Overview

Dataset statistics

Number of variables8
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory74.3 B

Variable types

DateTime1
Text1
Numeric6

Dataset

Description상수도요금 총괄 집계 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=092B1J2NN3H40A0CIVHN12133362&infSeq=1

Alerts

집계일자 has constant value ""Constant
부과량(톤) is highly overall correlated with 부과액(천원) and 3 other fieldsHigh correlation
부과액(천원) is highly overall correlated with 부과량(톤) and 2 other fieldsHigh correlation
판매단가(원/㎥) is highly overall correlated with 부과량(톤) and 1 other fieldsHigh correlation
생산액(천원) is highly overall correlated with 부과량(톤) and 2 other fieldsHigh correlation
생산원가(원/㎥) is highly overall correlated with 부과량(톤) and 3 other fieldsHigh correlation
시군명 has unique valuesUnique
부과량(톤) has unique valuesUnique
부과액(천원) has unique valuesUnique
판매단가(원/㎥) has unique valuesUnique
생산액(천원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:29:01.880968
Analysis finished2023-12-10 22:29:05.409440
Duration3.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계일자
Date

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2021-12-31 00:00:00
Maximum2021-12-31 00:00:00
2023-12-11T07:29:05.697948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:05.772653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시군명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:29:05.952127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters38
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

Unique31 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
가평군 1
 
3.2%
안양시 1
 
3.2%
하남시 1
 
3.2%
포천시 1
 
3.2%
평택시 1
 
3.2%
파주시 1
 
3.2%
이천시 1
 
3.2%
의정부시 1
 
3.2%
의왕시 1
 
3.2%
용인시 1
 
3.2%
Other values (21) 21
67.7%
2023-12-11T07:29:06.248923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

부과량(톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48759435
Minimum5685767
Maximum1.2278727 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:29:06.366870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5685767
5-th percentile7326946.5
Q118110056
median31687676
Q376452317
95-th percentile1.1368585 × 108
Maximum1.2278727 × 108
Range1.171015 × 108
Interquartile range (IQR)58342260

Descriptive statistics

Standard deviation38476807
Coefficient of variation (CV)0.78911511
Kurtosis-0.89787716
Mean48759435
Median Absolute Deviation (MAD)23229596
Skewness0.725714
Sum1.5115425 × 109
Variance1.4804647 × 1015
MonotonicityNot monotonic
2023-12-11T07:29:06.501234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5685767 1
 
3.2%
111111908 1
 
3.2%
114118384 1
 
3.2%
31687676 1
 
3.2%
17333099 1
 
3.2%
113253312 1
 
3.2%
55489404 1
 
3.2%
23058365 1
 
3.2%
44179378 1
 
3.2%
16544933 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
5685767 1
3.2%
6534897 1
3.2%
8118996 1
3.2%
8458080 1
3.2%
9626819 1
3.2%
12822813 1
3.2%
16544933 1
3.2%
17333099 1
3.2%
18887014 1
3.2%
21987142 1
3.2%
ValueCountFrequency (%)
122787269 1
3.2%
114118384 1
3.2%
113253312 1
3.2%
112329140 1
3.2%
111111908 1
3.2%
103261372 1
3.2%
88232670 1
3.2%
83197448 1
3.2%
69707186 1
3.2%
56856791 1
3.2%

부과액(천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33586332
Minimum5383003
Maximum93916810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:29:06.626598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5383003
5-th percentile7445369.5
Q115387516
median27240639
Q346823748
95-th percentile82600253
Maximum93916810
Range88533807
Interquartile range (IQR)31436231

Descriptive statistics

Standard deviation24418869
Coefficient of variation (CV)0.72704782
Kurtosis0.27423406
Mean33586332
Median Absolute Deviation (MAD)15072542
Skewness1.0116483
Sum1.0411763 × 109
Variance5.9628119 × 1014
MonotonicityNot monotonic
2023-12-11T07:29:06.732204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7239641 1
 
3.2%
65307184 1
 
3.2%
93916810 1
 
3.2%
16653590 1
 
3.2%
17979948 1
 
3.2%
87980116 1
 
3.2%
46574852 1
 
3.2%
27465629 1
 
3.2%
30913652 1
 
3.2%
14350489 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
5383003 1
3.2%
7239641 1
3.2%
7651098 1
3.2%
8875059 1
3.2%
10654976 1
3.2%
10878605 1
3.2%
12168097 1
3.2%
14350489 1
3.2%
16424544 1
3.2%
16653590 1
3.2%
ValueCountFrequency (%)
93916810 1
3.2%
87980116 1
3.2%
77220390 1
3.2%
68091903 1
3.2%
65307184 1
3.2%
49644505 1
3.2%
48753867 1
3.2%
47072643 1
3.2%
46574852 1
3.2%
41800121 1
3.2%

판매단가(원/㎥)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean784.26613
Minimum404.8
Maximum1339.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:29:06.841023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum404.8
5-th percentile539.055
Q1628.34
median712.19
Q3885.975
95-th percentile1232.215
Maximum1339.9
Range935.1
Interquartile range (IQR)257.635

Descriptive statistics

Standard deviation222.6168
Coefficient of variation (CV)0.28385364
Kurtosis0.47204027
Mean784.26613
Median Absolute Deviation (MAD)118.75
Skewness0.8920703
Sum24312.25
Variance49558.239
MonotonicityNot monotonic
2023-12-11T07:29:06.952107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1273.29 1
 
3.2%
587.76 1
 
3.2%
822.98 1
 
3.2%
525.55 1
 
3.2%
1037.32 1
 
3.2%
776.84 1
 
3.2%
839.35 1
 
3.2%
1191.14 1
 
3.2%
699.73 1
 
3.2%
867.36 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
404.8 1
3.2%
525.55 1
3.2%
552.56 1
3.2%
565.79 1
3.2%
587.76 1
3.2%
606.18 1
3.2%
611.7 1
3.2%
627.78 1
3.2%
628.9 1
3.2%
637.05 1
3.2%
ValueCountFrequency (%)
1339.9 1
3.2%
1273.29 1
3.2%
1191.14 1
3.2%
1076.49 1
3.2%
1037.32 1
3.2%
954.39 1
3.2%
921.91 1
3.2%
904.59 1
3.2%
867.36 1
3.2%
839.35 1
3.2%

생산액(천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42697992
Minimum8083863
Maximum1.0338142 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:29:07.083503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8083863
5-th percentile15317028
Q120680524
median33737679
Q358385530
95-th percentile94702641
Maximum1.0338142 × 108
Range95297561
Interquartile range (IQR)37705007

Descriptive statistics

Standard deviation27113926
Coefficient of variation (CV)0.63501642
Kurtosis-0.34862023
Mean42697992
Median Absolute Deviation (MAD)15389503
Skewness0.86351007
Sum1.3236378 × 109
Variance7.3516499 × 1014
MonotonicityNot monotonic
2023-12-11T07:29:07.205851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
14224615 1
 
3.2%
78077747 1
 
3.2%
103381424 1
 
3.2%
18029712 1
 
3.2%
30406861 1
 
3.2%
92120048 1
 
3.2%
54889976 1
 
3.2%
29000484 1
 
3.2%
46801885 1
 
3.2%
16858968 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
8083863 1
3.2%
14224615 1
3.2%
16409441 1
3.2%
16858968 1
3.2%
18029712 1
3.2%
18348176 1
3.2%
20117647 1
3.2%
20589000 1
3.2%
20772047 1
3.2%
21257169 1
3.2%
ValueCountFrequency (%)
103381424 1
3.2%
97285234 1
3.2%
92120048 1
3.2%
85874155 1
3.2%
78077747 1
3.2%
69387839 1
3.2%
67597713 1
3.2%
58769855 1
3.2%
58001206 1
3.2%
54889976 1
3.2%

생산원가(원/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1018.3226
Minimum562
Maximum2495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:29:07.309118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum562
5-th percentile655
Q1705.5
median858
Q31202
95-th percentile1887.5
Maximum2495
Range1933
Interquartile range (IQR)496.5

Descriptive statistics

Standard deviation445.75168
Coefficient of variation (CV)0.43773131
Kurtosis3.2432894
Mean1018.3226
Median Absolute Deviation (MAD)158
Skewness1.8008188
Sum31568
Variance198694.56
MonotonicityNot monotonic
2023-12-11T07:29:07.440571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
655 2
 
6.5%
2495 1
 
3.2%
1171 1
 
3.2%
906 1
 
3.2%
562 1
 
3.2%
1754 1
 
3.2%
813 1
 
3.2%
874 1
 
3.2%
1258 1
 
3.2%
700 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
562 1
3.2%
655 2
6.5%
671 1
3.2%
693 1
3.2%
700 1
3.2%
703 1
3.2%
704 1
3.2%
707 1
3.2%
765 1
3.2%
766 1
3.2%
ValueCountFrequency (%)
2495 1
3.2%
2021 1
3.2%
1754 1
3.2%
1569 1
3.2%
1441 1
3.2%
1391 1
3.2%
1258 1
3.2%
1233 1
3.2%
1171 1
3.2%
1019 1
3.2%

현실화율(%)
Real number (ℝ)

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.13871
Minimum51
Maximum107.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:29:07.573861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile56.05
Q172.1
median81.5
Q391.9
95-th percentile102.1
Maximum107.3
Range56.3
Interquartile range (IQR)19.8

Descriptive statistics

Standard deviation14.53753
Coefficient of variation (CV)0.17916886
Kurtosis-0.45585082
Mean81.13871
Median Absolute Deviation (MAD)11.5
Skewness-0.36873585
Sum2515.3
Variance211.33978
MonotonicityNot monotonic
2023-12-11T07:29:07.701742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
66.3 2
 
6.5%
51.0 1
 
3.2%
88.8 1
 
3.2%
90.8 1
 
3.2%
93.5 1
 
3.2%
59.1 1
 
3.2%
95.5 1
 
3.2%
96.0 1
 
3.2%
94.7 1
 
3.2%
100.0 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
51.0 1
3.2%
53.0 1
3.2%
59.1 1
3.2%
61.8 1
3.2%
62.8 1
3.2%
66.3 2
6.5%
66.8 1
3.2%
77.4 1
3.2%
79.1 1
3.2%
79.2 1
3.2%
ValueCountFrequency (%)
107.3 1
3.2%
104.2 1
3.2%
100.0 1
3.2%
96.0 1
3.2%
95.5 1
3.2%
94.7 1
3.2%
93.5 1
3.2%
93.0 1
3.2%
90.8 1
3.2%
90.5 1
3.2%

Interactions

2023-12-11T07:29:04.730613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.134752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.690720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.198741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.664404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.155749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.817979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.242125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.780756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.281697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.752958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.241915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.887954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.343151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.844214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.354650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.825983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.349057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.966889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.417853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.914463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.436624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.899852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.432843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:05.041428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.494797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.002482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.510498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.973415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.522948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:05.146794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:02.594749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.094215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:03.595001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.063193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:04.630635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:29:07.796613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명부과량(톤)부과액(천원)판매단가(원/㎥)생산액(천원)생산원가(원/㎥)현실화율(%)
시군명1.0001.0001.0001.0001.0001.0001.000
부과량(톤)1.0001.0000.5540.0000.8970.0000.456
부과액(천원)1.0000.5541.0000.0000.8700.0000.000
판매단가(원/㎥)1.0000.0000.0001.0000.0000.8950.791
생산액(천원)1.0000.8970.8700.0001.0000.0000.000
생산원가(원/㎥)1.0000.0000.0000.8950.0001.0000.685
현실화율(%)1.0000.4560.0000.7910.0000.6851.000
2023-12-11T07:29:07.899796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과량(톤)부과액(천원)판매단가(원/㎥)생산액(천원)생산원가(원/㎥)현실화율(%)
부과량(톤)1.0000.963-0.6520.921-0.7210.416
부과액(천원)0.9631.000-0.4660.947-0.5690.431
판매단가(원/㎥)-0.652-0.4661.000-0.4310.881-0.116
생산액(천원)0.9210.947-0.4311.000-0.5170.335
생산원가(원/㎥)-0.721-0.5690.881-0.5171.000-0.482
현실화율(%)0.4160.431-0.1160.335-0.4821.000

Missing values

2023-12-11T07:29:05.247070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:29:05.365428image/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

집계일자시군명부과량(톤)부과액(천원)판매단가(원/㎥)생산액(천원)생산원가(원/㎥)현실화율(%)
02021-12-31가평군568576772396411273.2914224615249551.0
12021-12-31고양시11111190865307184587.767807774770383.6
22021-12-31과천시65348975383003823.738083863123366.8
32021-12-31광명시2925018018633801637.052058900070490.5
42021-12-31광주시3986455224385024611.73403350176679.9
52021-12-31구리시1888701412168097644.261834817697266.3
62021-12-31군포시2705085818517897684.562337531586479.2
72021-12-31김포시5136163740983530797.944404322785893.0
82021-12-31남양주시6970718649644505712.195876985583485.4
92021-12-31동두천시96268198875059921.9121257169884104.2
집계일자시군명부과량(톤)부과액(천원)판매단가(원/㎥)생산액(천원)생산원가(원/㎥)현실화율(%)
212021-12-31오산시2479462016424544662.422077204783879.1
222021-12-31용인시11232914068091903606.188587415576579.3
232021-12-31의왕시1654493314350489867.3616858968101985.1
242021-12-31의정부시4417937830913652699.7346801885700100.0
252021-12-31이천시23058365274656291191.1429000484125894.7
262021-12-31파주시5548940446574852839.355488997687496.0
272021-12-31평택시11325331287980116776.849212004881395.5
282021-12-31포천시17333099179799481037.3230406861175459.1
292021-12-31하남시3168767616653590525.551802971256293.5
302021-12-31화성시11411838493916810822.9810338142490690.8