Overview

Dataset statistics

Number of variables8
Number of observations330
Missing cells65
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.4 KiB
Average record size in memory69.4 B

Variable types

Numeric5
Categorical2
Text1

Dataset

Description2009년-2010년 전국 330개 다중이용시설 대상으로 4계절(봄, 여름, 가을, 겨울) 시군구별 실내라돈농도 실태조사 결과
Author환경부 국립환경과학원
URLhttps://www.data.go.kr/data/15069143/fileData.do

Alerts

일련번호 is highly overall correlated with 구분High correlation
여름 실내 라돈 농도 (Bq/m3) is highly overall correlated with 가을 실내 라돈 농도 (Bq/m3) and 2 other fieldsHigh correlation
가을 실내 라돈 농도 (Bq/m3) is highly overall correlated with 여름 실내 라돈 농도 (Bq/m3) and 2 other fieldsHigh correlation
겨울 실내 라돈 농도 (Bq/m3) is highly overall correlated with 여름 실내 라돈 농도 (Bq/m3) and 2 other fieldsHigh correlation
봄 실내 라돈 농도 (Bq/m3) is highly overall correlated with 여름 실내 라돈 농도 (Bq/m3) and 2 other fieldsHigh correlation
구분 is highly overall correlated with 일련번호High correlation
여름 실내 라돈 농도 (Bq/m3) has 12 (3.6%) missing valuesMissing
가을 실내 라돈 농도 (Bq/m3) has 15 (4.5%) missing valuesMissing
겨울 실내 라돈 농도 (Bq/m3) has 23 (7.0%) missing valuesMissing
봄 실내 라돈 농도 (Bq/m3) has 15 (4.5%) missing valuesMissing
일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:31:00.491699
Analysis finished2023-12-12 12:31:04.681535
Duration4.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct330
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.5
Minimum1
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-12T21:31:04.842347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.45
Q183.25
median165.5
Q3247.75
95-th percentile313.55
Maximum330
Range329
Interquartile range (IQR)164.5

Descriptive statistics

Standard deviation95.407023
Coefficient of variation (CV)0.57647748
Kurtosis-1.2
Mean165.5
Median Absolute Deviation (MAD)82.5
Skewness0
Sum54615
Variance9102.5
MonotonicityStrictly increasing
2023-12-12T21:31:05.083309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
228 1
 
0.3%
226 1
 
0.3%
225 1
 
0.3%
224 1
 
0.3%
223 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
220 1
 
0.3%
219 1
 
0.3%
Other values (320) 320
97.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
330 1
0.3%
329 1
0.3%
328 1
0.3%
327 1
0.3%
326 1
0.3%
325 1
0.3%
324 1
0.3%
323 1
0.3%
322 1
0.3%
321 1
0.3%

구분
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
의료기관
54 
도서관
42 
지하역사
41 
대규모 점포
34 
찜질방
32 
Other values (11)
127 

Length

Max length10
Median length8
Mean length5.0363636
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공항여객터미널
2nd row공항여객터미널
3rd row노인전문요양시설
4th row노인전문요양시설
5th row노인전문요양시설

Common Values

ValueCountFrequency (%)
의료기관 54
16.4%
도서관 42
12.7%
지하역사 41
12.4%
대규모 점포 34
10.3%
찜질방 32
9.7%
지하도상가 19
 
5.8%
박물관 및 미술관 18
 
5.5%
보육시설 18
 
5.5%
실내 주차장 16
 
4.8%
노인전문요양시설 12
 
3.6%
Other values (6) 44
13.3%

Length

2023-12-12T21:31:05.286508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
의료기관 54
12.4%
도서관 42
 
9.6%
지하역사 41
 
9.4%
대규모 34
 
7.8%
점포 34
 
7.8%
찜질방 32
 
7.3%
대합실 21
 
4.8%
지하도상가 19
 
4.3%
미술관 18
 
4.1%
보육시설 18
 
4.1%
Other values (11) 124
28.4%

시/도
Categorical

Distinct16
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
경기도
72 
서울특별시
64 
부산광역시
27 
대구광역시
26 
인천광역시
21 
Other values (11)
120 

Length

Max length5
Median length5
Mean length4.2212121
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row부산광역시
2nd row서울특별시
3rd row경기도
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 72
21.8%
서울특별시 64
19.4%
부산광역시 27
 
8.2%
대구광역시 26
 
7.9%
인천광역시 21
 
6.4%
경상북도 18
 
5.5%
강원도 16
 
4.8%
광주광역시 15
 
4.5%
대전광역시 14
 
4.2%
충청북도 13
 
3.9%
Other values (6) 44
13.3%

Length

2023-12-12T21:31:05.483191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 72
21.8%
서울특별시 64
19.4%
부산광역시 27
 
8.2%
대구광역시 26
 
7.9%
인천광역시 21
 
6.4%
경상북도 18
 
5.5%
강원도 16
 
4.8%
광주광역시 15
 
4.5%
대전광역시 14
 
4.2%
충청북도 13
 
3.9%
Other values (6) 44
13.3%

시/군/구
Text

Distinct86
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T21:31:05.852364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8454545
Min length2

Characters and Unicode

Total characters939
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)6.4%

Sample

1st row강서구
2nd row강서구
3rd row광명시
4th row동두천
5th row수원시
ValueCountFrequency (%)
중구 21
 
6.4%
고양시 16
 
4.8%
서구 15
 
4.5%
강남구 14
 
4.2%
동구 14
 
4.2%
청주시 9
 
2.7%
북구 8
 
2.4%
포항시 7
 
2.1%
수원시 7
 
2.1%
안양시 7
 
2.1%
Other values (76) 212
64.2%
2023-12-12T21:31:06.398793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 175
18.6%
시 150
 
16.0%
주 33
 
3.5%
서 32
 
3.4%
양 29
 
3.1%
강 28
 
3.0%
천 27
 
2.9%
포 26
 
2.8%
남 26
 
2.8%
동 26
 
2.8%
Other values (73) 387
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 939
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 175
18.6%
시 150
 
16.0%
주 33
 
3.5%
서 32
 
3.4%
양 29
 
3.1%
강 28
 
3.0%
천 27
 
2.9%
포 26
 
2.8%
남 26
 
2.8%
동 26
 
2.8%
Other values (73) 387
41.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 939
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 175
18.6%
시 150
 
16.0%
주 33
 
3.5%
서 32
 
3.4%
양 29
 
3.1%
강 28
 
3.0%
천 27
 
2.9%
포 26
 
2.8%
남 26
 
2.8%
동 26
 
2.8%
Other values (73) 387
41.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 939
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 175
18.6%
시 150
 
16.0%
주 33
 
3.5%
서 32
 
3.4%
양 29
 
3.1%
강 28
 
3.0%
천 27
 
2.9%
포 26
 
2.8%
남 26
 
2.8%
동 26
 
2.8%
Other values (73) 387
41.2%

여름 실내 라돈 농도 (Bq/m3)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct218
Distinct (%)68.6%
Missing12
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean23.691824
Minimum5.3
Maximum186.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-12T21:31:06.601040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.3
5-th percentile8.6
Q113.9
median19.65
Q328.2
95-th percentile55.51
Maximum186.1
Range180.8
Interquartile range (IQR)14.3

Descriptive statistics

Standard deviation17.342823
Coefficient of variation (CV)0.7320172
Kurtosis27.063316
Mean23.691824
Median Absolute Deviation (MAD)6.7
Skewness3.9474983
Sum7534
Variance300.7735
MonotonicityNot monotonic
2023-12-12T21:31:06.812665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.1 5
 
1.5%
15.8 4
 
1.2%
9.8 4
 
1.2%
14.5 4
 
1.2%
22.4 4
 
1.2%
32.7 3
 
0.9%
25.4 3
 
0.9%
31.3 3
 
0.9%
22.7 3
 
0.9%
14.4 3
 
0.9%
Other values (208) 282
85.5%
(Missing) 12
 
3.6%
ValueCountFrequency (%)
5.3 1
 
0.3%
6.7 2
0.6%
7.0 1
 
0.3%
7.2 1
 
0.3%
7.3 1
 
0.3%
7.5 1
 
0.3%
7.7 3
0.9%
7.8 1
 
0.3%
7.9 1
 
0.3%
8.3 1
 
0.3%
ValueCountFrequency (%)
186.1 1
0.3%
114.7 1
0.3%
89.5 1
0.3%
81.4 1
0.3%
79.0 1
0.3%
75.6 1
0.3%
73.0 1
0.3%
70.1 1
0.3%
69.5 1
0.3%
65.2 1
0.3%

가을 실내 라돈 농도 (Bq/m3)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct217
Distinct (%)68.9%
Missing15
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean29.101587
Minimum5.2
Maximum188.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-12T21:31:07.000033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile11.57
Q117.95
median23.4
Q333.95
95-th percentile63.51
Maximum188.9
Range183.7
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20.059533
Coefficient of variation (CV)0.68929344
Kurtosis17.716072
Mean29.101587
Median Absolute Deviation (MAD)6.9
Skewness3.3478101
Sum9167
Variance402.38487
MonotonicityNot monotonic
2023-12-12T21:31:07.214069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 5
 
1.5%
17.2 4
 
1.2%
26.2 4
 
1.2%
23.4 4
 
1.2%
16.8 4
 
1.2%
19.3 4
 
1.2%
16.5 4
 
1.2%
22.6 3
 
0.9%
20.1 3
 
0.9%
18.0 3
 
0.9%
Other values (207) 277
83.9%
(Missing) 15
 
4.5%
ValueCountFrequency (%)
5.2 1
0.3%
6.4 1
0.3%
7.2 1
0.3%
8.0 1
0.3%
8.3 1
0.3%
8.6 1
0.3%
8.9 2
0.6%
9.4 2
0.6%
10.0 1
0.3%
11.0 1
0.3%
ValueCountFrequency (%)
188.9 1
0.3%
142.8 1
0.3%
113.4 1
0.3%
109.8 1
0.3%
105.9 1
0.3%
105.5 1
0.3%
84.6 1
0.3%
82.9 1
0.3%
71.7 1
0.3%
71.3 1
0.3%

겨울 실내 라돈 농도 (Bq/m3)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct230
Distinct (%)74.9%
Missing23
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean42.055049
Minimum13.6
Maximum291.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-12T21:31:07.394159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.6
5-th percentile19.83
Q126.15
median33.9
Q347.1
95-th percentile91.43
Maximum291.1
Range277.5
Interquartile range (IQR)20.95

Descriptive statistics

Standard deviation27.627518
Coefficient of variation (CV)0.656937
Kurtosis23.321874
Mean42.055049
Median Absolute Deviation (MAD)9.3
Skewness3.6573082
Sum12910.9
Variance763.27974
MonotonicityNot monotonic
2023-12-12T21:31:07.578485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 5
 
1.5%
34.1 4
 
1.2%
23.9 4
 
1.2%
31.2 3
 
0.9%
31.5 3
 
0.9%
34.5 3
 
0.9%
25.7 3
 
0.9%
33.7 3
 
0.9%
28.3 3
 
0.9%
26.3 3
 
0.9%
Other values (220) 273
82.7%
(Missing) 23
 
7.0%
ValueCountFrequency (%)
13.6 1
0.3%
14.3 1
0.3%
15.2 1
0.3%
16.2 1
0.3%
16.7 1
0.3%
17.2 1
0.3%
17.3 1
0.3%
18.2 1
0.3%
18.6 2
0.6%
19.1 1
0.3%
ValueCountFrequency (%)
291.1 1
0.3%
171.3 1
0.3%
138.5 1
0.3%
136.0 1
0.3%
129.5 1
0.3%
121.3 1
0.3%
112.7 1
0.3%
110.8 1
0.3%
110.6 1
0.3%
103.7 1
0.3%

봄 실내 라돈 농도 (Bq/m3)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct221
Distinct (%)70.2%
Missing15
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean34.407937
Minimum11.6
Maximum164.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-12T21:31:07.757105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile16.48
Q123.8
median30.1
Q340.7
95-th percentile60.55
Maximum164.8
Range153.2
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation18.084282
Coefficient of variation (CV)0.5255846
Kurtosis16.522679
Mean34.407937
Median Absolute Deviation (MAD)7.8
Skewness3.1336872
Sum10838.5
Variance327.04124
MonotonicityNot monotonic
2023-12-12T21:31:07.899995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.0 5
 
1.5%
31.5 5
 
1.5%
23.8 5
 
1.5%
26.8 4
 
1.2%
28.2 4
 
1.2%
38.7 4
 
1.2%
27.1 3
 
0.9%
25.7 3
 
0.9%
23.0 3
 
0.9%
47.4 3
 
0.9%
Other values (211) 276
83.6%
(Missing) 15
 
4.5%
ValueCountFrequency (%)
11.6 1
0.3%
12.2 1
0.3%
12.3 1
0.3%
12.5 1
0.3%
13.5 1
0.3%
13.9 1
0.3%
14.1 1
0.3%
14.2 2
0.6%
14.5 1
0.3%
14.6 1
0.3%
ValueCountFrequency (%)
164.8 1
0.3%
158.1 1
0.3%
113.7 1
0.3%
111.8 1
0.3%
87.7 1
0.3%
87.3 1
0.3%
86.5 1
0.3%
80.3 1
0.3%
67.7 1
0.3%
64.8 1
0.3%

Interactions

2023-12-12T21:31:03.483366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:00.921630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.363328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:02.002178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:02.876975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:03.619541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.004986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.474336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:02.132238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:02.989623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:03.755200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.094811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.612077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:02.252371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:03.104095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:03.893910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.183415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.737726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:02.375239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:03.228388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:04.034640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.270559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:01.873402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:02.477751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:31:03.364654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:31:08.027090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호구분시/도시/군/구여름 실내 라돈 농도 (Bq/m3)가을 실내 라돈 농도 (Bq/m3)겨울 실내 라돈 농도 (Bq/m3)봄 실내 라돈 농도 (Bq/m3)
일련번호1.0000.9400.4490.5280.3730.3940.3350.390
구분0.9401.0000.3360.6500.4560.5840.4020.426
시/도0.4490.3361.0000.9900.2990.4850.3640.410
시/군/구0.5280.6500.9901.0000.7500.8860.8710.602
여름 실내 라돈 농도 (Bq/m3)0.3730.4560.2990.7501.0000.7920.9070.898
가을 실내 라돈 농도 (Bq/m3)0.3940.5840.4850.8860.7921.0000.8150.687
겨울 실내 라돈 농도 (Bq/m3)0.3350.4020.3640.8710.9070.8151.0000.824
봄 실내 라돈 농도 (Bq/m3)0.3900.4260.4100.6020.8980.6870.8241.000
2023-12-12T21:31:08.155246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시/도구분
시/도1.0000.088
구분0.0881.000
2023-12-12T21:31:08.244823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호여름 실내 라돈 농도 (Bq/m3)가을 실내 라돈 농도 (Bq/m3)겨울 실내 라돈 농도 (Bq/m3)봄 실내 라돈 농도 (Bq/m3)구분시/도
일련번호1.000-0.344-0.292-0.229-0.1880.7460.191
여름 실내 라돈 농도 (Bq/m3)-0.3441.0000.6300.5800.5750.2240.139
가을 실내 라돈 농도 (Bq/m3)-0.2920.6301.0000.6810.5290.2400.187
겨울 실내 라돈 농도 (Bq/m3)-0.2290.5800.6811.0000.5380.1930.172
봄 실내 라돈 농도 (Bq/m3)-0.1880.5750.5290.5381.0000.2070.198
구분0.7460.2240.2400.1930.2071.0000.088
시/도0.1910.1390.1870.1720.1980.0881.000

Missing values

2023-12-12T21:31:04.242492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:31:04.407510image/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.
2023-12-12T21:31:04.569746image/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

일련번호구분시/도시/군/구여름 실내 라돈 농도 (Bq/m3)가을 실내 라돈 농도 (Bq/m3)겨울 실내 라돈 농도 (Bq/m3)봄 실내 라돈 농도 (Bq/m3)
01공항여객터미널부산광역시강서구22.420.427.224.1
12공항여객터미널서울특별시강서구25.533.237.123.0
23노인전문요양시설경기도광명시26.921.233.231.4
34노인전문요양시설경기도동두천23.444.146.741.2
45노인전문요양시설경기도수원시16.625.348.719.6
56노인전문요양시설경기도안산시18.523.437.621.3
67노인전문요양시설경기도안양시32.724.041.224.8
78노인전문요양시설경상남도양산시16.721.9<NA>32.5
89노인전문요양시설경상북도포항시8.522.420.514.1
910노인전문요양시설경상북도포항시10.016.720.223.8
일련번호구분시/도시/군/구여름 실내 라돈 농도 (Bq/m3)가을 실내 라돈 농도 (Bq/m3)겨울 실내 라돈 농도 (Bq/m3)봄 실내 라돈 농도 (Bq/m3)
320321철도역사 대합실경기도안양시20.719.4<NA><NA>
321322철도역사 대합실대구광역시동구<NA>18.922.628.2
322323철도역사 대합실대구광역시북구<NA>14.119.131.0
323324철도역사 대합실대전광역시동구12.421.725.238.7
324325철도역사 대합실부산광역시동구9.311.735.112.3
325326철도역사 대합실서울특별시용산구10.38.632.327.1
326327철도역사 대합실인천광역시부평구14.513.726.331.5
327328항만시설 대합실인천광역시중구12.624.524.521.4
328329항만시설 대합실인천광역시중구9.822.634.524.4
329330항만시설 대합실제주도제주시18.958.437.434.2