Overview

Dataset statistics

Number of variables8
Number of observations438
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.5 KiB
Average record size in memory71.3 B

Variable types

Numeric6
Categorical1
Text1

Dataset

Description고유번호,기준연도,행정동코드,행정동명,2005년사용량,2006년사용량,경도,위도
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1337/S/1/datasetView.do

Alerts

기준연도 has constant value ""Constant
고유번호 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
행정동코드 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
2005년사용량 is highly overall correlated with 2006년사용량High correlation
2006년사용량 is highly overall correlated with 2005년사용량High correlation
위도 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
고유번호 has unique valuesUnique
행정동코드 has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-11 08:54:40.226007
Analysis finished2023-12-11 08:54:46.569414
Duration6.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct438
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.5
Minimum1
Maximum438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:54:46.673332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.85
Q1110.25
median219.5
Q3328.75
95-th percentile416.15
Maximum438
Range437
Interquartile range (IQR)218.5

Descriptive statistics

Standard deviation126.58396
Coefficient of variation (CV)0.57669232
Kurtosis-1.2
Mean219.5
Median Absolute Deviation (MAD)109.5
Skewness0
Sum96141
Variance16023.5
MonotonicityNot monotonic
2023-12-11T17:54:46.855941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
0.2%
274 1
 
0.2%
316 1
 
0.2%
310 1
 
0.2%
381 1
 
0.2%
396 1
 
0.2%
290 1
 
0.2%
288 1
 
0.2%
287 1
 
0.2%
280 1
 
0.2%
Other values (428) 428
97.7%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
438 1
0.2%
437 1
0.2%
436 1
0.2%
435 1
0.2%
434 1
0.2%
433 1
0.2%
432 1
0.2%
431 1
0.2%
430 1
0.2%
429 1
0.2%

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2008
438 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2008
3rd row2008
4th row2008
5th row2008

Common Values

ValueCountFrequency (%)
2008 438
100.0%

Length

2023-12-11T17:54:47.048505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:54:47.152106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2008 438
100.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct438
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1113545.7
Minimum1101053
Maximum1125074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:54:47.288383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1102057.9
Q11107061.2
median1114060.5
Q31120072.8
95-th percentile1124075.3
Maximum1125074
Range24021
Interquartile range (IQR)13011.5

Descriptive statistics

Standard deviation7427.8897
Coefficient of variation (CV)0.0066704851
Kurtosis-1.2748023
Mean1113545.7
Median Absolute Deviation (MAD)6996
Skewness-0.052988859
Sum4.87733 × 108
Variance55173546
MonotonicityNot monotonic
2023-12-11T17:54:47.810477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103053 1
 
0.2%
1115069 1
 
0.2%
1122057 1
 
0.2%
1122062 1
 
0.2%
1124062 1
 
0.2%
1123055 1
 
0.2%
1119074 1
 
0.2%
1120052 1
 
0.2%
1116070 1
 
0.2%
1117052 1
 
0.2%
Other values (428) 428
97.7%
ValueCountFrequency (%)
1101053 1
0.2%
1101054 1
0.2%
1101055 1
0.2%
1101056 1
0.2%
1101057 1
0.2%
1101058 1
0.2%
1101060 1
0.2%
1101061 1
0.2%
1101063 1
0.2%
1101064 1
0.2%
ValueCountFrequency (%)
1125074 1
0.2%
1125073 1
0.2%
1125072 1
0.2%
1125071 1
0.2%
1125070 1
0.2%
1125067 1
0.2%
1125066 1
0.2%
1125065 1
0.2%
1125063 1
0.2%
1125061 1
0.2%

행정동명
Text

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2023-12-11T17:54:48.228725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.8287671
Min length2

Characters and Unicode

Total characters1677
Distinct characters187
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique436 ?
Unique (%)99.5%

Sample

1st row남영동
2nd row원효로2동
3rd row소공동
4th row숭인1동
5th row구의3동
ValueCountFrequency (%)
신사동 2
 
0.5%
오류1동 1
 
0.2%
연남동 1
 
0.2%
송파2동 1
 
0.2%
압구정2동 1
 
0.2%
영등포동 1
 
0.2%
노량진2동 1
 
0.2%
방화1동 1
 
0.2%
구로1동 1
 
0.2%
우장산동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T17:54:48.799531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 439
26.2%
1 101
 
6.0%
2 101
 
6.0%
3 52
 
3.1%
신 42
 
2.5%
4 30
 
1.8%
가 24
 
1.4%
계 19
 
1.1%
곡 18
 
1.1%
상 16
 
1.0%
Other values (177) 835
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1354
80.7%
Decimal Number 314
 
18.7%
Other Punctuation 9
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 439
32.4%
신 42
 
3.1%
가 24
 
1.8%
계 19
 
1.4%
곡 18
 
1.3%
상 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
성 16
 
1.2%
방 16
 
1.2%
Other values (166) 732
54.1%
Decimal Number
ValueCountFrequency (%)
1 101
32.2%
2 101
32.2%
3 52
16.6%
4 30
 
9.6%
5 11
 
3.5%
6 8
 
2.5%
7 6
 
1.9%
8 3
 
1.0%
0 1
 
0.3%
9 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1354
80.7%
Common 323
 
19.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 439
32.4%
신 42
 
3.1%
가 24
 
1.8%
계 19
 
1.4%
곡 18
 
1.3%
상 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
성 16
 
1.2%
방 16
 
1.2%
Other values (166) 732
54.1%
Common
ValueCountFrequency (%)
1 101
31.3%
2 101
31.3%
3 52
16.1%
4 30
 
9.3%
5 11
 
3.4%
. 9
 
2.8%
6 8
 
2.5%
7 6
 
1.9%
8 3
 
0.9%
0 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1354
80.7%
ASCII 323
 
19.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 439
32.4%
신 42
 
3.1%
가 24
 
1.8%
계 19
 
1.4%
곡 18
 
1.3%
상 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
성 16
 
1.2%
방 16
 
1.2%
Other values (166) 732
54.1%
ASCII
ValueCountFrequency (%)
1 101
31.3%
2 101
31.3%
3 52
16.1%
4 30
 
9.3%
5 11
 
3.4%
. 9
 
2.8%
6 8
 
2.5%
7 6
 
1.9%
8 3
 
0.9%
0 1
 
0.3%

2005년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.742398
Minimum0.051714
Maximum108.32401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:54:48.995802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.051714
5-th percentile2.4599057
Q17.0119985
median9.685008
Q313.134613
95-th percentile21.211712
Maximum108.32401
Range108.27229
Interquartile range (IQR)6.1226145

Descriptive statistics

Standard deviation7.6749776
Coefficient of variation (CV)0.71445664
Kurtosis64.231479
Mean10.742398
Median Absolute Deviation (MAD)3.016005
Skewness5.9012962
Sum4705.1703
Variance58.905281
MonotonicityNot monotonic
2023-12-11T17:54:49.151380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.462992 2
 
0.5%
5.650058 1
 
0.2%
9.247349 1
 
0.2%
4.462882 1
 
0.2%
6.148281 1
 
0.2%
15.117164 1
 
0.2%
8.842869 1
 
0.2%
12.432274 1
 
0.2%
8.330422 1
 
0.2%
12.608782 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
0.051714 1
0.2%
0.3285 1
0.2%
0.754094 1
0.2%
0.91246 1
0.2%
1.048089 1
0.2%
1.174169 1
0.2%
1.285106 1
0.2%
1.347645 1
0.2%
1.451321 1
0.2%
1.545803 1
0.2%
ValueCountFrequency (%)
108.324009 1
0.2%
62.029067 1
0.2%
43.653902 1
0.2%
33.123758 1
0.2%
30.968769 1
0.2%
30.675549 1
0.2%
30.617619 1
0.2%
29.905413 1
0.2%
28.519911 1
0.2%
27.88691 1
0.2%

2006년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.592128
Minimum0.05356
Maximum105.91331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:54:49.345917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05356
5-th percentile2.6153319
Q17.020612
median9.36404
Q312.753328
95-th percentile21.353556
Maximum105.91331
Range105.85975
Interquartile range (IQR)5.732716

Descriptive statistics

Standard deviation7.6502045
Coefficient of variation (CV)0.72225377
Kurtosis61.746774
Mean10.592128
Median Absolute Deviation (MAD)2.9119135
Skewness5.8999821
Sum4639.3522
Variance58.52563
MonotonicityNot monotonic
2023-12-11T17:54:49.493553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.985794 2
 
0.5%
5.348726 1
 
0.2%
8.728673 1
 
0.2%
4.275002 1
 
0.2%
6.497326 1
 
0.2%
14.898732 1
 
0.2%
8.431782 1
 
0.2%
11.507829 1
 
0.2%
8.433678 1
 
0.2%
13.379757 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
0.05356 1
0.2%
0.355241 1
0.2%
0.76463 1
0.2%
0.926535 1
0.2%
1.013304 1
0.2%
1.126996 1
0.2%
1.161073 1
0.2%
1.274211 1
0.2%
1.408414 1
0.2%
1.501606 1
0.2%
ValueCountFrequency (%)
105.913308 1
0.2%
63.603007 1
0.2%
54.334634 1
0.2%
31.790229 1
0.2%
30.067306 1
0.2%
29.682012 1
0.2%
29.379005 1
0.2%
29.145017 1
0.2%
26.061281 1
0.2%
25.118454 1
0.2%

경도
Real number (ℝ)

UNIQUE 

Distinct438
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99566
Minimum126.79474
Maximum127.17257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:54:49.635002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.79474
5-th percentile126.84818
Q1126.92419
median127.01051
Q3127.06216
95-th percentile127.12797
Maximum127.17257
Range0.3778334
Interquartile range (IQR)0.13797765

Descriptive statistics

Standard deviation0.085035585
Coefficient of variation (CV)0.0006695944
Kurtosis-0.84416473
Mean126.99566
Median Absolute Deviation (MAD)0.06347975
Skewness-0.20532014
Sum55624.101
Variance0.0072310508
MonotonicityNot monotonic
2023-12-11T17:54:49.790159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9740814 1
 
0.2%
126.8704306 1
 
0.2%
127.0167747 1
 
0.2%
126.9959053 1
 
0.2%
127.1167081 1
 
0.2%
127.0395405 1
 
0.2%
126.9092502 1
 
0.2%
126.938553 1
 
0.2%
126.8129904 1
 
0.2%
126.8744434 1
 
0.2%
Other values (428) 428
97.7%
ValueCountFrequency (%)
126.7947412 1
0.2%
126.8055663 1
0.2%
126.8129904 1
0.2%
126.8153977 1
0.2%
126.8253891 1
0.2%
126.8258043 1
0.2%
126.8282326 1
0.2%
126.8299898 1
0.2%
126.8313103 1
0.2%
126.8344994 1
0.2%
ValueCountFrequency (%)
127.1725746 1
0.2%
127.1650498 1
0.2%
127.1622577 1
0.2%
127.1557966 1
0.2%
127.1545404 1
0.2%
127.1516416 1
0.2%
127.1491472 1
0.2%
127.1489329 1
0.2%
127.1485334 1
0.2%
127.1473638 1
0.2%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct438
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.550548
Minimum37.440609
Maximum37.685297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:54:49.950331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.440609
5-th percentile37.474466
Q137.506891
median37.549624
Q337.584754
95-th percentile37.646762
Maximum37.685297
Range0.244688
Interquartile range (IQR)0.07786345

Descriptive statistics

Standard deviation0.052721623
Coefficient of variation (CV)0.0014040174
Kurtosis-0.55488934
Mean37.550548
Median Absolute Deviation (MAD)0.03906035
Skewness0.32431153
Sum16447.14
Variance0.0027795695
MonotonicityNot monotonic
2023-12-11T17:54:50.130384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5445003 1
 
0.2%
37.5162508 1
 
0.2%
37.5052427 1
 
0.2%
37.4850573 1
 
0.2%
37.5029784 1
 
0.2%
37.5291774 1
 
0.2%
37.5236448 1
 
0.2%
37.5110955 1
 
0.2%
37.569611 1
 
0.2%
37.4930275 1
 
0.2%
Other values (428) 428
97.7%
ValueCountFrequency (%)
37.4406086 1
0.2%
37.4489992 1
0.2%
37.4506941 1
0.2%
37.452285 1
0.2%
37.453328 1
0.2%
37.4541092 1
0.2%
37.4543585 1
0.2%
37.4595548 1
0.2%
37.4614434 1
0.2%
37.4617099 1
0.2%
ValueCountFrequency (%)
37.6852966 1
0.2%
37.6835121 1
0.2%
37.6815251 1
0.2%
37.676222 1
0.2%
37.668601 1
0.2%
37.6676017 1
0.2%
37.6661992 1
0.2%
37.6651501 1
0.2%
37.6633451 1
0.2%
37.6626127 1
0.2%

Interactions

2023-12-11T17:54:45.594572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:41.876622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:42.719452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:43.429803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.168728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.871345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:45.716914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:42.088574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:42.826041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:43.537555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.272623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.988670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:45.836341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:42.193802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:42.931734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:43.656362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.404713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:45.103244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:45.929658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:42.313228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:43.056092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:43.776364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.538387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:45.239131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:46.037567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:42.461499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:43.182303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:43.895785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.640308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:45.352792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:46.172981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:42.604300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:43.315044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.051701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:44.753558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:45.491948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:54:50.312303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2005년사용량2006년사용량경도위도
고유번호1.0000.9090.1700.1590.7710.742
행정동코드0.9091.0000.1650.1580.8950.878
2005년사용량0.1700.1651.0000.9290.0050.083
2006년사용량0.1590.1580.9291.0000.0000.128
경도0.7710.8950.0050.0001.0000.453
위도0.7420.8780.0830.1280.4531.000
2023-12-11T17:54:50.484136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2005년사용량2006년사용량경도위도
고유번호1.0000.9070.1910.202-0.020-0.602
행정동코드0.9071.0000.1600.1810.009-0.630
2005년사용량0.1910.1601.0000.984-0.100-0.116
2006년사용량0.2020.1810.9841.000-0.053-0.116
경도-0.0200.009-0.100-0.0531.0000.215
위도-0.602-0.630-0.116-0.1160.2151.000

Missing values

2023-12-11T17:54:46.327780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:54:46.500874image/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

고유번호기준연도행정동코드행정동명2005년사용량2006년사용량경도위도
0320081103053남영동5.6500585.348726126.97408137.5445
1420081103057원효로2동6.8248376.742827126.95262737.532593
2620081102052소공동25.96611525.118454126.9746837.564492
3820081101070숭인1동2.8015452.781448127.01691737.577577
4920081105062구의3동18.60579617.150443127.09614237.535739
59920081111072상계8동1.1741691.161073127.0537837.666199
6720081102062신당2동8.0478077.764107127.0087937.554878
7520081103063이촌1동1.9161661.969137126.97021137.517545
81020081109074우이동4.2596434.16336126.99899937.661931
91120081110064도봉1동21.26354723.022776127.02702537.685297
고유번호기준연도행정동코드행정동명2005년사용량2006년사용량경도위도
42836820081121073대학동24.0059222.751558126.95038337.454358
42942820081125051강일동2.2870665.268633127.17257537.563219
43042920081125052상일동11.59482313.236108127.1650537.550795
43143120081105067자양4동12.02823311.464335127.06360137.53388
43243220081106079답십리3동7.2262067.227326127.05045737.57044
43343320081108077장위2동9.2791769.090904127.0530437.612654
43443520081113076연희동22.71336221.746453126.93182137.572143
43543720081119072대림3동13.19281512.463053126.89854437.500099
43643820081123062대치3동14.68318515.40913127.06611237.501685
43740520081123076세곡동2.7422362.949599127.10347437.472439