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,경도,위도
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1339/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
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 09:09:17.458221
Analysis finished2023-12-11 09:09:23.570751
Duration6.11 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
2023-12-11T18:09:23.648952image/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
2023-12-11T18:09:23.788934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
0.2%
275 1
 
0.2%
318 1
 
0.2%
304 1
 
0.2%
303 1
 
0.2%
302 1
 
0.2%
301 1
 
0.2%
357 1
 
0.2%
287 1
 
0.2%
286 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

2023-12-11T18:09:23.913071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:09:24.005846image/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
2023-12-11T18:09:24.138768image/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
2023-12-11T18:09:24.323730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103053 1
 
0.2%
1118051 1
 
0.2%
1123061 1
 
0.2%
1122052 1
 
0.2%
1121069 1
 
0.2%
1119055 1
 
0.2%
1122056 1
 
0.2%
1121079 1
 
0.2%
1120055 1
 
0.2%
1120054 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
2023-12-11T18:09:24.664426image/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 row소공동
3rd row신당2동
4th row숭인1동
5th row구의3동
ValueCountFrequency (%)
신사동 2
 
0.5%
신정6동 1
 
0.2%
개봉1동 1
 
0.2%
신림동 1
 
0.2%
당산1동 1
 
0.2%
반포본동 1
 
0.2%
성현동 1
 
0.2%
상도3동 1
 
0.2%
상도2동 1
 
0.2%
가양1동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T18:09:25.125854image/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%
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%
화 16
 
1.2%
성 16
 
1.2%
정 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%
방 16
 
1.2%
상 16
 
1.2%
화 16
 
1.2%
성 16
 
1.2%
정 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%
방 16
 
1.2%
상 16
 
1.2%
화 16
 
1.2%
성 16
 
1.2%
정 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%

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
2023-12-11T18:09:25.287986image/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
2023-12-11T18:09:25.422195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.726021 2
 
0.5%
5.083092 1
 
0.2%
8.177839 1
 
0.2%
13.169142 1
 
0.2%
9.401928 1
 
0.2%
10.379859 1
 
0.2%
0.944227 1
 
0.2%
12.621774 1
 
0.2%
9.964331 1
 
0.2%
10.780839 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
2023-12-11T18:09:25.578776image/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
2023-12-11T18:09:25.715091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.727387 2
 
0.5%
5.550681 1
 
0.2%
8.184025 1
 
0.2%
15.284715 1
 
0.2%
9.563805 1
 
0.2%
10.227708 1
 
0.2%
0.902703 1
 
0.2%
12.338544 1
 
0.2%
9.850373 1
 
0.2%
11.797069 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%

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
2023-12-11T18:09:25.852913image/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
2023-12-11T18:09:26.005113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9740814 1
 
0.2%
126.8850721 1
 
0.2%
127.0662436 1
 
0.2%
127.0278351 1
 
0.2%
126.9269185 1
 
0.2%
126.8971277 1
 
0.2%
126.9873462 1
 
0.2%
126.950695 1
 
0.2%
126.9349944 1
 
0.2%
126.9416175 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
2023-12-11T18:09:26.155953image/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
2023-12-11T18:09:26.332700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5445003 1
 
0.2%
37.4756389 1
 
0.2%
37.4955294 1
 
0.2%
37.4890881 1
 
0.2%
37.4868818 1
 
0.2%
37.5241327 1
 
0.2%
37.5063564 1
 
0.2%
37.4898682 1
 
0.2%
37.4974897 1
 
0.2%
37.5032617 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

2023-12-11T18:09:22.343038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:18.626799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:19.291026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:20.034200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:20.825875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:21.531125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:22.458156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:18.733654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:19.388671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:20.190604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:20.942692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:21.665105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:22.587741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:18.852361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:19.498379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:20.302431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:21.071367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:21.790713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:22.696396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:18.949600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:19.616920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:20.438676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:21.194778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:21.936416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:23.055669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:19.088699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:19.750052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:20.558363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:21.313617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:22.064161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:23.175987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:19.198505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:19.890575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:20.687184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:21.420876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:09:22.211600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:09:26.433795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량경도위도
고유번호1.0000.9130.2040.1550.7610.759
행정동코드0.9131.0000.1190.1370.8950.878
2007년사용량0.2040.1191.0000.9610.0000.054
2008년사용량0.1550.1370.9611.0000.0800.076
경도0.7610.8950.0000.0801.0000.453
위도0.7590.8780.0540.0760.4531.000
2023-12-11T18:09:26.541055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량경도위도
고유번호1.0000.9400.1830.144-0.034-0.598
행정동코드0.9401.0000.1470.1070.009-0.630
2007년사용량0.1830.1471.0000.972-0.125-0.091
2008년사용량0.1440.1070.9721.000-0.090-0.072
경도-0.0340.009-0.125-0.0901.0000.215
위도-0.598-0.630-0.091-0.0720.2151.000

Missing values

2023-12-11T18:09:23.336064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:09:23.509075image/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

고유번호기준연도행정동코드행정동명2007년사용량2008년사용량경도위도
0320081103053남영동5.0830925.550681126.97408137.5445
1620081102052소공동22.86764825.273296126.9746837.564492
2720081102062신당2동7.4491777.517467127.0087937.554878
3820081101070숭인1동2.9677283.208023127.01691737.577577
4920081105062구의3동15.49546616.323043127.09614237.535739
54020081102067황학동3.4638034.239846127.02058437.568559
6420081103057원효로2동7.9674957.848416126.95262737.532593
7520081103063이촌1동2.0053762.397031126.97021137.517545
81020081109074우이동3.4960893.51827126.99899937.661931
91120081110064도봉1동25.19235523.807269127.02702537.685297
고유번호기준연도행정동코드행정동명2007년사용량2008년사용량경도위도
42843220081125051강일동13.04174514.206382127.17257537.563219
42943320081125054명일2동4.7458914.439935127.15579737.548492
43043520081125058암사2동2.4620272.48443127.1231537.558175
43143620081125059암사3동1.7815131.788522127.13842537.562507
43243720081125061천호1동10.21239910.259278127.13756437.546579
43342220081116070방화1동11.09505510.872201126.8129937.569611
43442420081123053논현2동22.73352622.611445127.03598837.51517
43542520081122066양재1동13.68965613.813689127.02369737.470474
43642720081125074길동17.60003317.597098127.14736437.539876
43739920081125066성내2동10.50695810.360192127.12779337.534341