Overview

Dataset statistics

Number of variables8
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)8.3%
Total size in memory1.7 KiB
Average record size in memory72.5 B

Variable types

Text3
Categorical2
Numeric3

Dataset

Description음식물 폐기물 처리시설 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=0B0FIT523OP806N198AL12314598&infSeq=1

Alerts

Dataset has 2 (8.3%) duplicate rowsDuplicates
소재지우편번호 is highly overall correlated with WGS84위도High correlation
WGS84위도 is highly overall correlated with 소재지우편번호High correlation

Reproduction

Analysis started2024-03-12 23:37:30.274354
Analysis finished2024-03-12 23:37:31.362010
Duration1.09 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-13T08:37:31.457663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Characters and Unicode

Total characters76
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)75.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광주시
5th row김포시
ValueCountFrequency (%)
남양주시 2
 
8.3%
성남시 2
 
8.3%
수원시 2
 
8.3%
여주시 1
 
4.2%
가평군 1
 
4.2%
안양시 1
 
4.2%
하남시 1
 
4.2%
평택시 1
 
4.2%
파주시 1
 
4.2%
의정부시 1
 
4.2%
Other values (11) 11
45.8%
2024-03-13T08:37:31.726171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 24
31.6%
남 5
 
6.6%
주 5
 
6.6%
양 4
 
5.3%
성 3
 
3.9%
천 3
 
3.9%
평 2
 
2.6%
의 2
 
2.6%
산 2
 
2.6%
안 2
 
2.6%
Other values (21) 24
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 24
31.6%
남 5
 
6.6%
주 5
 
6.6%
양 4
 
5.3%
성 3
 
3.9%
천 3
 
3.9%
평 2
 
2.6%
의 2
 
2.6%
산 2
 
2.6%
안 2
 
2.6%
Other values (21) 24
31.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 24
31.6%
남 5
 
6.6%
주 5
 
6.6%
양 4
 
5.3%
성 3
 
3.9%
천 3
 
3.9%
평 2
 
2.6%
의 2
 
2.6%
산 2
 
2.6%
안 2
 
2.6%
Other values (21) 24
31.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 24
31.6%
남 5
 
6.6%
주 5
 
6.6%
양 4
 
5.3%
성 3
 
3.9%
천 3
 
3.9%
평 2
 
2.6%
의 2
 
2.6%
산 2
 
2.6%
안 2
 
2.6%
Other values (21) 24
31.6%

시설명
Categorical

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
자원화시설
15 
자원화센터
2 
전처리시설
 
1
바이오매스에너지시설
 
1
자원정화센터
 
1
Other values (4)
4 

Length

Max length10
Median length5
Mean length5.6666667
Min length4

Unique

Unique7 ?
Unique (%)29.2%

Sample

1st row전처리시설
2nd row바이오매스에너지시설
3rd row자원정화센터
4th row자원화시설
5th row자원화센터

Common Values

ValueCountFrequency (%)
자원화시설 15
62.5%
자원화센터 2
 
8.3%
전처리시설 1
 
4.2%
바이오매스에너지시설 1
 
4.2%
자원정화센터 1
 
4.2%
음식물처리시설 1
 
4.2%
운정 환경관리센터 1
 
4.2%
에코센터 1
 
4.2%
동탄2크린에너지센터 1
 
4.2%

Length

2024-03-13T08:37:31.849640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:37:31.979434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자원화시설 15
60.0%
자원화센터 2
 
8.0%
전처리시설 1
 
4.0%
바이오매스에너지시설 1
 
4.0%
자원정화센터 1
 
4.0%
음식물처리시설 1
 
4.0%
운정 1
 
4.0%
환경관리센터 1
 
4.0%
에코센터 1
 
4.0%
동탄2크린에너지센터 1
 
4.0%

재활용방법
Categorical

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
사료화
9 
퇴비화
7 
기타
5 
바이오가스화
3 

Length

Max length6
Median length3
Mean length3.1666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row퇴비화
2nd row바이오가스화
3rd row기타
4th row퇴비화
5th row사료화

Common Values

ValueCountFrequency (%)
사료화 9
37.5%
퇴비화 7
29.2%
기타 5
20.8%
바이오가스화 3
 
12.5%

Length

2024-03-13T08:37:32.190523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:37:32.321224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사료화 9
37.5%
퇴비화 7
29.2%
기타 5
20.8%
바이오가스화 3
 
12.5%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13874.375
Minimum10066
Maximum18488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-13T08:37:32.428978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10066
5-th percentile10643.55
Q112207.5
median13108
Q315724.75
95-th percentile18113.65
Maximum18488
Range8422
Interquartile range (IQR)3517.25

Descriptive statistics

Standard deviation2461.5403
Coefficient of variation (CV)0.1774163
Kurtosis-0.78338521
Mean13874.375
Median Absolute Deviation (MAD)1574.5
Skewness0.46503973
Sum332985
Variance6059180.9
MonotonicityNot monotonic
2024-03-13T08:37:32.557073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13108 2
 
8.3%
16648 2
 
8.3%
12426 1
 
4.2%
14014 1
 
4.2%
18488 1
 
4.2%
12941 1
 
4.2%
18021 1
 
4.2%
10896 1
 
4.2%
11766 1
 
4.2%
16078 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
10066 1
4.2%
10599 1
4.2%
10896 1
4.2%
11301 1
4.2%
11766 1
4.2%
12092 1
4.2%
12246 1
4.2%
12426 1
4.2%
12666 1
4.2%
12813 1
4.2%
ValueCountFrequency (%)
18488 1
4.2%
18130 1
4.2%
18021 1
4.2%
16648 2
8.3%
16078 1
4.2%
15607 1
4.2%
15099 1
4.2%
14400 1
4.2%
14014 1
4.2%
13824 1
4.2%

소재지지번주소
Text

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-13T08:37:32.800349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length20.416667
Min length16

Characters and Unicode

Total characters490
Distinct characters85
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)83.3%

Sample

1st row경기도 가평군 가평읍 상색리 509번지
2nd row경기도 고양시 덕양구 동산동 333-1번지
3rd row경기도 과천시 갈현동 205-1번지
4th row경기도 광주시 곤지암읍 수양리 423번지
5th row경기도 김포시 마산동 618-2번지
ValueCountFrequency (%)
경기도 24
 
22.2%
수정구 2
 
1.9%
태평동 2
 
1.9%
7004번지 2
 
1.9%
수원시 2
 
1.9%
권선구 2
 
1.9%
고색동 2
 
1.9%
694-2번지 2
 
1.9%
성남시 2
 
1.9%
남양주시 2
 
1.9%
Other values (66) 66
61.1%
2024-03-13T08:37:33.336419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
 
17.1%
지 25
 
5.1%
경 24
 
4.9%
동 24
 
4.9%
도 24
 
4.9%
기 24
 
4.9%
시 24
 
4.9%
번 24
 
4.9%
2 14
 
2.9%
- 12
 
2.4%
Other values (75) 211
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
61.8%
Decimal Number 91
 
18.6%
Space Separator 84
 
17.1%
Dash Punctuation 12
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 25
 
8.3%
경 24
 
7.9%
동 24
 
7.9%
도 24
 
7.9%
기 24
 
7.9%
시 24
 
7.9%
번 24
 
7.9%
구 8
 
2.6%
양 6
 
2.0%
주 5
 
1.7%
Other values (63) 115
38.0%
Decimal Number
ValueCountFrequency (%)
2 14
15.4%
6 12
13.2%
1 12
13.2%
0 10
11.0%
3 10
11.0%
4 9
9.9%
7 9
9.9%
9 5
 
5.5%
8 5
 
5.5%
5 5
 
5.5%
Space Separator
ValueCountFrequency (%)
84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
61.8%
Common 187
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 25
 
8.3%
경 24
 
7.9%
동 24
 
7.9%
도 24
 
7.9%
기 24
 
7.9%
시 24
 
7.9%
번 24
 
7.9%
구 8
 
2.6%
양 6
 
2.0%
주 5
 
1.7%
Other values (63) 115
38.0%
Common
ValueCountFrequency (%)
84
44.9%
2 14
 
7.5%
- 12
 
6.4%
6 12
 
6.4%
1 12
 
6.4%
0 10
 
5.3%
3 10
 
5.3%
4 9
 
4.8%
7 9
 
4.8%
9 5
 
2.7%
Other values (2) 10
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
61.8%
ASCII 187
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
44.9%
2 14
 
7.5%
- 12
 
6.4%
6 12
 
6.4%
1 12
 
6.4%
0 10
 
5.3%
3 10
 
5.3%
4 9
 
4.8%
7 9
 
4.8%
9 5
 
2.7%
Other values (2) 10
 
5.3%
Hangul
ValueCountFrequency (%)
지 25
 
8.3%
경 24
 
7.9%
동 24
 
7.9%
도 24
 
7.9%
기 24
 
7.9%
시 24
 
7.9%
번 24
 
7.9%
구 8
 
2.6%
양 6
 
2.0%
주 5
 
1.7%
Other values (63) 115
38.0%

소재지도로명주소
Text

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-13T08:37:33.721928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22.5
Mean length19.708333
Min length15

Characters and Unicode

Total characters473
Distinct characters86
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)83.3%

Sample

1st row경기도 가평군 가평읍 경춘로 1639-50
2nd row경기도 고양시 덕양구 고양대로 1804-46
3rd row경기도 과천시 구리안로 177
4th row경기도 광주시 곤지암읍 경충대로311번길 36
5th row경기도 김포시 김포한강4로 419-37
ValueCountFrequency (%)
경기도 24
 
22.2%
권선구 2
 
1.9%
76 2
 
1.9%
남양주시 2
 
1.9%
432 2
 
1.9%
매송고색로804번길 2
 
1.9%
성남시 2
 
1.9%
수원시 2
 
1.9%
687 2
 
1.9%
수정구 2
 
1.9%
Other values (65) 66
61.1%
2024-03-13T08:37:34.234883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
 
17.8%
경 27
 
5.7%
시 25
 
5.3%
도 25
 
5.3%
기 24
 
5.1%
로 23
 
4.9%
1 17
 
3.6%
4 14
 
3.0%
3 13
 
2.7%
7 11
 
2.3%
Other values (76) 210
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 284
60.0%
Decimal Number 98
 
20.7%
Space Separator 84
 
17.8%
Dash Punctuation 7
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 27
 
9.5%
시 25
 
8.8%
도 25
 
8.8%
기 24
 
8.5%
로 23
 
8.1%
구 9
 
3.2%
길 7
 
2.5%
천 6
 
2.1%
양 6
 
2.1%
대 5
 
1.8%
Other values (64) 127
44.7%
Decimal Number
ValueCountFrequency (%)
1 17
17.3%
4 14
14.3%
3 13
13.3%
7 11
11.2%
6 10
10.2%
2 9
9.2%
0 8
8.2%
8 7
7.1%
5 5
 
5.1%
9 4
 
4.1%
Space Separator
ValueCountFrequency (%)
84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 284
60.0%
Common 189
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 27
 
9.5%
시 25
 
8.8%
도 25
 
8.8%
기 24
 
8.5%
로 23
 
8.1%
구 9
 
3.2%
길 7
 
2.5%
천 6
 
2.1%
양 6
 
2.1%
대 5
 
1.8%
Other values (64) 127
44.7%
Common
ValueCountFrequency (%)
84
44.4%
1 17
 
9.0%
4 14
 
7.4%
3 13
 
6.9%
7 11
 
5.8%
6 10
 
5.3%
2 9
 
4.8%
0 8
 
4.2%
- 7
 
3.7%
8 7
 
3.7%
Other values (2) 9
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 284
60.0%
ASCII 189
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
44.4%
1 17
 
9.0%
4 14
 
7.4%
3 13
 
6.9%
7 11
 
5.8%
6 10
 
5.3%
2 9
 
4.8%
0 8
 
4.2%
- 7
 
3.7%
8 7
 
3.7%
Other values (2) 9
 
4.8%
Hangul
ValueCountFrequency (%)
경 27
 
9.5%
시 25
 
8.8%
도 25
 
8.8%
기 24
 
8.5%
로 23
 
8.1%
구 9
 
3.2%
길 7
 
2.5%
천 6
 
2.1%
양 6
 
2.1%
대 5
 
1.8%
Other values (64) 127
44.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.456768
Minimum37.029517
Maximum37.945072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-13T08:37:34.534853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.029517
5-th percentile37.139451
Q137.289385
median37.429661
Q337.641603
95-th percentile37.788813
Maximum37.945072
Range0.91555472
Interquartile range (IQR)0.35221718

Descriptive statistics

Standard deviation0.23504309
Coefficient of variation (CV)0.0062750499
Kurtosis-0.66904164
Mean37.456768
Median Absolute Deviation (MAD)0.19478202
Skewness0.20489853
Sum898.96243
Variance0.055245253
MonotonicityNot monotonic
2024-03-13T08:37:34.770087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
37.45214336 2
 
8.3%
37.23487885 2
 
8.3%
37.79340683 1
 
4.2%
37.4030255 1
 
4.2%
37.17522667 1
 
4.2%
37.54654616 1
 
4.2%
37.02951734 1
 
4.2%
37.73713843 1
 
4.2%
37.76278255 1
 
4.2%
37.33777072 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
37.02951734 1
4.2%
37.1331373 1
4.2%
37.17522667 1
4.2%
37.22383391 1
4.2%
37.23487885 2
8.3%
37.30755424 1
4.2%
37.33426735 1
4.2%
37.33725726 1
4.2%
37.33777072 1
4.2%
37.4030255 1
4.2%
ValueCountFrequency (%)
37.94507206 1
4.2%
37.79340683 1
4.2%
37.76278255 1
4.2%
37.73713843 1
4.2%
37.67394447 1
4.2%
37.64760875 1
4.2%
37.63960051 1
4.2%
37.61121591 1
4.2%
37.54654616 1
4.2%
37.5423023 1
4.2%

WGS84경도
Real number (ℝ)

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03734
Minimum126.63805
Maximum127.65705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-13T08:37:34.930364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63805
5-th percentile126.70598
Q1126.88101
median127.03555
Q3127.11939
95-th percentile127.45151
Maximum127.65705
Range1.0190004
Interquartile range (IQR)0.23837455

Descriptive statistics

Standard deviation0.23989359
Coefficient of variation (CV)0.0018883707
Kurtosis0.86177149
Mean127.03734
Median Absolute Deviation (MAD)0.11870495
Skewness0.6580172
Sum3048.8962
Variance0.057548937
MonotonicityNot monotonic
2024-03-13T08:37:35.162691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
127.1193863 2
 
8.3%
126.9870237 2
 
8.3%
127.4663347 1
 
4.2%
126.8819764 1
 
4.2%
127.0973491 1
 
4.2%
127.2204967 1
 
4.2%
127.0144924 1
 
4.2%
126.7676914 1
 
4.2%
127.0975685 1
 
4.2%
126.9642548 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
126.6380538 1
4.2%
126.6967247 1
4.2%
126.7584274 1
4.2%
126.7653679 1
4.2%
126.7676914 1
4.2%
126.8781178 1
4.2%
126.8819764 1
4.2%
126.9642548 1
4.2%
126.9870237 2
8.3%
126.993275 1
4.2%
ValueCountFrequency (%)
127.6570542 1
4.2%
127.4663347 1
4.2%
127.3674834 1
4.2%
127.2204967 1
4.2%
127.1906942 1
4.2%
127.1193863 2
8.3%
127.1127564 1
4.2%
127.0975685 1
4.2%
127.0973491 1
4.2%
127.0586964 1
4.2%

Interactions

2024-03-13T08:37:30.970592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:37:30.584584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:37:30.785225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:37:31.031375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:37:30.656845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:37:30.848089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:37:31.101485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:37:30.717187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:37:30.907609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:37:35.348695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명시설명재활용방법소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
시군명1.0001.0000.8711.0001.0001.0000.9740.977
시설명1.0001.0000.4180.0001.0001.0000.5310.680
재활용방법0.8710.4181.0000.6511.0001.0000.3110.000
소재지우편번호1.0000.0000.6511.0001.0001.0000.7740.429
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.000
WGS84위도0.9740.5310.3110.7741.0001.0001.0000.000
WGS84경도0.9770.6800.0000.4291.0001.0000.0001.000
2024-03-13T08:37:35.560822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재활용방법시설명
재활용방법1.0000.208
시설명0.2081.000
2024-03-13T08:37:35.768032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시설명재활용방법
소재지우편번호1.000-0.841-0.1180.0000.353
WGS84위도-0.8411.0000.0350.2180.086
WGS84경도-0.1180.0351.0000.2380.000
시설명0.0000.2180.2381.0000.208
재활용방법0.3530.0860.0000.2081.000

Missing values

2024-03-13T08:37:31.217396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:37:31.321929image/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

시군명시설명재활용방법소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
0가평군전처리시설퇴비화12426경기도 가평군 가평읍 상색리 509번지경기도 가평군 가평읍 경춘로 1639-5037.793407127.466335
1고양시바이오매스에너지시설바이오가스화10599경기도 고양시 덕양구 동산동 333-1번지경기도 고양시 덕양구 고양대로 1804-4637.639601126.878118
2과천시자원정화센터기타13824경기도 과천시 갈현동 205-1번지경기도 과천시 구리안로 17737.407178126.993275
3광주시자원화시설퇴비화12813경기도 광주시 곤지암읍 수양리 423번지경기도 광주시 곤지암읍 경충대로311번길 3637.334267127.367483
4김포시자원화센터사료화10066경기도 김포시 마산동 618-2번지경기도 김포시 김포한강4로 419-3737.647609126.638054
5남양주시자원화시설기타12246경기도 남양주시 이패동 521-8번지경기도 남양주시 경강로163번길 4437.611216127.190694
6남양주시자원화시설바이오가스화12092경기도 남양주시 별내동 802번지경기도 남양주시 덕송3로 7637.673944127.112756
7동두천시자원화시설사료화11301경기도 동두천시 상봉암동 173번지경기도 동두천시 봉동로 2737.945072127.056614
8부천시자원화시설기타14400경기도 부천시 오정구 대장동 607번지경기도 부천시 오정구 벌말로 12237.542302126.765368
9성남시자원화시설사료화13108경기도 성남시 수정구 태평동 7004번지경기도 성남시 수정구 탄천로 68737.452143127.119386
시군명시설명재활용방법소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
14안산시자원화시설퇴비화15607경기도 안산시 단원구 성곡동 621-2번지경기도 안산시 단원구 해봉로 4537.307554126.758427
15안양시자원화시설기타14014경기도 안양시 만안구 박달동 751-7번지경기도 안양시 만안구 박달로 23237.403025126.881976
16여주시자원화센터퇴비화12666경기도 여주시 점동면 처리 739-63번지경기도 여주시 점동면 장여로 1381-6037.223834127.657054
17오산시자원화시설퇴비화18130경기도 오산시 누읍동 196-4번지경기도 오산시 오산천로 3-3537.133137127.058696
18의왕시음식물처리시설사료화16078경기도 의왕시 이동 478번지경기도 의왕시 가나무로 2037.337771126.964255
19의정부시자원화시설퇴비화11766경기도 의정부시 자일동 206-3번지경기도 의정부시 호국로 1778-5637.762783127.097568
20파주시운정 환경관리센터퇴비화10896경기도 파주시 와동동 1503번지경기도 파주시 가람로150번길 41-3437.737138126.767691
21평택시에코센터바이오가스화18021경기도 평택시 고덕면 해창리 1266번지경기도 평택시 고덕면 도시지원1길 9137.029517127.014492
22하남시자원화시설사료화12941경기도 하남시 신장동 27번지경기도 하남시 미사대로 71037.546546127.220497
23화성시동탄2크린에너지센터사료화18488경기도 화성시 송동 681-244번지경기도 화성시 동탄대로9길 7637.175227127.097349

Duplicate rows

Most frequently occurring

시군명시설명재활용방법소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도# duplicates
0성남시자원화시설사료화13108경기도 성남시 수정구 태평동 7004번지경기도 성남시 수정구 탄천로 68737.452143127.1193862
1수원시자원화시설사료화16648경기도 수원시 권선구 고색동 694-2번지경기도 수원시 권선구 매송고색로804번길 43237.234879126.9870242