Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory79.3 B

Variable types

Categorical3
Text1
Numeric5

Alerts

FILE_NAME has constant value ""Constant
base_ymd has constant value ""Constant
hadm_cd is highly overall correlated with atrctn_cnt and 2 other fieldsHigh correlation
atrctn_cnt is highly overall correlated with hadm_cdHigh correlation
rstrt_cnt is highly overall correlated with shopng_cnt and 1 other fieldsHigh correlation
shopng_cnt is highly overall correlated with rstrt_cnt and 1 other fieldsHigh correlation
residnt_cnt_sum is highly overall correlated with hadm_cd and 2 other fieldsHigh correlation
sido_nm is highly overall correlated with hadm_cdHigh correlation
hadm_cd has unique valuesUnique
rstrt_cnt has unique valuesUnique
residnt_cnt_sum has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:51:48.120930
Analysis finished2023-12-10 09:51:53.152624
Duration5.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

sido_nm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
42 
경상남도
22 
강원도
18 
경상북도
18 

Length

Max length4
Median length3
Mean length3.4
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 42
42.0%
경상남도 22
22.0%
강원도 18
18.0%
경상북도 18
18.0%

Length

2023-12-10T18:51:53.280995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:53.483099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 42
42.0%
경상남도 22
22.0%
강원도 18
18.0%
경상북도 18
18.0%

sgg_nm
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:53.991118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.97
Min length3

Characters and Unicode

Total characters397
Distinct characters93
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

Unique98 ?
Unique (%)98.0%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
창원시 5
 
4.1%
수원시 4
 
3.3%
고양시 3
 
2.5%
성남시 3
 
2.5%
용인시 3
 
2.5%
안양시 2
 
1.6%
안산시 2
 
1.6%
고성군 2
 
1.6%
영덕군 1
 
0.8%
안동시 1
 
0.8%
Other values (96) 96
78.7%
2023-12-10T18:51:54.797785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 68
 
17.1%
군 35
 
8.8%
구 25
 
6.3%
22
 
5.5%
양 16
 
4.0%
천 14
 
3.5%
원 14
 
3.5%
산 11
 
2.8%
주 11
 
2.8%
안 10
 
2.5%
Other values (83) 171
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 375
94.5%
Space Separator 22
 
5.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 68
18.1%
군 35
 
9.3%
구 25
 
6.7%
양 16
 
4.3%
천 14
 
3.7%
원 14
 
3.7%
산 11
 
2.9%
주 11
 
2.9%
안 10
 
2.7%
성 10
 
2.7%
Other values (82) 161
42.9%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 375
94.5%
Common 22
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 68
18.1%
군 35
 
9.3%
구 25
 
6.7%
양 16
 
4.3%
천 14
 
3.7%
원 14
 
3.7%
산 11
 
2.9%
주 11
 
2.9%
안 10
 
2.7%
성 10
 
2.7%
Other values (82) 161
42.9%
Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 375
94.5%
ASCII 22
 
5.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 68
18.1%
군 35
 
9.3%
구 25
 
6.7%
양 16
 
4.3%
천 14
 
3.7%
원 14
 
3.7%
산 11
 
2.9%
주 11
 
2.9%
안 10
 
2.7%
성 10
 
2.7%
Other values (82) 161
42.9%
ASCII
ValueCountFrequency (%)
22
100.0%

hadm_cd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44255.9
Minimum41111
Maximum48890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:55.208068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41111
5-th percentile41132.9
Q141425
median42725
Q347905
95-th percentile48840.5
Maximum48890
Range7779
Interquartile range (IQR)6480

Descriptive statistics

Standard deviation3170.1308
Coefficient of variation (CV)0.071631822
Kurtosis-1.7528404
Mean44255.9
Median Absolute Deviation (MAD)1543.5
Skewness0.39435263
Sum4425590
Variance10049729
MonotonicityNot monotonic
2023-12-10T18:51:55.543875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42150 1
 
1.0%
48840 1
 
1.0%
48123 1
 
1.0%
48127 1
 
1.0%
48125 1
 
1.0%
48740 1
 
1.0%
48170 1
 
1.0%
48720 1
 
1.0%
48330 1
 
1.0%
48860 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
41111 1
1.0%
41113 1
1.0%
41115 1
1.0%
41117 1
1.0%
41131 1
1.0%
41133 1
1.0%
41135 1
1.0%
41150 1
1.0%
41171 1
1.0%
41173 1
1.0%
ValueCountFrequency (%)
48890 1
1.0%
48880 1
1.0%
48870 1
1.0%
48860 1
1.0%
48850 1
1.0%
48840 1
1.0%
48820 1
1.0%
48740 1
1.0%
48730 1
1.0%
48720 1
1.0%

atrctn_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.72
Minimum13
Maximum625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:55.800331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile27.95
Q164
median130
Q3216.5
95-th percentile336.8
Maximum625
Range612
Interquartile range (IQR)152.5

Descriptive statistics

Standard deviation115.97083
Coefficient of variation (CV)0.75442904
Kurtosis2.8306972
Mean153.72
Median Absolute Deviation (MAD)68.5
Skewness1.4118647
Sum15372
Variance13449.234
MonotonicityNot monotonic
2023-12-10T18:51:56.025944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 3
 
3.0%
37 3
 
3.0%
153 3
 
3.0%
130 2
 
2.0%
280 2
 
2.0%
30 2
 
2.0%
27 2
 
2.0%
44 2
 
2.0%
233 2
 
2.0%
70 2
 
2.0%
Other values (72) 77
77.0%
ValueCountFrequency (%)
13 1
 
1.0%
17 1
 
1.0%
26 1
 
1.0%
27 2
2.0%
28 3
3.0%
29 1
 
1.0%
30 2
2.0%
34 1
 
1.0%
37 3
3.0%
40 1
 
1.0%
ValueCountFrequency (%)
625 1
1.0%
555 1
1.0%
423 1
1.0%
410 1
1.0%
409 1
1.0%
333 1
1.0%
309 1
1.0%
308 1
1.0%
303 1
1.0%
299 2
2.0%

rstrt_cnt
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2837.18
Minimum241
Maximum9380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:56.307591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum241
5-th percentile517.5
Q11098.25
median2387
Q33682.25
95-th percentile6954.95
Maximum9380
Range9139
Interquartile range (IQR)2584

Descriptive statistics

Standard deviation2102.8899
Coefficient of variation (CV)0.74119017
Kurtosis1.0619232
Mean2837.18
Median Absolute Deviation (MAD)1292.5
Skewness1.1604945
Sum283718
Variance4422146.1
MonotonicityNot monotonic
2023-12-10T18:51:56.574639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4685 1
 
1.0%
938 1
 
1.0%
3634 1
 
1.0%
3050 1
 
1.0%
3827 1
 
1.0%
1353 1
 
1.0%
6157 1
 
1.0%
480 1
 
1.0%
5659 1
 
1.0%
687 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
241 1
1.0%
268 1
1.0%
421 1
1.0%
480 1
1.0%
489 1
1.0%
519 1
1.0%
532 1
1.0%
564 1
1.0%
677 1
1.0%
687 1
1.0%
ValueCountFrequency (%)
9380 1
1.0%
9244 1
1.0%
8941 1
1.0%
7808 1
1.0%
7030 1
1.0%
6951 1
1.0%
6544 1
1.0%
6214 1
1.0%
6157 1
1.0%
5865 1
1.0%

shopng_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.59
Minimum48
Maximum977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:56.840137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile81.3
Q1149.75
median247.5
Q3360
95-th percentile640.3
Maximum977
Range929
Interquartile range (IQR)210.25

Descriptive statistics

Standard deviation175.21871
Coefficient of variation (CV)0.6200457
Kurtosis2.0737049
Mean282.59
Median Absolute Deviation (MAD)101
Skewness1.2933958
Sum28259
Variance30701.598
MonotonicityNot monotonic
2023-12-10T18:51:57.113237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 2
 
2.0%
271 2
 
2.0%
126 2
 
2.0%
300 2
 
2.0%
298 2
 
2.0%
138 2
 
2.0%
105 2
 
2.0%
211 2
 
2.0%
460 1
 
1.0%
401 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
48 1
1.0%
52 1
1.0%
54 1
1.0%
66 1
1.0%
68 1
1.0%
82 1
1.0%
83 1
1.0%
89 1
1.0%
95 1
1.0%
102 1
1.0%
ValueCountFrequency (%)
977 1
1.0%
751 1
1.0%
737 1
1.0%
674 1
1.0%
665 1
1.0%
639 1
1.0%
584 1
1.0%
575 1
1.0%
559 1
1.0%
553 1
1.0%

residnt_cnt_sum
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161769.75
Minimum5198
Maximum730558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:57.355488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5198
5-th percentile17990.55
Q135464.5
median132815.5
Q3246408.75
95-th percentile417416.75
Maximum730558
Range725360
Interquartile range (IQR)210944.25

Descriptive statistics

Standard deviation145624.98
Coefficient of variation (CV)0.90019905
Kurtosis1.8646798
Mean161769.75
Median Absolute Deviation (MAD)100570
Skewness1.2584857
Sum16176975
Variance2.1206633 × 1010
MonotonicityNot monotonic
2023-12-10T18:51:57.676110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162905 1
 
1.0%
30561 1
 
1.0%
191489 1
 
1.0%
162616 1
 
1.0%
144573 1
 
1.0%
45069 1
 
1.0%
280079 1
 
1.0%
18082 1
 
1.0%
269055 1
 
1.0%
24133 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
5198 1
1.0%
11314 1
1.0%
14731 1
1.0%
15570 1
1.0%
16253 1
1.0%
18082 1
1.0%
18628 1
1.0%
19462 1
1.0%
20388 1
1.0%
20917 1
1.0%
ValueCountFrequency (%)
730558 1
1.0%
618926 1
1.0%
521159 1
1.0%
447439 1
1.0%
423150 1
1.0%
417115 1
1.0%
414376 1
1.0%
364214 1
1.0%
352389 1
1.0%
351337 1
1.0%

FILE_NAME
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_2019
100 

Length

Max length44
Median length44
Mean length44
Min length44

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_2019
2nd rowKC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_2019
3rd rowKC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_2019
4th rowKC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_2019
5th rowKC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_2019

Common Values

ValueCountFrequency (%)
KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_2019 100
100.0%

Length

2023-12-10T18:51:57.919331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:58.078659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_619_dmstc_trv_cnsmp_statn_bizaea_map_2019 100
100.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200214
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200214
2nd row20200214
3rd row20200214
4th row20200214
5th row20200214

Common Values

ValueCountFrequency (%)
20200214 100
100.0%

Length

2023-12-10T18:51:58.262787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:58.443583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200214 100
100.0%

Interactions

2023-12-10T18:51:51.922376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:48.586664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:49.396697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:50.140766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:51.118781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.099907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:48.786014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:49.586205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:50.287999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:51.354260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.238622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:48.962645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:49.722608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:50.755486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:51.521066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.382985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:49.091318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:49.854541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:50.862060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:51.639529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:52.530247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:49.230364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:49.989256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:50.992135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:51.775505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:51:58.563956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
sido_nmsgg_nmhadm_cdatrctn_cntrstrt_cntshopng_cntresidnt_cnt_sum
sido_nm1.0000.6751.0000.5870.3830.2700.487
sgg_nm0.6751.0000.8620.9631.0001.0001.000
hadm_cd1.0000.8621.0000.6110.4310.4680.400
atrctn_cnt0.5870.9630.6111.0000.2580.2000.000
rstrt_cnt0.3831.0000.4310.2581.0000.9170.908
shopng_cnt0.2701.0000.4680.2000.9171.0000.857
residnt_cnt_sum0.4871.0000.4000.0000.9080.8571.000
2023-12-10T18:51:58.786147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
hadm_cdatrctn_cntrstrt_cntshopng_cntresidnt_cnt_sumsido_nm
hadm_cd1.0000.682-0.398-0.411-0.5670.990
atrctn_cnt0.6821.000-0.047-0.019-0.2620.406
rstrt_cnt-0.398-0.0471.0000.9700.9240.227
shopng_cnt-0.411-0.0190.9701.0000.8990.169
residnt_cnt_sum-0.567-0.2620.9240.8991.0000.304
sido_nm0.9900.4060.2270.1690.3041.000

Missing values

2023-12-10T18:51:52.768997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:51:53.065540image/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_nmhadm_cdatrctn_cntrstrt_cntshopng_cntresidnt_cnt_sumFILE_NAMEbase_ymd
0강원도강릉시421503084685460162905KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
1강원도고성군4282010579511718628KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
2강원도동해시4217085192924768772KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
3강원도삼척시42230135159720945409KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
4강원도속초시4221079259421063203KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
5강원도양구군42800725326814731KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
6강원도양양군4283013694712619462KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
7강원도영월군4275011979915225832KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
8강원도원주시421302866544553267203KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
9강원도인제군4281012197012320917KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
sido_nmsgg_nmhadm_cdatrctn_cntrstrt_cntshopng_cntresidnt_cnt_sumFILE_NAMEbase_ymd
90경상북도상주시47250255171524468854KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
91경상북도성주군4784018580811632010KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
92경상북도안동시471702752983375119201KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
93경상북도영덕군47770170127615325962KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
94경상북도영양군477601112415211314KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
95경상북도영주시47210177206529079658KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
96경상북도영천시47230555185423773702KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
97경상북도예천군4790011080412636291KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
98경상북도울릉군4794063268545198KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214
99경상북도울진군47930131126016536399KC_619_DMSTC_TRV_CNSMP_STATN_BIZAEA_MAP_201920200214