Overview

Dataset statistics

Number of variables8
Number of observations58
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory70.3 B

Variable types

Categorical1
Text4
Numeric3

Dataset

Description대전광역시 서구 조직별 세출예산 현황(기준연도, 구분, 예산액, 구성비, 전년도 예산액, 구성비, 비교증감, 증감률) 데이터 입니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15095171/fileData.do

Alerts

기준연도 has constant value ""Constant
예산액 is highly overall correlated with 전년도예산액High correlation
전년도예산액 is highly overall correlated with 예산액High correlation
구분 has unique valuesUnique
예산액 has unique valuesUnique
전년도예산액 has unique valuesUnique
비교증감 has unique valuesUnique
증감률 has unique valuesUnique

Reproduction

Analysis started2024-01-06 13:24:17.514567
Analysis finished2024-01-06 13:24:21.325083
Duration3.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size596.0 B
2024
58 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 58
100.0%

Length

2024-01-06T13:24:21.708451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T13:24:22.072820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024 58
100.0%

구분
Text

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size596.0 B
2024-01-06T13:24:22.702417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.1896552
Min length2

Characters and Unicode

Total characters243
Distinct characters97
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

Unique58 ?
Unique (%)100.0%

Sample

1st row기획조정실
2nd row홍보실
3rd row운영지원과
4th row회계과
5th row자치행정과
ValueCountFrequency (%)
기획조정실 1
 
1.7%
둔산2동 1
 
1.7%
관저2동 1
 
1.7%
의회사무국 1
 
1.7%
평생학습과 1
 
1.7%
도서관운영과 1
 
1.7%
복수동 1
 
1.7%
도마1동 1
 
1.7%
도마2동 1
 
1.7%
정림동 1
 
1.7%
Other values (48) 48
82.8%
2024-01-06T13:24:24.128726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 30
 
12.3%
동 26
 
10.7%
정 10
 
4.1%
지 7
 
2.9%
원 7
 
2.9%
도 6
 
2.5%
1 5
 
2.1%
2 5
 
2.1%
평 4
 
1.6%
관 4
 
1.6%
Other values (87) 139
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 231
95.1%
Decimal Number 12
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 30
 
13.0%
동 26
 
11.3%
정 10
 
4.3%
지 7
 
3.0%
원 7
 
3.0%
도 6
 
2.6%
평 4
 
1.7%
관 4
 
1.7%
건 4
 
1.7%
복 4
 
1.7%
Other values (84) 129
55.8%
Decimal Number
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 231
95.1%
Common 12
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 30
 
13.0%
동 26
 
11.3%
정 10
 
4.3%
지 7
 
3.0%
원 7
 
3.0%
도 6
 
2.6%
평 4
 
1.7%
관 4
 
1.7%
건 4
 
1.7%
복 4
 
1.7%
Other values (84) 129
55.8%
Common
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 231
95.1%
ASCII 12
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 30
 
13.0%
동 26
 
11.3%
정 10
 
4.3%
지 7
 
3.0%
원 7
 
3.0%
도 6
 
2.6%
평 4
 
1.7%
관 4
 
1.7%
건 4
 
1.7%
복 4
 
1.7%
Other values (84) 129
55.8%
ASCII
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

예산액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16646655
Minimum188711
Maximum3.012088 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2024-01-06T13:24:24.584446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum188711
5-th percentile200090.6
Q1228447.25
median926261
Q37923085
95-th percentile74323978
Maximum3.012088 × 108
Range3.0102009 × 108
Interquartile range (IQR)7694637.8

Descriptive statistics

Standard deviation49820720
Coefficient of variation (CV)2.9928367
Kurtosis21.229867
Mean16646655
Median Absolute Deviation (MAD)726694
Skewness4.454324
Sum9.65506 × 108
Variance2.4821042 × 1015
MonotonicityNot monotonic
2024-01-06T13:24:25.126642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7992585 1
 
1.7%
222721 1
 
1.7%
4954252 1
 
1.7%
4190362 1
 
1.7%
234544 1
 
1.7%
228193 1
 
1.7%
215265 1
 
1.7%
204164 1
 
1.7%
229676 1
 
1.7%
222880 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
188711 1
1.7%
196106 1
1.7%
198819 1
1.7%
200315 1
1.7%
204164 1
1.7%
207695 1
1.7%
211221 1
1.7%
211439 1
1.7%
215265 1
1.7%
218860 1
1.7%
ValueCountFrequency (%)
301208803 1
1.7%
182523263 1
1.7%
157168936 1
1.7%
59704280 1
1.7%
38371493 1
1.7%
34540302 1
1.7%
24891757 1
1.7%
20285240 1
1.7%
19146457 1
1.7%
17596208 1
1.7%

예산액_구성비
Text

Distinct32
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Memory size596.0 B
2024-01-06T13:24:25.615796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0517241
Min length6

Characters and Unicode

Total characters351
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)46.6%

Sample

1st row0.83 %
2nd row0.23 %
3rd row3.97 %
4th row6.18 %
5th row0.85 %
ValueCountFrequency (%)
58
50.0%
0.02 20
 
17.2%
0.03 5
 
4.3%
0.06 2
 
1.7%
0.24 2
 
1.7%
0.83 2
 
1.7%
0.80 1
 
0.9%
0.59 1
 
0.9%
1.48 1
 
0.9%
0.18 1
 
0.9%
Other values (23) 23
 
19.8%
2024-01-06T13:24:26.327127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 80
22.8%
. 58
16.5%
58
16.5%
% 58
16.5%
2 29
 
8.3%
1 15
 
4.3%
3 13
 
3.7%
8 13
 
3.7%
5 9
 
2.6%
6 6
 
1.7%
Other values (3) 12
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
50.4%
Other Punctuation 116
33.0%
Space Separator 58
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 80
45.2%
2 29
 
16.4%
1 15
 
8.5%
3 13
 
7.3%
8 13
 
7.3%
5 9
 
5.1%
6 6
 
3.4%
4 5
 
2.8%
9 5
 
2.8%
7 2
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 58
50.0%
% 58
50.0%
Space Separator
ValueCountFrequency (%)
58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 80
22.8%
. 58
16.5%
58
16.5%
% 58
16.5%
2 29
 
8.3%
1 15
 
4.3%
3 13
 
3.7%
8 13
 
3.7%
5 9
 
2.6%
6 6
 
1.7%
Other values (3) 12
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 80
22.8%
. 58
16.5%
58
16.5%
% 58
16.5%
2 29
 
8.3%
1 15
 
4.3%
3 13
 
3.7%
8 13
 
3.7%
5 9
 
2.6%
6 6
 
1.7%
Other values (3) 12
 
3.4%

전년도예산액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16105362
Minimum180011
Maximum2.7253832 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2024-01-06T13:24:26.772803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180011
5-th percentile210638.35
Q1246383
median1044101.5
Q38868521
95-th percentile71452166
Maximum2.7253832 × 108
Range2.7235831 × 108
Interquartile range (IQR)8622138

Descriptive statistics

Standard deviation45719131
Coefficient of variation (CV)2.8387521
Kurtosis19.990307
Mean16105362
Median Absolute Deviation (MAD)831917
Skewness4.3255222
Sum9.34111 × 108
Variance2.0902389 × 1015
MonotonicityNot monotonic
2024-01-06T13:24:27.257436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12265731 1
 
1.7%
256789 1
 
1.7%
3900580 1
 
1.7%
3956469 1
 
1.7%
227224 1
 
1.7%
234450 1
 
1.7%
244532 1
 
1.7%
277749 1
 
1.7%
298663 1
 
1.7%
223980 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
180011 1
1.7%
189998 1
1.7%
208538 1
1.7%
211009 1
1.7%
213360 1
1.7%
213775 1
1.7%
223980 1
1.7%
224966 1
1.7%
226183 1
1.7%
227224 1
1.7%
ValueCountFrequency (%)
272538325 1
1.7%
168006694 1
1.7%
150706176 1
1.7%
57466164 1
1.7%
35882334 1
1.7%
34867603 1
1.7%
26432403 1
1.7%
24231627 1
1.7%
19395654 1
1.7%
19237957 1
1.7%

전년도예산액_구성비
Text

Distinct32
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Memory size596.0 B
2024-01-06T13:24:27.634597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0517241
Min length6

Characters and Unicode

Total characters351
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)46.6%

Sample

1st row1.31 %
2nd row0.33 %
3rd row3.73 %
4th row6.15 %
5th row0.98 %
ValueCountFrequency (%)
58
50.0%
0.03 13
 
11.2%
0.02 12
 
10.3%
0.42 2
 
1.7%
0.11 2
 
1.7%
0.07 2
 
1.7%
0.68 1
 
0.9%
2.06 1
 
0.9%
0.05 1
 
0.9%
0.25 1
 
0.9%
Other values (23) 23
 
19.8%
2024-01-06T13:24:28.460288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 76
21.7%
. 58
16.5%
58
16.5%
% 58
16.5%
3 25
 
7.1%
2 22
 
6.3%
1 19
 
5.4%
9 7
 
2.0%
8 7
 
2.0%
5 7
 
2.0%
Other values (3) 14
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
50.4%
Other Punctuation 116
33.0%
Space Separator 58
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76
42.9%
3 25
 
14.1%
2 22
 
12.4%
1 19
 
10.7%
9 7
 
4.0%
8 7
 
4.0%
5 7
 
4.0%
6 6
 
3.4%
7 5
 
2.8%
4 3
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 58
50.0%
% 58
50.0%
Space Separator
ValueCountFrequency (%)
58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76
21.7%
. 58
16.5%
58
16.5%
% 58
16.5%
3 25
 
7.1%
2 22
 
6.3%
1 19
 
5.4%
9 7
 
2.0%
8 7
 
2.0%
5 7
 
2.0%
Other values (3) 14
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76
21.7%
. 58
16.5%
58
16.5%
% 58
16.5%
3 25
 
7.1%
2 22
 
6.3%
1 19
 
5.4%
9 7
 
2.0%
8 7
 
2.0%
5 7
 
2.0%
Other values (3) 14
 
4.0%

비교증감
Real number (ℝ)

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541293.1
Minimum-6635419
Maximum28670478
Zeros0
Zeros (%)0.0%
Negative36
Negative (%)62.1%
Memory size654.0 B
2024-01-06T13:24:29.113020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6635419
5-th percentile-3144095.2
Q1-89075.5
median-22768
Q318092.25
95-th percentile3947720.5
Maximum28670478
Range35305897
Interquartile range (IQR)107167.75

Descriptive statistics

Standard deviation4603139
Coefficient of variation (CV)8.5039677
Kurtosis25.992424
Mean541293.1
Median Absolute Deviation (MAD)61655.5
Skewness4.5253454
Sum31395000
Variance2.1188889 × 1013
MonotonicityNot monotonic
2024-01-06T13:24:29.572832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4273146 1
 
1.7%
-34068 1
 
1.7%
1053672 1
 
1.7%
233893 1
 
1.7%
7320 1
 
1.7%
-6257 1
 
1.7%
-29267 1
 
1.7%
-73585 1
 
1.7%
-68987 1
 
1.7%
-1100 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
-6635419 1
1.7%
-6615861 1
1.7%
-4273146 1
1.7%
-2944851 1
1.7%
-1895814 1
1.7%
-1540646 1
1.7%
-1342032 1
1.7%
-992608 1
1.7%
-937053 1
1.7%
-739864 1
1.7%
ValueCountFrequency (%)
28670478 1
1.7%
14516569 1
1.7%
6462760 1
1.7%
3503890 1
1.7%
2238116 1
1.7%
2088232 1
1.7%
1053672 1
1.7%
977210 1
1.7%
889586 1
1.7%
481503 1
1.7%

증감률
Text

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size596.0 B
2024-01-06T13:24:30.009440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.137931
Min length6

Characters and Unicode

Total characters414
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)100.0%

Sample

1st row△34.84 %
2nd row△30.03 %
3rd row10.05 %
4th row3.89 %
5th row△10.85 %
ValueCountFrequency (%)
58
50.0%
△3.80 1
 
0.9%
△5.83 1
 
0.9%
△11.96 1
 
0.9%
10.06 1
 
0.9%
27.01 1
 
0.9%
5.91 1
 
0.9%
3.22 1
 
0.9%
△2.67 1
 
0.9%
△11.97 1
 
0.9%
Other values (49) 49
42.2%
2024-01-06T13:24:31.102706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 58
14.0%
58
14.0%
% 58
14.0%
△ 36
8.7%
1 36
8.7%
2 26
6.3%
0 22
 
5.3%
3 21
 
5.1%
7 21
 
5.1%
8 19
 
4.6%
Other values (4) 59
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 204
49.3%
Other Punctuation 116
28.0%
Space Separator 58
 
14.0%
Other Symbol 36
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36
17.6%
2 26
12.7%
0 22
10.8%
3 21
10.3%
7 21
10.3%
8 19
9.3%
9 17
8.3%
5 17
8.3%
4 14
 
6.9%
6 11
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 58
50.0%
% 58
50.0%
Space Separator
ValueCountFrequency (%)
58
100.0%
Other Symbol
ValueCountFrequency (%)
△ 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 414
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 58
14.0%
58
14.0%
% 58
14.0%
△ 36
8.7%
1 36
8.7%
2 26
6.3%
0 22
 
5.3%
3 21
 
5.1%
7 21
 
5.1%
8 19
 
4.6%
Other values (4) 59
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 378
91.3%
Geometric Shapes 36
 
8.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 58
15.3%
58
15.3%
% 58
15.3%
1 36
9.5%
2 26
6.9%
0 22
 
5.8%
3 21
 
5.6%
7 21
 
5.6%
8 19
 
5.0%
9 17
 
4.5%
Other values (3) 42
11.1%
Geometric Shapes
ValueCountFrequency (%)
△ 36
100.0%

Interactions

2024-01-06T13:24:19.451488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:24:18.118402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:24:18.733588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:24:19.714127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:24:18.298646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:24:19.003405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:24:20.222185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:24:18.482680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:24:19.223803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-06T13:24:31.382454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분예산액예산액_구성비전년도예산액전년도예산액_구성비비교증감증감률
구분1.0001.0001.0001.0001.0001.0001.000
예산액1.0001.0001.0001.0001.0000.9181.000
예산액_구성비1.0001.0001.0001.0000.9990.9881.000
전년도예산액1.0001.0001.0001.0001.0000.9771.000
전년도예산액_구성비1.0001.0000.9991.0001.0000.9971.000
비교증감1.0000.9180.9880.9770.9971.0001.000
증감률1.0001.0001.0001.0001.0001.0001.000
2024-01-06T13:24:31.709043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예산액전년도예산액비교증감
예산액1.0000.9440.024
전년도예산액0.9441.000-0.119
비교증감0.024-0.1191.000

Missing values

2024-01-06T13:24:20.578333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-06T13:24:21.054129image/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

기준연도구분예산액예산액_구성비전년도예산액전년도예산액_구성비비교증감증감률
02024기획조정실79925850.83 %122657311.31 %-4273146△34.84 %
12024홍보실21836480.23 %31207010.33 %-937053△30.03 %
22024운영지원과383714933.97 %348676033.73 %350389010.05 %
32024회계과597042806.18 %574661646.15 %22381163.89 %
42024자치행정과81590140.85 %91516220.98 %-992608△10.85 %
52024문화체육과80307990.83 %70535890.76 %97721013.85 %
62024세정과10334880.11 %10569530.11 %-23465△2.22 %
72024세원관리과6070940.06 %6291650.07 %-22071△3.51 %
82024민원여권과9973060.10 %10998640.12 %-102558△9.32 %
92024복지정책과202852402.10 %193956542.08 %8895864.59 %
기준연도구분예산액예산액_구성비전년도예산액전년도예산액_구성비비교증감증감률
482024갈마2동2710130.03 %2137750.02 %5723826.77 %
492024월평1동1988190.02 %2110090.02 %-12190△5.78 %
502024월평2동2841140.03 %2819230.03 %21910.78 %
512024월평3동1887110.02 %1800110.02 %87004.83 %
522024만년동2112210.02 %1899980.02 %2122311.17 %
532024가수원동2337830.02 %2277080.02 %60752.67 %
542024도안동2447490.03 %2518160.03 %-7067△2.81 %
552024관저1동2114390.02 %2927240.03 %-81285△27.77 %
562024관저2동2623600.03 %2979930.03 %-35633△11.96 %
572024기성동2292100.02 %2261830.02 %30271.34 %