Overview

Dataset statistics

Number of variables16
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory144.0 B

Variable types

Categorical1
Numeric13
Text2

Dataset

Description도시재생 대상 지역의 인구감소, 사업체 수 감소, 생활환경 악화와 관련된 5개 법정 지표 기준을 설정하고 분석 인자별 도시쇠퇴 지표를 산출도시쇠퇴 예상지역을 도시쇠퇴지수 변화율 감소 지역을 대상으로 추출하고예상지역 결과가 2개 이상 중첩되는 후보 읍면동을 추출하여해당 지역의 유동인구와 카드 매출 변화율 하위 밀도 지역을 도시재생 후보 지역으로 도출
Author국토교통부
URLhttps://www.data.go.kr/data/15123180/fileData.do

Alerts

구분 has constant value ""Constant
행정동코드 is highly overall correlated with 인구변화율17년도High correlation
인구변화율17년도 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
인구변화율18년도 is highly overall correlated with 인구변화율19년도 and 1 other fieldsHigh correlation
인구변화율19년도 is highly overall correlated with 인구변화율18년도 and 1 other fieldsHigh correlation
사업체수변화율17년도 is highly overall correlated with 사업체수변화율종합 and 1 other fieldsHigh correlation
사업체수변화율18년도 is highly overall correlated with 사업체수변화율종합High correlation
노후건물변화율17년도 is highly overall correlated with 노후건물변화율18년도 and 3 other fieldsHigh correlation
노후건물변화율18년도 is highly overall correlated with 노후건물변화율17년도 and 3 other fieldsHigh correlation
노후건물변화율19년도 is highly overall correlated with 노후건물변화율17년도 and 3 other fieldsHigh correlation
인구변화율종합 is highly overall correlated with 인구변화율17년도 and 3 other fieldsHigh correlation
사업체수변화율종합 is highly overall correlated with 사업체수변화율17년도 and 2 other fieldsHigh correlation
노후건물평균 is highly overall correlated with 노후건물변화율17년도 and 3 other fieldsHigh correlation
변화율종합 is highly overall correlated with 인구변화율17년도 and 7 other fieldsHigh correlation
행정동코드 has unique valuesUnique
행정동명 has unique valuesUnique
인구변화율17년도 has unique valuesUnique
인구변화율18년도 has unique valuesUnique
인구변화율19년도 has unique valuesUnique
인구변화율종합 has unique valuesUnique
공간정보 has unique valuesUnique
사업체수변화율17년도 has 2 (4.5%) zerosZeros
사업체수변화율18년도 has 3 (6.8%) zerosZeros
노후건물변화율17년도 has 14 (31.8%) zerosZeros
노후건물변화율18년도 has 13 (29.5%) zerosZeros
노후건물변화율19년도 has 13 (29.5%) zerosZeros
사업체수변화율종합 has 2 (4.5%) zerosZeros
노후건물평균 has 12 (27.3%) zerosZeros
변화율종합 has 4 (9.1%) zerosZeros

Reproduction

Analysis started2023-12-12 17:41:09.873067
Analysis finished2023-12-12 17:41:25.185695
Duration15.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
종합(인구, 사업체수, 건물노후)
44 

Length

Max length18
Median length18
Mean length18
Min length18

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종합(인구, 사업체수, 건물노후)
2nd row종합(인구, 사업체수, 건물노후)
3rd row종합(인구, 사업체수, 건물노후)
4th row종합(인구, 사업체수, 건물노후)
5th row종합(인구, 사업체수, 건물노후)

Common Values

ValueCountFrequency (%)
종합(인구, 사업체수, 건물노후) 44
100.0%

Length

2023-12-13T02:41:25.237693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:41:25.356995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종합(인구 44
33.3%
사업체수 44
33.3%
건물노후 44
33.3%

행정동코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3101314.8
Minimum3101154
Maximum3101468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T02:41:25.836589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3101154
5-th percentile3101156.1
Q13101252.8
median3101309.5
Q33101451.2
95-th percentile3101465.9
Maximum3101468
Range314
Interquartile range (IQR)198.5

Descriptive statistics

Standard deviation114.27239
Coefficient of variation (CV)3.6846435 × 10-5
Kurtosis-1.3994862
Mean3101314.8
Median Absolute Deviation (MAD)102
Skewness-0.030338423
Sum1.3645785 × 108
Variance13058.18
MonotonicityNot monotonic
2023-12-13T02:41:25.998458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3101451 1
 
2.3%
3101253 1
 
2.3%
3101255 1
 
2.3%
3101256 1
 
2.3%
3101257 1
 
2.3%
3101260 1
 
2.3%
3101261 1
 
2.3%
3101262 1
 
2.3%
3101264 1
 
2.3%
3101265 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
3101154 1
2.3%
3101155 1
2.3%
3101156 1
2.3%
3101157 1
2.3%
3101158 1
2.3%
3101159 1
2.3%
3101160 1
2.3%
3101161 1
2.3%
3101162 1
2.3%
3101163 1
2.3%
ValueCountFrequency (%)
3101468 1
2.3%
3101467 1
2.3%
3101466 1
2.3%
3101465 1
2.3%
3101464 1
2.3%
3101463 1
2.3%
3101462 1
2.3%
3101460 1
2.3%
3101454 1
2.3%
3101453 1
2.3%

행정동명
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T02:41:26.275504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.5454545
Min length2

Characters and Unicode

Total characters156
Distinct characters51
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

Unique44 ?
Unique (%)100.0%

Sample

1st row매탄1동
2nd row매탄2동
3rd row매탄3동
4th row매탄4동
5th row원천동
ValueCountFrequency (%)
매탄1동 1
 
2.3%
매탄2동 1
 
2.3%
금곡동 1
 
2.3%
세류3동 1
 
2.3%
평동 1
 
2.3%
서둔동 1
 
2.3%
구운동 1
 
2.3%
권선1동 1
 
2.3%
곡선동 1
 
2.3%
입북동 1
 
2.3%
Other values (34) 34
77.3%
2023-12-13T02:41:26.664949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
28.2%
1 10
 
6.4%
2 10
 
6.4%
7
 
4.5%
3 4
 
2.6%
4
 
2.6%
4
 
2.6%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (41) 64
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 131
84.0%
Decimal Number 25
 
16.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
44
33.6%
7
 
5.3%
4
 
3.1%
4
 
3.1%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
Other values (37) 54
41.2%
Decimal Number
ValueCountFrequency (%)
1 10
40.0%
2 10
40.0%
3 4
 
16.0%
4 1
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 131
84.0%
Common 25
 
16.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
44
33.6%
7
 
5.3%
4
 
3.1%
4
 
3.1%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
Other values (37) 54
41.2%
Common
ValueCountFrequency (%)
1 10
40.0%
2 10
40.0%
3 4
 
16.0%
4 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 131
84.0%
ASCII 25
 
16.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
44
33.6%
7
 
5.3%
4
 
3.1%
4
 
3.1%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
Other values (37) 54
41.2%
ASCII
ValueCountFrequency (%)
1 10
40.0%
2 10
40.0%
3 4
 
16.0%
4 1
 
4.0%

인구변화율17년도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31.264605
Minimum-368.45639
Maximum491.41791
Zeros0
Zeros (%)0.0%
Negative28
Negative (%)63.6%
Memory size528.0 B
2023-12-13T02:41:26.809305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-368.45639
5-th percentile-331.20355
Q1-202.80172
median-55.213201
Q3149.05699
95-th percentile333.32184
Maximum491.41791
Range859.87429
Interquartile range (IQR)351.85871

Descriptive statistics

Standard deviation228.1275
Coefficient of variation (CV)-7.2966697
Kurtosis-0.63995827
Mean-31.264605
Median Absolute Deviation (MAD)156.29477
Skewness0.5335199
Sum-1375.6426
Variance52042.155
MonotonicityNot monotonic
2023-12-13T02:41:26.944505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
9.865265018626816 1
 
2.3%
-125.8073536915726 1
 
2.3%
-309.1966340510044 1
 
2.3%
-368.456387487382 1
 
2.3%
-281.7476926022282 1
 
2.3%
-108.2211488649518 1
 
2.3%
152.8718644287255 1
 
2.3%
31.783844733347 1
 
2.3%
215.3816964156867 1
 
2.3%
-41.2517170850042 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
-368.456387487382 1
2.3%
-338.6458156496374 1
2.3%
-331.4340609082174 1
2.3%
-329.89728996952 1
2.3%
-309.1966340510044 1
2.3%
-282.5530051593778 1
2.3%
-281.7476926022282 1
2.3%
-274.8245575775072 1
2.3%
-267.1453865473304 1
2.3%
-218.3254829487814 1
2.3%
ValueCountFrequency (%)
491.4179063005868 1
2.3%
426.2943736135421 1
2.3%
336.1527680665313 1
2.3%
317.2799428578874 1
2.3%
308.1507295215379 1
2.3%
271.9057720837591 1
2.3%
261.6542763668804 1
2.3%
251.9902678887447 1
2.3%
237.4977956838775 1
2.3%
215.3816964156867 1
2.3%

인구변화율18년도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-77.522031
Minimum-4449.2662
Maximum10347.885
Zeros0
Zeros (%)0.0%
Negative33
Negative (%)75.0%
Memory size528.0 B
2023-12-13T02:41:27.073802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4449.2662
5-th percentile-2787.7521
Q1-1306.8068
median-928.85716
Q3-109.3726
95-th percentile5668.5967
Maximum10347.885
Range14797.151
Interquartile range (IQR)1197.4342

Descriptive statistics

Standard deviation2802.0248
Coefficient of variation (CV)-36.144884
Kurtosis4.3975638
Mean-77.522031
Median Absolute Deviation (MAD)516.92341
Skewness2.0083645
Sum-3410.9694
Variance7851343.1
MonotonicityNot monotonic
2023-12-13T02:41:27.212000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
-872.3296221644887 1
 
2.3%
-1958.68208404789 1
 
2.3%
5699.906796737389 1
 
2.3%
-1858.99120764832 1
 
2.3%
-1469.956866011733 1
 
2.3%
-329.0453758051808 1
 
2.3%
-1276.852793937949 1
 
2.3%
-631.2386651263314 1
 
2.3%
-1360.115081512638 1
 
2.3%
6955.080511586988 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
-4449.266216915581 1
2.3%
-3134.770866567509 1
2.3%
-2886.988404055963 1
2.3%
-2225.413281163404 1
2.3%
-1958.68208404789 1
2.3%
-1858.99120764832 1
2.3%
-1697.976111283628 1
2.3%
-1623.360510268983 1
2.3%
-1469.956866011733 1
2.3%
-1360.115081512638 1
2.3%
ValueCountFrequency (%)
10347.8847512651 1
2.3%
6955.080511586988 1
2.3%
5699.906796737389 1
2.3%
5491.172545829933 1
2.3%
4225.241737683044 1
2.3%
3979.789122261631 1
2.3%
1887.314281911738 1
2.3%
999.9790281130736 1
2.3%
309.742245721638 1
2.3%
284.6108183685428 1
2.3%

인구변화율19년도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.72666
Minimum-8404.4009
Maximum2163.3326
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)13.6%
Memory size528.0 B
2023-12-13T02:41:27.352802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8404.4009
5-th percentile-3777.1135
Q1297.53878
median549.88916
Q3779.79162
95-th percentile1675.814
Maximum2163.3326
Range10567.733
Interquartile range (IQR)482.25284

Descriptive statistics

Standard deviation1830.4794
Coefficient of variation (CV)15.162181
Kurtosis11.511443
Mean120.72666
Median Absolute Deviation (MAD)241.09593
Skewness-3.1543832
Sum5311.973
Variance3350654.9
MonotonicityNot monotonic
2023-12-13T02:41:27.477730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
553.021625151925 1
 
2.3%
1437.846163474933 1
 
2.3%
-4614.350215978426 1
 
2.3%
1048.381298749628 1
 
2.3%
683.5242497551226 1
 
2.3%
121.7379222009549 1
 
2.3%
655.7648452771318 1
 
2.3%
270.1257459883145 1
 
2.3%
1569.776795426646 1
 
2.3%
-139.5051791581864 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
-8404.40089556783 1
2.3%
-4614.350215978426 1
2.3%
-4068.041116939345 1
2.3%
-2128.523603289927 1
2.3%
-2110.864317637763 1
2.3%
-139.5051791581864 1
2.3%
99.79633834854212 1
2.3%
121.7379222009549 1
2.3%
199.6895846951429 1
2.3%
270.1257459883145 1
2.3%
ValueCountFrequency (%)
2163.332557104779 1
2.3%
1797.170926879844 1
2.3%
1694.526452841531 1
2.3%
1569.776795426646 1
2.3%
1437.846163474933 1
2.3%
1048.381298749628 1
2.3%
962.4645175608456 1
2.3%
949.8952193599508 1
2.3%
949.591138109212 1
2.3%
837.0838279005657 1
2.3%

사업체수변화율17년도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.90909
Minimum-49
Maximum3293
Zeros2
Zeros (%)4.5%
Negative14
Negative (%)31.8%
Memory size528.0 B
2023-12-13T02:41:27.601990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-49
5-th percentile-40.7
Q1-13.25
median18.5
Q370.5
95-th percentile743.45
Maximum3293
Range3342
Interquartile range (IQR)83.75

Descriptive statistics

Standard deviation571.06684
Coefficient of variation (CV)3.3413486
Kurtosis22.752083
Mean170.90909
Median Absolute Deviation (MAD)35.5
Skewness4.622384
Sum7520
Variance326117.34
MonotonicityNot monotonic
2023-12-13T02:41:27.747727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
7 4
 
9.1%
0 2
 
4.5%
-15 2
 
4.5%
-14 2
 
4.5%
-47 1
 
2.3%
31 1
 
2.3%
83 1
 
2.3%
63 1
 
2.3%
9 1
 
2.3%
127 1
 
2.3%
Other values (28) 28
63.6%
ValueCountFrequency (%)
-49 1
2.3%
-47 1
2.3%
-41 1
2.3%
-39 1
2.3%
-24 1
2.3%
-23 1
2.3%
-19 1
2.3%
-15 2
4.5%
-14 2
4.5%
-13 1
2.3%
ValueCountFrequency (%)
3293 1
2.3%
1858 1
2.3%
797 1
2.3%
440 1
2.3%
245 1
2.3%
209 1
2.3%
127 1
2.3%
91 1
2.3%
85 1
2.3%
83 1
2.3%

사업체수변화율18년도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.204545
Minimum-147
Maximum447
Zeros3
Zeros (%)6.8%
Negative14
Negative (%)31.8%
Memory size528.0 B
2023-12-13T02:41:27.886043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-147
5-th percentile-75.45
Q1-6.25
median10
Q341.5
95-th percentile269.15
Maximum447
Range594
Interquartile range (IQR)47.75

Descriptive statistics

Standard deviation108.54097
Coefficient of variation (CV)3.173291
Kurtosis4.8382286
Mean34.204545
Median Absolute Deviation (MAD)23
Skewness1.9145006
Sum1505
Variance11781.143
MonotonicityNot monotonic
2023-12-13T02:41:27.998971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 3
 
6.8%
53 3
 
6.8%
25 3
 
6.8%
18 2
 
4.5%
4 2
 
4.5%
-147 1
 
2.3%
15 1
 
2.3%
9 1
 
2.3%
253 1
 
2.3%
272 1
 
2.3%
Other values (26) 26
59.1%
ValueCountFrequency (%)
-147 1
2.3%
-136 1
2.3%
-78 1
2.3%
-61 1
2.3%
-57 1
2.3%
-53 1
2.3%
-33 1
2.3%
-21 1
2.3%
-16 1
2.3%
-11 1
2.3%
ValueCountFrequency (%)
447 1
 
2.3%
284 1
 
2.3%
272 1
 
2.3%
253 1
 
2.3%
212 1
 
2.3%
126 1
 
2.3%
55 1
 
2.3%
53 3
6.8%
52 1
 
2.3%
38 1
 
2.3%

노후건물변화율17년도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.19796
Minimum0
Maximum100
Zeros14
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T02:41:28.125745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.888889
Q333.333333
95-th percentile62.125
Maximum100
Range100
Interquartile range (IQR)33.333333

Descriptive statistics

Standard deviation24.872279
Coefficient of variation (CV)1.1204759
Kurtosis0.96316333
Mean22.19796
Median Absolute Deviation (MAD)13.888889
Skewness1.173002
Sum976.71022
Variance618.63028
MonotonicityNot monotonic
2023-12-13T02:41:28.247282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 14
31.8%
33.33333333333333 3
 
6.8%
25.0 2
 
4.5%
11.11111111111111 2
 
4.5%
20.0 2
 
4.5%
50.0 2
 
4.5%
2.380952380952381 1
 
2.3%
57.14285714285714 1
 
2.3%
100.0 1
 
2.3%
28.57142857142857 1
 
2.3%
Other values (15) 15
34.1%
ValueCountFrequency (%)
0.0 14
31.8%
2.380952380952381 1
 
2.3%
2.912621359223301 1
 
2.3%
6.25 1
 
2.3%
7.692307692307693 1
 
2.3%
8.333333333333332 1
 
2.3%
9.090909090909092 1
 
2.3%
11.11111111111111 2
 
4.5%
16.66666666666666 1
 
2.3%
17.64705882352941 1
 
2.3%
ValueCountFrequency (%)
100.0 1
 
2.3%
78.04878048780488 1
 
2.3%
62.5 1
 
2.3%
60.0 1
 
2.3%
58.62068965517241 1
 
2.3%
57.14285714285714 1
 
2.3%
50.0 2
4.5%
46.15384615384615 1
 
2.3%
40.0 1
 
2.3%
33.33333333333333 3
6.8%

노후건물변화율18년도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.446956
Minimum0
Maximum100
Zeros13
Zeros (%)29.5%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T02:41:28.349358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18.333333
Q340
95-th percentile62.125
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation25.37179
Coefficient of variation (CV)1.0820931
Kurtosis0.52350816
Mean23.446956
Median Absolute Deviation (MAD)18.333333
Skewness1.0211805
Sum1031.6661
Variance643.72775
MonotonicityNot monotonic
2023-12-13T02:41:28.454565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 13
29.5%
20.0 3
 
6.8%
50.0 3
 
6.8%
33.33333333333333 3
 
6.8%
40.0 2
 
4.5%
25.0 1
 
2.3%
2.325581395348837 1
 
2.3%
57.14285714285714 1
 
2.3%
16.66666666666666 1
 
2.3%
100.0 1
 
2.3%
Other values (15) 15
34.1%
ValueCountFrequency (%)
0.0 13
29.5%
2.325581395348837 1
 
2.3%
2.912621359223301 1
 
2.3%
3.03030303030303 1
 
2.3%
5.263157894736842 1
 
2.3%
6.666666666666667 1
 
2.3%
7.692307692307693 1
 
2.3%
8.333333333333332 1
 
2.3%
11.11111111111111 1
 
2.3%
16.66666666666666 1
 
2.3%
ValueCountFrequency (%)
100.0 1
 
2.3%
79.48717948717949 1
 
2.3%
62.5 1
 
2.3%
60.0 1
 
2.3%
59.25925925925925 1
 
2.3%
57.14285714285714 1
 
2.3%
50.0 3
6.8%
46.66666666666666 1
 
2.3%
40.0 2
4.5%
33.33333333333333 3
6.8%

노후건물변화율19년도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.626127
Minimum0
Maximum100
Zeros13
Zeros (%)29.5%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T02:41:28.555229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18.333333
Q340
95-th percentile74.0625
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation26.833187
Coefficient of variation (CV)1.0896227
Kurtosis0.079776369
Mean24.626127
Median Absolute Deviation (MAD)18.333333
Skewness0.95904306
Sum1083.5496
Variance720.01992
MonotonicityNot monotonic
2023-12-13T02:41:28.658928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 13
29.5%
20.0 3
 
6.8%
33.33333333333333 3
 
6.8%
40.0 2
 
4.5%
50.0 2
 
4.5%
25.0 1
 
2.3%
2.912621359223301 1
 
2.3%
57.14285714285714 1
 
2.3%
16.66666666666666 1
 
2.3%
100.0 1
 
2.3%
Other values (16) 16
36.4%
ValueCountFrequency (%)
0.0 13
29.5%
2.325581395348837 1
 
2.3%
2.912621359223301 1
 
2.3%
3.03030303030303 1
 
2.3%
5.263157894736842 1
 
2.3%
6.666666666666667 1
 
2.3%
7.692307692307693 1
 
2.3%
8.333333333333332 1
 
2.3%
11.11111111111111 1
 
2.3%
16.66666666666666 1
 
2.3%
ValueCountFrequency (%)
100.0 1
2.3%
79.48717948717949 1
2.3%
75.0 1
2.3%
68.75 1
2.3%
62.96296296296296 1
2.3%
60.0 1
2.3%
57.14285714285714 1
2.3%
53.33333333333334 1
2.3%
50.0 2
4.5%
40.0 2
4.5%

인구변화율종합
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.940023
Minimum-3500.6842
Maximum6774.3236
Zeros0
Zeros (%)0.0%
Negative30
Negative (%)68.2%
Memory size528.0 B
2023-12-13T02:41:28.768464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3500.6842
5-th percentile-1162.4334
Q1-743.73508
median-409.37228
Q3228.72384
95-th percentile3553.8435
Maximum6774.3236
Range10275.008
Interquartile range (IQR)972.45892

Descriptive statistics

Standard deviation1653.9825
Coefficient of variation (CV)138.52424
Kurtosis7.1109976
Mean11.940023
Median Absolute Deviation (MAD)424.95962
Skewness2.1944046
Sum525.361
Variance2735658.2
MonotonicityNot monotonic
2023-12-13T02:41:28.878719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
-309.4427319939368 1
 
2.3%
-646.6432742645302 1
 
2.3%
776.3599467079584 1
 
2.3%
-1179.066296386074 1
 
2.3%
-1068.180308858839 1
 
2.3%
-315.5286024691777 1
 
2.3%
-468.2160842320918 1
 
2.3%
-329.3290744046699 1
 
2.3%
425.0434103296939 1
 
2.3%
6774.323615343797 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
-3500.684246512519 1
2.3%
-1607.027464095043 1
2.3%
-1179.066296386074 1
2.3%
-1068.180308858839 1
2.3%
-1065.408883692302 1
2.3%
-1062.285809321354 1
2.3%
-948.3797586649072 1
2.3%
-861.680345278437 1
2.3%
-810.5939550535732 1
2.3%
-806.6790251000748 1
2.3%
ValueCountFrequency (%)
6774.323615343797 1
2.3%
4469.717241459135 1
2.3%
3854.066848840594 1
2.3%
1852.577544775766 1
2.3%
1342.698440811644 1
2.3%
1196.251023333507 1
2.3%
937.8997691839686 1
2.3%
776.3599467079584 1
2.3%
766.8762632336111 1
2.3%
425.0434103296939 1
2.3%

사업체수변화율종합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.11364
Minimum-150
Maximum3236
Zeros2
Zeros (%)4.5%
Negative14
Negative (%)31.8%
Memory size528.0 B
2023-12-13T02:41:28.985329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-150
5-th percentile-56.55
Q1-11
median21.5
Q3122
95-th percentile874.1
Maximum3236
Range3386
Interquartile range (IQR)133

Descriptive statistics

Standard deviation579.06389
Coefficient of variation (CV)2.823137
Kurtosis18.894731
Mean205.11364
Median Absolute Deviation (MAD)54
Skewness4.1336766
Sum9025
Variance335314.99
MonotonicityNot monotonic
2023-12-13T02:41:29.102232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-11 2
 
4.5%
59 2
 
4.5%
0 2
 
4.5%
9 2
 
4.5%
225 1
 
2.3%
84 1
 
2.3%
-64 1
 
2.3%
78 1
 
2.3%
18 1
 
2.3%
152 1
 
2.3%
Other values (30) 30
68.2%
ValueCountFrequency (%)
-150 1
2.3%
-64 1
2.3%
-57 1
2.3%
-54 1
2.3%
-35 1
2.3%
-34 1
2.3%
-31 1
2.3%
-30 1
2.3%
-26 1
2.3%
-17 1
2.3%
ValueCountFrequency (%)
3236 1
2.3%
1896 1
2.3%
887 1
2.3%
801 1
2.3%
498 1
2.3%
493 1
2.3%
250 1
2.3%
225 1
2.3%
169 1
2.3%
152 1
2.3%

노후건물평균
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.423681
Minimum0
Maximum100
Zeros12
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T02:41:29.211015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q340
95-th percentile63.937979
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation25.367287
Coefficient of variation (CV)1.0829761
Kurtosis0.5348696
Mean23.423681
Median Absolute Deviation (MAD)15
Skewness1.0338898
Sum1030.642
Variance643.49925
MonotonicityNot monotonic
2023-12-13T02:41:29.323371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 12
27.3%
50.0 2
 
4.5%
33.33333333333333 2
 
4.5%
40.0 2
 
4.5%
11.11111111111111 2
 
4.5%
7.474747474747475 1
 
2.3%
2.912621359223301 1
 
2.3%
2.344038390550018 1
 
2.3%
7.692307692307693 1
 
2.3%
8.333333333333332 1
 
2.3%
Other values (19) 19
43.2%
ValueCountFrequency (%)
0.0 12
27.3%
2.02020202020202 1
 
2.3%
2.344038390550018 1
 
2.3%
2.912621359223301 1
 
2.3%
3.703703703703704 1
 
2.3%
5.592105263157895 1
 
2.3%
7.474747474747475 1
 
2.3%
7.692307692307693 1
 
2.3%
8.333333333333332 1
 
2.3%
11.11111111111111 2
 
4.5%
ValueCountFrequency (%)
100.0 1
2.3%
79.00771315405461 1
2.3%
64.58333333333333 1
2.3%
60.2809706257982 1
2.3%
60.0 1
2.3%
57.14285714285714 1
2.3%
50.0 2
4.5%
48.71794871794872 1
2.3%
43.33333333333334 1
2.3%
40.0 2
4.5%

변화율종합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6136364
Minimum0
Maximum6
Zeros4
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T02:41:29.420210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.6170014
Coefficient of variation (CV)0.61867879
Kurtosis-0.56296412
Mean2.6136364
Median Absolute Deviation (MAD)1
Skewness0.32186722
Sum115
Variance2.6146934
MonotonicityNot monotonic
2023-12-13T02:41:29.509301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 12
27.3%
3 9
20.5%
1 7
15.9%
4 5
11.4%
5 5
11.4%
0 4
 
9.1%
6 2
 
4.5%
ValueCountFrequency (%)
0 4
 
9.1%
1 7
15.9%
2 12
27.3%
3 9
20.5%
4 5
11.4%
5 5
11.4%
6 2
 
4.5%
ValueCountFrequency (%)
6 2
 
4.5%
5 5
11.4%
4 5
11.4%
3 9
20.5%
2 12
27.3%
1 7
15.9%
0 4
 
9.1%

공간정보
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T02:41:29.697467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length1024
Mean length1024
Min length1024

Characters and Unicode

Total characters45056
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st rowMULTIPOLYGON (((127.042009377406 37.2752132021241,127.041655744911 37.2753127791338,127.041290796847 37.2754164213666,127.041210246757 37.2754390525052,127.040856614262 37.2755684271807,127.040778746382 37.275511262601,127.040773130507 37.2755024616027,127.040766844079 37.2754927805045,127.04052871421 37.2751911996283,127.040358477757 37.2751651319094,127.039971736744 37.2751340769581,127.039939131141 37.2751370106242,127.039851875529 37.2751434646897,127.03968557857 37.2750510122977,127.039569489211 37.2749616612098,127.03937041901 37.2748072246438,127.03930462107 37.2748048777109,127.03923102796 37.2748016925877,127.039055678546 37.2747959090745,127.038926178142 37.2747887425473,127.038859542012 37.2747850964194,127.038681007474 37.2747830009436,127.038621076866 37.2747762535116,127.038414295315 37.274753706192,127.038254200964 37.2748021955019,127.037969300075 37.2749218890792,127.037981370016 37.2749381080619,127.037908028363 37.2749944344512,127.03785027705 37.2750096476055,127.037820940389 37.2749842923
2nd rowMULTIPOLYGON (((127.048709954623 37.2733881265267,127.048706182767 37.2733887970789,127.04850853749 37.2734136075123,127.048099835891 37.2735113824129,127.048016771231 37.2735333849087,127.048015011031 37.2735338878229,127.047986261103 37.2735212311491,127.047764224488 37.2734240848913,127.046828133542 37.2730143355546,127.0466707214 37.2729454363105,127.046652365032 37.2729373477739,127.046107206049 37.2726987149905,127.04567000598 37.272507356141,127.044913539218 37.2721762709656,127.04490557641 37.2721727086567,127.044902223649 37.2721712418237,127.044894260841 37.2721677214243,127.044511794599 37.2720003348179,127.044487989994 37.271989941258,127.043353667037 37.2714933973139,127.043179239632 37.2711973065842,127.043167672606 37.2711775672022,127.043159290703 37.2711633179668,127.043070442529 37.2710125275287,127.042909090893 37.2707387326615,127.042795432286 37.270490670237,127.043864879312 37.2701887959941,127.043864879312 37.2701887540846,127.043877787443 37.2701851917758,127.043955571504 37.2701631473
3rd rowMULTIPOLYGON (((127.058939732173 37.2663097350236,127.058909389683 37.2663270855631,127.058876029709 37.2663422987174,127.058661033892 37.2664513053682,127.058563803816 37.2664103178617,127.058411420816 37.2663461543929,127.0583954952 37.2663393650513,127.058487696135 37.266301562668,127.058362302863 37.2663126686897,127.058343443581 37.2663172368269,127.05814965398 37.2662319090526,127.05814529539 37.2662182046409,127.058127609574 37.2662073081668,127.0580682657 37.2661408396746,127.058075390318 37.2661339246045,127.058056698673 37.2661112515564,127.058032810249 37.2660962898592,127.05809592598 37.2660317911143,127.058093662866 37.2660263847867,127.057885037296 37.2659001533249,127.057724356213 37.2658038871669,127.057649841093 37.265747267411,127.057612122529 37.2657254325532,127.057571218842 37.2657325990804,127.057569039547 37.2657341497325,127.057121110641 37.2655761927672,127.057115662404 37.265565212474,127.057119937175 37.265521584668,127.057102670454 37.2655302599378,127.057085152276 37.2655437548019
4th rowMULTIPOLYGON (((127.059980429271 37.2656301722236,127.05996844315 37.2656122349508,127.059952266077 37.2656191081114,127.059801643277 37.2655145019598,127.059784544194 37.2655023482002,127.059647164801 37.2654194930873,127.059563848683 37.2653699560395,127.059472821215 37.2653159765831,127.059434599736 37.2652878133884,127.059304512599 37.265187775374,127.059221615577 37.2650773018902,127.05915464417 37.2649878669833,127.059147184277 37.2649728633766,127.059104939485 37.2648816263605,127.059007877046 37.2647457557101,127.058955071056 37.2646503277424,127.05893998363 37.2646220388192,127.058926488766 37.2645974798429,127.058929925346 37.2645691071006,127.059000920066 37.264412910335,127.059012906188 37.2642884809823,127.058978624204 37.2641923405529,127.058937217602 37.2640791429505,127.058931769365 37.2640625048727,127.058918274501 37.2640914643482,127.058858427712 37.2640335873068,127.058814338901 37.2639441104904,127.058764047482 37.2638436533808,127.058720880681 37.2637904702052,127.058671511271 37.2637292
5th rowMULTIPOLYGON (((127.048459670995 37.2854620065938,127.047664898936 37.2856041636717,127.047314116288 37.285880892205,127.046882364455 37.2864127239615,127.046647084433 37.2867553761633,127.046647084433 37.2867561305346,127.046646581519 37.2867561724441,127.046419348124 37.2870870061624,127.045469594675 37.2870842820439,127.045002890306 37.2863105066523,127.044961148428 37.286241272132,127.044944971355 37.2862145338609,127.04481379457 37.2861239673971,127.044546998592 37.2856794331622,127.044483547585 37.2855736954536,127.044438369127 37.2855620446082,127.044254805448 37.2855143096696,127.044195964487 37.2854913013454,127.044024219291 37.2854237432058,127.043727332281 37.2853071090231,127.043271943481 37.2853230765487,127.043225926833 37.2853246691103,127.042869109215 37.2850331046083,127.042853435056 37.284893671649,127.042706332655 37.2847084315888,127.04262712367 37.2846597746409,127.042380695717 37.2847494610049,127.042164694072 37.2845643047637,127.04201340072 37.2843665756678,127.041751130969 37.28407295
ValueCountFrequency (%)
multipolygon 44
 
3.1%
37.2538882993413,127.039426074847 1
 
0.1%
37.2546976978214,127.038584699407 1
 
0.1%
37.2546870947139,127.038617221191 1
 
0.1%
37.254674102764,127.03864848569 1
 
0.1%
37.2546606917189,127.038678325265 1
 
0.1%
37.254577752787,127.038707829564 1
 
0.1%
37.2545127092183,127.038882256969 1
 
0.1%
37.2542853920042,127.038920813724 1
 
0.1%
37.2541303267955,127.039221137315 1
 
0.1%
Other values (1355) 1355
96.2%
2023-12-13T02:41:30.021771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5354
11.9%
7 5318
11.8%
3 4781
10.6%
1 4367
9.7%
0 3591
8.0%
6 3487
7.7%
9 3227
7.2%
8 3115
6.9%
5 3018
6.7%
4 2854
6.3%
Other values (14) 5944
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39112
86.8%
Other Punctuation 3920
 
8.7%
Space Separator 1364
 
3.0%
Uppercase Letter 528
 
1.2%
Open Punctuation 132
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5354
13.7%
7 5318
13.6%
3 4781
12.2%
1 4367
11.2%
0 3591
9.2%
6 3487
8.9%
9 3227
8.3%
8 3115
8.0%
5 3018
7.7%
4 2854
7.3%
Uppercase Letter
ValueCountFrequency (%)
O 88
16.7%
L 88
16.7%
G 44
8.3%
N 44
8.3%
U 44
8.3%
Y 44
8.3%
P 44
8.3%
I 44
8.3%
T 44
8.3%
M 44
8.3%
Other Punctuation
ValueCountFrequency (%)
. 2641
67.4%
, 1279
32.6%
Space Separator
ValueCountFrequency (%)
1364
100.0%
Open Punctuation
ValueCountFrequency (%)
( 132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44528
98.8%
Latin 528
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5354
12.0%
7 5318
11.9%
3 4781
10.7%
1 4367
9.8%
0 3591
8.1%
6 3487
7.8%
9 3227
7.2%
8 3115
7.0%
5 3018
6.8%
4 2854
6.4%
Other values (4) 5416
12.2%
Latin
ValueCountFrequency (%)
O 88
16.7%
L 88
16.7%
G 44
8.3%
N 44
8.3%
U 44
8.3%
Y 44
8.3%
P 44
8.3%
I 44
8.3%
T 44
8.3%
M 44
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5354
11.9%
7 5318
11.8%
3 4781
10.6%
1 4367
9.7%
0 3591
8.0%
6 3487
7.7%
9 3227
7.2%
8 3115
6.9%
5 3018
6.7%
4 2854
6.3%
Other values (14) 5944
13.2%

Interactions

2023-12-13T02:41:23.818286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:10.239257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.465874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.661753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.949399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.286887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.370758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.457829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.478735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.547088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.835525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.732603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.731640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.897417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:10.353954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.580875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.750933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.048295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.387798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.447711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.536484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.560334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.636420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.913909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.809068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.809002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.975484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:10.436126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.700315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.848884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.152712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.467549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.512842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.604373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.635460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.710652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.980139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.877217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.889315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.072643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:10.529485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.830899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.966114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.229566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.565136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.594841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.683956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.716012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.790034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.057669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.953442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.974304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.162893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:10.613863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.906941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.078340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.304526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.665407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.684121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.753414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.791017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.870200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.133177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.038438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.067046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.251469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:10.709308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.987062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.197431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.380584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.744298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.789462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.839283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.869437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.954892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.201391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.117867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.160681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.337546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:10.793539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.063577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.317314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.452354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.827926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.862668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.929521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.956602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.028309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.266485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.206687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.241089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.423998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:10.892219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.165545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.437168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.779539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.904993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.939015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.005450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.036889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.100687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.332552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.286077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.326990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.502839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.007094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.250862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.527511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.851840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.979234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.018586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.093462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.119014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.464608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.400335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.355965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.408608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.581378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.113235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.327309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.619784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:14.925119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.058045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.107102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.181778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.203955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.539024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.465786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.429691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.503652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.665021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.208309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.404986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.694959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.005918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.132423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.192354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.255433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.280659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.616411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.532767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.503143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.582225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.739060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.298489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.485016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.780157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.088835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.215058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.281202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.333632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.362678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.696131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.599065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.583536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.662321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:24.816548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:11.380403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:12.570982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:13.858823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:15.165609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:16.290760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:17.377212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:18.400941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:19.455250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:20.764826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:21.665145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:22.661875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:41:23.741562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:41:30.122168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드행정동명인구변화율17년도인구변화율18년도인구변화율19년도사업체수변화율17년도사업체수변화율18년도노후건물변화율17년도노후건물변화율18년도노후건물변화율19년도인구변화율종합사업체수변화율종합노후건물평균변화율종합공간정보
행정동코드1.0001.0000.6160.3710.2480.0000.4740.5020.5540.5820.2990.1710.6130.4481.000
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인구변화율17년도0.6161.0001.0000.0000.5820.4090.0940.0000.0000.0000.2840.3600.0000.4821.000
인구변화율18년도0.3711.0000.0001.0000.8850.0780.8630.0000.0000.0000.9320.6530.0000.4081.000
인구변화율19년도0.2481.0000.5820.8851.0000.1630.8330.0000.0000.0000.8670.8030.0000.0001.000
사업체수변화율17년도0.0001.0000.4090.0780.1631.0000.6230.0000.0000.0000.3771.0000.0000.0001.000
사업체수변화율18년도0.4741.0000.0940.8630.8330.6231.0000.0000.0000.0000.8110.7810.0000.0001.000
노후건물변화율17년도0.5021.0000.0000.0000.0000.0000.0001.0000.9870.9650.0000.0000.9860.6661.000
노후건물변화율18년도0.5541.0000.0000.0000.0000.0000.0000.9871.0000.9940.0000.0000.9910.5991.000
노후건물변화율19년도0.5821.0000.0000.0000.0000.0000.0000.9650.9941.0000.0000.0000.9900.5751.000
인구변화율종합0.2991.0000.2840.9320.8670.3770.8110.0000.0000.0001.0000.7930.0000.6001.000
사업체수변화율종합0.1711.0000.3600.6530.8031.0000.7810.0000.0000.0000.7931.0000.0000.0001.000
노후건물평균0.6131.0000.0000.0000.0000.0000.0000.9860.9910.9900.0000.0001.0000.6591.000
변화율종합0.4481.0000.4820.4080.0000.0000.0000.6660.5990.5750.6000.0000.6591.0001.000
공간정보1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T02:41:30.514705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드인구변화율17년도인구변화율18년도인구변화율19년도사업체수변화율17년도사업체수변화율18년도노후건물변화율17년도노후건물변화율18년도노후건물변화율19년도인구변화율종합사업체수변화율종합노후건물평균변화율종합
행정동코드1.0000.5090.318-0.2650.168-0.029-0.322-0.350-0.3830.3500.171-0.360-0.413
인구변화율17년도0.5091.0000.321-0.1570.4750.060-0.367-0.327-0.3090.6480.379-0.327-0.626
인구변화율18년도0.3180.3211.000-0.8980.2410.223-0.232-0.280-0.2640.8140.289-0.244-0.299
인구변화율19년도-0.265-0.157-0.8981.000-0.169-0.1870.1770.2010.195-0.577-0.2070.1790.107
사업체수변화율17년도0.1680.4750.241-0.1691.0000.201-0.377-0.351-0.3390.4140.704-0.366-0.546
사업체수변화율18년도-0.0290.0600.223-0.1870.2011.000-0.306-0.273-0.2680.2600.702-0.285-0.471
노후건물변화율17년도-0.322-0.367-0.2320.177-0.377-0.3061.0000.9440.932-0.310-0.4800.9650.655
노후건물변화율18년도-0.350-0.327-0.2800.201-0.351-0.2730.9441.0000.995-0.352-0.4340.9900.711
노후건물변화율19년도-0.383-0.309-0.2640.195-0.339-0.2680.9320.9951.000-0.327-0.4180.9900.691
인구변화율종합0.3500.6480.814-0.5770.4140.260-0.310-0.352-0.3271.0000.452-0.311-0.526
사업체수변화율종합0.1710.3790.289-0.2070.7040.702-0.480-0.434-0.4180.4521.000-0.454-0.633
노후건물평균-0.360-0.327-0.2440.179-0.366-0.2850.9650.9900.990-0.311-0.4541.0000.683
변화율종합-0.413-0.626-0.2990.107-0.546-0.4710.6550.7110.691-0.526-0.6330.6831.000

Missing values

2023-12-13T02:41:24.927154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:41:25.110316image/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

구분행정동코드행정동명인구변화율17년도인구변화율18년도인구변화율19년도사업체수변화율17년도사업체수변화율18년도노후건물변화율17년도노후건물변화율18년도노후건물변화율19년도인구변화율종합사업체수변화율종합노후건물평균변화율종합공간정보
0종합(인구, 사업체수, 건물노후)3101451매탄1동9.865265-872.329622553.021625-8-325.025.025.0-309.442732-1125.03MULTIPOLYGON (((127.042009377406 37.2752132021241,127.041655744911 37.2753127791338,127.041290796847 37.2754164213666,127.041210246757 37.2754390525052,127.040856614262 37.2755684271807,127.040778746382 37.275511262601,127.040773130507 37.2755024616027,127.040766844079 37.2754927805045,127.04052871421 37.2751911996283,127.040358477757 37.2751651319094,127.039971736744 37.2751340769581,127.039939131141 37.2751370106242,127.039851875529 37.2751434646897,127.03968557857 37.2750510122977,127.039569489211 37.2749616612098,127.03937041901 37.2748072246438,127.03930462107 37.2748048777109,127.03923102796 37.2748016925877,127.039055678546 37.2747959090745,127.038926178142 37.2747887425473,127.038859542012 37.2747850964194,127.038681007474 37.2747830009436,127.038621076866 37.2747762535116,127.038414295315 37.274753706192,127.038254200964 37.2748021955019,127.037969300075 37.2749218890792,127.037981370016 37.2749381080619,127.037908028363 37.2749944344512,127.03785027705 37.2750096476055,127.037820940389 37.2749842923
1종합(인구, 사업체수, 건물노후)3101452매탄2동-202.172146-853.092975312.45467-15-233.33333333.33333333.333333-742.810451-1733.3333334MULTIPOLYGON (((127.048709954623 37.2733881265267,127.048706182767 37.2733887970789,127.04850853749 37.2734136075123,127.048099835891 37.2735113824129,127.048016771231 37.2735333849087,127.048015011031 37.2735338878229,127.047986261103 37.2735212311491,127.047764224488 37.2734240848913,127.046828133542 37.2730143355546,127.0466707214 37.2729454363105,127.046652365032 37.2729373477739,127.046107206049 37.2726987149905,127.04567000598 37.272507356141,127.044913539218 37.2721762709656,127.04490557641 37.2721727086567,127.044902223649 37.2721712418237,127.044894260841 37.2721677214243,127.044511794599 37.2720003348179,127.044487989994 37.271989941258,127.043353667037 37.2714933973139,127.043179239632 37.2711973065842,127.043167672606 37.2711775672022,127.043159290703 37.2711633179668,127.043070442529 37.2710125275287,127.042909090893 37.2707387326615,127.042795432286 37.270490670237,127.043864879312 37.2701887959941,127.043864879312 37.2701887540846,127.043877787443 37.2701851917758,127.043955571504 37.2701631473
2종합(인구, 사업체수, 건물노후)3101453매탄3동261.654276-1172.224214949.895219692817.64705920.020.039.3252829719.2156861MULTIPOLYGON (((127.058939732173 37.2663097350236,127.058909389683 37.2663270855631,127.058876029709 37.2663422987174,127.058661033892 37.2664513053682,127.058563803816 37.2664103178617,127.058411420816 37.2663461543929,127.0583954952 37.2663393650513,127.058487696135 37.266301562668,127.058362302863 37.2663126686897,127.058343443581 37.2663172368269,127.05814965398 37.2662319090526,127.05814529539 37.2662182046409,127.058127609574 37.2662073081668,127.0580682657 37.2661408396746,127.058075390318 37.2661339246045,127.058056698673 37.2661112515564,127.058032810249 37.2660962898592,127.05809592598 37.2660317911143,127.058093662866 37.2660263847867,127.057885037296 37.2659001533249,127.057724356213 37.2658038871669,127.057649841093 37.265747267411,127.057612122529 37.2657254325532,127.057571218842 37.2657325990804,127.057569039547 37.2657341497325,127.057121110641 37.2655761927672,127.057115662404 37.265565212474,127.057119937175 37.265521584668,127.057102670454 37.2655302599378,127.057085152276 37.2655437548019
3종합(인구, 사업체수, 건물노후)3101454매탄4동7.413868-1193.861706674.191655-491840.040.040.0-512.256183-3140.02MULTIPOLYGON (((127.059980429271 37.2656301722236,127.05996844315 37.2656122349508,127.059952266077 37.2656191081114,127.059801643277 37.2655145019598,127.059784544194 37.2655023482002,127.059647164801 37.2654194930873,127.059563848683 37.2653699560395,127.059472821215 37.2653159765831,127.059434599736 37.2652878133884,127.059304512599 37.265187775374,127.059221615577 37.2650773018902,127.05915464417 37.2649878669833,127.059147184277 37.2649728633766,127.059104939485 37.2648816263605,127.059007877046 37.2647457557101,127.058955071056 37.2646503277424,127.05893998363 37.2646220388192,127.058926488766 37.2645974798429,127.058929925346 37.2645691071006,127.059000920066 37.264412910335,127.059012906188 37.2642884809823,127.058978624204 37.2641923405529,127.058937217602 37.2640791429505,127.058931769365 37.2640625048727,127.058918274501 37.2640914643482,127.058858427712 37.2640335873068,127.058814338901 37.2639441104904,127.058764047482 37.2638436533808,127.058720880681 37.2637904702052,127.058671511271 37.2637292
4종합(인구, 사업체수, 건물노후)3101460원천동426.2943743979.789122-4068.04111767-786.255.2631585.263158338.042379-115.5921052MULTIPOLYGON (((127.048459670995 37.2854620065938,127.047664898936 37.2856041636717,127.047314116288 37.285880892205,127.046882364455 37.2864127239615,127.046647084433 37.2867553761633,127.046647084433 37.2867561305346,127.046646581519 37.2867561724441,127.046419348124 37.2870870061624,127.045469594675 37.2870842820439,127.045002890306 37.2863105066523,127.044961148428 37.286241272132,127.044944971355 37.2862145338609,127.04481379457 37.2861239673971,127.044546998592 37.2856794331622,127.044483547585 37.2855736954536,127.044438369127 37.2855620446082,127.044254805448 37.2855143096696,127.044195964487 37.2854913013454,127.044024219291 37.2854237432058,127.043727332281 37.2853071090231,127.043271943481 37.2853230765487,127.043225926833 37.2853246691103,127.042869109215 37.2850331046083,127.042853435056 37.284893671649,127.042706332655 37.2847084315888,127.04262712367 37.2846597746409,127.042380695717 37.2847494610049,127.042164694072 37.2845643047637,127.04201340072 37.2843665756678,127.041751130969 37.28407295
5종합(인구, 사업체수, 건물노후)3101462광교1동491.4179065491.172546-2128.5236034404470.03.0303033.0303033854.0668498872.0202021MULTIPOLYGON (((127.039820275754 37.320995450635,127.039695553034 37.3209371544984,127.039698905796 37.3209461650443,127.039685410932 37.3209397109789,127.039612488374 37.3209049679902,127.039353739023 37.3208064387184,127.039197667986 37.3207211528536,127.038851327747 37.3204845317269,127.038787206187 37.3201529436373,127.038929027989 37.3195325989833,127.038907737955 37.3191024816218,127.0386943347 37.3188895812812,127.038644630014 37.317820050436,127.038806400746 37.3173180163453,127.038685449883 37.3171562037045,127.038571036904 37.316827549281,127.038407254516 37.3163584560698,127.038119587599 37.3160675621201,127.037896042242 37.3157249518278,127.03723915249 37.3153626859725,127.037103617115 37.3152792022169,127.0366045586 37.31517245868,127.036257715447 37.3148796368925,127.035890671907 37.3145015292403,127.035499404667 37.314323832893,127.035400749666 37.31427907353,127.03515541136 37.3140337771336,127.034890962315 37.3138651751512,127.03471477471 37.3137654305034,127.03464034341 37.31372331144,127.03
6종합(인구, 사업체수, 건물노후)3101463광교2동271.9057721887.314282-2110.864318382120.00.00.048.3557362500.01MULTIPOLYGON (((127.087244832463 37.2981162496368,127.086502531118 37.2982916828703,127.085840360767 37.2986224327696,127.085295369423 37.2977778302961,127.08464023987 37.2973319968662,127.084339832461 37.2974273410148,127.083401310762 37.2977805125051,127.08280938076 37.2976672310837,127.082747522314 37.2976334520139,127.082549374123 37.2970854012748,127.082483576183 37.297024003834,127.082155843769 37.2968611853648,127.081797936504 37.2965841215554,127.081466767509 37.2961703908146,127.081095449199 37.2960845182166,127.080896043722 37.2958745934516,127.080896043722 37.2958745096325,127.080606532786 37.2954200847519,127.080055003557 37.2951503131982,127.079963976089 37.2951301966306,127.079762810413 37.295116743676,127.079620569516 37.2950642310526,127.079233409408 37.293434118523,127.079180603418 37.2932310250091,127.079060490746 37.2932514349433,127.079046828243 37.2932682825687,127.0789744086 37.2932815259757,127.078762849364 37.2933201665493,127.077609331849 37.2935294626716,127.077387295234 37.293567139
7종합(인구, 사업체수, 건물노후)3101464영통1동237.497796-1146.248954697.67993779740.00.00.0-211.0712218010.01MULTIPOLYGON (((127.084875855169 37.2699123189179,127.084718275389 37.2698062459332,127.084595061412 37.2697244385582,127.084265987894 37.2694920502927,127.08425416941 37.26948362648,127.084249643183 37.2694807766329,127.084221899083 37.269463887098,127.083973208016 37.2692934410969,127.083681014871 37.269094328987,127.083609517237 37.2690439537489,127.083523015996 37.2689863281646,127.083377170881 37.2688867511549,127.083138789555 37.2687171014346,127.082956902256 37.2685877267591,127.082835448479 37.2685098169691,127.082840896716 37.2685036143607,127.082841818725 37.2685024828038,127.082392716353 37.2681736188327,127.082268664186 37.2681061026026,127.082133967002 37.268033263864,127.082066241224 37.2679973054994,127.081873708908 37.2678901428673,127.081794080828 37.2678552741501,127.081693246533 37.2678136580008,127.08162510166 37.2677830221447,127.081557962616 37.2677552361357,127.081400131379 37.2677015081363,127.081252358426 37.26764924697,127.08102604704 37.2675697027089,127.080948933531 37.267543844537
8종합(인구, 사업체수, 건물노후)3101465영통2동317.27994365.825012383.7713081858380.00.00.0766.87626318960.00MULTIPOLYGON (((127.060990280966 37.252256258974,127.060966224904 37.252270801576,127.060997824679 37.2523034071793,127.061141741956 37.2524524374178,127.061257579858 37.2525722986332,127.061627305607 37.2529547229656,127.061403341154 37.2528882125639,127.061256406392 37.2528139908113,127.061186501319 37.2527963888146,127.061159679229 37.2527936646961,127.061103269021 37.2527728775762,127.060837059776 37.2526182733721,127.060800011764 37.2525955584145,127.06071552218 37.2525706641621,127.060614687885 37.2525395253918,127.060599600459 37.2525332808739,127.060278657386 37.2524474920949,127.060197939659 37.2524249866849,127.06010775038 37.2523809397837,127.06000473679 37.2522965340187,127.059945476735 37.2522246172895,127.059873811463 37.2522104518731,127.059873057092 37.2522185404097,127.059823771501 37.2524330752215,127.059787561679 37.2524857973924,127.059762918884 37.2525176486245,127.059757554466 37.2525755256659,127.059732660213 37.2525991207234,127.059719500625 37.2525817282743,127.059633167023 37.2524674
9종합(인구, 사업체수, 건물노후)3101466영통3동308.15073-436.110054320.243653293-5716.66666720.020.0192.284326323618.8888892MULTIPOLYGON (((127.065397737113 37.2577581402183,127.065311990243 37.2579471102254,127.065323808727 37.2579563303189,127.065242252809 37.2581001218679,127.065209563386 37.2581719128686,127.065205204797 37.2581816777858,127.065195817065 37.2581970166686,127.06480413073 37.2579849964278,127.064698434931 37.2578702062638,127.064567425784 37.2578060008855,127.064472123545 37.2577661449359,127.064377659496 37.2577389875696,127.064328290086 37.2577362215415,127.064119161602 37.2577724732728,127.063971053373 37.2577635465459,127.063770306792 37.2577598585085,127.063668382849 37.2577007660911,127.063584647637 37.2576272987098,127.063494877454 37.2575734449819,127.063338638778 37.2574876562029,127.063192961301 37.257395622906,127.062919962715 37.2572338521747,127.062947958271 37.2572362410171,127.062970924686 37.257239970964,127.062994226377 37.2572417311637,127.063004284661 37.2572341455413,127.06305801266 37.2572088321937,127.063221124496 37.2570716623483,127.063330173056 37.2570118574691,127.063562435593 37.256973
구분행정동코드행정동명인구변화율17년도인구변화율18년도인구변화율19년도사업체수변화율17년도사업체수변화율18년도노후건물변화율17년도노후건물변화율18년도노후건물변화율19년도인구변화율종합사업체수변화율종합노후건물평균변화율종합공간정보
34종합(인구, 사업체수, 건물노후)3101353지동-193.769164-1351.762352837.083828-191160.060.060.0-708.447689-860.05MULTIPOLYGON (((127.025275578268 37.2891976951106,127.02509796574 37.2892313065423,127.024967459508 37.2892559493376,127.024914904975 37.2892012155099,127.02488674178 37.2891786262808,127.024819770374 37.289081312385,127.024855225824 37.2890661830497,127.024954970472 37.2890430909065,127.025027138658 37.2890158497212,127.025125374564 37.288978466433,127.025143311836 37.2889925061208,127.025171391212 37.2889958169726,127.025196117826 37.2889659354878,127.025209780329 37.2889319468704,127.025179605477 37.2889072621655,127.025182455324 37.2888799790707,127.025206092291 37.2886804897751,127.025242721208 37.2884843532408,127.025253701501 37.288424087357,127.025281026506 37.28826453783,127.025282451429 37.2882557787412,127.025290833332 37.288206367422,127.025288905495 37.2880816027932,127.025287061476 37.2879985800423,127.025279433944 37.2879498811848,127.025281277963 37.2879131265394,127.025217994594 37.2879051218219,127.025325702049 37.2879059181027,127.025340202742 37.2877734002135,127.025347243541 37.2877119189
35종합(인구, 사업체수, 건물노후)3101354우만1동-204.690456-1290.445996546.756693-39530.76923123.52941223.529412-948.379759-3425.9426853MULTIPOLYGON (((127.035820096282 37.2936094260279,127.03581598915 37.2935867110703,127.035829651652 37.2935286663908,127.035794363839 37.2934797160763,127.035867956949 37.2934810990903,127.035806769056 37.2934252337056,127.035796962229 37.2934253175247,127.035755639447 37.2934254013437,127.035629407985 37.2934256528008,127.035608369408 37.293420791297,127.03536495894 37.2933640458125,127.035255742741 37.293366686112,127.035191369725 37.2933681948545,127.034941002277 37.2933352120655,127.034743021724 37.2933003014388,127.034539425296 37.2932618285032,127.034473124442 37.2932493394675,127.034370865223 37.29323353958,127.034319400338 37.2932328690278,127.034190570486 37.2933425881403,127.034109349844 37.293365470736,127.034082779211 37.2933623275223,127.034063333196 37.2933412051263,127.033808020425 37.2930577291609,127.033735097867 37.2929900452928,127.033637783971 37.2929375326694,127.033395295513 37.292854719466,127.033263448176 37.2928084513605,127.033134534505 37.2927561482847,127.032881903943 37.2926751371
36종합(인구, 사업체수, 건물노후)3101355우만2동-26.684832-692.719062472.066754282520.020.020.0-247.337145320.02MULTIPOLYGON (((127.042922082843 37.2859775774581,127.042912192197 37.2859775355486,127.042912192197 37.2859775774581,127.042712283806 37.2859769907249,127.042588231639 37.285961484204,127.042526205556 37.2859724225877,127.04244875677 37.2859408228127,127.042405422331 37.2859373862324,127.042352951617 37.2859104803232,127.042286986039 37.2858987875683,127.042220601366 37.2859247295586,127.042157653273 37.2859342430187,127.042093783171 37.2859189460454,127.042058495359 37.2859259868441,127.042054639683 37.2859267412154,127.042037456782 37.2859301777957,127.041940478162 37.2859186526788,127.041914829538 37.2859024336962,127.041812654138 37.2858352527422,127.041747694389 37.285833366814,127.041741324142 37.2858154295412,127.041728835107 37.2857793035385,127.041664210633 37.2857707959068,127.041594976113 37.2857433870834,127.041535632239 37.2856841689375,127.04147234887 37.2856747812059,127.041412921176 37.2856347157087,127.041370257289 37.2855867293131,127.041266573147 37.2855636790793,127.041228938401 37.285586
37종합(인구, 사업체수, 건물노후)3101356인계동-1.009168-4449.266217949.591138853528.57142933.33333333.333333-3500.68424712031.7460322MULTIPOLYGON (((127.040358477757 37.2751651319094,127.04052871421 37.2751911996283,127.040766844079 37.2754927805045,127.040773130507 37.2755024616027,127.040778746382 37.275511262601,127.040856614262 37.2755684271807,127.040385132209 37.275777890941,127.040157898814 37.2758789766933,127.039723045677 37.2760781307128,127.039300094843 37.2762355009449,127.039034388512 37.2763063280267,127.038265935629 37.2765664603918,127.03768062733 37.2767427318156,127.036926926597 37.2769794367612,127.035651033295 37.2773386432218,127.035062539873 37.2775010006863,127.034563229901 37.2776600472991,127.034186379534 37.2777829259996,127.033938610476 37.2778540464481,127.033444245827 37.2779593650615,127.033029006344 37.2780270070201,127.032912330252 37.2780406695223,127.032371194583 37.2781057130909,127.032095094692 37.278119040317,127.032061902355 37.2781204652405,127.032042623978 37.2781213034308,127.031953608166 37.2781238180018,127.031962912079 37.2781557530529,127.031065545525 37.2782197907931,127.030120988856 37.2782370
38종합(인구, 사업체수, 건물노후)3101367매교동-166.78305-3134.7708671694.526453-14-13633.33333333.33333333.333333-1607.027464-15033.3333334MULTIPOLYGON (((127.014323364484 37.2774297545093,127.013913824695 37.2774281200382,127.013092901098 37.277336463927,127.012575402396 37.2772658044832,127.012600967201 37.2771894453453,127.012569115969 37.2771658083783,127.01249887562 37.2772321092325,127.011578039737 37.2770469529913,127.012211795436 37.2764181007056,127.012234594213 37.2763733832521,127.012371973606 37.2759210538473,127.012287064927 37.2753607655295,127.012233588385 37.2752430416994,127.01213267027 37.2750207955367,127.012094532611 37.2749046223587,127.011850954505 37.2741687331694,127.011727321433 37.2740459382879,127.011452395009 37.2730315184561,127.011438061954 37.2728763694283,127.011470164643 37.2726942306723,127.011877441319 37.2716523181981,127.011945586191 37.2715325408017,127.011963272007 37.2715032460501,127.011417023377 37.2712902618904,127.011459854902 37.2712003240693,127.011479720013 37.2711091708723,127.01144359401 37.2710249327454,127.011370587634 37.2708863379764,127.0113215535 37.2707879344331,127.01125148079 37.270591546
39종합(인구, 사업체수, 건물노후)3101368매산동-147.801044-598.626525274.7597319114100.0100.0100.0-471.667838105100.04MULTIPOLYGON (((126.998974088113 37.2660514047677,126.998987666796 37.2660355210612,126.99915614305 37.2658382948795,126.999532658141 37.2653066726706,127.000088629779 37.2643473638521,127.00026942743 37.26441420953,127.000552735757 37.2644217113333,127.001021954697 37.263941805467,127.001166710165 37.263796547085,127.001209960785 37.2637356944679,127.001260838938 37.2637550566642,127.001304424834 37.2637695154472,127.001387740952 37.2637971338182,127.00142856082 37.2638154901861,127.001440379304 37.2637986006512,127.001458735672 37.2637720300182,127.001606508625 37.2638337627351,127.001736847219 37.2639073558449,127.001837430057 37.2639738662466,127.001916471404 37.2640525304079,127.001934492496 37.2640705514998,127.001980173868 37.2641294762791,127.001993584913 37.2641427196861,127.00207522465 37.264247912571,127.002172790003 37.2643984934615,127.00219558878 37.2644322725313,127.002203048674 37.2644434204625,127.002220818308 37.2644354576545,127.002369932366 37.2643691987099,127.002660281492 37.264338479034
40종합(인구, 사업체수, 건물노후)3101369고등동-267.145387-890.958928538.360779-14-210.00.00.0-619.743535-350.04MULTIPOLYGON (((127.006966819524 37.2798742108419,127.006963885858 37.2799077384546,127.006766827315 37.2798485622182,127.006706309974 37.2798483945801,127.006706812888 37.2798229974135,127.006707399621 37.2797902241721,127.006622407123 37.2797897631674,127.006630789026 37.2793996693936,127.006645708814 37.278771152384,127.00600021845 37.2787753852451,127.004192996306 37.2787835156912,127.004067351578 37.2787163766468,127.003985963298 37.2786729164788,127.003799885048 37.2786150394374,127.003761412112 37.2786031371349,127.003602491228 37.2785528457158,127.003435942812 37.2777815848951,127.003395709676 37.2775982726727,127.003365702463 37.277444758116,127.003222958652 37.2773225499677,127.003187503202 37.2773079654562,127.003154310865 37.2772926265734,127.003114664463 37.277274395934,127.003093793524 37.2772648824739,127.003011650873 37.2772273734572,127.002983655316 37.2772145910548,127.002946020571 37.2772148844214,127.002816352529 37.2772166027116,127.002680984793 37.2772178599971,127.002660868225 37.277217
41종합(인구, 사업체수, 건물노후)3101370화서1동-179.793984-1291.821638609.9352775900.00.00.0-861.680345590.02MULTIPOLYGON (((127.005630827977 37.286381166096,127.005599814935 37.2863961277932,127.005568047522 37.286411466676,127.005465117751 37.2864610456333,127.005311561285 37.2865383267806,127.005309633447 37.2865392487899,127.005256827457 37.2865669928894,127.005262527152 37.286513013433,127.005243835508 37.2864874905378,127.00514057046 37.2863523742586,127.005089943765 37.2863671264082,127.005047615154 37.286367922689,127.005037640689 37.2863549726486,127.005005957095 37.2863141108706,127.004923143892 37.2862040145724,127.004903530239 37.2861777792155,127.00482985331 37.2860806329577,127.004780232443 37.2861074131383,127.004763384818 37.2860941697313,127.004705801143 37.28603801098,127.00472507952 37.2860185649647,127.00466707675 37.2859560778765,127.004647463097 37.2859363384945,127.004670010416 37.2859239751873,127.004675458653 37.2858930459646,127.004703873305 37.2858774556247,127.004724576606 37.2858736837683,127.004769671245 37.2859770326344,127.004821806683 37.286024767573,127.004817783369 37.2859579218952
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