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-337/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
2007년사용량 has 325 (74.2%) zerosZeros
2008년사용량 has 322 (73.5%) zerosZeros

Reproduction

Analysis started2023-12-11 08:49:22.115101
Analysis finished2023-12-11 08:49:27.716280
Duration5.6 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
2023-12-11T17:49:27.837486image/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
2023-12-11T17:49:28.043521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 1
 
0.2%
321 1
 
0.2%
271 1
 
0.2%
270 1
 
0.2%
269 1
 
0.2%
293 1
 
0.2%
373 1
 
0.2%
411 1
 
0.2%
354 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

2023-12-11T17:49:28.218629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:49:28.347734image/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
2023-12-11T17:49:28.522974image/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
2023-12-11T17:49:28.751767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1106060 1
 
0.2%
1120056 1
 
0.2%
1114075 1
 
0.2%
1114074 1
 
0.2%
1114073 1
 
0.2%
1117070 1
 
0.2%
1123062 1
 
0.2%
1122064 1
 
0.2%
1122052 1
 
0.2%
1121066 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
2023-12-11T17:49:29.171059image/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 row답십리1동
2nd row창신3동
3rd row창신1동
4th row사근동
5th row성수2가3동
ValueCountFrequency (%)
신사동 2
 
0.5%
목5동 1
 
0.2%
상도3동 1
 
0.2%
성산2동 1
 
0.2%
가리봉동 1
 
0.2%
대치3동 1
 
0.2%
방배3동 1
 
0.2%
서초2동 1
 
0.2%
서림동 1
 
0.2%
청림동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T17:49:29.769374image/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%
상 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
성 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%
방 16
 
1.2%
상 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
성 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%
방 16
 
1.2%
상 16
 
1.2%
정 16
 
1.2%
화 16
 
1.2%
성 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  ZEROS 

Distinct114
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10474.456
Minimum0
Maximum203553.2
Zeros325
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:49:29.977031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3762.075
95-th percentile63353.47
Maximum203553.2
Range203553.2
Interquartile range (IQR)762.075

Descriptive statistics

Standard deviation25629.818
Coefficient of variation (CV)2.4468878
Kurtosis16.115005
Mean10474.456
Median Absolute Deviation (MAD)0
Skewness3.5307757
Sum4587811.6
Variance6.5688755 × 108
MonotonicityNot monotonic
2023-12-11T17:49:30.214366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 325
74.2%
23389.4 1
 
0.2%
78574.7 1
 
0.2%
32543.9 1
 
0.2%
43570.4 1
 
0.2%
22189.5 1
 
0.2%
30525.9 1
 
0.2%
14402.1 1
 
0.2%
9649.5 1
 
0.2%
14246.9 1
 
0.2%
Other values (104) 104
 
23.7%
ValueCountFrequency (%)
0.0 325
74.2%
193.4 1
 
0.2%
458.5 1
 
0.2%
516.3 1
 
0.2%
844.0 1
 
0.2%
845.7 1
 
0.2%
2036.3 1
 
0.2%
2228.9 1
 
0.2%
4889.9 1
 
0.2%
5266.6 1
 
0.2%
ValueCountFrequency (%)
203553.2 1
0.2%
191024.0 1
0.2%
133120.7 1
0.2%
123813.7 1
0.2%
106250.8 1
0.2%
102988.7 1
0.2%
98252.8 1
0.2%
97237.2 1
0.2%
94072.9 1
0.2%
93923.5 1
0.2%

2008년사용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct117
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11037.428
Minimum0
Maximum211015.9
Zeros322
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:49:30.434129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31731.925
95-th percentile65729.615
Maximum211015.9
Range211015.9
Interquartile range (IQR)1731.925

Descriptive statistics

Standard deviation26121.704
Coefficient of variation (CV)2.3666478
Kurtosis15.864685
Mean11037.428
Median Absolute Deviation (MAD)0
Skewness3.4427309
Sum4834393.3
Variance6.823434 × 108
MonotonicityNot monotonic
2023-12-11T17:49:30.651166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 322
73.5%
26503.4 1
 
0.2%
32146.1 1
 
0.2%
41911.4 1
 
0.2%
43862.9 1
 
0.2%
30041.4 1
 
0.2%
14372.5 1
 
0.2%
9622.5 1
 
0.2%
14782.5 1
 
0.2%
20959.7 1
 
0.2%
Other values (107) 107
 
24.4%
ValueCountFrequency (%)
0.0 322
73.5%
136.7 1
 
0.2%
227.0 1
 
0.2%
401.9 1
 
0.2%
432.2 1
 
0.2%
585.1 1
 
0.2%
899.2 1
 
0.2%
2009.5 1
 
0.2%
2115.2 1
 
0.2%
4852.4 1
 
0.2%
ValueCountFrequency (%)
211015.9 1
0.2%
194738.3 1
0.2%
127257.2 1
0.2%
125331.7 1
0.2%
105779.4 1
0.2%
101331.3 1
0.2%
95960.1 1
0.2%
94883.9 1
0.2%
94479.8 1
0.2%
92550.4 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
2023-12-11T17:49:30.835224image/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
2023-12-11T17:49:31.032612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204828.138 1
 
0.2%
194705.213 1
 
0.2%
193295.628 1
 
0.2%
195297.15 1
 
0.2%
189425.035 1
 
0.2%
190112.471 1
 
0.2%
205845.671 1
 
0.2%
199823.794 1
 
0.2%
200863.446 1
 
0.2%
194525.99 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
2023-12-11T17:49:31.244043image/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-0.55467902
Mean450117.74
Median Absolute Deviation (MAD)4332.767
Skewness0.32397248
Sum1.9715157 × 108
Variance34230250
MonotonicityNot monotonic
2023-12-11T17:49:31.448441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452466.809 1
 
0.2%
444247.615 1
 
0.2%
449646.73 1
 
0.2%
448546.697 1
 
0.2%
452579.793 1
 
0.2%
442613.672 1
 
0.2%
444693.458 1
 
0.2%
441675.182 1
 
0.2%
442912.533 1
 
0.2%
441596.917 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

2023-12-11T17:49:26.183837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:22.530724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:23.202043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:23.955192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:24.806352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.491352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:26.308124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:22.658941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:23.312773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:24.114953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:24.921887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.608094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:26.420291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:22.769821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:23.435236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:24.254174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.037472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.729557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:26.546682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:22.881958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:23.559151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:24.406436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.157603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.851063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:26.689722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:22.983472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:23.679289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:24.546942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.265283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.957100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:26.898175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:23.092622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:23.806122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:24.692472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:25.371619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:49:26.075528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:49:31.556876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량X 좌표Y 좌표
고유번호1.0000.9480.2930.2680.8540.814
행정동코드0.9481.0000.2840.2730.8870.855
2007년사용량0.2930.2841.0000.9900.1750.218
2008년사용량0.2680.2730.9901.0000.1540.199
X 좌표0.8540.8870.1750.1541.0000.454
Y 좌표0.8140.8550.2180.1990.4541.000
2023-12-11T17:49:31.714689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량X 좌표Y 좌표
고유번호1.0000.9810.2650.268-0.011-0.629
행정동코드0.9811.0000.3040.3100.011-0.629
2007년사용량0.2650.3041.0000.9850.136-0.092
2008년사용량0.2680.3100.9851.0000.130-0.085
X 좌표-0.0110.0110.1360.1301.0000.215
Y 좌표-0.629-0.629-0.092-0.0850.2151.000

Missing values

2023-12-11T17:49:27.440668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:49:27.631814image/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
08020081106060답십리1동0.00.0204828.138452466.809
11320081101069창신3동0.00.0201147.803453250.932
23820081101067창신1동0.00.0201255.885452639.421
34920081104055사근동0.00.0204007.697451051.69
45720081104068성수2가3동0.00.0205174.84449557.35
56620081105057중곡3동0.00.0207191.668452012.858
67220081105064자양1동0.00.0207092.778448584.67
74320081104066성수1가2동0.00.0203702.617449912.407
817020081111061중계1동47185.349420.1207552.187460939.842
9820081101063종로5.6가동0.00.0200364.167452627.462
고유번호기준연도행정동코드행정동명2007년사용량2008년사용량X 좌표Y 좌표
42843820081125074길동0.00.0213023.986448939.589
42941620081123053논현2동12761.112711.1203181.696446188.395
43041920081123057청담2동5873.25723.5203952.789447006.484
43142020081124077잠실6동42924.442774.3208776.964446524.327
43242220081124080잠실3동87959.384980.5208380.132446309.836
43342320081125051강일동0.00.0215247.599451535.363
43442620081125054명일2동0.00.0213768.099449897.966
43542720081125056고덕2동0.00.0214335.251451907.084
43643020081125061천호1동0.00.0212156.58449683.208
437720081101061종로1.2.3.4가동0.00.0199052.485453135.715