Overview

Dataset statistics

Number of variables10
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory86.8 B

Variable types

Categorical6
Text1
Numeric3

Dataset

Description부산광역시사상구_급경사지정보_20230628
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15025594

Alerts

관리주체 is highly overall correlated with 비탈면기능 and 1 other fieldsHigh correlation
비탈면기능 is highly overall correlated with 관리주체 and 1 other fieldsHigh correlation
시설관리부서 is highly overall correlated with 관리주체 and 1 other fieldsHigh correlation
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:05:49.162394
Analysis finished2023-12-10 17:05:50.790530
Duration1.63 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리주체
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
사상구청
20 
(주)이비알
 
1
(재)부산테크노파크
 
1
동양아파트
 
1
벽산아파트
 
1
Other values (11)
11 

Length

Max length10
Median length4
Mean length5.0571429
Min length4

Unique

Unique15 ?
Unique (%)42.9%

Sample

1st row사상구청
2nd row(주)이비알
3rd row(재)부산테크노파크
4th row동양아파트
5th row벽산아파트

Common Values

ValueCountFrequency (%)
사상구청 20
57.1%
(주)이비알 1
 
2.9%
(재)부산테크노파크 1
 
2.9%
동양아파트 1
 
2.9%
벽산아파트 1
 
2.9%
목화아파트 1
 
2.9%
엄궁아파트 1
 
2.9%
동인병원 1
 
2.9%
엄궁삼성타워아파트 1
 
2.9%
엄궁쌍용스윗닷홈 1
 
2.9%
Other values (6) 6
 
17.1%

Length

2023-12-11T02:05:50.868926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사상구청 20
57.1%
주)이비알 1
 
2.9%
재)부산테크노파크 1
 
2.9%
동양아파트 1
 
2.9%
벽산아파트 1
 
2.9%
목화아파트 1
 
2.9%
엄궁아파트 1
 
2.9%
동인병원 1
 
2.9%
엄궁삼성타워아파트 1
 
2.9%
엄궁쌍용스윗닷홈 1
 
2.9%
Other values (6) 6
 
17.1%

소재지
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-11T02:05:51.136180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length27
Mean length21.971429
Min length8

Characters and Unicode

Total characters769
Distinct characters118
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row덕포동 775-1(덕상로 32), 덕상초등학교 주변 괘내교 인근
2nd row엄궁동 141-15, ㈜이비알 근처
3rd row엄궁동 138-2(엄궁로 70-10)
4th row학장동 210-3번지, (동양아파트 일원)
5th row주례동 616-4(주례로139번길 8), 주례벽산아파트 경사지
ValueCountFrequency (%)
엄궁동 10
 
7.0%
덕포동 6
 
4.2%
학장동 5
 
3.5%
주례동 5
 
3.5%
주변 4
 
2.8%
인근 4
 
2.8%
모라동 4
 
2.8%
괘법동 3
 
2.1%
아래 3
 
2.1%
경사면 3
 
2.1%
Other values (85) 95
66.9%
2023-12-11T02:05:51.553499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108
 
14.0%
1 40
 
5.2%
동 39
 
5.1%
- 27
 
3.5%
, 25
 
3.3%
0 19
 
2.5%
3 19
 
2.5%
지 18
 
2.3%
2 18
 
2.3%
번 16
 
2.1%
Other values (108) 440
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 413
53.7%
Decimal Number 168
21.8%
Space Separator 108
 
14.0%
Dash Punctuation 27
 
3.5%
Other Punctuation 26
 
3.4%
Close Punctuation 13
 
1.7%
Open Punctuation 13
 
1.7%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 39
 
9.4%
지 18
 
4.4%
번 16
 
3.9%
엄 13
 
3.1%
궁 13
 
3.1%
주 13
 
3.1%
산 12
 
2.9%
로 12
 
2.9%
사 11
 
2.7%
덕 11
 
2.7%
Other values (91) 255
61.7%
Decimal Number
ValueCountFrequency (%)
1 40
23.8%
0 19
11.3%
3 19
11.3%
2 18
10.7%
8 15
 
8.9%
7 14
 
8.3%
9 12
 
7.1%
4 12
 
7.1%
6 10
 
6.0%
5 9
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 25
96.2%
: 1
 
3.8%
Space Separator
ValueCountFrequency (%)
108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 414
53.8%
Common 355
46.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 39
 
9.4%
지 18
 
4.3%
번 16
 
3.9%
엄 13
 
3.1%
궁 13
 
3.1%
주 13
 
3.1%
산 12
 
2.9%
로 12
 
2.9%
사 11
 
2.7%
덕 11
 
2.7%
Other values (92) 256
61.8%
Common
ValueCountFrequency (%)
108
30.4%
1 40
 
11.3%
- 27
 
7.6%
, 25
 
7.0%
0 19
 
5.4%
3 19
 
5.4%
2 18
 
5.1%
8 15
 
4.2%
7 14
 
3.9%
) 13
 
3.7%
Other values (6) 57
16.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 413
53.7%
ASCII 355
46.2%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108
30.4%
1 40
 
11.3%
- 27
 
7.6%
, 25
 
7.0%
0 19
 
5.4%
3 19
 
5.4%
2 18
 
5.1%
8 15
 
4.2%
7 14
 
3.9%
) 13
 
3.7%
Other values (6) 57
16.1%
Hangul
ValueCountFrequency (%)
동 39
 
9.4%
지 18
 
4.4%
번 16
 
3.9%
엄 13
 
3.1%
궁 13
 
3.1%
주 13
 
3.1%
산 12
 
2.9%
로 12
 
2.9%
사 11
 
2.7%
덕 11
 
2.7%
Other values (91) 255
61.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

높이(m)
Real number (ℝ)

Distinct14
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.734286
Minimum4
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T02:05:51.692069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.7
Q18
median12
Q316.5
95-th percentile20
Maximum30
Range26
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.94212
Coefficient of variation (CV)0.46662374
Kurtosis0.40487362
Mean12.734286
Median Absolute Deviation (MAD)4
Skewness0.66916883
Sum445.7
Variance35.30879
MonotonicityNot monotonic
2023-12-11T02:05:51.860390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
15.0 6
17.1%
10.0 6
17.1%
20.0 6
17.1%
13.0 2
 
5.7%
18.0 2
 
5.7%
4.0 2
 
5.7%
8.0 2
 
5.7%
7.2 2
 
5.7%
6.0 2
 
5.7%
5.0 1
 
2.9%
Other values (4) 4
11.4%
ValueCountFrequency (%)
4.0 2
 
5.7%
5.0 1
 
2.9%
6.0 2
 
5.7%
7.0 1
 
2.9%
7.2 2
 
5.7%
8.0 2
 
5.7%
9.3 1
 
2.9%
10.0 6
17.1%
12.0 1
 
2.9%
13.0 2
 
5.7%
ValueCountFrequency (%)
30.0 1
 
2.9%
20.0 6
17.1%
18.0 2
 
5.7%
15.0 6
17.1%
13.0 2
 
5.7%
12.0 1
 
2.9%
10.0 6
17.1%
9.3 1
 
2.9%
8.0 2
 
5.7%
7.2 2
 
5.7%

길이(m)
Real number (ℝ)

Distinct19
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136
Minimum30
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T02:05:51.992825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q162.5
median90
Q3122.5
95-th percentile212
Maximum1500
Range1470
Interquartile range (IQR)60

Descriptive statistics

Standard deviation243.10129
Coefficient of variation (CV)1.7875095
Kurtosis31.507501
Mean136
Median Absolute Deviation (MAD)30
Skewness5.4908985
Sum4760
Variance59098.235
MonotonicityNot monotonic
2023-12-11T02:05:52.110912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
100 6
17.1%
30 4
11.4%
80 3
 
8.6%
70 3
 
8.6%
150 2
 
5.7%
90 2
 
5.7%
200 2
 
5.7%
50 2
 
5.7%
180 1
 
2.9%
75 1
 
2.9%
Other values (9) 9
25.7%
ValueCountFrequency (%)
30 4
11.4%
40 1
 
2.9%
45 1
 
2.9%
50 2
5.7%
60 1
 
2.9%
65 1
 
2.9%
70 3
8.6%
75 1
 
2.9%
80 3
8.6%
90 2
5.7%
ValueCountFrequency (%)
1500 1
 
2.9%
240 1
 
2.9%
200 2
 
5.7%
180 1
 
2.9%
160 1
 
2.9%
150 2
 
5.7%
135 1
 
2.9%
110 1
 
2.9%
100 6
17.1%
90 2
 
5.7%

경사
Real number (ℝ)

Distinct10
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.6
Minimum30
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T02:05:52.245933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q142.5
median55
Q372.5
95-th percentile90
Maximum90
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.395711
Coefficient of variation (CV)0.37145332
Kurtosis-1.1201645
Mean57.6
Median Absolute Deviation (MAD)15
Skewness0.41732788
Sum2016
Variance457.77647
MonotonicityNot monotonic
2023-12-11T02:05:52.348908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
90 8
22.9%
60 6
17.1%
45 6
17.1%
30 5
14.3%
40 3
 
8.6%
50 2
 
5.7%
65 2
 
5.7%
55 1
 
2.9%
31 1
 
2.9%
80 1
 
2.9%
ValueCountFrequency (%)
30 5
14.3%
31 1
 
2.9%
40 3
 
8.6%
45 6
17.1%
50 2
 
5.7%
55 1
 
2.9%
60 6
17.1%
65 2
 
5.7%
80 1
 
2.9%
90 8
22.9%
ValueCountFrequency (%)
90 8
22.9%
80 1
 
2.9%
65 2
 
5.7%
60 6
17.1%
55 1
 
2.9%
50 2
 
5.7%
45 6
17.1%
40 3
 
8.6%
31 1
 
2.9%
30 5
14.3%

위험등급
Categorical

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
B
18 
C
17 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
B 18
51.4%
C 17
48.6%

Length

2023-12-11T02:05:52.491041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:05:52.607894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 18
51.4%
c 17
48.6%

비탈면유형
Categorical

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
인공(토)
19 
인공(임)
15 
자연(임)
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row인공(토)
2nd row인공(토)
3rd row인공(토)
4th row인공(토)
5th row인공(토)

Common Values

ValueCountFrequency (%)
인공(토) 19
54.3%
인공(임) 15
42.9%
자연(임) 1
 
2.9%

Length

2023-12-11T02:05:52.728411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:05:52.842008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인공(토 19
54.3%
인공(임 15
42.9%
자연(임 1
 
2.9%

비탈면기능
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
도로
19 
아파트
11 
기타
5 

Length

Max length3
Median length2
Mean length2.3142857
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도로
2nd row기타
3rd row기타
4th row아파트
5th row아파트

Common Values

ValueCountFrequency (%)
도로 19
54.3%
아파트 11
31.4%
기타 5
 
14.3%

Length

2023-12-11T02:05:53.018384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:05:53.152746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로 19
54.3%
아파트 11
31.4%
기타 5
 
14.3%

비탈면 보호구조
Categorical

Distinct4
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size412.0 B
복합
17 
토사
10 
옹벽
6 
콘크리트 옹벽
2 

Length

Max length7
Median length2
Mean length2.2857143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
복합 17
48.6%
토사 10
28.6%
옹벽 6
 
17.1%
콘크리트 옹벽 2
 
5.7%

Length

2023-12-11T02:05:53.304345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:05:53.466385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
복합 17
45.9%
토사 10
27.0%
옹벽 8
21.6%
콘크리트 2
 
5.4%

시설관리부서
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
건설과
17 
건축과
10 
녹지공원과
4 
안전총괄과
2 
한국토지주택공사
2 

Length

Max length8
Median length3
Mean length3.6285714
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건설과
2nd row안전총괄과
3rd row안전총괄과
4th row건축과
5th row건축과

Common Values

ValueCountFrequency (%)
건설과 17
48.6%
건축과 10
28.6%
녹지공원과 4
 
11.4%
안전총괄과 2
 
5.7%
한국토지주택공사 2
 
5.7%

Length

2023-12-11T02:05:53.637522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:05:53.774632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설과 17
48.6%
건축과 10
28.6%
녹지공원과 4
 
11.4%
안전총괄과 2
 
5.7%
한국토지주택공사 2
 
5.7%

Interactions

2023-12-11T02:05:50.271228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:49.690602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:49.986242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:50.360186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:49.776937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:50.073572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:50.457355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:49.896056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:05:50.173997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:05:53.881388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리주체소재지높이(m)길이(m)경사위험등급비탈면유형비탈면기능비탈면 보호구조시설관리부서
관리주체1.0001.0000.4490.0000.0000.1220.0000.8900.4040.935
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
높이(m)0.4491.0001.0000.0000.0000.5370.4750.3520.0000.509
길이(m)0.0001.0000.0001.0000.0000.0000.0000.6300.0000.000
경사0.0001.0000.0000.0001.0000.4780.6100.5780.6610.597
위험등급0.1221.0000.5370.0000.4781.0000.0000.1400.5140.000
비탈면유형0.0001.0000.4750.0000.6100.0001.0000.0000.3540.401
비탈면기능0.8901.0000.3520.6300.5780.1400.0001.0000.4370.772
비탈면 보호구조0.4041.0000.0000.0000.6610.5140.3540.4371.0000.386
시설관리부서0.9351.0000.5090.0000.5970.0000.4010.7720.3861.000
2023-12-11T02:05:54.074140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비탈면유형시설관리부서비탈면 보호구조비탈면기능위험등급관리주체
비탈면유형1.0000.3160.3350.0000.0000.000
시설관리부서0.3161.0000.3140.7560.0000.640
비탈면 보호구조0.3350.3141.0000.4220.3350.105
비탈면기능0.0000.7560.4221.0000.2240.593
위험등급0.0000.0000.3350.2241.0000.000
관리주체0.0000.6400.1050.5930.0001.000
2023-12-11T02:05:54.240410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
높이(m)길이(m)경사관리주체위험등급비탈면유형비탈면기능비탈면 보호구조시설관리부서
높이(m)1.0000.288-0.3000.0750.3590.3070.2060.0000.317
길이(m)0.2881.000-0.1380.0000.0000.0000.2920.0000.000
경사-0.300-0.1381.0000.0000.3170.4350.4030.3140.392
관리주체0.0750.0000.0001.0000.0000.0000.5930.1050.640
위험등급0.3590.0000.3170.0001.0000.0000.2240.3350.000
비탈면유형0.3070.0000.4350.0000.0001.0000.0000.3350.316
비탈면기능0.2060.2920.4030.5930.2240.0001.0000.4220.756
비탈면 보호구조0.0000.0000.3140.1050.3350.3350.4221.0000.314
시설관리부서0.3170.0000.3920.6400.0000.3160.7560.3141.000

Missing values

2023-12-11T02:05:50.582948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:05:50.730198image/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

관리주체소재지높이(m)길이(m)경사위험등급비탈면유형비탈면기능비탈면 보호구조시설관리부서
0사상구청덕포동 775-1(덕상로 32), 덕상초등학교 주변 괘내교 인근15.015055C인공(토)도로복합건설과
1(주)이비알엄궁동 141-15, ㈜이비알 근처13.03060B인공(토)기타복합안전총괄과
2(재)부산테크노파크엄궁동 138-2(엄궁로 70-10)18.016031C인공(토)기타복합안전총괄과
3동양아파트학장동 210-3번지, (동양아파트 일원)18.03040C인공(토)아파트복합건축과
4벽산아파트주례동 616-4(주례로139번길 8), 주례벽산아파트 경사지10.010030C인공(토)아파트복합건축과
5목화아파트학장동 산574-82, 목화아파트 뒤 경사지15.010030C인공(임)기타복합건축과
6사상구청엄궁동 산104-8, 골드리버골프클럽입구 입구10.020060C인공(토)도로복합건설과
7사상구청엄궁동 산104-9, 골드리버골프클럽입구 아래10.010050B인공(토)도로복합건설과
8사상구청주례동 산14-1, 보훈병원 인근 경사지5.03030C인공(임)도로토사녹지공원과
9사상구청괘법동 산1-1(백양대로700번길 140), 예비군교장 인근 경사면4.03040B인공(임)기타토사녹지공원과
관리주체소재지높이(m)길이(m)경사위험등급비탈면유형비탈면기능비탈면 보호구조시설관리부서
25사상구청감전동 41-2번지 일원15.04545C인공(토)도로토사건설과
26사상구청덕포동 792,오양힐타운아파트 주변15.018060B인공(토)도로복합건설과
27엄궁삼성타워아파트엄궁동 1-10번지7.06590B인공(임)아파트옹벽건축과
28엄궁쌍용스윗닷홈엄궁동 680-2번지7.211090B인공(임)아파트복합건축과
29주례센텀시티주례동 221-1번지6.05090B인공(임)아파트옹벽건축과
30벽산하나로아파트덕포동 38번지7.28090B인공(임)아파트옹벽건축과
31모라우성2차아파트모라동 313-7번지9.37590B인공(임)아파트옹벽건축과
32신흥동백아파트엄궁동 37-3번지6.05090B인공(임)아파트옹벽건축과
33부산모라1모라동 121번지20.09030B인공(토)아파트토사한국토지주택공사
34부산모라2모라동 122번지20.09030B인공(임)아파트옹벽한국토지주택공사