Overview

Dataset statistics

Number of variables8
Number of observations206
Missing cells2
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 KiB
Average record size in memory66.6 B

Variable types

Categorical4
Numeric2
Text2

Dataset

Description고양시 디바이스(센서) 현황
Author고양시
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=8Y92B5QJ216YRLAFO02A26426113&infSeq=1

Alerts

영문서비스명 is highly overall correlated with 서비스아이디 and 2 other fieldsHigh correlation
국문서비스명 is highly overall correlated with 서비스아이디 and 2 other fieldsHigh correlation
서비스아이디 is highly overall correlated with 설치장소 and 2 other fieldsHigh correlation
설치위도 is highly overall correlated with 설치장소High correlation
설치경도 is highly overall correlated with 설치장소High correlation
설치장소 is highly overall correlated with 설치위도 and 4 other fieldsHigh correlation
서비스아이디 is highly imbalanced (59.4%)Imbalance
국문서비스명 is highly imbalanced (59.4%)Imbalance
영문서비스명 is highly imbalanced (59.4%)Imbalance

Reproduction

Analysis started2023-12-10 21:40:11.962888
Analysis finished2023-12-10 21:40:13.050986
Duration1.09 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

서비스아이디
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
its_parking
150 
iot_sclass
40 
ReliefEducation
 
10
coliform
 
2
AirQuality
 
1
Other values (3)
 
3

Length

Max length18
Median length11
Mean length11
Min length8

Unique

Unique4 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
its_parking 150
72.8%
iot_sclass 40
 
19.4%
ReliefEducation 10
 
4.9%
coliform 2
 
1.0%
AirQuality 1
 
0.5%
waterquality 1
 
0.5%
WeatherInformation 1
 
0.5%
waterdrone 1
 
0.5%

Length

2023-12-11T06:40:13.118461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:40:13.235600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
its_parking 150
72.8%
iot_sclass 40
 
19.4%
reliefeducation 10
 
4.9%
coliform 2
 
1.0%
airquality 1
 
0.5%
waterquality 1
 
0.5%
weatherinformation 1
 
0.5%
waterdrone 1
 
0.5%

설치장소
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
냉천초등학교
60 
호수공원
41 
성저초등학교
30 
킨텍스IC
30 
이산포IC
20 
Other values (16)
25 

Length

Max length11
Median length10
Mean length5.3883495
Min length4

Unique

Unique15 ?
Unique (%)7.3%

Sample

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

Common Values

ValueCountFrequency (%)
냉천초등학교 60
29.1%
호수공원 41
19.9%
성저초등학교 30
14.6%
킨텍스IC 30
14.6%
이산포IC 20
 
9.7%
장항IC 10
 
4.9%
덕양구청어린이집 1
 
0.5%
시립서정어린이집 1
 
0.5%
시립앵두어린이집 1
 
0.5%
성실어린이집 1
 
0.5%
Other values (11) 11
 
5.3%

Length

2023-12-11T06:40:13.515753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
냉천초등학교 60
28.4%
호수공원 42
19.9%
성저초등학교 30
14.2%
킨텍스ic 30
14.2%
이산포ic 20
 
9.5%
장항ic 10
 
4.7%
계단바닦분수(좌측 1
 
0.5%
내 1
 
0.5%
시립햇빛어린이집 1
 
0.5%
고양바이오메스 1
 
0.5%
Other values (15) 15
 
7.1%

설치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)22.9%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean37.661054
Minimum37.56
Maximum37.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T06:40:13.977757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.56
5-th percentile37.64
Q137.655692
median37.66315
Q337.67
95-th percentile37.69
Maximum37.69
Range0.13
Interquartile range (IQR)0.014308

Descriptive statistics

Standard deviation0.022510515
Coefficient of variation (CV)0.00059771336
Kurtosis6.4233375
Mean37.661054
Median Absolute Deviation (MAD)0.00685
Skewness-1.9240107
Sum7720.516
Variance0.00050672327
MonotonicityNot monotonic
2023-12-11T06:40:14.152420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
37.67 61
29.6%
37.64 40
19.4%
37.69 32
15.5%
37.66 25
12.1%
37.57 5
 
2.4%
37.660737 1
 
0.5%
37.662065 1
 
0.5%
37.661216 1
 
0.5%
37.661161 1
 
0.5%
37.661072 1
 
0.5%
Other values (37) 37
18.0%
ValueCountFrequency (%)
37.56 1
 
0.5%
37.57 5
 
2.4%
37.62 1
 
0.5%
37.64 40
19.4%
37.650523 1
 
0.5%
37.650629 1
 
0.5%
37.650718 1
 
0.5%
37.651674 1
 
0.5%
37.655692 1
 
0.5%
37.656983 1
 
0.5%
ValueCountFrequency (%)
37.69 32
15.5%
37.67 61
29.6%
37.663924 1
 
0.5%
37.663822 1
 
0.5%
37.663712 1
 
0.5%
37.663656 1
 
0.5%
37.663495 1
 
0.5%
37.663398 1
 
0.5%
37.663355 1
 
0.5%
37.663249 1
 
0.5%

설치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)23.4%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean126.77236
Minimum126.72
Maximum126.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-11T06:40:14.344278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72
5-th percentile126.72
Q1126.76
median126.76
Q3126.79
95-th percentile126.79
Maximum126.98
Range0.26
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.042369424
Coefficient of variation (CV)0.00033421658
Kurtosis14.876747
Mean126.77236
Median Absolute Deviation (MAD)0.001196
Skewness3.4054302
Sum25988.333
Variance0.0017951681
MonotonicityNot monotonic
2023-12-11T06:40:14.496941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
126.76 71
34.5%
126.79 60
29.1%
126.72 20
 
9.7%
126.98 6
 
2.9%
126.75 4
 
1.9%
126.77 2
 
1.0%
126.759971 1
 
0.5%
126.761045 1
 
0.5%
126.759939 1
 
0.5%
126.759885 1
 
0.5%
Other values (38) 38
18.4%
ValueCountFrequency (%)
126.72 20
9.7%
126.75 4
 
1.9%
126.758712 1
 
0.5%
126.75883 1
 
0.5%
126.758868 1
 
0.5%
126.758945 1
 
0.5%
126.759152 1
 
0.5%
126.759173 1
 
0.5%
126.75926 1
 
0.5%
126.759314 1
 
0.5%
ValueCountFrequency (%)
126.98 6
 
2.9%
126.87 1
 
0.5%
126.85 1
 
0.5%
126.79 60
29.1%
126.770837 1
 
0.5%
126.77 2
 
1.0%
126.767763 1
 
0.5%
126.766749 1
 
0.5%
126.7664 1
 
0.5%
126.766046 1
 
0.5%

국문서비스명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
주정차무인관제서비스
150 
IOT보안등
40 
안심교육서비스
 
10
안심분수서비스(대장균)
 
2
대기질모니터링서비스
 
1
Other values (3)
 
3

Length

Max length12
Median length10
Mean length9.0679612
Min length4

Unique

Unique4 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
주정차무인관제서비스 150
72.8%
IOT보안등 40
 
19.4%
안심교육서비스 10
 
4.9%
안심분수서비스(대장균) 2
 
1.0%
대기질모니터링서비스 1
 
0.5%
안심분수수질서비스 1
 
0.5%
생활기상 정보 서비스 1
 
0.5%
수상드론 1
 
0.5%

Length

2023-12-11T06:40:14.658504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:40:14.811883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주정차무인관제서비스 150
72.1%
iot보안등 40
 
19.2%
안심교육서비스 10
 
4.8%
안심분수서비스(대장균 2
 
1.0%
대기질모니터링서비스 1
 
0.5%
안심분수수질서비스 1
 
0.5%
생활기상 1
 
0.5%
정보 1
 
0.5%
서비스 1
 
0.5%
수상드론 1
 
0.5%

영문서비스명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
AMPS
150 
iot_stclass
40 
Relief Education
 
10
Relief Fountain(coliform)
 
2
Air Quality
 
1
Other values (3)
 
3

Length

Max length30
Median length4
Mean length6.4126214
Min length4

Unique

Unique4 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
AMPS 150
72.8%
iot_stclass 40
 
19.4%
Relief Education 10
 
4.9%
Relief Fountain(coliform) 2
 
1.0%
Air Quality 1
 
0.5%
Relief Fountain(Water Quality) 1
 
0.5%
Weather Information 1
 
0.5%
Water Drone 1
 
0.5%

Length

2023-12-11T06:40:14.987592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:40:15.141983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
amps 150
67.3%
iot_stclass 40
 
17.9%
relief 13
 
5.8%
education 10
 
4.5%
fountain(coliform 2
 
0.9%
quality 2
 
0.9%
air 1
 
0.4%
fountain(water 1
 
0.4%
weather 1
 
0.4%
information 1
 
0.4%
Other values (2) 2
 
0.9%

실증서비스디바이스명
Text

Distinct61
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T06:40:15.424423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length9
Mean length9.6213592
Min length9

Characters and Unicode

Total characters1982
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)27.2%

Sample

1st rowae-its-05
2nd rowae-its-05
3rd rowae-its-05
4th rowae-its-05
5th rowae-its-05
ValueCountFrequency (%)
ae-its-05 60
29.1%
ae-its-04 30
14.6%
ae-its-02 30
14.6%
ae-its-03 20
 
9.7%
ae-its-01 10
 
4.9%
ae-esclass22 1
 
0.5%
ae-esclass21 1
 
0.5%
ae-escrtu0 1
 
0.5%
ae-esclass23 1
 
0.5%
ae-esclass24 1
 
0.5%
Other values (51) 51
24.8%
2023-12-11T06:40:15.900352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 354
17.9%
s 253
12.8%
a 248
12.5%
e 219
11.0%
i 158
8.0%
t 155
7.8%
0 154
7.8%
5 64
 
3.2%
2 45
 
2.3%
c 42
 
2.1%
Other values (36) 290
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1180
59.5%
Decimal Number 363
 
18.3%
Dash Punctuation 354
 
17.9%
Uppercase Letter 83
 
4.2%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 253
21.4%
a 248
21.0%
e 219
18.6%
i 158
13.4%
t 155
13.1%
c 42
 
3.6%
l 35
 
3.0%
o 14
 
1.2%
n 10
 
0.8%
g 8
 
0.7%
Other values (12) 38
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
E 40
48.2%
U 10
 
12.0%
R 10
 
12.0%
T 10
 
12.0%
G 3
 
3.6%
S 2
 
2.4%
D 2
 
2.4%
H 2
 
2.4%
L 1
 
1.2%
A 1
 
1.2%
Other values (2) 2
 
2.4%
Decimal Number
ValueCountFrequency (%)
0 154
42.4%
5 64
17.6%
2 45
 
12.4%
4 34
 
9.4%
1 25
 
6.9%
3 25
 
6.9%
9 4
 
1.1%
8 4
 
1.1%
6 4
 
1.1%
7 4
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 354
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1263
63.7%
Common 719
36.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 253
20.0%
a 248
19.6%
e 219
17.3%
i 158
12.5%
t 155
12.3%
c 42
 
3.3%
E 40
 
3.2%
l 35
 
2.8%
o 14
 
1.1%
U 10
 
0.8%
Other values (24) 89
 
7.0%
Common
ValueCountFrequency (%)
- 354
49.2%
0 154
21.4%
5 64
 
8.9%
2 45
 
6.3%
4 34
 
4.7%
1 25
 
3.5%
3 25
 
3.5%
9 4
 
0.6%
8 4
 
0.6%
6 4
 
0.6%
Other values (2) 6
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 354
17.9%
s 253
12.8%
a 248
12.5%
e 219
11.0%
i 158
8.0%
t 155
7.8%
0 154
7.8%
5 64
 
3.2%
2 45
 
2.3%
c 42
 
2.1%
Other values (36) 290
14.6%

스마트시티플랫폼컨테이너아이디정보
Text

Distinct62
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-11T06:40:16.181062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.8834951
Min length8

Characters and Unicode

Total characters1830
Distinct characters21
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

Unique30 ?
Unique (%)14.6%

Sample

1st rowcnt-06-02
2nd rowcnt-06-03
3rd rowcnt-06-04
4th rowcnt-06-05
5th rowcnt-06-06
ValueCountFrequency (%)
cnt-data 40
 
19.4%
cnt-period 16
 
7.8%
cnt-01-05 5
 
2.4%
cnt-01-02 5
 
2.4%
cnt-01-01 5
 
2.4%
cnt-01-04 5
 
2.4%
cnt-01-03 5
 
2.4%
cnt-01-06 5
 
2.4%
cnt-01-07 5
 
2.4%
cnt-01-08 5
 
2.4%
Other values (52) 110
53.4%
2023-12-11T06:40:16.635459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 356
19.5%
0 300
16.4%
t 246
13.4%
c 206
11.3%
n 206
11.3%
a 80
 
4.4%
1 80
 
4.4%
d 56
 
3.1%
2 55
 
3.0%
3 45
 
2.5%
Other values (11) 200
10.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 858
46.9%
Decimal Number 600
32.8%
Dash Punctuation 356
19.5%
Uppercase Letter 16
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 300
50.0%
1 80
 
13.3%
2 55
 
9.2%
3 45
 
7.5%
6 25
 
4.2%
5 25
 
4.2%
4 25
 
4.2%
9 15
 
2.5%
8 15
 
2.5%
7 15
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
t 246
28.7%
c 206
24.0%
n 206
24.0%
a 80
 
9.3%
d 56
 
6.5%
i 16
 
1.9%
r 16
 
1.9%
e 16
 
1.9%
o 16
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 356
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 956
52.2%
Latin 874
47.8%

Most frequent character per script

Common
ValueCountFrequency (%)
- 356
37.2%
0 300
31.4%
1 80
 
8.4%
2 55
 
5.8%
3 45
 
4.7%
6 25
 
2.6%
5 25
 
2.6%
4 25
 
2.6%
9 15
 
1.6%
8 15
 
1.6%
Latin
ValueCountFrequency (%)
t 246
28.1%
c 206
23.6%
n 206
23.6%
a 80
 
9.2%
d 56
 
6.4%
i 16
 
1.8%
r 16
 
1.8%
e 16
 
1.8%
P 16
 
1.8%
o 16
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 356
19.5%
0 300
16.4%
t 246
13.4%
c 206
11.3%
n 206
11.3%
a 80
 
4.4%
1 80
 
4.4%
d 56
 
3.1%
2 55
 
3.0%
3 45
 
2.5%
Other values (11) 200
10.9%

Interactions

2023-12-11T06:40:12.544449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.332414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.657226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.434905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:40:16.749746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서비스아이디설치장소설치위도설치경도국문서비스명영문서비스명실증서비스디바이스명스마트시티플랫폼컨테이너아이디정보
서비스아이디1.0000.9820.6620.6081.0001.0001.0000.000
설치장소0.9821.0000.9981.0000.9820.9821.0000.000
설치위도0.6620.9981.0000.9810.6620.6621.0000.000
설치경도0.6081.0000.9811.0000.6080.6081.0000.000
국문서비스명1.0000.9820.6620.6081.0001.0001.0000.000
영문서비스명1.0000.9820.6620.6081.0001.0001.0000.000
실증서비스디바이스명1.0001.0001.0001.0001.0001.0001.0000.000
스마트시티플랫폼컨테이너아이디정보0.0000.0000.0000.0000.0000.0000.0001.000
2023-12-11T06:40:16.890086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영문서비스명국문서비스명설치장소서비스아이디
영문서비스명1.0001.0000.8871.000
국문서비스명1.0001.0000.8871.000
설치장소0.8870.8871.0000.887
서비스아이디1.0001.0000.8871.000
2023-12-11T06:40:17.021706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치위도설치경도서비스아이디설치장소국문서비스명영문서비스명
설치위도1.0000.2330.4700.9520.4700.470
설치경도0.2331.0000.4180.9640.4180.418
서비스아이디0.4700.4181.0000.8871.0001.000
설치장소0.9520.9640.8871.0000.8870.887
국문서비스명0.4700.4181.0000.8871.0001.000
영문서비스명0.4700.4181.0000.8871.0001.000

Missing values

2023-12-11T06:40:12.766119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:40:12.886648image/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.
2023-12-11T06:40:13.001629image/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

서비스아이디설치장소설치위도설치경도국문서비스명영문서비스명실증서비스디바이스명스마트시티플랫폼컨테이너아이디정보
0its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-02
1its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-03
2its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-04
3its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-05
4its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-06
5its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-07
6its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-08
7its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-09
8its_parking킨텍스IC37.64126.76주정차무인관제서비스AMPSae-its-02cnt-01-05
9its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-10
서비스아이디설치장소설치위도설치경도국문서비스명영문서비스명실증서비스디바이스명스마트시티플랫폼컨테이너아이디정보
196its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-05-04
197its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-05-05
198its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-05-06
199its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-05-07
200its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-05-08
201its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-05-09
202its_parking킨텍스IC37.64126.76주정차무인관제서비스AMPSae-its-02cnt-01-04
203its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-05-10
204its_parking냉천초등학교37.67126.79주정차무인관제서비스AMPSae-its-05cnt-06-01
205its_parking성저초등학교37.69126.76주정차무인관제서비스AMPSae-its-04cnt-03-02