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,X 좌표,Y 좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-331/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
Y 좌표 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
고유번호 has unique valuesUnique
행정동코드 has unique valuesUnique
X 좌표 has unique valuesUnique
Y 좌표 has unique valuesUnique

Reproduction

Analysis started2023-12-11 05:20:17.852652
Analysis finished2023-12-11 05:20:24.771695
Duration6.92 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
2023-12-11T14:20:24.907706image/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
2023-12-11T14:20:25.171159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 1
 
0.2%
410 1
 
0.2%
269 1
 
0.2%
267 1
 
0.2%
266 1
 
0.2%
264 1
 
0.2%
369 1
 
0.2%
409 1
 
0.2%
402 1
 
0.2%
342 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

2023-12-11T14:20:25.407267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:20:25.568435image/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
2023-12-11T14:20:26.259280image/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
2023-12-11T14:20:26.530386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101061 1
 
0.2%
1122065 1
 
0.2%
1114072 1
 
0.2%
1114070 1
 
0.2%
1114069 1
 
0.2%
1114066 1
 
0.2%
1123057 1
 
0.2%
1122064 1
 
0.2%
1124064 1
 
0.2%
1121069 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
2023-12-11T14:20:27.100311image/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 row종로1.2.3.4가동
2nd row종로5.6가동
3rd row남영동
4th row원효로2동
5th row소공동
ValueCountFrequency (%)
신사동 2
 
0.5%
신월3동 1
 
0.2%
가락본동 1
 
0.2%
망원1동 1
 
0.2%
서교동 1
 
0.2%
청담2동 1
 
0.2%
방배3동 1
 
0.2%
삼전동 1
 
0.2%
신림동 1
 
0.2%
압구정2동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T14:20:27.787806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 439
26.2%
2 101
 
6.0%
1 101
 
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%
방 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%
방 16
 
1.2%
화 16
 
1.2%
성 16
 
1.2%
정 16
 
1.2%
상 16
 
1.2%
Other values (166) 732
54.1%
Decimal Number
ValueCountFrequency (%)
2 101
32.2%
1 101
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%
9 1
 
0.3%
0 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%
방 16
 
1.2%
화 16
 
1.2%
성 16
 
1.2%
정 16
 
1.2%
상 16
 
1.2%
Other values (166) 732
54.1%
Common
ValueCountFrequency (%)
2 101
31.3%
1 101
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%
9 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%
방 16
 
1.2%
화 16
 
1.2%
성 16
 
1.2%
정 16
 
1.2%
상 16
 
1.2%
Other values (166) 732
54.1%
ASCII
ValueCountFrequency (%)
2 101
31.3%
1 101
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%
9 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
2023-12-11T14:20:27.992072image/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
2023-12-11T14:20:28.198591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.462992 2
 
0.5%
26.169964 1
 
0.2%
8.171087 1
 
0.2%
7.975041 1
 
0.2%
9.586977 1
 
0.2%
25.56381 1
 
0.2%
11.389721 1
 
0.2%
14.463206 1
 
0.2%
14.388346 1
 
0.2%
10.19354 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
2023-12-11T14:20:28.418354image/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
2023-12-11T14:20:28.613244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.985794 2
 
0.5%
24.864863 1
 
0.2%
7.909982 1
 
0.2%
7.607289 1
 
0.2%
9.13546 1
 
0.2%
24.547517 1
 
0.2%
12.098901 1
 
0.2%
13.819295 1
 
0.2%
14.38939 1
 
0.2%
9.720244 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%

X 좌표
Real number (ℝ)

UNIQUE 

Distinct438
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199615.65
Minimum181863.61
Maximum215247.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T14:20:28.796437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum181863.61
5-th percentile186582.34
Q1193301.75
median200928.42
Q3205491.4
95-th percentile211311.35
Maximum215247.6
Range33383.994
Interquartile range (IQR)12189.656

Descriptive statistics

Standard deviation7515.4609
Coefficient of variation (CV)0.037649658
Kurtosis-0.84397398
Mean199615.65
Median Absolute Deviation (MAD)5611.313
Skewness-0.20514805
Sum87431654
Variance56482152
MonotonicityNot monotonic
2023-12-11T14:20:28.963521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199052.485 1
 
0.2%
199244.368 1
 
0.2%
192119.502 1
 
0.2%
190783.78 1
 
0.2%
191227.788 1
 
0.2%
193183.412 1
 
0.2%
203952.789 1
 
0.2%
199823.794 1
 
0.2%
208003.497 1
 
0.2%
193536.714 1
 
0.2%
Other values (428) 428
97.7%
ValueCountFrequency (%)
181863.605 1
0.2%
182826.453 1
0.2%
183479.251 1
0.2%
183694.055 1
0.2%
184558.927 1
0.2%
184607.027 1
0.2%
184818.062 1
0.2%
184974.596 1
0.2%
185086.826 1
0.2%
185371.643 1
0.2%
ValueCountFrequency (%)
215247.599 1
0.2%
214585.692 1
0.2%
214335.251 1
0.2%
213768.099 1
0.2%
213665.901 1
0.2%
213409.003 1
0.2%
213176.714 1
0.2%
213171.669 1
0.2%
213127.875 1
0.2%
213023.986 1
0.2%

Y 좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct438
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450117.74
Minimum437916.66
Maximum465070.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T14:20:29.185889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437916.66
5-th percentile441670.65
Q1445271.95
median450016.76
Q3453913.19
95-th percentile460794.65
Maximum465070.19
Range27153.533
Interquartile range (IQR)8641.2373

Descriptive statistics

Standard deviation5850.6624
Coefficient of variation (CV)0.012998071
Kurtosis-0.55467903
Mean450117.74
Median Absolute Deviation (MAD)4332.767
Skewness0.32397248
Sum1.9715157 × 108
Variance34230250
MonotonicityNot monotonic
2023-12-11T14:20:29.465809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
453135.715 1
 
0.2%
443372.06 1
 
0.2%
451429.174 1
 
0.2%
450807.413 1
 
0.2%
450300.486 1
 
0.2%
450614.914 1
 
0.2%
447006.484 1
 
0.2%
441675.182 1
 
0.2%
444695.558 1
 
0.2%
443050.798 1
 
0.2%
Other values (428) 428
97.7%
ValueCountFrequency (%)
437916.657 1
0.2%
438847.03 1
0.2%
439034.702 1
0.2%
439211.042 1
0.2%
439328.644 1
0.2%
439411.965 1
0.2%
439440.063 1
0.2%
440019.112 1
0.2%
440227.137 1
0.2%
440254.738 1
0.2%
ValueCountFrequency (%)
465070.19 1
0.2%
464874.245 1
0.2%
464652.49 1
0.2%
464065.786 1
0.2%
463217.382 1
0.2%
463107.98 1
0.2%
462951.716 1
0.2%
462834.562 1
0.2%
462635.878 1
0.2%
462552.518 1
0.2%

Interactions

2023-12-11T14:20:23.412884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:18.418376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:19.283545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:20.367771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:21.365338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:22.387504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:23.550441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:18.559495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:19.473511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:20.503047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:21.546331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:22.558099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:23.708722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:18.711420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:19.652623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:20.668797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:21.741382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:22.722354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:23.900692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:18.840818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:19.819079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:20.847248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:21.905833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:22.898329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:24.065408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:18.977181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:20.002519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:21.016078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:22.051604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:23.085752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:24.227954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:19.117553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:20.181135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:21.191694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:22.221820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:20:23.261003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:20:29.657366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2005년사용량2006년사용량X 좌표Y 좌표
고유번호1.0000.9490.1900.1500.8440.810
행정동코드0.9491.0000.1650.1580.8940.878
2005년사용량0.1900.1651.0000.9290.0000.086
2006년사용량0.1500.1580.9291.0000.0000.133
X 좌표0.8440.8940.0000.0001.0000.454
Y 좌표0.8100.8780.0860.1330.4541.000
2023-12-11T14:20:29.865718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2005년사용량2006년사용량X 좌표Y 좌표
고유번호1.0000.9790.1820.200-0.012-0.627
행정동코드0.9791.0000.1600.1810.009-0.630
2005년사용량0.1820.1601.0000.984-0.100-0.116
2006년사용량0.2000.1810.9841.000-0.053-0.115
X 좌표-0.0120.009-0.100-0.0531.0000.215
Y 좌표-0.627-0.630-0.116-0.1150.2151.000

Missing values

2023-12-11T14:20:24.452497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:20:24.676472image/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

고유번호기준연도행정동코드행정동명2005년사용량2006년사용량X 좌표Y 좌표
0720081101061종로1.2.3.4가동26.16996424.864863199052.485453135.715
1820081101063종로5.6가동7.4337596.887036200364.167452627.462
23220081103053남영동5.6500585.348726197709.584449443.419
33320081103057원효로2동6.8248376.742827195812.758448122.786
41720081102052소공동25.96611525.118454197763.058451662.285
52420081102062신당2동8.0478077.764107200776.666450594.952
61420081101070숭인1동2.8015452.781448201494.367453114.366
77020081105062구의3동18.60579617.150443208497.268448474.65
84320081104065성수1가1동7.9847487.772014203549.769448964.249
94120081103063이촌1동1.9161661.969137197366.442446451.673
고유번호기준연도행정동코드행정동명2005년사용량2006년사용량X 좌표Y 좌표
42834720081121079성현동14.69404213.449697195639.647443380.734
42942020081124078잠실7동0.7540940.76463206556.921445284.388
43042220081124080잠실3동11.84137511.191679208380.132446309.836
43142320081125051강일동2.2870665.268633215247.599451535.363
4326420081105055중곡1동7.1240256.904084206869.179451378.271
4337420081105067자양4동12.02823311.464335205621.339448266.35
43410620081106079답십리3동7.2262067.227326204457.593452323.377
43515220081108077장위2동9.2791769.090904204683.096457008.478
43626120081113076연희동22.71336221.746453193977.015452512.949
43735120081122051서초1동13.80846714.48477201801.851442999.793