Overview

Dataset statistics

Number of variables7
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory65.9 B

Variable types

Numeric5
Text2

Dataset

Description고유번호,관측소코드,관측소명,주소,평균강수량,경도,위도
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1367/S/1/datasetView.do

Alerts

고유번호 is highly overall correlated with 위도High correlation
위도 is highly overall correlated with 고유번호High correlation
고유번호 has unique valuesUnique
관측소코드 has unique valuesUnique
관측소명 has unique valuesUnique
주소 has unique valuesUnique
평균강수량 has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-11 08:00:09.016716
Analysis finished2023-12-11 08:00:12.366140
Duration3.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T17:00:12.439259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-11T17:00:12.591312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

관측소코드
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean407.59259
Minimum108
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T17:00:12.753622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile400.3
Q1405.5
median412
Q3418.5
95-th percentile483.5
Maximum510
Range402
Interquartile range (IQR)13

Descriptive statistics

Standard deviation65.66363
Coefficient of variation (CV)0.16110114
Kurtosis18.18311
Mean407.59259
Median Absolute Deviation (MAD)7
Skewness-3.632236
Sum11005
Variance4311.7123
MonotonicityNot monotonic
2023-12-11T17:00:12.907743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
509 1
 
3.7%
417 1
 
3.7%
108 1
 
3.7%
406 1
 
3.7%
424 1
 
3.7%
407 1
 
3.7%
420 1
 
3.7%
414 1
 
3.7%
416 1
 
3.7%
409 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
108 1
3.7%
400 1
3.7%
401 1
3.7%
402 1
3.7%
403 1
3.7%
404 1
3.7%
405 1
3.7%
406 1
3.7%
407 1
3.7%
408 1
3.7%
ValueCountFrequency (%)
510 1
3.7%
509 1
3.7%
424 1
3.7%
423 1
3.7%
421 1
3.7%
420 1
3.7%
419 1
3.7%
418 1
3.7%
417 1
3.7%
416 1
3.7%

관측소명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T17:00:13.128349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1851852
Min length2

Characters and Unicode

Total characters59
Distinct characters39
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

Unique27 ?
Unique (%)100.0%

Sample

1st row관악
2nd row금천
3rd row서초
4th row구로
5th row기상청
ValueCountFrequency (%)
관악 1
 
3.7%
강서 1
 
3.7%
도봉 1
 
3.7%
강북 1
 
3.7%
노원 1
 
3.7%
북한산 1
 
3.7%
성북 1
 
3.7%
은평 1
 
3.7%
중랑 1
 
3.7%
동대문 1
 
3.7%
Other values (17) 17
63.0%
2023-12-11T17:00:13.484002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 5
 
8.5%
서 4
 
6.8%
동 3
 
5.1%
북 3
 
5.1%
문 2
 
3.4%
포 2
 
3.4%
천 2
 
3.4%
성 2
 
3.4%
구 2
 
3.4%
중 2
 
3.4%
Other values (29) 32
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 5
 
8.5%
서 4
 
6.8%
동 3
 
5.1%
북 3
 
5.1%
문 2
 
3.4%
포 2
 
3.4%
천 2
 
3.4%
성 2
 
3.4%
구 2
 
3.4%
중 2
 
3.4%
Other values (29) 32
54.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 5
 
8.5%
서 4
 
6.8%
동 3
 
5.1%
북 3
 
5.1%
문 2
 
3.4%
포 2
 
3.4%
천 2
 
3.4%
성 2
 
3.4%
구 2
 
3.4%
중 2
 
3.4%
Other values (29) 32
54.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 5
 
8.5%
서 4
 
6.8%
동 3
 
5.1%
북 3
 
5.1%
문 2
 
3.4%
포 2
 
3.4%
천 2
 
3.4%
성 2
 
3.4%
구 2
 
3.4%
중 2
 
3.4%
Other values (29) 32
54.2%

주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T17:00:13.795497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length27.333333
Min length22

Characters and Unicode

Total characters738
Distinct characters129
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

Unique27 ?
Unique (%)100.0%

Sample

1st row서울특별시 관악구 신림동 산56-1 (서울대학교)
2nd row서울특별시 금천구 독산동 1034 (독산초등학교)
3rd row서울특별시 서초구 서초동 1650 (서울교육대학교)
4th row서울특별시 구로구 궁동 213-42 (수궁동사무소)
5th row서울특별시 동작구 신대방동 460-18 (기상청)
ValueCountFrequency (%)
서울특별시 27
 
19.9%
영등포구 2
 
1.5%
종로구 2
 
1.5%
신촌동 1
 
0.7%
551 1
 
0.7%
면목동 1
 
0.7%
중랑구 1
 
0.7%
서울시립대 1
 
0.7%
90 1
 
0.7%
전농동 1
 
0.7%
Other values (98) 98
72.1%
2023-12-11T17:00:14.269222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
14.8%
서 35
 
4.7%
동 35
 
4.7%
울 31
 
4.2%
구 31
 
4.2%
1 31
 
4.2%
시 28
 
3.8%
( 28
 
3.8%
특 27
 
3.7%
별 27
 
3.7%
Other values (119) 356
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 458
62.1%
Space Separator 109
 
14.8%
Decimal Number 99
 
13.4%
Open Punctuation 28
 
3.8%
Close Punctuation 27
 
3.7%
Dash Punctuation 17
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 35
 
7.6%
동 35
 
7.6%
울 31
 
6.8%
구 31
 
6.8%
시 28
 
6.1%
특 27
 
5.9%
별 27
 
5.9%
학 17
 
3.7%
교 15
 
3.3%
대 11
 
2.4%
Other values (105) 201
43.9%
Decimal Number
ValueCountFrequency (%)
1 31
31.3%
2 12
 
12.1%
4 10
 
10.1%
0 10
 
10.1%
3 9
 
9.1%
5 7
 
7.1%
6 6
 
6.1%
9 6
 
6.1%
8 5
 
5.1%
7 3
 
3.0%
Space Separator
ValueCountFrequency (%)
109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 458
62.1%
Common 280
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 35
 
7.6%
동 35
 
7.6%
울 31
 
6.8%
구 31
 
6.8%
시 28
 
6.1%
특 27
 
5.9%
별 27
 
5.9%
학 17
 
3.7%
교 15
 
3.3%
대 11
 
2.4%
Other values (105) 201
43.9%
Common
ValueCountFrequency (%)
109
38.9%
1 31
 
11.1%
( 28
 
10.0%
) 27
 
9.6%
- 17
 
6.1%
2 12
 
4.3%
4 10
 
3.6%
0 10
 
3.6%
3 9
 
3.2%
5 7
 
2.5%
Other values (4) 20
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 458
62.1%
ASCII 280
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
38.9%
1 31
 
11.1%
( 28
 
10.0%
) 27
 
9.6%
- 17
 
6.1%
2 12
 
4.3%
4 10
 
3.6%
0 10
 
3.6%
3 9
 
3.2%
5 7
 
2.5%
Other values (4) 20
 
7.1%
Hangul
ValueCountFrequency (%)
서 35
 
7.6%
동 35
 
7.6%
울 31
 
6.8%
구 31
 
6.8%
시 28
 
6.1%
특 27
 
5.9%
별 27
 
5.9%
학 17
 
3.7%
교 15
 
3.3%
대 11
 
2.4%
Other values (105) 201
43.9%

평균강수량
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1384.0509
Minimum1189.6667
Maximum1591.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T17:00:14.427324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1189.6667
5-th percentile1254.4444
Q11322.6042
median1358.9583
Q31437.3333
95-th percentile1556.5271
Maximum1591.5
Range401.83333
Interquartile range (IQR)114.72917

Descriptive statistics

Standard deviation95.644026
Coefficient of variation (CV)0.069104415
Kurtosis0.03569488
Mean1384.0509
Median Absolute Deviation (MAD)48.416666
Skewness0.39483006
Sum37369.374
Variance9147.7796
MonotonicityNot monotonic
2023-12-11T17:00:14.598738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1404.416667 1
 
3.7%
1337.833333 1
 
3.7%
1560.583333 1
 
3.7%
1591.5 1
 
3.7%
1547.0625 1
 
3.7%
1442.916667 1
 
3.7%
1310.541667 1
 
3.7%
1489.083333 1
 
3.7%
1296.541667 1
 
3.7%
1427.333333 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1189.666667 1
3.7%
1240.277778 1
3.7%
1287.5 1
3.7%
1296.541667 1
3.7%
1310.541667 1
3.7%
1316.958333 1
3.7%
1319.5 1
3.7%
1325.708333 1
3.7%
1330.083333 1
3.7%
1337.833333 1
3.7%
ValueCountFrequency (%)
1591.5 1
3.7%
1560.583333 1
3.7%
1547.0625 1
3.7%
1489.083333 1
3.7%
1480.208333 1
3.7%
1465.708333 1
3.7%
1442.916667 1
3.7%
1431.75 1
3.7%
1427.333333 1
3.7%
1404.416667 1
3.7%

경도
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9833
Minimum126.83123
Maximum127.1463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T17:00:15.092080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.83123
5-th percentile126.85672
Q1126.93287
median126.97542
Q3127.04246
95-th percentile127.09501
Maximum127.1463
Range0.3150733
Interquartile range (IQR)0.10958895

Descriptive statistics

Standard deviation0.079407578
Coefficient of variation (CV)0.00062533874
Kurtosis-0.53620192
Mean126.9833
Median Absolute Deviation (MAD)0.0576934
Skewness0.0879341
Sum3428.5492
Variance0.0063055635
MonotonicityNot monotonic
2023-12-11T17:00:15.229506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
126.9502188 1
 
3.7%
126.9141209 1
 
3.7%
126.9635149 1
 
3.7%
127.0331096 1
 
3.7%
126.9996128 1
 
3.7%
127.0873044 1
 
3.7%
126.9544188 1
 
3.7%
126.9972197 1
 
3.7%
126.9336226 1
 
3.7%
127.0868052 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
126.8312293 1
3.7%
126.8479308 1
3.7%
126.8772291 1
3.7%
126.9059224 1
3.7%
126.9141209 1
3.7%
126.920722 1
3.7%
126.9321232 1
3.7%
126.9336226 1
3.7%
126.9373233 1
3.7%
126.9445225 1
3.7%
ValueCountFrequency (%)
127.1463026 1
3.7%
127.0983106 1
3.7%
127.0873044 1
3.7%
127.0868052 1
3.7%
127.0770084 1
3.7%
127.0480106 1
3.7%
127.0464123 1
3.7%
127.0385114 1
3.7%
127.0331096 1
3.7%
127.0179133 1
3.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.551016
Minimum37.452863
Maximum37.666091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T17:00:15.395883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.452863
5-th percentile37.467064
Q137.515906
median37.547803
Q337.583149
95-th percentile37.631924
Maximum37.666091
Range0.2132273
Interquartile range (IQR)0.0672425

Descriptive statistics

Standard deviation0.054194593
Coefficient of variation (CV)0.0014432257
Kurtosis-0.45590083
Mean37.551016
Median Absolute Deviation (MAD)0.0355952
Skewness0.18460134
Sum1013.8774
Variance0.0029370539
MonotonicityNot monotonic
2023-12-11T17:00:15.548777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
37.4528634 1
 
3.7%
37.4603141 1
 
3.7%
37.5741861 1
 
3.7%
37.6660907 1
 
3.7%
37.6360933 1
 
3.7%
37.6221956 1
 
3.7%
37.6182946 1
 
3.7%
37.613296 1
 
3.7%
37.6111958 1
 
3.7%
37.5862996 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
37.4528634 1
3.7%
37.4603141 1
3.7%
37.4828143 1
3.7%
37.4861128 1
3.7%
37.4963142 1
3.7%
37.5110096 1
3.7%
37.5122075 1
3.7%
37.5196055 1
3.7%
37.5241055 1
3.7%
37.5268081 1
3.7%
ValueCountFrequency (%)
37.6660907 1
3.7%
37.6360933 1
3.7%
37.6221956 1
3.7%
37.6182946 1
3.7%
37.613296 1
3.7%
37.6111958 1
3.7%
37.5862996 1
3.7%
37.5799984 1
3.7%
37.5741861 1
3.7%
37.5703995 1
3.7%

Interactions

2023-12-11T17:00:11.598038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:09.342189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:09.952939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.497389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:11.068003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:11.687668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:09.430883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.066053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.596881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:11.179220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:11.793301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:09.573399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.176209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.718266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:11.285821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:11.910791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:09.718199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.294409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.858527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:11.406209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:12.011702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:09.848966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.397574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:10.976522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:00:11.510010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:00:15.675194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호관측소코드관측소명주소평균강수량경도위도
고유번호1.0000.0001.0001.0000.5260.0000.933
관측소코드0.0001.0001.0001.0000.3380.0000.000
관측소명1.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.000
평균강수량0.5260.3381.0001.0001.0000.0000.621
경도0.0000.0001.0001.0000.0001.0000.000
위도0.9330.0001.0001.0000.6210.0001.000
2023-12-11T17:00:15.798030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호관측소코드평균강수량경도위도
고유번호1.000-0.1100.3930.3020.978
관측소코드-0.1101.000-0.383-0.371-0.042
평균강수량0.393-0.3831.0000.3800.356
경도0.302-0.3710.3801.0000.326
위도0.978-0.0420.3560.3261.000

Missing values

2023-12-11T17:00:12.138943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:00:12.308814image/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

고유번호관측소코드관측소명주소평균강수량경도위도
01509관악서울특별시 관악구 신림동 산56-1 (서울대학교)1404.416667126.95021937.452863
12417금천서울특별시 금천구 독산동 1034 (독산초등학교)1337.833333126.91412137.460314
23401서초서울특별시 서초구 서초동 1650 (서울교육대학교)1394.083333127.01791337.482814
34423구로서울특별시 구로구 궁동 213-42 (수궁동사무소)1388.0126.83122937.486113
45410기상청서울특별시 동작구 신대방동 460-18 (기상청)1319.5126.92072237.496314
56403송파서울특별시 송파구 잠실동 40-1 (롯데월드)1352.166667127.09831137.51101
67400강남서울특별시 강남구 삼성동 42 (삼릉초등학교)1381.583333127.04641237.512208
78415용산서울특별시 용산구 이촌동 301-75 (신용산초등학교)1325.708333126.97541637.519605
89418한강서울특별시 영등포구 여의도동 85-1 (세모유람선)1189.666667126.93732337.524105
910405양천서울특별시 양천구 목동 915 (목동주차장)1316.958333126.87722937.526808
고유번호관측소코드관측소명주소평균강수량경도위도
1718412서대문서울특별시 서대문구 신촌동 134 (연세대학교)1358.958333126.94452337.5704
1819408동대문서울특별시 동대문구 전농동 90 (서울시립대)1480.208333127.04801137.579998
1920409중랑서울특별시 중랑구 면목동 551 (면동초등학교)1427.333333127.08680537.5863
2021416은평서울특별시 은평구 불광동 280-17 (국립환경연구원)1296.541667126.93362337.611196
2122414성북서울특별시 성북구 정릉동 861-1 (국민대학교)1489.083333126.9972237.613296
2223420북한산서울특별시 종로구 구기동 산1 (승가사)1310.541667126.95441937.618295
2324407노원서울특별시 노원구 공릉동 230-3 (육군사관학교)1442.916667127.08730437.622196
2425424강북서울특별시 강북구 수유동 192-49 (강북구청 본관)1547.0625126.99961337.636093
2526406도봉서울특별시 도봉구 방학동 310 (신방학초등학교)1591.5127.0331137.666091
2627108서울서울특별시 종로구 송월동 1번지 (서울기상대)1560.583333126.96351537.574186