Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory722.7 KiB
Average record size in memory74.0 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description지자체별 토지(지적)의 축척, 소유구분, 지목에 따른 면적 및 지번수에 대한 지적기본통계입니다.매년 연말을 기준으로 작성되며 해당수치는 토지소유현황 통계 및 지적통계연보의 기초자료로 활용됩니다.제공항목은 통계 기준일자,행정구역,대장 구분,축척,소유구분,지목,지번수,면적으로 구분되어 있습니다.
Author국토교통부
URLhttps://www.data.go.kr/data/15063997/fileData.do

Alerts

통계 기준일자 has constant value ""Constant
축척 is highly overall correlated with 대장 구분High correlation
대장 구분 is highly overall correlated with 축척High correlation
지번수 is highly overall correlated with 면적High correlation
면적 is highly overall correlated with 지번수High correlation
지번수 is highly skewed (γ1 = 27.07633747)Skewed
면적 is highly skewed (γ1 = 42.23675668)Skewed

Reproduction

Analysis started2023-12-12 10:26:31.819267
Analysis finished2023-12-12 10:26:33.359747
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계 기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-12-31
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 10000
100.0%

Length

2023-12-12T19:26:33.408212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:26:33.484270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 10000
100.0%

행정구역
Text

Distinct236
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T19:26:33.764036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.5735
Min length7

Characters and Unicode

Total characters85735
Distinct characters144
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row전라북도 군산시
2nd row대구광역시 달성군
3rd row경상북도 문경시
4th row서울특별시 관악구
5th row경상북도 고령군
ValueCountFrequency (%)
경기도 1679
 
7.9%
전라남도 1201
 
5.7%
경상남도 1158
 
5.5%
경상북도 1094
 
5.2%
충청남도 844
 
4.0%
전라북도 780
 
3.7%
충청북도 733
 
3.5%
서울특별시 588
 
2.8%
부산광역시 470
 
2.2%
인천광역시 340
 
1.6%
Other values (230) 12342
58.1%
2023-12-12T19:26:34.232696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11229
 
13.1%
도 7812
 
9.1%
시 6815
 
7.9%
경 4099
 
4.8%
남 3895
 
4.5%
구 3800
 
4.4%
군 3567
 
4.2%
북 2916
 
3.4%
상 2435
 
2.8%
전 2288
 
2.7%
Other values (134) 36879
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74506
86.9%
Space Separator 11229
 
13.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 7812
 
10.5%
시 6815
 
9.1%
경 4099
 
5.5%
남 3895
 
5.2%
구 3800
 
5.1%
군 3567
 
4.8%
북 2916
 
3.9%
상 2435
 
3.3%
전 2288
 
3.1%
광 2221
 
3.0%
Other values (133) 34658
46.5%
Space Separator
ValueCountFrequency (%)
11229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74506
86.9%
Common 11229
 
13.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 7812
 
10.5%
시 6815
 
9.1%
경 4099
 
5.5%
남 3895
 
5.2%
구 3800
 
5.1%
군 3567
 
4.8%
북 2916
 
3.9%
상 2435
 
3.3%
전 2288
 
3.1%
광 2221
 
3.0%
Other values (133) 34658
46.5%
Common
ValueCountFrequency (%)
11229
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74506
86.9%
ASCII 11229
 
13.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11229
100.0%
Hangul
ValueCountFrequency (%)
도 7812
 
10.5%
시 6815
 
9.1%
경 4099
 
5.5%
남 3895
 
5.2%
구 3800
 
5.1%
군 3567
 
4.8%
북 2916
 
3.9%
상 2435
 
3.3%
전 2288
 
3.1%
광 2221
 
3.0%
Other values (133) 34658
46.5%

대장 구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
토지
8631 
임야
1369 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토지
2nd row임야
3rd row토지
4th row토지
5th row토지

Common Values

ValueCountFrequency (%)
토지 8631
86.3%
임야 1369
 
13.7%

Length

2023-12-12T19:26:34.352158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:26:34.446576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토지 8631
86.3%
임야 1369
 
13.7%

축척
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1:1200
4207 
수치
2264 
1:1000
1220 
1:6000
1027 
1:600
552 
Other values (4)
730 

Length

Max length6
Median length6
Mean length5.0003
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1:600
2nd row1:6000
3rd row1:1200
4th row1:1200
5th row1:1200

Common Values

ValueCountFrequency (%)
1:1200 4207
42.1%
수치 2264
22.6%
1:1000 1220
 
12.2%
1:6000 1027
 
10.3%
1:600 552
 
5.5%
1:500 361
 
3.6%
1:3000 361
 
3.6%
기타 7
 
0.1%
1:2400 1
 
< 0.1%

Length

2023-12-12T19:26:34.565296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:26:34.691220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1:1200 4207
42.1%
수치 2264
22.6%
1:1000 1220
 
12.2%
1:6000 1027
 
10.3%
1:600 552
 
5.5%
1:500 361
 
3.6%
1:3000 361
 
3.6%
기타 7
 
0.1%
1:2400 1
 
< 0.1%

소유구분
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인
1739 
군유지
1546 
법인
1520 
국유지
1418 
시, 도유지
1104 
Other values (6)
2673 

Length

Max length11
Median length9
Mean length3.519
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종중
2nd row개인
3rd row개인
4th row개인
5th row군유지

Common Values

ValueCountFrequency (%)
개인 1739
17.4%
군유지 1546
15.5%
법인 1520
15.2%
국유지 1418
14.2%
시, 도유지 1104
11.0%
종중 695
 
7.0%
기타단체 632
 
6.3%
종교단체 591
 
5.9%
외국인, 외국공공기관 358
 
3.6%
일본인, 창씨명등 306
 
3.1%

Length

2023-12-12T19:26:34.838904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
개인 1739
14.8%
군유지 1546
13.1%
법인 1520
12.9%
국유지 1418
12.0%
시 1104
9.4%
도유지 1104
9.4%
종중 695
 
5.9%
기타단체 632
 
5.4%
종교단체 591
 
5.0%
외국인 358
 
3.0%
Other values (4) 1061
9.0%

지목
Categorical

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
도로
928 
대
873 
임야
825 
답
664 
전
615 
Other values (24)
6095 

Length

Max length5
Median length4
Mean length2.437
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도로
2nd row도로
3rd row답
4th row대
5th row목장용지

Common Values

ValueCountFrequency (%)
도로 928
 
9.3%
대 873
 
8.7%
임야 825
 
8.2%
답 664
 
6.6%
전 615
 
6.2%
잡종지 557
 
5.6%
구거 530
 
5.3%
하천 403
 
4.0%
유지 397
 
4.0%
주차장 372
 
3.7%
Other values (19) 3836
38.4%

Length

2023-12-12T19:26:35.019949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도로 928
 
9.3%
대 873
 
8.7%
임야 825
 
8.2%
답 664
 
6.6%
전 615
 
6.2%
잡종지 557
 
5.6%
구거 530
 
5.3%
하천 403
 
4.0%
유지 397
 
4.0%
주차장 372
 
3.7%
Other values (19) 3836
38.4%

지번수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1259
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean495.8195
Minimum1
Maximum219597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:26:35.167598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q370
95-th percentile1206
Maximum219597
Range219596
Interquartile range (IQR)67

Descriptive statistics

Standard deviation3808.5657
Coefficient of variation (CV)7.6813553
Kurtosis1227.4606
Mean495.8195
Median Absolute Deviation (MAD)10
Skewness27.076337
Sum4958195
Variance14505173
MonotonicityNot monotonic
2023-12-12T19:26:35.344653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1587
 
15.9%
2 908
 
9.1%
3 568
 
5.7%
4 408
 
4.1%
5 348
 
3.5%
6 295
 
2.9%
7 225
 
2.2%
8 213
 
2.1%
9 177
 
1.8%
10 156
 
1.6%
Other values (1249) 5115
51.1%
ValueCountFrequency (%)
1 1587
15.9%
2 908
9.1%
3 568
 
5.7%
4 408
 
4.1%
5 348
 
3.5%
6 295
 
2.9%
7 225
 
2.2%
8 213
 
2.1%
9 177
 
1.8%
10 156
 
1.6%
ValueCountFrequency (%)
219597 1
< 0.1%
102272 1
< 0.1%
85585 1
< 0.1%
61677 1
< 0.1%
60807 1
< 0.1%
58982 1
< 0.1%
58542 1
< 0.1%
57522 1
< 0.1%
57461 1
< 0.1%
52057 1
< 0.1%

면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8807
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1228539.8
Minimum0.7
Maximum1.042636 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:26:35.515614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile120
Q11576
median11276
Q392795.025
95-th percentile1935201.5
Maximum1.042636 × 109
Range1.042636 × 109
Interquartile range (IQR)91219.025

Descriptive statistics

Standard deviation15010699
Coefficient of variation (CV)12.218326
Kurtosis2528.6453
Mean1228539.8
Median Absolute Deviation (MAD)11011.5
Skewness42.236757
Sum1.2285398 × 1010
Variance2.2532108 × 1014
MonotonicityNot monotonic
2023-12-12T19:26:35.692054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198.0 16
 
0.2%
99.0 12
 
0.1%
20.0 11
 
0.1%
496.0 11
 
0.1%
298.0 11
 
0.1%
397.0 10
 
0.1%
17.0 9
 
0.1%
3.0 9
 
0.1%
330.0 8
 
0.1%
660.0 8
 
0.1%
Other values (8797) 9895
99.0%
ValueCountFrequency (%)
0.7 2
 
< 0.1%
1.0 6
0.1%
2.0 1
 
< 0.1%
3.0 9
0.1%
3.1 1
 
< 0.1%
3.3 1
 
< 0.1%
4.0 6
0.1%
5.0 1
 
< 0.1%
5.2 1
 
< 0.1%
5.4 1
 
< 0.1%
ValueCountFrequency (%)
1042635969.0 1
< 0.1%
522331066.0 1
< 0.1%
313817660.0 1
< 0.1%
267877457.3 1
< 0.1%
256399926.0 1
< 0.1%
240056983.8 1
< 0.1%
217607073.0 1
< 0.1%
210946022.0 1
< 0.1%
202471113.0 1
< 0.1%
191022945.0 1
< 0.1%

Interactions

2023-12-12T19:26:32.712796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:26:32.485564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:26:32.818287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:26:32.614751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:26:35.824204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장 구분축척소유구분지목지번수면적
대장 구분1.0000.9500.0700.4150.0100.139
축척0.9501.0000.2330.7180.5280.533
소유구분0.0700.2331.0000.4240.1900.156
지목0.4150.7180.4241.0000.6060.608
지번수0.0100.5280.1900.6061.0000.624
면적0.1390.5330.1560.6080.6241.000
2023-12-12T19:26:35.968744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소유구분축척대장 구분지목
소유구분1.0000.1070.0670.159
축척0.1071.0000.9860.366
대장 구분0.0670.9861.0000.355
지목0.1590.3660.3551.000
2023-12-12T19:26:36.080318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지번수면적대장 구분축척소유구분지목
지번수1.0000.8500.0070.2950.0980.319
면적0.8501.0000.1000.2980.0800.320
대장 구분0.0070.1001.0000.9860.0670.355
축척0.2950.2980.9861.0000.1070.366
소유구분0.0980.0800.0670.1071.0000.159
지목0.3190.3200.3550.3660.1591.000

Missing values

2023-12-12T19:26:32.929791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:26:33.311656image/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

통계 기준일자행정구역대장 구분축척소유구분지목지번수면적
472082022-12-31전라북도 군산시토지1:600종중도로4258.3
109902022-12-31대구광역시 달성군임야1:6000개인도로11480239.0
663412022-12-31경상북도 문경시토지1:1200개인답2867940677551.0
38052022-12-31서울특별시 관악구토지1:1200개인대165502874459.0
689662022-12-31경상북도 고령군토지1:1200군유지목장용지201423.0
329352022-12-31경기도 양평군토지1:1000국유지창고용지3682.0
565452022-12-31전라남도 보성군토지1:1000개인유지31902.0
258072022-12-31경기도 오산시토지수치국유지공장용지2322.7
639282022-12-31경상북도 김천시토지1:600법인주유소용지2771.2
189852022-12-31울산광역시 울주군토지수치개인구거52668.0
통계 기준일자행정구역대장 구분축척소유구분지목지번수면적
781362022-12-31경상남도 남해군토지1:1000개인임야2799.0
234102022-12-31경기도 안산시 상록구토지수치법인주유소용지1622074.6
566432022-12-31전라남도 보성군토지1:1200국유지묘지3758365.0
53752022-12-31부산광역시 영도구토지수치개인대509101684.0
506032022-12-31전라북도 장수군토지1:1200법인목장용지98475458.0
575852022-12-31전라남도 장흥군토지1:1200시, 도유지대16557711.0
405572022-12-31충청남도 공주시토지1:600법인대14645867.0
505392022-12-31전라북도 장수군토지1:1200국유지대30774835.0
223962022-12-31경기도 광명시임야1:6000군유지잡종지114.0
143042022-12-31광주광역시 서구토지1:1200국유지목장용지31462.0