Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory56.3 B

Variable types

Numeric4
Text2

Dataset

Description광주광역시 개발제한구역 조정(해제)에 대한 관련 자료- 해제지역 주요 위치, 기정 면적, 해제 면적, 변경 면적, 변경사유 등
Author광주광역시
URLhttps://www.data.go.kr/data/15010492/fileData.do

Alerts

연번 is highly overall correlated with 기정(제곱미터) and 1 other fieldsHigh correlation
기정(제곱미터) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
변경(제곱미터) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
위치 has unique valuesUnique
기정(제곱미터) has unique valuesUnique
증감(해제총량)(제곱미터) has unique valuesUnique
변경(제곱미터) has unique valuesUnique
변경사유 has unique valuesUnique

Reproduction

Analysis started2024-03-14 09:12:33.914862
Analysis finished2024-03-14 09:12:38.331260
Duration4.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T18:12:38.448460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2024-03-14T18:12:38.826067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

위치
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
2024-03-14T18:12:39.678174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length23.566667
Min length11

Characters and Unicode

Total characters707
Distinct characters133
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row 동구 운림동외3개소
2nd row 남구 진월동, 노대동, 행암동(진월택지개발지구)
3rd row 남구 진월택지개발지구
4th row 남구 향등동 및 광산 신야촌 이주단지
5th row 동구 내남동 남계마을외 195개 집단취락지구
ValueCountFrequency (%)
남구 7
 
6.5%
동구 6
 
5.6%
일원 6
 
5.6%
소규모 4
 
3.7%
진월동 3
 
2.8%
광주 2
 
1.9%
압촌 2
 
1.9%
효천2국민임대주택단지 2
 
1.9%
노대동 2
 
1.9%
내남동 2
 
1.9%
Other values (70) 72
66.7%
2024-03-14T18:12:40.980617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102
 
14.4%
동 38
 
5.4%
구 37
 
5.2%
지 35
 
5.0%
) 28
 
4.0%
( 28
 
4.0%
, 24
 
3.4%
남 21
 
3.0%
단 19
 
2.7%
산 15
 
2.1%
Other values (123) 360
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 486
68.7%
Space Separator 102
 
14.4%
Decimal Number 34
 
4.8%
Close Punctuation 28
 
4.0%
Open Punctuation 28
 
4.0%
Other Punctuation 26
 
3.7%
Other Symbol 1
 
0.1%
Math Symbol 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 38
 
7.8%
구 37
 
7.6%
지 35
 
7.2%
남 21
 
4.3%
단 19
 
3.9%
산 15
 
3.1%
원 14
 
2.9%
일 13
 
2.7%
광 11
 
2.3%
북 10
 
2.1%
Other values (105) 273
56.2%
Decimal Number
ValueCountFrequency (%)
1 10
29.4%
5 6
17.6%
3 5
14.7%
2 3
 
8.8%
9 3
 
8.8%
8 2
 
5.9%
6 2
 
5.9%
7 2
 
5.9%
0 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 24
92.3%
. 1
 
3.8%
/ 1
 
3.8%
Space Separator
ValueCountFrequency (%)
102
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 486
68.7%
Common 221
31.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 38
 
7.8%
구 37
 
7.6%
지 35
 
7.2%
남 21
 
4.3%
단 19
 
3.9%
산 15
 
3.1%
원 14
 
2.9%
일 13
 
2.7%
광 11
 
2.3%
북 10
 
2.1%
Other values (105) 273
56.2%
Common
ValueCountFrequency (%)
102
46.2%
) 28
 
12.7%
( 28
 
12.7%
, 24
 
10.9%
1 10
 
4.5%
5 6
 
2.7%
3 5
 
2.3%
2 3
 
1.4%
9 3
 
1.4%
8 2
 
0.9%
Other values (8) 10
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 486
68.7%
ASCII 220
31.1%
CJK Compat 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
102
46.4%
) 28
 
12.7%
( 28
 
12.7%
, 24
 
10.9%
1 10
 
4.5%
5 6
 
2.7%
3 5
 
2.3%
2 3
 
1.4%
9 3
 
1.4%
8 2
 
0.9%
Other values (7) 9
 
4.1%
Hangul
ValueCountFrequency (%)
동 38
 
7.8%
구 37
 
7.6%
지 35
 
7.2%
남 21
 
4.3%
단 19
 
3.9%
산 15
 
3.1%
원 14
 
2.9%
일 13
 
2.7%
광 11
 
2.3%
북 10
 
2.1%
Other values (105) 273
56.2%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

기정(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5273982 × 108
Minimum2.4361365 × 108
Maximum2.6766 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T18:12:41.223067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4361365 × 108
5-th percentile2.4393931 × 108
Q12.4664699 × 108
median2.4959683 × 108
Q32.5851176 × 108
95-th percentile2.6734993 × 108
Maximum2.6766 × 108
Range24046346
Interquartile range (IQR)11864769

Descriptive statistics

Standard deviation8181988.6
Coefficient of variation (CV)0.032373168
Kurtosis-0.788396
Mean2.5273982 × 108
Median Absolute Deviation (MAD)5303272
Skewness0.7198653
Sum7.5821946 × 109
Variance6.6944938 × 1013
MonotonicityNot monotonic
2024-03-14T18:12:41.624316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
267660000 1
 
3.3%
249145618 1
 
3.3%
243613654 1
 
3.3%
243671231 1
 
3.3%
244266961 1
 
3.3%
244382087 1
 
3.3%
244431078 1
 
3.3%
244449274 1
 
3.3%
245378879 1
 
3.3%
246525702 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
243613654 1
3.3%
243671231 1
3.3%
244266961 1
3.3%
244382087 1
3.3%
244431078 1
3.3%
244449274 1
3.3%
245378879 1
3.3%
246525702 1
3.3%
247010839 1
3.3%
247233184 1
3.3%
ValueCountFrequency (%)
267660000 1
3.3%
267650916 1
3.3%
266982066 1
3.3%
266978144 1
3.3%
266926410 1
3.3%
258883505 1
3.3%
258615505 1
3.3%
258613505 1
3.3%
258206505 1
3.3%
255428505 1
3.3%

증감(해제총량)(제곱미터)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802885.2
Minimum-2000
Maximum8042905
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.3%
Memory size398.0 B
2024-03-14T18:12:42.015671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2000
5-th percentile5663.05
Q149676.75
median166238.5
Q3650570
95-th percentile3918519.6
Maximum8042905
Range8044905
Interquartile range (IQR)600893.25

Descriptive statistics

Standard deviation1700373.5
Coefficient of variation (CV)2.1178289
Kurtosis12.077335
Mean802885.2
Median Absolute Deviation (MAD)156791
Skewness3.3610832
Sum24086556
Variance2.8912699 × 1012
MonotonicityNot monotonic
2024-03-14T18:12:42.455037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9084 1
 
3.3%
1755355 1
 
3.3%
40210 1
 
3.3%
57577 1
 
3.3%
595730 1
 
3.3%
115126 1
 
3.3%
48991 1
 
3.3%
18196 1
 
3.3%
929605 1
 
3.3%
1146823 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-2000 1
3.3%
3922 1
3.3%
7791 1
3.3%
9084 1
3.3%
9811 1
3.3%
18196 1
3.3%
40210 1
3.3%
48991 1
3.3%
51734 1
3.3%
57577 1
3.3%
ValueCountFrequency (%)
8042905 1
3.3%
4851672 1
3.3%
2778000 1
3.3%
1755355 1
3.3%
1146823 1
3.3%
929605 1
3.3%
694000 1
3.3%
668850 1
3.3%
595730 1
3.3%
485137 1
3.3%

변경(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5193693 × 108
Minimum2.4357344 × 108
Maximum2.6765092 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-14T18:12:42.860031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4357344 × 108
5-th percentile2.4363956 × 108
Q12.4566558 × 108
median2.4919772 × 108
Q32.5751200 × 108
95-th percentile2.669803 × 108
Maximum2.6765092 × 108
Range24077472
Interquartile range (IQR)11846420

Descriptive statistics

Standard deviation7842138.9
Coefficient of variation (CV)0.031127389
Kurtosis-0.478794
Mean2.5193693 × 108
Median Absolute Deviation (MAD)5228628.5
Skewness0.83440016
Sum7.558108 × 109
Variance6.1499142 × 1013
MonotonicityNot monotonic
2024-03-14T18:12:43.263136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
267650916 1
 
3.3%
247390263 1
 
3.3%
243573444 1
 
3.3%
243613654 1
 
3.3%
243671231 1
 
3.3%
244266961 1
 
3.3%
244382087 1
 
3.3%
244431078 1
 
3.3%
244449274 1
 
3.3%
245378879 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
243573444 1
3.3%
243613654 1
3.3%
243671231 1
3.3%
244266961 1
3.3%
244382087 1
3.3%
244431078 1
3.3%
244449274 1
3.3%
245378879 1
3.3%
246525702 1
3.3%
247010839 1
3.3%
ValueCountFrequency (%)
267650916 1
3.3%
266982066 1
3.3%
266978144 1
3.3%
266926410 1
3.3%
258883505 1
3.3%
258615505 1
3.3%
258613505 1
3.3%
258206505 1
3.3%
255428505 1
3.3%
255181505 1
3.3%

변경사유
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
2024-03-14T18:12:44.258109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length41
Mean length37.133333
Min length30

Characters and Unicode

Total characters1114
Distinct characters141
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row 개발제한구역우선해제경계선관통, 취락지역(2001. 5. 8)광고 제221호
2nd row서민층의 주거불안정을 긴급 해소,국책사업(2003. 3.13)건고 제52호
3rd row지구계분할결과 변경(2007.12.21)건고 제609호
4th row공공이주단지(2003.4.8)광고43호,남구(35,667㎡), 광산(16,067㎡)
5th row196개 1차집단취락지구 해제,(2003. 8.14) 광고 제109호
ValueCountFrequency (%)
건고 10
 
6.2%
광고 9
 
5.6%
소규모 4
 
2.5%
국토고 4
 
2.5%
7 3
 
1.9%
제 3
 
1.9%
지역 2
 
1.2%
6.24 2
 
1.2%
제459호 2
 
1.2%
제216호 2
 
1.2%
Other values (116) 120
74.5%
2024-03-14T18:12:45.531425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
134
 
12.0%
2 74
 
6.6%
. 70
 
6.3%
0 63
 
5.7%
1 58
 
5.2%
) 39
 
3.5%
( 39
 
3.5%
제 30
 
2.7%
호 30
 
2.7%
고 30
 
2.7%
Other values (131) 547
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 473
42.5%
Decimal Number 337
30.3%
Space Separator 134
 
12.0%
Other Punctuation 82
 
7.4%
Close Punctuation 39
 
3.5%
Open Punctuation 39
 
3.5%
Dash Punctuation 5
 
0.4%
Other Symbol 2
 
0.2%
Uppercase Letter 2
 
0.2%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 30
 
6.3%
호 30
 
6.3%
고 30
 
6.3%
지 29
 
6.1%
단 19
 
4.0%
광 18
 
3.8%
건 12
 
2.5%
구 12
 
2.5%
업 11
 
2.3%
주 10
 
2.1%
Other values (111) 272
57.5%
Decimal Number
ValueCountFrequency (%)
2 74
22.0%
0 63
18.7%
1 58
17.2%
9 24
 
7.1%
6 22
 
6.5%
4 21
 
6.2%
3 21
 
6.2%
7 20
 
5.9%
5 18
 
5.3%
8 16
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 70
85.4%
, 12
 
14.6%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 639
57.4%
Hangul 473
42.5%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 30
 
6.3%
호 30
 
6.3%
고 30
 
6.3%
지 29
 
6.1%
단 19
 
4.0%
광 18
 
3.8%
건 12
 
2.5%
구 12
 
2.5%
업 11
 
2.3%
주 10
 
2.1%
Other values (111) 272
57.5%
Common
ValueCountFrequency (%)
134
21.0%
2 74
11.6%
. 70
11.0%
0 63
9.9%
1 58
9.1%
) 39
 
6.1%
( 39
 
6.1%
9 24
 
3.8%
6 22
 
3.4%
4 21
 
3.3%
Other values (8) 95
14.9%
Latin
ValueCountFrequency (%)
G 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 639
57.4%
Hangul 473
42.5%
CJK Compat 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
21.0%
2 74
11.6%
. 70
11.0%
0 63
9.9%
1 58
9.1%
) 39
 
6.1%
( 39
 
6.1%
9 24
 
3.8%
6 22
 
3.4%
4 21
 
3.3%
Other values (9) 95
14.9%
Hangul
ValueCountFrequency (%)
제 30
 
6.3%
호 30
 
6.3%
고 30
 
6.3%
지 29
 
6.1%
단 19
 
4.0%
광 18
 
3.8%
건 12
 
2.5%
구 12
 
2.5%
업 11
 
2.3%
주 10
 
2.1%
Other values (111) 272
57.5%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%

Interactions

2024-03-14T18:12:37.250768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:34.254592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:35.248388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:36.221680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:37.496279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:34.500774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:35.491530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:36.480167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:37.722930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:34.741727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:35.723765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:36.729133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:37.885350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:35.005430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:35.986624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:12:36.999456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:12:45.795415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위치기정(제곱미터)증감(해제총량)(제곱미터)변경(제곱미터)변경사유
연번1.0001.0000.9290.6030.9271.000
위치1.0001.0001.0001.0001.0001.000
기정(제곱미터)0.9291.0001.0000.3980.9751.000
증감(해제총량)(제곱미터)0.6031.0000.3981.0000.5321.000
변경(제곱미터)0.9271.0000.9750.5321.0001.000
변경사유1.0001.0001.0001.0001.0001.000
2024-03-14T18:12:46.070907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번기정(제곱미터)증감(해제총량)(제곱미터)변경(제곱미터)
연번1.000-1.000-0.038-1.000
기정(제곱미터)-1.0001.0000.0460.999
증감(해제총량)(제곱미터)-0.0380.0461.0000.030
변경(제곱미터)-1.0000.9990.0301.000

Missing values

2024-03-14T18:12:38.069259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:12:38.253217image/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

연번위치기정(제곱미터)증감(해제총량)(제곱미터)변경(제곱미터)변경사유
01동구 운림동외3개소2676600009084267650916개발제한구역우선해제경계선관통, 취락지역(2001. 5. 8)광고 제221호
12남구 진월동, 노대동, 행암동(진월택지개발지구)267650916668850266982066서민층의 주거불안정을 긴급 해소,국책사업(2003. 3.13)건고 제52호
23남구 진월택지개발지구2669820663922266978144지구계분할결과 변경(2007.12.21)건고 제609호
34남구 향등동 및 광산 신야촌 이주단지26697814451734266926410공공이주단지(2003.4.8)광고43호,남구(35,667㎡), 광산(16,067㎡)
45동구 내남동 남계마을외 195개 집단취락지구2669264108042905258883505196개 1차집단취락지구 해제,(2003. 8.14) 광고 제109호
56효천2국민임대주택단지 (남구노대동,행암,송하일원)258883505270000258613505광주효천2 국민임대주택단지,(2005. 3.19) 건고 제59호
67효천2국민임대주택단지 (남구노대동,일원)258613505-2000258615505효천2 단지 구적오차 정정(증). (2005.12.29) 건고 제472호
78남구 노대동 565일원 (노인건강문화타운조성사업)258615505409000258206505부족한 노인복지시설을 확보하기위함.(2005. 7. 1) 건고 제187호
89광산구 운수동 산170번지 일원 (빛과 예술의 테마파크)2582065052778000255428505유원지개발, 지역 현안사업,(2005. 7. 1) 건고 제 188호
910동구 월남동 158번지일원 (임대주택지개발)255428505247000255181505임대주택단지개발 지역 현안사업. (2005.12.27) 건고 제459호
연번위치기정(제곱미터)증감(해제총량)(제곱미터)변경(제곱미터)변경사유
2021경계선 관통대지(동,서,남,북,광산 859필지)247233184222345247010839경계선관통대지 정비(2014. 6.24) 광고 제89~128호
2122도시첨단산업단지 (남구 압촌, 지석)(국가)247010839485137246525702도시첨단산업단지 (2015. 12.29) 국토고 2015-974호
2223평동3차 산업단지 조성(광산구 연산동 일원)2465257021146823245378879평동3차산업단지 조성(2016. 2.19) 국토고 제2016-49호
2324도시첨단산업단지(지방)(남구 석정, 압촌, 지석, 칠석)245378879929605244449274남구 에너지밸리 일반산업단지 조성(2017. 6.21) 국토고 2017-409호
2425소규모 단절토지(북구 각화,문흥. 남구 노대)24444927418196244431078소규모 단절토지(재정비)(2018.9.1) 광고 제175호
2526소규모 단절토지(동,남,북,광산 15필지)24443107848991244382087소규모 단절토지(재정비)(2019.12.27) 광고 제347호
2627광주 생태문화마을(북구 충효동)244382087115126244266961광주 생태문화마을 조성사업(2020.6.15) 광고 제208호
2728광주 첨단3지구(광주 북구,광산구/전남 장성군)244266961595730243671231광주연구개발특구 첨단3지구 개발사업(2020.7.3) 국고 제484호
2829단절토지(1만~3만㎡, 남구양과동,북구수곡동,광산구도덕동)24367123157577243613654GB 이용불편 해소, 광고2022-304호(2022.11.23)
2930운전면허시험장(북구 삼각동)24361365440210243573444운전면허시험장 조성사업(도로교통공단), 광고2023-36호(2023.2.9)