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-1334/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 07:56:59.893545
Analysis finished2023-12-11 07:57:04.880360
Duration4.99 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
2023-12-11T16:57:04.968223image/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
2023-12-11T16:57:05.156312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
0.2%
234 1
 
0.2%
307 1
 
0.2%
275 1
 
0.2%
272 1
 
0.2%
271 1
 
0.2%
281 1
 
0.2%
279 1
 
0.2%
278 1
 
0.2%
243 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

2023-12-11T16:57:05.296649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:57:05.399175image/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
2023-12-11T16:57:05.536270image/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
2023-12-11T16:57:05.697285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103058 1
 
0.2%
1115052 1
 
0.2%
1123054 1
 
0.2%
1117062 1
 
0.2%
1118052 1
 
0.2%
1118051 1
 
0.2%
1115062 1
 
0.2%
1115059 1
 
0.2%
1115058 1
 
0.2%
1116052 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
2023-12-11T16:57:06.069230image/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 row효창동
2nd row숭인2동
3rd row소공동
4th row구의3동
5th row하계2동
ValueCountFrequency (%)
신사동 2
 
0.5%
난곡동 1
 
0.2%
아현동 1
 
0.2%
고척2동 1
 
0.2%
독산1동 1
 
0.2%
가산동 1
 
0.2%
신월6동 1
 
0.2%
신월3동 1
 
0.2%
신월2동 1
 
0.2%
등촌1동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T16:57:06.576008image/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%
화 16
 
1.2%
상 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%
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%
화 16
 
1.2%
상 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%
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%
화 16
 
1.2%
상 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%
9 1
 
0.3%

2007년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.806861
Minimum7.689708
Maximum831.76975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T16:57:06.772888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.689708
5-th percentile29.565866
Q150.59604
median68.728149
Q3105.82203
95-th percentile257.35967
Maximum831.76975
Range824.08005
Interquartile range (IQR)55.225989

Descriptive statistics

Standard deviation94.714691
Coefficient of variation (CV)0.96838493
Kurtosis19.452535
Mean97.806861
Median Absolute Deviation (MAD)22.90389
Skewness3.746478
Sum42839.405
Variance8970.8726
MonotonicityNot monotonic
2023-12-11T16:57:06.982486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.489527 2
 
0.5%
20.814328 1
 
0.2%
98.79742 1
 
0.2%
59.729832 1
 
0.2%
243.05191 1
 
0.2%
547.700436 1
 
0.2%
28.442686 1
 
0.2%
38.918728 1
 
0.2%
45.785454 1
 
0.2%
97.621055 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
7.689708 1
0.2%
8.222036 1
0.2%
12.637248 1
0.2%
13.391452 1
0.2%
13.518065 1
0.2%
14.357689 1
0.2%
16.753378 1
0.2%
17.042226 1
0.2%
18.183449 1
0.2%
20.814328 1
0.2%
ValueCountFrequency (%)
831.769753 1
0.2%
811.389233 1
0.2%
548.405825 1
0.2%
547.700436 1
0.2%
494.563072 1
0.2%
494.54089 1
0.2%
467.512156 1
0.2%
458.031508 1
0.2%
434.279556 1
0.2%
423.513379 1
0.2%

2008년사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct437
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.37198
Minimum7.425011
Maximum862.38961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T16:57:07.180021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.425011
5-th percentile30.730494
Q151.001925
median70.178257
Q3107.37879
95-th percentile267.52692
Maximum862.38961
Range854.9646
Interquartile range (IQR)56.376867

Descriptive statistics

Standard deviation97.057949
Coefficient of variation (CV)0.96698255
Kurtosis19.821936
Mean100.37198
Median Absolute Deviation (MAD)23.534489
Skewness3.7555051
Sum43962.925
Variance9420.2454
MonotonicityNot monotonic
2023-12-11T16:57:07.358023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.389609 2
 
0.5%
21.459975 1
 
0.2%
93.903146 1
 
0.2%
61.589036 1
 
0.2%
245.081038 1
 
0.2%
571.652817 1
 
0.2%
29.105478 1
 
0.2%
40.666991 1
 
0.2%
46.951724 1
 
0.2%
100.65776 1
 
0.2%
Other values (427) 427
97.5%
ValueCountFrequency (%)
7.425011 1
0.2%
8.429381 1
0.2%
14.025424 1
0.2%
14.903593 1
0.2%
15.349535 1
0.2%
16.982302 1
0.2%
17.382672 1
0.2%
17.516867 1
0.2%
19.178458 1
0.2%
21.459975 1
0.2%
ValueCountFrequency (%)
862.389607 1
0.2%
839.363475 1
0.2%
571.652817 1
0.2%
532.941004 1
0.2%
498.36079 1
0.2%
493.660601 1
0.2%
473.315272 1
0.2%
451.500206 1
0.2%
447.570895 1
0.2%
424.742285 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
2023-12-11T16:57:07.544295image/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
2023-12-11T16:57:07.708446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.960713 1
 
0.2%
126.8756178 1
 
0.2%
127.0282296 1
 
0.2%
126.8545802 1
 
0.2%
126.8921307 1
 
0.2%
126.8850721 1
 
0.2%
126.8417608 1
 
0.2%
126.8282326 1
 
0.2%
126.846252 1
 
0.2%
126.859834 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
2023-12-11T16:57:07.880289image/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
2023-12-11T16:57:08.072659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5436889 1
 
0.2%
37.5427256 1
 
0.2%
37.5292279 1
 
0.2%
37.5050342 1
 
0.2%
37.4654438 1
 
0.2%
37.4756389 1
 
0.2%
37.51656 1
 
0.2%
37.532608 1
 
0.2%
37.5224464 1
 
0.2%
37.5560676 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

2023-12-11T16:57:03.720389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:00.307986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.047565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.664668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.308927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.998694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:03.812765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:00.436475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.140052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.744910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.442129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:03.120744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:04.243209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:00.581465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.247049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.848851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.580691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:03.258118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:04.345592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:00.690686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.345320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.953166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.683485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:03.374814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:04.437587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:00.796706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.455344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.055743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.789529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:03.489331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:04.549276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:00.940458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:01.566591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.194896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:02.902113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:57:03.610579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:57:08.213702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량경도위도
고유번호1.0000.9100.1180.1280.7620.751
행정동코드0.9101.0000.0430.0000.8950.878
2007년사용량0.1180.0431.0000.9970.0000.000
2008년사용량0.1280.0000.9971.0000.0000.000
경도0.7620.8950.0000.0001.0000.453
위도0.7510.8780.0000.0000.4531.000
2023-12-11T16:57:08.365161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량경도위도
고유번호1.0000.9120.0810.086-0.009-0.572
행정동코드0.9121.0000.1230.1300.009-0.630
2007년사용량0.0810.1231.0000.9970.005-0.150
2008년사용량0.0860.1300.9971.0000.004-0.157
경도-0.0090.0090.0050.0041.0000.215
위도-0.572-0.630-0.150-0.1570.2151.000

Missing values

2023-12-11T16:57:04.672431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:57:04.817302image/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
0320081103058효창동20.81432821.459975126.96071337.543689
1620081101071숭인2동72.80962174.027617127.01952737.574927
2820081102052소공동423.513379424.742285126.9746837.564492
3920081105062구의3동133.85004131.664698127.09614237.535739
410320081111059하계2동36.93036537.577989127.06460237.635166
5720081102064신당4동47.60079850.101068127.01527737.55663
6520081103063이촌1동78.77672479.079995126.97021137.517545
71020081109074우이동51.86797752.548616126.99899937.661931
81120081110064도봉1동67.51409168.453704127.02702537.685297
91220081111056공릉2동158.930946165.70109127.09185837.631411
고유번호기준연도행정동코드행정동명2007년사용량2008년사용량경도위도
42838920081124077잠실6동172.244889178.32093127.09928337.518159
42939120081124053거여1동54.74935554.898508127.1421237.494644
43036820081123055압구정2동149.626668152.095066127.03954137.529177
43136920081121063남현동54.7457958.414975126.97659237.46171
43237020081121066서림동57.4072259.19018126.93811737.473792
43337220081121072조원동101.818272103.651986126.90702137.483343
43437420081121073대학동221.959497225.027668126.95038337.454358
43537520081121078은천동68.12676769.248238126.94058337.487133
43637620081121079성현동73.32230673.596809126.95069537.489868
43737920081121054청림동37.54456537.542942126.95924937.489553