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-1333/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 04:45:32.865291
Analysis finished2023-12-11 04:45:40.106762
Duration7.24 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
2023-12-11T13:45:40.192783image/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
2023-12-11T13:45:40.381100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1
 
0.2%
271 1
 
0.2%
284 1
 
0.2%
283 1
 
0.2%
296 1
 
0.2%
286 1
 
0.2%
282 1
 
0.2%
281 1
 
0.2%
265 1
 
0.2%
264 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

2023-12-11T13:45:40.531633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:45:40.645139image/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
2023-12-11T13:45:40.801477image/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
2023-12-11T13:45:40.992980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103057 1
 
0.2%
1118054 1
 
0.2%
1117068 1
 
0.2%
1117067 1
 
0.2%
1121054 1
 
0.2%
1124051 1
 
0.2%
1120054 1
 
0.2%
1114072 1
 
0.2%
1117054 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
2023-12-11T13:45:41.395442image/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 row원효로2동
2nd row숭인2동
3rd row숭인1동
4th row원효로1동
5th row사근동
ValueCountFrequency (%)
신사동 2
 
0.5%
대치2동 1
 
0.2%
송파2동 1
 
0.2%
오류1동 1
 
0.2%
청림동 1
 
0.2%
풍납1동 1
 
0.2%
상도2동 1
 
0.2%
성산1동 1
 
0.2%
구로3동 1
 
0.2%
구로1동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T13:45:41.956110image/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%
정 16
 
1.2%
화 16
 
1.2%
상 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%
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%
정 16
 
1.2%
화 16
 
1.2%
상 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%
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%
정 16
 
1.2%
화 16
 
1.2%
상 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%
9 1
 
0.3%

2005년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.116144
Minimum7.021582
Maximum745.59215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T13:45:42.151969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.021582
5-th percentile27.825696
Q145.13542
median61.319471
Q392.485717
95-th percentile237.73866
Maximum745.59215
Range738.57057
Interquartile range (IQR)47.350297

Descriptive statistics

Standard deviation86.159803
Coefficient of variation (CV)0.97779816
Kurtosis18.111776
Mean88.116144
Median Absolute Deviation (MAD)21.11911
Skewness3.6640708
Sum38594.871
Variance7423.5117
MonotonicityNot monotonic
2023-12-11T13:45:42.338818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.871981 2
 
0.5%
56.066604 1
 
0.2%
52.311661 1
 
0.2%
51.338002 1
 
0.2%
36.563337 1
 
0.2%
56.875634 1
 
0.2%
46.141866 1
 
0.2%
51.767772 1
 
0.2%
211.016771 1
 
0.2%
328.04949 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
7.021582 1
0.2%
9.793491 1
0.2%
10.162799 1
0.2%
14.611261 1
0.2%
15.01936 1
0.2%
15.551121 1
0.2%
15.761751 1
0.2%
17.612789 1
0.2%
17.878851 1
0.2%
19.210865 1
0.2%
ValueCountFrequency (%)
745.59215 1
0.2%
710.758701 1
0.2%
510.695359 1
0.2%
481.094198 1
0.2%
464.848832 1
0.2%
447.644492 1
0.2%
435.101217 1
0.2%
416.37423 1
0.2%
408.913542 1
0.2%
357.56073 1
0.2%

2006년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.136764
Minimum8.12449
Maximum785.55754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T13:45:42.507133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.12449
5-th percentile28.837305
Q147.760129
median63.823172
Q397.258358
95-th percentile247.60642
Maximum785.55754
Range777.43305
Interquartile range (IQR)49.498229

Descriptive statistics

Standard deviation89.972957
Coefficient of variation (CV)0.97651527
Kurtosis18.46165
Mean92.136764
Median Absolute Deviation (MAD)21.556883
Skewness3.6803716
Sum40355.903
Variance8095.133
MonotonicityNot monotonic
2023-12-11T13:45:43.050656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.153428 2
 
0.5%
57.183068 1
 
0.2%
53.74952 1
 
0.2%
55.788854 1
 
0.2%
37.010321 1
 
0.2%
57.776225 1
 
0.2%
48.116412 1
 
0.2%
53.754417 1
 
0.2%
263.795408 1
 
0.2%
314.156419 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
8.12449 1
0.2%
9.676906 1
0.2%
10.939988 1
0.2%
14.685397 1
0.2%
15.801553 1
0.2%
18.321737 1
0.2%
20.88436 1
0.2%
20.915793 1
0.2%
21.184328 1
0.2%
21.186319 1
0.2%
ValueCountFrequency (%)
785.557543 1
0.2%
750.761009 1
0.2%
526.338071 1
0.2%
487.605265 1
0.2%
468.130495 1
0.2%
461.263275 1
0.2%
457.54841 1
0.2%
436.947854 1
0.2%
432.249003 1
0.2%
396.668101 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
2023-12-11T13:45:43.245414image/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
2023-12-11T13:45:43.417485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.952627 1
 
0.2%
126.9050746 1
 
0.2%
126.8346877 1
 
0.2%
126.8424577 1
 
0.2%
126.959249 1
 
0.2%
127.1137027 1
 
0.2%
126.9416175 1
 
0.2%
126.9108057 1
 
0.2%
126.8947943 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
2023-12-11T13:45:43.618806image/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
2023-12-11T13:45:43.807379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5325935 1
 
0.2%
37.4758607 1
 
0.2%
37.4845164 1
 
0.2%
37.4969287 1
 
0.2%
37.4895535 1
 
0.2%
37.5382922 1
 
0.2%
37.5032617 1
 
0.2%
37.5623625 1
 
0.2%
37.4852754 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

2023-12-11T13:45:39.134357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:35.334927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.358117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.017977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.680717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:38.454330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:39.224365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:35.461385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.461756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.115673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.802588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:38.552691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:39.350720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:35.577257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.561457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.218278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.956023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:38.674545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:39.465948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.067230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.684917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.323792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:38.097303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:38.793267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:39.589408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.171844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.809674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.444475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:38.221370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:38.932498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:39.695496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.271786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:36.919253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:37.570679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:38.347137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:45:39.042014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T13:45:43.929028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2005년사용량2006년사용량경도위도
고유번호1.0000.8990.1760.1900.7560.752
행정동코드0.8991.0000.1800.1740.8950.878
2005년사용량0.1760.1801.0000.9930.0000.000
2006년사용량0.1900.1740.9931.0000.1340.000
경도0.7560.8950.0000.1341.0000.453
위도0.7520.8780.0000.0000.4531.000
2023-12-11T13:45:44.088089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2005년사용량2006년사용량경도위도
고유번호1.0000.9040.1320.133-0.029-0.588
행정동코드0.9041.0000.1580.1560.009-0.630
2005년사용량0.1320.1581.0000.9960.022-0.148
2006년사용량0.1330.1560.9961.0000.018-0.148
경도-0.0290.0090.0220.0181.0000.215
위도-0.588-0.630-0.148-0.1480.2151.000

Missing values

2023-12-11T13:45:39.841472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:45:40.040852image/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
0420081103057원효로2동56.06660457.183068126.95262737.532593
1620081101071숭인2동50.30579154.707591127.01952737.574927
2820081101070숭인1동19.21086520.915793127.01691737.577577
31320081103072원효로1동45.06015148.707363126.96544237.537645
41420081104055사근동104.951558107.352963127.04535937.558984
51520081104068성수2가3동312.86556338.525931127.05855837.545516
69820081106066장안2동57.56134769.22035127.07372937.574129
7720081102062신당2동47.63527950.173432127.0087937.554878
81720081105067자양4동76.05027278.806103127.06360137.53388
9920081103064이촌2동17.61278918.321737126.95406537.523457
고유번호기준연도행정동코드행정동명2005년사용량2006년사용량경도위도
42838120081121069신림동85.75549186.591174126.92691837.486882
42943220081122068내곡동69.86758174.436983127.06969237.452285
43043420081123054압구정1동117.303315123.234316127.0282337.529228
43143520081124070장지동48.77300453.448314127.13679137.481112
43243820081124080잠실3동160.593047173.411804127.09478337.516232
43341820081123058삼성1동416.37423436.947854127.05938737.516232
43441920081123059삼성2동156.679523169.632541127.04899337.511692
43542020081123063대치4동199.75281208.771687127.05494337.501589
43642120081123064역삼1동710.758701750.761009127.03675837.499921
43742320081101054삼청동29.52687730.72611126.98154137.586048