Overview

Dataset statistics

Number of variables7
Number of observations51
Missing cells14
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory60.5 B

Variable types

Numeric1
Text4
Categorical2

Dataset

Description경기도 주요관광지 방문객 실태조사(국가승인통계 제210015호)의 조사지점 정보(지점명, 조사위치, 주소 등)를 제공합니다. 경기도 주요관광지 관련 관광정책 수립, 관광업계의 시장 대응, 학계 및 연구기관 등에서 활용 가능합니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15105191/fileData.do

Alerts

연번 is highly overall correlated with 계수방법High correlation
계수방법 is highly overall correlated with 연번High correlation
출구개수 is highly imbalanced (76.1%)Imbalance
도로명주소 has 9 (17.6%) missing valuesMissing
지번주소 has 5 (9.8%) missing valuesMissing
연번 has unique valuesUnique
지점명 has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:42:53.115886
Analysis finished2024-04-21 02:42:54.460149
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-04-21T11:42:54.591018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q113.5
median26
Q338.5
95-th percentile48.5
Maximum51
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.866069
Coefficient of variation (CV)0.57177187
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)13
Skewness0
Sum1326
Variance221
MonotonicityStrictly increasing
2024-04-21T11:42:54.842488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
2 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
51 1
2.0%
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%

지점명
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size536.0 B
2024-04-21T11:42:55.550497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length6.9215686
Min length2

Characters and Unicode

Total characters353
Distinct characters168
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st row구름산산림욕장
2nd row북한산국립공원
3rd row임진각관광지
4th row용문산관광지
5th row소요산관광지(자재암)
ValueCountFrequency (%)
구름산산림욕장 1
 
1.9%
허브아일랜드 1
 
1.9%
신구대학교식물원 1
 
1.9%
행주산성 1
 
1.9%
남한산성행궁 1
 
1.9%
포천아트밸리 1
 
1.9%
헤이리예술마을 1
 
1.9%
수원화성박물관 1
 
1.9%
융건릉 1
 
1.9%
영릉(세종대왕 1
 
1.9%
Other values (42) 42
80.8%
2024-04-21T11:42:56.467355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 13
 
3.7%
원 11
 
3.1%
관 9
 
2.5%
장 8
 
2.3%
대 7
 
2.0%
한 7
 
2.0%
) 6
 
1.7%
( 6
 
1.7%
지 6
 
1.7%
구 6
 
1.7%
Other values (158) 274
77.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
96.3%
Close Punctuation 6
 
1.7%
Open Punctuation 6
 
1.7%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 13
 
3.8%
원 11
 
3.2%
관 9
 
2.6%
장 8
 
2.4%
대 7
 
2.1%
한 7
 
2.1%
지 6
 
1.8%
구 6
 
1.8%
리 6
 
1.8%
수 6
 
1.8%
Other values (155) 261
76.8%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
96.3%
Common 13
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 13
 
3.8%
원 11
 
3.2%
관 9
 
2.6%
장 8
 
2.4%
대 7
 
2.1%
한 7
 
2.1%
지 6
 
1.8%
구 6
 
1.8%
리 6
 
1.8%
수 6
 
1.8%
Other values (155) 261
76.8%
Common
ValueCountFrequency (%)
) 6
46.2%
( 6
46.2%
1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
96.3%
ASCII 13
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 13
 
3.8%
원 11
 
3.2%
관 9
 
2.6%
장 8
 
2.4%
대 7
 
2.1%
한 7
 
2.1%
지 6
 
1.8%
구 6
 
1.8%
리 6
 
1.8%
수 6
 
1.8%
Other values (155) 261
76.8%
ASCII
ValueCountFrequency (%)
) 6
46.2%
( 6
46.2%
1
 
7.7%

조사위치
Text

Distinct36
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Memory size536.0 B
2024-04-21T11:42:57.011720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length7.5294118
Min length4

Characters and Unicode

Total characters384
Distinct characters105
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)60.8%

Sample

1st row계측기 설치지점(하안동 229)
2nd row매표소 앞 입구
3rd row주차장 왼편
4th row용문산 매표소 앞
5th row소요산관광지원센터 앞
ValueCountFrequency (%)
앞 30
24.8%
매표소 15
12.4%
출입구 14
11.6%
입구 8
 
6.6%
출구 6
 
5.0%
정문 4
 
3.3%
주차장 3
 
2.5%
및 3
 
2.5%
건물 2
 
1.7%
다리 2
 
1.7%
Other values (32) 34
28.1%
2024-04-21T11:42:57.806082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
18.2%
앞 30
 
7.8%
구 29
 
7.6%
입 23
 
6.0%
출 20
 
5.2%
소 18
 
4.7%
매 17
 
4.4%
표 17
 
4.4%
장 8
 
2.1%
문 7
 
1.8%
Other values (95) 145
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 306
79.7%
Space Separator 70
 
18.2%
Decimal Number 4
 
1.0%
Dash Punctuation 2
 
0.5%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 30
 
9.8%
구 29
 
9.5%
입 23
 
7.5%
출 20
 
6.5%
소 18
 
5.9%
매 17
 
5.6%
표 17
 
5.6%
장 8
 
2.6%
문 7
 
2.3%
차 4
 
1.3%
Other values (88) 133
43.5%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 1
25.0%
9 1
25.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 306
79.7%
Common 78
 
20.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 30
 
9.8%
구 29
 
9.5%
입 23
 
7.5%
출 20
 
6.5%
소 18
 
5.9%
매 17
 
5.6%
표 17
 
5.6%
장 8
 
2.6%
문 7
 
2.3%
차 4
 
1.3%
Other values (88) 133
43.5%
Common
ValueCountFrequency (%)
70
89.7%
2 2
 
2.6%
- 2
 
2.6%
1 1
 
1.3%
) 1
 
1.3%
9 1
 
1.3%
( 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 306
79.7%
ASCII 78
 
20.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
70
89.7%
2 2
 
2.6%
- 2
 
2.6%
1 1
 
1.3%
) 1
 
1.3%
9 1
 
1.3%
( 1
 
1.3%
Hangul
ValueCountFrequency (%)
앞 30
 
9.8%
구 29
 
9.5%
입 23
 
7.5%
출 20
 
6.5%
소 18
 
5.9%
매 17
 
5.6%
표 17
 
5.6%
장 8
 
2.6%
문 7
 
2.3%
차 4
 
1.3%
Other values (88) 133
43.5%

출구개수
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size536.0 B
1
49 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 49
96.1%
2 2
 
3.9%

Length

2024-04-21T11:42:58.020075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:42:58.183567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 49
96.1%
2 2
 
3.9%

계수방법
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size536.0 B
입장권
33 
계수기
15 
예약
 
3

Length

Max length3
Median length3
Mean length2.9411765
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row계수기
2nd row계수기
3rd row입장권
4th row계수기
5th row입장권

Common Values

ValueCountFrequency (%)
입장권 33
64.7%
계수기 15
29.4%
예약 3
 
5.9%

Length

2024-04-21T11:42:58.355471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:42:58.530603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
입장권 33
64.7%
계수기 15
29.4%
예약 3
 
5.9%

도로명주소
Text

MISSING 

Distinct41
Distinct (%)97.6%
Missing9
Missing (%)17.6%
Memory size536.0 B
2024-04-21T11:42:59.397961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length19.142857
Min length13

Characters and Unicode

Total characters804
Distinct characters143
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)95.2%

Sample

1st row경기 양주시 장흥면 호국로550번길 102-108
2nd row경기 파주시 문산읍 임진각로 164
3rd row경기 동두천시 평화로2910번길 145
4th row경기 가평군 가평읍 자라섬로 60
5th row경기 연천군 전곡읍 선사로 76
ValueCountFrequency (%)
경기 41
 
20.1%
용인시 4
 
2.0%
파주시 4
 
2.0%
가평군 4
 
2.0%
포천시 3
 
1.5%
과천시 3
 
1.5%
양평군 3
 
1.5%
신북면 2
 
1.0%
광명로 2
 
1.0%
상면 2
 
1.0%
Other values (123) 136
66.7%
2024-04-21T11:43:00.845916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
162
20.1%
기 46
 
5.7%
경 42
 
5.2%
로 36
 
4.5%
시 35
 
4.4%
1 31
 
3.9%
2 19
 
2.4%
9 15
 
1.9%
면 15
 
1.9%
0 14
 
1.7%
Other values (133) 389
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 498
61.9%
Space Separator 162
 
20.1%
Decimal Number 140
 
17.4%
Dash Punctuation 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 46
 
9.2%
경 42
 
8.4%
로 36
 
7.2%
시 35
 
7.0%
면 15
 
3.0%
구 14
 
2.8%
길 13
 
2.6%
양 11
 
2.2%
평 10
 
2.0%
천 8
 
1.6%
Other values (121) 268
53.8%
Decimal Number
ValueCountFrequency (%)
1 31
22.1%
2 19
13.6%
9 15
10.7%
0 14
10.0%
4 12
 
8.6%
3 12
 
8.6%
7 10
 
7.1%
6 10
 
7.1%
8 10
 
7.1%
5 7
 
5.0%
Space Separator
ValueCountFrequency (%)
162
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 498
61.9%
Common 306
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 46
 
9.2%
경 42
 
8.4%
로 36
 
7.2%
시 35
 
7.0%
면 15
 
3.0%
구 14
 
2.8%
길 13
 
2.6%
양 11
 
2.2%
평 10
 
2.0%
천 8
 
1.6%
Other values (121) 268
53.8%
Common
ValueCountFrequency (%)
162
52.9%
1 31
 
10.1%
2 19
 
6.2%
9 15
 
4.9%
0 14
 
4.6%
4 12
 
3.9%
3 12
 
3.9%
7 10
 
3.3%
6 10
 
3.3%
8 10
 
3.3%
Other values (2) 11
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 498
61.9%
ASCII 306
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
162
52.9%
1 31
 
10.1%
2 19
 
6.2%
9 15
 
4.9%
0 14
 
4.6%
4 12
 
3.9%
3 12
 
3.9%
7 10
 
3.3%
6 10
 
3.3%
8 10
 
3.3%
Other values (2) 11
 
3.6%
Hangul
ValueCountFrequency (%)
기 46
 
9.2%
경 42
 
8.4%
로 36
 
7.2%
시 35
 
7.0%
면 15
 
3.0%
구 14
 
2.8%
길 13
 
2.6%
양 11
 
2.2%
평 10
 
2.0%
천 8
 
1.6%
Other values (121) 268
53.8%

지번주소
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing5
Missing (%)9.8%
Memory size536.0 B
2024-04-21T11:43:02.065288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length18.521739
Min length13

Characters and Unicode

Total characters852
Distinct characters108
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row경기 광명시 하안동 141-7
2nd row경기 파주시 마정리 1325-5
3rd row경기 양평군 용문면 신점리 520
4th row경기 동두천시 상봉암동 산1-1
5th row경기 가평군 가평읍 달전리 산7
ValueCountFrequency (%)
경기 43
 
19.9%
양평군 4
 
1.9%
가평군 4
 
1.9%
용인시 4
 
1.9%
안산시 4
 
1.9%
포천시 3
 
1.4%
과천시 3
 
1.4%
경기도 3
 
1.4%
막계동 3
 
1.4%
성남시 2
 
0.9%
Other values (129) 143
66.2%
2024-04-21T11:43:03.361837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
171
20.1%
기 49
 
5.8%
경 46
 
5.4%
시 37
 
4.3%
1 36
 
4.2%
- 32
 
3.8%
동 28
 
3.3%
2 25
 
2.9%
리 22
 
2.6%
3 20
 
2.3%
Other values (98) 386
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 485
56.9%
Space Separator 171
 
20.1%
Decimal Number 164
 
19.2%
Dash Punctuation 32
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 49
 
10.1%
경 46
 
9.5%
시 37
 
7.6%
동 28
 
5.8%
리 22
 
4.5%
산 20
 
4.1%
구 14
 
2.9%
면 13
 
2.7%
양 13
 
2.7%
평 10
 
2.1%
Other values (86) 233
48.0%
Decimal Number
ValueCountFrequency (%)
1 36
22.0%
2 25
15.2%
3 20
12.2%
6 15
9.1%
4 14
 
8.5%
7 13
 
7.9%
5 13
 
7.9%
9 10
 
6.1%
8 9
 
5.5%
0 9
 
5.5%
Space Separator
ValueCountFrequency (%)
171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 485
56.9%
Common 367
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 49
 
10.1%
경 46
 
9.5%
시 37
 
7.6%
동 28
 
5.8%
리 22
 
4.5%
산 20
 
4.1%
구 14
 
2.9%
면 13
 
2.7%
양 13
 
2.7%
평 10
 
2.1%
Other values (86) 233
48.0%
Common
ValueCountFrequency (%)
171
46.6%
1 36
 
9.8%
- 32
 
8.7%
2 25
 
6.8%
3 20
 
5.4%
6 15
 
4.1%
4 14
 
3.8%
7 13
 
3.5%
5 13
 
3.5%
9 10
 
2.7%
Other values (2) 18
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 485
56.9%
ASCII 367
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
171
46.6%
1 36
 
9.8%
- 32
 
8.7%
2 25
 
6.8%
3 20
 
5.4%
6 15
 
4.1%
4 14
 
3.8%
7 13
 
3.5%
5 13
 
3.5%
9 10
 
2.7%
Other values (2) 18
 
4.9%
Hangul
ValueCountFrequency (%)
기 49
 
10.1%
경 46
 
9.5%
시 37
 
7.6%
동 28
 
5.8%
리 22
 
4.5%
산 20
 
4.1%
구 14
 
2.9%
면 13
 
2.7%
양 13
 
2.7%
평 10
 
2.1%
Other values (86) 233
48.0%

Interactions

2024-04-21T11:42:53.838415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:43:03.528987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지점명조사위치출구개수계수방법도로명주소지번주소
연번1.0001.0000.7940.0000.8101.0001.000
지점명1.0001.0001.0001.0001.0001.0001.000
조사위치0.7941.0001.0001.0000.9871.0001.000
출구개수0.0001.0001.0001.0000.0001.0001.000
계수방법0.8101.0000.9870.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.000
2024-04-21T11:43:03.708355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출구개수계수방법
출구개수1.0000.000
계수방법0.0001.000
2024-04-21T11:43:03.849063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번출구개수계수방법
연번1.0000.0000.510
출구개수0.0001.0000.000
계수방법0.5100.0001.000

Missing values

2024-04-21T11:42:54.023750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:42:54.224158image/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.
2024-04-21T11:42:54.381782image/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

연번지점명조사위치출구개수계수방법도로명주소지번주소
01구름산산림욕장계측기 설치지점(하안동 229)1계수기<NA>경기 광명시 하안동 141-7
12북한산국립공원매표소 앞 입구1계수기경기 양주시 장흥면 호국로550번길 102-108<NA>
23임진각관광지주차장 왼편2입장권경기 파주시 문산읍 임진각로 164경기 파주시 마정리 1325-5
34용문산관광지용문산 매표소 앞1계수기<NA>경기 양평군 용문면 신점리 520
45소요산관광지(자재암)소요산관광지원센터 앞1입장권경기 동두천시 평화로2910번길 145경기 동두천시 상봉암동 산1-1
56자라섬캠핑장캠핑장 내1예약경기 가평군 가평읍 자라섬로 60경기 가평군 가평읍 달전리 산7
67한탄강관광지오토캠핑장캠핑장 내1예약경기 연천군 전곡읍 선사로 76경기 연천군 전곡읍 전곡리 692-1
78남한강자전거길(양평구간)철교쉼터 앞1계수기경기 양평군 양서면 남한강자전거길 1043<NA>
89안산갈대습지공원주차장-환경생태관 사이 탐방로 입구1예약경기 안산시 상록구 갈대습지로 76경기 안산시 상록구 사동 1031-8
910의정부실내빙상장건물 입구1입장권경기 의정부시 체육로 136 의정부실내빙상장경기 의정부시 녹양동 284-4
연번지점명조사위치출구개수계수방법도로명주소지번주소
4142시흥갯골생태공원주차장 출구 앞1계수기경기 시흥시 동서로 287경기 시흥시 장곡동 724-10
4243수리산입구등산객 출입구 앞1계수기<NA>경기 안산시 수암동 277-2
4344관악산자연학습장학습장 입구1계수기<NA>경기 안양시 동안구 비산동 산42-1
4445대부해솔길(종현어촌마을입구)종현어촌체험마을 입구1계수기경기 안산시 단원구 구봉길 240경기 안산시 단원구 대부북동 1870-37
4546잣향기푸른숲정문 매표소1입장권경기 가평군 상면 축령로 289-146경기 가평군 상면 행현리 922-1
4647비둘기낭폭포출입구 앞1계수기<NA>경기 포천시 영북면 대회산리 415-2
4748두물머리두물머리나루터1계수기<NA>경기 양평군 양서면 양수리 711-1
4849마장호수마장근린공원1계수기경기 파주시 광탄면 기산로 313<NA>
4950포천한탄강하늘다리다리 입구1계수기경기 포천시 영북면 비둘기낭길 207경기 포천시 영북면 대회산리 377
5051재인폭포스카이워크 앞 다리1계수기<NA>경기 연천군 연천읍 고문리 산21