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-1343/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
2007년사용량 has 325 (74.2%) zerosZeros
2008년사용량 has 322 (73.5%) zerosZeros

Reproduction

Analysis started2023-12-11 08:59:38.183238
Analysis finished2023-12-11 08:59:43.181334
Duration5 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
2023-12-11T17:59:43.292260image/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
2023-12-11T17:59:43.496550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 1
 
0.2%
353 1
 
0.2%
290 1
 
0.2%
286 1
 
0.2%
283 1
 
0.2%
289 1
 
0.2%
282 1
 
0.2%
281 1
 
0.2%
232 1
 
0.2%
322 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

2023-12-11T17:59:43.680210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:59:43.824072image/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
2023-12-11T17:59:43.951438image/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
2023-12-11T17:59:44.206297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1102052 1
 
0.2%
1123051 1
 
0.2%
1121054 1
 
0.2%
1117069 1
 
0.2%
1120054 1
 
0.2%
1122055 1
 
0.2%
1120053 1
 
0.2%
1114072 1
 
0.2%
1114061 1
 
0.2%
1119062 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
2023-12-11T17:59:44.617320image/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 row무악동
3rd row마장동
4th row응봉동
5th row군자동
ValueCountFrequency (%)
신사동 2
 
0.5%
상도4동 1
 
0.2%
공덕동 1
 
0.2%
수궁동 1
 
0.2%
상도2동 1
 
0.2%
잠원동 1
 
0.2%
상도1동 1
 
0.2%
성산1동 1
 
0.2%
염리동 1
 
0.2%
양평2동 1
 
0.2%
Other values (427) 427
97.5%
2023-12-11T17:59:45.226473image/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%
상 16
 
1.2%
방 16
 
1.2%
정 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%
화 16
 
1.2%
상 16
 
1.2%
방 16
 
1.2%
정 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%
화 16
 
1.2%
상 16
 
1.2%
방 16
 
1.2%
정 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%

2007년사용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10474.456
Minimum0
Maximum203553.2
Zeros325
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:59:45.411324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3762.075
95-th percentile63353.47
Maximum203553.2
Range203553.2
Interquartile range (IQR)762.075

Descriptive statistics

Standard deviation25629.818
Coefficient of variation (CV)2.4468878
Kurtosis16.115005
Mean10474.456
Median Absolute Deviation (MAD)0
Skewness3.5307757
Sum4587811.6
Variance6.5688755 × 108
MonotonicityNot monotonic
2023-12-11T17:59:45.593631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 325
74.2%
21185.5 1
 
0.2%
18205.4 1
 
0.2%
20934.3 1
 
0.2%
41730.3 1
 
0.2%
89038.3 1
 
0.2%
46652.1 1
 
0.2%
23295.0 1
 
0.2%
50390.2 1
 
0.2%
14388.4 1
 
0.2%
Other values (104) 104
 
23.7%
ValueCountFrequency (%)
0.0 325
74.2%
193.4 1
 
0.2%
458.5 1
 
0.2%
516.3 1
 
0.2%
844.0 1
 
0.2%
845.7 1
 
0.2%
2036.3 1
 
0.2%
2228.9 1
 
0.2%
4889.9 1
 
0.2%
5266.6 1
 
0.2%
ValueCountFrequency (%)
203553.2 1
0.2%
191024.0 1
0.2%
133120.7 1
0.2%
123813.7 1
0.2%
106250.8 1
0.2%
102988.7 1
0.2%
98252.8 1
0.2%
97237.2 1
0.2%
94072.9 1
0.2%
93923.5 1
0.2%

2008년사용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct117
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11037.428
Minimum0
Maximum211015.9
Zeros322
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-11T17:59:45.805143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31731.925
95-th percentile65729.615
Maximum211015.9
Range211015.9
Interquartile range (IQR)1731.925

Descriptive statistics

Standard deviation26121.704
Coefficient of variation (CV)2.3666478
Kurtosis15.864685
Mean11037.428
Median Absolute Deviation (MAD)0
Skewness3.4427309
Sum4834393.3
Variance6.823434 × 108
MonotonicityNot monotonic
2023-12-11T17:59:45.999629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 322
73.5%
4852.4 1
 
0.2%
2009.5 1
 
0.2%
17708.0 1
 
0.2%
20494.9 1
 
0.2%
42785.5 1
 
0.2%
86467.7 1
 
0.2%
46279.6 1
 
0.2%
24864.8 1
 
0.2%
56106.6 1
 
0.2%
Other values (107) 107
 
24.4%
ValueCountFrequency (%)
0.0 322
73.5%
136.7 1
 
0.2%
227.0 1
 
0.2%
401.9 1
 
0.2%
432.2 1
 
0.2%
585.1 1
 
0.2%
899.2 1
 
0.2%
2009.5 1
 
0.2%
2115.2 1
 
0.2%
4852.4 1
 
0.2%
ValueCountFrequency (%)
211015.9 1
0.2%
194738.3 1
0.2%
127257.2 1
0.2%
125331.7 1
0.2%
105779.4 1
0.2%
101331.3 1
0.2%
95960.1 1
0.2%
94883.9 1
0.2%
94479.8 1
0.2%
92550.4 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
2023-12-11T17:59:46.152632image/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
2023-12-11T17:59:46.349873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9746802 1
 
0.2%
127.0201823 1
 
0.2%
126.959249 1
 
0.2%
126.8253891 1
 
0.2%
126.9416175 1
 
0.2%
127.0128992 1
 
0.2%
126.9543551 1
 
0.2%
126.9108057 1
 
0.2%
126.9472006 1
 
0.2%
126.8946775 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
2023-12-11T17:59:46.525761image/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
2023-12-11T17:59:46.678040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5644921 1
 
0.2%
37.5258447 1
 
0.2%
37.4895535 1
 
0.2%
37.4987947 1
 
0.2%
37.5032617 1
 
0.2%
37.5180129 1
 
0.2%
37.4999435 1
 
0.2%
37.5623625 1
 
0.2%
37.5501423 1
 
0.2%
37.5396988 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

2023-12-11T17:59:41.977843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:38.548391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.303907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.995160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.664303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:41.282677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:42.084862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:38.649825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.413682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.115181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.780047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:41.386513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:42.174457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:38.761774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.523573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.237537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.882689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:41.491863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:42.572732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:38.901615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.651674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.386005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.986690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:41.612770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:42.690208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.040917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.771370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.476137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:41.077256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:41.738241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:42.821013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.184417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:39.897458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:40.578260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:41.179658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:59:41.859604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:59:46.792185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량경도위도
고유번호1.0000.8940.1470.1330.7760.754
행정동코드0.8941.0000.2840.2730.8950.878
2007년사용량0.1470.2841.0000.9900.2210.227
2008년사용량0.1330.2730.9901.0000.1930.198
경도0.7760.8950.2210.1931.0000.453
위도0.7540.8780.2270.1980.4531.000
2023-12-11T17:59:46.932060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호행정동코드2007년사용량2008년사용량경도위도
고유번호1.0000.9060.2040.205-0.046-0.602
행정동코드0.9061.0000.3040.3100.009-0.630
2007년사용량0.2040.3041.0000.9850.142-0.098
2008년사용량0.2050.3100.9851.0000.135-0.090
경도-0.0460.0090.1420.1351.0000.215
위도-0.602-0.630-0.098-0.0900.2151.000

Missing values

2023-12-11T17:59:42.957608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:59:43.103508image/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
0720081102052소공동458.5432.2126.9746837.564492
1920081101057무악동0.00.0126.95827837.577329
21420081104054마장동0.00.0127.0402937.567482
31520081104058응봉동0.00.0127.0345937.550568
41620081105054군자동0.00.0127.07351437.55337
51720081105063광장동0.00.0127.10355637.547503
6320081103063이촌1동106250.8105779.4126.97021137.517545
7820081102065신당5동0.00.0127.02139937.563164
8620081102067황학동0.00.0127.02058437.568559
91120081109074우이동0.00.0126.99899937.661931
고유번호기준연도행정동코드행정동명2007년사용량2008년사용량경도위도
42843820081125061천호1동0.00.0127.13756437.546579
42940820081124060오금동38982.538328.3127.13407137.503719
43041020081124063석촌동0.00.0127.10411437.502851
43141120081120070신대방2동0.00.0126.92221737.494006
43241220081120071흑석동0.00.0126.9646537.505401
43341320081120072노량진1동0.00.0126.9504637.512746
43441420081101061종로1.2.3.4가동0.00.0126.98927437.57777
43541820081117065개봉3동0.00.0126.85388537.486412
43641920081117054구로3동0.00.0126.89479437.485275
43742220081122066양재1동0.00.0127.02369737.470474