Overview

Dataset statistics

Number of variables7
Number of observations86
Missing cells51
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory60.5 B

Variable types

Text2
Numeric3
Categorical1
Boolean1

Dataset

Description전라남도 여수시 도시계획정보시스템(UPIS)용도구역 결정 조서 현황 데이터 자료로 용도구역 결정 조서를 제공합니다.
URLhttps://www.data.go.kr/data/15119178/fileData.do

Alerts

면적(기정) is highly overall correlated with 비고High correlation
면적(변경후) is highly overall correlated with 비고High correlation
비고 is highly overall correlated with 면적(기정) and 2 other fieldsHigh correlation
공간도형존재여부 is highly overall correlated with 비고High correlation
위치명 has 5 (5.8%) missing valuesMissing
지역명 has 4 (4.7%) missing valuesMissing
면적(기정) has 8 (9.3%) missing valuesMissing
면적(변경) has 14 (16.3%) missing valuesMissing
면적(변경후) has 1 (1.2%) missing valuesMissing
공간도형존재여부 has 19 (22.1%) missing valuesMissing
면적(기정) has 38 (44.2%) zerosZeros
면적(변경후) has 10 (11.6%) zerosZeros

Reproduction

Analysis started2023-12-12 23:45:35.347740
Analysis finished2023-12-12 23:45:36.928490
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위치명
Text

MISSING 

Distinct58
Distinct (%)71.6%
Missing5
Missing (%)5.8%
Memory size820.0 B
2023-12-13T08:45:37.079256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length122
Median length41
Mean length19.395062
Min length3

Characters and Unicode

Total characters1571
Distinct characters138
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

Unique42 ?
Unique (%)51.9%

Sample

1st row충무동, 광림동, 서강동, 대교동, 중앙동 일원
2nd row돌산읍(우두리, 평사리, 죽포리, 율림리, 금성리, 금봉리, 둔전리) 율촌면(상봉리, 봉전리, 반월리, 가장리) 소라면(사곡리, 복산리, 현천리) 화양면(창무리, 이천리, 서촌리, 이목리, 나진리, 용주리) 쌍봉동 일원
3rd row월호동, 국동, 대교동, 여서동, 광림동, 서강동, 동문동, 만덕동 일원
4th row한려동, 만덕동 일원
5th row월호동, 국동, 대교동 일원
ValueCountFrequency (%)
일원 34
 
9.3%
여수시 28
 
7.7%
돌산읍 15
 
4.1%
전라남도 13
 
3.6%
화정면 8
 
2.2%
우두리 8
 
2.2%
일부 7
 
1.9%
5개 6
 
1.6%
읍면동 6
 
1.6%
소라면 6
 
1.6%
Other values (136) 234
64.1%
2023-12-13T08:45:37.390664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
284
 
18.1%
, 82
 
5.2%
동 70
 
4.5%
일 54
 
3.4%
면 50
 
3.2%
여 45
 
2.9%
원 45
 
2.9%
리 41
 
2.6%
수 37
 
2.4%
산 35
 
2.2%
Other values (128) 828
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1075
68.4%
Space Separator 284
 
18.1%
Decimal Number 87
 
5.5%
Other Punctuation 85
 
5.4%
Math Symbol 14
 
0.9%
Close Punctuation 8
 
0.5%
Open Punctuation 8
 
0.5%
Dash Punctuation 7
 
0.4%
Lowercase Letter 2
 
0.1%
Other Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 70
 
6.5%
일 54
 
5.0%
면 50
 
4.7%
여 45
 
4.2%
원 45
 
4.2%
리 41
 
3.8%
수 37
 
3.4%
산 35
 
3.3%
시 35
 
3.3%
전 32
 
3.0%
Other values (106) 631
58.7%
Decimal Number
ValueCountFrequency (%)
1 22
25.3%
3 13
14.9%
7 11
12.6%
5 10
11.5%
6 9
10.3%
2 8
 
9.2%
0 6
 
6.9%
8 4
 
4.6%
9 3
 
3.4%
4 1
 
1.1%
Math Symbol
ValueCountFrequency (%)
~ 10
71.4%
∼ 2
 
14.3%
～ 2
 
14.3%
Other Punctuation
ValueCountFrequency (%)
, 82
96.5%
. 3
 
3.5%
Lowercase Letter
ValueCountFrequency (%)
m 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
284
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1075
68.4%
Common 494
31.4%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 70
 
6.5%
일 54
 
5.0%
면 50
 
4.7%
여 45
 
4.2%
원 45
 
4.2%
리 41
 
3.8%
수 37
 
3.4%
산 35
 
3.3%
시 35
 
3.3%
전 32
 
3.0%
Other values (106) 631
58.7%
Common
ValueCountFrequency (%)
284
57.5%
, 82
 
16.6%
1 22
 
4.5%
3 13
 
2.6%
7 11
 
2.2%
~ 10
 
2.0%
5 10
 
2.0%
6 9
 
1.8%
2 8
 
1.6%
) 8
 
1.6%
Other values (10) 37
 
7.5%
Latin
ValueCountFrequency (%)
m 1
50.0%
k 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1075
68.4%
ASCII 491
31.3%
None 3
 
0.2%
Math Operators 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
284
57.8%
, 82
 
16.7%
1 22
 
4.5%
3 13
 
2.6%
7 11
 
2.2%
~ 10
 
2.0%
5 10
 
2.0%
6 9
 
1.8%
2 8
 
1.6%
) 8
 
1.6%
Other values (9) 34
 
6.9%
Hangul
ValueCountFrequency (%)
동 70
 
6.5%
일 54
 
5.0%
면 50
 
4.7%
여 45
 
4.2%
원 45
 
4.2%
리 41
 
3.8%
수 37
 
3.4%
산 35
 
3.3%
시 35
 
3.3%
전 32
 
3.0%
Other values (106) 631
58.7%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%
None
ValueCountFrequency (%)
～ 2
66.7%
² 1
33.3%

지역명
Text

MISSING 

Distinct43
Distinct (%)52.4%
Missing4
Missing (%)4.7%
Memory size820.0 B
2023-12-13T08:45:37.557379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length32
Mean length16.182927
Min length2

Characters and Unicode

Total characters1327
Distinct characters118
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)31.7%

Sample

1st row연등천 중점경관관리구역(연등천 일원)
2nd row해안변 중점경관관리구역(여자만, 여수만, 가막만등 해안변에 연접한 지역)
3rd row원도심 배후 산림경관 중점경관관리구역(구봉산, 장군단, 종고산, 마래산 일원)
4th row여수 EXPO 중점경관관리구역(여수 EXPO 및 배후 단지 일원)
5th row원도심 중점경관관리구역(국동항 배후 원도심 일원)
ValueCountFrequency (%)
개발행위허가 25
 
11.5%
제한지역 25
 
11.5%
도시자연공원구역 10
 
4.6%
여수시 9
 
4.1%
일원 8
 
3.7%
도시계획구역 7
 
3.2%
행정구역 6
 
2.8%
수산자원보호구역 6
 
2.8%
행정구역(전라남도 6
 
2.8%
수변축 5
 
2.3%
Other values (72) 110
50.7%
2023-12-13T08:45:37.827948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
135
 
10.2%
역 102
 
7.7%
구 66
 
5.0%
지 54
 
4.1%
원 41
 
3.1%
행 39
 
2.9%
수 35
 
2.6%
도 34
 
2.6%
가 30
 
2.3%
한 30
 
2.3%
Other values (108) 761
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1113
83.9%
Space Separator 135
 
10.2%
Close Punctuation 26
 
2.0%
Open Punctuation 26
 
2.0%
Decimal Number 10
 
0.8%
Uppercase Letter 8
 
0.6%
Other Punctuation 5
 
0.4%
Math Symbol 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 102
 
9.2%
구 66
 
5.9%
지 54
 
4.9%
원 41
 
3.7%
행 39
 
3.5%
수 35
 
3.1%
도 34
 
3.1%
가 30
 
2.7%
한 30
 
2.7%
개 29
 
2.6%
Other values (96) 653
58.7%
Uppercase Letter
ValueCountFrequency (%)
X 2
25.0%
P 2
25.0%
O 2
25.0%
E 2
25.0%
Decimal Number
ValueCountFrequency (%)
2 6
60.0%
1 4
40.0%
Math Symbol
ValueCountFrequency (%)
∼ 2
50.0%
～ 2
50.0%
Space Separator
ValueCountFrequency (%)
135
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1113
83.9%
Common 206
 
15.5%
Latin 8
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 102
 
9.2%
구 66
 
5.9%
지 54
 
4.9%
원 41
 
3.7%
행 39
 
3.5%
수 35
 
3.1%
도 34
 
3.1%
가 30
 
2.7%
한 30
 
2.7%
개 29
 
2.6%
Other values (96) 653
58.7%
Common
ValueCountFrequency (%)
135
65.5%
) 26
 
12.6%
( 26
 
12.6%
2 6
 
2.9%
, 5
 
2.4%
1 4
 
1.9%
∼ 2
 
1.0%
～ 2
 
1.0%
Latin
ValueCountFrequency (%)
X 2
25.0%
P 2
25.0%
O 2
25.0%
E 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1113
83.9%
ASCII 210
 
15.8%
Math Operators 2
 
0.2%
None 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135
64.3%
) 26
 
12.4%
( 26
 
12.4%
2 6
 
2.9%
, 5
 
2.4%
1 4
 
1.9%
X 2
 
1.0%
P 2
 
1.0%
O 2
 
1.0%
E 2
 
1.0%
Hangul
ValueCountFrequency (%)
역 102
 
9.2%
구 66
 
5.9%
지 54
 
4.9%
원 41
 
3.7%
행 39
 
3.5%
수 35
 
3.1%
도 34
 
3.1%
가 30
 
2.7%
한 30
 
2.7%
개 29
 
2.6%
Other values (96) 653
58.7%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%
None
ValueCountFrequency (%)
～ 2
100.0%

면적(기정)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct31
Distinct (%)39.7%
Missing8
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean53952124
Minimum0
Maximum5.19388 × 108
Zeros38
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-13T08:45:37.931988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median79240
Q326450000
95-th percentile4.1256222 × 108
Maximum5.19388 × 108
Range5.19388 × 108
Interquartile range (IQR)26450000

Descriptive statistics

Standard deviation1.3018211 × 108
Coefficient of variation (CV)2.412919
Kurtosis6.4483109
Mean53952124
Median Absolute Deviation (MAD)79240
Skewness2.7498976
Sum4.2082657 × 109
Variance1.6947381 × 1016
MonotonicityNot monotonic
2023-12-13T08:45:38.050965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 38
44.2%
519388000.0 3
 
3.5%
7780568.0 2
 
2.3%
412438893.0 2
 
2.3%
24300000.0 2
 
2.3%
4050000.0 2
 
2.3%
31550000.0 2
 
2.3%
100000.0 2
 
2.3%
59870000.0 2
 
2.3%
26940000.0 2
 
2.3%
Other values (21) 21
24.4%
(Missing) 8
 
9.3%
ValueCountFrequency (%)
0.0 38
44.2%
58480.0 1
 
1.2%
100000.0 2
 
2.3%
197284.1 1
 
1.2%
363688.0 1
 
1.2%
595641.0 1
 
1.2%
668595.0 1
 
1.2%
1126865.0 1
 
1.2%
1791346.0 1
 
1.2%
2460838.0 1
 
1.2%
ValueCountFrequency (%)
519388000.0 3
3.5%
413261093.0 1
 
1.2%
412438893.0 2
2.3%
294830000.0 1
 
1.2%
239820000.0 1
 
1.2%
181860000.0 1
 
1.2%
68976000.0 1
 
1.2%
65658000.0 1
 
1.2%
65538000.0 1
 
1.2%
63539000.0 1
 
1.2%

면적(변경)
Real number (ℝ)

MISSING 

Distinct56
Distinct (%)77.8%
Missing14
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean9889380.5
Minimum1606
Maximum1.0694911 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-13T08:45:38.167132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1606
5-th percentile15500
Q1196454.58
median974532.5
Q37780568
95-th percentile42222876
Maximum1.0694911 × 108
Range1.069475 × 108
Interquartile range (IQR)7584113.4

Descriptive statistics

Standard deviation22703019
Coefficient of variation (CV)2.2956968
Kurtosis12.438284
Mean9889380.5
Median Absolute Deviation (MAD)942382.5
Skewness3.483676
Sum7.120354 × 108
Variance5.1542709 × 1014
MonotonicityNot monotonic
2023-12-13T08:45:38.283041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106949107.0 3
 
3.5%
595641.0 2
 
2.3%
24300000.0 2
 
2.3%
20369596.0 2
 
2.3%
10000.0 2
 
2.3%
42222876.0 2
 
2.3%
15996635.0 2
 
2.3%
13340000.0 2
 
2.3%
3805938.0 2
 
2.3%
1791346.0 2
 
2.3%
Other values (46) 51
59.3%
(Missing) 14
 
16.3%
ValueCountFrequency (%)
1606.0 1
1.2%
3411.0 1
1.2%
10000.0 2
2.3%
20000.0 1
1.2%
22000.0 1
1.2%
23400.0 1
1.2%
30000.0 1
1.2%
34300.0 1
1.2%
35000.0 1
1.2%
54000.0 1
1.2%
ValueCountFrequency (%)
106949107.0 3
3.5%
42222876.0 2
2.3%
40558000.0 1
 
1.2%
26670000.0 1
 
1.2%
24300000.0 2
2.3%
20369596.0 2
2.3%
15996635.0 2
2.3%
13500000.0 1
 
1.2%
13340000.0 2
2.3%
9193306.0 1
 
1.2%

면적(변경후)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct63
Distinct (%)74.1%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean51109800
Minimum0
Maximum4.1345506 × 108
Zeros10
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-13T08:45:38.389112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q190000
median1184579
Q355476000
95-th percentile4.1233799 × 108
Maximum4.1345506 × 108
Range4.1345506 × 108
Interquartile range (IQR)55386000

Descriptive statistics

Standard deviation1.131604 × 108
Coefficient of variation (CV)2.2140647
Kurtosis5.3544752
Mean51109800
Median Absolute Deviation (MAD)1184579
Skewness2.5601519
Sum4.344333 × 109
Variance1.2805277 × 1016
MonotonicityNot monotonic
2023-12-13T08:45:38.490280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
11.6%
412438893.0 3
 
3.5%
197284.1 2
 
2.3%
11180404.0 2
 
2.3%
90000.0 2
 
2.3%
17647124.0 2
 
2.3%
63562400.0 2
 
2.3%
63281000.0 2
 
2.3%
10687000.0 2
 
2.3%
55476000.0 2
 
2.3%
Other values (53) 56
65.1%
ValueCountFrequency (%)
0.0 10
11.6%
3411.0 1
 
1.2%
20000.0 1
 
1.2%
22000.0 1
 
1.2%
30000.0 1
 
1.2%
34300.0 1
 
1.2%
35000.0 1
 
1.2%
54000.0 1
 
1.2%
56874.0 1
 
1.2%
58480.0 1
 
1.2%
ValueCountFrequency (%)
413455059.0 1
 
1.2%
413261093.0 1
 
1.2%
412438893.0 3
3.5%
411934389.0 1
 
1.2%
281490000.0 1
 
1.2%
240550000.0 1
 
1.2%
239820000.0 1
 
1.2%
181860000.0 1
 
1.2%
68976000.0 1
 
1.2%
65658000.0 1
 
1.2%

비고
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size820.0 B
<NA>
45 
개발행위허가 제한지역 지정고시
13 
개발행위허가 제한지역 해제고시
6 
근린공원→도시자연공원구역
5 
제한기간 변경
 
2
Other values (14)
15 

Length

Max length44
Median length4
Mean length9.9418605
Min length3

Unique

Unique13 ?
Unique (%)15.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 45
52.3%
개발행위허가 제한지역 지정고시 13
 
15.1%
개발행위허가 제한지역 해제고시 6
 
7.0%
근린공원→도시자연공원구역 5
 
5.8%
제한기간 변경 2
 
2.3%
해면(372,548,000㎡) 2
 
2.3%
경호동 매립지 0.796㎢포함 1
 
1.2%
매립지추가 1
 
1.2%
재결정 1
 
1.2%
건설부고시제171호(86.4.25) 1
 
1.2%
Other values (9) 9
 
10.5%

Length

2023-12-13T08:45:38.583867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 45
31.0%
제한지역 21
14.5%
개발행위허가 21
14.5%
지정고시 15
 
10.3%
해제고시 6
 
4.1%
근린공원→도시자연공원구역 5
 
3.4%
해면(372,548,000㎡ 3
 
2.1%
제한기간 2
 
1.4%
변경 2
 
1.4%
경호동 2
 
1.4%
Other values (21) 23
15.9%

공간도형존재여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)3.0%
Missing19
Missing (%)22.1%
Memory size304.0 B
False
55 
True
12 
(Missing)
19 
ValueCountFrequency (%)
False 55
64.0%
True 12
 
14.0%
(Missing) 19
 
22.1%
2023-12-13T08:45:38.651929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-13T08:45:36.415286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:35.735033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:35.965457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:36.487539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:35.806786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:36.275717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:36.566843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:35.878853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:36.347281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:45:38.701849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
위치명1.0000.9940.0000.8010.9200.7770.796
지역명0.9941.0000.7430.0000.8940.2230.853
면적(기정)0.0000.7431.0000.8820.9710.9820.118
면적(변경)0.8010.0000.8821.0000.4840.5300.079
면적(변경후)0.9200.8940.9710.4841.0001.0000.297
비고0.7770.2230.9820.5301.0001.0000.973
공간도형존재여부0.7960.8530.1180.0790.2970.9731.000
2023-12-13T08:45:38.782140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간도형존재여부비고
공간도형존재여부1.0000.536
비고0.5361.000
2023-12-13T08:45:38.844537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
면적(기정)1.0000.4660.4960.7060.113
면적(변경)0.4661.0000.4340.4210.115
면적(변경후)0.4960.4341.0000.7780.204
비고0.7060.4210.7781.0000.536
공간도형존재여부0.1130.1150.2040.5361.000

Missing values

2023-12-13T08:45:36.652061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:45:36.752350image/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.
2023-12-13T08:45:36.851902image/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

위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
0충무동, 광림동, 서강동, 대교동, 중앙동 일원연등천 중점경관관리구역(연등천 일원)0.0120000.0120000.0<NA>N
1돌산읍(우두리, 평사리, 죽포리, 율림리, 금성리, 금봉리, 둔전리) 율촌면(상봉리, 봉전리, 반월리, 가장리) 소라면(사곡리, 복산리, 현천리) 화양면(창무리, 이천리, 서촌리, 이목리, 나진리, 용주리) 쌍봉동 일원해안변 중점경관관리구역(여자만, 여수만, 가막만등 해안변에 연접한 지역)0.026670000.026670000.0<NA>N
2월호동, 국동, 대교동, 여서동, 광림동, 서강동, 동문동, 만덕동 일원원도심 배후 산림경관 중점경관관리구역(구봉산, 장군단, 종고산, 마래산 일원)0.04360000.04360000.0<NA>N
3한려동, 만덕동 일원여수 EXPO 중점경관관리구역(여수 EXPO 및 배후 단지 일원)0.01880000.01880000.0<NA>N
4월호동, 국동, 대교동 일원원도심 중점경관관리구역(국동항 배후 원도심 일원)0.01480000.01480000.0<NA>N
5충무동, 동문동, 중앙동 일원전라좌수영성(진남관) 중점경관관리구역(전라좌수영성 일원)0.0280000.0280000.0<NA>N
6돌산읍 우두리, 남산동, 교동, 중앙동, 종화동 일원(돌산공원～남산공원～자산공원 수변축)돌산공원～남산공원～자산공원 수변축 개발행위허가 제한지역668595.0668595.00.0개발행위허가 제한지역 해제고시N
7율촌면 등 17개 읍, 면, 동 수변축 일원수변축 경관관리계획수립구역 개발행위허가 제한지역7780568.0<NA>7780568.0제한기간 변경N
8율촌면 등 17개 읍, 면, 동 수변축 일원수변축 경관관리계획수립구역 개발행위허가 제한지역7780568.07780568.00.0개발행위허가 제한지역 해제고시N
9화정면 외 5개 읍면동수산자원보호구역412438893.0504504.0411934389.0해면(372,548,000㎡)Y
위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
76월호동 일원경도 중점경관관리구역(대경도 일원)0.02330000.02330000.0<NA>N
77소호 디오션리조트~화양 용주, 화양 대서이~구미, 돌산 월전포~안굴전, 돌산 무슬목~소율해안 수변축(2구역) 개발행위허가 제한지역3805938.03805938.00.0개발행위허가 제한지역 해제고시<NA>
78오림동 산30번지 일원오림공원 개발행위허가 제한지역0.030000.030000.0개발행위허가 제한지역 지정고시<NA>
79돌산읍 우두리 657-12번지 일원굴밭공원 개발행위허가 제한지역0.034300.034300.0개발행위허가 제한지역 지정고시<NA>
80시전동 181-1번지 일원여천체육공원 개발행위허가 제한지역0.0527099.0527099.0개발행위허가 제한지역 지정고시 / 집행부지 제외<NA>
81둔덕동 330번지 일원전남대 여수캠퍼스 개발행위허가 제한지역0.0269130.0269130.0개발행위허가 제한지역 지정고시 / 집행부지 제외(둔덕저류지 39,755㎡ 포함)<NA>
82여천동 903-10번지 일원여천역 주변지역 개발행위허가 제한지역0.0363688.0363688.0개발행위허가 제한지역 지정고시<NA>
83화정면 외 5개 읍면동수산자원보호구역413261093.0193966.0413455059.0해면(372,741,966㎡)Y
84여천동 903-10번지 일원여천역 주변지역 개발행위허가 제한지역363688.0<NA>363688.0<NA><NA>
85우두리 661-6 일원도시자연공원구역58480.01606.056874.0<NA>Y