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,2007년사용량,2008년사용량,X 좌표,Y 좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-333/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
2007년사용량 is highly overall correlated with 2008년사용량High correlation
2008년사용량 is highly overall correlated with 2007년사용량High 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 05:59:59.548683
Analysis finished2023-12-11 06:00:05.330648
Duration5.78 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
2023-12-11T15:00:05.828771image/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
2023-12-11T15:00:06.039751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 1
 
0.2%
355 1
 
0.2%
272 1
 
0.2%
271 1
 
0.2%
433 1
 
0.2%
374 1
 
0.2%
366 1
 
0.2%
405 1
 
0.2%
353 1
 
0.2%
302 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

2023-12-11T15:00:06.224452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:00:06.375640image/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
2023-12-11T15:00:06.532580image/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
2023-12-11T15:00:06.784914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1102065 1
 
0.2%
1122055 1
 
0.2%
1116071 1
 
0.2%
1116070 1
 
0.2%
1125067 1
 
0.2%
1123062 1
 
0.2%
1123053 1
 
0.2%
1122058 1
 
0.2%
1122052 1
 
0.2%
1123073 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
2023-12-11T15:00:07.258877image/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 row신당5동
2nd row신당1동
3rd row군자동
4th row이태원2동
5th row자양1동
ValueCountFrequency (%)
신사동 2
 
0.5%
고척2동 1
 
0.2%
가양1동 1
 
0.2%
성내3동 1
 
0.2%
대치3동 1
 
0.2%
논현2동 1
 
0.2%
반포2동 1
 
0.2%
서초2동 1
 
0.2%
일원1동 1
 
0.2%
반포본동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T15:00:07.822957image/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%
성 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%
성 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
상 16
 
1.2%
방 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%
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%
성 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
상 16
 
1.2%
방 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%
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%
성 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
상 16
 
1.2%
방 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%
0 1
 
0.3%

2007년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.272784
Minimum0.105726
Maximum96.543881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T15:00:08.005448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.105726
5-th percentile2.2810962
Q16.5731015
median8.9292195
Q312.411614
95-th percentile21.06466
Maximum96.543881
Range96.438155
Interquartile range (IQR)5.8385123

Descriptive statistics

Standard deviation7.9528354
Coefficient of variation (CV)0.77416551
Kurtosis46.755084
Mean10.272784
Median Absolute Deviation (MAD)2.8772371
Skewness5.3880508
Sum4499.4796
Variance63.247591
MonotonicityNot monotonic
2023-12-11T15:00:08.227264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.726021 2
 
0.5%
3.968509 1
 
0.2%
15.196486 1
 
0.2%
10.131355 1
 
0.2%
14.777525 1
 
0.2%
22.733526 1
 
0.2%
1.891113 1
 
0.2%
13.169142 1
 
0.2%
52.622083 1
 
0.2%
0.944227 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
0.105726 1
0.2%
0.41099 1
0.2%
0.469326 1
0.2%
0.54488 1
0.2%
0.944227 1
0.2%
0.962463 1
0.2%
1.117913 1
0.2%
1.16327 1
0.2%
1.171394 1
0.2%
1.237687 1
0.2%
ValueCountFrequency (%)
96.543881 1
0.2%
76.960833 1
0.2%
60.090084 1
0.2%
52.622083 1
0.2%
29.046991 1
0.2%
28.756018 1
0.2%
27.777639 1
0.2%
26.390219 1
0.2%
25.891209 1
0.2%
25.867369 1
0.2%

2008년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.394986
Minimum0.116264
Maximum102.61198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T15:00:08.411674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.116264
5-th percentile2.3951543
Q16.5274815
median8.9874745
Q312.383406
95-th percentile21.377385
Maximum102.61198
Range102.49572
Interquartile range (IQR)5.855924

Descriptive statistics

Standard deviation8.3808803
Coefficient of variation (CV)0.80624257
Kurtosis57.31522
Mean10.394986
Median Absolute Deviation (MAD)2.802458
Skewness6.0566653
Sum4553.0039
Variance70.239155
MonotonicityNot monotonic
2023-12-11T15:00:08.590974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.727387 2
 
0.5%
4.035609 1
 
0.2%
6.164665 1
 
0.2%
9.853181 1
 
0.2%
14.393642 1
 
0.2%
22.611445 1
 
0.2%
1.856192 1
 
0.2%
15.284715 1
 
0.2%
32.57433 1
 
0.2%
0.902703 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
0.116264 1
0.2%
0.410427 1
0.2%
0.434542 1
0.2%
0.537682 1
0.2%
0.810669 1
0.2%
0.902703 1
0.2%
1.015512 1
0.2%
1.055612 1
0.2%
1.154264 1
0.2%
1.170693 1
0.2%
ValueCountFrequency (%)
102.61198 1
0.2%
93.720453 1
0.2%
59.307558 1
0.2%
36.221153 1
0.2%
32.57433 1
0.2%
29.750465 1
0.2%
28.173288 1
0.2%
28.041599 1
0.2%
27.556485 1
0.2%
26.96501 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
2023-12-11T15:00:08.781415image/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
2023-12-11T15:00:08.968777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201890.623 1
 
0.2%
198881.227 1
 
0.2%
182826.453 1
 
0.2%
183479.251 1
 
0.2%
211815.756 1
 
0.2%
204858.263 1
 
0.2%
196874.495 1
 
0.2%
211614.877 1
 
0.2%
200863.446 1
 
0.2%
208026.839 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
2023-12-11T15:00:09.166191image/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
2023-12-11T15:00:09.367414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451514.806 1
 
0.2%
445209.663 1
 
0.2%
453754.87 1
 
0.2%
452247.062 1
 
0.2%
447665.923 1
 
0.2%
444681.936 1
 
0.2%
445104.241 1
 
0.2%
444099.164 1
 
0.2%
442912.533 1
 
0.2%
443739.302 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

2023-12-11T15:00:04.184722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:59:59.895416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.594653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:01.411157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:02.394992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:03.285154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:04.316212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.034116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.700823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:01.581321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:02.520059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:03.438599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:04.476618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.155557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.829241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:01.767171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:02.669492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:03.588014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:04.608883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.258052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.981655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:01.903250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:02.814034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:03.730578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:04.755529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.365008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:01.109816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:02.077019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:02.957967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:03.881095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:04.890301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:00.493006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:01.282312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:02.241715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:03.128084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:00:04.033869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:00:09.500875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량X 좌표Y 좌표
고유번호1.0000.9490.1650.1300.8440.810
행정동코드0.9491.0000.1190.1370.8760.865
2007년사용량0.1650.1191.0000.9610.0000.000
2008년사용량0.1300.1370.9611.0000.0820.000
X 좌표0.8440.8760.0000.0821.0000.454
Y 좌표0.8100.8650.0000.0000.4541.000
2023-12-11T15:00:09.648697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량X 좌표Y 좌표
고유번호1.0000.9780.1680.128-0.012-0.627
행정동코드0.9781.0000.1470.1070.005-0.623
2007년사용량0.1680.1471.0000.972-0.140-0.093
2008년사용량0.1280.1070.9721.000-0.108-0.074
X 좌표-0.0120.005-0.140-0.1081.0000.215
Y 좌표-0.627-0.623-0.093-0.0740.2151.000

Missing values

2023-12-11T15:00:05.060739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:00:05.255547image/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

고유번호기준연도행정동코드행정동명2007년사용량2008년사용량X 좌표Y 좌표
02720081102065신당5동3.9685094.035609201890.623451514.806
12320081102061신당1동8.8445348.884614201213.632451748.381
26320081105054군자동9.74439810.026433206495.914450430.124
38320081103066이태원2동6.8196317.264627199322.876449128.968
47220081105064자양1동10.12271610.029272207092.778448584.67
58620081103071청파동12.42637112.263737197048.524449875.04
64420081105053화양동16.40373117.179769206466.719449336.664
74720081106061답십리2동5.1806565.273614205283.699452469.857
85620081104067성수2가1동8.0834138.523298204945.831448533.352
916820081111053월계3동5.9253415.89841205047.708459107.823
고유번호기준연도행정동코드행정동명2007년사용량2008년사용량X 좌표Y 좌표
42843220081125066성내2동10.50695810.360192211294.978448323.149
4296420081105055중곡1동6.3331666.564086206869.179451378.271
4307420081105067자양4동2.0964781.880025205621.339448266.35
43122720081112066신사2동8.0329837.932096191676.32453692.664
43226020081116063화곡8동9.88374410.326117187828.332451478.498
43327520081116074우장산동12.46600213.01819186011.435450076.827
43432420081118058시흥2동9.5116079.917495191747.068437916.657
43531420081119072대림3동11.98752411.581343191898.2445995.248
43633920081121063남현동8.5094728.514167194086.155442301.543
43741120081122065방배4동12.24677712.355708202096.054441227.636