Overview

Dataset statistics

Number of variables12
Number of observations69
Missing cells201
Missing cells (%)24.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory101.9 B

Variable types

Text8
Categorical1
Numeric3

Dataset

Description23~25 공공데이터 중장기개방계획에 의한 도시계획정보시스템 개방데이터로서 용도구역 결정조서, 용도구역 변경사유서, 용도구역 승인조서 테이블을 병합하여 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=16&beforeMenuCd=DOM_000000201001001000&publicdatapk=15122321

Alerts

면적_기정 is highly overall correlated with 도면번호High correlation
면적_변경 is highly overall correlated with 도면번호High correlation
면적_변경후 is highly overall correlated with 도면번호High correlation
도면번호 is highly overall correlated with 면적_기정 and 2 other fieldsHigh correlation
도면번호 is highly imbalanced (68.2%)Imbalance
이전 조서관리번호 has 36 (52.2%) missing valuesMissing
승인고시 관리번호 has 30 (43.5%) missing valuesMissing
지역명 has 52 (75.4%) missing valuesMissing
결정(변경)사유 has 66 (95.7%) missing valuesMissing
비고 has 17 (24.6%) missing valuesMissing
조서관리번호 has unique valuesUnique
면적_기정 has 36 (52.2%) zerosZeros
면적_변경 has 11 (15.9%) zerosZeros
면적_변경후 has 6 (8.7%) zerosZeros

Reproduction

Analysis started2024-01-09 20:20:01.487730
Analysis finished2024-01-09 20:20:03.096328
Duration1.61 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조서관리번호
Text

UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size684.0 B
2024-01-10T05:20:03.218055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters1380
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)100.0%

Sample

1st row44200BTZ198504080001
2nd row44200BTZ198601040001
3rd row44200BTZ199208240001
4th row44200BTZ200302270001
5th row44200BTZ200302270002
ValueCountFrequency (%)
44200btz198504080001 1
 
1.4%
44200btz200607050001 1
 
1.4%
44200btz201007140001 1
 
1.4%
44200btz201004130001 1
 
1.4%
44200btz200701100002 1
 
1.4%
44200btz200904200001 1
 
1.4%
44200btz200909040002 1
 
1.4%
44200btz200909040001 1
 
1.4%
44200btz200712100002 1
 
1.4%
44200btz200903160001 1
 
1.4%
Other values (59) 59
85.5%
2024-01-10T05:20:03.494851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 532
38.6%
2 194
 
14.1%
4 165
 
12.0%
1 132
 
9.6%
B 69
 
5.0%
T 69
 
5.0%
Z 69
 
5.0%
7 41
 
3.0%
3 28
 
2.0%
9 27
 
2.0%
Other values (3) 54
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1173
85.0%
Uppercase Letter 207
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 532
45.4%
2 194
 
16.5%
4 165
 
14.1%
1 132
 
11.3%
7 41
 
3.5%
3 28
 
2.4%
9 27
 
2.3%
5 25
 
2.1%
8 20
 
1.7%
6 9
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
B 69
33.3%
T 69
33.3%
Z 69
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1173
85.0%
Latin 207
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 532
45.4%
2 194
 
16.5%
4 165
 
14.1%
1 132
 
11.3%
7 41
 
3.5%
3 28
 
2.4%
9 27
 
2.3%
5 25
 
2.1%
8 20
 
1.7%
6 9
 
0.8%
Latin
ValueCountFrequency (%)
B 69
33.3%
T 69
33.3%
Z 69
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 532
38.6%
2 194
 
14.1%
4 165
 
12.0%
1 132
 
9.6%
B 69
 
5.0%
T 69
 
5.0%
Z 69
 
5.0%
7 41
 
3.0%
3 28
 
2.0%
9 27
 
2.0%
Other values (3) 54
 
3.9%

이전 조서관리번호
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing36
Missing (%)52.2%
Memory size684.0 B
2024-01-10T05:20:03.670017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters660
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row44200BTZ197612100001
2nd row44200BTZ197703170002
3rd row44200BTZ197703170001
4th row44200BTZ199303170001
5th row44200BTZ199401280001
ValueCountFrequency (%)
44200btz199200000001 1
 
3.0%
44200btz200707240001 1
 
3.0%
44200btz200804110002 1
 
3.0%
44200btz200607050002 1
 
3.0%
44200btz200802110001 1
 
3.0%
44200btz200811100001 1
 
3.0%
44200btz200806050002 1
 
3.0%
44200btz200806050001 1
 
3.0%
44200btz200701100001 1
 
3.0%
44200btz200909040002 1
 
3.0%
Other values (23) 23
69.7%
2024-01-10T05:20:03.929277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 249
37.7%
2 83
 
12.6%
4 78
 
11.8%
1 69
 
10.5%
B 33
 
5.0%
T 33
 
5.0%
Z 33
 
5.0%
9 20
 
3.0%
7 20
 
3.0%
8 13
 
2.0%
Other values (3) 29
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 561
85.0%
Uppercase Letter 99
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249
44.4%
2 83
 
14.8%
4 78
 
13.9%
1 69
 
12.3%
9 20
 
3.6%
7 20
 
3.6%
8 13
 
2.3%
3 12
 
2.1%
5 10
 
1.8%
6 7
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 33
33.3%
T 33
33.3%
Z 33
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 561
85.0%
Latin 99
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249
44.4%
2 83
 
14.8%
4 78
 
13.9%
1 69
 
12.3%
9 20
 
3.6%
7 20
 
3.6%
8 13
 
2.3%
3 12
 
2.1%
5 10
 
1.8%
6 7
 
1.2%
Latin
ValueCountFrequency (%)
B 33
33.3%
T 33
33.3%
Z 33
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249
37.7%
2 83
 
12.6%
4 78
 
11.8%
1 69
 
10.5%
B 33
 
5.0%
T 33
 
5.0%
Z 33
 
5.0%
9 20
 
3.0%
7 20
 
3.0%
8 13
 
2.0%
Other values (3) 29
 
4.4%

결정고시 관리번호
Text

Distinct52
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
2024-01-10T05:20:04.135803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters1380
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)59.4%

Sample

1st row44200NTC198504080001
2nd row44200NTC198601040001
3rd row44200NTC199208240001
4th row44200NTC200302270001
5th row44200NTC200302270001
ValueCountFrequency (%)
44200ntc200302270001 5
 
7.2%
44200ntc201105020001 4
 
5.8%
44200ntc201805210001 3
 
4.3%
44200ntc200712100001 2
 
2.9%
44200ntc200607050001 2
 
2.9%
44200ntc200701100001 2
 
2.9%
44200ntc201407070175 2
 
2.9%
44200ntc201905310001 2
 
2.9%
44200ntc200707240001 2
 
2.9%
44200ntc197703170001 2
 
2.9%
Other values (42) 43
62.3%
2024-01-10T05:20:04.451328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 530
38.4%
2 189
 
13.7%
4 166
 
12.0%
1 140
 
10.1%
N 69
 
5.0%
T 69
 
5.0%
C 69
 
5.0%
7 42
 
3.0%
9 27
 
2.0%
5 26
 
1.9%
Other values (3) 53
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1173
85.0%
Uppercase Letter 207
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 530
45.2%
2 189
 
16.1%
4 166
 
14.2%
1 140
 
11.9%
7 42
 
3.6%
9 27
 
2.3%
5 26
 
2.2%
3 25
 
2.1%
8 19
 
1.6%
6 9
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N 69
33.3%
T 69
33.3%
C 69
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1173
85.0%
Latin 207
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 530
45.2%
2 189
 
16.1%
4 166
 
14.2%
1 140
 
11.9%
7 42
 
3.6%
9 27
 
2.3%
5 26
 
2.2%
3 25
 
2.1%
8 19
 
1.6%
6 9
 
0.8%
Latin
ValueCountFrequency (%)
N 69
33.3%
T 69
33.3%
C 69
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 530
38.4%
2 189
 
13.7%
4 166
 
12.0%
1 140
 
10.1%
N 69
 
5.0%
T 69
 
5.0%
C 69
 
5.0%
7 42
 
3.0%
9 27
 
2.0%
5 26
 
1.9%
Other values (3) 53
 
3.8%

승인고시 관리번호
Text

MISSING 

Distinct31
Distinct (%)79.5%
Missing30
Missing (%)43.5%
Memory size684.0 B
2024-01-10T05:20:04.639148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters780
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)66.7%

Sample

1st row44200NTC198612200001
2nd row44200NTC199209070001
3rd row44200NTC199402160001
4th row44200NTC199307090001
5th row44200NTC197703170001
ValueCountFrequency (%)
44200ntc201105160002 4
 
10.3%
44200ntc201807250001 3
 
7.7%
44200ntc201905310001 2
 
5.1%
44200ntc201407070175 2
 
5.1%
44200ntc200707240001 2
 
5.1%
44200ntc201507270003 1
 
2.6%
44200ntc200909040002 1
 
2.6%
44200ntc200904200001 1
 
2.6%
44200ntc201004130002 1
 
2.6%
44200ntc201007140001 1
 
2.6%
Other values (21) 21
53.8%
2024-01-10T05:20:04.918596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 286
36.7%
2 108
 
13.8%
4 95
 
12.2%
1 77
 
9.9%
N 39
 
5.0%
T 39
 
5.0%
C 39
 
5.0%
7 27
 
3.5%
9 20
 
2.6%
5 16
 
2.1%
Other values (3) 34
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
85.0%
Uppercase Letter 117
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 286
43.1%
2 108
 
16.3%
4 95
 
14.3%
1 77
 
11.6%
7 27
 
4.1%
9 20
 
3.0%
5 16
 
2.4%
3 13
 
2.0%
8 11
 
1.7%
6 10
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
N 39
33.3%
T 39
33.3%
C 39
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 663
85.0%
Latin 117
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 286
43.1%
2 108
 
16.3%
4 95
 
14.3%
1 77
 
11.6%
7 27
 
4.1%
9 20
 
3.0%
5 16
 
2.4%
3 13
 
2.0%
8 11
 
1.7%
6 10
 
1.5%
Latin
ValueCountFrequency (%)
N 39
33.3%
T 39
33.3%
C 39
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 286
36.7%
2 108
 
13.8%
4 95
 
12.2%
1 77
 
9.9%
N 39
 
5.0%
T 39
 
5.0%
C 39
 
5.0%
7 27
 
3.5%
9 20
 
2.6%
5 16
 
2.1%
Other values (3) 34
 
4.4%

도면번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size684.0 B
<NA>
61 
1
 
3
2
 
2
3
 
2
4
 
1

Length

Max length4
Median length4
Mean length3.6521739
Min length1

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 61
88.4%
1 3
 
4.3%
2 2
 
2.9%
3 2
 
2.9%
4 1
 
1.4%

Length

2024-01-10T05:20:05.035062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:20:05.124668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 61
88.4%
1 3
 
4.3%
2 2
 
2.9%
3 2
 
2.9%
4 1
 
1.4%

지역명
Text

MISSING 

Distinct13
Distinct (%)76.5%
Missing52
Missing (%)75.4%
Memory size684.0 B
2024-01-10T05:20:05.259869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length11.117647
Min length5

Characters and Unicode

Total characters189
Distinct characters56
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)52.9%

Sample

1st row용화1구역
2nd row용화2구역
3rd row용화3구역
4th row용화4구역
5th row아산 온천지구 도시개발사업
ValueCountFrequency (%)
도시개발사업 4
 
13.3%
아산 3
 
10.0%
용화1구역 2
 
6.7%
용화2구역 2
 
6.7%
도시개발예정지 2
 
6.7%
온천지구 2
 
6.7%
용화3구역 2
 
6.7%
용화4구역 1
 
3.3%
신정호지구 1
 
3.3%
온양중심상권재정비촉진지구 1
 
3.3%
Other values (10) 10
33.3%
2024-01-10T05:20:05.568940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 15
 
7.9%
구 15
 
7.9%
13
 
6.9%
정 9
 
4.8%
역 9
 
4.8%
발 9
 
4.8%
개 9
 
4.8%
용 7
 
3.7%
화 7
 
3.7%
도 7
 
3.7%
Other values (46) 89
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 163
86.2%
Space Separator 13
 
6.9%
Decimal Number 7
 
3.7%
Uppercase Letter 4
 
2.1%
Other Punctuation 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 15
 
9.2%
구 15
 
9.2%
정 9
 
5.5%
역 9
 
5.5%
발 9
 
5.5%
개 9
 
5.5%
용 7
 
4.3%
화 7
 
4.3%
도 7
 
4.3%
시 7
 
4.3%
Other values (38) 69
42.3%
Decimal Number
ValueCountFrequency (%)
1 2
28.6%
2 2
28.6%
3 2
28.6%
4 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
R 2
50.0%
D 2
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 163
86.2%
Common 22
 
11.6%
Latin 4
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 15
 
9.2%
구 15
 
9.2%
정 9
 
5.5%
역 9
 
5.5%
발 9
 
5.5%
개 9
 
5.5%
용 7
 
4.3%
화 7
 
4.3%
도 7
 
4.3%
시 7
 
4.3%
Other values (38) 69
42.3%
Common
ValueCountFrequency (%)
13
59.1%
& 2
 
9.1%
1 2
 
9.1%
2 2
 
9.1%
3 2
 
9.1%
4 1
 
4.5%
Latin
ValueCountFrequency (%)
R 2
50.0%
D 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 163
86.2%
ASCII 26
 
13.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 15
 
9.2%
구 15
 
9.2%
정 9
 
5.5%
역 9
 
5.5%
발 9
 
5.5%
개 9
 
5.5%
용 7
 
4.3%
화 7
 
4.3%
도 7
 
4.3%
시 7
 
4.3%
Other values (38) 69
42.3%
ASCII
ValueCountFrequency (%)
13
50.0%
R 2
 
7.7%
& 2
 
7.7%
D 2
 
7.7%
1 2
 
7.7%
2 2
 
7.7%
3 2
 
7.7%
4 1
 
3.8%

위치명
Text

Distinct55
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Memory size684.0 B
2024-01-10T05:20:05.718746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length24
Mean length16.492754
Min length3

Characters and Unicode

Total characters1138
Distinct characters115
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)60.9%

Sample

1st row충청남도 아산군 온양읍 일부지역
2nd row도고면:신언리,기곡리,금산리,향산리, 선장면:신성리
3rd row충청남도 아산군 둔포면 일부지역
4th row아산시 온양
5th row아산시 배방
ValueCountFrequency (%)
아산시 44
 
17.3%
일원 39
 
15.4%
배방면 7
 
2.8%
신창면 6
 
2.4%
충청남도 6
 
2.4%
용화동 6
 
2.4%
온천동 5
 
2.0%
방축동 4
 
1.6%
둔포면 4
 
1.6%
탕정면 4
 
1.6%
Other values (80) 129
50.8%
2024-01-10T05:20:05.993580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
185
 
16.3%
리 67
 
5.9%
산 65
 
5.7%
일 59
 
5.2%
, 57
 
5.0%
아 49
 
4.3%
시 49
 
4.3%
면 47
 
4.1%
원 46
 
4.0%
동 37
 
3.3%
Other values (105) 477
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 809
71.1%
Space Separator 185
 
16.3%
Decimal Number 62
 
5.4%
Other Punctuation 59
 
5.2%
Dash Punctuation 13
 
1.1%
Open Punctuation 5
 
0.4%
Close Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 67
 
8.3%
산 65
 
8.0%
일 59
 
7.3%
아 49
 
6.1%
시 49
 
6.1%
면 47
 
5.8%
원 46
 
5.7%
동 37
 
4.6%
방 17
 
2.1%
지 13
 
1.6%
Other values (89) 360
44.5%
Decimal Number
ValueCountFrequency (%)
1 21
33.9%
2 9
14.5%
4 8
 
12.9%
0 6
 
9.7%
9 5
 
8.1%
6 5
 
8.1%
5 3
 
4.8%
3 2
 
3.2%
7 2
 
3.2%
8 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
, 57
96.6%
: 2
 
3.4%
Space Separator
ValueCountFrequency (%)
185
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 809
71.1%
Common 329
28.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 67
 
8.3%
산 65
 
8.0%
일 59
 
7.3%
아 49
 
6.1%
시 49
 
6.1%
면 47
 
5.8%
원 46
 
5.7%
동 37
 
4.6%
방 17
 
2.1%
지 13
 
1.6%
Other values (89) 360
44.5%
Common
ValueCountFrequency (%)
185
56.2%
, 57
 
17.3%
1 21
 
6.4%
- 13
 
4.0%
2 9
 
2.7%
4 8
 
2.4%
0 6
 
1.8%
( 5
 
1.5%
) 5
 
1.5%
9 5
 
1.5%
Other values (6) 15
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 809
71.1%
ASCII 329
28.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
185
56.2%
, 57
 
17.3%
1 21
 
6.4%
- 13
 
4.0%
2 9
 
2.7%
4 8
 
2.4%
0 6
 
1.8%
( 5
 
1.5%
) 5
 
1.5%
9 5
 
1.5%
Other values (6) 15
 
4.6%
Hangul
ValueCountFrequency (%)
리 67
 
8.3%
산 65
 
8.0%
일 59
 
7.3%
아 49
 
6.1%
시 49
 
6.1%
면 47
 
5.8%
원 46
 
5.7%
동 37
 
4.6%
방 17
 
2.1%
지 13
 
1.6%
Other values (89) 360
44.5%

면적_기정
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0549325 × 108
Minimum0
Maximum6.99408 × 109
Zeros36
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T05:20:06.100892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32650000
95-th percentile27275000
Maximum6.99408 × 109
Range6.99408 × 109
Interquartile range (IQR)2650000

Descriptive statistics

Standard deviation8.4153621 × 108
Coefficient of variation (CV)7.9771571
Kurtosis68.982043
Mean1.0549325 × 108
Median Absolute Deviation (MAD)0
Skewness8.3050296
Sum7.2790342 × 109
Variance7.081832 × 1017
MonotonicityNot monotonic
2024-01-10T05:20:06.214954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 36
52.2%
27275000 4
 
5.8%
1180000 2
 
2.9%
41130000 2
 
2.9%
3634000 2
 
2.9%
2650000 2
 
2.9%
823483 1
 
1.4%
465015 1
 
1.4%
690367 1
 
1.4%
130560 1
 
1.4%
Other values (17) 17
24.6%
ValueCountFrequency (%)
0 36
52.2%
49099 1
 
1.4%
54720 1
 
1.4%
95000 1
 
1.4%
130560 1
 
1.4%
296780 1
 
1.4%
333400 1
 
1.4%
465015 1
 
1.4%
690367 1
 
1.4%
731732 1
 
1.4%
ValueCountFrequency (%)
6994080000 1
 
1.4%
41130000 2
2.9%
27275000 4
5.8%
18116000 1
 
1.4%
11176000 1
 
1.4%
10152000 1
 
1.4%
8265000 1
 
1.4%
7603000 1
 
1.4%
7596000 1
 
1.4%
4759000 1
 
1.4%

면적_변경
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6397447 × 108
Minimum0
Maximum6.99408 × 109
Zeros11
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T05:20:06.367571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150238
median404358
Q33000000
95-th percentile2.255588 × 108
Maximum6.99408 × 109
Range6.99408 × 109
Interquartile range (IQR)2949762

Descriptive statistics

Standard deviation9.2317976 × 108
Coefficient of variation (CV)5.6300214
Kurtosis46.973997
Mean1.6397447 × 108
Median Absolute Deviation (MAD)404358
Skewness6.6899141
Sum1.1314238 × 1010
Variance8.5226086 × 1017
MonotonicityNot monotonic
2024-01-10T05:20:06.525665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 11
 
15.9%
49099.0 2
 
2.9%
404358.0 2
 
2.9%
7596000.0 2
 
2.9%
568392.0 1
 
1.4%
225352.0 1
 
1.4%
6994080000.0 1
 
1.4%
465015.0 1
 
1.4%
1194000.0 1
 
1.4%
5393000.0 1
 
1.4%
Other values (46) 46
66.7%
ValueCountFrequency (%)
0.0 11
15.9%
2927.0 1
 
1.4%
24283.0 1
 
1.4%
33810.0 1
 
1.4%
42092.0 1
 
1.4%
49099.0 2
 
2.9%
50238.0 1
 
1.4%
54720.0 1
 
1.4%
64260.0 1
 
1.4%
77032.0 1
 
1.4%
ValueCountFrequency (%)
6994080000.0 1
1.4%
3241300000.0 1
1.4%
523850000.0 1
1.4%
318102000.0 1
1.4%
86744000.0 1
1.4%
43842000.0 1
1.4%
13855000.0 1
1.4%
11776000.0 1
1.4%
8265000.0 1
1.4%
8256000.0 1
1.4%

면적_변경후
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66104125
Minimum0
Maximum3.2413 × 109
Zeros6
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T05:20:06.681772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1330000
median900515
Q37603000
95-th percentile69583200
Maximum3.2413 × 109
Range3.2413 × 109
Interquartile range (IQR)7273000

Descriptive statistics

Standard deviation3.94791 × 108
Coefficient of variation (CV)5.9722597
Kurtosis64.105105
Mean66104125
Median Absolute Deviation (MAD)866705
Skewness7.9028308
Sum4.5611846 × 109
Variance1.5585993 × 1017
MonotonicityNot monotonic
2024-01-10T05:20:06.832862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
8.7%
41130000.0 3
 
4.3%
1180000.0 3
 
4.3%
3634000.0 3
 
4.3%
27275000.0 2
 
2.9%
404358.0 2
 
2.9%
3980000.0 2
 
2.9%
333400.0 2
 
2.9%
272497.0 1
 
1.4%
180798.0 1
 
1.4%
Other values (44) 44
63.8%
ValueCountFrequency (%)
0.0 6
8.7%
33810.0 1
 
1.4%
42092.0 1
 
1.4%
49099.0 1
 
1.4%
51793.0 1
 
1.4%
54720.0 1
 
1.4%
104785.0 1
 
1.4%
130560.0 1
 
1.4%
163830.0 1
 
1.4%
180798.0 1
 
1.4%
ValueCountFrequency (%)
3241300000.0 1
 
1.4%
523850000.0 1
 
1.4%
318102000.0 1
 
1.4%
86744000.0 1
 
1.4%
43842000.0 1
 
1.4%
41130000.0 3
4.3%
35531000.0 1
 
1.4%
27275000.0 2
2.9%
18116000.0 1
 
1.4%
11776000.0 1
 
1.4%

결정(변경)사유
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing66
Missing (%)95.7%
Memory size684.0 B
2024-01-10T05:20:07.027950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length65
Mean length57
Min length39

Characters and Unicode

Total characters171
Distinct characters56
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row훼손으로 인하여 기능이 현저하게 떨어진 도시자연공원구역 경계 조정 및 도시계획시설(도로) 폐지 계획에 따른 구역 환원
2nd row훼손으로 인하여 기능이 현저하게 떨어진 도시자연공원구역 경계 조정 및 인접 도시계획시설(배수시설 및 공원)로 경계 정형화
3rd row도시계획시설(운동장) 축소에 따라 종전 공원시설의 도시자연공원구역 편입
ValueCountFrequency (%)
도시자연공원구역 3
 
8.1%
경계 3
 
8.1%
및 3
 
8.1%
훼손으로 2
 
5.4%
기능이 2
 
5.4%
현저하게 2
 
5.4%
떨어진 2
 
5.4%
조정 2
 
5.4%
인하여 2
 
5.4%
공원)로 1
 
2.7%
Other values (15) 15
40.5%
2024-01-10T05:20:07.275817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34
19.9%
시 11
 
6.4%
계 7
 
4.1%
도 7
 
4.1%
원 6
 
3.5%
공 5
 
2.9%
설 5
 
2.9%
구 4
 
2.3%
로 4
 
2.3%
하 4
 
2.3%
Other values (46) 84
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 131
76.6%
Space Separator 34
 
19.9%
Open Punctuation 3
 
1.8%
Close Punctuation 3
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 11
 
8.4%
계 7
 
5.3%
도 7
 
5.3%
원 6
 
4.6%
공 5
 
3.8%
설 5
 
3.8%
구 4
 
3.1%
로 4
 
3.1%
하 4
 
3.1%
획 4
 
3.1%
Other values (43) 74
56.5%
Space Separator
ValueCountFrequency (%)
34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 131
76.6%
Common 40
 
23.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 11
 
8.4%
계 7
 
5.3%
도 7
 
5.3%
원 6
 
4.6%
공 5
 
3.8%
설 5
 
3.8%
구 4
 
3.1%
로 4
 
3.1%
하 4
 
3.1%
획 4
 
3.1%
Other values (43) 74
56.5%
Common
ValueCountFrequency (%)
34
85.0%
( 3
 
7.5%
) 3
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 131
76.6%
ASCII 40
 
23.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
85.0%
( 3
 
7.5%
) 3
 
7.5%
Hangul
ValueCountFrequency (%)
시 11
 
8.4%
계 7
 
5.3%
도 7
 
5.3%
원 6
 
4.6%
공 5
 
3.8%
설 5
 
3.8%
구 4
 
3.1%
로 4
 
3.1%
하 4
 
3.1%
획 4
 
3.1%
Other values (43) 74
56.5%

비고
Text

MISSING 

Distinct34
Distinct (%)65.4%
Missing17
Missing (%)24.6%
Memory size684.0 B
2024-01-10T05:20:07.504483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length181
Median length69
Mean length27.019231
Min length4

Characters and Unicode

Total characters1405
Distinct characters172
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)48.1%

Sample

1st row기정구역:도고면-신언리,기곡리 선장면-신성리
2nd row충남고시 제58호(19770317)
3rd row아산시 인주면
4th row개발행위허가제한기간:고시일로부터 2년
5th row개발행위허가제한기간:고시일로부터 2년
ValueCountFrequency (%)
2년 10
 
5.4%
제한기간:고시일로부터 9
 
4.9%
개발행위허가제한기간:고시일로부터 6
 
3.2%
도시개발사업 5
 
2.7%
3년간 4
 
2.2%
개발행위허가제한지역 4
 
2.2%
사전에 3
 
1.6%
제2011-115호 3
 
1.6%
3년 3
 
1.6%
난개발 3
 
1.6%
Other values (104) 135
73.0%
2024-01-10T05:20:07.853067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
134
 
9.5%
0 58
 
4.1%
2 56
 
4.0%
시 44
 
3.1%
제 42
 
3.0%
1 39
 
2.8%
일 34
 
2.4%
한 33
 
2.3%
기 33
 
2.3%
간 33
 
2.3%
Other values (162) 899
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 907
64.6%
Decimal Number 236
 
16.8%
Space Separator 134
 
9.5%
Other Punctuation 76
 
5.4%
Close Punctuation 13
 
0.9%
Open Punctuation 13
 
0.9%
Dash Punctuation 10
 
0.7%
Math Symbol 9
 
0.6%
Uppercase Letter 4
 
0.3%
Other Symbol 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 44
 
4.9%
제 42
 
4.6%
일 34
 
3.7%
한 33
 
3.6%
기 33
 
3.6%
간 33
 
3.6%
고 33
 
3.6%
년 33
 
3.6%
지 29
 
3.2%
개 26
 
2.9%
Other values (137) 567
62.5%
Decimal Number
ValueCountFrequency (%)
0 58
24.6%
2 56
23.7%
1 39
16.5%
7 18
 
7.6%
5 15
 
6.4%
3 14
 
5.9%
8 11
 
4.7%
4 10
 
4.2%
9 9
 
3.8%
6 6
 
2.5%
Other Punctuation
ValueCountFrequency (%)
: 27
35.5%
. 27
35.5%
, 21
27.6%
& 1
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
R 1
25.0%
D 1
25.0%
L 1
25.0%
H 1
25.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
66.7%
○ 1
33.3%
Space Separator
ValueCountFrequency (%)
134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 907
64.6%
Common 494
35.2%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 44
 
4.9%
제 42
 
4.6%
일 34
 
3.7%
한 33
 
3.6%
기 33
 
3.6%
간 33
 
3.6%
고 33
 
3.6%
년 33
 
3.6%
지 29
 
3.2%
개 26
 
2.9%
Other values (137) 567
62.5%
Common
ValueCountFrequency (%)
134
27.1%
0 58
11.7%
2 56
11.3%
1 39
 
7.9%
: 27
 
5.5%
. 27
 
5.5%
, 21
 
4.3%
7 18
 
3.6%
5 15
 
3.0%
3 14
 
2.8%
Other values (11) 85
17.2%
Latin
ValueCountFrequency (%)
R 1
25.0%
D 1
25.0%
L 1
25.0%
H 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 907
64.6%
ASCII 495
35.2%
CJK Compat 2
 
0.1%
Geometric Shapes 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
27.1%
0 58
11.7%
2 56
11.3%
1 39
 
7.9%
: 27
 
5.5%
. 27
 
5.5%
, 21
 
4.2%
7 18
 
3.6%
5 15
 
3.0%
3 14
 
2.8%
Other values (13) 86
17.4%
Hangul
ValueCountFrequency (%)
시 44
 
4.9%
제 42
 
4.6%
일 34
 
3.7%
한 33
 
3.6%
기 33
 
3.6%
간 33
 
3.6%
고 33
 
3.6%
년 33
 
3.6%
지 29
 
3.2%
개 26
 
2.9%
Other values (137) 567
62.5%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%
Geometric Shapes
ValueCountFrequency (%)
○ 1
100.0%

Interactions

2024-01-10T05:20:02.539027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:02.047183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:02.313011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:02.614166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:02.172328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:02.392335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:02.686668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:02.240877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:20:02.468977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:20:07.940459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조서관리번호이전 조서관리번호결정고시 관리번호승인고시 관리번호도면번호지역명위치명면적_기정면적_변경면적_변경후결정(변경)사유비고
조서관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
이전 조서관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
결정고시 관리번호1.0001.0001.0001.0000.0000.8770.9751.0001.0001.000NaN0.982
승인고시 관리번호1.0001.0001.0001.0000.0000.8770.957NaNNaNNaNNaN0.968
도면번호1.0001.0000.0000.0001.0001.0001.000NaNNaNNaN1.0000.629
지역명1.0001.0000.8770.8771.0001.0001.000NaNNaNNaN1.0000.735
위치명1.0001.0000.9750.9571.0001.0001.0000.0000.0000.0001.0000.861
면적_기정1.0001.0001.000NaNNaNNaN0.0001.0001.0000.000NaN0.000
면적_변경1.0001.0001.000NaNNaNNaN0.0001.0001.0000.939NaN0.769
면적_변경후1.0001.0001.000NaNNaNNaN0.0000.0000.9391.000NaN1.000
결정(변경)사유1.0001.000NaNNaN1.0001.0001.000NaNNaNNaN1.000NaN
비고1.0001.0000.9820.9680.6290.7350.8610.0000.7691.000NaN1.000
2024-01-10T05:20:08.268842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적_기정면적_변경면적_변경후도면번호
면적_기정1.000-0.2660.2241.000
면적_변경-0.2661.0000.3101.000
면적_변경후0.2240.3101.0001.000
도면번호1.0001.0001.0001.000

Missing values

2024-01-10T05:20:02.786895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:20:02.918457image/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.
2024-01-10T05:20:03.019607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

조서관리번호이전 조서관리번호결정고시 관리번호승인고시 관리번호도면번호지역명위치명면적_기정면적_변경면적_변경후결정(변경)사유비고
044200BTZ19850408000144200BTZ19761210000144200NTC198504080001<NA><NA><NA>충청남도 아산군 온양읍 일부지역272750000.027275000.0<NA>기정구역:도고면-신언리,기곡리 선장면-신성리
144200BTZ19860104000144200BTZ19770317000244200NTC19860104000144200NTC198612200001<NA><NA>도고면:신언리,기곡리,금산리,향산리, 선장면:신성리2650000984000.03634000.0<NA>충남고시 제58호(19770317)
244200BTZ19920824000144200BTZ19770317000144200NTC19920824000144200NTC199209070001<NA><NA>충청남도 아산군 둔포면 일부지역11800000.01180000.0<NA><NA>
344200BTZ20030227000144200BTZ19930317000144200NTC200302270001<NA><NA><NA>아산시 온양272750008256000.035531000.0<NA><NA>
444200BTZ20030227000244200BTZ19940128000144200NTC200302270001<NA><NA><NA>아산시 배방7603000381000.07984000.0<NA><NA>
544200BTZ200302270003<NA>44200NTC200302270001<NA><NA><NA>아산시 인주면011776000.011776000.0<NA><NA>
644200BTZ197301050001<NA>44200NTC197301050001<NA><NA><NA>충남아산군둔포면(송용리,시모리,둔포리)04400000.04400000.0<NA><NA>
744200BTZ199401280001<NA>44200NTC19940128000144200NTC199402160001<NA><NA>배방면 일부지역07603000.07603000.0<NA><NA>
844200BTZ19930317000144200BTZ19850408000144200NTC19930317000144200NTC199307090001<NA><NA>충남 온양272750000.027275000.0<NA><NA>
944200BTZ196810220001<NA>44200NTC196810220001<NA><NA><NA>충청남도 아산시 온양읍일도(12개리)탕정면일부권곡리모종리신창면일부점양리득산리411300000.041130000.0<NA><NA>
조서관리번호이전 조서관리번호결정고시 관리번호승인고시 관리번호도면번호지역명위치명면적_기정면적_변경면적_변경후결정(변경)사유비고
5944200BTZ201707310004<NA>44200NTC20170731000444200NTC201707310004<NA>온양중심상권재정비촉진지구아산시 온천동 일원9500095000.00.0<NA>제한기간:고시일로부터 3년
6044200BTZ201905310001<NA>44200NTC20190531000144200NTC201905310001<NA>풍기지구 도시개발예정지아산시 풍기동 159-3번지(풍기동, 남동, 읍내동)0703491.0703491.0<NA>제한기간:고시일로부터 3년
6144200BTZ201905310002<NA>44200NTC20190531000144200NTC201905310001<NA>모종지구 도시개발예정지아산시 모종동 113-10번지 일원(모종동, 신동, 배방읍 구령리)0580453.0580453.0<NA>제한기간:고시일로부터 3년
6244200BTZ20180521000144200BTZ20110502000144200NTC20180521000144200NTC2018072500011용화1구역용화동 201-4 일원547202927.051793.0훼손으로 인하여 기능이 현저하게 떨어진 도시자연공원구역 경계 조정 및 도시계획시설(도로) 폐지 계획에 따른 구역 환원충청남도고시 제2011-115호
6344200BTZ20180521000244200BTZ20110502000244200NTC20180521000144200NTC2018072500012용화2구역용화동 111-41 일원29678024283.0272497.0훼손으로 인하여 기능이 현저하게 떨어진 도시자연공원구역 경계 조정 및 인접 도시계획시설(배수시설 및 공원)로 경계 정형화충청남도고시 제2011-115호
6444200BTZ20180521000344200BTZ20110502000344200NTC20180521000144200NTC2018072500013용화3구역읍내동 산7-9 일원13056050238.0180798.0도시계획시설(운동장) 축소에 따라 종전 공원시설의 도시자연공원구역 편입충청남도고시 제2011-115호
6544200BTZ202203150001<NA>44200NTC20220315000244200NTC2022031500021탕정R&D집적지구아산시 배방읍 장재리, 탕정면 매곡리 및 호산리 일원690367225352.0465015.0<NA>탕정R&D집적지구, 개발행위허가 제한 고시일(2019.3.15.)로부터 5년간 (2년연장)
6644200BTZ202211070001<NA>44200NTC20221107000244200NTC202211070002<NA>아산둔포센트럴파크도시개발사업예정지개발행위허가제한지역둔포면 둔포리 122-4번지 일원(송용리 일부)0568392.0568392.0<NA>도시개발사업 예정지 내 무분별한 난개발 및 보상투기행위 등을 사전에 방지하고 계획적이고 체계적인 도시개발사업을 시행- 보상투기행위를 사전에 차단하여 사회적비용 손실 방지, 환지방식 도시개발사업 구역내 토지소유자의 재산보호, 도시개발사업 시행으로 새롭게 도시관리계획이 수립될 예정으로 개발, 행위허가의 기준이 크게 달리질 지역
6744200BTZ202302270001<NA>44200NTC20230227000144200NTC202302270001<NA>도시개발사업 예정지 개발행위허가 제한지역 지정아산시 방축동 86번지 일원0984431.0984431.0<NA>도시개발사업 예정지 내 무분별한 난개발 및 보상투기행위 등을 사전에 방지하고 계획적이고 체계적인 도시개발사업 시행, 정주여건 마련을 위한 난개발 방지와 보상투기행위 등을 차단하여 사회,경제적비용 손실 방지.
6844200BTZ20230522000144200BTZ20220315000144200NTC20230522000444200NTC202305220004<NA>아산탕정 R&D집적지구아산시 배방읍 장재리, 탕전면 매곡리 및 호산리 일원465015465015.00.0<NA>개발행위허가제한의 제한 목적 상실 및 아산시 성장관리계획 고시에 따른 개발행위허가 제한지역 해제