Overview

Dataset statistics

Number of variables9
Number of observations782
Missing cells6
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.4 KiB
Average record size in memory75.2 B

Variable types

Numeric3
Categorical3
Text2
Unsupported1

Dataset

Description민방위비상대피소현황1510월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202471

Alerts

대피소 구분 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
시군구 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
면적 is highly overall correlated with 수용 인원(명) and 1 other fieldsHigh correlation
수용 인원(명) is highly overall correlated with 면적 and 1 other fieldsHigh correlation
시도명 is highly imbalanced (98.6%)Imbalance
대피소 구분 is highly imbalanced (94.5%)Imbalance
설치년도 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 00:14:18.705781
Analysis finished2024-03-14 00:14:20.333064
Duration1.63 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION 

Distinct781
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean391
Minimum1
Maximum781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2024-03-14T09:14:20.388944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40
Q1196
median391
Q3586
95-th percentile742
Maximum781
Range780
Interquartile range (IQR)390

Descriptive statistics

Standard deviation225.59957
Coefficient of variation (CV)0.576981
Kurtosis-1.2
Mean391
Median Absolute Deviation (MAD)195
Skewness0
Sum305371
Variance50895.167
MonotonicityStrictly increasing
2024-03-14T09:14:20.504644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
538 1
 
0.1%
516 1
 
0.1%
517 1
 
0.1%
518 1
 
0.1%
519 1
 
0.1%
520 1
 
0.1%
521 1
 
0.1%
522 1
 
0.1%
523 1
 
0.1%
Other values (771) 771
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
781 1
0.1%
780 1
0.1%
779 1
0.1%
778 1
0.1%
777 1
0.1%
776 1
0.1%
775 1
0.1%
774 1
0.1%
773 1
0.1%
772 1
0.1%

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
전라북도
781 
<NA>
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 781
99.9%
<NA> 1
 
0.1%

Length

2024-03-14T09:14:20.607951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:14:20.684828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 781
99.9%
na 1
 
0.1%

시군구
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
전주시
331 
군산시
129 
익산시
86 
정읍시
83 
남원시
 
33
Other values (10)
120 

Length

Max length4
Median length3
Mean length3.0012788
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 331
42.3%
군산시 129
 
16.5%
익산시 86
 
11.0%
정읍시 83
 
10.6%
남원시 33
 
4.2%
고창군 24
 
3.1%
김제시 20
 
2.6%
완주군 20
 
2.6%
부안군 17
 
2.2%
순창군 14
 
1.8%
Other values (5) 25
 
3.2%

Length

2024-03-14T09:14:20.783854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 331
42.3%
군산시 129
 
16.5%
익산시 86
 
11.0%
정읍시 83
 
10.6%
남원시 33
 
4.2%
고창군 24
 
3.1%
김제시 20
 
2.6%
완주군 20
 
2.6%
부안군 17
 
2.2%
순창군 14
 
1.8%
Other values (5) 25
 
3.2%

읍면동
Text

Distinct98
Distinct (%)12.5%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2024-03-14T09:14:20.974656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2650448
Min length2

Characters and Unicode

Total characters2550
Distinct characters100
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)1.8%

Sample

1st row노송동
2nd row중앙동
3rd row풍남동
4th row완산동
5th row완산동
ValueCountFrequency (%)
평화2 34
 
4.4%
효자4동 28
 
3.6%
내장상동 28
 
3.6%
조촌동 27
 
3.5%
고창읍 24
 
3.1%
중앙동 22
 
2.8%
수성동 22
 
2.8%
시기동 20
 
2.6%
수송동 18
 
2.3%
서신동 15
 
1.9%
Other values (88) 543
69.5%
2024-03-14T09:14:21.308913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 571
22.4%
2 105
 
4.1%
1 97
 
3.8%
읍 92
 
3.6%
화 76
 
3.0%
효 61
 
2.4%
자 61
 
2.4%
중 56
 
2.2%
산 54
 
2.1%
삼 50
 
2.0%
Other values (90) 1327
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2273
89.1%
Decimal Number 277
 
10.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 571
25.1%
읍 92
 
4.0%
화 76
 
3.3%
효 61
 
2.7%
자 61
 
2.7%
중 56
 
2.5%
산 54
 
2.4%
삼 50
 
2.2%
평 49
 
2.2%
천 47
 
2.1%
Other values (86) 1156
50.9%
Decimal Number
ValueCountFrequency (%)
2 105
37.9%
1 97
35.0%
3 47
17.0%
4 28
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2273
89.1%
Common 277
 
10.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 571
25.1%
읍 92
 
4.0%
화 76
 
3.3%
효 61
 
2.7%
자 61
 
2.7%
중 56
 
2.5%
산 54
 
2.4%
삼 50
 
2.2%
평 49
 
2.2%
천 47
 
2.1%
Other values (86) 1156
50.9%
Common
ValueCountFrequency (%)
2 105
37.9%
1 97
35.0%
3 47
17.0%
4 28
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2273
89.1%
ASCII 277
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 571
25.1%
읍 92
 
4.0%
화 76
 
3.3%
효 61
 
2.7%
자 61
 
2.7%
중 56
 
2.5%
산 54
 
2.4%
삼 50
 
2.2%
평 49
 
2.2%
천 47
 
2.1%
Other values (86) 1156
50.9%
ASCII
ValueCountFrequency (%)
2 105
37.9%
1 97
35.0%
3 47
17.0%
4 28
 
10.1%

대피소 구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
공공
772 
정부지원
 
8
<NA>
 
1
정부
 
1

Length

Max length4
Median length2
Mean length2.0230179
Min length2

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
공공 772
98.7%
정부지원 8
 
1.0%
<NA> 1
 
0.1%
정부 1
 
0.1%

Length

2024-03-14T09:14:21.694521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:14:21.805370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공 772
98.7%
정부지원 8
 
1.0%
na 1
 
0.1%
정부 1
 
0.1%

설치년도
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.1%
Memory size6.2 KiB

대피소위치
Text

Distinct767
Distinct (%)98.2%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2024-03-14T09:14:22.074407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length18.393086
Min length4

Characters and Unicode

Total characters14365
Distinct characters397
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique757 ?
Unique (%)96.9%

Sample

1st row전주시청
2nd row다가대피소
3rd row풍남초등학교대피소
4th row시립도서관(곤지산 4길12)
5th row완산교회(전주천서로 181)
ValueCountFrequency (%)
남원시 33
 
1.6%
고창군 24
 
1.1%
고창읍 24
 
1.1%
전라북도 21
 
1.0%
완주군 20
 
0.9%
아파트 18
 
0.8%
부안군 16
 
0.8%
순창읍 14
 
0.7%
부안읍 14
 
0.7%
10 12
 
0.6%
Other values (1345) 1925
90.8%
2024-03-14T09:14:22.474850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1393
 
9.7%
( 816
 
5.7%
) 795
 
5.5%
1 707
 
4.9%
로 525
 
3.7%
2 430
 
3.0%
아 387
 
2.7%
동 356
 
2.5%
트 345
 
2.4%
파 341
 
2.4%
Other values (387) 8270
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8400
58.5%
Decimal Number 2718
 
18.9%
Space Separator 1393
 
9.7%
Open Punctuation 816
 
5.7%
Close Punctuation 795
 
5.5%
Dash Punctuation 107
 
0.7%
Control 58
 
0.4%
Other Punctuation 50
 
0.3%
Lowercase Letter 15
 
0.1%
Uppercase Letter 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 525
 
6.2%
아 387
 
4.6%
동 356
 
4.2%
트 345
 
4.1%
파 341
 
4.1%
길 280
 
3.3%
대 168
 
2.0%
산 139
 
1.7%
차 134
 
1.6%
지 124
 
1.5%
Other values (357) 5601
66.7%
Decimal Number
ValueCountFrequency (%)
1 707
26.0%
2 430
15.8%
3 302
11.1%
0 273
 
10.0%
5 208
 
7.7%
4 188
 
6.9%
6 179
 
6.6%
9 162
 
6.0%
7 148
 
5.4%
8 121
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
k 5
33.3%
s 2
 
13.3%
e 2
 
13.3%
b 2
 
13.3%
v 1
 
6.7%
i 1
 
6.7%
w 1
 
6.7%
t 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 36
72.0%
@ 13
 
26.0%
. 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
A 6
50.0%
P 3
25.0%
T 3
25.0%
Space Separator
ValueCountFrequency (%)
1393
100.0%
Open Punctuation
ValueCountFrequency (%)
( 816
100.0%
Close Punctuation
ValueCountFrequency (%)
) 795
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 107
100.0%
Control
ValueCountFrequency (%)
58
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8401
58.5%
Common 5937
41.3%
Latin 27
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 525
 
6.2%
아 387
 
4.6%
동 356
 
4.2%
트 345
 
4.1%
파 341
 
4.1%
길 280
 
3.3%
대 168
 
2.0%
산 139
 
1.7%
차 134
 
1.6%
지 124
 
1.5%
Other values (358) 5602
66.7%
Common
ValueCountFrequency (%)
1393
23.5%
( 816
13.7%
) 795
13.4%
1 707
11.9%
2 430
 
7.2%
3 302
 
5.1%
0 273
 
4.6%
5 208
 
3.5%
4 188
 
3.2%
6 179
 
3.0%
Other values (8) 646
10.9%
Latin
ValueCountFrequency (%)
A 6
22.2%
k 5
18.5%
P 3
11.1%
T 3
11.1%
s 2
 
7.4%
e 2
 
7.4%
b 2
 
7.4%
v 1
 
3.7%
i 1
 
3.7%
w 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8400
58.5%
ASCII 5964
41.5%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1393
23.4%
( 816
13.7%
) 795
13.3%
1 707
11.9%
2 430
 
7.2%
3 302
 
5.1%
0 273
 
4.6%
5 208
 
3.5%
4 188
 
3.2%
6 179
 
3.0%
Other values (19) 673
11.3%
Hangul
ValueCountFrequency (%)
로 525
 
6.2%
아 387
 
4.6%
동 356
 
4.2%
트 345
 
4.1%
파 341
 
4.1%
길 280
 
3.3%
대 168
 
2.0%
산 139
 
1.7%
차 134
 
1.6%
지 124
 
1.5%
Other values (357) 5601
66.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct657
Distinct (%)84.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3477.9329
Minimum2.251
Maximum57600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2024-03-14T09:14:22.586301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.251
5-th percentile192
Q1607
median1683
Q33934
95-th percentile13713
Maximum57600
Range57597.749
Interquartile range (IQR)3327

Descriptive statistics

Standard deviation5541.5495
Coefficient of variation (CV)1.5933457
Kurtosis22.226319
Mean3477.9329
Median Absolute Deviation (MAD)1246
Skewness3.9848893
Sum2716265.6
Variance30708771
MonotonicityNot monotonic
2024-03-14T09:14:22.690506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1887.0 7
 
0.9%
2981.0 5
 
0.6%
2000.0 4
 
0.5%
330.0 4
 
0.5%
2805.0 4
 
0.5%
1518.0 4
 
0.5%
825.0 4
 
0.5%
7014.0 3
 
0.4%
495.0 3
 
0.4%
2640.0 3
 
0.4%
Other values (647) 740
94.6%
ValueCountFrequency (%)
2.251 1
 
0.1%
3.152 1
 
0.1%
12.536 1
 
0.1%
24.0 1
 
0.1%
66.0 3
0.4%
70.0 1
 
0.1%
72.0 1
 
0.1%
80.0 2
0.3%
82.5 1
 
0.1%
85.0 1
 
0.1%
ValueCountFrequency (%)
57600.0 1
0.1%
42580.88999999999 1
0.1%
37348.0 1
0.1%
33000.0 1
0.1%
32800.0 1
0.1%
32401.0 1
0.1%
31332.0 1
0.1%
29271.0 1
0.1%
27954.0 1
0.1%
26572.0 1
0.1%

수용 인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct614
Distinct (%)78.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4151.7051
Minimum3.82
Maximum69120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2024-03-14T09:14:22.828439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.82
5-th percentile233
Q1724
median1900
Q34744
95-th percentile16000
Maximum69120
Range69116.18
Interquartile range (IQR)4020

Descriptive statistics

Standard deviation6585.2931
Coefficient of variation (CV)1.5861659
Kurtosis22.793637
Mean4151.7051
Median Absolute Deviation (MAD)1412
Skewness4.0063619
Sum3242481.7
Variance43366085
MonotonicityNot monotonic
2024-03-14T09:14:22.956034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000.0 8
 
1.0%
800.0 7
 
0.9%
2287.0 7
 
0.9%
3300.0 6
 
0.8%
3560.0 5
 
0.6%
1300.0 5
 
0.6%
760.0 5
 
0.6%
400.0 5
 
0.6%
1200.0 4
 
0.5%
2340.0 4
 
0.5%
Other values (604) 725
92.7%
ValueCountFrequency (%)
3.82 1
 
0.1%
29.0 1
 
0.1%
80.0 3
0.4%
85.0 1
 
0.1%
87.27272727272728 1
 
0.1%
96.0 1
 
0.1%
97.0 1
 
0.1%
100.0 1
 
0.1%
103.03030303030303 1
 
0.1%
110.30303030303031 1
 
0.1%
ValueCountFrequency (%)
69120.0 1
0.1%
51613.19999999999 1
0.1%
45271.0 1
0.1%
39757.0 1
0.1%
39274.0 1
0.1%
37978.0 1
0.1%
35480.0 1
0.1%
33883.0 1
0.1%
32208.0 1
0.1%
30628.0 1
0.1%

Interactions

2024-03-14T09:14:19.754790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:14:19.205569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:14:19.461641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:14:19.841058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:14:19.305705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:14:19.547433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:14:19.916127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:14:19.382639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:14:19.651737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:14:23.031192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구읍면동대피소 구분면적수용 인원(명)
연번1.0000.9190.9980.1020.1550.160
시군구0.9191.0001.0000.1920.0000.000
읍면동0.9981.0001.0000.5950.0000.000
대피소 구분0.1020.1920.5951.0000.0000.000
면적0.1550.0000.0000.0001.0001.000
수용\n인원(명)0.1600.0000.0000.0001.0001.000
2024-03-14T09:14:23.115624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대피소 구분시도명시군구
대피소 구분1.0001.0000.107
시도명1.0001.0001.000
시군구0.1071.0001.000
2024-03-14T09:14:23.187416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적수용 인원(명)시도명시군구대피소 구분
연번1.000-0.146-0.1571.0000.7040.060
면적-0.1461.0000.9771.0000.0000.000
수용\n인원(명)-0.1570.9771.0001.0000.0000.000
시도명1.0001.0001.0001.0001.0001.000
시군구0.7040.0000.0001.0001.0000.107
대피소 구분0.0600.0000.0001.0000.1071.000

Missing values

2024-03-14T09:14:20.036482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:14:20.143106image/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.
2024-03-14T09:14:20.251664image/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

연번시도명시군구읍면동대피소 구분설치년도대피소위치면적수용 인원(명)
0<NA><NA><NA><NA><NA>NaN<NA><NA><NA>
11전라북도전주시노송동정부지원03.05.26전주시청1372.01660.0
22전라북도전주시중앙동정부지원80.09.02다가대피소1296.01568.0
33전라북도전주시풍남동정부지원88.10.20풍남초등학교대피소840.01016.0
44전라북도전주시완산동공공89.10.14시립도서관(곤지산 4길12)621.0751.0
55전라북도전주시완산동공공11.1.20완산교회(전주천서로 181)626.0758.0
66전라북도전주시완산동공공11.1.20오페라하우스(안행로 150)14197.017179.0
77전라북도전주시완산동공공91.1.30완산동주민센터(강당2길7)112.0136.0
88전라북도전주시완산동공공02.1.30대풍한마을(따박골7길22)396.0480.0
99전라북도전주시완산동공공91.1.30농협완산동지점(용머리로 201)264.0320.0
연번시도명시군구읍면동대피소 구분설치년도대피소위치면적수용 인원(명)
772772전라북도부안군부안읍공공1998-11-20 00:00:00전라북도 부안군 부안읍 낭주길 5(대림아파트)992.01200.0
773773전라북도부안군부안읍공공1998-0101전라북도 부안군 부안읍 학동길 11-6(동원아파트)2248.02720.0
774774전라북도부안군부안읍공공1997-08-29 00:00:00전라북도 부안군 부안읍 봉신길 5(동영아파트)2136.02572.0
775775전라북도부안군부안읍공공1991-02-28 00:00:00전라북도 부안군 부안읍 상원길 19(상원아파트)774.0936.0
776776전라북도부안군부안읍공공1990-12-15 00:00:00전아북도 부안군 부안읍 오리정로 172(하이안아파트)3620.04380.0
777777전라북도부안군부안읍공공1993-11-19 00:00:00전라북도 부안군 부안읍 당산로 91(부안군청)8834.010707.0
778778전라북도부안군부안읍공공2005-10-21 00:00:00전라북도 부안군 변산면 지서로 80(송림아파트)2621.03176.0
779779전라북도부안군변산면공공2010-11-03 00:00:00전라북도 부안군변산면 변산해변로 51(대명리조트)14833.017978.0
780780전라북도부안군변산면공공2000-11-29 00:00:00전라북도 부안군 변산면 격포윗길 20(양우아파트)1400.01696.0
781781전라북도부안군부안읍공공2008-07-11 00:00:00전라북도 부안군 부안읍 봉신길 19(현대아파트)1924.02328.0