Overview

Dataset statistics

Number of variables7
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory60.1 B

Variable types

Categorical3
Numeric3
Text1

Dataset

Description등록된 토지정보와 실제 지적정보가 일치하지 않는 지역을 분석하여지적재조사 사업 우선순위 지역을 도출지적정보와 중첩되는 지역을 제외한 지적 추출새주소 건물상의 건물과 연속지적도상의 도로 지적과 중첩되는 지적불부합지를 도출
Author국토교통부
URLhttps://www.data.go.kr/data/15123053/fileData.do

Alerts

구분 has constant value ""Constant
일련번호 is highly overall correlated with 지적번호High correlation
지적번호 is highly overall correlated with 일련번호 and 1 other fieldsHigh correlation
지적면적 is highly overall correlated with 계산면적High correlation
광역시도명 is highly overall correlated with 지적번호High correlation
계산면적 is highly overall correlated with 지적면적High correlation
계산면적 is highly imbalanced (89.7%)Imbalance
지적면적 has unique valuesUnique
공간정보 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:22:05.199455
Analysis finished2023-12-12 11:22:07.816483
Duration2.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
전체
1000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전체
2nd row전체
3rd row전체
4th row전체
5th row전체

Common Values

ValueCountFrequency (%)
전체 1000
100.0%

Length

2023-12-12T20:22:07.937507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:22:08.115351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 1000
100.0%

광역시도명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
인천광역시
619 
경기도
176 
충청남도
104 
충청북도
 
44
대구광역시
 
18
Other values (5)
 
39

Length

Max length7
Median length5
Mean length4.491
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row인천광역시
2nd row경기도
3rd row인천광역시
4th row경기도
5th row인천광역시

Common Values

ValueCountFrequency (%)
인천광역시 619
61.9%
경기도 176
 
17.6%
충청남도 104
 
10.4%
충청북도 44
 
4.4%
대구광역시 18
 
1.8%
경상남도 13
 
1.3%
경상북도 10
 
1.0%
대전광역시 8
 
0.8%
세종특별자치시 7
 
0.7%
서울특별시 1
 
0.1%

Length

2023-12-12T20:22:08.325923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:22:08.589733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 619
61.9%
경기도 176
 
17.6%
충청남도 104
 
10.4%
충청북도 44
 
4.4%
대구광역시 18
 
1.8%
경상남도 13
 
1.3%
경상북도 10
 
1.0%
대전광역시 8
 
0.8%
세종특별자치시 7
 
0.7%
서울특별시 1
 
0.1%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct827
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean825578.38
Minimum4
Maximum4987859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T20:22:08.881774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile21117.05
Q1139881.75
median290720.5
Q3961256.5
95-th percentile2927765
Maximum4987859
Range4987855
Interquartile range (IQR)821374.75

Descriptive statistics

Standard deviation1084273.8
Coefficient of variation (CV)1.3133505
Kurtosis1.2371621
Mean825578.38
Median Absolute Deviation (MAD)182277.5
Skewness1.5362509
Sum8.2557838 × 108
Variance1.1756497 × 1012
MonotonicityNot monotonic
2023-12-12T20:22:09.182048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192804 17
 
1.7%
238357 6
 
0.6%
2469460 5
 
0.5%
1709742 4
 
0.4%
94389 4
 
0.4%
505530 4
 
0.4%
1588445 4
 
0.4%
135840 4
 
0.4%
154512 4
 
0.4%
108443 4
 
0.4%
Other values (817) 944
94.4%
ValueCountFrequency (%)
4 1
 
0.1%
1246 1
 
0.1%
1853 1
 
0.1%
2204 1
 
0.1%
2400 1
 
0.1%
2493 1
 
0.1%
2693 1
 
0.1%
3253 3
0.3%
3272 1
 
0.1%
3609 2
0.2%
ValueCountFrequency (%)
4987859 1
0.1%
4987707 1
0.1%
4986453 1
0.1%
4915333 1
0.1%
4913740 1
0.1%
4604873 1
0.1%
4581145 1
0.1%
4581135 1
0.1%
4562534 1
0.1%
4418916 1
0.1%

지적번호
Real number (ℝ)

HIGH CORRELATION 

Distinct827
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3479512 × 1018
Minimum1.1440124 × 1018
Maximum4.889036 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T20:22:09.461566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1440124 × 1018
5-th percentile2.8110134 × 1018
Q12.8200101 × 1018
median2.871025 × 1018
Q34.157025 × 1018
95-th percentile4.420036 × 1018
Maximum4.889036 × 1018
Range3.7450236 × 1018
Interquartile range (IQR)1.3370149 × 1018

Descriptive statistics

Standard deviation7.1223509 × 1017
Coefficient of variation (CV)0.2127376
Kurtosis-1.2845821
Mean3.3479512 × 1018
Median Absolute Deviation (MAD)5.3314728 × 1016
Skewness0.67253036
Sum9.0905013 × 1018
Variance5.0727882 × 1035
MonotonicityNot monotonic
2023-12-12T20:22:09.764200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2823710100101260010 17
 
1.7%
2824510100106570054 6
 
0.6%
4150037025105170000 5
 
0.5%
4375034023110240002 4
 
0.4%
2817710200105010000 4
 
0.4%
2871037024109560004 4
 
0.4%
4423035023103540000 4
 
0.4%
4413135022104620002 4
 
0.4%
2820010100114410000 4
 
0.4%
2817710400106410061 4
 
0.4%
Other values (817) 944
94.4%
ValueCountFrequency (%)
1144012400105610055 1
0.1%
2711010300102710143 1
0.1%
2711014000101860001 1
0.1%
2711015400102730015 1
0.1%
2711015600105720063 1
0.1%
2711015600105720064 1
0.1%
2711015600109320043 1
0.1%
2714011900108870014 1
0.1%
2714012000105120004 1
0.1%
2717010100112900000 2
0.2%
ValueCountFrequency (%)
4889036024105110000 1
0.1%
4889036024105020000 1
0.1%
4889035026111010000 1
0.1%
4888025021107490071 1
0.1%
4874042027109190002 1
0.1%
4874042022104660006 1
0.1%
4874038023200280006 1
0.1%
4874025023105470001 1
0.1%
4872031026109360000 1
0.1%
4872025023112010002 1
0.1%

지적면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.568799
Minimum1.4983153 × 10-5
Maximum471.45932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T20:22:10.042776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4983153 × 10-5
5-th percentile0.058822484
Q11.8347555
median6.4086274
Q315.178162
95-th percentile55.462319
Maximum471.45932
Range471.45931
Interquartile range (IQR)13.343406

Descriptive statistics

Standard deviation29.117431
Coefficient of variation (CV)1.9986158
Kurtosis78.086779
Mean14.568799
Median Absolute Deviation (MAD)5.6138499
Skewness7.0181129
Sum14568.799
Variance847.82481
MonotonicityNot monotonic
2023-12-12T20:22:10.340368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.47931311208342 1
 
0.1%
9.634732301908654 1
 
0.1%
9.634238222010874 1
 
0.1%
3.845152718301906 1
 
0.1%
12.58968900523019 1
 
0.1%
0.2330337903990251 1
 
0.1%
2.40017056348124 1
 
0.1%
16.93113424842279 1
 
0.1%
16.5324858963416 1
 
0.1%
6.24818375588052 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1.498315316951991e-05 1
0.1%
3.670044635511361e-05 1
0.1%
0.0004738716785892 1
0.1%
0.0005570062321788 1
0.1%
0.0005947383815948 1
0.1%
0.0007608901000171 1
0.1%
0.0007676397565232 1
0.1%
0.0010773675067982 1
0.1%
0.0013050122192948 1
0.1%
0.0013900860762433 1
0.1%
ValueCountFrequency (%)
471.4593212273118 1
0.1%
274.7130486109978 1
0.1%
242.4586807494307 1
0.1%
221.1301745291861 1
0.1%
219.9891722013836 1
0.1%
176.1658065531116 1
0.1%
159.2252361531096 1
0.1%
144.5427364966716 1
0.1%
139.9590012981454 1
0.1%
129.0444509741291 1
0.1%

계산면적
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
979 
2
 
16
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 979
97.9%
2 16
 
1.6%
3 5
 
0.5%

Length

2023-12-12T20:22:10.591706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:22:10.774193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 979
97.9%
2 16
 
1.6%
3 5
 
0.5%

공간정보
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-12T20:22:11.155501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length559
Mean length227.079
Min length148

Characters and Unicode

Total characters227079
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowMULTIPOLYGON (((126.491316913856 37.6402849832737,126.491353542773 37.6402960892954,126.491348932726 37.6403113443592,126.491291097594 37.640308075417,126.491289337394 37.6402962569335,126.491288499204 37.6402900962347,126.491316913856 37.6402849832737)))
2nd rowMULTIPOLYGON (((126.811651460273 37.0869336543078,126.811500418378 37.0868978635813,126.811499999283 37.0868900265018,126.81163444501 37.0869285413469,126.811651460273 37.0869336543078)))
3rd rowMULTIPOLYGON (((126.436998241785 37.5973543839517,126.436985585111 37.5973610475647,126.436974940094 37.5973423559206,126.436950884032 37.5973150728258,126.436947950365 37.5973122229787,126.436961864325 37.5973049726325,126.436997571232 37.5973530428472,126.436998241785 37.5973543839517)))
4th rowMULTIPOLYGON (((126.56972199053 37.6896723721594,126.569696677183 37.6896305883721,126.569736658861 37.6896189794362,126.569722661083 37.6896694804028,126.56972199053 37.6896723721594)))
5th rowMULTIPOLYGON (((126.67873182645 37.4619201372666,126.678718666862 37.4619205144522,126.678715984653 37.4618520343033,126.678753284122 37.4618225300041,126.67875864854 37.4618225300041,126.678728306051 37.4618470051614,126.67873182645 37.4619201372666)))
ValueCountFrequency (%)
multipolygon 1000
 
12.2%
36.870358729209,127.146211186026 2
 
< 0.1%
37.4475932854449 2
 
< 0.1%
126.641419197731 2
 
< 0.1%
37.7899255919223,126.372802083554 2
 
< 0.1%
37.4467595794456,126.676976320649 2
 
< 0.1%
37.5284064409921,126.706821344191 2
 
< 0.1%
37.7250179358409,126.506292441164 1
 
< 0.1%
36.894327661439,126.765286459944 1
 
< 0.1%
126.668167191869 1
 
< 0.1%
Other values (7171) 7171
87.6%
2023-12-12T20:22:11.832813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 23279
10.3%
7 22505
9.9%
3 21135
9.3%
2 20944
9.2%
1 20680
9.1%
4 17684
7.8%
5 15918
 
7.0%
8 14876
 
6.6%
9 14536
 
6.4%
0 12633
 
5.6%
Other values (15) 42889
18.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184190
81.1%
Other Punctuation 17558
 
7.7%
Uppercase Letter 12000
 
5.3%
Space Separator 7186
 
3.2%
Open Punctuation 3074
 
1.4%
Close Punctuation 3071
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 23279
12.6%
7 22505
12.2%
3 21135
11.5%
2 20944
11.4%
1 20680
11.2%
4 17684
9.6%
5 15918
8.6%
8 14876
8.1%
9 14536
7.9%
0 12633
6.9%
Uppercase Letter
ValueCountFrequency (%)
O 2000
16.7%
L 2000
16.7%
U 1000
8.3%
N 1000
8.3%
G 1000
8.3%
Y 1000
8.3%
P 1000
8.3%
I 1000
8.3%
T 1000
8.3%
M 1000
8.3%
Other Punctuation
ValueCountFrequency (%)
. 12372
70.5%
, 5186
29.5%
Space Separator
ValueCountFrequency (%)
7186
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3074
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3071
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 215079
94.7%
Latin 12000
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
6 23279
10.8%
7 22505
10.5%
3 21135
9.8%
2 20944
9.7%
1 20680
9.6%
4 17684
8.2%
5 15918
7.4%
8 14876
6.9%
9 14536
6.8%
0 12633
5.9%
Other values (5) 30889
14.4%
Latin
ValueCountFrequency (%)
O 2000
16.7%
L 2000
16.7%
U 1000
8.3%
N 1000
8.3%
G 1000
8.3%
Y 1000
8.3%
P 1000
8.3%
I 1000
8.3%
T 1000
8.3%
M 1000
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 227079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 23279
10.3%
7 22505
9.9%
3 21135
9.3%
2 20944
9.2%
1 20680
9.1%
4 17684
7.8%
5 15918
 
7.0%
8 14876
 
6.6%
9 14536
 
6.4%
0 12633
 
5.6%
Other values (15) 42889
18.9%

Interactions

2023-12-12T20:22:06.762639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:05.575463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:06.129198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:06.941412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:05.757388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:06.321970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:07.179613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:05.960729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:06.558234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:22:11.999697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역시도명일련번호지적번호지적면적계산면적
광역시도명1.0000.7720.9380.0000.000
일련번호0.7721.0000.7510.0000.000
지적번호0.9380.7511.0000.0000.000
지적면적0.0000.0000.0001.0000.949
계산면적0.0000.0000.0000.9491.000
2023-12-12T20:22:12.182045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역시도명계산면적
광역시도명1.0000.000
계산면적0.0001.000
2023-12-12T20:22:12.341294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호지적번호지적면적광역시도명계산면적
일련번호1.0000.8000.1590.4920.000
지적번호0.8001.0000.0920.8970.000
지적면적0.1590.0921.0000.0000.970
광역시도명0.4920.8970.0001.0000.000
계산면적0.0000.0000.9700.0001.000

Missing values

2023-12-12T20:22:07.448495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:22:07.717344image/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

구분광역시도명일련번호지적번호지적면적계산면적공간정보
0전체인천광역시412015287103302110511000312.4793131MULTIPOLYGON (((126.491316913856 37.6402849832737,126.491353542773 37.6402960892954,126.491348932726 37.6403113443592,126.491291097594 37.640308075417,126.491289337394 37.6402962569335,126.491288499204 37.6402900962347,126.491316913856 37.6402849832737)))
1전체경기도309371641590256321040700095.9159881MULTIPOLYGON (((126.811651460273 37.0869336543078,126.811500418378 37.0868978635813,126.811499999283 37.0868900265018,126.81163444501 37.0869285413469,126.811651460273 37.0869336543078)))
2전체인천광역시45225328710340261041400008.0726471MULTIPOLYGON (((126.436998241785 37.5973543839517,126.436985585111 37.5973610475647,126.436974940094 37.5973423559206,126.436950884032 37.5973150728258,126.436947950365 37.5973122229787,126.436961864325 37.5973049726325,126.436997571232 37.5973530428472,126.436998241785 37.5973543839517)))
3전체경기도282994741570250291033200009.5587791MULTIPOLYGON (((126.56972199053 37.6896723721594,126.569696677183 37.6896305883721,126.569736658861 37.6896189794362,126.569722661083 37.6896694804028,126.56972199053 37.6896723721594)))
4전체인천광역시112572281771050010217000010.3951871MULTIPOLYGON (((126.67873182645 37.4619201372666,126.678718666862 37.4619205144522,126.678715984653 37.4618520343033,126.678753284122 37.4618225300041,126.67875864854 37.4618225300041,126.678728306051 37.4618470051614,126.67873182645 37.4619201372666)))
5전체인천광역시29724528260110001053300550.0746421MULTIPOLYGON (((126.671206134684 37.5062511430617,126.671205547951 37.5062511849712,126.671204542122 37.5062257458951,126.671206134684 37.5062511430617)))
6전체인천광역시10201281101330010198000221.2640731MULTIPOLYGON (((126.629056980195 37.4723831831815,126.629032169762 37.4723927804606,126.629016495603 37.4723679700272,126.629002078729 37.4723470571788,126.629052873062 37.4723260605113,126.629067876669 37.4723478953691,126.629041641312 37.4723578279244,126.629056980195 37.4723831831815)))
7전체인천광역시524266287103802410491000432.900151MULTIPOLYGON (((126.392211134728 37.801544334647,126.392168973755 37.8015658761382,126.39212094545 37.801516548638,126.392167884108 37.8014909838333,126.392211134728 37.801544334647)))
8전체충청북도205113443770310281139000008.8024991MULTIPOLYGON (((127.735758045565 36.9218835453139,127.735760057222 36.9218566813142,127.735798613977 36.9218577290521,127.735796686139 36.9218773427056,127.735758045565 36.9218835453139)))
9전체경기도2926011415703503010202000417.5045441MULTIPOLYGON (((126.593769670741 37.734775602694,126.593727761225 37.7347472718613,126.593729605243 37.7347454278426,126.593769670741 37.734775602694)),((126.59389866823 37.7348461364093,126.59388835849 37.7348557755979,126.593783249424 37.7347847389685,126.593841000737 37.7348008322226,126.59389866823 37.7348461364093)))
구분광역시도명일련번호지적번호지적면적계산면적공간정보
990전체인천광역시59722228720310241117500120.137471MULTIPOLYGON (((126.338624454261 37.5326931558236,126.338625292451 37.5326975563228,126.338622358785 37.5326983106941,126.338621269138 37.5326939940139,126.338624454261 37.5326931558236)))
991전체경기도293470941570360221070400178.9411371MULTIPOLYGON (((126.646619917195 37.7231183032147,126.646549760665 37.7230918164007,126.646583288278 37.7230784053556,126.646619917195 37.7231183032147)))
992전체경상북도491533347840360331079300017.0058431MULTIPOLYGON (((128.215861138057 35.9280546549373,128.21585652801 35.9280438422822,128.215874800559 35.9280341192745,128.215872202169 35.9280174811967,128.215886619043 35.9280135417022,128.215893659841 35.928039357964,128.215882679548 35.9280444709249,128.215861138057 35.9280546549373)))
993전체인천광역시349826287102502211003013120.7137691MULTIPOLYGON (((126.485417730401 37.7499960492132,126.485383029322 37.7499956301181,126.485425357933 37.7499461349798,126.485449078719 37.7499276109738,126.48547581699 37.7499263117788,126.485463914688 37.7499347355915,126.485417730401 37.7499960492132)))
994전체경기도289124141570340261013400027.3910461MULTIPOLYGON (((126.604025935104 37.6563186001295,126.604028533494 37.6563149959111,126.6040228338 37.6563120203355,126.604040268159 37.6562904369348,126.604078824913 37.6563092123979,126.604025935104 37.6563186001295)))
995전체인천광역시19693628237101001043100409.8914721MULTIPOLYGON (((126.721862920896 37.5047286122594,126.721862837077 37.5047289475355,126.721833668054 37.504726307236,126.721832997502 37.5047316297445,126.721670556218 37.5047167518664,126.721647254527 37.5046928215328,126.721648511813 37.5046843558106,126.721678351388 37.5047136924717,126.721862920896 37.5047286122594)))
996전체충청남도135840441313502210462000214.7437571MULTIPOLYGON (((127.263433610677 36.7302193430859,127.263468814671 36.7302188820812,127.263469149947 36.7302610430542,127.263433862135 36.7302616716969,127.263433610677 36.7302193430859)))
997전체인천광역시352856287102502310879001142.9316991MULTIPOLYGON (((126.475573017483 37.7493046679298,126.475635462662 37.7493432246844,126.475579723006 37.7493991319786,126.475540160423 37.7493761655639,126.47556983236 37.7493542887966,126.47555256564 37.7493238624881,126.475573017483 37.7493046679298)))
998전체인천광역시213838282371030010087000113.7482511MULTIPOLYGON (((126.7012611249 37.5049459969182,126.701233632258 37.5049472122942,126.70122994422 37.5048965436895,126.701256933949 37.5048942386661,126.701260621986 37.5049386208434,126.7012611249 37.5049459969182)))
999전체충청북도17326943112310211094700007.6025641MULTIPOLYGON (((127.428975360202 36.5629423719658,127.428993046018 36.5629455151795,127.428988855066 36.5629594710483,127.428981227534 36.5629658412947,127.428948789569 36.5629601416006,127.428952812883 36.5629433358847,127.428972761812 36.5629446769892,127.428975360202 36.5629423719658)))