Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells260
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory752.0 KiB
Average record size in memory77.0 B

Variable types

Text1
Numeric5
Categorical2

Dataset

Description전국의 건설 현장에서 생산되는 시추조사 결과로 생산된 지반조사자료(보고서)의 전산화 데이터 중 시추공에 대한 정보임
Author국토교통부
URLhttps://www.data.go.kr/data/15069365/fileData.do

Alerts

시추공종류 is highly imbalanced (67.5%)Imbalance
지하수위 has 224 (2.2%) missing valuesMissing
고도 is highly skewed (γ1 = 31.03722429)Skewed
시추심도 is highly skewed (γ1 = 25.66890644)Skewed
지하수위 is highly skewed (γ1 = -44.60882221)Skewed
시추공코드 has unique valuesUnique
고도 has 1388 (13.9%) zerosZeros
지하수위 has 2787 (27.9%) zerosZeros

Reproduction

Analysis started2023-12-12 19:49:53.336992
Analysis finished2023-12-12 19:49:58.584229
Duration5.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시추공코드
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:49:58.829536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.6973
Min length8

Characters and Unicode

Total characters96973
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowB1290BH001
2nd rowP118BB021
3rd rowP0475TB044
4th rowR078SB088
5th rowK140BH019
ValueCountFrequency (%)
b1290bh001 1
 
< 0.1%
b0329bh034 1
 
< 0.1%
p0570bh292 1
 
< 0.1%
b0346bh039 1
 
< 0.1%
p0243bh014 1
 
< 0.1%
b0052bh047 1
 
< 0.1%
p1210bh002 1
 
< 0.1%
p278bb041 1
 
< 0.1%
p0999bb053 1
 
< 0.1%
b0381bh002 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-13T04:49:59.444777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23069
23.8%
B 15229
15.7%
1 9012
 
9.3%
2 6117
 
6.3%
3 5601
 
5.8%
H 5357
 
5.5%
P 4769
 
4.9%
4 4377
 
4.5%
5 4375
 
4.5%
6 3687
 
3.8%
Other values (23) 15380
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66858
68.9%
Uppercase Letter 30109
31.0%
Dash Punctuation 3
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 15229
50.6%
H 5357
 
17.8%
P 4769
 
15.8%
T 1132
 
3.8%
K 1051
 
3.5%
A 794
 
2.6%
C 639
 
2.1%
R 583
 
1.9%
S 482
 
1.6%
W 46
 
0.2%
Other values (9) 27
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 23069
34.5%
1 9012
 
13.5%
2 6117
 
9.1%
3 5601
 
8.4%
4 4377
 
6.5%
5 4375
 
6.5%
6 3687
 
5.5%
7 3600
 
5.4%
9 3518
 
5.3%
8 3502
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
o 1
33.3%
g 1
33.3%
y 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66861
68.9%
Latin 30112
31.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 15229
50.6%
H 5357
 
17.8%
P 4769
 
15.8%
T 1132
 
3.8%
K 1051
 
3.5%
A 794
 
2.6%
C 639
 
2.1%
R 583
 
1.9%
S 482
 
1.6%
W 46
 
0.2%
Other values (12) 30
 
0.1%
Common
ValueCountFrequency (%)
0 23069
34.5%
1 9012
 
13.5%
2 6117
 
9.1%
3 5601
 
8.4%
4 4377
 
6.5%
5 4375
 
6.5%
6 3687
 
5.5%
7 3600
 
5.4%
9 3518
 
5.3%
8 3502
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23069
23.8%
B 15229
15.7%
1 9012
 
9.3%
2 6117
 
6.3%
3 5601
 
5.8%
H 5357
 
5.5%
P 4769
 
4.9%
4 4377
 
4.5%
5 4375
 
4.5%
6 3687
 
3.8%
Other values (23) 15380
15.9%

고도
Real number (ℝ)

SKEWED  ZEROS 

Distinct5425
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.317751
Minimum-147.89
Maximum7190
Zeros1388
Zeros (%)13.9%
Negative131
Negative (%)1.3%
Memory size166.0 KiB
2023-12-13T04:49:59.650531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-147.89
5-th percentile0
Q16.9
median38.675
Q389.4475
95-th percentile206.01
Maximum7190
Range7337.89
Interquartile range (IQR)82.5475

Descriptive statistics

Standard deviation106.61309
Coefficient of variation (CV)1.6837788
Kurtosis2000.2308
Mean63.317751
Median Absolute Deviation (MAD)35.375
Skewness31.037224
Sum633177.51
Variance11366.35
MonotonicityNot monotonic
2023-12-13T04:49:59.859581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1388
 
13.9%
1.0 24
 
0.2%
6.0 16
 
0.2%
3.1 13
 
0.1%
104.2 12
 
0.1%
3.4 11
 
0.1%
4.3 11
 
0.1%
40.0 10
 
0.1%
317.9 10
 
0.1%
4.8 10
 
0.1%
Other values (5415) 8495
85.0%
ValueCountFrequency (%)
-147.89 1
< 0.1%
-83.8 1
< 0.1%
-74.6 1
< 0.1%
-51.2 1
< 0.1%
-26.5 1
< 0.1%
-19.7 1
< 0.1%
-19.0 1
< 0.1%
-18.2 1
< 0.1%
-18.1 1
< 0.1%
-15.5 1
< 0.1%
ValueCountFrequency (%)
7190.0 1
< 0.1%
1116.19 1
< 0.1%
875.0 1
< 0.1%
808.5 1
< 0.1%
747.5 1
< 0.1%
692.01 1
< 0.1%
673.07 1
< 0.1%
658.7 1
< 0.1%
658.1 1
< 0.1%
635.01 1
< 0.1%

시추심도
Real number (ℝ)

SKEWED 

Distinct591
Distinct (%)5.9%
Missing36
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean30.02999
Minimum-8.44
Maximum10056.8
Zeros5
Zeros (%)< 0.1%
Negative6
Negative (%)0.1%
Memory size166.0 KiB
2023-12-13T04:50:00.070587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8.44
5-th percentile1
Q16.5
median12
Q320
95-th percentile36
Maximum10056.8
Range10065.24
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation388.79394
Coefficient of variation (CV)12.946856
Kurtosis657.87954
Mean30.02999
Median Absolute Deviation (MAD)6.5
Skewness25.668906
Sum299218.82
Variance151160.73
MonotonicityNot monotonic
2023-12-13T04:50:00.335760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 389
 
3.9%
10.0 327
 
3.3%
1.0 322
 
3.2%
12.0 320
 
3.2%
2.0 308
 
3.1%
9.0 249
 
2.5%
11.0 241
 
2.4%
20.0 240
 
2.4%
18.0 218
 
2.2%
8.0 215
 
2.1%
Other values (581) 7135
71.4%
ValueCountFrequency (%)
-8.44 1
 
< 0.1%
-3.83 1
 
< 0.1%
-3.65 1
 
< 0.1%
-1.95 1
 
< 0.1%
-0.51 1
 
< 0.1%
-0.26 1
 
< 0.1%
0.0 5
0.1%
0.15 1
 
< 0.1%
0.2 3
< 0.1%
0.3 4
< 0.1%
ValueCountFrequency (%)
10056.8 1
< 0.1%
10056.5 1
< 0.1%
10052.2 1
< 0.1%
10049.63 1
< 0.1%
10049.1 1
< 0.1%
10039.9 1
< 0.1%
10034.99 1
< 0.1%
10034.7 1
< 0.1%
10034.35 1
< 0.1%
10026.9 1
< 0.1%

지하수위
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct747
Distinct (%)7.6%
Missing224
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean-995.80784
Minimum-999999
Maximum12.5
Zeros2787
Zeros (%)27.9%
Negative6944
Negative (%)69.4%
Memory size166.0 KiB
2023-12-13T04:50:00.568716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999999
5-th percentile-13.5
Q1-4.1
median-1.6
Q30
95-th percentile0
Maximum12.5
Range1000011.5
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation16804.237
Coefficient of variation (CV)-16.87498
Kurtosis2563.7393
Mean-995.80784
Median Absolute Deviation (MAD)1.6
Skewness-44.608822
Sum-9735017.4
Variance2.8238238 × 108
MonotonicityNot monotonic
2023-12-13T04:50:00.805054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2787
27.9%
-1.2 179
 
1.8%
-0.5 163
 
1.6%
-1.5 152
 
1.5%
-1.0 152
 
1.5%
-2.5 148
 
1.5%
-2.0 141
 
1.4%
-1.3 139
 
1.4%
-0.7 133
 
1.3%
-0.8 131
 
1.3%
Other values (737) 5651
56.5%
(Missing) 224
 
2.2%
ValueCountFrequency (%)
-999999.0 2
 
< 0.1%
-99999.0 77
0.8%
-491.93 1
 
< 0.1%
-287.01 1
 
< 0.1%
-276.3 1
 
< 0.1%
-252.67 1
 
< 0.1%
-219.86 1
 
< 0.1%
-173.17 1
 
< 0.1%
-172.66 1
 
< 0.1%
-171.99 1
 
< 0.1%
ValueCountFrequency (%)
12.5 1
< 0.1%
12.2 1
< 0.1%
11.6 1
< 0.1%
11.47 1
< 0.1%
11.0 1
< 0.1%
10.8 1
< 0.1%
9.5 1
< 0.1%
8.21 1
< 0.1%
8.1 1
< 0.1%
8.0 1
< 0.1%

시추방법
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
회전수세식
6418 
기타
2490 
회전유압식
1087 
1
 
4
26
 
1

Length

Max length5
Median length5
Mean length4.2511
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row회전유압식
2nd row회전수세식
3rd row회전수세식
4th row회전수세식
5th row회전수세식

Common Values

ValueCountFrequency (%)
회전수세식 6418
64.2%
기타 2490
 
24.9%
회전유압식 1087
 
10.9%
1 4
 
< 0.1%
26 1
 
< 0.1%

Length

2023-12-13T04:50:01.058211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:50:01.234466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
회전수세식 6418
64.2%
기타 2490
 
24.9%
회전유압식 1087
 
10.9%
1 4
 
< 0.1%
26 1
 
< 0.1%

시추공종류
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Boring
8537 
Boring_Ex
 
647
Boring_Hand
 
615
Borig_EX
 
99
<NA>
 
67

Length

Max length11
Median length6
Mean length6.508
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Boring 8537
85.4%
Boring_Ex 647
 
6.5%
Boring_Hand 615
 
6.2%
Borig_EX 99
 
1.0%
<NA> 67
 
0.7%
boring 35
 
0.4%

Length

2023-12-13T04:50:01.437792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:50:01.667360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
boring 8572
85.7%
boring_ex 647
 
6.5%
boring_hand 615
 
6.2%
borig_ex 99
 
1.0%
na 67
 
0.7%

X좌표
Real number (ℝ)

Distinct9962
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259695.43
Minimum-7296.2427
Maximum427417.6
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-13T04:50:02.268651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7296.2427
5-th percentile164777.09
Q1194469.03
median227116.15
Q3335852.7
95-th percentile402513.99
Maximum427417.6
Range434713.84
Interquartile range (IQR)141383.67

Descriptive statistics

Standard deviation81114.462
Coefficient of variation (CV)0.31234458
Kurtosis-1.0740107
Mean259695.43
Median Absolute Deviation (MAD)48860.308
Skewness0.53145995
Sum2.5969543 × 109
Variance6.5795559 × 109
MonotonicityNot monotonic
2023-12-13T04:50:02.522813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
222384.1991 4
 
< 0.1%
205271.2863 4
 
< 0.1%
238073.4015 3
 
< 0.1%
204228.9261 2
 
< 0.1%
238664.0306 2
 
< 0.1%
206290.6972 2
 
< 0.1%
182790.0646 2
 
< 0.1%
217062.5313 2
 
< 0.1%
349052.1415 2
 
< 0.1%
209234.4196 2
 
< 0.1%
Other values (9952) 9975
99.8%
ValueCountFrequency (%)
-7296.2427 1
< 0.1%
-7279.7441 1
< 0.1%
-7227.1837 1
< 0.1%
-7213.2735 1
< 0.1%
186.6718 1
< 0.1%
329.3097 1
< 0.1%
120009.2939 1
< 0.1%
130132.3188 1
< 0.1%
131171.1681 1
< 0.1%
134372.2684 1
< 0.1%
ValueCountFrequency (%)
427417.6011 1
< 0.1%
426792.8532 1
< 0.1%
426704.8215 1
< 0.1%
425280.6524 1
< 0.1%
425085.0923 1
< 0.1%
425030.1982 1
< 0.1%
424953.4035 1
< 0.1%
424784.3371 1
< 0.1%
424723.1863 1
< 0.1%
424322.7758 1
< 0.1%

Y좌표
Real number (ℝ)

Distinct9964
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean423017.7
Minimum72903.811
Maximum661202.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:50:02.701907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72903.811
5-th percentile262456.25
Q1326873.89
median424210.93
Q3522644.05
95-th percentile576026.05
Maximum661202.21
Range588298.4
Interquartile range (IQR)195770.16

Descriptive statistics

Standard deviation110426.41
Coefficient of variation (CV)0.26104443
Kurtosis-1.028789
Mean423017.7
Median Absolute Deviation (MAD)98109.842
Skewness-0.17428703
Sum4.230177 × 109
Variance1.2193993 × 1010
MonotonicityNot monotonic
2023-12-13T04:50:02.934536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
507261.8676 4
 
< 0.1%
562316.7127 4
 
< 0.1%
457035.1781 3
 
< 0.1%
560864.5288 2
 
< 0.1%
483395.8886 2
 
< 0.1%
523139.0802 2
 
< 0.1%
486744.1938 2
 
< 0.1%
297854.6399 2
 
< 0.1%
523992.414 2
 
< 0.1%
512298.4788 2
 
< 0.1%
Other values (9954) 9975
99.8%
ValueCountFrequency (%)
72903.8115 1
< 0.1%
73180.8749 1
< 0.1%
73225.4259 1
< 0.1%
73265.4424 1
< 0.1%
73321.7068 1
< 0.1%
95175.2676 1
< 0.1%
95177.7488 1
< 0.1%
95206.5541 1
< 0.1%
95212.5834 1
< 0.1%
98282.6479 1
< 0.1%
ValueCountFrequency (%)
661202.2098 1
< 0.1%
659913.2408 1
< 0.1%
659707.0117 1
< 0.1%
659044.8705 1
< 0.1%
658183.0383 1
< 0.1%
656840.9264 1
< 0.1%
656409.1761 1
< 0.1%
655418.1853 1
< 0.1%
654745.8686 1
< 0.1%
652545.2998 1
< 0.1%

Interactions

2023-12-13T04:49:57.416661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:54.407871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:55.050502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:55.763672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:56.632617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:57.563707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:54.513697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:55.229308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:55.954973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:56.791817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:57.705796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:54.638613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:55.380363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:56.119541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:56.950759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:57.860898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:54.751580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:55.518254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:56.297896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:57.103005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:57.981836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:54.875414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:55.625730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:56.474461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:57.261475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:50:03.108406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고도시추심도지하수위시추방법시추공종류X좌표Y좌표
고도1.0000.0000.0000.0000.0000.1240.042
시추심도0.0001.0000.0000.0210.2840.0450.043
지하수위0.0000.0001.0000.0160.0330.0460.070
시추방법0.0000.0210.0161.0000.6510.1830.168
시추공종류0.0000.2840.0330.6511.0000.1070.131
X좌표0.1240.0450.0460.1830.1071.0000.643
Y좌표0.0420.0430.0700.1680.1310.6431.000
2023-12-13T04:50:03.263124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시추방법시추공종류
시추방법1.0000.294
시추공종류0.2941.000
2023-12-13T04:50:03.400426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고도시추심도지하수위X좌표Y좌표시추방법시추공종류
고도1.0000.146-0.3040.1880.0220.0000.000
시추심도0.1461.000-0.388-0.083-0.0010.0260.347
지하수위-0.304-0.3881.0000.013-0.0380.0070.025
X좌표0.188-0.0830.0131.000-0.2510.1060.062
Y좌표0.022-0.001-0.038-0.2511.0000.0970.075
시추방법0.0000.0260.0070.1060.0971.0000.294
시추공종류0.0000.3470.0250.0620.0750.2941.000

Missing values

2023-12-13T04:49:58.143187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:49:58.316165image/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.
2023-12-13T04:49:58.500079image/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

시추공코드고도시추심도지하수위시추방법시추공종류X좌표Y좌표
87958B1290BH00131.1716.0-4.9회전유압식Boring204731.7562565352.9213
34278P118BB021109.346.5-1.5회전수세식Boring209770.0543268009.8897
46490P0475TB04458.2433.00.0회전수세식Boring203609.9263430654.6942
23582R078SB0883.1911.00.0회전수세식Boring165786.3043486421.9064
41569K140BH01932.414.5-2.1회전수세식Boring373855.0168296178.0809
81960B0919BH0296.033.2-2.5회전수세식Boring181239.4682555414.0226
20712P0452BH0033.624.4-3.0회전수세식Boring392392.1284100.8964
17047P034BH026118.9416.00.0기타Boring238195.5106472562.2975
35263K129BHH29104.210.00.0회전수세식Boring152500.7955100734.8435
45948P0487BB048112.0112.5-11.0회전수세식Boring279863.3069500967.0702
시추공코드고도시추심도지하수위시추방법시추공종류X좌표Y좌표
34652P225BH00490.5450.0-32.0회전수세식Boring306241.2709332769.6768
54904R053BH0024.1845.0<NA>기타Boring374517.0753281613.3088
83544B0774TB012241.8231.5-5.1회전수세식Boring267704.6879573652.3688
91913B1885BH01019.1211.5-3.8회전수세식Boring209027.0316399149.8169
25262B0104BH01254.1410.8-3.58회전유압식Boring230774.8066415001.8455
92188B2001BH007119.114.0-5.7회전수세식boring206419.8506557234.0032
61020B0176BH02235.330.0-6.1회전수세식Boring200319.2112518461.7633
19060P0890BB037556.0911.2-1.2회전수세식Boring340775.1436564023.9521
1227K151TP0040.01.00.0기타Boring_Ex383546.8469306736.708
43664P0091BH012541.83.90.0회전수세식Boring364513.5534547614.8089