Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory976.6 KiB
Average record size in memory100.0 B

Variable types

Categorical7
Text1
DateTime1
Numeric2

Dataset

Description제주 신규 공공 와이파이 설치제거를 위한 도내 카테고리별 설치 및 이용 현황 데이터 매쉬업 결과 정보입니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15074775/fileData.do

Alerts

서비스 사용시간 has constant value ""Constant
사용 횟수 has constant value ""Constant
데이터기준일자 has constant value ""Constant
시도명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
읍면동명 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 읍면동명High correlation

Reproduction

Analysis started2023-12-12 13:41:15.435732
Analysis finished2023-12-12 13:41:17.118793
Duration1.68 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Categorical

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2016-11-04
 
454
2016-11-02
 
448
2016-11-11
 
447
2016-11-15
 
445
2016-11-19
 
439
Other values (19)
7767 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-11-08
2nd row2016-11-15
3rd row2016-11-07
4th row2016-11-24
5th row2016-11-15

Common Values

ValueCountFrequency (%)
2016-11-04 454
 
4.5%
2016-11-02 448
 
4.5%
2016-11-11 447
 
4.5%
2016-11-15 445
 
4.5%
2016-11-19 439
 
4.4%
2016-11-06 433
 
4.3%
2016-11-20 427
 
4.3%
2016-11-09 427
 
4.3%
2016-11-01 427
 
4.3%
2016-11-18 426
 
4.3%
Other values (14) 5627
56.3%

Length

2023-12-12T22:41:17.175622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-11-04 454
 
4.5%
2016-11-02 448
 
4.5%
2016-11-11 447
 
4.5%
2016-11-15 445
 
4.5%
2016-11-19 439
 
4.4%
2016-11-06 433
 
4.3%
2016-11-20 427
 
4.3%
2016-11-09 427
 
4.3%
2016-11-01 427
 
4.3%
2016-11-18 426
 
4.3%
Other values (14) 5627
56.3%

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
제주시
5416 
서귀포시
4584 

Length

Max length4
Median length3
Mean length3.4584
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주시
2nd row제주시
3rd row제주시
4th row서귀포시
5th row서귀포시

Common Values

ValueCountFrequency (%)
제주시 5416
54.2%
서귀포시 4584
45.8%

Length

2023-12-12T22:41:17.278594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:41:17.393604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 5416
54.2%
서귀포시 4584
45.8%

읍면동명
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
송산동
 
545
천지동
 
523
성산읍
 
507
조천읍
 
477
구좌읍
 
367
Other values (44)
7581 

Length

Max length4
Median length3
Mean length3.1001
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연동
2nd row삼도1동
3rd row조천읍
4th row천지동
5th row남원읍

Common Values

ValueCountFrequency (%)
송산동 545
 
5.5%
천지동 523
 
5.2%
성산읍 507
 
5.1%
조천읍 477
 
4.8%
구좌읍 367
 
3.7%
애월읍 358
 
3.6%
남원읍 352
 
3.5%
연동 345
 
3.5%
노형동 337
 
3.4%
봉개동 331
 
3.3%
Other values (39) 5858
58.6%

Length

2023-12-12T22:41:17.510543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송산동 545
 
5.5%
천지동 523
 
5.2%
성산읍 507
 
5.1%
조천읍 477
 
4.8%
구좌읍 367
 
3.7%
애월읍 358
 
3.6%
남원읍 352
 
3.5%
연동 345
 
3.5%
노형동 337
 
3.4%
봉개동 331
 
3.3%
Other values (39) 5858
58.6%

맥주소
Text

Distinct2259
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:41:17.806007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters640000
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)1.7%

Sample

1st rowd34e8abccc30dbe54433dc1276999e067b8ea0f76c3d7691f7661b39dac43cb9
2nd rowae5f179f611b0573bb597c560930a372f13fa153116af242c1e1e1dda22814b3
3rd row631d82191bbfc8da4942a6a0941764b94a2bd015343368765ffa80d5964b76f4
4th row24dd350392b837bfb5242b1c3ec6027a9ea34e1d6c14dc2d50a38409f9b4ce02
5th row8f69870ae23c16f8bd74f02c86136329bf516cb8a1cae8d511e9427c50f55578
ValueCountFrequency (%)
ec04eeed817821450773819ce4471ee6bcc5ccab5bbb73a57a65f1f7448f8869 24
 
0.2%
76663cf2d0d755d5ba92ed1541d0b15eee31d4e2c37118df9fd4be9f42ce245f 24
 
0.2%
1a413f41f6df4f24fefd1f563fb21e33f96f4dca0d1be3c6d444be1a3b356ed2 24
 
0.2%
65b4649cca287547663b640db32f304e9aba815be63505e6b02a89972cfbd769 24
 
0.2%
bb6ec9a47a178c3f13cd7182a3f6751c5a9efd5208c90e780d11317a12cbb1d5 23
 
0.2%
3952cbb42ffbac195cfe64a706268628bc86589bca37ff28455e877091a87f80 20
 
0.2%
e53ee7e3dd32c69b86a84a19d4b59d1ce390020f2071e1933542cbdf5c00ec5a 20
 
0.2%
befba7b91e539de9430a43a494e7fda1a067e81e5a7545f7461001de70d4714d 19
 
0.2%
e7bd3191c740beecf041b91bf7eb711ccc4c71fefb3c4633e9f0bfcfe6548260 19
 
0.2%
9aed4b38baa88c851e83f28901a6d21f61d37c0a447f9f7f446f7341bb86a939 19
 
0.2%
Other values (2249) 9784
97.8%
2023-12-12T22:41:18.286878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 40939
 
6.4%
6 40769
 
6.4%
f 40615
 
6.3%
3 40593
 
6.3%
4 40287
 
6.3%
e 40230
 
6.3%
0 40190
 
6.3%
a 40159
 
6.3%
c 39739
 
6.2%
d 39635
 
6.2%
Other values (6) 236844
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400094
62.5%
Lowercase Letter 239906
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 40939
10.2%
6 40769
10.2%
3 40593
10.1%
4 40287
10.1%
0 40190
10.0%
1 39599
9.9%
8 39562
9.9%
9 39508
9.9%
5 39496
9.9%
2 39151
9.8%
Lowercase Letter
ValueCountFrequency (%)
f 40615
16.9%
e 40230
16.8%
a 40159
16.7%
c 39739
16.6%
d 39635
16.5%
b 39528
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400094
62.5%
Latin 239906
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
7 40939
10.2%
6 40769
10.2%
3 40593
10.1%
4 40287
10.1%
0 40190
10.0%
1 39599
9.9%
8 39562
9.9%
9 39508
9.9%
5 39496
9.9%
2 39151
9.8%
Latin
ValueCountFrequency (%)
f 40615
16.9%
e 40230
16.8%
a 40159
16.7%
c 39739
16.6%
d 39635
16.5%
b 39528
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 640000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 40939
 
6.4%
6 40769
 
6.4%
f 40615
 
6.3%
3 40593
 
6.3%
4 40287
 
6.3%
e 40230
 
6.3%
0 40190
 
6.3%
a 40159
 
6.3%
c 39739
 
6.2%
d 39635
 
6.2%
Other values (6) 236844
37.0%

개통일
Date

Distinct242
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-07-19 00:00:00
Maximum2020-04-27 00:00:00
2023-12-12T22:41:18.489700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:18.629187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1518
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.389177
Minimum33.166454
Maximum33.559583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:18.784642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.166454
5-th percentile33.240936
Q133.254105
median33.433833
Q333.496519
95-th percentile33.526917
Maximum33.559583
Range0.393129
Interquartile range (IQR)0.242414

Descriptive statistics

Standard deviation0.1144584
Coefficient of variation (CV)0.003428009
Kurtosis-1.6668248
Mean33.389177
Median Absolute Deviation (MAD)0.084395
Skewness-0.17862633
Sum333891.77
Variance0.013100725
MonotonicityNot monotonic
2023-12-12T22:41:18.951431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.249836 62
 
0.6%
33.245962 60
 
0.6%
33.511806 52
 
0.5%
33.437048 48
 
0.5%
33.247744 46
 
0.5%
33.243868 46
 
0.5%
33.246184 43
 
0.4%
33.248487 42
 
0.4%
33.244669 42
 
0.4%
33.248697 40
 
0.4%
Other values (1508) 9519
95.2%
ValueCountFrequency (%)
33.166453999999995 10
0.1%
33.199104 8
0.1%
33.205119 2
 
< 0.1%
33.2059 2
 
< 0.1%
33.205906 5
 
0.1%
33.206792 3
 
< 0.1%
33.208525 9
0.1%
33.208902 3
 
< 0.1%
33.209822 2
 
< 0.1%
33.210076 15
0.1%
ValueCountFrequency (%)
33.559583 7
0.1%
33.558634999999995 11
0.1%
33.557889 6
0.1%
33.557666 5
 
0.1%
33.55761 8
0.1%
33.557227000000005 13
0.1%
33.557167 7
0.1%
33.556926000000004 8
0.1%
33.556553 8
0.1%
33.556414000000004 5
 
0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1527
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.55512
Minimum126.16361
Maximum126.96873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:41:19.112411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16361
5-th percentile126.25855
Q1126.48999
median126.55359
Q3126.60837
95-th percentile126.90059
Maximum126.96873
Range0.805128
Interquartile range (IQR)0.11838475

Descriptive statistics

Standard deviation0.16237647
Coefficient of variation (CV)0.0012830494
Kurtosis0.52924243
Mean126.55512
Median Absolute Deviation (MAD)0.062324
Skewness0.27761551
Sum1265551.2
Variance0.026366118
MonotonicityNot monotonic
2023-12-12T22:41:19.275880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.559971 62
 
0.6%
126.563879 60
 
0.6%
126.526056 52
 
0.5%
126.628152 48
 
0.5%
126.5603 46
 
0.5%
126.569025 46
 
0.5%
126.557365 43
 
0.4%
126.565978 42
 
0.4%
126.564107 40
 
0.4%
126.56658600000002 39
 
0.4%
Other values (1517) 9522
95.2%
ValueCountFrequency (%)
126.163606 5
0.1%
126.163778 5
0.1%
126.164197 8
0.1%
126.165759 6
0.1%
126.166233 2
 
< 0.1%
126.166301 1
 
< 0.1%
126.166819 6
0.1%
126.167783 3
 
< 0.1%
126.168003 4
< 0.1%
126.171035 7
0.1%
ValueCountFrequency (%)
126.968734 15
0.1%
126.967512 5
 
0.1%
126.967145 7
0.1%
126.9658 4
 
< 0.1%
126.965055 2
 
< 0.1%
126.964697 4
 
< 0.1%
126.963074 4
 
< 0.1%
126.959746 5
 
0.1%
126.959703 4
 
< 0.1%
126.959575 3
 
< 0.1%

카테고리
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
버스정류소
2340 
관광지
1950 
전기차충전소
1015 
공원
938 
올레코스
852 
Other values (15)
2905 

Length

Max length13
Median length7
Mean length4.0983
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row버스정류소
2nd row공공기관
3rd row버스정류소
4th row공원
5th row버스정류소

Common Values

ValueCountFrequency (%)
버스정류소 2340
23.4%
관광지 1950
19.5%
전기차충전소 1015
10.2%
공원 938
9.4%
올레코스 852
 
8.5%
테마거리 851
 
8.5%
해변 481
 
4.8%
전통시장 426
 
4.3%
정류소 231
 
2.3%
공공기관 228
 
2.3%
Other values (10) 688
 
6.9%

Length

2023-12-12T22:41:19.424508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
버스정류소 2340
23.2%
관광지 1950
19.3%
전기차충전소 1015
10.1%
공원 938
9.3%
올레코스 852
 
8.5%
테마거리 851
 
8.4%
해변 481
 
4.8%
전통시장 426
 
4.2%
정류소 231
 
2.3%
공공기관 228
 
2.3%
Other values (10) 768
 
7.6%

서비스 사용시간
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-12T22:41:19.535872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:41:19.638995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

사용 횟수
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-12T22:41:19.734350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:41:19.855455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-12-15
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-15
2nd row2020-12-15
3rd row2020-12-15
4th row2020-12-15
5th row2020-12-15

Common Values

ValueCountFrequency (%)
2020-12-15 10000
100.0%

Length

2023-12-12T22:41:19.964605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:41:20.069969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-15 10000
100.0%

Interactions

2023-12-12T22:41:16.572348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:16.291974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:16.699565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:41:16.448399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:41:20.128263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자시도명읍면동명위도경도카테고리
일자1.0000.0000.0000.0000.0000.000
시도명0.0001.0001.0000.9780.2760.346
읍면동명0.0001.0001.0000.9120.9610.794
위도0.0000.9780.9121.0000.7680.635
경도0.0000.2760.9610.7681.0000.581
카테고리0.0000.3460.7940.6350.5811.000
2023-12-12T22:41:20.267459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명일자카테고리읍면동명
시도명1.0000.0000.2730.998
일자0.0001.0000.0000.000
카테고리0.2730.0001.0000.313
읍면동명0.9980.0000.3131.000
2023-12-12T22:41:20.386276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도일자시도명읍면동명카테고리
위도1.0000.1070.0000.8710.6050.252
경도0.1071.0000.0000.2120.7550.221
일자0.0000.0001.0000.0000.0000.000
시도명0.8710.2120.0001.0000.9980.273
읍면동명0.6050.7550.0000.9981.0000.313
카테고리0.2520.2210.0000.2730.3131.000

Missing values

2023-12-12T22:41:16.867627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:41:17.030899image/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

일자시도명읍면동명맥주소개통일위도경도카테고리서비스 사용시간사용 횟수데이터기준일자
210362016-11-08제주시연동d34e8abccc30dbe54433dc1276999e067b8ea0f76c3d7691f7661b39dac43cb92019-07-1733.48305126.489983버스정류소002020-12-15
395812016-11-15제주시삼도1동ae5f179f611b0573bb597c560930a372f13fa153116af242c1e1e1dda22814b32018-11-1233.504171126.522423공공기관002020-12-15
167772016-11-07제주시조천읍631d82191bbfc8da4942a6a0941764b94a2bd015343368765ffa80d5964b76f42018-04-1333.545154126.652773버스정류소002020-12-15
635952016-11-24서귀포시천지동24dd350392b837bfb5242b1c3ec6027a9ea34e1d6c14dc2d50a38409f9b4ce022018-04-1833.243535126.557743공원002020-12-15
400092016-11-15서귀포시남원읍8f69870ae23c16f8bd74f02c86136329bf516cb8a1cae8d511e9427c50f555782019-06-2533.278735126.717906버스정류소002020-12-15
331812016-11-13제주시조천읍d9b13e7b271d9b797b64dc89231c250ea7e89a97ef276cd48f171b0a2f23f7d22019-10-2133.437266126.677236테마거리002020-12-15
433062016-11-16제주시화북동f8483b15830bde55a6ec7aadab5766ea3121c1ebd17da28d6ebd2a35ab4f922a2018-04-0433.51255126.551023정류소002020-12-15
426742016-11-16제주시이도2동6060555a395b74de745a7ca7878467f9075e06eb03da2cf028a54dfbd75435222018-10-2933.494525126.537958공공기관002020-12-15
183002016-11-07제주시한림읍f04d386ca5ac84bd73a7b06904b0cba42933cf5c356c57198345b56a156efe2d2019-06-1333.402906126.270066전기차충전소002020-12-15
234242016-11-09제주시노형동afbc5adebf8dde8ab22519a755aad451458f195b6ab23bf023c1d0a1d7aed26f2019-11-3033.484493126.471052공원002020-12-15
일자시도명읍면동명맥주소개통일위도경도카테고리서비스 사용시간사용 횟수데이터기준일자
430482016-11-16제주시용담2동13997dd74b34bd47372c17865055a709e8f24f84dd959eb12fca3132d21a467c2018-04-2833.500325126.5103버스정류소002020-12-15
442392016-11-17제주시아라동87e9200943747fc15bb6149c4d6122bc9a087c374a04f06260173f4b3cc62e9c2018-03-2933.477612126.562756버스정류소002020-12-15
298672016-11-12제주시이호동739591fda515121e013c8922be8458e49238952780ab1da5cde914a7e1558d5b2017-08-0333.499833126.455083해변002020-12-15
187652016-11-07서귀포시천지동ba0adc1c5d77cfa2ceb8ca9e29a7dc7720c0e6bc59367e945f047157c3eaca6b2020-04-1733.250194126.564118전통시장002020-12-15
116692016-11-05서귀포시표선면5d0657d84efeb3ebcd3603f16743411d1e0defab20d7993565fcdd84246af9772017-10-2733.386424126.800172관광지002020-12-15
238792016-11-09서귀포시천지동6649f72d34c7e92de429e6fa40cc6e5ef4a547177e3f6f62f11cb29509138ddd2019-11-0533.254167126.547237버스정류소002020-12-15
70712016-11-03서귀포시동홍동f6ce1d7057875d39e5cad71241901592428c70eea7a16d56a2bd0bba64270add2018-10-3033.258789126.563526공공기관002020-12-15
467362016-11-18서귀포시서홍동295fd1acff2c95b423510b251a02f3010d7ef8174b4f376e744350b31a6099122019-11-1333.240385126.548188해변002020-12-15
477532016-11-18서귀포시예래동1a413f41f6df4f24fefd1f563fb21e33f96f4dca0d1be3c6d444be1a3b356ed22019-06-2833.255514126.413161버스정류소002020-12-15
137202016-11-06제주시봉개동12e03b73c63981637f997a83bbf0e602e9cbe0609c2eba586503d807a744a8472018-05-0933.440686126.628602관광지002020-12-15