Overview

Dataset statistics

Number of variables9
Number of observations232
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.8 KiB
Average record size in memory78.6 B

Variable types

Categorical3
Numeric6

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR001

Alerts

SIDO_NM is highly overall correlated with MESR_DPNT_LA and 3 other fieldsHigh correlation
TRGET_AREA_NM is highly overall correlated with MESR_DPNT_LA and 3 other fieldsHigh correlation
SGG_NM is highly overall correlated with MESR_DPNT_LA and 3 other fieldsHigh correlation
MESR_DPNT_LA is highly overall correlated with MESR_DPNT_LO and 3 other fieldsHigh correlation
MESR_DPNT_LO is highly overall correlated with MESR_DPNT_LA and 3 other fieldsHigh correlation

Reproduction

Analysis started2024-03-13 12:45:36.293010
Analysis finished2024-03-13 12:45:42.558769
Duration6.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
인천광역시
140 
충청남도
54 
경기도
38 

Length

Max length5
Median length5
Mean length4.4396552
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 140
60.3%
충청남도 54
 
23.3%
경기도 38
 
16.4%

Length

2024-03-13T21:45:42.673576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:45:42.830364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 140
60.3%
충청남도 54
 
23.3%
경기도 38
 
16.4%

SGG_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
옹진군
84 
태안군
54 
중구
46 
안산시
38 
강화군
10 

Length

Max length3
Median length3
Mean length2.8017241
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
옹진군 84
36.2%
태안군 54
23.3%
중구 46
19.8%
안산시 38
16.4%
강화군 10
 
4.3%

Length

2024-03-13T21:45:42.993091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:45:43.192108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
옹진군 84
36.2%
태안군 54
23.3%
중구 46
19.8%
안산시 38
16.4%
강화군 10
 
4.3%

TRGET_AREA_NM
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
신두리
30 
만리포
24 
장골
22 
실미
16 
서위
16 
Other values (13)
124 

Length

Max length5
Median length4
Mean length2.8189655
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신두리 30
12.9%
만리포 24
 
10.3%
장골 22
 
9.5%
실미 16
 
6.9%
서위 16
 
6.9%
큰풀안 14
 
6.0%
구봉도남측 12
 
5.2%
장경리 12
 
5.2%
이일레 12
 
5.2%
서포리 10
 
4.3%
Other values (8) 64
27.6%

Length

2024-03-13T21:45:43.377990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신두리 30
12.9%
만리포 24
 
10.3%
장골 22
 
9.5%
실미 16
 
6.9%
서위 16
 
6.9%
큰풀안 14
 
6.0%
구봉도남측 12
 
5.2%
장경리 12
 
5.2%
이일레 12
 
5.2%
동막 10
 
4.3%
Other values (8) 64
27.6%

MESR_BSLN_NO
Real number (ℝ)

Distinct15
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5172414
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:45:43.561625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum15
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1731633
Coefficient of variation (CV)0.702456
Kurtosis0.94081187
Mean4.5172414
Median Absolute Deviation (MAD)2
Skewness1.1571367
Sum1048
Variance10.068966
MonotonicityNot monotonic
2024-03-13T21:45:43.720441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 36
15.5%
2 36
15.5%
3 36
15.5%
4 32
13.8%
5 24
10.3%
6 18
7.8%
7 12
 
5.2%
8 10
 
4.3%
9 6
 
2.6%
10 6
 
2.6%
Other values (5) 16
6.9%
ValueCountFrequency (%)
1 36
15.5%
2 36
15.5%
3 36
15.5%
4 32
13.8%
5 24
10.3%
6 18
7.8%
7 12
 
5.2%
8 10
 
4.3%
9 6
 
2.6%
10 6
 
2.6%
ValueCountFrequency (%)
15 2
 
0.9%
14 2
 
0.9%
13 2
 
0.9%
12 4
 
1.7%
11 6
 
2.6%
10 6
 
2.6%
9 6
 
2.6%
8 10
4.3%
7 12
5.2%
6 18
7.8%

MESR_AZ
Real number (ℝ)

Distinct110
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.84741
Minimum4.2
Maximum359.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:45:43.888543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile29.76
Q1209.05
median254.5
Q3310.575
95-th percentile338.185
Maximum359.9
Range355.7
Interquartile range (IQR)101.525

Descriptive statistics

Standard deviation82.785324
Coefficient of variation (CV)0.33949642
Kurtosis1.41071
Mean243.84741
Median Absolute Deviation (MAD)48.85
Skewness-1.2480001
Sum56572.6
Variance6853.4099
MonotonicityNot monotonic
2024-03-13T21:45:44.075149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321.4 8
 
3.4%
209.9 4
 
1.7%
249.0 4
 
1.7%
314.2 4
 
1.7%
219.6 2
 
0.9%
359.9 2
 
0.9%
195.1 2
 
0.9%
189.7 2
 
0.9%
196.0 2
 
0.9%
235.6 2
 
0.9%
Other values (100) 200
86.2%
ValueCountFrequency (%)
4.2 2
0.9%
6.1 2
0.9%
18.6 2
0.9%
21.3 2
0.9%
25.8 2
0.9%
26.9 2
0.9%
32.1 2
0.9%
53.4 2
0.9%
60.4 2
0.9%
83.2 2
0.9%
ValueCountFrequency (%)
359.9 2
0.9%
356.4 2
0.9%
353.9 2
0.9%
346.3 2
0.9%
341.4 2
0.9%
338.9 2
0.9%
337.6 2
0.9%
336.6 2
0.9%
334.9 2
0.9%
334.2 2
0.9%

MESR_DPNT_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.18959
Minimum36.782658
Maximum37.593472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:45:44.270693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.782658
5-th percentile36.785692
Q137.162925
median37.249826
Q337.289033
95-th percentile37.457239
Maximum37.593472
Range0.81081389
Interquartile range (IQR)0.12610834

Descriptive statistics

Standard deviation0.2292315
Coefficient of variation (CV)0.006163862
Kurtosis-0.66713044
Mean37.18959
Median Absolute Deviation (MAD)0.08651528
Skewness-0.54994728
Sum8627.9849
Variance0.052547081
MonotonicityNot monotonic
2024-03-13T21:45:44.475274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.591525 2
 
0.9%
37.28971667 2
 
0.9%
37.28086389 2
 
0.9%
37.28056111 2
 
0.9%
37.27945278 2
 
0.9%
37.27813611 2
 
0.9%
37.28265278 2
 
0.9%
37.28040556 2
 
0.9%
37.27948056 2
 
0.9%
37.27841944 2
 
0.9%
Other values (106) 212
91.4%
ValueCountFrequency (%)
36.78265833 2
0.9%
36.78273056 2
0.9%
36.78310278 2
0.9%
36.78353611 2
0.9%
36.78483056 2
0.9%
36.78512778 2
0.9%
36.78615278 2
0.9%
36.787375 2
0.9%
36.78888333 2
0.9%
36.79044444 2
0.9%
ValueCountFrequency (%)
37.59347222 2
0.9%
37.59279167 2
0.9%
37.59238611 2
0.9%
37.59196667 2
0.9%
37.591525 2
0.9%
37.45781389 2
0.9%
37.45676944 2
0.9%
37.45525556 2
0.9%
37.45368611 2
0.9%
37.44917222 2
0.9%

MESR_DPNT_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.33249
Minimum126.1121
Maximum126.5776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:45:44.738170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.1121
5-th percentile126.13417
Q1126.19803
median126.3134
Q3126.44792
95-th percentile126.56812
Maximum126.5776
Range0.4655
Interquartile range (IQR)0.249893

Descriptive statistics

Standard deviation0.14162277
Coefficient of variation (CV)0.0011210321
Kurtosis-1.0648757
Mean126.33249
Median Absolute Deviation (MAD)0.11645135
Skewness0.2061447
Sum29309.137
Variance0.020057008
MonotonicityNot monotonic
2024-03-13T21:45:44.996526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4604861 2
 
0.9%
126.5776028 2
 
0.9%
126.5449222 2
 
0.9%
126.5468028 2
 
0.9%
126.5491167 2
 
0.9%
126.5495361 2
 
0.9%
126.5684444 2
 
0.9%
126.5678583 2
 
0.9%
126.5668944 2
 
0.9%
126.5652944 2
 
0.9%
Other values (106) 212
91.4%
ValueCountFrequency (%)
126.1121028 2
0.9%
126.1146806 2
0.9%
126.1152444 2
0.9%
126.1154278 2
0.9%
126.1162083 2
0.9%
126.1331361 2
0.9%
126.1350222 2
0.9%
126.1365472 2
0.9%
126.1381111 2
0.9%
126.1414167 2
0.9%
ValueCountFrequency (%)
126.5776028 2
0.9%
126.5763944 2
0.9%
126.5741472 2
0.9%
126.5724028 2
0.9%
126.570225 2
0.9%
126.5684444 2
0.9%
126.5678583 2
0.9%
126.5668944 2
0.9%
126.5652944 2
0.9%
126.563325 2
0.9%

MESR_WTCH_YMD
Real number (ℝ)

Distinct15
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210662
Minimum20210401
Maximum20210917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:45:45.179921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210401
5-th percentile20210401
Q120210413
median20210667
Q320210909
95-th percentile20210916
Maximum20210917
Range516
Interquartile range (IQR)496

Descriptive statistics

Standard deviation248.84844
Coefficient of variation (CV)1.2312731 × 10-5
Kurtosis-2.0143177
Mean20210662
Median Absolute Deviation (MAD)248
Skewness-0.0015695914
Sum4.6888736 × 109
Variance61925.546
MonotonicityNot monotonic
2024-03-13T21:45:45.385194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20210908 29
12.5%
20210412 28
12.1%
20210401 25
10.8%
20210909 21
9.1%
20210906 17
 
7.3%
20210427 15
 
6.5%
20210916 15
 
6.5%
20210414 14
 
6.0%
20210428 12
 
5.2%
20210917 12
 
5.2%
Other values (5) 44
19.0%
ValueCountFrequency (%)
20210401 25
10.8%
20210412 28
12.1%
20210413 11
 
4.7%
20210414 14
6.0%
20210415 5
 
2.2%
20210416 6
 
2.6%
20210427 15
6.5%
20210428 12
5.2%
20210906 17
7.3%
20210907 11
 
4.7%
ValueCountFrequency (%)
20210917 12
5.2%
20210916 15
6.5%
20210914 11
 
4.7%
20210909 21
9.1%
20210908 29
12.5%
20210907 11
 
4.7%
20210906 17
7.3%
20210428 12
5.2%
20210427 15
6.5%
20210416 6
 
2.6%

MESR_BCH_WDTH
Real number (ℝ)

Distinct212
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.65431
Minimum0
Maximum245.7
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:45:45.571744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.785
Q141.5
median57.45
Q392.175
95-th percentile120.255
Maximum245.7
Range245.7
Interquartile range (IQR)50.675

Descriptive statistics

Standard deviation36.836041
Coefficient of variation (CV)0.55264305
Kurtosis2.153906
Mean66.65431
Median Absolute Deviation (MAD)22.75
Skewness1.0159989
Sum15463.8
Variance1356.8939
MonotonicityNot monotonic
2024-03-13T21:45:45.801836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.4 3
 
1.3%
57.1 2
 
0.9%
89.2 2
 
0.9%
101.3 2
 
0.9%
75.1 2
 
0.9%
84.7 2
 
0.9%
16.5 2
 
0.9%
38.5 2
 
0.9%
25.2 2
 
0.9%
54.4 2
 
0.9%
Other values (202) 211
90.9%
ValueCountFrequency (%)
0.0 1
0.4%
1.7 1
0.4%
10.2 1
0.4%
10.5 1
0.4%
13.5 1
0.4%
14.5 1
0.4%
15.2 1
0.4%
16.1 1
0.4%
16.5 2
0.9%
16.7 1
0.4%
ValueCountFrequency (%)
245.7 1
0.4%
189.8 1
0.4%
170.3 1
0.4%
169.6 1
0.4%
169.1 1
0.4%
165.3 1
0.4%
146.6 1
0.4%
133.7 1
0.4%
126.9 1
0.4%
123.4 1
0.4%

Interactions

2024-03-13T21:45:40.724913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:36.834462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:37.567608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:38.426703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:39.212915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:39.906662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:40.855520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:36.951473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:37.748661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:38.544417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:39.329453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:40.074790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:41.022184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:37.098456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:37.907229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:38.683182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:39.431944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:40.219895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:41.194723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:37.225723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:38.057379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:38.878235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:39.547521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:40.339395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:41.370606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:37.337143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:38.189599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:38.987481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:39.658674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:40.471793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:41.545752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:37.454845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:38.311342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:39.083928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:39.760138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:40.590473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:45:45.959495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMSGG_NMTRGET_AREA_NMMESR_BSLN_NOMESR_AZMESR_DPNT_LAMESR_DPNT_LOMESR_WTCH_YMDMESR_BCH_WDTH
SIDO_NM1.0001.0001.0000.4730.7010.9110.9980.1610.615
SGG_NM1.0001.0001.0000.4400.5640.9590.9440.2050.432
TRGET_AREA_NM1.0001.0001.0000.0000.8781.0001.0000.2120.793
MESR_BSLN_NO0.4730.4400.0001.0000.5650.3100.2730.0000.081
MESR_AZ0.7010.5640.8780.5651.0000.6220.8040.0000.708
MESR_DPNT_LA0.9110.9591.0000.3100.6221.0000.9420.2660.421
MESR_DPNT_LO0.9980.9441.0000.2730.8040.9421.0000.2300.653
MESR_WTCH_YMD0.1610.2050.2120.0000.0000.2660.2301.0000.000
MESR_BCH_WDTH0.6150.4320.7930.0810.7080.4210.6530.0001.000
2024-03-13T21:45:46.120756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMTRGET_AREA_NMSGG_NM
SIDO_NM1.0000.9670.996
TRGET_AREA_NM0.9671.0000.971
SGG_NM0.9960.9711.000
2024-03-13T21:45:46.240172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MESR_BSLN_NOMESR_AZMESR_DPNT_LAMESR_DPNT_LOMESR_WTCH_YMDMESR_BCH_WDTHSIDO_NMSGG_NMTRGET_AREA_NM
MESR_BSLN_NO1.0000.143-0.279-0.2060.1500.0930.3240.2020.022
MESR_AZ0.1431.000-0.1270.1200.0260.0590.4030.3660.496
MESR_DPNT_LA-0.279-0.1271.0000.792-0.398-0.1150.9100.9500.975
MESR_DPNT_LO-0.2060.1200.7921.000-0.424-0.2740.9390.8790.980
MESR_WTCH_YMD0.1500.026-0.398-0.4241.0000.0910.0000.0000.000
MESR_BCH_WDTH0.0930.059-0.115-0.2740.0911.0000.3320.2640.386
SIDO_NM0.3240.4030.9100.9390.0000.3321.0000.9960.967
SGG_NM0.2020.3660.9500.8790.0000.2640.9961.0000.971
TRGET_AREA_NM0.0220.4960.9750.9800.0000.3860.9670.9711.000

Missing values

2024-03-13T21:45:42.137751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:45:42.442876image/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

SIDO_NMSGG_NMTRGET_AREA_NMMESR_BSLN_NOMESR_AZMESR_DPNT_LAMESR_DPNT_LOMESR_WTCH_YMDMESR_BCH_WDTH
0인천광역시강화군동막1219.637.591525126.4604862021041236.3
1인천광역시강화군동막1219.637.591525126.4604862021090637.3
2인천광역시강화군동막2216.037.591967126.4598532021041240.5
3인천광역시강화군동막2216.037.591967126.4598532021090639.7
4인천광역시강화군동막3208.937.592386126.4587142021041222.4
5인천광역시강화군동막3208.937.592386126.4587142021090621.8
6인천광역시강화군동막4209.937.592792126.4577062021041222.1
7인천광역시강화군동막4209.937.592792126.4577062021090619.4
8인천광역시강화군동막5198.937.593472126.4560782021041277.6
9인천광역시강화군동막5198.937.593472126.4560782021090666.0
SIDO_NMSGG_NMTRGET_AREA_NMMESR_BSLN_NOMESR_AZMESR_DPNT_LAMESR_DPNT_LOMESR_WTCH_YMDMESR_BCH_WDTH
222충청남도태안군만리포8302.236.787375126.1440862021042852.1
223충청남도태안군만리포8302.236.787375126.1440862021091751.4
224충청남도태안군만리포9295.436.788883126.1451222021042847.9
225충청남도태안군만리포9295.436.788883126.1451222021091737.7
226충청남도태안군만리포10293.336.790444126.1460222021042846.8
227충청남도태안군만리포10293.336.790444126.1460222021091745.0
228충청남도태안군만리포11285.436.792083126.1467222021042842.1
229충청남도태안군만리포11285.436.792083126.1467222021091738.5
230충청남도태안군만리포12274.336.793189126.1469362021042844.6
231충청남도태안군만리포12274.336.793189126.1469362021091743.6