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),
[email protected]
** (Ph.D. Student, Department of Geography,
Seoul National University and Assistant research fellow, division
of geospatial information research, Korea Research
Institute for Human Settlements),
[email protected],
[email protected]
*** (Professor, Department of Geography, Seoul National
University
and Researcher, Institute for Korean Regional Studies),
[email protected]
?
?
? ?
?
?
?
? ?
?
?
?
?
?
: .
. (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/
Karki, M. and Nemani, R., 2015, DeepSat: a learn-
ing framework for satellite imagery, Proceedings of
the 23rd SIGSPATIAL International Conference on
Advances in Geographic Information Systems. ACM,
37.
Fisher, P. F., Comber, A. J., and Wadsworth, R. A., 2005,
Land use and land cover: contradiction or comple-
ment, Re-Presenting GIS, Wiley, Chichester, 85-98.
Géron, A, 2017, Hands-on machine learning with Scikit-
Learn and TensorFlow: concepts, tools, and techniques
to build intelligent systems, O’Reilly Media, Inc.,
Sebastopol.
learning, MIT press, Cambridge.
He, K., Zhang, X., Ren, S. and Sun, J., 2016, Deep residual
learning for image recognition, Proceedings of the
IEEE conference on computer vision and pattern rec-
ognition, 770-778.
Helber, P., Bischke, B., Dengel, A., and Borthm D., 2017,
Eurosat: A Novel Dataset and Deep Learning
Benchmark for Land use and Land cover Classifica-
tion, arXiv:1709.00029 (v1).
Hu, F., Xia, G. S., Hu, J. and Zhang, L., 2015, Transferring
deep convolutional neural networks for the scene
classification of high-resolution remote sensing im-
agery, Remote Sensing, 7(11), 14680-14707.
Hubel, D. H. and Wiesel, T. N., 1962, Receptive fields,
binocular interaction and functional architecture in
the cat’s visual cortex, The Journal of physiology, 160
(1), 106-154.
Krizhevsky, A., Sutskever, I. and Hinton, G. E., 2012, Ima-
genet classification with deep convolutional neural
networks, Advances in neural information processing
systems, 1097-1105.
tion: advantages and limitations, Remote Sensing
Letters, 1(4) 187-194.
manesh, F. and Zhang, Y., 2018, Very deep con-
volutional neural networks for complex land cover
mapping using multispectral remote sensing imag-
ery, Remote Sensing, 10(7), 1119.
Schöpfer, E., Lang, S. and Strobl, J., 2010, Segmentation
and object-based image analysis, Remote Sensing of
Urban and Suburban Areas, Springer, Dordrecht,
181-192.
Scott, G. J., England, M. R., Starms, W. A., Marcum, R.
A. and Davis, C. H., 2017, Training deep convolu-
tional neural networks for land–cover classification
of high-resolution imagery, IEEE Geoscience and
Remote Sensing Letters, 14(4), 549-553.
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R.
and LeCun, Y., 2014, OverFeat: Integrated Recog-
nition, Localization and Detection using Convolu-
tional Networks, arXiv:1312.6229 (v4).
lutional Networks for Large-Scale Image Recogni-
tion, arXiv:1409.1556 (v6).
Salakhutdinov, R., 2014, Dropout: a simple way to
prevent neural networks from overfitting, The Jour-
nal of Machine Learning Research, 15(1), 1929-1958.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Angue-
lov, D., Erhan, D., Vanhoucke, V. and Rabinovich,
A., 2015, Going deeper with convolutions, Proceed-
ings of the IEEE conference on computer vision and
pattern recognition, 1-9.
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T.,
Kendall, C. B., Gotway, M. B. and Liang, J., 2016,
Convolutional Neural Networks for Medical Im-
age Analysis: Full Training or Fine Tuning?, IEEE
Transactions on Medical Imaging, 35(5), 1299-1312.
Yang, Y. and Newsam, S., 2010, Bag-of-visual-words and
spatial extensions for land-use classification, Pro-
ceedings of the 18th SIGSPATIAL international con-
ference on advances in geographic information systems.
ACM, 270-279.
Zeiler, M. D. and Fergus, R., 2014, Visualizing and under-
standing convolutional networks, European confer-
ence on computer vision, Springer, Cham, 818-833.
Zhao, W., Du, S. and Emery, W. J, 2017, Object-based con-
volutional neural network for high-resolution im-
agery classification, IEEE Journal of Selected Topics
in Applied Earth Observations and Remote Sensing,
10(7), 3386-3396.
Zhong, Y., Fei, F., Liu, Y., Zhao, B. and Jiao, H., 2017,
SatCNN: satellite image dataset classification using
agile convolutional neural networks, Remote Sensing
Letters, 8(2), 136-145.
(:
[email protected], : 02-880
-6453)
phy, Seoul National University, 1 Gwanak-ro, Gwanak-gu,
Seoul, 08826, Korea(e-mail:
[email protected], 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.),
[email protected]
** (Associate Research Fellow, The Seoul Institute),
[email protected]
*** (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
2016 2
,
,
( 5).
2)
, ,
< 4> .
R&D
< 5> .
100 8.51 43.71, 0
. 100
, () .
(2)
2. 2016 TCB -
- 27 -
.
20 ,
1 ‘
’, 2 ‘ LINC’,
3 ‘()
4. 2012, 2014, 2016 TCB
- 28 -
.
R&D
. TCB
,
90
.
R&D R&D
, ·
R&D .
R&D ,
.
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
2 j591 LINC 2012 0.00 0.00 0.00 0.43 43.26
3 c203 ()
4 c262
5 c264
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
.
R&D
.
.
R&D
.
R&D
.
30,207
R&D-TCB
. 20 R&D
8 , 7
, 2, 1, 1, 1
.
.
1 LINC
0.43
0
.
, , ,
0
.
30km
. R&D
30km ,
R&D
.
, 30km
.
. , R&D
, R&D
.
R&D
,
.
,
,
,
.
R&D
. , R&D
30km
R&D
.
6.
R&D TCB
5 30,207
.
. R&D
(area) (driving
distance), (time distance)
.
R&D ,
20
.
- 31 -
. TCB
7,920 1
, 3,900 2
, , ,
3,240 3 .
4
, 9 R&D
.
R&D TCB
.
,
,
.
TCB
R&D
. R&D
.
, TCB ,
, , ,
, R&D
.
, 159 R&D ,
, 2
LINC, 3 ()
.
, TCB
, 90
.
R&D
. 30km
, .
0 ,
,
.
,
R&D
.
,
.
,
R&D
. ,
R&D .
R&D
.
,
. , R&D
,
.
R&D
, R&D
. , R&D
. , R&D
.
, R&D
. R&D
, R&D
.
,
- 32 -
,
.
.
R&D
. , ,
, , ,
.
.
, , R&D
.
R&D
.
’
(, 2018) .
’ - 100
9 ,” , 16(2),
19-25.
.
,” , 24(4), 79-96.
·, 2017, “
,”
, 33(1), 29-41.
, 41(1), 58-72.
,” , 43(3),
392-411.
,” , 27(3), 81-99.
,” ,
21(2), 80-93.
,” , 23(4), 101-113.
·, 2015, “
,” ,
50(5), 527-541.
, .
, 2016, “
,” KISO , 24, 33-35.
, 2015, .
.
Ahmadi-Javid, A., Seyedi, P., and Syam, S. S., 2017, A survey
of healthcare facility location, Computers & Opera-
tions Research, 79, 223-263.
General Theory of Land Rent, Harvard University
Press.
2018, The impact of traffic congestion when opti-
mising delivery routes in real time: A case study in
Spain, International Journal of Logistics Research and
Applications, 21(5), 529-541.
Asheim, B. T., Coenen, L., and Vang, J., 2007, Face-to-face,
- 33 -
buzz, and knowledge bases: sociospatial implica-
tions for learning, innovation, and innovation
policy, Environment and Planning C: Government
and Policy, 25(5), 655-670.
Audretsch, D. B., and Feldman, M. P., 1996, R&D spill-
overs and the geography of innovation and produc-
tion, The American Economic Review, 86(3), 630-
640.
Scientific knowledge dynamics and relatedness in
biotech cities, Research Policy, 43(1), 107-114.
Brown, P. A., and Gibson, D. F., 1972, A Quantified Model
for Facility Site Selection-Application to a Multi-
plant Location Problem, AIIE Transactions, 4(1),
1-10.
Communications Revolution is Changing Our Lives,
Harvard Business Review Press.
Transportation Networks, Geographical Analysis,
Cooke, P., 1996, The new wave of regional innovation net-
works: analysis, characteristics and strategy, Small
Business Economics, 8(2), 159-171.
ceptual foundations, classic works, and recent
developments, Journal of Planning Literature, 18(2),
131-172.
tion of Advanced Manufacturing Technologies,
Economic Geography, 71(1), 1-26.
Problem in Heragu, S. (ed.), Facilities Design, 3rd
Edition, CRC Press, 435-476.
mizing consumers’ welfare, Regional Studies, 15(6),
493-506.
Kang, K. N., and Park, H., 2012, Influence of government
R&D support and inter-firm collaborations on in-
novation in Korean biotechnology SMEs, Techno-
vation, 32(1), 68-78.
raphy, Journal of Political Economy, 99(3), 483-499.
Lee, C-. Y-., 2011, The differential effects of public
R&D
support on firm R&D: Theory and evidence from
multi-country data, Technovation, 31(5-6), 256-
259.
Lee, B. S. and McDonald, J. F., 2003, Determinants of
commuting time and distance for Seoul residents:
The impact of family status on the commuting of
women, Urban Studies, 40(7), 1283-1302.
Malecki, E. J., 1981, Governmentfunded R&D: some
regional economic implications, The Professional
Geographer, 33(1), 72-82.
Malecki, E. J., 1987, The R&D location decision of the
firm
and “creative” regions—a survey, Technovation,
6(3), 205-222.
industrial policies: evidence from four countries.
International Regional Science Review, 19(1-2), 49-
77.
Marshall, Alfred, 1919, Industry and Trade: A Study of In-
dustrial Technique and Business Organization and of
Their Influences on the Condition of Various Classes
and Nations, MacMillian and Co. Limited.
Ohmae, Kenichi, 1990, The Borderless World: Power and
Strategy in the Interlinked Economy, Harper Busi-
ness.
Free Press.
Siedschlag, I., Smith, D., Turcu, C., and Zhang, X., 2013,
What determines the location choice of R&D
activities by multinational firms?, Research Policy,
42(8), 1420-1430.
contact and the urban economy. Journal of Eco-
nomic Geography, 4(4), 351-370.
Friedrich, C. J.(Trans.), University of Chicago
Press.
43 KD 808(:
[email protected])
Dunsan-ro 123beon-gil, Seo-gu, Daejeon, 35240, Republic
of Korea (e-mail:
[email protected])
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).
‘ ’
‘ ’
, ‘
’ (,
2009; , 2011).
20
.
,
( , 2016).
()
.
,
(, 2011).
’
.
,
,
,
.
(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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- 42 -
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- 43 -
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- 44 -
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- 46 -
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- 47 -
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- 48 -
,
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··, 2013, “
: ,”
, 25(1), 99-113.
: , ·
.
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.
,” ,
18(2), 39-52.
,” , 48(6), 929-943.
, 2014, “ ‘’
: ‘ ’
,” , 49(4), 563-584.
.
(: euihan@kangwon.
Education, College of Education, Kangwon National Uni-
versity, Kangwondaehakgil 1, Chuncheon, 24341, Korea
(e-mail:
[email protected], 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
.
.
.
(Bosco, 2004).
(Alderman,
2003).
1991
.
.
(Dwyer and Alderman, 2008a).
(constancy)
(Till, 2012a, 7). ,
,
. 2011
12 4 1000
.
· (, 2010;
, 2008; , 2014)
(, 2018; , 2006; ,
2014; , 2012)
(, 2010; , 2017)
.
- 53 -
.
.
(2013) .
. (2013)
(Lefebvre, 1991a) (production of
.
(
, 2013).
(2013)
(public memory)
, , 2
.
.
,
,
, . ,
.
.
,
.
.
.
(Honneth, 1996).
(Till, 2012b).
.
2015 12 30
2017 12 13 2018 1 10
.
,
. (semi-
structured)
- 54 -
(USB) .
()
(, 1993, 1997,
·
.
90
.
.
. Lowenthal
(1985, 210) “ …
”
.
(Alderman, 2002, 104).
·
(Loader and Mulcahy,
2003; Onken, 2007).
.
(Dwyer, 2000; Osborne, 2001; Till,
2005).
(Tuan, 1977).
.
.
(Mitchell, 2003).
.
(Little and Painter, 1995;
Wcever, 2005; Weiss and Wodak, 2003).
(, 2006; Barker and
Galasiski, 2001; Jørgensen and Phillips, 2002).
··
.
(1980a)
- 55 -
.
(Dittmer, 2010, 279).
,
(Gramsci, 1992; Lees, 2004; Waitt, 2010).
(Foucault, 1980b).
.
(Simonsen, 2005).
,
(Lefebvre,
1991a; McCann, 1999).
(Elden, 2007).
(Pile, 2013).
(Dwyer, 2000; Lowenthal, 1975;
Marschall, 2010).
(Alderman, 2003).
(Herod, 1991).
·
(Alderman, 2003),
(Bosco, 2004, 382).
(Till, 2003).
(Dwyer, 2000; Schein, 1997),
.
,
(Till, 2012a).
.
(, 2009, 780),
(, 2015).
(Dwyer
- 56 -
.
(, 2014).
.
Bourdieu(2011) (capital)
.
, ,
(Duncan and Duncan, 2001; Mitchell et al., 2001).
.
(Leib, 2002).
(Till, 2012a).
(Honneth, 1996).
.
(aesthetic)
(Till, 2008, 104).
.
(Lefebvre, 1991a).
.
.
90
. 20
.
.
3.
.
- 57 -
(Inwood,
2010).
.
(Nash, 1996).
(Foucault, 1980a).
.
.
, ,
(,
2004).
.
, , ,
(Min, 2003).
.
(Min,
2003)
‘ ’
(, 1993).
.
.
.
.
1965
(, 2017).
·
(Bhabha, 2012).
(morality) .
.
(Honneth, 1992).
- 58 -
.
(Honneth, 1992).
(Calhoun,
1991, 1994).
.
(self-respect) (Campbell
.
(Higgins et al., 1986).
.
.
… . (
, 1997, p.98)
.
.
95 96 …
.
…
“ ”
. “ ?
.”
.
( A , 2015. 12. 28.)
.
, ,
.
,
(storyteller)
.
(Feinberg, 2014; Honneth, 2004),
.
- 59 -
.
,
,
(, 2001; ,
2010).
.
1990
.
.
.
,
.
· (intersectional)
(Crenshaw, 1989).
.
(Foucault, 1980b).
(Ball, 2012).
.
.1)
2
.
1650
85
, 70
(2007)
.2)
, “
”, “
”
.
,
(
, 2010).
- 60 -
.
‘ ’
(, 2010).
.
(memory-work)
.
,
.
.
, ,
, ,
.
.
.
. ( 1)
.
. 32
(
1. . .
: , 2015. 12. 29
- 61 -
20, 2014).
.
.
.
(counter-memory)
. 2011
12 14 1000
(20
.
.
.
.
.
.
(Calhoun,
1994).
.
.
(Honneth and Farrell, 1997).
(Hall, 2001).
.
(Ball, 2012).
, ,
.
,
,
(Lefebvre, 1991).
.
(Foucault, 1980a).
- 62 -
.
.
(Courtheyn, 2016).
.
(Cresswell, 2014).
(Tuan, 1977).
.
.
.
(Calhoun, 2001).
.
.
.
.
.
.
(Dutton, 2009; Till, 2012a).
(Calhoun,
2001).
.
(Alderman and In-
wood, 2013).
.
(Duminy, 2014).
(Foucault, 1980a).
6.
1000
.
.
.
.
.
…
…
.
,
. ‘
’ . ( B
, 2015. 12. 30.)
.
.
…
.
. ( C
, 2015. 12. 30.)
.
.
.
( 2).
… ‘
’
.
. ( D ,
2018. 1. 5.)
. 22
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.
www.seoul.go.kr/storyw/museum/list.do
, 2013. 6. 6. http://news1.kr/articles/?1165031
3) “ ‘ , ’ ”,
, 2016. 12. 31. http://www.ohmynews.com/NWS_
Web/View/at_pg.aspx?CNTN_CD=A0002275451
,” , 61, 317-334.
: , ,
,” , 13, 7-59.
: ,”
, 34(3), 113-146.
,” , 35, 177-203.
, 2006, “ ‘’
,” , 69, 108-118.
, 2010, “ ‘’ ,
, 53(3), 79-108.
,” , 53(3), 41-68.
, 2014, “ ‘’
:
,” , 39, 477-492.
, 2006, “ ,” ,
14(3), 106-145.
,” , 53(3), 41-78.
, 2004, ‘’
: · ,
.
,” , 44(6), 779-796.
: ‘’
,” , 16(3), 101-116.
,” , 49, 119-147.
, 41, 559-576.
1, , .
2, , .
3, , .
4: , ,
- 67 -
.
, 2014, “
,” , 26(2), 104-125.
, 2017, “
‘’ : 2014
,” , 33(1),
267-304.
The reputational politics of commemorating Mar-
tin Luther King in a Georgia county, Historical
Geography, 30, 99-120.
Alderman, D. H., 2003, Street names and the scaling of
memory: The politics of commemorating Martin
Luther King, Jr within the African American com-
munity, Area, 35(2), 163-173.
memory and socially just futures, in Johnson, N.
C., Schein, R. H., and Winders, J. (eds.), The Wiley-
Blackwell companion to cultural geography, Wiley-
Blackwell, Malden.
New York.
Discourse Analysis: A Dialogue on Language and
Identity, Sage, Thousand Oaks.
London.
Bosco, F. J., 2004, Human rights politics and scaled perfor-
mances of memory: Conflicts among the Madres
de Plaza de Mayo in Argentina, Social & Cultural
Geography, 5(3), 381-402.
Bourdieu, P., 2011, The forms of capital (1986), in Szeman,
I. and Kaposy, T. (eds.), Cultural Theory: An Anthol-
ogy, Wiley-Blackwell, Malden.
nation: Charles Taylor on the sources of the self,
Sociological Theory, 9(2), 232-263.
Calhoun, C., 1994, Social Theory and the Politics of
Identity,
Blackwell, Malden.
Passionate politics: Emotions and social movements,
University of Chicago Press, Chicago.
Courtheyn, C., 2016, ‘Memory is the strength of our resis-
tance’: an ‘other politics’ through embodied and
material commemoration in the San José Peace
Community, Colombia, Social & Cultural Geogra-
phy, 17(7), 933-958.
race and sex: A black feminist critique of antidis-
crimination doctrine, feminist theory and antiracist
politics, University of Chicago Legal Forum (1989),
139-167.
Malden.
McDowell, L. (eds.), The SAGE Handbook of Quali-
tative Geography, Sage, Thousand Oaks.
Duminy, J., 2014, Street renaming, symbolic capital, and re-
sistance in Durban, South Africa, Environment and
Planning D: Society and Space, 32(2), 310-328.
Duncan, J. and Duncan, N., 2001, Sense of place as a posi-
tional good: Locating Bedford in space and time,
in Adams, P. C., Hoelscher, S. D., and Till, K. E.
(eds.), Textures of Place: Exploring Humanist Geogra-
phies, University of Minnesota Press, Minneapolis.
Dutton, D., 2009, The Art Instinct: Beauty, Pleasure, &
Hu-
man Evolution. Oxford University Press, New York.
Dwyer, O. J., 2000, Interpreting the civil rights movement:
Place, memory, and conflict, The Professional Geog-
rapher, 52(4), 660-671.
Dwyer, O. J. and Alderman, D. H., 2008a, Civil Rights Me-
morials and the Geography of Memory, University of
Georgia Press, Athens.
Dwyer, O. J. and Alderman, D. H., 2008b, Memorial land-
scapes: Analytic questions and metaphors, GeoJour-
nal, 73(3), 165-178.
Elden, S., 2007, There is a politics of space because space
is political: Henri Lefebvre and the production of
space, Radical Philosophy Review, 10(2), 101-116.
Feinberg, J., 2014, Rights, Justice, and the Bounds of
Liberty:
- 68 -
University Press, Princeton.
York.
Press, Ithaca.
versity Press, New York.
in Wetherell, M., Taylor, S., and Yates, S. J. (eds.),
Discourse Theory and Practice: A Reader, Sage, Thou-
sand Oaks.
Herod, A., 1991, The production of scale in United States
labour relations, Area, 23(1), 82-88.
Higgins, E. T., Bond, R. N., Klein, R., and Strauman, T.,
1986, Self-discrepancies and emotional vulner-
ability: How magnitude, accessibility, and type of
discrepancy influence affect, Journal of Personality
and Social Psychology, 51(1), 5-15.
Honneth, A., 1992, Integrity and disrespect: principles of a
conception of morality based on the theory of rec-
ognition, Political Theory, 20(2), 187-201.
Honneth, A., 1996, The Struggle for Recognition: The Moral
Grammar of Social Conf licts, MIT Press, Cam-
bridge.
plural theory of justice, Acta Sociologica, 47(4), 351-
364.
obligation, Social Research, 64(1), 16-35.
Inwood, J. F., 2010, Sweet Auburn: Constructing Atlanta’s
Auburn Avenue as a heritage tourist destination,
Urban Geography, 31(5), 573-594.
Jørgensen, M. W. and Phillips, L. J., 2002, Discourse
Analysis
as Theory and Method, Sage, Thousand Oaks.
Lees, L., 2004, Urban geography: discourse analysis and ur-
ban research, Progress in Human Geography, 28(1),
101-107.
Malden.
for a Sociology of the Everyday (Vol. 2), Verso, New
York.
Leib, J. I., 2002, Separate times, shared spaces: Arthur
Ashe,
Monument Avenue and the politics of Richmond,
Virginia’s symbolic landscape, Cultural geographies,
9(3), 286-312.
Little, P. D. and Painter, M., 1995, Discourse, politics, and
the development process: Reflections on Escobar’s
“anthropology and the development encounter”,
American Ethnologist, 22(3), 602-609.
Loader, I., and Mulcahy, A, 2003, Policing and the Condition
of England: Memory, Politics and Culture, Oxford
University Press, Oxford.
memory, Geographical Review, 65(1), 1-36.
Lowenthal, D., 2015, The Past is a Foreign Country-Revisited,
Cambridge University Press, Cambridge.
apartheid South Africa, Brill, Leiden.
McCann, E. J., 1999, Race, protest, and public space: Con-
textualizing Lefebvre in the US city, Antipode,
31(2), 163-184.
tion of colonial power, gender, and class, Gender &
Society, 17(6), 938-957.
Mitchell, C. J., Atkinson, R. G., and Clark, A., 2001, The
creative destruction of NiagaraontheLake, Cana-
dian Geographer/Le Géographe canadien, 45(2), 285
-299.
Mitchell, D., 2003, The Right to the City: Social Justice and
the Fight for Public Space, Guilford Press, New York.
Nash, C., 1996, Reclaiming vision: looking at landscape and
the body, Gender, Place and Culture: A Journal of
Feminist Geography, 3(2), 149-170.
Onken, E. C., 2007, The Baltic states and Moscow’s 9 May
commemoration: Analysing memory politics in
Europe, Europe-Asia Studies, 59(1), 23-46.
Osborne, B. S., 2001, Landscapes, memory, monuments,
and commemoration: Putting identity in its place,
Canadian Ethnic Studies, 33(3), 39-77.
- 69 -
Pile, S., 2013, The Body and the City: Psychoanalysis, Space
and Subjectivity, Routledge, Abingdon.
2008, Collective memory and the politics of urban
space: an introduction, GeoJournal, 73(3), 161-164.
Schein, R. H., 1997, The place of landscape: a conceptual
framework for interpreting an American scene,
Annals of the Association of American Geographers,
87(4), 660-680.
contribution from Henri Lefebvre, Geografiska An-
naler: Series B, Human Geography, 87(1), 1-14.
Till, K., 2003, Places of memory, in Agnew, J. A., Mitchell,
K., and Toal, G. (eds.) A Companion to Political Ge-
ography, Blackwell, Malden.
Till, K. E., 2005, The New Berlin: Memory, Politics, Place,
University of Minnesota Press, Minneapolis.
Till, K. E., 2008, Artistic and activist memory-work: Ap-
proaching place-based practice, Memory Studies,
1(1), 99-113.
place-based ethics of care, Political Geography,
31(1), 3-14.
Till, K., 2012b, Resilient politics and a place-based ethics
of
care: Rethinking the city through the District Six
Museum in Cape Town, South Africa, in Gold-
stein, B. E. (ed.), Collaborative Resilience: Moving
through Crisis to Opportunity, MIT Press, Cam-
bridge.
Tuan, Y. F., 1977, Space and Place: The Perspective of
Experi-
ence, University of Minnesota Press, Minneapolis.
Waitt, G., 2010, Doing Foucauldian discourse analysis-
revealing social realities, in Hay, I. (ed.), Qualitative
Research Methods in Human Geography, Oxford
University Press, Oxford.
nation, and Europe, in Howarth, D. and Torfing, J.
(eds.), Discourse theory in European politics, Palgrave
Macmillan, London.
terdisciplinarity and critical discourse analysis, in
Weiss, G. and Wodak, R. (eds.), Critical Discourse
Analysis, Palgrave Macmillan, London.
121 (: amyjh07@
konkuk.ac.kr,
[email protected])
jin-gu, Room 121 Department of Geography, Science Build-
ing, Seoul, 05029, Korea(e-mail:
[email protected],
[email protected])
IT
*
Realities and Improvements in the Resilience of the Gumi IT
Industry Cluster
Ji-Hye Jeon*
* (Post Doctoral Researcher, Kyungpook National University),
[email protected]
?
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: IT ,
, . IT 2010
, · .
IT , ·
. , R&D
R&D
. ·
,
. 3
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: , , , IT
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)
,
(·, 2018).
,
·
(Martin, 2011; Fingleton et al., 2012; Martin et
al., 2016; , 2016; Sensier et al., 2016; Eray-
din, 2016), ,
(Simmie and Martin,
2010; Cowell, 2013),
(Park and Østergaard,
2012),
(Kiese and Hundt, 2014; Svoboda and Applová,
2014; Eraydin, 2014) .
, ,
.
,
.
,
.
·
··
. IT
, LG
. , IT
.
, , ,
(Martin and Sunley,
2015; ·, 2017).
·(2018) .
, ,
.
IT
2017 1 20 4 21
158
.
, ,
- 73 -
2.
.
.
,
. ,
Martin and Sunley(2015)
‘ ’, ‘’, ‘ ’,
‘ ’ ‘’ 5, Bos-
chma(2015) ‘’, ‘ ’
‘’ 3 . , Palekiene et
al.(2015) ‘ ’ ‘ ’, ‘·
’, ‘ ’, ‘· ’
‘ ’ 6
.
,
(·, 2003),
‘, ’
3 .
.
. ,
(·, 2018).
,
. ,
.
,
(Essletzbichler, 2007;
Evans and Karecha, 2014; Doran and Fingleton,
2018)1). ,
(Boschma, 2015).
,
.
,
.
(Boschma, 2015;
Martin and Sunley, 2015). ,
,
(Boschma and Frenken, 2010).
,
(Martin and Sunley, 2015).
,
. ,
,
· ,
(Krugman, 2005; Martin and
Sunley, 2015 ; Eraydin, 2014).
,
·
.
·
, R&D
- 74 -
·
(Christopherson et
al., 2010). R&D
, R&D
. , R&D
. R&D
(Geroski and
Machn, 1992).
(Martin
and Sunley, 2015). ,
··
.
.
(Pendall et al., 2010; Christopherson et al.,
2010). , ·
, R&D
(Boschma, 2015). ,
R&D
, , ·
(, 2012).
.
(North, 1994; Scott, 2003).
, ,
,
(Martin
and Sunley, 2015).
(Boschma,
2015).
(Trembac-
zowski, 2012; Bristow and Healey, 2014; Martin
and Sunley, 2015). ,
,
. ,
(Pike et al., 2010).
(Hill et al., 2008; Dawley et al., 2010;
Wolfe, 2011; Eraydin, 2016). ,
. , ·
(Boschma, 2015).
3
( 1).
,
.
: Martin and Sunley(2015), Palekiene et al.(2015),
Boschma(2015)
3. IT
IT 1~5 5
IT ···
( , 2016).
1969
IT ,
. IT
.
1,909,
28,819
4.2%, 6.8% .
, 50
, 233 15 .
(5 ) 3
.
43.4%(834) ,
(30.6%), (11.0%), (4.7%)
, 3D,
, IT
.
(58.2%), (23.4%), (6.8%),
63.7%(283,233 ), 87.9%(25,318 )
( 1).
2012 12 1,757
54 3% ,
70% ( ,
2012). 59.6%
( , 2016).
.
‘
’(18.5%). , LG
IT
. ‘
1. (2017 12 )
(: , , , , %)
’(14.1%), ‘ ·’
(12.5%), ‘ ’(12.3%)
‘ ’(11.7%)
. , ‘, , ’(4.3%) ‘
· ’(2.7%)
.
IT
(mono-culture) ,
·
(hub-spoke)
. 2010
LG , ,
IT
. , 2016
77.6% 2018 64.8%
30 25
(, 2019). IT
·
.
IT
,
.
1)
.
‘’2)
. 2 IT ‘·
’ (2.56) 10
3
. ‘1’
‘’(1.79) ‘’(1.08)
. IT
· IT
·