Overview

Dataset statistics

Number of variables15
Number of observations88
Missing cells12
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.0 KiB
Average record size in memory128.5 B

Variable types

Categorical2
Text5
Numeric7
DateTime1

Dataset

Description경기도_노인주거복지시설(양로시설, 공동생활가정) 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=7QSG3H7LGJ3HMYQ3QBHJ27657101&infSeq=1

Alerts

우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
입소정원 is highly overall correlated with 입소현원-계 and 3 other fieldsHigh correlation
입소현원-계 is highly overall correlated with 입소정원 and 3 other fieldsHigh correlation
입소현원-남자 is highly overall correlated with 입소정원 and 1 other fieldsHigh correlation
입소현원-여자 is highly overall correlated with 입소정원 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 우편번호 and 2 other fieldsHigh correlation
시설종류 is highly overall correlated with 입소정원 and 1 other fieldsHigh correlation
전화번호 has 1 (1.1%) missing valuesMissing
FAX번호 has 10 (11.4%) missing valuesMissing
설치일자 has 1 (1.1%) missing valuesMissing
시설명 has unique valuesUnique
입소정원 has 1 (1.1%) zerosZeros
입소현원-계 has 3 (3.4%) zerosZeros
입소현원-남자 has 27 (30.7%) zerosZeros
입소현원-여자 has 6 (6.8%) zerosZeros

Reproduction

Analysis started2024-03-12 23:14:42.506396
Analysis finished2024-03-12 23:14:47.053443
Duration4.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size836.0 B
고양시
8 
여주시
7 
양평군
6 
포천시
6 
안성시
6 
Other values (22)
55 

Length

Max length4
Median length3
Mean length3.0568182
Min length3

Unique

Unique9 ?
Unique (%)10.2%

Sample

1st row가평군
2nd row가평군
3rd row가평군
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
고양시 8
 
9.1%
여주시 7
 
8.0%
양평군 6
 
6.8%
포천시 6
 
6.8%
안성시 6
 
6.8%
화성시 5
 
5.7%
수원시 5
 
5.7%
안산시 5
 
5.7%
파주시 4
 
4.5%
남양주시 4
 
4.5%
Other values (17) 32
36.4%

Length

2024-03-13T08:14:47.119020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 8
 
9.1%
여주시 7
 
8.0%
양평군 6
 
6.8%
포천시 6
 
6.8%
안성시 6
 
6.8%
화성시 5
 
5.7%
수원시 5
 
5.7%
안산시 5
 
5.7%
파주시 4
 
4.5%
남양주시 4
 
4.5%
Other values (17) 32
36.4%

시설종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
양로시설
59 
노인공동생활가정
29 

Length

Max length8
Median length4
Mean length5.3181818
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양로시설
2nd row양로시설
3rd row노인공동생활가정
4th row노인공동생활가정
5th row양로시설

Common Values

ValueCountFrequency (%)
양로시설 59
67.0%
노인공동생활가정 29
33.0%

Length

2024-03-13T08:14:47.212450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:14:47.294262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양로시설 59
67.0%
노인공동생활가정 29
33.0%

시설명
Text

UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:47.498106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length6.4318182
Min length3

Characters and Unicode

Total characters566
Distinct characters178
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)100.0%

Sample

1st row아름다운집
2nd row열린효경원
3rd row원방의집
4th row기쁨이가득한집
5th row늘푸른실버타운
ValueCountFrequency (%)
집 4
 
3.8%
사랑의 2
 
1.9%
실버타운 2
 
1.9%
노인공동생활가정 2
 
1.9%
무지개양로원 1
 
0.9%
나눔의샘양로원 1
 
0.9%
의왕vip실버타운 1
 
0.9%
양로원 1
 
0.9%
마리아의집 1
 
0.9%
더힐실버타운 1
 
0.9%
Other values (90) 90
84.9%
2024-03-13T08:14:47.841455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 36
 
6.4%
원 34
 
6.0%
집 29
 
5.1%
양 22
 
3.9%
로 21
 
3.7%
18
 
3.2%
운 16
 
2.8%
타 15
 
2.7%
버 14
 
2.5%
실 14
 
2.5%
Other values (168) 347
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 543
95.9%
Space Separator 18
 
3.2%
Uppercase Letter 3
 
0.5%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 36
 
6.6%
원 34
 
6.3%
집 29
 
5.3%
양 22
 
4.1%
로 21
 
3.9%
운 16
 
2.9%
타 15
 
2.8%
버 14
 
2.6%
실 14
 
2.6%
마 11
 
2.0%
Other values (162) 331
61.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
V 1
33.3%
I 1
33.3%
Space Separator
ValueCountFrequency (%)
18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 543
95.9%
Common 20
 
3.5%
Latin 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 36
 
6.6%
원 34
 
6.3%
집 29
 
5.3%
양 22
 
4.1%
로 21
 
3.9%
운 16
 
2.9%
타 15
 
2.8%
버 14
 
2.6%
실 14
 
2.6%
마 11
 
2.0%
Other values (162) 331
61.0%
Common
ValueCountFrequency (%)
18
90.0%
) 1
 
5.0%
( 1
 
5.0%
Latin
ValueCountFrequency (%)
P 1
33.3%
V 1
33.3%
I 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 543
95.9%
ASCII 23
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 36
 
6.6%
원 34
 
6.3%
집 29
 
5.3%
양 22
 
4.1%
로 21
 
3.9%
운 16
 
2.9%
타 15
 
2.8%
버 14
 
2.6%
실 14
 
2.6%
마 11
 
2.0%
Other values (162) 331
61.0%
ASCII
ValueCountFrequency (%)
18
78.3%
) 1
 
4.3%
( 1
 
4.3%
P 1
 
4.3%
V 1
 
4.3%
I 1
 
4.3%

도로명 주소
Text

Distinct87
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:48.069724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length20.75
Min length14

Characters and Unicode

Total characters1826
Distinct characters156
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

Unique86 ?
Unique (%)97.7%

Sample

1st row경기도 가평군 가평읍 복장포길 50
2nd row경기도 가평군 설악면 유명로 1906-22
3rd row경기도 가평군 가평읍 호반로 2135
4th row경기도 고양시 덕양구 원당로319번길 73
5th row경기도 고양시 일산동구 성현로 185-33
ValueCountFrequency (%)
경기도 88
 
20.8%
고양시 8
 
1.9%
여주시 7
 
1.7%
양평군 6
 
1.4%
포천시 6
 
1.4%
안성시 6
 
1.4%
수원시 5
 
1.2%
안산시 5
 
1.2%
화성시 5
 
1.2%
장안구 4
 
0.9%
Other values (234) 283
66.9%
2024-03-13T08:14:48.411166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
335
 
18.3%
도 91
 
5.0%
기 90
 
4.9%
경 88
 
4.8%
시 82
 
4.5%
1 73
 
4.0%
로 60
 
3.3%
2 51
 
2.8%
길 49
 
2.7%
- 40
 
2.2%
Other values (146) 867
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1096
60.0%
Decimal Number 355
 
19.4%
Space Separator 335
 
18.3%
Dash Punctuation 40
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 91
 
8.3%
기 90
 
8.2%
경 88
 
8.0%
시 82
 
7.5%
로 60
 
5.5%
길 49
 
4.5%
면 37
 
3.4%
양 33
 
3.0%
구 23
 
2.1%
성 22
 
2.0%
Other values (134) 521
47.5%
Decimal Number
ValueCountFrequency (%)
1 73
20.6%
2 51
14.4%
3 38
10.7%
4 34
9.6%
7 32
9.0%
5 31
8.7%
6 28
 
7.9%
9 25
 
7.0%
0 22
 
6.2%
8 21
 
5.9%
Space Separator
ValueCountFrequency (%)
335
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1096
60.0%
Common 730
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 91
 
8.3%
기 90
 
8.2%
경 88
 
8.0%
시 82
 
7.5%
로 60
 
5.5%
길 49
 
4.5%
면 37
 
3.4%
양 33
 
3.0%
구 23
 
2.1%
성 22
 
2.0%
Other values (134) 521
47.5%
Common
ValueCountFrequency (%)
335
45.9%
1 73
 
10.0%
2 51
 
7.0%
- 40
 
5.5%
3 38
 
5.2%
4 34
 
4.7%
7 32
 
4.4%
5 31
 
4.2%
6 28
 
3.8%
9 25
 
3.4%
Other values (2) 43
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1096
60.0%
ASCII 730
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
335
45.9%
1 73
 
10.0%
2 51
 
7.0%
- 40
 
5.5%
3 38
 
5.2%
4 34
 
4.7%
7 32
 
4.4%
5 31
 
4.2%
6 28
 
3.8%
9 25
 
3.4%
Other values (2) 43
 
5.9%
Hangul
ValueCountFrequency (%)
도 91
 
8.3%
기 90
 
8.2%
경 88
 
8.0%
시 82
 
7.5%
로 60
 
5.5%
길 49
 
4.5%
면 37
 
3.4%
양 33
 
3.0%
구 23
 
2.1%
성 22
 
2.0%
Other values (134) 521
47.5%

지번 주소
Text

Distinct87
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:48.620725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length21.920455
Min length17

Characters and Unicode

Total characters1929
Distinct characters144
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

Unique86 ?
Unique (%)97.7%

Sample

1st row경기도 가평군 가평읍 복장리 243번지
2nd row경기도 가평군 설악면 회곡리 6-2번지
3rd row경기도 가평군 가평읍 이화리 118번지
4th row경기도 고양시 덕양구 원당동 889번지
5th row경기도 고양시 일산동구 성석동 524-28번지
ValueCountFrequency (%)
경기도 88
 
20.8%
고양시 8
 
1.9%
여주시 7
 
1.7%
포천시 6
 
1.4%
안성시 6
 
1.4%
양평군 6
 
1.4%
수원시 5
 
1.2%
안산시 5
 
1.2%
화성시 5
 
1.2%
파주시 4
 
0.9%
Other values (238) 283
66.9%
2024-03-13T08:14:48.922494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
335
 
17.4%
지 93
 
4.8%
도 91
 
4.7%
기 90
 
4.7%
경 88
 
4.6%
번 88
 
4.6%
시 83
 
4.3%
- 66
 
3.4%
1 65
 
3.4%
리 48
 
2.5%
Other values (134) 882
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1201
62.3%
Space Separator 335
 
17.4%
Decimal Number 327
 
17.0%
Dash Punctuation 66
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 93
 
7.7%
도 91
 
7.6%
기 90
 
7.5%
경 88
 
7.3%
번 88
 
7.3%
시 83
 
6.9%
리 48
 
4.0%
동 47
 
3.9%
면 37
 
3.1%
양 30
 
2.5%
Other values (122) 506
42.1%
Decimal Number
ValueCountFrequency (%)
1 65
19.9%
2 46
14.1%
3 40
12.2%
5 39
11.9%
4 36
11.0%
6 30
9.2%
8 26
 
8.0%
7 21
 
6.4%
0 13
 
4.0%
9 11
 
3.4%
Space Separator
ValueCountFrequency (%)
335
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1201
62.3%
Common 728
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 93
 
7.7%
도 91
 
7.6%
기 90
 
7.5%
경 88
 
7.3%
번 88
 
7.3%
시 83
 
6.9%
리 48
 
4.0%
동 47
 
3.9%
면 37
 
3.1%
양 30
 
2.5%
Other values (122) 506
42.1%
Common
ValueCountFrequency (%)
335
46.0%
- 66
 
9.1%
1 65
 
8.9%
2 46
 
6.3%
3 40
 
5.5%
5 39
 
5.4%
4 36
 
4.9%
6 30
 
4.1%
8 26
 
3.6%
7 21
 
2.9%
Other values (2) 24
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1201
62.3%
ASCII 728
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
335
46.0%
- 66
 
9.1%
1 65
 
8.9%
2 46
 
6.3%
3 40
 
5.5%
5 39
 
5.4%
4 36
 
4.9%
6 30
 
4.1%
8 26
 
3.6%
7 21
 
2.9%
Other values (2) 24
 
3.3%
Hangul
ValueCountFrequency (%)
지 93
 
7.7%
도 91
 
7.6%
기 90
 
7.5%
경 88
 
7.3%
번 88
 
7.3%
시 83
 
6.9%
리 48
 
4.0%
동 47
 
3.9%
면 37
 
3.1%
양 30
 
2.5%
Other values (122) 506
42.1%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13855.636
Minimum10023
Maximum18586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:49.033033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10023
5-th percentile10256.2
Q111425.75
median12687.5
Q316291.5
95-th percentile18312
Maximum18586
Range8563
Interquartile range (IQR)4865.75

Descriptive statistics

Standard deviation2721.5085
Coefficient of variation (CV)0.19641887
Kurtosis-1.3173998
Mean13855.636
Median Absolute Deviation (MAD)2239
Skewness0.28477128
Sum1219296
Variance7406608.5
MonotonicityNot monotonic
2024-03-13T08:14:49.141684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17500 2
 
2.3%
10311 2
 
2.3%
12430 1
 
1.1%
12649 1
 
1.1%
11807 1
 
1.1%
16107 1
 
1.1%
16066 1
 
1.1%
16011 1
 
1.1%
16895 1
 
1.1%
11015 1
 
1.1%
Other values (76) 76
86.4%
ValueCountFrequency (%)
10023 1
1.1%
10026 1
1.1%
10038 1
1.1%
10251 1
1.1%
10252 1
1.1%
10264 1
1.1%
10290 1
1.1%
10311 2
2.3%
10464 1
1.1%
10503 1
1.1%
ValueCountFrequency (%)
18586 1
1.1%
18556 1
1.1%
18545 1
1.1%
18541 1
1.1%
18522 1
1.1%
17922 1
1.1%
17800 1
1.1%
17742 1
1.1%
17603 1
1.1%
17598 1
1.1%

전화번호
Text

MISSING 

Distinct87
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Memory size836.0 B
2024-03-13T08:14:49.340625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.988506
Min length11

Characters and Unicode

Total characters1043
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)100.0%

Sample

1st row031-582-8061
2nd row031-585-5671
3rd row031-581-1435
4th row031-963-2872
5th row031-977-0421
ValueCountFrequency (%)
031-582-8061 1
 
1.1%
031-851-5453 1
 
1.1%
031-462-7776 1
 
1.1%
031-457-7654 1
 
1.1%
031-426-3886 1
 
1.1%
031-282-5601 1
 
1.1%
031-834-4084 1
 
1.1%
031-886-2442 1
 
1.1%
031-548-1201 1
 
1.1%
031-884-0214 1
 
1.1%
Other values (77) 77
88.5%
2024-03-13T08:14:49.620808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 174
16.7%
1 150
14.4%
3 147
14.1%
0 138
13.2%
7 73
7.0%
5 72
6.9%
8 66
 
6.3%
2 61
 
5.8%
6 60
 
5.8%
9 56
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 869
83.3%
Dash Punctuation 174
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 150
17.3%
3 147
16.9%
0 138
15.9%
7 73
8.4%
5 72
8.3%
8 66
7.6%
2 61
7.0%
6 60
 
6.9%
9 56
 
6.4%
4 46
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 174
16.7%
1 150
14.4%
3 147
14.1%
0 138
13.2%
7 73
7.0%
5 72
6.9%
8 66
 
6.3%
2 61
 
5.8%
6 60
 
5.8%
9 56
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 174
16.7%
1 150
14.4%
3 147
14.1%
0 138
13.2%
7 73
7.0%
5 72
6.9%
8 66
 
6.3%
2 61
 
5.8%
6 60
 
5.8%
9 56
 
5.4%

FAX번호
Text

MISSING 

Distinct78
Distinct (%)100.0%
Missing10
Missing (%)11.4%
Memory size836.0 B
2024-03-13T08:14:49.848313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.012821
Min length11

Characters and Unicode

Total characters937
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)100.0%

Sample

1st row031-584-5675
2nd row031-624-7452
3rd row031-976-0424
4th row031-901-8445
5th row031-979-7719
ValueCountFrequency (%)
031-577-7924 1
 
1.3%
031-631-3520 1
 
1.3%
031-851-2209 1
 
1.3%
031-462-7776 1
 
1.3%
0505-315-2000 1
 
1.3%
031-426-3888 1
 
1.3%
031-834-8380 1
 
1.3%
031-886-2440 1
 
1.3%
031-548-1202 1
 
1.3%
031-882-1738 1
 
1.3%
Other values (68) 68
87.2%
2024-03-13T08:14:50.178529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 156
16.6%
0 132
14.1%
3 123
13.1%
1 121
12.9%
7 75
8.0%
5 63
6.7%
6 59
 
6.3%
2 58
 
6.2%
8 56
 
6.0%
9 49
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 781
83.4%
Dash Punctuation 156
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 132
16.9%
3 123
15.7%
1 121
15.5%
7 75
9.6%
5 63
8.1%
6 59
7.6%
2 58
7.4%
8 56
7.2%
9 49
 
6.3%
4 45
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 937
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 156
16.6%
0 132
14.1%
3 123
13.1%
1 121
12.9%
7 75
8.0%
5 63
6.7%
6 59
 
6.3%
2 58
 
6.2%
8 56
 
6.0%
9 49
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 156
16.6%
0 132
14.1%
3 123
13.1%
1 121
12.9%
7 75
8.0%
5 63
6.7%
6 59
 
6.3%
2 58
 
6.2%
8 56
 
6.0%
9 49
 
5.2%

입소정원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.227273
Minimum0
Maximum216
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:50.296192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.35
Q19
median24
Q338.5
95-th percentile100
Maximum216
Range216
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation40.034677
Coefficient of variation (CV)1.1364682
Kurtosis7.8278888
Mean35.227273
Median Absolute Deviation (MAD)15
Skewness2.602931
Sum3100
Variance1602.7753
MonotonicityNot monotonic
2024-03-13T08:14:50.415533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
9 23
26.1%
29 13
14.8%
20 7
 
8.0%
25 3
 
3.4%
60 2
 
2.3%
22 2
 
2.3%
65 2
 
2.3%
100 2
 
2.3%
8 2
 
2.3%
5 2
 
2.3%
Other values (28) 30
34.1%
ValueCountFrequency (%)
0 1
 
1.1%
5 2
 
2.3%
6 1
 
1.1%
7 1
 
1.1%
8 2
 
2.3%
9 23
26.1%
10 1
 
1.1%
12 1
 
1.1%
15 1
 
1.1%
19 1
 
1.1%
ValueCountFrequency (%)
216 1
1.1%
188 1
1.1%
187 1
1.1%
115 1
1.1%
100 2
2.3%
99 1
1.1%
90 2
2.3%
80 1
1.1%
69 1
1.1%
67 1
1.1%

입소현원-계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.965909
Minimum0
Maximum136
Zeros3
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:50.524979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q328.5
95-th percentile58.95
Maximum136
Range136
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation22.987477
Coefficient of variation (CV)1.2120419
Kurtosis9.046491
Mean18.965909
Median Absolute Deviation (MAD)5.5
Skewness2.6075521
Sum1669
Variance528.42411
MonotonicityNot monotonic
2024-03-13T08:14:50.627632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
5 13
 
14.8%
6 9
 
10.2%
3 5
 
5.7%
8 5
 
5.7%
7 4
 
4.5%
2 4
 
4.5%
0 3
 
3.4%
4 3
 
3.4%
17 3
 
3.4%
25 2
 
2.3%
Other values (29) 37
42.0%
ValueCountFrequency (%)
0 3
 
3.4%
2 4
 
4.5%
3 5
 
5.7%
4 3
 
3.4%
5 13
14.8%
6 9
10.2%
7 4
 
4.5%
8 5
 
5.7%
9 2
 
2.3%
10 1
 
1.1%
ValueCountFrequency (%)
136 1
1.1%
109 1
1.1%
72 1
1.1%
63 1
1.1%
60 1
1.1%
57 1
1.1%
50 1
1.1%
47 1
1.1%
46 1
1.1%
44 1
1.1%

입소현원-남자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6136364
Minimum0
Maximum43
Zeros27
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:50.723441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q311
95-th percentile24
Maximum43
Range43
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.9525336
Coefficient of variation (CV)1.3536477
Kurtosis4.3326696
Mean6.6136364
Median Absolute Deviation (MAD)3
Skewness1.9626128
Sum582
Variance80.147858
MonotonicityNot monotonic
2024-03-13T08:14:50.806994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 27
30.7%
1 8
 
9.1%
3 7
 
8.0%
5 7
 
8.0%
2 5
 
5.7%
4 4
 
4.5%
11 4
 
4.5%
6 3
 
3.4%
20 3
 
3.4%
14 2
 
2.3%
Other values (13) 18
20.5%
ValueCountFrequency (%)
0 27
30.7%
1 8
 
9.1%
2 5
 
5.7%
3 7
 
8.0%
4 4
 
4.5%
5 7
 
8.0%
6 3
 
3.4%
7 1
 
1.1%
8 1
 
1.1%
10 2
 
2.3%
ValueCountFrequency (%)
43 1
 
1.1%
41 1
 
1.1%
27 1
 
1.1%
26 1
 
1.1%
24 2
2.3%
20 3
3.4%
18 2
2.3%
17 2
2.3%
15 1
 
1.1%
14 2
2.3%

입소현원-여자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.352273
Minimum0
Maximum95
Zeros6
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:50.914436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q318.25
95-th percentile43
Maximum95
Range95
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation15.812144
Coefficient of variation (CV)1.2801
Kurtosis8.9937886
Mean12.352273
Median Absolute Deviation (MAD)4
Skewness2.6147772
Sum1087
Variance250.0239
MonotonicityNot monotonic
2024-03-13T08:14:51.017205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 9
 
10.2%
2 9
 
10.2%
5 8
 
9.1%
4 7
 
8.0%
0 6
 
6.8%
6 5
 
5.7%
1 4
 
4.5%
20 4
 
4.5%
24 3
 
3.4%
12 3
 
3.4%
Other values (21) 30
34.1%
ValueCountFrequency (%)
0 6
6.8%
1 4
4.5%
2 9
10.2%
3 9
10.2%
4 7
8.0%
5 8
9.1%
6 5
5.7%
7 3
 
3.4%
8 2
 
2.3%
9 1
 
1.1%
ValueCountFrequency (%)
95 1
1.1%
66 1
1.1%
48 1
1.1%
46 1
1.1%
43 2
2.3%
40 1
1.1%
37 1
1.1%
34 1
1.1%
28 1
1.1%
25 2
2.3%

설치일자
Date

MISSING 

Distinct85
Distinct (%)97.7%
Missing1
Missing (%)1.1%
Memory size836.0 B
Minimum1956-12-01 00:00:00
Maximum2022-08-01 00:00:00
2024-03-13T08:14:51.114326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:51.212233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.464596
Minimum36.944178
Maximum38.109321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:51.326306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.944178
5-th percentile37.048219
Q137.254748
median37.414244
Q337.679534
95-th percentile37.91539
Maximum38.109321
Range1.1651429
Interquartile range (IQR)0.4247863

Descriptive statistics

Standard deviation0.27197576
Coefficient of variation (CV)0.0072595407
Kurtosis-0.72429239
Mean37.464596
Median Absolute Deviation (MAD)0.22550382
Skewness0.19108786
Sum3296.8845
Variance0.073970816
MonotonicityNot monotonic
2024-03-13T08:14:51.468570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6790621918 2
 
2.3%
37.7423153809 1
 
1.1%
37.2512586564 1
 
1.1%
37.7391586169 1
 
1.1%
37.3038156832 1
 
1.1%
37.3451938013 1
 
1.1%
37.3908982727 1
 
1.1%
37.2914679287 1
 
1.1%
38.109320683 1
 
1.1%
37.3440826546 1
 
1.1%
Other values (77) 77
87.5%
ValueCountFrequency (%)
36.9441777641 1
1.1%
36.9850594317 1
1.1%
36.9883431619 1
1.1%
37.0418132631 1
1.1%
37.0433510308 1
1.1%
37.0572592808 1
1.1%
37.0574920261 1
1.1%
37.066882376 1
1.1%
37.1168485233 1
1.1%
37.1376762309 1
1.1%
ValueCountFrequency (%)
38.109320683 1
1.1%
38.0384850625 1
1.1%
38.0157474516 1
1.1%
37.9703767522 1
1.1%
37.9319580327 1
1.1%
37.8846202925 1
1.1%
37.8112500742 1
1.1%
37.8010572442 1
1.1%
37.7945497914 1
1.1%
37.7809821167 1
1.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.105
Minimum126.54289
Maximum127.7501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:51.580585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.54289
5-th percentile126.64809
Q1126.84546
median127.08717
Q3127.29532
95-th percentile127.62272
Maximum127.7501
Range1.2072113
Interquartile range (IQR)0.4498624

Descriptive statistics

Standard deviation0.30688249
Coefficient of variation (CV)0.0024144014
Kurtosis-0.83377657
Mean127.105
Median Absolute Deviation (MAD)0.23430801
Skewness0.26730532
Sum11185.24
Variance0.094176862
MonotonicityNot monotonic
2024-03-13T08:14:51.901773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.790557918 2
 
2.3%
127.5050736008 1
 
1.1%
127.5671722854 1
 
1.1%
127.0948589488 1
 
1.1%
126.9544080192 1
 
1.1%
126.9814985354 1
 
1.1%
127.0144421496 1
 
1.1%
127.1562887436 1
 
1.1%
127.0694504981 1
 
1.1%
127.7500971008 1
 
1.1%
Other values (77) 77
87.5%
ValueCountFrequency (%)
126.5428857821 1
1.1%
126.569437195 1
1.1%
126.586413828 1
1.1%
126.5962497405 1
1.1%
126.6358813441 1
1.1%
126.6707765339 1
1.1%
126.6921160509 1
1.1%
126.7517779592 1
1.1%
126.7521202539 1
1.1%
126.7762067177 1
1.1%
ValueCountFrequency (%)
127.7500971008 1
1.1%
127.6713203888 1
1.1%
127.6480438195 1
1.1%
127.6431226307 1
1.1%
127.6242979674 1
1.1%
127.619783337 1
1.1%
127.6123313482 1
1.1%
127.6009014847 1
1.1%
127.5822950244 1
1.1%
127.5729719273 1
1.1%

Interactions

2024-03-13T08:14:46.223683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.436263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.848012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.262378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.709490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.164289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.570875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:46.297899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.490358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.903786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.321001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.767020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.218762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.633632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:46.369733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.545906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.957502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.379617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.839311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.272423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.690604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:46.456949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.611948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.023763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.450611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.924813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.334275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.759073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:46.531864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.669778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.083181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.509173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.983740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.389021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.816919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:46.592683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.726758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.141668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.568620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.042083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.444075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.876334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:46.655756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:43.785511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.199927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:44.632275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.101012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:45.508250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:46.144439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:14:51.983533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명시설종류시설명도로명 주소지번 주소우편번호전화번호FAX번호입소정원입소현원-계입소현원-남자입소현원-여자설치일자WGS84위도WGS84경도
시군명1.0000.3551.0001.0001.0000.9951.0001.0000.0000.0000.0000.0000.9800.8860.928
시설종류0.3551.0001.0001.0001.0000.4641.0001.0000.8980.7170.4950.6580.6050.2130.000
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명 주소1.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0000.9961.0001.000
지번 주소1.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0000.9961.0001.000
우편번호0.9950.4641.0001.0001.0001.0001.0001.0000.0000.1760.0000.2530.9440.8550.814
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
FAX번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
입소정원0.0000.8981.0001.0001.0000.0001.0001.0001.0000.9450.9020.9260.9940.0000.071
입소현원-계0.0000.7171.0000.0000.0000.1761.0001.0000.9451.0000.9270.9880.9170.0000.000
입소현원-남자0.0000.4951.0001.0001.0000.0001.0001.0000.9020.9271.0000.8860.0000.0000.000
입소현원-여자0.0000.6581.0000.0000.0000.2531.0001.0000.9260.9880.8861.0000.0000.0000.000
설치일자0.9800.6051.0000.9960.9960.9441.0001.0000.9940.9170.0000.0001.0000.9810.866
WGS84위도0.8860.2131.0001.0001.0000.8551.0001.0000.0000.0000.0000.0000.9811.0000.298
WGS84경도0.9280.0001.0001.0001.0000.8141.0001.0000.0710.0000.0000.0000.8660.2981.000
2024-03-13T08:14:52.092609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명시설종류
시군명1.0000.251
시설종류0.2511.000
2024-03-13T08:14:52.161602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호입소정원입소현원-계입소현원-남자입소현원-여자WGS84위도WGS84경도시군명시설종류
우편번호1.0000.1290.0910.0420.079-0.8920.0660.8570.344
입소정원0.1291.0000.8440.6500.765-0.2170.0300.0000.699
입소현원-계0.0910.8441.0000.7210.883-0.1520.0020.0000.529
입소현원-남자0.0420.6500.7211.0000.417-0.0900.0620.0000.358
입소현원-여자0.0790.7650.8830.4171.000-0.141-0.0310.0000.481
WGS84위도-0.892-0.217-0.152-0.090-0.1411.000-0.0510.5100.152
WGS84경도0.0660.0300.0020.062-0.031-0.0511.0000.6000.000
시군명0.8570.0000.0000.0000.0000.5100.6001.0000.251
시설종류0.3440.6990.5290.3580.4810.1520.0000.2511.000

Missing values

2024-03-13T08:14:46.755651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:14:46.901349image/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.
2024-03-13T08:14:47.000927image/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

시군명시설종류시설명도로명 주소지번 주소우편번호전화번호FAX번호입소정원입소현원-계입소현원-남자입소현원-여자설치일자WGS84위도WGS84경도
0가평군양로시설아름다운집경기도 가평군 가평읍 복장포길 50경기도 가평군 가평읍 복장리 243번지12430031-582-8061<NA>296152007-02-0737.742315127.505074
1가평군양로시설열린효경원경기도 가평군 설악면 유명로 1906-22경기도 가평군 설악면 회곡리 6-2번지12459031-585-5671031-584-5675224221999-12-0737.680949127.471829
2가평군노인공동생활가정원방의집경기도 가평군 가평읍 호반로 2135경기도 가평군 가평읍 이화리 118번지12427031-581-1435031-624-745297432006-09-1137.780982127.508188
3고양시노인공동생활가정기쁨이가득한집경기도 고양시 덕양구 원당로319번길 73경기도 고양시 덕양구 원당동 889번지10290031-963-2872<NA>54042004-08-2537.67738126.845721
4고양시양로시설늘푸른실버타운경기도 고양시 일산동구 성현로 185-33경기도 고양시 일산동구 성석동 524-28번지10252031-977-0421031-976-0424804420242019-04-1237.709867126.806794
5고양시양로시설베아투스 카운티 실버타운경기도 고양시 일산동구 고봉로770번길 112-11경기도 고양시 일산동구 설문동 756-52번지10251031-977-1003031-901-844529165112018-06-0137.719533126.792373
6고양시양로시설예승실버타운경기도 고양시 덕양구 중앙로 628경기도 고양시 덕양구 화정동 1148-1번지10503031-979-9919031-979-7719693818202015-10-0237.627532126.82891
7고양시양로시설은혜실버타운경기도 고양시 덕양구 고양대로 1387경기도 고양시 덕양구 성사동 704-4번지10464031-968-9001031-965-900229151052019-03-0837.654517126.838315
8고양시양로시설효누림실버타운경기도 고양시 일산동구 고양대로 864-7경기도 고양시 일산동구 중산동 35-15번지10311031-975-7371031-976-7375905714432016-08-2537.679062126.790558
9고양시양로시설효누림실버타운신관경기도 고양시 일산동구 고양대로 864-7경기도 고양시 일산동구 중산동 35-15번지10311031-976-6891031-976-7377993713242022-04-2737.679062126.790558
시군명시설종류시설명도로명 주소지번 주소우편번호전화번호FAX번호입소정원입소현원-계입소현원-남자입소현원-여자설치일자WGS84위도WGS84경도
78포천시양로시설은혜의 집경기도 포천시 일동면 성장로 379-4경기도 포천시 일동면 수입리 895-3번지11115031-535-7567031-535-7507292511142020-05-1538.015747127.295235
79포천시양로시설포천실버타운경기도 포천시 내촌면 금강로2536번길 112-12경기도 포천시 내촌면 소학리 314-8번지11188031-533-0056031-531-56462014682011-05-3137.79455127.248744
80포천시노인공동생활가정화평의집경기도 포천시 창수면 창동로 241-36경기도 포천시 창수면 오가리 87번지11133031-535-5822031-535-582596512007-01-1138.038485127.201715
81포천시노인공동생활가정효사랑의집경기도 포천시 신북면 호국로 2039-155경기도 포천시 신북면 기지리 934-5번지11139031-535-9937031-535-051596422021-08-0137.931958127.222321
82하남시양로시설영락경로원경기도 하남시 안터로 55-1경기도 하남시 풍산동 산33번지12984031-792-2155031-792-93651006317461957-10-0137.546259127.185144
83화성시양로시설성녀루이제의집경기도 화성시 정남면 서봉로921번길 45경기도 화성시 정남면 문학리 586-2번지18522031-353-8214031-352-672044280281992-04-3037.15669126.960298
84화성시양로시설성신양로원경기도 화성시 장안면 장명길 26경기도 화성시 장안면 장안리 154-15번지18586031-351-3078031-351-3118297342000-11-0137.057259126.85312
85화성시양로시설에벤에셀공동체경기도 화성시 서신면 박고지길 209경기도 화성시 서신면 백미리 594-1번지18556031-356-7408031-355-618020165112006-01-1837.137867126.692116
86화성시양로시설엘림교회복지원경기도 화성시 마도면 쌍송북로 63-1경기도 화성시 마도면 쌍송리 12-2번지18541031-356-7548031-356-4127293211994-03-0137.19786126.790004
87화성시양로시설하늘정원경기도 화성시 송산면 고포길 56경기도 화성시 송산면 고포리 182-1번지18545031-357-0471031-357-047325201192009-03-0937.236088126.670777