Overview

Dataset statistics

Number of variables20
Number of observations148
Missing cells96
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.8 KiB
Average record size in memory171.9 B

Variable types

Numeric9
Categorical6
Text1
DateTime4

Dataset

Description전라남도 광양시 착공신고 현황(연번,건축구분,대지위치,지목,대지면적,건축면적,연면적 등)을 제공합니다.
Author전라남도 광양시
URLhttps://www.data.go.kr/data/15063827/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 증축연면적(제곱미터) and 1 other fieldsHigh correlation
대지면적(제곱미터) is highly overall correlated with 건축면적(제곱미터) and 3 other fieldsHigh correlation
건축면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
증축연면적(제곱미터) is highly overall correlated with 연번 and 2 other fieldsHigh correlation
건폐율(퍼센트) is highly overall correlated with 용적률(퍼센트)High correlation
용적률(퍼센트) is highly overall correlated with 건폐율(퍼센트)High correlation
최고높이(미터) is highly overall correlated with 연번 and 5 other fieldsHigh correlation
동수 is highly overall correlated with 대지면적(제곱미터) and 2 other fieldsHigh correlation
건축구분 is highly overall correlated with 증축연면적(제곱미터) and 2 other fieldsHigh correlation
구조 is highly overall correlated with 최대지하층수High correlation
최대지상층수 is highly overall correlated with 최고높이(미터) and 1 other fieldsHigh correlation
최대지하층수 is highly overall correlated with 대지면적(제곱미터) and 8 other fieldsHigh correlation
주용도 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
최대지상층수 is highly imbalanced (54.0%)Imbalance
증축연면적(제곱미터) has 96 (64.9%) missing valuesMissing
연번 has unique valuesUnique
건폐율(퍼센트) has 2 (1.4%) zerosZeros
용적률(퍼센트) has 2 (1.4%) zerosZeros

Reproduction

Analysis started2024-03-16 04:17:24.694879
Analysis finished2024-03-16 04:17:45.372863
Duration20.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct148
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.5
Minimum1
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:45.490953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.35
Q137.75
median74.5
Q3111.25
95-th percentile140.65
Maximum148
Range147
Interquartile range (IQR)73.5

Descriptive statistics

Standard deviation42.868014
Coefficient of variation (CV)0.57540959
Kurtosis-1.2
Mean74.5
Median Absolute Deviation (MAD)37
Skewness0
Sum11026
Variance1837.6667
MonotonicityStrictly increasing
2024-03-16T13:17:45.663413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
95 1
 
0.7%
97 1
 
0.7%
98 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
102 1
 
0.7%
103 1
 
0.7%
104 1
 
0.7%
Other values (138) 138
93.2%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
148 1
0.7%
147 1
0.7%
146 1
0.7%
145 1
0.7%
144 1
0.7%
143 1
0.7%
142 1
0.7%
141 1
0.7%
140 1
0.7%
139 1
0.7%

건축구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
신축
92 
증축
53 
대수선
 
2
가설건축물축조허가
 
1

Length

Max length9
Median length2
Mean length2.0608108
Min length2

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row신축
2nd row증축
3rd row증축
4th row신축
5th row신축

Common Values

ValueCountFrequency (%)
신축 92
62.2%
증축 53
35.8%
대수선 2
 
1.4%
가설건축물축조허가 1
 
0.7%

Length

2024-03-16T13:17:45.928337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:17:46.119575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 92
62.2%
증축 53
35.8%
대수선 2
 
1.4%
가설건축물축조허가 1
 
0.7%
Distinct131
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-03-16T13:17:46.684018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length21.27027
Min length16

Characters and Unicode

Total characters3148
Distinct characters90
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

Unique125 ?
Unique (%)84.5%

Sample

1st row전라남도 광양시 다압면 신원리 1686-1 외5필지
2nd row전라남도 광양시 성황동 556-1
3rd row전라남도 광양시 광영동 729-4
4th row전라남도 광양시 광양읍 칠성리 937-1
5th row전라남도 광양시 중동 1867-11
ValueCountFrequency (%)
전라남도 148
20.5%
광양시 148
20.5%
광양읍 31
 
4.3%
외1필지 20
 
2.8%
태인동 16
 
2.2%
옥곡면 16
 
2.2%
마동 14
 
1.9%
옥룡면 11
 
1.5%
신금리 11
 
1.5%
28-117 10
 
1.4%
Other values (184) 296
41.1%
2024-03-16T13:17:47.480342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
573
18.2%
185
 
5.9%
179
 
5.7%
1 164
 
5.2%
153
 
4.9%
150
 
4.8%
148
 
4.7%
148
 
4.7%
148
 
4.7%
- 102
 
3.2%
Other values (80) 1198
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1824
57.9%
Decimal Number 649
 
20.6%
Space Separator 573
 
18.2%
Dash Punctuation 102
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
185
 
10.1%
179
 
9.8%
153
 
8.4%
150
 
8.2%
148
 
8.1%
148
 
8.1%
148
 
8.1%
85
 
4.7%
64
 
3.5%
54
 
3.0%
Other values (68) 510
28.0%
Decimal Number
ValueCountFrequency (%)
1 164
25.3%
2 77
11.9%
7 69
10.6%
5 62
 
9.6%
6 57
 
8.8%
3 51
 
7.9%
8 47
 
7.2%
9 44
 
6.8%
4 44
 
6.8%
0 34
 
5.2%
Space Separator
ValueCountFrequency (%)
573
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1824
57.9%
Common 1324
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
185
 
10.1%
179
 
9.8%
153
 
8.4%
150
 
8.2%
148
 
8.1%
148
 
8.1%
148
 
8.1%
85
 
4.7%
64
 
3.5%
54
 
3.0%
Other values (68) 510
28.0%
Common
ValueCountFrequency (%)
573
43.3%
1 164
 
12.4%
- 102
 
7.7%
2 77
 
5.8%
7 69
 
5.2%
5 62
 
4.7%
6 57
 
4.3%
3 51
 
3.9%
8 47
 
3.5%
9 44
 
3.3%
Other values (2) 78
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1824
57.9%
ASCII 1324
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
573
43.3%
1 164
 
12.4%
- 102
 
7.7%
2 77
 
5.8%
7 69
 
5.2%
5 62
 
4.7%
6 57
 
4.3%
3 51
 
3.9%
8 47
 
3.5%
9 44
 
3.3%
Other values (2) 78
 
5.9%
Hangul
ValueCountFrequency (%)
185
 
10.1%
179
 
9.8%
153
 
8.4%
150
 
8.2%
148
 
8.1%
148
 
8.1%
148
 
8.1%
85
 
4.7%
64
 
3.5%
54
 
3.0%
Other values (68) 510
28.0%

지목
Categorical

Distinct12
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
62 
공장용지
30 
임야
18 
17 
10 
Other values (7)
11 

Length

Max length5
Median length1
Mean length1.9121622
Min length1

Unique

Unique4 ?
Unique (%)2.7%

Sample

1st row도로
2nd row주유소용지
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
62
41.9%
공장용지 30
20.3%
임야 18
 
12.2%
17
 
11.5%
10
 
6.8%
창고용지 3
 
2.0%
도로 2
 
1.4%
잡종지 2
 
1.4%
주유소용지 1
 
0.7%
학교용지 1
 
0.7%
Other values (2) 2
 
1.4%

Length

2024-03-16T13:17:47.770963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
62
41.9%
공장용지 30
20.3%
임야 18
 
12.2%
17
 
11.5%
10
 
6.8%
창고용지 3
 
2.0%
도로 2
 
1.4%
잡종지 2
 
1.4%
주유소용지 1
 
0.7%
학교용지 1
 
0.7%
Other values (2) 2
 
1.4%

대지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86180.507
Minimum120
Maximum3293527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:48.036725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile200
Q1360.5
median674
Q31670
95-th percentile469379.65
Maximum3293527
Range3293407
Interquartile range (IQR)1309.5

Descriptive statistics

Standard deviation382360.5
Coefficient of variation (CV)4.43674
Kurtosis39.977712
Mean86180.507
Median Absolute Deviation (MAD)354
Skewness5.8899945
Sum12754715
Variance1.4619955 × 1011
MonotonicityNot monotonic
2024-03-16T13:17:48.278064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
683 10
 
6.8%
1206256 3
 
2.0%
284 2
 
1.4%
200 2
 
1.4%
231 2
 
1.4%
206 2
 
1.4%
226 2
 
1.4%
1094792 2
 
1.4%
1028 2
 
1.4%
659 2
 
1.4%
Other values (118) 119
80.4%
ValueCountFrequency (%)
120 1
0.7%
180 1
0.7%
185 1
0.7%
189 1
0.7%
192 1
0.7%
195 1
0.7%
197 1
0.7%
200 2
1.4%
201 1
0.7%
206 2
1.4%
ValueCountFrequency (%)
3293527 1
 
0.7%
2120439 1
 
0.7%
1206256 3
2.0%
1094792 2
1.4%
611494 1
 
0.7%
205453 1
 
0.7%
195300 1
 
0.7%
126766 1
 
0.7%
96836 1
 
0.7%
45695 1
 
0.7%

건축면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10131.486
Minimum13
Maximum724041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:48.506270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile44.35
Q187
median148
Q3417.25
95-th percentile38598.7
Maximum724041
Range724028
Interquartile range (IQR)330.25

Descriptive statistics

Standard deviation65334.32
Coefficient of variation (CV)6.4486411
Kurtosis101.06922
Mean10131.486
Median Absolute Deviation (MAD)77
Skewness9.6720165
Sum1499460
Variance4.2685734 × 109
MonotonicityNot monotonic
2024-03-16T13:17:48.757500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197 10
 
6.8%
99 8
 
5.4%
120 3
 
2.0%
84 3
 
2.0%
73 2
 
1.4%
90 2
 
1.4%
50 2
 
1.4%
88 2
 
1.4%
60 2
 
1.4%
165 2
 
1.4%
Other values (107) 112
75.7%
ValueCountFrequency (%)
13 1
0.7%
18 2
1.4%
24 1
0.7%
29 1
0.7%
32 1
0.7%
36 1
0.7%
44 1
0.7%
45 1
0.7%
48 1
0.7%
49 1
0.7%
ValueCountFrequency (%)
724041 1
0.7%
308747 1
0.7%
67824 1
0.7%
67479 1
0.7%
59754 1
0.7%
59706 1
0.7%
59380 1
0.7%
48097 1
0.7%
20959 1
0.7%
15342 1
0.7%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13544.73
Minimum11
Maximum752844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:48.987388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile44.35
Q188.75
median173.5
Q3444.25
95-th percentile66373.8
Maximum752844
Range752833
Interquartile range (IQR)355.5

Descriptive statistics

Standard deviation73369.019
Coefficient of variation (CV)5.4167946
Kurtosis76.13355
Mean13544.73
Median Absolute Deviation (MAD)103.5
Skewness8.2479633
Sum2004620
Variance5.3830129 × 109
MonotonicityNot monotonic
2024-03-16T13:17:49.680997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
254 10
 
6.8%
99 8
 
5.4%
84 4
 
2.7%
98 4
 
2.7%
120 3
 
2.0%
60 2
 
1.4%
123 2
 
1.4%
165 2
 
1.4%
57 2
 
1.4%
145 2
 
1.4%
Other values (101) 109
73.6%
ValueCountFrequency (%)
11 1
0.7%
18 2
1.4%
24 1
0.7%
29 1
0.7%
32 2
1.4%
44 1
0.7%
45 1
0.7%
48 1
0.7%
49 1
0.7%
50 2
1.4%
ValueCountFrequency (%)
752844 1
0.7%
408656 1
0.7%
127014 1
0.7%
125801 1
0.7%
120277 1
0.7%
120229 1
0.7%
119030 1
0.7%
90325 1
0.7%
21893 1
0.7%
18578 1
0.7%

증축연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)73.1%
Missing96
Missing (%)64.9%
Infinite0
Infinite (%)0.0%
Mean274.28846
Minimum19
Maximum2471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:50.102990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile31.1
Q132
median81.5
Q3146.5
95-th percentile1414.4
Maximum2471
Range2452
Interquartile range (IQR)114.5

Descriptive statistics

Standard deviation519.74177
Coefficient of variation (CV)1.8948729
Kurtosis7.3250174
Mean274.28846
Median Absolute Deviation (MAD)49.5
Skewness2.745885
Sum14263
Variance270131.5
MonotonicityNot monotonic
2024-03-16T13:17:50.407220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
32 11
 
7.4%
84 4
 
2.7%
36 2
 
1.4%
108 1
 
0.7%
85 1
 
0.7%
59 1
 
0.7%
135 1
 
0.7%
83 1
 
0.7%
82 1
 
0.7%
30 1
 
0.7%
Other values (28) 28
 
18.9%
(Missing) 96
64.9%
ValueCountFrequency (%)
19 1
 
0.7%
27 1
 
0.7%
30 1
 
0.7%
32 11
7.4%
33 1
 
0.7%
35 1
 
0.7%
36 2
 
1.4%
44 1
 
0.7%
48 1
 
0.7%
50 1
 
0.7%
ValueCountFrequency (%)
2471 1
0.7%
1824 1
0.7%
1608 1
0.7%
1256 1
0.7%
1214 1
0.7%
1199 1
0.7%
622 1
0.7%
527 1
0.7%
366 1
0.7%
338 1
0.7%

건폐율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.202703
Minimum0
Maximum71
Zeros2
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:50.608487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q113
median21
Q338
95-th percentile60
Maximum71
Range71
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.156523
Coefficient of variation (CV)0.66745289
Kurtosis-0.49396304
Mean27.202703
Median Absolute Deviation (MAD)10
Skewness0.74183354
Sum4026
Variance329.65931
MonotonicityNot monotonic
2024-03-16T13:17:50.850376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 11
 
7.4%
20 9
 
6.1%
60 8
 
5.4%
13 8
 
5.4%
19 7
 
4.7%
10 7
 
4.7%
15 6
 
4.1%
12 4
 
2.7%
44 4
 
2.7%
14 4
 
2.7%
Other values (47) 80
54.1%
ValueCountFrequency (%)
0 2
 
1.4%
2 1
 
0.7%
3 2
 
1.4%
4 1
 
0.7%
5 4
2.7%
6 3
2.0%
7 1
 
0.7%
8 2
 
1.4%
9 2
 
1.4%
10 7
4.7%
ValueCountFrequency (%)
71 1
 
0.7%
70 1
 
0.7%
69 1
 
0.7%
68 1
 
0.7%
63 2
 
1.4%
60 8
5.4%
59 2
 
1.4%
57 1
 
0.7%
56 1
 
0.7%
55 1
 
0.7%

용적률(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.108108
Minimum0
Maximum336
Zeros2
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:51.109658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.35
Q113
median23
Q346.25
95-th percentile93.85
Maximum336
Range336
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation37.38677
Coefficient of variation (CV)1.0649041
Kurtosis29.30522
Mean35.108108
Median Absolute Deviation (MAD)13
Skewness4.3483924
Sum5196
Variance1397.7705
MonotonicityNot monotonic
2024-03-16T13:17:51.324941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 11
 
7.4%
19 10
 
6.8%
10 9
 
6.1%
20 6
 
4.1%
13 6
 
4.1%
11 5
 
3.4%
16 5
 
3.4%
12 5
 
3.4%
60 5
 
3.4%
14 4
 
2.7%
Other values (55) 82
55.4%
ValueCountFrequency (%)
0 2
 
1.4%
2 1
 
0.7%
3 1
 
0.7%
4 1
 
0.7%
5 1
 
0.7%
6 2
 
1.4%
7 1
 
0.7%
8 2
 
1.4%
9 2
 
1.4%
10 9
6.1%
ValueCountFrequency (%)
336 1
0.7%
179 1
0.7%
134 1
0.7%
122 1
0.7%
120 1
0.7%
101 1
0.7%
98 1
0.7%
97 1
0.7%
88 1
0.7%
74 1
0.7%

구조
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
경량철골구조
70 
일반철골구조
35 
철근콘크리트구조
29 
일반목구조
 
6
컨테이너조
 
3
Other values (5)
 
5

Length

Max length13
Median length6
Mean length6.3851351
Min length4

Unique

Unique5 ?
Unique (%)3.4%

Sample

1st row철근콘크리트구조
2nd row일반철골구조
3rd row경량철골구조
4th row철근콘크리트구조
5th row일반철골구조

Common Values

ValueCountFrequency (%)
경량철골구조 70
47.3%
일반철골구조 35
23.6%
철근콘크리트구조 29
19.6%
일반목구조 6
 
4.1%
컨테이너조 3
 
2.0%
철골철근콘크리트구조 1
 
0.7%
공업화박판강구조(PEB) 1
 
0.7%
<NA> 1
 
0.7%
기타강구조 1
 
0.7%
강파이프구조 1
 
0.7%

Length

2024-03-16T13:17:51.523770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:17:51.745457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경량철골구조 70
47.3%
일반철골구조 35
23.6%
철근콘크리트구조 29
19.6%
일반목구조 6
 
4.1%
컨테이너조 3
 
2.0%
철골철근콘크리트구조 1
 
0.7%
공업화박판강구조(peb 1
 
0.7%
na 1
 
0.7%
기타강구조 1
 
0.7%
강파이프구조 1
 
0.7%
Distinct97
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2021-09-27 00:00:00
Maximum2024-01-26 00:00:00
2024-03-16T13:17:52.639932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:52.997949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct64
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-09-05 00:00:00
Maximum2024-01-31 00:00:00
2024-03-16T13:17:53.355666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:53.603136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct78
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2022-11-12 00:00:00
Maximum2024-12-25 00:00:00
2024-03-16T13:17:53.891915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:54.200492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최대지상층수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
112 
2
28 
4
 
4
5
 
2
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 112
75.7%
2 28
 
18.9%
4 4
 
2.7%
5 2
 
1.4%
3 2
 
1.4%

Length

2024-03-16T13:17:54.400010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:17:54.632835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 112
75.7%
2 28
 
18.9%
4 4
 
2.7%
5 2
 
1.4%
3 2
 
1.4%

최대지하층수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0
91 
<NA>
53 
1
 
4

Length

Max length4
Median length1
Mean length2.0743243
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row<NA>
3rd row<NA>
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 91
61.5%
<NA> 53
35.8%
1 4
 
2.7%

Length

2024-03-16T13:17:54.877482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:17:55.063398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 91
61.5%
na 53
35.8%
1 4
 
2.7%

최고높이(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2162162
Minimum3
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:55.215904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median6
Q38
95-th percentile15
Maximum26
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.2722655
Coefficient of variation (CV)0.59203679
Kurtosis7.1124502
Mean7.2162162
Median Absolute Deviation (MAD)2
Skewness2.456694
Sum1068
Variance18.252252
MonotonicityNot monotonic
2024-03-16T13:17:55.473637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
5 36
24.3%
4 28
18.9%
6 21
14.2%
8 19
12.8%
7 10
 
6.8%
10 7
 
4.7%
12 6
 
4.1%
9 4
 
2.7%
3 4
 
2.7%
15 3
 
2.0%
Other values (7) 10
 
6.8%
ValueCountFrequency (%)
3 4
 
2.7%
4 28
18.9%
5 36
24.3%
6 21
14.2%
7 10
 
6.8%
8 19
12.8%
9 4
 
2.7%
10 7
 
4.7%
12 6
 
4.1%
13 1
 
0.7%
ValueCountFrequency (%)
26 1
 
0.7%
25 3
2.0%
18 1
 
0.7%
17 1
 
0.7%
16 1
 
0.7%
15 3
2.0%
14 2
 
1.4%
13 1
 
0.7%
12 6
4.1%
10 7
4.7%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2837838
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-16T13:17:55.672431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4.65
Maximum42
Range41
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.0224537
Coefficient of variation (CV)2.1991809
Kurtosis48.324612
Mean2.2837838
Median Absolute Deviation (MAD)0
Skewness6.7113964
Sum338
Variance25.225041
MonotonicityNot monotonic
2024-03-16T13:17:55.846000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 96
64.9%
2 36
 
24.3%
3 4
 
2.7%
4 4
 
2.7%
5 3
 
2.0%
42 1
 
0.7%
11 1
 
0.7%
18 1
 
0.7%
16 1
 
0.7%
40 1
 
0.7%
ValueCountFrequency (%)
1 96
64.9%
2 36
 
24.3%
3 4
 
2.7%
4 4
 
2.7%
5 3
 
2.0%
11 1
 
0.7%
16 1
 
0.7%
18 1
 
0.7%
40 1
 
0.7%
42 1
 
0.7%
ValueCountFrequency (%)
42 1
 
0.7%
40 1
 
0.7%
18 1
 
0.7%
16 1
 
0.7%
11 1
 
0.7%
5 3
 
2.0%
4 4
 
2.7%
3 4
 
2.7%
2 36
 
24.3%
1 96
64.9%

주용도
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
단독주택
41 
제2종근린생활시설
29 
공장
27 
제1종근린생활시설
24 
창고시설
13 
Other values (9)
14 

Length

Max length10
Median length9
Mean length5.6756757
Min length2

Unique

Unique6 ?
Unique (%)4.1%

Sample

1st row제1종근린생활시설
2nd row위험물저장및처리시설
3rd row제2종근린생활시설
4th row제1종근린생활시설
5th row제2종근린생활시설

Common Values

ValueCountFrequency (%)
단독주택 41
27.7%
제2종근린생활시설 29
19.6%
공장 27
18.2%
제1종근린생활시설 24
16.2%
창고시설 13
 
8.8%
위험물저장및처리시설 3
 
2.0%
자원순환관련시설 3
 
2.0%
교육연구시설 2
 
1.4%
장례시설 1
 
0.7%
판매시설 1
 
0.7%
Other values (4) 4
 
2.7%

Length

2024-03-16T13:17:56.062722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단독주택 41
27.7%
제2종근린생활시설 29
19.6%
공장 27
18.2%
제1종근린생활시설 24
16.2%
창고시설 13
 
8.8%
위험물저장및처리시설 3
 
2.0%
자원순환관련시설 3
 
2.0%
교육연구시설 2
 
1.4%
장례시설 1
 
0.7%
판매시설 1
 
0.7%
Other values (4) 4
 
2.7%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2024-03-08 00:00:00
Maximum2024-03-08 00:00:00
2024-03-16T13:17:56.298216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:56.506238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-16T13:17:43.279999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:27.594504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:29.192550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:31.707867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:33.267472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:34.895833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:36.248370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:37.910616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:41.402565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:43.440457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:27.787667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:29.353209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:31.941579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:33.466449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:35.024706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:36.414134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:38.159607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:41.588427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:43.627857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:27.968524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:29.687300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:32.120773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:33.647942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:35.178982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:36.593560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:38.354904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:41.742339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:43.775418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:28.135286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:30.604635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:32.355003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:33.817963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:35.339279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:36.745432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:38.525793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:41.939312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:43.933329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:28.327130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:30.783263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:32.515429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:34.016110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:35.498018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:36.912380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:38.679398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:42.578636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:44.146483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:28.523557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:30.938204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:32.662291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:34.185216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:35.687313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:37.040113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:38.922515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:42.735901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:44.413369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:28.689357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:31.189817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:32.809169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:34.395591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:35.863134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:37.214197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:39.633033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:42.893357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:44.561349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:28.889854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:31.373498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:32.981247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:34.602890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:36.011317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:37.416712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:40.703811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:43.030198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:44.672975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:29.045633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:31.533430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:33.123659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:34.744463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:36.128254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:37.663347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:41.064214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:17:43.157798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:17:56.658484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번건축구분지목대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일착공예정일최대지상층수최대지하층수최고높이(미터)동수주용도
연번1.0000.3140.3860.2120.0000.1210.4650.6020.3300.3430.9140.9330.9180.4330.4350.4000.0000.395
건축구분0.3141.0000.5810.1050.0000.295NaN0.3600.1510.4760.0000.7870.0000.2180.4450.5690.0000.742
지목0.3860.5811.0000.0000.0000.0540.2580.4940.4120.0000.7680.8100.6510.0000.0000.5730.6740.796
대지면적(제곱미터)0.2120.1050.0001.0001.0000.9470.1580.6050.0000.0000.0000.7800.0000.406NaN0.3840.6830.728
건축면적(제곱미터)0.0000.0000.0001.0001.0001.0000.0000.2470.0000.0000.7750.9180.0000.000NaN0.0000.0000.000
연면적(제곱미터)0.1210.2950.0540.9471.0001.0000.6710.4800.0000.0000.5600.9130.0000.208NaN0.5440.0000.000
증축연면적(제곱미터)0.465NaN0.2580.1580.0000.6711.0000.0000.0000.6480.7360.0000.0000.565NaN0.9400.6270.000
건폐율(퍼센트)0.6020.3600.4940.6050.2470.4800.0001.0000.7170.0000.0000.6170.4060.4420.3000.4100.0720.491
용적률(퍼센트)0.3300.1510.4120.0000.0000.0000.0000.7171.0000.0000.0000.0000.0000.6240.6910.5680.0000.318
구조0.3430.4760.0000.0000.0000.0000.6480.0000.0001.0000.9590.0000.0000.3660.4960.5980.6670.786
허가일0.9140.0000.7680.0000.7750.5600.7360.0000.0000.9591.0000.8750.9630.0000.6370.6760.0000.880
착공처리일0.9330.7870.8100.7800.9180.9130.0000.6170.0000.0000.8751.0000.9840.5940.2890.8040.0000.000
착공예정일0.9180.0000.6510.0000.0000.0000.0000.4060.0000.0000.9630.9841.0000.0000.0000.0000.0000.000
최대지상층수0.4330.2180.0000.4060.0000.2080.5650.4420.6240.3660.0000.5940.0001.0000.7970.7320.0000.493
최대지하층수0.4350.4450.000NaNNaNNaNNaN0.3000.6910.4960.6370.2890.0000.7971.0000.932NaN0.840
최고높이(미터)0.4000.5690.5730.3840.0000.5440.9400.4100.5680.5980.6760.8040.0000.7320.9321.0000.3080.715
동수0.0000.0000.6740.6830.0000.0000.6270.0720.0000.6670.0000.0000.0000.000NaN0.3081.0000.800
주용도0.3950.7420.7960.7280.0000.0000.0000.4910.3180.7860.8800.0000.0000.4930.8400.7150.8001.000
2024-03-16T13:17:56.939101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구조건축구분지목주용도최대지하층수최대지상층수
구조1.0000.3180.0000.4730.5180.217
건축구분0.3181.0000.2930.5070.6880.178
지목0.0000.2931.0000.4500.0000.000
주용도0.4730.5070.4501.0000.6430.274
최대지하층수0.5180.6880.0000.6431.0000.582
최대지상층수0.2170.1780.0000.2740.5821.000
2024-03-16T13:17:57.202221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)최고높이(미터)동수건축구분지목구조최대지상층수최대지하층수주용도
연번1.000-0.114-0.305-0.323-0.789-0.210-0.240-0.5630.1500.1870.1700.1610.1900.3180.167
대지면적(제곱미터)-0.1141.0000.7380.7120.407-0.350-0.2770.2850.5510.0840.0000.0000.1611.0000.472
건축면적(제곱미터)-0.3050.7381.0000.9810.4780.2400.3010.5220.4800.0000.0000.0000.0001.0000.000
연면적(제곱미터)-0.3230.7120.9811.0000.4680.2560.3480.5730.4560.1180.0100.0000.1701.0000.000
증축연면적(제곱미터)-0.7890.4070.4780.4681.000-0.248-0.2300.6960.2411.0000.1130.4190.3750.0000.000
건폐율(퍼센트)-0.210-0.3500.2400.256-0.2481.0000.9610.259-0.1490.2170.2300.0000.1940.2180.218
용적률(퍼센트)-0.240-0.2770.3010.348-0.2300.9611.0000.359-0.1500.0960.1680.0000.4820.4960.156
최고높이(미터)-0.5630.2850.5220.5730.6960.2590.3591.000-0.0950.2780.2800.3440.5600.7470.406
동수0.1500.5510.4800.4560.241-0.149-0.150-0.0951.0000.0000.4440.4590.0001.0000.559
건축구분0.1870.0840.0000.1181.0000.2170.0960.2780.0001.0000.2930.3180.1780.6880.507
지목0.1700.0000.0000.0100.1130.2300.1680.2800.4440.2931.0000.0000.0000.0000.450
구조0.1610.0000.0000.0000.4190.0000.0000.3440.4590.3180.0001.0000.2170.5180.473
최대지상층수0.1900.1610.0000.1700.3750.1940.4820.5600.0000.1780.0000.2171.0000.5820.274
최대지하층수0.3181.0001.0001.0000.0000.2180.4960.7471.0000.6880.0000.5180.5821.0000.643
주용도0.1670.4720.0000.0000.0000.2180.1560.4060.5590.5070.4500.4730.2740.6431.000

Missing values

2024-03-16T13:17:44.906593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:17:45.236536image/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

연번건축구분대지위치지목대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일착공예정일최대지상층수최대지하층수최고높이(미터)동수주용도데이터기준일
01신축전라남도 광양시 다압면 신원리 1686-1 외5필지도로2154286399<NA>1319철근콘크리트구조2023-08-282023-09-052023-09-0420101제1종근린생활시설2024-03-08
12증축전라남도 광양시 성황동 556-1주유소용지17302673191671518일반철골구조2023-08-232023-09-052023-09-072<NA>83위험물저장및처리시설2024-03-08
23증축전라남도 광양시 광영동 729-41971031989952101경량철골구조2023-09-052023-09-112023-09-112<NA>81제2종근린생활시설2024-03-08
34신축전라남도 광양시 광양읍 칠성리 937-1332199398<NA>60120철근콘크리트구조2023-09-012023-09-112023-09-112091제1종근린생활시설2024-03-08
45신축전라남도 광양시 중동 1867-11532318318<NA>6060일반철골구조2023-08-312023-09-112023-09-111071제2종근린생활시설2024-03-08
56신축전라남도 광양시 진월면 망덕리 210-621000397397<NA>4040일반철골구조2023-07-262023-09-122023-08-141071창고시설2024-03-08
67증축전라남도 광양시 옥곡면 신금리 365-3 외3필지446789116121632036경량철골구조2023-08-032023-09-132023-09-142<NA>103장례시설2024-03-08
78증축전라남도 광양시 광양읍 초남리 313-28잡종지132161842184218241414일반철골구조2023-07-182023-09-132023-08-141<NA>175자원순환관련시설2024-03-08
89증축전라남도 광양시 광양읍 초남리 763-3 외6필지공장용지456951174133333833일반철골구조2023-09-012023-09-142023-09-181<NA>75위험물저장및처리시설2024-03-08
910증축전라남도 광양시 광양읍 초남리 761-7 외5필지공장용지159943649260624712316철골철근콘크리트구조2023-09-132023-09-152023-09-181<NA>132공장2024-03-08
연번건축구분대지위치지목대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율(퍼센트)용적률(퍼센트)구조허가일착공처리일착공예정일최대지상층수최대지하층수최고높이(미터)동수주용도데이터기준일
138139증축전라남도 광양시 광영동 583-101939443404842321경량철골구조2023-11-282023-12-222023-12-291<NA>42위험물저장및처리시설2024-03-08
139140증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-032024-01-082024-01-111<NA>42공장2024-03-08
140141증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-102024-01-152024-01-311<NA>42공장2024-03-08
141142증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-102024-01-162024-01-171<NA>42공장2024-03-08
142143증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-102024-01-172024-01-221<NA>42공장2024-03-08
143144증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-152024-01-242024-01-261<NA>42공장2024-03-08
144145증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-042024-01-252024-01-301<NA>42공장2024-03-08
145146증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-262024-01-312024-02-021<NA>42공장2024-03-08
146147증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-252024-01-312024-02-031<NA>42공장2024-03-08
147148증축전라남도 광양시 태인동 28-117공장용지683197254322937경량철골구조2024-01-182024-01-312024-02-011<NA>42공장2024-03-08