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-1341/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
2005년사용량 has 326 (74.4%) zerosZeros
2006년사용량 has 325 (74.2%) zerosZeros

Reproduction

Analysis started2023-12-11 08:45:51.439428
Analysis finished2023-12-11 08:45:56.363161
Duration4.92 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
2023-12-11T17:45:56.466999image/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
2023-12-11T17:45:56.659450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1
 
0.2%
275 1
 
0.2%
283 1
 
0.2%
294 1
 
0.2%
293 1
 
0.2%
292 1
 
0.2%
289 1
 
0.2%
287 1
 
0.2%
286 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

2023-12-11T17:45:56.830327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:45:56.969929image/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
2023-12-11T17:45:57.132823image/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
2023-12-11T17:45:57.310966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103057 1
 
0.2%
1118052 1
 
0.2%
1115063 1
 
0.2%
1119076 1
 
0.2%
1121065 1
 
0.2%
1121061 1
 
0.2%
1122053 1
 
0.2%
1120065 1
 
0.2%
1120056 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
2023-12-11T17:45:57.616656image/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 row창신3동
3rd row신당1동
4th row중곡1동
5th row원효로1동
ValueCountFrequency (%)
신사동 2
 
0.5%
수궁동 1
 
0.2%
가산동 1
 
0.2%
신원동 1
 
0.2%
중앙동 1
 
0.2%
서초3동 1
 
0.2%
사당3동 1
 
0.2%
상도4동 1
 
0.2%
오류1동 1
 
0.2%
신월6동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T17:45:58.066457image/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%
상 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%
상 16
 
1.2%
화 16
 
1.2%
정 16
 
1.2%
성 16
 
1.2%
방 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%
상 16
 
1.2%
화 16
 
1.2%
정 16
 
1.2%
성 16
 
1.2%
방 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%
상 16
 
1.2%
화 16
 
1.2%
정 16
 
1.2%
성 16
 
1.2%
방 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%

2005년사용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct113
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11565.648
Minimum0
Maximum299852.16
Zeros326
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:45:58.242827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3334.21
95-th percentile73337.627
Maximum299852.16
Range299852.16
Interquartile range (IQR)334.21

Descriptive statistics

Standard deviation30676.595
Coefficient of variation (CV)2.6523887
Kurtosis25.631933
Mean11565.648
Median Absolute Deviation (MAD)0
Skewness4.2956773
Sum5065754
Variance9.4105347 × 108
MonotonicityNot monotonic
2023-12-11T17:45:58.401182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 326
74.4%
20705.99 1
 
0.2%
48486.7 1
 
0.2%
16441.38 1
 
0.2%
16711.2 1
 
0.2%
350.08 1
 
0.2%
62456.37 1
 
0.2%
47679.06 1
 
0.2%
39454.5 1
 
0.2%
6411.1 1
 
0.2%
Other values (103) 103
 
23.5%
ValueCountFrequency (%)
0.0 326
74.4%
142.8 1
 
0.2%
286.6 1
 
0.2%
350.08 1
 
0.2%
367.6 1
 
0.2%
374.1 1
 
0.2%
563.02 1
 
0.2%
805.0 1
 
0.2%
2035.0 1
 
0.2%
2401.5 1
 
0.2%
ValueCountFrequency (%)
299852.16 1
0.2%
198098.9 1
0.2%
180946.18 1
0.2%
172459.0099 1
0.2%
143993.0001 1
0.2%
115285.3 1
0.2%
113897.3 1
0.2%
112021.3 1
0.2%
111990.0 1
0.2%
104380.2 1
0.2%

2006년사용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10576.106
Minimum0
Maximum262022.67
Zeros325
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:45:58.624577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3347.35
95-th percentile65001.734
Maximum262022.67
Range262022.67
Interquartile range (IQR)347.35

Descriptive statistics

Standard deviation28031.928
Coefficient of variation (CV)2.6504961
Kurtosis24.875471
Mean10576.106
Median Absolute Deviation (MAD)0
Skewness4.2913354
Sum4632334.6
Variance7.8578901 × 108
MonotonicityNot monotonic
2023-12-11T17:45:59.240793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 325
74.2%
83577.99 1
 
0.2%
14678.8 1
 
0.2%
15090.0 1
 
0.2%
572.26 1
 
0.2%
57613.82 1
 
0.2%
39686.25 1
 
0.2%
34867.8 1
 
0.2%
5908.8 1
 
0.2%
40222.55 1
 
0.2%
Other values (104) 104
 
23.7%
ValueCountFrequency (%)
0.0 325
74.2%
141.0 1
 
0.2%
222.2 1
 
0.2%
280.3 1
 
0.2%
369.7 1
 
0.2%
403.04 1
 
0.2%
572.26 1
 
0.2%
667.8 1
 
0.2%
1499.7 1
 
0.2%
1525.7 1
 
0.2%
ValueCountFrequency (%)
262022.67 1
0.2%
200547.7 1
0.2%
175677.5 1
0.2%
163398.46 1
0.2%
124751.7 1
0.2%
105319.5 1
0.2%
103321.2 1
0.2%
98582.1 1
0.2%
97807.0 1
0.2%
97337.9 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
2023-12-11T17:45:59.436287image/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-0.84416474
Mean126.99566
Median Absolute Deviation (MAD)0.06347975
Skewness-0.20532013
Sum55624.101
Variance0.0072310508
MonotonicityNot monotonic
2023-12-11T17:45:59.612588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.952627 1
 
0.2%
126.8921307 1
 
0.2%
126.8313103 1
 
0.2%
126.8917569 1
 
0.2%
126.9267456 1
 
0.2%
126.9513359 1
 
0.2%
127.0097616 1
 
0.2%
126.9711251 1
 
0.2%
126.9401218 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
2023-12-11T17:45:59.816435image/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
Skewness0.32431151
Sum16447.14
Variance0.0027795695
MonotonicityNot monotonic
2023-12-11T17:46:00.049870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5325935 1
 
0.2%
37.4654438 1
 
0.2%
37.5169283 1
 
0.2%
37.515614 1
 
0.2%
37.4791405 1
 
0.2%
37.484121 1
 
0.2%
37.4856591 1
 
0.2%
37.4889213 1
 
0.2%
37.4976735 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

2023-12-11T17:45:55.365513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:51.786716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.435130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:53.117857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:53.889485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:54.665623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:55.489948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:51.895159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.548814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:53.238823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:54.014193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:54.775609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:55.621427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.019393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.659767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:53.353804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:54.148970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:54.901538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:55.762741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.121764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.786348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:53.482111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:54.282129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:55.023330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:55.871921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.219684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.892579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:53.615303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:54.412204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:55.136137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:56.001154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:52.338314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:53.007236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:53.761340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:54.550357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:45:55.244004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:46:00.174670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2005년사용량2006년사용량경도위도
고유번호1.0000.9090.1920.1790.7550.755
행정동코드0.9091.0000.2450.2600.8950.878
2005년사용량0.1920.2451.0000.9980.1760.145
2006년사용량0.1790.2600.9981.0000.2070.171
경도0.7550.8950.1760.2071.0000.453
위도0.7550.8780.1450.1710.4531.000
2023-12-11T17:46:00.334942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2005년사용량2006년사용량경도위도
고유번호1.0000.9180.2090.203-0.034-0.584
행정동코드0.9181.0000.2940.2870.009-0.630
2005년사용량0.2090.2941.0000.9960.169-0.119
2006년사용량0.2030.2870.9961.0000.168-0.119
경도-0.0340.0090.1690.1681.0000.215
위도-0.584-0.630-0.119-0.1190.2151.000

Missing values

2023-12-11T17:45:56.146947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:45:56.294513image/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
0420081103057원효로2동10290.09306.7126.95262737.532593
1520081101069창신3동0.00.0127.01299437.578808
2820081102061신당1동0.00.0127.01373737.56527
3920081105055중곡1동0.00.0127.07774837.56191
41320081103072원효로1동0.00.0126.96544237.537645
51420081104058응봉동0.00.0127.0345937.550568
610520081106066장안2동0.00.0127.07372937.574129
7720081102066신당6동0.00.0127.01858937.560813
81720081106055제기2동0.00.0127.0340537.584026
91020081109074우이동0.00.0126.99899937.661931
고유번호기준연도행정동코드행정동명2005년사용량2006년사용량경도위도
42843420081124065가락본동17385.9214444.08127.12139637.497192
42943520081123076세곡동0.00.0127.10347437.472439
43041020081125059암사3동0.00.0127.13842537.562507
43141120081125061천호1동0.00.0127.13756437.546579
43241220081125063천호3동0.00.0127.13407337.539353
43341320081125066성내2동0.00.0127.12779337.534341
43441420081125071둔촌2동0.00.0127.14853337.531799
43541620081125073천호2동0.00.0127.12208637.543896
43641820081122068내곡동0.00.0127.06969237.452285
43741920081123052논현1동0.00.0127.02674137.512057