands [46–49], land use planning for further development
and densification regulation in flood-prone areas , flood-proofing buildings , insurance
programs and tax incentives for flood risk [52–54], flood risk response strategy communication (e.g.,
flood information, early disaster warning, risk mapping and distribution, and evacuation training
[50,55]), involvement of local community members in the flood planning and recovery processes
[49,50], minimization of the constraints of the disaster vulnerable population [49,56] and mitigation
efforts for flood risk factors through continuous monitoring of the local community [49,50]. The
strategy presented in this paper can be additionally considered when establishing flood prevention
measures on the local community level. Therefore, the proposed strategy cannot encompass all
elements of community flood disaster prevention planning. A flood risk prevention strategy
appropriate to the area type can be developed through additional research. Moreover, the suitability
of the proposed strategy should be verified through continuous monitoring.
This study differs from previous studies in that it categorized flood-vulnerable areas into three
types considering the demographic change factors and improved the accuracy of the regional
differences by subdividing Korea into 229 municipalities and performing a detailed analysis.
However, as suggested in Subsection 3.4, flood damage and demographic factors are constantly
Sustainability 2020, 12, 4727 15 of 17
changing variables. Therefore, flood-vulnerable areas must be re-evaluated through monitoring and
continuous improvement and update of response measures and planning. In addition, in terms of
demographic change, the quality of research is expected to be further improved if various disaster
vulnerable group variables, such as foreigners, the disabled, and the poor, can be additionally
considered and if long-term demographic data are supplemented.
Author Contributions: Conceptualization, H.-K.L.; Methodology, H.-K.L.; Writing—original draft, H.-K.L.;
Writing—review and editing, Y.-H.B. and J.-Y.S.; Visualization, Y.-H.B. and J.-Y.S.; Supervision, W.-H.H. All
authors have read and agreed to the published version of the manuscript. All authors have read and agreed to
the published version of the manuscript.
Funding: This research was funded by NATIONAL RESEARCH FOUNDATION OF KOREA (NRF), funded by
the government of Korea (MSIT), grant number 2019R1A2C3002219.
Conflicts of Interest: The authors declare no conflict of interest.
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