Overview

Dataset statistics

Number of variables11
Number of observations199
Missing cells4
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.4 KiB
Average record size in memory94.7 B

Variable types

Text1
Numeric4
Categorical6

Alerts

방화2동 has a high cardinality: 51 distinct valuesHigh cardinality
고속도로 is highly overall correlated with 938450 and 3 other fieldsHigh correlation
방화2동 is highly overall correlated with 7505 and 7 other fieldsHigh correlation
1150 is highly overall correlated with 7505 and 7 other fieldsHigh correlation
11 is highly overall correlated with 938450 and 7 other fieldsHigh correlation
서울특별시 is highly overall correlated with 7505 and 8 other fieldsHigh correlation
강서구 is highly overall correlated with 7505 and 7 other fieldsHigh correlation
7505 is highly overall correlated with 서울특별시 and 3 other fieldsHigh correlation
938450 is highly overall correlated with 고속도로 and 5 other fieldsHigh correlation
1952750 is highly overall correlated with 11500640 and 6 other fieldsHigh correlation
11500640 is highly overall correlated with 1952750 and 5 other fieldsHigh correlation
11 is highly imbalanced (92.2%)Imbalance
서울특별시 is highly imbalanced (85.8%)Imbalance
11500640 has 4 (2.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 06:19:22.628456
Analysis finished2023-12-10 06:19:26.764545
Duration4.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

다사38405270
Text

Distinct196
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:19:26.996261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.8190955
Min length1

Characters and Unicode

Total characters1954
Distinct characters12
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

Unique195 ?
Unique (%)98.0%

Sample

1st row다사64004240
2nd row다사41305160
3rd row다사42805190
4th row다사59005850
5th row다사58905980
ValueCountFrequency (%)
6 4
 
2.0%
다사56805900 1
 
0.5%
다사39905010 1
 
0.5%
다사58204760 1
 
0.5%
다사56106220 1
 
0.5%
다사37905110 1
 
0.5%
다사61204250 1
 
0.5%
다사58005830 1
 
0.5%
다사41304960 1
 
0.5%
다사54706140 1
 
0.5%
Other values (186) 186
93.5%
2023-12-10T15:19:27.614486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 478
24.5%
5 209
10.7%
4 200
10.2%
다 195
10.0%
사 195
10.0%
6 136
 
7.0%
3 130
 
6.7%
2 90
 
4.6%
9 84
 
4.3%
1 82
 
4.2%
Other values (2) 155
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1564
80.0%
Other Letter 390
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 478
30.6%
5 209
13.4%
4 200
12.8%
6 136
 
8.7%
3 130
 
8.3%
2 90
 
5.8%
9 84
 
5.4%
1 82
 
5.2%
8 80
 
5.1%
7 75
 
4.8%
Other Letter
ValueCountFrequency (%)
다 195
50.0%
사 195
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1564
80.0%
Hangul 390
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 478
30.6%
5 209
13.4%
4 200
12.8%
6 136
 
8.7%
3 130
 
8.3%
2 90
 
5.8%
9 84
 
5.4%
1 82
 
5.2%
8 80
 
5.1%
7 75
 
4.8%
Hangul
ValueCountFrequency (%)
다 195
50.0%
사 195
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1564
80.0%
Hangul 390
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 478
30.6%
5 209
13.4%
4 200
12.8%
6 136
 
8.7%
3 130
 
8.3%
2 90
 
5.8%
9 84
 
5.4%
1 82
 
5.2%
8 80
 
5.1%
7 75
 
4.8%
Hangul
ValueCountFrequency (%)
다 195
50.0%
사 195
50.0%

7505
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5671.6281
Minimum1000
Maximum10542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:19:27.853776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1002
Q16005
median6350
Q37521
95-th percentile7740.2
Maximum10542
Range9542
Interquartile range (IQR)1516

Descriptive statistics

Standard deviation2581.7
Coefficient of variation (CV)0.45519556
Kurtosis-0.48578642
Mean5671.6281
Median Absolute Deviation (MAD)1168
Skewness-1.0252335
Sum1128654
Variance6665174.7
MonotonicityNot monotonic
2023-12-10T15:19:28.115256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7505 20
 
10.1%
1000 9
 
4.5%
1002 9
 
4.5%
7500 5
 
2.5%
2021 4
 
2.0%
6321 4
 
2.0%
7524 3
 
1.5%
6001 3
 
1.5%
1018 2
 
1.0%
6371 2
 
1.0%
Other values (130) 138
69.3%
ValueCountFrequency (%)
1000 9
4.5%
1002 9
4.5%
1009 1
 
0.5%
1013 1
 
0.5%
1018 2
 
1.0%
1019 2
 
1.0%
1061 1
 
0.5%
1095 1
 
0.5%
1096 1
 
0.5%
1118 1
 
0.5%
ValueCountFrequency (%)
10542 2
1.0%
7813 1
0.5%
7809 1
0.5%
7799 1
0.5%
7795 1
0.5%
7789 1
0.5%
7782 1
0.5%
7755 1
0.5%
7751 1
0.5%
7739 1
0.5%

고속도로
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
산
27 
주거지역
25 
하천
24 
기타
22 
상업지역
16 
Other values (11)
85 

Length

Max length4
Median length3
Mean length2.6934673
Min length1

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st row주거지역
2nd row기타
3rd row주거지역
4th row주거지역
5th row하천

Common Values

ValueCountFrequency (%)
산 27
13.6%
주거지역 25
12.6%
하천 24
12.1%
기타 22
11.1%
상업지역 16
8.0%
업무지역 16
8.0%
학교 15
7.5%
공원 15
7.5%
주차장 14
7.0%
고속도로 13
6.5%
Other values (6) 12
6.0%

Length

2023-12-10T15:19:28.413380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산 27
13.6%
주거지역 25
12.6%
하천 24
12.1%
기타 22
11.1%
상업지역 16
8.0%
업무지역 16
8.0%
학교 15
7.5%
공원 15
7.5%
주차장 14
7.0%
고속도로 13
6.5%
Other values (6) 12
6.0%

938450
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean932060.06
Minimum1147
Maximum966450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:19:28.635728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1147
5-th percentile936740
Q1940750
median955450
Q3959650
95-th percentile963870
Maximum966450
Range965303
Interquartile range (IQR)18900

Descriptive statistics

Standard deviation133779.22
Coefficient of variation (CV)0.14353068
Kurtosis45.428806
Mean932060.06
Median Absolute Deviation (MAD)8600
Skewness-6.8339149
Sum1.8547995 × 108
Variance1.7896879 × 1010
MonotonicityNot monotonic
2023-12-10T15:19:28.971724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
939850 5
 
2.5%
960250 4
 
2.0%
956850 4
 
2.0%
958950 4
 
2.0%
942450 4
 
2.0%
960850 4
 
2.0%
958050 3
 
1.5%
957750 3
 
1.5%
958550 3
 
1.5%
940050 3
 
1.5%
Other values (122) 162
81.4%
ValueCountFrequency (%)
1147 1
0.5%
2920 1
0.5%
3117 1
0.5%
4117 1
0.5%
936050 1
0.5%
936150 1
0.5%
936250 1
0.5%
936350 1
0.5%
936450 1
0.5%
936650 1
0.5%
ValueCountFrequency (%)
966450 1
0.5%
966350 1
0.5%
966250 1
0.5%
965450 1
0.5%
965050 1
0.5%
964750 2
1.0%
964350 1
0.5%
964250 1
0.5%
964050 1
0.5%
963850 1
0.5%

1952750
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1912958.9
Minimum20270
Maximum1964650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:19:29.222393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20270
5-th percentile1941790
Q11945050
median1950750
Q31954350
95-th percentile1962680
Maximum1964650
Range1944380
Interquartile range (IQR)9300

Descriptive statistics

Standard deviation266631.82
Coefficient of variation (CV)0.13938189
Kurtosis46.053712
Mean1912958.9
Median Absolute Deviation (MAD)4800
Skewness-6.8936643
Sum3.8067882 × 108
Variance7.1092529 × 1010
MonotonicityNot monotonic
2023-12-10T15:19:29.472661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1951050 7
 
3.5%
1950450 4
 
2.0%
1951350 4
 
2.0%
1949950 3
 
1.5%
1943050 3
 
1.5%
1952650 3
 
1.5%
1952450 3
 
1.5%
1951250 3
 
1.5%
1943250 3
 
1.5%
1943950 3
 
1.5%
Other values (123) 163
81.9%
ValueCountFrequency (%)
20270 1
0.5%
22517 1
0.5%
36339 1
0.5%
149941 1
0.5%
1940350 1
0.5%
1940650 1
0.5%
1940750 1
0.5%
1940850 1
0.5%
1940950 1
0.5%
1941250 1
0.5%
ValueCountFrequency (%)
1964650 1
0.5%
1963650 1
0.5%
1963550 2
1.0%
1963450 1
0.5%
1963350 1
0.5%
1963250 1
0.5%
1963150 2
1.0%
1962950 1
0.5%
1962650 1
0.5%
1962550 1
0.5%

11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
11.0
195 
6.83
 
1
16.13
 
1
10.9
 
1
9.01
 
1

Length

Max length5
Median length4
Mean length4.0050251
Min length4

Unique

Unique4 ?
Unique (%)2.0%

Sample

1st row11.0
2nd row11.0
3rd row11.0
4th row11.0
5th row11.0

Common Values

ValueCountFrequency (%)
11.0 195
98.0%
6.83 1
 
0.5%
16.13 1
 
0.5%
10.9 1
 
0.5%
9.01 1
 
0.5%

Length

2023-12-10T15:19:29.716751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:19:29.904178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11.0 195
98.0%
6.83 1
 
0.5%
16.13 1
 
0.5%
10.9 1
 
0.5%
9.01 1
 
0.5%

서울특별시
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울특별시
195 
<NA>
 
4

Length

Max length5
Median length5
Mean length4.9798995
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 195
98.0%
<NA> 4
 
2.0%

Length

2023-12-10T15:19:30.102907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:19:30.321734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 195
98.0%
na 4
 
2.0%

1150
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1150
86 
1168
67 
1130
42 
<NA>
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1168
2nd row1150
3rd row1150
4th row1130
5th row1130

Common Values

ValueCountFrequency (%)
1150 86
43.2%
1168 67
33.7%
1130 42
21.1%
<NA> 4
 
2.0%

Length

2023-12-10T15:19:30.525276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:19:30.706905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1150 86
43.2%
1168 67
33.7%
1130 42
21.1%
na 4
 
2.0%

강서구
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
강서구
86 
강남구
67 
강북구
42 
<NA>
 
4

Length

Max length4
Median length3
Mean length3.0201005
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강남구
2nd row강서구
3rd row강서구
4th row강북구
5th row강북구

Common Values

ValueCountFrequency (%)
강서구 86
43.2%
강남구 67
33.7%
강북구 42
21.1%
<NA> 4
 
2.0%

Length

2023-12-10T15:19:30.953829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:19:31.156850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강서구 86
43.2%
강남구 67
33.7%
강북구 42
21.1%
na 4
 
2.0%

11500640
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)25.6%
Missing4
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean11520462
Minimum11305535
Maximum11680750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:19:31.449039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11305535
5-th percentile11305569
Q111500515
median11500620
Q311680595
95-th percentile11680700
Maximum11680750
Range375215
Interquartile range (IQR)180080

Descriptive statistics

Standard deviation137946.37
Coefficient of variation (CV)0.011974031
Kurtosis-1.0852693
Mean11520462
Median Absolute Deviation (MAD)179925
Skewness-0.28147042
Sum2.2464901 × 109
Variance1.9029201 × 1010
MonotonicityNot monotonic
2023-12-10T15:19:31.734043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11305645 21
 
10.6%
11500620 20
 
10.1%
11500640 12
 
6.0%
11500603 10
 
5.0%
11680700 8
 
4.0%
11500510 7
 
3.5%
11680670 7
 
3.5%
11500611 6
 
3.0%
11500641 6
 
3.0%
11680510 6
 
3.0%
Other values (40) 92
46.2%
ValueCountFrequency (%)
11305535 5
 
2.5%
11305545 4
 
2.0%
11305555 1
 
0.5%
11305575 1
 
0.5%
11305595 1
 
0.5%
11305603 2
 
1.0%
11305608 2
 
1.0%
11305615 3
 
1.5%
11305645 21
10.6%
11305660 2
 
1.0%
ValueCountFrequency (%)
11680750 1
 
0.5%
11680740 3
 
1.5%
11680730 2
 
1.0%
11680720 3
 
1.5%
11680700 8
4.0%
11680690 3
 
1.5%
11680670 7
3.5%
11680660 5
2.5%
11680656 2
 
1.0%
11680655 3
 
1.5%

방화2동
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
우이동
21 
공항동
20 
방화2동
 
12
가양1동
 
10
세곡동
 
8
Other values (46)
128 

Length

Max length4
Median length4
Mean length3.5879397
Min length3

Unique

Unique10 ?
Unique (%)5.0%

Sample

1st row일원본동
2nd row가양1동
3rd row가양2동
4th row번2동
5th row번1동

Common Values

ValueCountFrequency (%)
우이동 21
 
10.6%
공항동 20
 
10.1%
방화2동 12
 
6.0%
가양1동 10
 
5.0%
세곡동 8
 
4.0%
염창동 7
 
3.5%
개포2동 7
 
3.5%
발산1동 6
 
3.0%
방화3동 6
 
3.0%
신사동 6
 
3.0%
Other values (41) 96
48.2%

Length

2023-12-10T15:19:31.971411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
우이동 21
 
10.6%
공항동 20
 
10.1%
방화2동 12
 
6.0%
가양1동 10
 
5.0%
세곡동 8
 
4.0%
염창동 7
 
3.5%
개포2동 7
 
3.5%
발산1동 6
 
3.0%
방화3동 6
 
3.0%
신사동 6
 
3.0%
Other values (41) 96
48.2%

Interactions

2023-12-10T15:19:25.331284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:23.566588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:24.132216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:24.693339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:25.489388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:23.710173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:24.258090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:24.841489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:25.615251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:23.838947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:24.385777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:25.001707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:26.115197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:23.986391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:24.524694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:19:25.171604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:19:32.128581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
7505고속도로9384501952750111150강서구11500640방화2동
75051.0000.6451.0001.0000.6201.0001.0001.0000.957
고속도로0.6451.000NaNNaNNaN0.7760.7760.7930.904
9384501.000NaN1.0000.9211.000NaNNaNNaNNaN
19527501.000NaN0.9211.0001.000NaNNaNNaNNaN
110.620NaN1.0001.0001.000NaNNaNNaNNaN
11501.0000.776NaNNaNNaN1.0001.0001.0001.000
강서구1.0000.776NaNNaNNaN1.0001.0001.0001.000
115006401.0000.793NaNNaNNaN1.0001.0001.0001.000
방화2동0.9570.904NaNNaNNaN1.0001.0001.0001.000
2023-12-10T15:19:32.380623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고속도로방화2동115011서울특별시강서구
고속도로1.0000.4600.4891.0001.0000.489
방화2동0.4601.0000.8691.0001.0000.869
11500.4890.8691.0001.0001.0001.000
111.0001.0001.0001.0001.0001.000
서울특별시1.0001.0001.0001.0001.0001.000
강서구0.4890.8691.0001.0001.0001.000
2023-12-10T15:19:32.596653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
7505938450195275011500640고속도로11서울특별시1150강서구방화2동
75051.000-0.459-0.2160.1270.3680.4791.0000.9950.9950.671
938450-0.4591.000-0.4400.4161.0000.9921.0001.0001.0001.000
1952750-0.216-0.4401.000-0.8361.0000.9921.0001.0001.0001.000
115006400.1270.416-0.8361.0000.4891.0001.0001.0001.0000.869
고속도로0.3681.0001.0000.4891.0001.0001.0000.4890.4890.460
110.4790.9920.9921.0001.0001.0001.0001.0001.0001.000
서울특별시1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
11500.9951.0001.0001.0000.4891.0001.0001.0001.0000.869
강서구0.9951.0001.0001.0000.4891.0001.0001.0001.0000.869
방화2동0.6711.0001.0000.8690.4601.0001.0000.8690.8691.000

Missing values

2023-12-10T15:19:26.351084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:19:26.652913image/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

다사384052707505고속도로938450195275011서울특별시1150강서구11500640방화2동
0다사640042406362주거지역964050194245011.0서울특별시1168강남구11680720일원본동
1다사413051607795기타941350195165011.0서울특별시1150강서구11500603가양1동
2다사428051907527주거지역942850195195011.0서울특별시1150강서구11500604가양2동
3다사590058501151주거지역959050195855011.0서울특별시1130강북구11305603번2동
4다사589059801061하천958950195985011.0서울특별시1130강북구11305595번1동
5다사549062501002산954950196255011.0서울특별시1130강북구11305645우이동
6다사566061201009산956650196125011.0서울특별시1130강북구11305645우이동
7다사593058301228하천959350195835011.0서울특별시1130강북구11305608번3동
8다사608042306321주거지역960850194235011.0서울특별시1168강남구11680660개포1동
9다사397050207506기타939750195025011.0서울특별시1150강서구11500611발산1동
다사384052707505고속도로938450195275011서울특별시1150강서구11500640방화2동
189다사563063101000산956350196315011.0서울특별시1130강북구11305645우이동
190다사664041206378하천966450194125011.0서울특별시1168강남구11680700세곡동
191다사407052907520기타940750195295011.0서울특별시1150강서구11500603가양1동
192다사608042506321주거지역960850194255011.0서울특별시1168강남구11680660개포1동
193다사638043006353하천963850194305011.0서울특별시1168강남구11680720일원본동
194다사596043806272업무지역959650194385011.0서울특별시1168강남구11680655도곡1동
195다사400050107642상업지역940050195015011.0서울특별시1150강서구11500611발산1동
196다사435051207532공장943550195125011.0서울특별시1150강서구11500605가양3동
197다사602041806312학교960250194185011.0서울특별시1168강남구11680690개포4동
198다사566062601002하천956650196265011.0서울특별시1130강북구11305645우이동