eographical Society Vol. 54, No. 1 February 2019
Volume 54, Number 1 (Series No. 190), February 2019 54 1 ( 190) 2019. 2
Articles
A Location Assessment Model for R&D Institutions Considering Time-Distance and Industrial Sectors ...........................................Chang-Hyun Kim · Young-Long Kim · Young-Hyun Jin ( 17 )
Types and Characteristics of Scenic Spots in the Central Region: Focusing on New Scenic Spot Classification Criteria ............................................................................................Euihan Lee ( 35 )
A Study on the Memoryscape of Comfort Women and Symbolic Significance of the Statue of Peace .....................................................................................................................Jihwan Yoon ( 51 )
Realities and Improvements in the Resilience of the Gumi IT Industry Cluster ......Ji-Hye Jeon ( 71 )
The Making of Mt. Geumgang Tourism Space Through Travelers’ Railway Guidebooks and Japanese Travelogues During the Japanese Colonial Period..............................Jiyoung Kim ( 89 )
The Limits to Subsumption of Nature by Capital ...........................................Byung-Doo Choi ( 111 )
The Humanities Meaning of Teaching Geography ...........................................Seungkyu, Park ( 135 )
Book Reveiw

:
.........................................................................................................·· ( 1 )
R&D ..............·· ( 17 )
: ............... ( 35 )
....................... ( 51 )
IT ........................................................... ( 71 )
.................... ( 89 )
.............................................................................. ( 111 )

- 1 -
:
*·**·***
Wonho Jo* · Yongho Lim** · Key-Ho Park***
BK21 (4-Zero , )
. 2018 · .
* (Master Student, Department of Geography, Seoul National University), jwh3320@snu.ac.kr
** (Ph.D. Student, Department of Geography,
Seoul National University and Assistant research fellow, division of geospatial information research, Korea Research
Institute for Human Settlements), beast75@snu.ac.kr, yhlim@krihs.re.kr
*** (Professor, Department of Geography, Seoul National University
and Researcher, Institute for Korean Regional Studies), khp@snu.ac.kr
?
?
? ?
?
?
?
? ?
?
?
?
?
?
: .

. (deep learning)

. (Convolutional neural network)
.
EuroSAT
. Sentinel-2
.
98.28% EuroSAT . ImageNet
(Pre-trained parameter) (99.66%)
ImageNet .
.
: , , ,
Abstract : Land cover map best reflects land surface phenomena and change so it is used in various research. However, the land cover maps provided by the Ministry of Environment have limitation in terms of ac- curacy and temporal resolution. This study proposes the possibility of deep learning based alternative land cover classification method for shortening the renewal cycle of land cover maps through automation and development of a land cover classification that is suitable for Korea. Land cover classification is conducted using Convolutional neural network, which is the deep learning architecture specialized in image analysis.
- 2 -
(Fisher et al., 2005),

(
, https://egis.me.go.kr/intro/land.do).
( ,
2009),
(·, 2011;
, 2014; ·, 2016; ,
2017).
Sat
, 1980 , 1990 , 2000
. 2010


.
2000
2007
2013 3
. 2010 2017

(, https:
//egis.me.go.kr/intro/land.do).
. ·

.

.

.



.


. Liu and Xia(2010)

. Schöp-
fer et al.(2010)



·

.
, on
In order to confirm the heterogeneity of the land cover according to region even though the same land cover type, land cover classification of Korea was experimented using the model trained by EuroSAT land cover data composed of Europe region’s land cover data. Using Sentinel-2 satellite images the training and test data are constructed for land cover classification of Korea. The convolutional neural network model trained by the data constructed in this study showed better performance with high accuracy(98.28%) than the model trained by EuroSAT data. In addition, The learning speed and accuracy(99.66%) of the convolutional neural network model was improved by using ImageNet pre-trained parameters and it showed the possibility of using ImageNet parameters in land cover classification of Korea. We hope that this study will prompt the research on land cover classification using convolutional neural network which has not been illuminated in Korea.
Key Words : deep learning, Convolutional neural network, remote sensing, land cover
- 3 -
. on screen
digitizing

.
75%, 70% (
, https://egis.me.go.kr/intro/land.do) ·

.

. Basu
et al.(2015)


.


.




.

.


(Convolutional neural network)
(Hu et al.,2015; Zhong
et al., 2017; Scott et al., 2017; Zhao et al., 2017;
Mahdianpari et al., 2018),

Yang and Newsam(2010), Basu et
al.(2015), Helber et al.(2017) .




. ,

. ,



.






·.
EuroSAT(Helber et al., 2017), UC
Merced Land Use Dataset(Yang and Newsam.,
2010), SAT-4·SAT-6(Basu et al.,(2015)

.



( 1).

Helber et al.(2017)
EuroSAT1)

. EuroSAT
Sentinel-22)
- 4 -
Sentinel-2


.
EuroSAT
. EuroSAT

EuroSAT
. EuroSAT

. Annual crop·perma-
nent crop , river·sea·lake ,
industrial·residential·highway ·
, pasture·herbaceous vegetation , forest
1. ( )
() USGS ESA UN(FAO)
Agricultural Land Mechanically
Forest Nonmechanically
Wet land Forest Mosaic vegetation / cropland Tree-covered areas
Barren Grassland/
Water Wetland Tree broadleaved deciduous Shrub-covered areas
-
aquatic or regularly flooded
Mining Tree mixed leaf type Sparsely natural vegetated areas
Ice/Snow Mosaic tree, shrub /
-
Shrubland Coastal water bodies and intertidal areas
Grassland Permanent snow and glaciers
Lichens and mosses
.


.
Sentinel-2 3).

30m LandSat
. Sentinel-2
10m LandSat
(5)

.
()
,
Sentinel-2
.

EuroSAT
.

.
2018
6 2, 6 7, 6 22, 7 7, 7
22, 7 30, 8 1, 9 25, 9 30, 10
25, 11 1

2.

·

2
1,500
(training set)
(test set) .
8:2 . 2
.


(Krizhevsky et al., 2012). Seltinel-2
1.6TB

(Supervised learning)
(labeling) .

,
.
(Data Augmentation)
.

.
, ,
, , , , ,
4).

.



(Over fitting) .
matplotlib .
1) (Convolutional neural
2012; Zeiler and Fergus, 2014; Sermanet et al.,
2014; Simonyan and Zisserman, 2015; Szegedy et
al., 2015; He et al., 2016). Hubel and Wiesel (1962)
(local
receptive field)
.
(convolu-
tion)5) (pooling)6) (layer)
(deep)
(fully connected layer)
(Krizhevsky et al., 2012).

VGG16(Simonyan and Zisserman, 2015)
. VGG16 3×3
(filter)
.
(hidden layer) (Activation
function) ReLU7) . Simonyan and
Zisserman(2015) VGG16 ,


VGG16 .
3 VGG16
.
function) . Categorical
cross entropy . Categorical
cross entropy (multi-class)
. Cate-
gorical cross entropy .
- 7 -
Categorical Cross entropy
.

.

RMSProp
(learning rate) 0.00002
. RMSProp


(gradient)
(Géron,
2017). RMSProp .
G=βG+(1-β)(∇θJ(θt)) 2 (2)
θ=θ- η
G+ε ∇θJ(θt)
, β , η .
2) (Fully connected layer)

.

2,048 (node) .
.
3. VGG16
Layer filter size number of channels number of filters stride output size
Convolution 11
Convolution 12
Pooling 1
3 × 3
3 × 3
3 × 3
4 × 4
4 × 4
4 × 4
2 × 2
: Stride .
: Simonyan & Zisserman(2015)
- 8 -
256
. softmax

. Softmax


. 7
0
1 1.

(regularization) .
dropout
. Dropout

(validation set)
0.5
(Srivastava et al. 2014). Dropout 0.5
8). 1

dropout
. 4
.
3) (Pre-trained



(transfer learning) .
Tajbakhsh et al.(2016)
(convergence)

(hyper parameter)

. VGG16
1,470
.

(random initialization)

.

ImageNet9)

. Goodfel-
low et al.(2016)

. ImageNet
1,500


1.

Layer Output

. ImageNet



. Ima-
geNet


.

(random split)
.
.


(confusion matrix) .
(precision),





.

.

.
4.

EuroSAT

. ,

. , ImageNet

.

EuroSAT

.
93%
,

.

(gradient vanishing) (gradient
exploding)
.


(Géron, 2017).

50×
500
10). EuroSAT
82.39
92.92% .
(fluc-
tuation) ( 2).
EuroSAT
65.21
% .
- 10 -
. 3 EuroSAT

.
· 93%,
98% .
, , 22%, 37%,
43% .
42% , 22% ·
14%
.

.


.
93%
63%
.

.
43%
, 30% 27%
.
,

.



.

98.88%
. 32.81 EuroSAT

2. EuroSAT
3. EuroSAT

( 4).


98.28% .
0.6%
.

EuroSAT
, ·, 100%
( 5).
94%,
97%, 97%, 97%
. ·
4%, 2% , 3%
, 3%
, 3% .
·
2
,

.


.



.

EuroSAT


.


.
4.
5.

- 12 -
ImageNet VGG16


ImageNet

. ImageNet


8.23
,
( 6). 99.71%
.
99.66% Ima-
geNet
.
0.05%
. ImageNet

, , , ·
, , 100% .
98%
2% ( 7).
ImageNet

ImageNet
.
5.
.
·
6. ImageNet
7. ImageNet


- 13 -

.

.



.


.
EuroSAT



.
,
.



.
,
ImageNet
. ImageNet




ImageNet
.
,

.


.


.




.


.



(crowdsourcing)
.


(segmentation)
.


.



.



.


11).
- 14 -



.

.

.
30
Annual Crop, Forest, Herbaceous Vegeta-
tion, Highway, Industrial, Pasture, Permanent Crop,
Residential, River, SeaLake .
program)
10m . Sentinel-2
Sentinel-2A Sentinel-2B
2015 6, 2017 3 .
5
.

. Sentinel-2 10
10m .
Sentinel-2 ESA
. https://sentinel.esa.int/web/sentinel
/missions/sentinel-2/data-products
. 40°
· 0.2
.
0.2
· 0.2
.
. .



.
6)
.


. VGG16

max pooling .
ReLU
0 0
0 . ReLU
.
0.5 .

Dropout
. Dropout 0, 0.1, 0.2, 0.3,
0.4, 0.5 dropout
0.5
.
. ImageNet
2
.
, .
10)
.
steps_per_epoch 50 . EuroSAT
epoch 500
, epoch 50 (steps_per_
epoch=50) 50×500 .

GPU
. GPU
GTX 1050 GPU 2Gb. GPU
.
11)
URL . https://mys
nu-my.sharepoint.com/:f:/g/personal/jwh3320_seoul
- 15 -
_ac_kr/EqIFZfmts0JEovt5_C_EI8IBAkBLeFJFfqDiFCM5
,”
, 14(2), 28-39.

:
,” , 33(6), 1101-1118.
“
,” , 25(1), 71-83.
·, 2016, “
CALMET ,”
, 16(4), 383-392.

,” , 48(3), 363-373.
, , https://egis.me.go.kr/
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(: khp@snu.ac.kr, : 02-880
-6453)
phy, Seoul National University, 1 Gwanak-ro, Gwanak-gu,
Seoul, 08826, Korea(e-mail:khp@snu.ac.kr, phone: +82-2-
880-6453)
R&D
*·**·***
A Location Assessment Model for R&D Institutions Considering Time-Distance and Industrial Sectors
Chang-Hyun Kim* · Young-Long Kim** · Young-Hyun Jin***
* MBG (Director, MBG Inc.), skytreesea@mbg-world.com
** (Associate Research Fellow, The Seoul Institute), ylkim@si.re.kr
*** (Research Fellow, Korea Institute of S&T Evaluation and Planning(KISTEP)), yhjin@
kistep.re.kr
?
?
?
? ?
?
?
?
?
?
: (R&D)
R&D . R&D
(TCB ) R&D
. , 159 R&D 49,963 TCB
TCB
. ,
. , ,
. 1 20
. , TCB
,
. R&D ,
, , , R&D
.
: , R&D , , ,
Abstract : A model to assess the location of R&D institutions is required to increase the sustainability and beneficiary of the regional R&D institutions for regional economic development. This research aims to estab- lish a location assessment model of government-funded R&D institutions and apply it to the existing R&D institutions in South Korea based on the databases of the national R&D institutions and the firms with technology credit rating from the Technology Credit Bureau (TCB firms). First, we mapped and analyzed the spatial distribution and clusters of 159 R&D institutions and 49,963 firms and visualized the spatial difference between them. The difference between them was evident; the TCB firms are clustered mainly in the Seoul Metropolitan Area while the R&D institutions are more evenly distributed all over the country. Second, we calculated the location score of the R&D institutions considering their locational clusters and physical-, time-, and driving-distances to firms in the relevant industrial sectors. The top 20 ranked R&D in- stitutions was classified into two types: those in the Seoul Metropolitan Area and the others. The former type of institutions has high scores in the cluster index because most of the firms in the same industrial sector are
- 18 -

(
, 2018). R&D
,

(, 2016). (regional innova-
,
R&D ‘’
.
R&D
, 2017-2021
R&D
.
R&D
. R&D

, R&D

. R&D

R&D
.

. , R&D
.
, , ,

, R&D

. , R&D
,

. , R&D
‘’ ‘
’ ‘’(time-distance)
,
.
.
‘
’ ,

.
,

.

,

. R&D

‘’ R&D
.
R&D

R&D ,
R&D
located in the Seoul Metropolitan Area while the latter tends to have high scores in the distance indices. This model is an attempt to assess the location of R&D institutions using their spatial clustering and time- and driving-distances, which should be considered for better utilization of R&D institutions.
Key Words : location assessment, R&D institution, technology credit rating, spatial clustering, time-distance
- 19 -
. 159
R&D
R&D
,

. ‘NICE’
(Technology-Credit Bureau)
30,207
.
GIS

. ·
,
R&D
(clustering)
.
.
R&D
.
,


.
, , R&D
,

.

, R&D
.


(Dawkins, 2003).
(Christaller)
, (Thünen) , (We-
ber) .
(Lösch), (Hoover), (Isard),

(Alonso, 1964) ,

(Krugman, 1991).

.
‘ ’
R&D
.
, ‘
’(selection of the region), ‘
’(selection of the location)
(Brown and Gibson, 1972, 21).
‘ ’
R&D .
20

(Cairncross, 2001; Ohmae, 1990)
.

(explicit knowledge) (tacit
knowledge)
(Asheim et al., 2007;
Gertler, 1995; Storper and Venables, 2004).


(Audretsch and
Feldman, 1996; Boschma et al., 2014).


- 20 -
(Marshall, 1919),
(Markusen, 1996; Por-
ter, 1990) .
R&D
, R&D
.
R&D
,
R&D (
, 2016).
R&D
.
Brown and Gibson(1972) Heragu
(2008) ,
, ,
·(2015)
.
R&D
. R&D

.
R&D
.
,
(Hodgson, 1981; Ahmadi-
Javid et al., 2017). (time distance)
(cost distance)
(·
, 2015; Clark, 1977)
.
,
(raw data)
.

(Lee and McDonald, 2003).

(·, 2017; , 2018)
,
.
, R&D

. (2006)
R&D
,
R&D
. Malecki(1981;
1987)
, R&D
.
, Lee(2011) R&D
,
. R&D
, R&D

. R&D
, R&D

(Cooke, 1996; Siedschlag et al., 2013; Kang and
Park, 2012).

‘ ’ R&D

. (vehicle rout-
ing problem)
(KTDB) (UTIC)
, 2016).
,


.
- 21 -
‘ ’(Google Maps)
(Al-
varez et al., 2018).


(
, 2016).
,
(raw
data) .


.

R&D


.
R&D

.
3. R&D
1)

, ,


.
. TCB
2014 7~2017 8 ‘NICE’
. TCB
1)
, 2)
TCB R&D
.
R&D
‘NICE’ 3
(2018)
, R&D
.
30km
.
49,963 . 2016
R&D R&D
30,207
, ( 1).
R&D
R&D

.
(4),
(8), (9)
( 2). R&D
. , R&D
1 10
,
, ,
( 3).
- 22 -
1 7920(15.9) 35 153(0.3)
2 3900(7.8) 36 142(0.3)
3 , , ,
3240(6.5) 37 139(0.3)
4 2677(5.4) 38 132(0.3)
5 2531(5.1) 39 118(0.2)
6 2527(5.1) 40 113(0.2)
7 2443(4.9) 41 88(0.2)
8 2341(4.7) 42 , 73(0.1)
9 2271(4.5) 43 72(0.1)
10 1774(3.6) 44 ( ) 65(0.1)
11 1735(3.5) 45 60(0.1)
12 , , 1434(2.9) 46 58(0.1)
13 1 1252(2.5) 47 54(0.1)
14 1238(2.5) 48 49(0.1)
15 , 1038(2.1) 49 39(0.1)
16 975(2) 50 38(0.1)
17 965(1.9) 51 , 35(0.1)
18 ,
956(1.9) 52 ( ) 34(0.1)
19 779(1.6) 53 32(0.1)
20 761(1.5) 54 , , 28(0.1)
21 , 746(1.5) 55 , 24(0)
22 629(1.3) 56 14(0)
23 599(1.2) 57 10(0)
24 470(0.9) 58 8(0)
25 461(0.9) 59 7(0)
26 , 425(0.9) 60 6(0)
27 384(0.8) 61 5(0)
28 384(0.8) 62 4(0)
29 357(0.7) 63 3(0)
30 292(0.6) 64 1(0)
31 · 276(0.6) 65 1(0)
32 , 209(0.4) 66 1(0)
33 ; 178(0.4) 67 1(0)
34 168(0.3) 49,962
- 23 -
2)
(2012, 2014, 2016) .

.
(Local Moran’s I)
.
,

.
Ii =zi ∑ j
i j
, (w) (z)

. i
,
.
(Moran scatter plot) 0
,
,

.
(·, 2008; ·
, 2011)
(
3. R&D
() () () ()
1 20(11.5) 20 2(1.1)
2 16(9.2) 21 2(1.1)
3 14(8) 22 2(1.1)
4 12(6.9) 23 , , 2(1.1)
5 11(6.3) 24 2(1.1)
6 9(5.2) 25 2(1.1)
7 8(4.6) 26 2(1.1)
8 7(4) 27 1 1(0.6)
9 7(4) 28 1 1(0.6)
10 6(3.4) 29 1(0.6)
11 6(3.4) 30 , 1(0.6)
12 6(3.4) 31 , , 1(0.6)
13 , , , 5(2.9) 32 , 1(0.6)
14 , 4(2.3) 33 , 1(0.6)
15 4(2.3) 34 1(0.6)
16 , · 4(2.3) 35 1(0.6)
17 3(1.7) 36 , 1(0.6)
18 3(1.7) 37 1(0.6)
19 3(1.7) 38 1(0.6)
Total 174(100)
(
) .
3)
(1)
.
R&D
,

.
,
‘ ’(selection of the location),
‘ ’(selection
of the region) (Brown
and Gibson, 1972, 1). ‘ ’
, R&D
.
R&D
‘ ’
‘’
, ‘
’ . , “R&D

?” ,
“
?”
. .
. ,
R&D
. 30km
‘’
‘’
. , R&D

. ‘ ’
. (door-to-
door) (‘’)
(‘’) .

, .
,
,

.
, R&D
.
40km,
90
40km, 90
. 2015
60-90
23.7%, 14.7%
, 90
10% .
1
90 (, 2015).

.
( )

.
- 25 -


. ,
R&D

( 1).
=( )/(5*40km
)
- 5*15 /90

- 4*30 /90

- 3*45 /90

- 2*60 /90

- 1*90 /90

=( )/(5*90
)
1) R&D TCB
, TCB
.
TCB ,

1.
- 26 -
, ,
, TCB
. R&D
, TCB
, (
3). ,

R&D

. R&D
, R&D

.
2012 2016 2
,

( 4). R&D , 2012
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, ,
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100 8.51 43.71, 0
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2. 2016 TCB -
- 27 -
.
20 ,
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’, 2 ‘ LINC’,
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4. 2012, 2014, 2016 TCB
- 28 -
.
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. TCB
,
90
.
R&D R&D
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.
5. 2012, 2014, 2016 R&D
4.

(km) 74.9 0.1 75.0 27.3 14.7
(km) 198.9 0.0 199.0 38.2 20.8
() 151.0 0.0 151.0 50.9 22.0
5. R&D

0.08 0.14 1.00 0.00 1.00
0.07 0.13 0.80 0.00 0.07
0.03 0.05 0.35 0.00 0.03
0.06 0.08 0.43 0.00 0.06
8.51 8.62 43.71 0.00 8.51
- 29 -
5.

. R&D TCB
.
TCB
. TCB
6. 20 ( )




(c+d)*100
1 c143 2006 0.01 0.01 0.00 0.43 43.71
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3 c203 ()
4 c262
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3D 2010 0.00 0.00 0.00 0.31 31.31
6 c262 2013 0.00 0.00 0.00 0.29 28.82
7 c171 2009 0.80 0.30 0.28 0.01 28.07
8 c213
9 c284
10 c261
2006 0.50 0.50 0.25 0.00 25.30
11 c272 2012 0.00 0.00 0.00 0.24 24.26
12 c264 IoT 2006 0.00 0.00 0.00 0.24 24.22
13 c211 2014 0.02 0.02 0.01 0.22 23.22
13 c211 () 2015 0.02 0.02 0.01 0.22 23.22
15 c213
IBS 2013 0.03 0.02 0.01 0.21 22.41
16 c271
17 c204
18 c282
ESS 2010 0.03 0.02 0.01 0.20 21.35
19 c262 2008 0.00 0.00 0.00 0.21 21.11
20 c204
- 30 -

, 2016
.
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- 31 -
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.

,
- 32 -

,

.

.
R&D

. , ,

, , ,

.

.
, , R&D

.
R&D

.

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(, 2018) .

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,” , 43(3),
392-411.

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43 KD 808(: skytreesea@mbg-world.com)
Dunsan-ro 123beon-gil, Seo-gu, Daejeon, 35240, Republic
of Korea (e-mail: skytreesea@mbg-world.com)
2018. 11. 23
2019. 1. 17
2019. 1. 31
:
*
Types and Characteristics of Scenic Spots in the Central Region: Focusing on New Scenic Spot Classification Criteria
Euihan Lee*
kangwon.ac.kr
?
?
?
? ?
?
?
?
?
?
:
. 41
, . 2007 8
29
.
. , , , ,
, , , .
29, 12
17 . 13,
7, 4, 2, 2, 1,
5, 4, 2, 1.
.
, , , ,
.
: , , , , , ,
Abstract : Although the designation of scenic spots has increased rapidly in recent years, the systematic clas- sification and systematic management of scenic spots have not been conducted very well. In order to solve this problem, this researcher first classified and organized 41 scenic spots in the central region among South Korean scenic spots from a new viewpoint and examined the distribution and characteristics of these scenic spots. After the complete revision of the scenic spot designation criteria on August 29, 2007, the Cultural Heritage Administration divided scenic spots into natural scenic spots and historical and cultural scenic spots but made mistakes of wrongly classifying quite a few natural scenic spots into historical and cultural scenic spots. In light of this problem, this researcher intended to propose new scenic spot classification crite- ria. First, this researcher largely classified the natural scenic spots into mountain landforms, river landforms, coastal landforms, volcanic landforms, karst landforms, view landscapes, and animal and plant habitats and subdivided them according to minimum landform units. The large classification of scenic spots according to
- 36 -

,
() .
.



(, 2011).
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2009; , 2011).
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.
,
,
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(2013) ,

.
,
.
. (2013)
‘’
.

, , ,

,
. (2014)
the new criteria indicated that natural scenic spots outnumbered historical and cultural scenic spots by 17 as 29 scenic spots were classified into natural scenic spots while 12 were classified into historical and cultural scenic spots. The subdivision of those scenic spots classified the natural scenic spots into thirteen river land- forms, seven mountain landforms, four view landscapes, two karst landforms, two coastal landforms, and one volcanic landform and the historical and cultural scenic spots into five traditional traffic landscapes, four traditional artificial landscapes, two historical remains, and one traditional industrial landscape. The new scenic spot classification criteria proposed by this researcher should be carefully reviewed and supplemented as additional scenic spots are designated hereafter. In addition, in this study, the scenic spots in the central region were examined in various aspects such as locations, formation periods, grounds for the origins of the names, related persons, and related paintings.
Key Words : scenic spots, characteristics, scenic spot designation criteria, natural scenic spots, historical and cultural scenic spots, new scenic spot classification criteria
- 37 -

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- 48 -

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, 2014, “ ‘’
: ‘ ’
,” , 49(4), 563-584.
.

(: euihan@kangwon.
Education, College of Education, Kangwon National Uni-
versity, Kangwondaehakgil 1, Chuncheon, 24341, Korea
(e-mail: euihan@kangwon.ac.kr, phone: +82-33-250-6692)
2018. 12. 10
2019. 1. 2
2019. 2. 1

*
A Study on the Memoryscape of Comfort Women and Symbolic Significance of the Statue of Peace
Jihwan Yoon*
?
?
? ?
?
?
?
? ?
?
?
?
?
?
:
. ,
.
30
. ,

. ,

,

.
: , , , ,
Abstract : This study examines how the statue of comfort women in front of the Japanese Embassy in Seoul contributes to forming a public discourse with its symbolic and affective characteristics. Since the indepen- dence in 1945, former comfort women have gone through traumatic experiences as well as the periods of sexual slavery during World War II. Even though they were set free from sexual abuses since World War II, family members and neighbors of former comfort women have had critical views on their experiences within comfort stations. This indicates that numerous people in Korea have considered former comfort women as violators of virginity rather than victims of sexual crimes committed by the Japanese army and government. Taking account of this vulnerable condition for human rights of comfort women, this study focuses on sym- bolic strengths of the statue of comfort women, which have played an important role in reversing negative views about sexual crime victims and forging collective affection for former comfort women. In analyzing the landscape of memory in front of the Japanese Embassy in Seoul, we can be aware of how counter-memory of marginal groups can be represented within urban space despite the sociocultural vulnerability and utilized for conducting place-based politics of victims of war crimes.
Key Words : Comfort Women, Statue of Peace, landscape of memory, symbolic capital, place-based politics
- 52 -


.




.

12 28

.



.

.



.
,
, 2015


.


.


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(Bosco, 2004).



(Alderman,
2003).


1991

.


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(Dwyer and Alderman, 2008a).

(constancy)

(Till, 2012a, 7). ,
,


. 2011
12 4 1000



.

· (, 2010;
, 2008; , 2014)
(, 2018; , 2006; ,
2014; , 2012)
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- 53 -



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(2013) .


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structured)
- 54 -

(USB) .

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2003; Onken, 2007).


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(Dwyer, 2000; Osborne, 2001; Till,
2005).


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(Mitchell, 2003).


.
(Little and Painter, 1995;
Wcever, 2005; Weiss and Wodak, 2003).



(, 2006; Barker and
Galasiski, 2001; Jørgensen and Phillips, 2002).


··
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(1980a)
- 55 -


.



(Dittmer, 2010, 279).



,

(Gramsci, 1992; Lees, 2004; Waitt, 2010).



(Foucault, 1980b).


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(Simonsen, 2005).

,

(Lefebvre,
1991a; McCann, 1999).


(Elden, 2007).


(Pile, 2013).

(Dwyer, 2000; Lowenthal, 1975;
Marschall, 2010).

(Alderman, 2003).



(Herod, 1991).
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(Alderman, 2003),

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(Till, 2003).


(Dwyer, 2000; Schein, 1997),


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(Till, 2012a).



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(, 2009, 780),


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- 56 -




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(Lefebvre, 1991a).



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- 57 -


(Inwood,
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- 58 -


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(Honneth, 1992).



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1991, 1994).


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(self-respect) (Campbell


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- 59 -

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.1)
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(2007)
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(
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- 60 -


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(memory-work)
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. ( 1)


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. 32
(
1. . .
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20, 2014).

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1994).

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(Foucault, 1980a).

- 62 -




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.
(Courtheyn, 2016).



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(Cresswell, 2014).




(Tuan, 1977).

.


.



.
(Calhoun, 2001).

.


.



.


.
.

.



(Dutton, 2009; Till, 2012a).



(Calhoun,
2001).
.



(Alderman and In-
wood, 2013).


.



(Duminy, 2014).



(Foucault, 1980a).

6.


1000
.


.


.



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…
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, 2015. 12. 30.)

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2018. 1. 5.)

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- 65 -

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- 66 -

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www.seoul.go.kr/storyw/museum/list.do
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121 (: amyjh07@
konkuk.ac.kr, amyjh07.yoon@gmail.com)
jin-gu, Room 121 Department of Geography, Science Build-
ing, Seoul, 05029, Korea(e-mail: amyjh07@konkuk.ac.kr,
amyjh07.yoon@gmail.com)
IT
*
Realities and Improvements in the Resilience of the Gumi IT Industry Cluster
Ji-Hye Jeon*
* (Post Doctoral Researcher, Kyungpook National University), hanamotomika@knu.ac.kr
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Abstract : This study attempts to explore the current status of resilience in the Gumi IT industry cluster in terms of the production domain, technological innovation domain, and institutional domain, and to suggest methods of improvement for resilience. The Gumi IT industry cluster has entered a period of decline because it was unable to respond to and adapt to external shocks such as the crisis in its major industry and the out- flow of large enterprises since the 2010s. In this regard, in terms of the production domain, the industrial structure specialized on the IT industry for production capability, the local and closed supply chain, and the weak capital of the SMEs have weakened the cluster’s resilience. In the technological innovation domain, the limitation of supply and demand in high-quality human resources and a weak R&D network have not strengthened the resilience, despite increased interest and investment in R&D. In the institutional domain, improving resilience has been impeded by the low companies’ reliability on institutional actors and the low ripple effect of the regional embeddedness of institutions, even though the Gumi City and the Korea Indus- trial Complex Corporation have actively promoted policies and projects. Therefore, in order to improve the cluster’s resilience, it is necessary to construct integrated platform for crisis response, and to attract and oper- ate ‘Special Area for Responding to Industrial Crisis’ that allow each domain to enhance their functions and three domains to complement each other’s functions.
Key Words : realities of resilience, improvements of resilience, external shocks, Gumi IT industry cluster
- 72 -


.
·
.

. ,

,
(resilience)
.
··

.

·
. (Hill et al., 2008;
Hassink, 2010; Christopherson et al., 2010; Suire
and Vicente, 2014; Boschma, 2015; Martin and
Sunley, 2015)

,
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(Martin, 2011; Fingleton et al., 2012; Martin et
al., 2016; , 2016; Sensier et al., 2016; Eray-
din, 2016), ,

(Simmie and Martin,
2010; Cowell, 2013),
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2012),
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- 73 -
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chma(2015) ‘’, ‘ ’
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2018)1). ,

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- 74 -


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