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-1331/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 08:22:44.219723
Analysis finished2023-12-11 08:22:48.822378
Duration4.6 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
2023-12-11T17:22:48.926563image/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
2023-12-11T17:22:49.437039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 1
 
0.2%
275 1
 
0.2%
297 1
 
0.2%
294 1
 
0.2%
293 1
 
0.2%
292 1
 
0.2%
288 1
 
0.2%
281 1
 
0.2%
280 1
 
0.2%
279 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

2023-12-11T17:22:49.610059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:22:49.768981image/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
2023-12-11T17:22:49.954791image/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
2023-12-11T17:22:50.200926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1102052 1
 
0.2%
1118051 1
 
0.2%
1122051 1
 
0.2%
1121066 1
 
0.2%
1120063 1
 
0.2%
1118060 1
 
0.2%
1117069 1
 
0.2%
1114066 1
 
0.2%
1117068 1
 
0.2%
1117067 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
2023-12-11T17:22:50.672637image/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 row소공동
2nd row신당2동
3rd row숭인1동
4th row구의3동
5th row성수1가1동
ValueCountFrequency (%)
신사동 2
 
0.5%
신월7동 1
 
0.2%
개봉1동 1
 
0.2%
사당1동 1
 
0.2%
시흥4동 1
 
0.2%
수궁동 1
 
0.2%
서교동 1
 
0.2%
오류2동 1
 
0.2%
오류1동 1
 
0.2%
연남동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T17:22:51.281489image/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%
화 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%
화 16
 
1.2%
정 16
 
1.2%
성 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%
화 16
 
1.2%
정 16
 
1.2%
성 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%
화 16
 
1.2%
정 16
 
1.2%
성 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%
Mean2996.3754
Minimum227.536
Maximum12579.895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:22:51.493869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum227.536
5-th percentile1191.578
Q12197.5332
median2797.554
Q33607.5075
95-th percentile5364.2455
Maximum12579.895
Range12352.359
Interquartile range (IQR)1409.9743

Descriptive statistics

Standard deviation1349.7239
Coefficient of variation (CV)0.4504522
Kurtosis6.7826558
Mean2996.3754
Median Absolute Deviation (MAD)730.324
Skewness1.603834
Sum1312412.4
Variance1821754.5
MonotonicityNot monotonic
2023-12-11T17:22:51.717355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2809.554 2
 
0.5%
4206.258 1
 
0.2%
4196.931 1
 
0.2%
2974.475 1
 
0.2%
2727.895 1
 
0.2%
2969.112 1
 
0.2%
6235.65 1
 
0.2%
2981.047 1
 
0.2%
2512.556 1
 
0.2%
2252.54 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
227.536 1
0.2%
463.809 1
0.2%
574.738 1
0.2%
585.27 1
0.2%
601.47 1
0.2%
610.093 1
0.2%
667.094 1
0.2%
680.951 1
0.2%
779.803 1
0.2%
809.758 1
0.2%
ValueCountFrequency (%)
12579.895 1
0.2%
9010.809 1
0.2%
7679.323 1
0.2%
7603.71 1
0.2%
7537.367 1
0.2%
7154.793 1
0.2%
6755.571 1
0.2%
6574.332 1
0.2%
6572.53 1
0.2%
6354.487 1
0.2%

2008년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2717.0478
Minimum90.143
Maximum10381.521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:22:51.951210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90.143
5-th percentile1005.8668
Q11917.6015
median2554.8795
Q33318.7407
95-th percentile4856.2843
Maximum10381.521
Range10291.378
Interquartile range (IQR)1401.1392

Descriptive statistics

Standard deviation1225.1343
Coefficient of variation (CV)0.45090642
Kurtosis4.2109525
Mean2717.0478
Median Absolute Deviation (MAD)681.153
Skewness1.3086765
Sum1190066.9
Variance1500954.1
MonotonicityNot monotonic
2023-12-11T17:22:52.187660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2801.291 2
 
0.5%
3436.076 1
 
0.2%
4009.488 1
 
0.2%
2478.381 1
 
0.2%
2222.404 1
 
0.2%
2453.978 1
 
0.2%
5909.811 1
 
0.2%
2926.134 1
 
0.2%
2555.067 1
 
0.2%
1819.045 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
90.143 1
0.2%
480.65 1
0.2%
561.162 1
0.2%
581.703 1
0.2%
593.459 1
0.2%
599.777 1
0.2%
634.032 1
0.2%
647.954 1
0.2%
660.024 1
0.2%
688.798 1
0.2%
ValueCountFrequency (%)
10381.521 1
0.2%
7394.406 1
0.2%
7389.959 1
0.2%
6980.014 1
0.2%
6835.164 1
0.2%
6399.271 1
0.2%
5945.513 1
0.2%
5909.811 1
0.2%
5692.283 1
0.2%
5620.421 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
2023-12-11T17:22:52.425093image/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
2023-12-11T17:22:52.666868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9746802 1
 
0.2%
126.8850721 1
 
0.2%
127.020373 1
 
0.2%
126.938117 1
 
0.2%
126.9761499 1
 
0.2%
126.9092028 1
 
0.2%
126.8253891 1
 
0.2%
126.9228533 1
 
0.2%
126.8346877 1
 
0.2%
126.8424577 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
2023-12-11T17:22:52.874185image/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
2023-12-11T17:22:53.066616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5644921 1
 
0.2%
37.4756389 1
 
0.2%
37.486444 1
 
0.2%
37.4737918 1
 
0.2%
37.4803836 1
 
0.2%
37.4595548 1
 
0.2%
37.4987947 1
 
0.2%
37.5550337 1
 
0.2%
37.4845164 1
 
0.2%
37.4969287 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

2023-12-11T17:22:47.769216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:44.603452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.240936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.894370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.441397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:47.062105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:47.915993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:44.693293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.332646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.984785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.538054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:47.160980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:48.015982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:44.808992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.419811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.080759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.637505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:47.259189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:48.123307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:44.918862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.549223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.164541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.748869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:47.366367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:48.234843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.006684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.682213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.256071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.851368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:47.488607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:48.399312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.116790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:45.784267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.350941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:46.977730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:22:47.617876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:22:53.213076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량경도위도
고유번호1.0000.9090.2880.3200.7590.747
행정동코드0.9091.0000.3030.2850.8950.878
2007년사용량0.2880.3031.0000.9800.1700.148
2008년사용량0.3200.2850.9801.0000.1250.138
경도0.7590.8950.1700.1251.0000.453
위도0.7470.8780.1480.1380.4531.000
2023-12-11T17:22:53.353369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량경도위도
고유번호1.0000.9210.2910.293-0.052-0.591
행정동코드0.9211.0000.3220.3260.009-0.630
2007년사용량0.2910.3221.0000.974-0.033-0.103
2008년사용량0.2930.3260.9741.000-0.042-0.104
경도-0.0520.009-0.033-0.0421.0000.215
위도-0.591-0.630-0.103-0.1040.2151.000

Missing values

2023-12-11T17:22:48.573925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:22:48.752013image/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
0620081102052소공동4206.2583436.076126.9746837.564492
1720081102062신당2동2264.2392262.996127.0087937.554878
2820081101070숭인1동667.094688.798127.01691737.577577
3920081105062구의3동4156.1783972.142127.09614237.535739
41320081104065성수1가1동2208.1331818.306127.04016537.540179
51620081105063광장동5880.885692.283127.10355637.547503
6420081103057원효로2동1624.8591363.161126.95262737.532593
71520081104071왕십리도선동2934.9752503.278127.02994637.566514
81020081109074우이동2684.9452684.228126.99899937.661931
91120081110064도봉1동2961.2642924.577127.02702537.685297
고유번호기준연도행정동코드행정동명2007년사용량2008년사용량경도위도
42843420081124059오륜동2892.5842798.322127.12899837.517569
42943620081123062대치3동4172.4453476.942127.06611237.501685
43043720081122066양재1동4377.5573577.649127.02369737.470474
43140720081125056고덕2동2399.0471949.43127.16225837.566594
43240920081125063천호3동3577.023534.563127.13407337.539353
43341120081125070둔촌1동2727.1342691.32127.14005337.522872
43441220081125071둔촌2동3462.1292871.711127.14853337.531799
43541420081125073천호2동5110.5094607.285127.12208637.543896
43641520081125074길동7537.3676980.014127.14736437.539876
43741620081123054압구정1동2342.961990.722127.0282337.529228