Analysis of Flood-Vulnerable Areas for Disaster Planning Considering Demographic Changes in South Korea

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.

Full text

sustainability
Article
Analysis of Flood-Vulnerable Areas for Disaster
Planning Considering Demographic Changes
in South Korea
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; trot36@hanmail.net (H.L.); byh0105@gmail.com (Y.B.); ssonjy2239@gmail.com (J.S.)
*Correspondence: hongwh@knu.ac.kr; 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
1. Introduction
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 strategies [12,13].
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.
Cardona et al. [
14
] 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. [
15
]
Sustainability 2020,12, 4727; doi:10.3390/su12114727 www.mdpi.com/journal/sustainability

Sustainability 2020,12, 4727 2 of 16
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. [
16
] 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. [
17
] 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 [
18
]. Regional safety assessment
techniques have been proposed to identify the risk of disaster related to natural hazards in specific
regions [
19
]. 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 [
20
]. In areas were divided into risk and vulnerability groups through the excess flood
vulnerability index (EFVI) [21].
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 [29].
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 [
32
]. In addition, more than two-thirds of the
entire land consists of mountainous terrain with weathered rocks, and most of the heavy rainfall
is concentrated in the west and south river basin because of the tilted landform (high altitudes in

Sustainability 2020,12, 4727 3 of 16
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 [
33
]. 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
are increasing.
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.
Sustainability 2020, 12, x FOR PEER REVIEW 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 [33]. 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
are increasing.
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
Gun
Gu
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
3
-
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
-
14
Jeollanam-do
12,343.58
5
17
-
15
Gyeongsangbuk-do
19,032.87
10
13
-
16
Gyeongsangnam-do
10,540.12
8
10
-
17
Jeju-si
1850.16
2
-
-
Total
100,377.68
78
82
69
2.2. Methods
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).
Regions Area
(km2)Si Gun Gu
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 3 -
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 -
14 Jeollanam-do 12,343.58 5 17 -
15 Gyeongsangbuk-do 19,032.87 10 13 -
16 Gyeongsangnam-do 10,540.12 8 10 -
17 Jeju-si 1850.16 2 - -
Total
100,377.68
78 82 69
2.2. Methods
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).

Sustainability 2020,12, 4727 4 of 16
Sustainability 2020, 12, x FOR PEER REVIEW 4 of 17
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 [37]. 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
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 (km
2
) 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 [
37
]. 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 km
2
, and through comparison, the 30 most damaged
regions in each category, i.e., property damage (one million South Korean won/km
2
), casualties
(persons/km2), and flooded area (km2/km2) were selected as the flood vulnerable areas.

Sustainability 2020,12, 4727 5 of 16
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 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 [38].
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/km
2
), casualties (person/km
2
), and flooded area (km
2
/km
2
), 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
Demographic characteristics
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 years old (Population in 2018–Population in
2000)/Population in 2018 ×100
Population proportion (%) 65–74 years old Population by age group or
gender/population in 2018 ×100
Rate of population proportion change (%) +75 years old Population proportion in
2018–Population proportion in 2000

Sustainability 2020,12, 4727 6 of 16
3. Results
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).
Sustainability 2020, 12, x FOR PEER REVIEW 6 of 17
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).
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/km
2
(36.45%), followed by Seoul-si with 2085 million won/km
2
(14.97%), and
Gyeonggi-do with 1767 million won/km
2
(12.68%). The casualties were 4632 persons/km
2
(63.12%),
1061 persons/km
2
(14.46%), and 801 persons/km
2
(10.91%) in Seoul-si, Incheon-si, and Gyeonggi-do,
respectively. Seoul-si, Jeollabuk-do, and Busan-si had flooded areas of 23.68 km
2
/km
2
(77.60%),
0.38 km
2
/km
2
(11.07%), and 0.14 km
2
/km
2
(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 3and Table 3).

Sustainability 2020,12, 4727 7 of 16
Sustainability 2020, 12, x FOR PEER REVIEW 7 of 17
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
Municipalities
Si/Do/Gun/Gu
Million
won/k
m2
%
Municipalities
Si/Do/Gun/Gu
Person/k
m2
%
Municipalities
Si/Do/Gun/Gu
km2/k
m2
%
Total
1,392,9.54
100.00
Total
7337.55
100.0
0
Total
3.46
100.0
0
Sub-Total : Top 30
796,5.27
57.18
Sub-Total : Top 30
6440.45
87.77
Sub-Total: Top 30
3.46
99.94
Busan
Suyeong
843.49
6.06
Seoul
Yangcheon
658.19
8.97
Seoul
Gangseo
2.683
77.59
Busan
Seo
763.30
5.48
Seoul
Dongjak
497.73
6.78
Chungcheongb
uk
Jeonju
0.368
10.36
Busan
Yeongdo
702.68
5.04
Seoul
Gwanak
496.70
6.77
Chungcheongn
am
Seocheon
0.080
2.31
Busan
Saha
555.82
3.99
Incheo
n
Michuhol
441.16
6.01
Busan
Yeonje
0.076
2.20
Busan
Nam
438.00
3.14
Seoul
Gangdong
364.00
4.96
Busan
Haeundea
0.066
1.91
Seoul
Seocho
436.77
3.14
Seoul
Guro
352.38
4.80
Gyeongsangna
m
Sacheon
0.028
0.82
Busan
Yeonje
332.96
2.39
Seoul
Geumcheo
n
339.93
4.63
Gyeongsangna
m
Changnyeong
0.028
0.81
Busan
Gijang
316.96
2.28
Seoul
Gwangjin
314.18
4.28
Jeollanam
Suncheon
0.024
0.68
Seoul
Yangcheo
n
299.54
2.15
Incheo
n
Bupyeong
313.88
4.28
Gyeonggi
Hwaseong
0.017
0.50
Busan
Buk
235.70
1.69
Seoul
Seocho
267.71
3.65
Chungcheongb
uk
Gimje
0.017
0.49
Jeollanam
Wando
228.65
1.64
Seoul
Gangseo
240.85
3.28
Gyeongsangna
m
Miryang
0.011
0.31
Seoul
Gwanak
228.59
1.64
Gyeon
ggi
Bucheon
239.78
3.27
Chungcheongn
am
Taean
0.010
0.29
Busan
Dongnae
227.67
1.63
Gyeon
ggi
Gwangmyeong
178.56
2.43
Gwanju
Gwangsan
0.009
0.29
Busan
Haeundea
226.73
1.63
Busan
Dongnae
167.68
2.29
Gyeongsangbu
k
Pohang
0.007
0.20
Gyeonggi
Dongducheo
n
213.61
1.53
Seoul
Gangnam
162.22
2.21
Chungcheongb
uk
Iksan
0.007
0.19
Jeollanam
Mokpo
191.51
1.37
Seoul
Yeongdeungpo
145.02
1.98
Chungcheongn
am
Cheongyang
0.006
0.18
Busan
Geumjeon
g
176.15
1.26
Seoul
Songpa
119.21
1.62
Gyeongsangna
m
Jinju
0.006
0.18
Gwanju
Nam
149.98
1.08
Seoul
Mapo
115.68
1.58
Chungcheongn
am
Buyeo
0.005
0.16
Seoul
Seodaem
un
145.63
1.05
Busan
Yeonje
112.49
1.53
Jeollanam
Naju
0.005
0.14
Ulsan
Buk
138.71
1.00
Seoul
Eunpyeong
106.54
1.45
Jeollanam
Boseong
0.002
0.06
Gyeonggi
Gwangmyeo
ng
137.73
0.99
Incheo
n
Namdong
102.09
1.39
Chungcheongn
am
Yesan
0.002
0.06
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
Municipalities
Si/Do/Gun/Gu
Million
won/km
2%Municipalities
Si/Do/Gun/Gu
Person/km
2
%Municipalities
Si/Do/Gun/Gu
km
2
/km
2%
Total
1,392,9.54100.00
Total
7337.55 100.00
Total 3.46
100.00
Sub-Total: Top 30
796,5.27
57.18 Sub-Total: Top 30
6440.45
87.77 Sub-Total: Top 30 3.46 99.94
Busan Suyeong
843.49
6.06 Seoul Yangcheon
658.19
8.97 Seoul Gangseo 2.683 77.59
Busan Seo
763.30
5.48 Seoul Dongjak
497.73
6.78 Chungcheongbuk Jeonju 0.368 10.36
Busan Yeongdo
702.68
5.04 Seoul Gwanak
496.70
6.77 Chungcheongnam Seocheon 0.080 2.31
Busan Saha
555.82
3.99 Incheon Michuhol
441.16
6.01 Busan Yeonje 0.076 2.20
Busan Nam
438.00
3.14 Seoul Gangdong
364.00
4.96 Busan Haeundea 0.066 1.91
Seoul Seocho
436.77
3.14 Seoul Guro
352.38
4.80 Gyeongsangnam Sacheon 0.028 0.82
Busan Yeonje
332.96
2.39 Seoul Geumcheon
339.93
4.63 Gyeongsangnam
Changnyeong
0.028 0.81
Busan Gijang
316.96
2.28 Seoul Gwangjin
314.18
4.28 Jeollanam Suncheon 0.024 0.68
Seoul Yangcheon
299.54
2.15 Incheon Bupyeong
313.88
4.28 Gyeonggi Hwaseong 0.017 0.50
Busan Buk
235.70
1.69 Seoul Seocho
267.71
3.65 Chungcheongbuk Gimje 0.017 0.49
Jeollanam Wando
228.65
1.64 Seoul Gangseo
240.85
3.28 Gyeongsangnam Miryang 0.011 0.31
Seoul Gwanak
228.59
1.64 Gyeonggi Bucheon
239.78
3.27 Chungcheongnam Taean 0.010 0.29
Busan Dongnae
227.67
1.63 Gyeonggi Gwangmyeong
178.56
2.43 Gwanju Gwangsan 0.009 0.29
Busan Haeundea
226.73
1.63 Busan Dongnae
167.68
2.29 Gyeongsangbuk Pohang 0.007 0.20
Gyeonggi Dongducheon
213.61
1.53 Seoul Gangnam
162.22
2.21 Chungcheongbuk Iksan 0.007 0.19
Jeollanam Mokpo
191.51
1.37 Seoul Yeongdeungpo
145.02
1.98 Chungcheongnam Cheongyang 0.006 0.18
Busan Geumjeong
176.15
1.26 Seoul Songpa
119.21
1.62 Gyeongsangnam Jinju 0.006 0.18
Gwanju Nam
149.98
1.08 Seoul Mapo
115.68
1.58 Chungcheongnam Buyeo 0.005 0.16
Seoul Seodaemun
145.63
1.05 Busan Yeonje
112.49
1.53 Jeollanam Naju 0.005 0.14
Ulsan Buk
138.71
1.00 Seoul Eunpyeong
106.54
1.45 Jeollanam Boseong 0.002 0.06
Gyeonggi Gwangmyeong
137.73
0.99 Incheon Namdong
102.09
1.39 Chungcheongnam Yesan 0.002 0.06
Gyeonggi Gwangju
125.54
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
0.82 Incheon Gyeyang 87.37 1.19 Incheon Bupyeong 0.001 0.04
Ulsan Jung
114.17
0.82 Seoul Dongdaemun 82.94 1.13 Chungcheongbuk Imsil 0.001 0.02
Gyeonggi Uiwang
111.31
0.80 Seoul Seodaemun 80.57 1.10 Incheon Ongjin 0.001 0.02
Gyeongsangnam
Tongyeong 99.70 0.72 Seoul Gangbuk 72.96 0.99 Deagu Dalseong 0.001 0.01
Jeollanam Yeosu 98.03 0.70 Gyeonggi Anyang 70.73 0.96 Busan Gangseo 0.000 0.01
Seoul Songpa 97.71 0.70 Busan Yeongdo 68.80 0.94 Chungcheongnam Hongseong 0.000 0.01
Jeollanam Shinan 94.65 0.68 Incheon 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,

Sustainability 2020,12, 4727 8 of 16
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 planning.
Sustainability 2020, 12, x FOR PEER REVIEW 9 of 17
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.
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 km
2
) 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.

Sustainability 2020,12, 4727 9 of 16
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
Population (×103)Proportion (%) Population (×103)Proportion (%)
2000 2018 2018–2000 2000 2018 2018–2000 2000 2018 2018–2000 2000 2018 2018–2000
Total 45,985 51,630 +5644 100.00 100.00 - 19,255 20,799 +1544 100.00 100.00 -
Male 23,068 25,877 +2809 50.16 50.12 −0.04 9660 10,340 +681 50.17 49.72 −0.45
Female 22,917 25,752 +2835 49.84 49.88 +0.04 9595 10,459 +864 49.83 50.28 +0.45
Infants 6574 4280 −2294 14.30 8.29 −6.01 2621 1683 −938 13.61 8.09 −5.52
Male 3473 2198 −1275 7.55 4.26 −3.30 1383 864 −519 7.18 4.15 −3.03
Female 3102 2083 −1019 6.74 4.03 −2.71 1238 819 −419 6.43 3.94 −2.49
School-age 6756 5036 −1720 14.69 9.75 −4.94 2853 1943 −910 14.82 9.34 −5.48
Male 3529 2614 −915 7.67 5.06 −2.61 1494 1005 −489 7.76 4.83 −2.93
Female 3227 2421 −806 7.02 4.69 −2.33 1360 939 −421 7.06 4.51 −2.55
Working-age
29,281 34,859 +5577 63.68 67.52 +3.84 12,536 14,215 +1678 65.11 68.34 +3.23
Male 14,778 17,877 +3099 32.14 34.63 +2.49 6311 7190 +879 32.78 34.57 +1.79
Female 14,503 16,982 +2478 31.54 32.89 +1.35 6226 7025 +799 32.33 33.77 +1.44
Aged 2294 4202 +1907 4.99 8.14 +3.15 853 1735 +883 4.43 8.34 +3.92
Male 942 1984 +1041 2.05 3.84 +1.79 352 821 +470 1.83 3.95 +2.12
Female 1352 2218 +866 2.94 4.30 +1.36 501 914 +413 2.60 4.40 +1.79
Super-aged 1078 3254 +2176 2.34 6.30 +3.96 391 1222 +831 2.03 5.88 +3.85
Male 345 1205 +860 0.75 2.33 +1.58 120 460 +340 0.63 2.21 +1.59
Female 735 2049 +1316 1.59 3.97 +2.38 270 762 +492 1.40 3.66 +2.26

Sustainability 2020,12, 4727 10 of 16
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.
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).
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

Sustainability 2020,12, 4727 11 of 16
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.
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/km2112.43 million won/km2173.53 million won/km2
Casualties 241.09 person/km210.24 person/km271.83 person/km2
Flooded area 0.17 km2/km20.01 km2/km20.00 km2/km2
Male Female Male Female Male Female
Population (×103)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
Population change (×103)4273 13,851 −478 −2088 10,025 13,011
Infants −15,278 −12,679 −1924 −1571 −7734 −6231
School-age −14,940 −12,943 −2447 −2173 −6325 −5457
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
Rate of population change
Infants −77.55% −67.95% −101.40% −91.73% −71.65% −61.23%
School-age −65.15% −60.77% −86.37% −88.36% −50.66% −46.68%
Working-age +6.71% +8.63% −3.44% −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
proportion change
Infants −3.23% −2.70% −2.23% −1.80% −3.22% −2.65%
School-age −3.19% −2.78% −2.84% −2.52% −2.77% −2.42%
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%
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 population
(
−
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.

Sustainability 2020,12, 4727 12 of 16
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 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 largest.
Table 7. Regional classification by type.
Cluster (number) Characteristics Regions (Si/Do/Gun/Gu)
1
(18)
Large number of
casualties;
large flooded areas;
low population growth
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
2
(25)
Small and medium cities;
population reduction;
low birth rate; fast
population aging
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
3
(30)
Large property damage;
high population growth;
high proportion of young
people
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
3.4. Recommendations
Flood disaster risk reduction management is a continuous process that involves identifying issues,
defining objectives, assessing risks, appraising strategies, implementation, monitoring, and review [
43
].
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 5and explained below:
1.
Data monitoring: Identify trends of change through flood damage and population census
data monitoring.
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 13 of 16
4
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 [50], flood-proofing buildings [51], 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
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/km
2
). 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/km
2
). 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/km
2
)
larger than the overall average population size of Korea (514 people/km
2
), 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 [
50
], flood-proofing buildings [
51
], 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

Sustainability 2020,12, 4727 14 of 16
in Section 3.4, flood damage and demographic factors are constantly 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.
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.
References
1.
Wisner, B.; Blaikie, P.; Cannon, T.; Davis, I. At Risk; Taylor & Francis: Abingdon, UK, 1994; ISBN
978-0-203-44423-8.
2.
Jonkman, S.N. Global Perspectives on loss of human life caused by floods. Nat. Hazards
2005
,34, 151–175.
[CrossRef]
3.
Douben, K.-J. Characteristics of river floods and flooding: A global overview, 1985–2003. Irrig. Drain.
2006
,
55, S9–S21. [CrossRef]
4.
Botzen, W.; Van den Bergh, J.C.J.M.; Bouwer, L.M. Climate change and increased risk for the insurance sector:
A global perspective and an assessment for the Netherlands. Nat. Hazards 2009,52, 577–598. [CrossRef]
5.
Kvoˇcka, D.; Falconer, R.A.; Bray, M. Flood hazard assessment for extreme flood events. Nat. Hazards
2016
,84,
1569–1599. [CrossRef]
6.
Diffenbaugh, N.; Singh, D.; Mankin, J.S.; Horton, D.E.; Swain, D.L.; Touma, D.; Charland, A.; Liu, Y.;
Haugen, M.; Tsiang, M.; et al. Quantifying the influence of global warming on unprecedented extreme
climate events. Proc. Natl. Acad. Sci. USA 2017,114, 4881–4886. [CrossRef]
7. Davoudi, S.; Crawford, J.; Mehmood, A. Planning for Climate Change: Strategies for Mitigation and Adaptation
for Spatial Planners; Routledge: London, UK, 2009; ISBN 9781849770156. [CrossRef]
8.
Biesbroek, R.; Swart, R.; Van Der Knaap, W.G. The mitigation–adaptation dichotomy and the role of spatial
planning. Habitat Int. 2009,33, 230–237. [CrossRef]
9.
Kim, Y.J.; Lee, J.E.; Kim, K.B. Development of flood mitigation strategies for urban resilience-focused on
the assessment of gulpo stream’s watershed in Incheon, South Korea. Urban Des. Inst. Korea
2018
,19, 5–20.
[CrossRef]
10.
Pielke, R.A.; Downton, M.W. Precipitation and damaging floods: Trends in the United States, 1932–1997. J.
Clim. 2000,13, 3625–3637. [CrossRef]
11.
Brody, S.D.; Kang, J.E.; Bernhardt, S. Identifying factors influencing flood mitigation at the local level in
Texas and Florida: The role of organizational capacity. Nat. Hazards 2009,52, 167–184. [CrossRef]
12.
Laurien, F.; Hochrainer-Stigler, S.; Keating, A.; Campbell, K.; Mechler, R.; Czajkowski, J. A typology of
community flood resilience. Reg. Environ. Chang. 2020,20, 1–14. [CrossRef]
13.
Jongman, B.; Winsemius, H.C.; Fraser, S.A.; Muis, S.; Ward, P.J. Assessment and adaptation to climate
change-related flood risks. In Oxford Research Encyclopedia of Natural Hazard Science; Oxford University Press:
Oxford, UK, 2018. [CrossRef]
14. Cardona, O.D. Indicators of disaster risk and risk management. Science 2005,284, 939–942. [CrossRef]
15. Kubal, C.; Haase, D.; Meyer, V.; Scheuer, S. Integrated urban flood risk assessment-adapting a multicriteria
approach to a city. Nat. Hazards Earth Syst. Sci. 2009,9, 1881–1895. [CrossRef]
16.
Balica, S.F.; Douben, N.; Wright, N. Flood vulnerability indices at varying spatial scales. Water Sci. Technol.
2009,60, 2571–2580. [CrossRef]
17.
Balica, S.F.; Wright, N.; van Der Meulen, F. A flood vulnerability index for coastal cities and its use in
assessing climate change impacts. Nat. Hazards 2012,64, 73–105. [CrossRef]

Sustainability 2020,12, 4727 15 of 16
18.
Ministry of Land, Infrastructure and Transport. Comprehensive Water Resources Plan—Water Vision 2020;
Ministry of Land, Infrastructure and Transport: 2000. Available online: http://molit.go.kr (accessed on 5
March 2020).
19.
National Disaster Management Research Institute. Development of Evaluation and Diagnosis Methods of
Safer City; National Disaster Management Research Institute: 2013. Available online: http://ndmi.go.kr
(accessed on 5 March 2020).
20.
Park, T.S.; Kim, K.M.; Yun, Y.S.; Lee, S.B. Analysis of Flood Damage Characteristics and Development
of Flood Damage Index; Korea Research Institute for Human Settlements: Daejeon, Korea, 2005;
Available online: https://scholar.google.co.kr/scholar?hl=ko&as_sdt=0%2C5&q=Analysis+of+flood+
damage+characteristics+and+development+of+flood+damage+index&btnG (accessed on 5 March 2020).
21.
Korea Agency for Infrastructure Technology Advancement. Development of Evaluation Technique on the
Abnormal Flood; Korea Agency for Infrastructure Technology Advancement: 2008. pp. 1–202. Available
online: https://www.kaia.re.kr (accessed on 5 March 2020).
22.
Alkema, D. Flood risk assessment for EIA: An example of a motorway near Trento, Italy. Stud. Trentini Di Sci.
Nat. Acta Geol. 2003,78, 147–153.
23.
Tingsanchali, T.; Karim, M.F. Flood hazard and risk analysis in the southwest region of Bangladesh. Hydrol.
Process. 2005,19, 2055–2069. [CrossRef]
24.
Dang, N.M.; Babel, M.S.; Luong, H.T. Evaluation of food risk parameters in the day river flood diversion
area, red river delta, vietnam. Nat. Hazards 2010,56, 169–194. [CrossRef]
25. Rauscher, M. Demographic change and climate change. Environ. Dev. Econ. 2019,25, 5–20. [CrossRef]
26.
Raker, E.J. Natural hazards, disasters, and demographic change: The case of severe tornadoes in the United
States, 1980–2010. Demography 2020,57, 653–674. [CrossRef]
27.
Liddle, B.; Lung, S.; Liddle, B. Age-structure, urbanization, and climate change in developed countries:
Revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Popul.
Environ. 2010,31, 317–343. [CrossRef]
28.
Donner, W.; Rodriguez, H. Population composition, migration and inequality: The Influence of demographic
changes on disaster risk and vulnerability. Soc. Forces 2008,87, 1089–1114. [CrossRef]
29.
Oriangi, G.; Albrecht, F.; Di Baldassarre, G.; Bamutaze, Y.; Mukwaya, P.I.; Ardö, J.; Pilesjö, P. Household
resilience to climate change hazards in Uganda. Int. J. Clim. Chang. Strat. Manag.
2020
,12, 59–73. [CrossRef]
30.
Kim, E.J.; Kim, M.-Y.; Kim, H. Spatio-temporal trend of aging regions and their neighborhood environment:
Findings from daegu metropolitan City, Korea. Sustainability 2020,12, 1218. [CrossRef]
31.
Park, E.H. Ultra-low fertility and policy response in South Korea: Lessons from the case of Japan. Ageing Int.
2020,45, 191–205. [CrossRef]
32.
Han River Flood Control Office. Characteristics of Flooding in Korea. Available online: http://www.hrfco.go.
kr/web/floodPage/floodClass.do (accessed on 5 March 2020).
33.
Lee, J.S.; Myeong, S.; Choi, H.I.; Korea Environment Institute; Yeungnam University. Development of a
flood vulnerability assessment methodology for coastal areas. J. Korean Soc. Hazard Mitig.
2017
,17, 397–402.
[CrossRef]
34.
Messner, F.; Meyer, V. Flood damage, vulnerability and risk perception-Challenges for flood damage research.
In Flood Risk Management: Hazards, Vulnerability and Mitigation Measures; Springer: Dordrecht, Netherlands,
2007; pp. 149–167.
35.
Scheuer, S.; Haase, D.; Meyer, V. Exploring multicriteria flood vulnerability by integrating economic, social
and ecological dimensions of flood risk and coping capacity: From a starting point view towards an end
point view of vulnerability. Nat. Hazards 2010,58, 731–751. [CrossRef]
36.
Kim, J.K.; Park, I.C.; Yang, D.M. The development of a disaster situation analysis system based on national
disaster statistics and damage information-A case of heavy rains and typhoons in South Korea. Crisis Emerg.
Manag. Theory Prax. 2020,16, 175–183. [CrossRef]
37.
The Ministry of the Interior and Safety (MOIS). Statistical Yearbook of Natural Disaster. Available
online: http://kosis.kr/statHtml/statHtml.do?orgId=110&tblId=DT_15603N_004&conn_path=I2 (accessed on
5 March 2020).
38.
Statistics Korea. Census. Available online: http://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_
1IN1503&conn_path=I3 (accessed on 5 March 2020).

Sustainability 2020,12, 4727 16 of 16
39.
Barbieri, A.F.; Domingues, E.; Queiroz, B.L.; Ruiz, R.M.; Rigotti, J.I.; Carvalho, J.A.M.; Resende, M.F. Climate
change and population migration in Brazil’s Northeast: Scenarios for 2025–2050. Popul. Environ.
2010
,31,
344–370. [CrossRef]
40.
Grosso, N.; Dias, L.; Costa, H.P.; Santos, F.D.; Garrett, P. Continental portuguese territory flood social
susceptibility index. Nat. Hazards Earth Syst. Sci. 2015,15, 1921–1931. [CrossRef]
41.
Rao, M.R. Cluster analysis and mathematical programming. J. Am. Stat. Assoc.
1971
,66, 622–626. [CrossRef]
42.
Hennig, C.; Meila, M.; Murtagh, F.; Rocci, R. Handbook of Cluster Analysis; Chapman and Hall/CRC: New
York, NY, USA, 2015; ISBN 9781466551893. [CrossRef]
43.
Tasseff, B.; Bent, R.W.; Van Hentenryck, P. Optimization of structural flood mitigation strategies. Water Resour.
Res. 2019,55, 1490–1509. [CrossRef]
44.
Tasseff, B.; Bent, R.; van Hentenryck, P. Optimal flood mitigation over flood propagation approximations.
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics); Springer: Cham, Switzerland, 2016; pp. 358–373.
45.
Judi, D.; Tasseff, B.; Bent, R.; Pan, F. Topography-based Flood Planning and Optimization Capability Development
Report; Los Alamos National Laboratory: Los Alamos, NM, USA, 2014. [CrossRef]
46.
Brody, S.D.; Highfield, W.E. Open space protection and flood mitigation: A national study. Land Use Policy
2013,32, 89–95. [CrossRef]
47.
Brody, S.; Highfield, W.E.; Blessing, R.; Makino, T.; Shepard, C. Evaluating the effects of open space
configurations in reducing flood damage along the Gulf of Mexico coast. Landsc. Urban Plan.
2017
,167,
225–231. [CrossRef]
48.
Calil, J.; Newkirk, S. Aligning natural resource conservation, flood hazard mitigation, and social vulnerability
remediation in Florida. J. Ocean Coast. Econ. 2017,4. [CrossRef]
49.
Tyler, J.; Sadiq, A.-A.; Noonan, D.S. A review of the community flood risk management literature in the USA:
Lessons for improving community resilience to floods. Nat. Hazards 2019,96, 1223–1248. [CrossRef]
50.
Thieken, A.H.; Kienzler, S.; Kreibich, H.; Kuhlicke, C.; Kunz, M.; Muller, M.; Otto, A.; Petrow, T.; Pisi, S.;
Schroeter, K. Review of the flood risk management system in Germany after the major flood in 2013. Ecol.
Soc. 2016,21. [CrossRef]
51.
Veerbeek, W.; Ashley, R.M.; Zevenbergen, C.; Rijke, J.; Gersonius, B. Building adaptive capacity for
flood proofing in urban areas through synergistic interventions. In Proceedings of the WSUD 2012-7th
International Conference on Water Sensitive Urban Design: Building the Water Sensitive Community, Final
Program and Abstract Book; Engineers Australia: Barton, Australia, 21 January 2012; ISBN 9780858258952.
Available online: https://scholar.google.be/scholar?hl=ko&as_sdt=0%2C5&q=Building+adaptive+capacity+
for+flood+proofing+in+urban+areas+through+synergistic+interventions.&btnG=.
52. Michel-Kerjan, E.; Kunreuther, H. Redesigning flood insurance. Science 2011,333, 408–409. [CrossRef]
53.
Aerts, J.C.; Botzen, W. Climate change impacts on pricing long-term flood insurance: A comprehensive study
for the Netherlands. Glob. Environ. Chang. 2011,21, 1045–1060. [CrossRef]
54.
Ermolieva, T.; Filatova, T.; Ermoliev, Y.; Obersteiner, M.; De Bruijn, K.; Jeuken, A. Flood catastrophe model
for designing optimal flood insurance program: Estimating location-specific premiums in the netherlands.
Risk Anal. 2016,37, 82–98. [CrossRef]
55.
Goniewicz, K.; Burkle, F.M. Analysis of the potential of IT system support in early warning systems:
Mitigating flood risk in Poland. Prehospital Disaster Med. 2019,34, 563–565. [CrossRef]
56.
Kellens, W.; Terpstra, T.; De Maeyer, P. Perception and communication of flood risks: A systematic review of
empirical research. Risk Anal. 2012,33, 24–49. [CrossRef] [PubMed]
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