Overview

Dataset statistics

Number of variables14
Number of observations32
Missing cells251
Missing cells (%)56.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory125.1 B

Variable types

Categorical3
Numeric2
Text2
Unsupported7

Dataset

Description소재지대륙명,소재지국가명,소재지도시명,SEQNO,기구명,가입연도,설립목적,설립연혁,회원도시,회원자격,회비,회의주기,주요활동,사무국담당자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2488/S/1/datasetView.do

Alerts

소재지국가명 is highly overall correlated with 가입연도 and 2 other fieldsHigh correlation
소재지도시명 is highly overall correlated with 소재지대륙명 and 1 other fieldsHigh correlation
가입연도 is highly overall correlated with 소재지국가명High correlation
소재지대륙명 is highly overall correlated with 소재지국가명 and 1 other fieldsHigh correlation
설립목적 has 32 (100.0%) missing valuesMissing
설립연혁 has 32 (100.0%) missing valuesMissing
회원도시 has 32 (100.0%) missing valuesMissing
회원자격 has 32 (100.0%) missing valuesMissing
회비 has 32 (100.0%) missing valuesMissing
회의주기 has 32 (100.0%) missing valuesMissing
주요활동 has 32 (100.0%) missing valuesMissing
사무국담당자 has 27 (84.4%) missing valuesMissing
SEQNO has unique valuesUnique
설립목적 is an unsupported type, check if it needs cleaning or further analysisUnsupported
설립연혁 is an unsupported type, check if it needs cleaning or further analysisUnsupported
회원도시 is an unsupported type, check if it needs cleaning or further analysisUnsupported
회원자격 is an unsupported type, check if it needs cleaning or further analysisUnsupported
회비 is an unsupported type, check if it needs cleaning or further analysisUnsupported
회의주기 is an unsupported type, check if it needs cleaning or further analysisUnsupported
주요활동 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-03 19:52:59.288856
Analysis finished2024-05-03 19:53:02.398685
Duration3.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지대륙명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
유럽
16 
아시아
11 
북아메리카
5 

Length

Max length5
Median length4
Mean length2.8125
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유럽
2nd row유럽
3rd row유럽
4th row유럽
5th row북아메리카

Common Values

ValueCountFrequency (%)
유럽 16
50.0%
아시아 11
34.4%
북아메리카 5
 
15.6%

Length

2024-05-03T19:53:02.717301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:53:03.079649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유럽 16
50.0%
아시아 11
34.4%
북아메리카 5
 
15.6%

소재지국가명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
미국
5 
한국
5 
프랑스
4 
영국
3 
벨기에
3 
Other values (8)
12 

Length

Max length5
Median length2
Mean length2.5
Min length2

Unique

Unique6 ?
Unique (%)18.8%

Sample

1st row오스트리아
2nd row프랑스
3rd row프랑스
4th row영국
5th row미국

Common Values

ValueCountFrequency (%)
미국 5
15.6%
한국 5
15.6%
프랑스 4
12.5%
영국 3
9.4%
벨기에 3
9.4%
일본 3
9.4%
스페인 3
9.4%
오스트리아 1
 
3.1%
싱가포르 1
 
3.1%
중국 1
 
3.1%
Other values (3) 3
9.4%

Length

2024-05-03T19:53:03.434359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미국 5
15.6%
한국 5
15.6%
프랑스 4
12.5%
영국 3
9.4%
벨기에 3
9.4%
일본 3
9.4%
스페인 3
9.4%
오스트리아 1
 
3.1%
싱가포르 1
 
3.1%
중국 1
 
3.1%
Other values (3) 3
9.4%

소재지도시명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Memory size388.0 B
서울
5 
뉴욕
4 
파리
3 
런던
3 
브뤼셀
3 
Other values (10)
14 

Length

Max length5
Median length2
Mean length2.53125
Min length1

Unique

Unique8 ?
Unique (%)25.0%

Sample

1st row비엔나
2nd row파리
3rd row파리
4th row런던
5th row뉴욕

Common Values

ValueCountFrequency (%)
서울 5
15.6%
뉴욕 4
12.5%
파리 3
9.4%
런던 3
9.4%
브뤼셀 3
9.4%
도쿄 3
9.4%
바르셀로나 3
9.4%
비엔나 1
 
3.1%
워싱턴 1
 
3.1%
싱가포르 1
 
3.1%
Other values (5) 5
15.6%

Length

2024-05-03T19:53:03.843181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 5
15.6%
뉴욕 4
12.5%
파리 3
9.4%
런던 3
9.4%
브뤼셀 3
9.4%
도쿄 3
9.4%
바르셀로나 3
9.4%
비엔나 1
 
3.1%
워싱턴 1
 
3.1%
싱가포르 1
 
3.1%
Other values (5) 5
15.6%

SEQNO
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.3125
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-05-03T19:53:04.262731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.55
Q18.75
median18.5
Q361.25
95-th percentile67.45
Maximum69
Range68
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation24.532319
Coefficient of variation (CV)0.86648367
Kurtosis-1.1613552
Mean28.3125
Median Absolute Deviation (MAD)12
Skewness0.7299073
Sum906
Variance601.83468
MonotonicityNot monotonic
2024-05-03T19:53:04.698483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
61 1
 
3.1%
16 1
 
3.1%
8 1
 
3.1%
69 1
 
3.1%
2 1
 
3.1%
32 1
 
3.1%
3 1
 
3.1%
68 1
 
3.1%
4 1
 
3.1%
5 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1 1
3.1%
2 1
3.1%
3 1
3.1%
4 1
3.1%
5 1
3.1%
6 1
3.1%
7 1
3.1%
8 1
3.1%
9 1
3.1%
12 1
3.1%
ValueCountFrequency (%)
69 1
3.1%
68 1
3.1%
67 1
3.1%
66 1
3.1%
65 1
3.1%
64 1
3.1%
63 1
3.1%
62 1
3.1%
61 1
3.1%
32 1
3.1%

기구명
Text

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-05-03T19:53:05.257383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length21
Mean length18.75
Min length3

Characters and Unicode

Total characters600
Distinct characters148
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)87.5%

Sample

1st row세계옴부즈만협회(IOI)
2nd row국제 박물관 협의회(ICOM)
3rd row글로벌 도시 대기오염 관측망(GUAPO)
4th row세계식물원보전연맹(BGCI)
5th row세계 지방정부 건강도시 협의체(Partnership for Healthy Cities)
ValueCountFrequency (%)
글로벌 3
 
4.1%
세계 2
 
2.7%
건강도시 2
 
2.7%
협의체(partnership 2
 
2.7%
for 2
 
2.7%
healthy 2
 
2.7%
cities 2
 
2.7%
세계전자정부협의체 2
 
2.7%
wego 2
 
2.7%
도시 2
 
2.7%
Other values (51) 52
71.2%
2024-05-03T19:53:06.111675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41
 
6.8%
( 28
 
4.7%
) 28
 
4.7%
C 19
 
3.2%
시 15
 
2.5%
협 15
 
2.5%
I 12
 
2.0%
e 12
 
2.0%
회 12
 
2.0%
세 11
 
1.8%
Other values (138) 407
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
50.5%
Uppercase Letter 118
 
19.7%
Lowercase Letter 76
 
12.7%
Space Separator 41
 
6.8%
Open Punctuation 28
 
4.7%
Close Punctuation 28
 
4.7%
Decimal Number 6
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 15
 
5.0%
협 15
 
5.0%
회 12
 
4.0%
세 11
 
3.6%
도 11
 
3.6%
계 11
 
3.6%
의 9
 
3.0%
부 8
 
2.6%
국 8
 
2.6%
체 7
 
2.3%
Other values (95) 196
64.7%
Uppercase Letter
ValueCountFrequency (%)
C 19
16.1%
I 12
10.2%
G 10
 
8.5%
O 10
 
8.5%
A 8
 
6.8%
T 7
 
5.9%
P 7
 
5.9%
U 7
 
5.9%
M 5
 
4.2%
W 5
 
4.2%
Other values (9) 28
23.7%
Lowercase Letter
ValueCountFrequency (%)
e 12
15.8%
r 8
10.5%
t 8
10.5%
i 8
10.5%
a 6
7.9%
o 5
 
6.6%
s 5
 
6.6%
n 4
 
5.3%
h 4
 
5.3%
y 3
 
3.9%
Other values (7) 13
17.1%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
0 1
16.7%
4 1
16.7%
Space Separator
ValueCountFrequency (%)
41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
50.5%
Latin 194
32.3%
Common 103
 
17.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 15
 
5.0%
협 15
 
5.0%
회 12
 
4.0%
세 11
 
3.6%
도 11
 
3.6%
계 11
 
3.6%
의 9
 
3.0%
부 8
 
2.6%
국 8
 
2.6%
체 7
 
2.3%
Other values (95) 196
64.7%
Latin
ValueCountFrequency (%)
C 19
 
9.8%
I 12
 
6.2%
e 12
 
6.2%
G 10
 
5.2%
O 10
 
5.2%
r 8
 
4.1%
t 8
 
4.1%
i 8
 
4.1%
A 8
 
4.1%
T 7
 
3.6%
Other values (26) 92
47.4%
Common
ValueCountFrequency (%)
41
39.8%
( 28
27.2%
) 28
27.2%
2 2
 
1.9%
1 2
 
1.9%
0 1
 
1.0%
4 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
50.5%
ASCII 297
49.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41
 
13.8%
( 28
 
9.4%
) 28
 
9.4%
C 19
 
6.4%
I 12
 
4.0%
e 12
 
4.0%
G 10
 
3.4%
O 10
 
3.4%
r 8
 
2.7%
t 8
 
2.7%
Other values (33) 121
40.7%
Hangul
ValueCountFrequency (%)
시 15
 
5.0%
협 15
 
5.0%
회 12
 
4.0%
세 11
 
3.6%
도 11
 
3.6%
계 11
 
3.6%
의 9
 
3.0%
부 8
 
2.6%
국 8
 
2.6%
체 7
 
2.3%
Other values (95) 196
64.7%

가입연도
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.0625
Minimum1970
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-05-03T19:53:06.638674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1987
Q12000.5
median2010
Q32014.5
95-th percentile2018.45
Maximum2020
Range50
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.104218
Coefficient of variation (CV)0.0060338191
Kurtosis1.0748655
Mean2006.0625
Median Absolute Deviation (MAD)6.5
Skewness-1.2129954
Sum64194
Variance146.5121
MonotonicityDecreasing
2024-05-03T19:53:07.016961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2017 3
 
9.4%
2014 3
 
9.4%
2012 3
 
9.4%
2010 3
 
9.4%
2016 2
 
6.2%
1987 2
 
6.2%
1989 2
 
6.2%
2020 1
 
3.1%
2001 1
 
3.1%
1970 1
 
3.1%
Other values (11) 11
34.4%
ValueCountFrequency (%)
1970 1
3.1%
1987 2
6.2%
1988 1
3.1%
1989 2
6.2%
1998 1
3.1%
1999 1
3.1%
2001 1
3.1%
2003 1
3.1%
2004 1
3.1%
2005 1
3.1%
ValueCountFrequency (%)
2020 1
 
3.1%
2019 1
 
3.1%
2018 1
 
3.1%
2017 3
9.4%
2016 2
6.2%
2014 3
9.4%
2013 1
 
3.1%
2012 3
9.4%
2010 3
9.4%
2007 1
 
3.1%

설립목적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

설립연혁
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

회원도시
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

회원자격
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

회비
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

회의주기
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

주요활동
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing32
Missing (%)100.0%
Memory size420.0 B

사무국담당자
Text

MISSING 

Distinct4
Distinct (%)80.0%
Missing27
Missing (%)84.4%
Memory size388.0 B
2024-05-03T19:53:07.346224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length14
Mean length18.8
Min length9

Characters and Unicode

Total characters94
Distinct characters42
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)60.0%

Sample

1st row영국 런던
2nd row일본 도쿄 요시히로 마쯔자카 Yoshihiro Matsuzaka 남 동경도 지사본국
3rd row독일 본
4th row스페인 바르셀로나
5th row스페인 바르셀로나
ValueCountFrequency (%)
스페인 2
 
11.8%
바르셀로나 2
 
11.8%
영국 1
 
5.9%
런던 1
 
5.9%
일본 1
 
5.9%
도쿄 1
 
5.9%
요시히로 1
 
5.9%
마쯔자카 1
 
5.9%
yoshihiro 1
 
5.9%
matsuzaka 1
 
5.9%
Other values (5) 5
29.4%
2024-05-03T19:53:07.946832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33
35.1%
본 3
 
3.2%
a 3
 
3.2%
로 3
 
3.2%
일 2
 
2.1%
h 2
 
2.1%
s 2
 
2.1%
o 2
 
2.1%
페 2
 
2.1%
도 2
 
2.1%
Other values (32) 40
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43
45.7%
Space Separator 33
35.1%
Lowercase Letter 16
 
17.0%
Uppercase Letter 2
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
본 3
 
7.0%
로 3
 
7.0%
일 2
 
4.7%
페 2
 
4.7%
도 2
 
4.7%
스 2
 
4.7%
나 2
 
4.7%
국 2
 
4.7%
인 2
 
4.7%
바 2
 
4.7%
Other values (19) 21
48.8%
Lowercase Letter
ValueCountFrequency (%)
a 3
18.8%
h 2
12.5%
s 2
12.5%
o 2
12.5%
i 2
12.5%
t 1
 
6.2%
z 1
 
6.2%
k 1
 
6.2%
r 1
 
6.2%
u 1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
M 1
50.0%
Y 1
50.0%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43
45.7%
Common 33
35.1%
Latin 18
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
본 3
 
7.0%
로 3
 
7.0%
일 2
 
4.7%
페 2
 
4.7%
도 2
 
4.7%
스 2
 
4.7%
나 2
 
4.7%
국 2
 
4.7%
인 2
 
4.7%
바 2
 
4.7%
Other values (19) 21
48.8%
Latin
ValueCountFrequency (%)
a 3
16.7%
h 2
11.1%
s 2
11.1%
o 2
11.1%
i 2
11.1%
M 1
 
5.6%
t 1
 
5.6%
z 1
 
5.6%
k 1
 
5.6%
r 1
 
5.6%
Other values (2) 2
11.1%
Common
ValueCountFrequency (%)
33
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
54.3%
Hangul 43
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33
64.7%
a 3
 
5.9%
h 2
 
3.9%
s 2
 
3.9%
o 2
 
3.9%
i 2
 
3.9%
M 1
 
2.0%
t 1
 
2.0%
z 1
 
2.0%
k 1
 
2.0%
Other values (3) 3
 
5.9%
Hangul
ValueCountFrequency (%)
본 3
 
7.0%
로 3
 
7.0%
일 2
 
4.7%
페 2
 
4.7%
도 2
 
4.7%
스 2
 
4.7%
나 2
 
4.7%
국 2
 
4.7%
인 2
 
4.7%
바 2
 
4.7%
Other values (19) 21
48.8%

Interactions

2024-05-03T19:53:00.713543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:53:00.215297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:53:00.963013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:53:00.468132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T19:53:08.165843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지대륙명소재지국가명소재지도시명SEQNO기구명가입연도사무국담당자
소재지대륙명1.0001.0001.0000.5861.0000.3581.000
소재지국가명1.0001.0001.0000.5491.0000.8761.000
소재지도시명1.0001.0001.0000.5721.0000.7721.000
SEQNO0.5860.5490.5721.0000.6430.488NaN
기구명1.0001.0001.0000.6431.0001.0001.000
가입연도0.3580.8760.7720.4881.0001.0000.913
사무국담당자1.0001.0001.000NaN1.0000.9131.000
2024-05-03T19:53:08.376484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지국가명소재지대륙명소재지도시명
소재지국가명1.0000.8090.946
소재지대륙명0.8091.0000.766
소재지도시명0.9460.7661.000
2024-05-03T19:53:08.698533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQNO가입연도소재지대륙명소재지국가명소재지도시명
SEQNO1.0000.4530.4380.2270.215
가입연도0.4531.0000.1210.5370.492
소재지대륙명0.4380.1211.0000.8090.766
소재지국가명0.2270.5370.8091.0000.946
소재지도시명0.2150.4920.7660.9461.000

Missing values

2024-05-03T19:53:01.325249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T19:53:02.119129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

소재지대륙명소재지국가명소재지도시명SEQNO기구명가입연도설립목적설립연혁회원도시회원자격회비회의주기주요활동사무국담당자
0유럽오스트리아비엔나61세계옴부즈만협회(IOI)2020<NA><NA><NA><NA><NA><NA><NA><NA>
1유럽프랑스파리65국제 박물관 협의회(ICOM)2019<NA><NA><NA><NA><NA><NA><NA><NA>
2유럽프랑스파리63글로벌 도시 대기오염 관측망(GUAPO)2018<NA><NA><NA><NA><NA><NA><NA><NA>
3유럽영국런던67세계식물원보전연맹(BGCI)2017<NA><NA><NA><NA><NA><NA><NA><NA>
4북아메리카미국뉴욕29세계 지방정부 건강도시 협의체(Partnership for Healthy Cities)2017<NA><NA><NA><NA><NA><NA><NA><NA>
5북아메리카미국뉴욕28세계 지방정부 건강도시 협의체(Partnership for Healthy Cities)2017<NA><NA><NA><NA><NA><NA><NA><NA>
6북아메리카미국워싱턴22열린정부파트너쉽(OGP)2016<NA><NA><NA><NA><NA><NA><NA><NA>
7아시아싱가포르싱가포르66글로벌 회복력 도시 네트워크(GRCN)2016<NA><NA><NA><NA><NA><NA><NA><NA>
8아시아한국서울19국제사회적경제협의체 (GSEF)2014<NA><NA><NA><NA><NA><NA><NA><NA>
9북아메리카미국뉴욕18유엔글로벌콤팩트(UNGC) 한국협회2014<NA><NA><NA><NA><NA><NA><NA><NA>
소재지대륙명소재지국가명소재지도시명SEQNO기구명가입연도설립목적설립연혁회원도시회원자격회비회의주기주요활동사무국담당자
22아시아일본도쿄64위기관리 네트워크2003<NA><NA><NA><NA><NA><NA><NA><NA>
23아시아일본도쿄1아시아대도시네트워크21(ANMC21)2001<NA><NA><NA><NA><NA><NA><NA>일본 도쿄 요시히로 마쯔자카 Yoshihiro Matsuzaka 남 동경도 지사본국
24유럽독일본5자치단체국제환경협의회(ICLEI)1999<NA><NA><NA><NA><NA><NA><NA>독일 본
25유럽스페인바르셀로나4세계지방자치단체연합(UCLG)1998<NA><NA><NA><NA><NA><NA><NA>스페인 바르셀로나
26아시아한국서울68시티넷1989<NA><NA><NA><NA><NA><NA><NA><NA>
27아시아한국서울3인간정주관리를 위한 지방정부망(CITYNET)1989<NA><NA><NA><NA><NA><NA><NA><NA>
28유럽스위스제네바32국제이주기구 한국 대표부(IOM)1988<NA><NA><NA><NA><NA><NA><NA><NA>
29유럽스페인바르셀로나2세계대도시협의회(METROPOLIS)1987<NA><NA><NA><NA><NA><NA><NA>스페인 바르셀로나
30유럽스페인바르셀로나69메트로폴리스1987<NA><NA><NA><NA><NA><NA><NA><NA>
31아시아태국방콕8아시아태평양관광협회 (PATA)1970<NA><NA><NA><NA><NA><NA><NA><NA>