Overview

Dataset statistics

Number of variables9
Number of observations256
Missing cells24
Missing cells (%)1.0%
Duplicate rows1
Duplicate rows (%)0.4%
Total size in memory19.4 KiB
Average record size in memory77.5 B

Variable types

Numeric5
Text1
Categorical1
DateTime2

Dataset

Description광주 광역시 광산구 소방서 활동실적(출동위치, 재산피해, 화재요인, 출동일자, 위도, 경도 등) 현황을 제공합니다.
Author광주광역시 광산구
URLhttps://www.data.go.kr/data/15049612/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (0.4%) duplicate rowsDuplicates
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
연번 has 3 (1.2%) missing valuesMissing
출동위치 has 3 (1.2%) missing valuesMissing
재산피해(천원) has 3 (1.2%) missing valuesMissing
피해면적 has 3 (1.2%) missing valuesMissing
출동일자 has 3 (1.2%) missing valuesMissing
위도 has 3 (1.2%) missing valuesMissing
경도 has 3 (1.2%) missing valuesMissing
데이터기준일자 has 3 (1.2%) missing valuesMissing
재산피해(천원) has 4 (1.6%) zerosZeros
피해면적 has 167 (65.2%) zerosZeros

Reproduction

Analysis started2024-05-04 07:40:51.632067
Analysis finished2024-05-04 07:41:02.681070
Duration11.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

MISSING 

Distinct253
Distinct (%)100.0%
Missing3
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean127
Minimum1
Maximum253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:41:02.975708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.6
Q164
median127
Q3190
95-th percentile240.4
Maximum253
Range252
Interquartile range (IQR)126

Descriptive statistics

Standard deviation73.179004
Coefficient of variation (CV)0.57621263
Kurtosis-1.2
Mean127
Median Absolute Deviation (MAD)63
Skewness0
Sum32131
Variance5355.1667
MonotonicityStrictly increasing
2024-05-04T07:41:03.464225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175 1
 
0.4%
162 1
 
0.4%
163 1
 
0.4%
164 1
 
0.4%
165 1
 
0.4%
166 1
 
0.4%
167 1
 
0.4%
168 1
 
0.4%
169 1
 
0.4%
170 1
 
0.4%
Other values (243) 243
94.9%
(Missing) 3
 
1.2%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
253 1
0.4%
252 1
0.4%
251 1
0.4%
250 1
0.4%
249 1
0.4%
248 1
0.4%
247 1
0.4%
246 1
0.4%
245 1
0.4%
244 1
0.4%

출동위치
Text

MISSING 

Distinct242
Distinct (%)95.7%
Missing3
Missing (%)1.2%
Memory size2.1 KiB
2024-05-04T07:41:04.523385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.703557
Min length16

Characters and Unicode

Total characters4985
Distinct characters64
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

Unique231 ?
Unique (%)91.3%

Sample

1st row광주광역시 광산구 임곡동 178
2nd row광주광역시 광산구 월곡1동 614-1
3rd row광주광역시 광산구 비아동 56-4
4th row광주광역시 광산구 유계동 238-4
5th row광주광역시 광산구 평동 1279
ValueCountFrequency (%)
광주광역시 253
25.0%
광산구 253
25.0%
하남동 31
 
3.1%
평동 26
 
2.6%
수완동 20
 
2.0%
우산동 17
 
1.7%
어룡동 16
 
1.6%
신창동 14
 
1.4%
첨단2동 13
 
1.3%
비아동 13
 
1.3%
Other values (272) 356
35.2%
2024-05-04T07:41:05.763822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1008
20.2%
광 759
15.2%
산 287
 
5.8%
동 259
 
5.2%
역 253
 
5.1%
시 253
 
5.1%
구 253
 
5.1%
주 253
 
5.1%
1 230
 
4.6%
- 170
 
3.4%
Other values (54) 1260
25.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2757
55.3%
Decimal Number 1050
 
21.1%
Space Separator 1008
 
20.2%
Dash Punctuation 170
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 759
27.5%
산 287
 
10.4%
동 259
 
9.4%
역 253
 
9.2%
시 253
 
9.2%
구 253
 
9.2%
주 253
 
9.2%
남 41
 
1.5%
하 31
 
1.1%
신 27
 
1.0%
Other values (42) 341
12.4%
Decimal Number
ValueCountFrequency (%)
1 230
21.9%
2 127
12.1%
5 104
9.9%
6 99
9.4%
7 92
 
8.8%
8 89
 
8.5%
4 80
 
7.6%
9 79
 
7.5%
0 75
 
7.1%
3 75
 
7.1%
Space Separator
ValueCountFrequency (%)
1008
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2757
55.3%
Common 2228
44.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 759
27.5%
산 287
 
10.4%
동 259
 
9.4%
역 253
 
9.2%
시 253
 
9.2%
구 253
 
9.2%
주 253
 
9.2%
남 41
 
1.5%
하 31
 
1.1%
신 27
 
1.0%
Other values (42) 341
12.4%
Common
ValueCountFrequency (%)
1008
45.2%
1 230
 
10.3%
- 170
 
7.6%
2 127
 
5.7%
5 104
 
4.7%
6 99
 
4.4%
7 92
 
4.1%
8 89
 
4.0%
4 80
 
3.6%
9 79
 
3.5%
Other values (2) 150
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2757
55.3%
ASCII 2228
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1008
45.2%
1 230
 
10.3%
- 170
 
7.6%
2 127
 
5.7%
5 104
 
4.7%
6 99
 
4.4%
7 92
 
4.1%
8 89
 
4.0%
4 80
 
3.6%
9 79
 
3.5%
Other values (2) 150
 
6.7%
Hangul
ValueCountFrequency (%)
광 759
27.5%
산 287
 
10.4%
동 259
 
9.4%
역 253
 
9.2%
시 253
 
9.2%
구 253
 
9.2%
주 253
 
9.2%
남 41
 
1.5%
하 31
 
1.1%
신 27
 
1.0%
Other values (42) 341
12.4%

재산피해(천원)
Real number (ℝ)

MISSING  ZEROS 

Distinct227
Distinct (%)89.7%
Missing3
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean5975.2174
Minimum0
Maximum210803
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:41:06.121013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q1175
median526
Q32948
95-th percentile29483
Maximum210803
Range210803
Interquartile range (IQR)2773

Descriptive statistics

Standard deviation18768.378
Coefficient of variation (CV)3.1410368
Kurtosis62.369142
Mean5975.2174
Median Absolute Deviation (MAD)454
Skewness6.9338196
Sum1511730
Variance3.5225201 × 108
MonotonicityNot monotonic
2024-05-04T07:41:06.515940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.6%
220 3
 
1.2%
110 3
 
1.2%
231 2
 
0.8%
165 2
 
0.8%
95 2
 
0.8%
120 2
 
0.8%
535 2
 
0.8%
390 2
 
0.8%
104 2
 
0.8%
Other values (217) 229
89.5%
(Missing) 3
 
1.2%
ValueCountFrequency (%)
0 4
1.6%
10 1
 
0.4%
15 1
 
0.4%
17 1
 
0.4%
22 2
0.8%
26 1
 
0.4%
38 1
 
0.4%
43 1
 
0.4%
48 1
 
0.4%
53 1
 
0.4%
ValueCountFrequency (%)
210803 1
0.4%
102719 1
0.4%
100430 1
0.4%
81068 1
0.4%
74744 1
0.4%
59957 1
0.4%
39591 1
0.4%
38959 1
0.4%
38362 1
0.4%
37810 1
0.4%

피해면적
Real number (ℝ)

MISSING  ZEROS 

Distinct44
Distinct (%)17.4%
Missing3
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean45.565217
Minimum0
Maximum3300
Zeros167
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:41:07.121553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile200
Maximum3300
Range3300
Interquartile range (IQR)5

Descriptive statistics

Standard deviation238.73212
Coefficient of variation (CV)5.2393499
Kurtosis139.64296
Mean45.565217
Median Absolute Deviation (MAD)0
Skewness10.819732
Sum11528
Variance56993.023
MonotonicityNot monotonic
2024-05-04T07:41:07.550104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 167
65.2%
10.0 12
 
4.7%
5.0 8
 
3.1%
1.0 6
 
2.3%
3.3 5
 
2.0%
40.0 4
 
1.6%
3.0 4
 
1.6%
15.0 4
 
1.6%
60.0 2
 
0.8%
65.0 2
 
0.8%
Other values (34) 39
 
15.2%
(Missing) 3
 
1.2%
ValueCountFrequency (%)
0.0 167
65.2%
1.0 6
 
2.3%
1.5 1
 
0.4%
2.0 1
 
0.4%
3.0 4
 
1.6%
3.3 5
 
2.0%
5.0 8
 
3.1%
6.5 1
 
0.4%
6.6 1
 
0.4%
7.0 1
 
0.4%
ValueCountFrequency (%)
3300.0 1
0.4%
990.0 1
0.4%
900.0 1
0.4%
825.0 1
0.4%
500.0 1
0.4%
495.0 2
0.8%
428.0 1
0.4%
345.2 1
0.4%
330.0 1
0.4%
280.0 1
0.4%

화재요인
Categorical

Distinct37
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
미상(미상)
39 
부주의(담배꽁초)
35 
전기적 요인(절연열화에 의한 단락)
18 
부주의(기기(전기, 기계 등) 사용.설치부주의)
14 
부주의(음식물 조리중)
 
13
Other values (32)
137 

Length

Max length26
Median length19
Mean length12.613281
Min length4

Unique

Unique9 ?
Unique (%)3.5%

Sample

1st row부주의(불씨,불꽃,화원방치)
2nd row부주의(기기(전기, 기계 등) 사용.설치부주의)
3rd row교통사고(교통사고)
4th row기계적 요인(과열, 과부하)
5th row부주의(담배꽁초)

Common Values

ValueCountFrequency (%)
미상(미상) 39
15.2%
부주의(담배꽁초) 35
13.7%
전기적 요인(절연열화에 의한 단락) 18
 
7.0%
부주의(기기(전기, 기계 등) 사용.설치부주의) 14
 
5.5%
부주의(음식물 조리중) 13
 
5.1%
기계적 요인(과열, 과부하) 13
 
5.1%
기계적 요인(노후) 13
 
5.1%
부주의(불씨,불꽃,화원방치) 12
 
4.7%
부주의(쓰레기 소각) 10
 
3.9%
전기적 요인(트래킹에 의한 단락) 10
 
3.9%
Other values (27) 79
30.9%

Length

2024-05-04T07:41:07.965000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전기적 50
 
9.5%
기계적 41
 
7.8%
미상(미상 39
 
7.4%
단락 35
 
6.7%
부주의(담배꽁초 35
 
6.7%
의한 35
 
6.7%
요인(절연열화에 18
 
3.4%
기계 14
 
2.7%
등 14
 
2.7%
사용.설치부주의 14
 
2.7%
Other values (48) 229
43.7%

출동일자
Date

MISSING 

Distinct182
Distinct (%)71.9%
Missing3
Missing (%)1.2%
Memory size2.1 KiB
Minimum2023-01-09 00:00:00
Maximum2023-12-31 00:00:00
2024-05-04T07:41:08.381503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:41:08.851597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct241
Distinct (%)95.3%
Missing3
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean35.171938
Minimum35.078607
Maximum35.251487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:41:09.410547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.078607
5-th percentile35.110925
Q135.147538
median35.177451
Q335.193333
95-th percentile35.220533
Maximum35.251487
Range0.17288
Interquartile range (IQR)0.045795

Descriptive statistics

Standard deviation0.034303615
Coefficient of variation (CV)0.00097531207
Kurtosis-0.54444089
Mean35.171938
Median Absolute Deviation (MAD)0.024394
Skewness-0.44087381
Sum8898.5003
Variance0.001176738
MonotonicityNot monotonic
2024-05-04T07:41:09.733129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.193124 2
 
0.8%
35.192324 2
 
0.8%
35.192992 2
 
0.8%
35.181217 2
 
0.8%
35.202716 2
 
0.8%
35.183606 2
 
0.8%
35.184258 2
 
0.8%
35.165107 2
 
0.8%
35.191952 2
 
0.8%
35.201845 2
 
0.8%
Other values (231) 233
91.0%
(Missing) 3
 
1.2%
ValueCountFrequency (%)
35.078607 1
0.4%
35.083553 1
0.4%
35.087521 1
0.4%
35.093488 1
0.4%
35.098571 1
0.4%
35.101219 1
0.4%
35.107716 1
0.4%
35.10801 1
0.4%
35.108269 1
0.4%
35.109556 1
0.4%
ValueCountFrequency (%)
35.251487 1
0.4%
35.226157 1
0.4%
35.225568 1
0.4%
35.223728 1
0.4%
35.2229979 1
0.4%
35.222242 1
0.4%
35.22134 1
0.4%
35.221276 1
0.4%
35.221209 1
0.4%
35.221071 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct241
Distinct (%)95.3%
Missing3
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean126.79367
Minimum126.65807
Maximum126.85806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:41:10.247631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.65807
5-th percentile126.69687
Q1126.77423
median126.80476
Q3126.82779
95-th percentile126.84945
Maximum126.85806
Range0.199984
Interquartile range (IQR)0.053554

Descriptive statistics

Standard deviation0.04545444
Coefficient of variation (CV)0.00035849139
Kurtosis0.53834918
Mean126.79367
Median Absolute Deviation (MAD)0.024534
Skewness-1.0564874
Sum32078.799
Variance0.0020661062
MonotonicityNot monotonic
2024-05-04T07:41:10.830503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.856059 2
 
0.8%
126.829236 2
 
0.8%
126.828033 2
 
0.8%
126.798934 2
 
0.8%
126.814915 2
 
0.8%
126.839133 2
 
0.8%
126.850407 2
 
0.8%
126.803686 2
 
0.8%
126.828201 2
 
0.8%
126.685111 2
 
0.8%
Other values (231) 233
91.0%
(Missing) 3
 
1.2%
ValueCountFrequency (%)
126.658073 1
0.4%
126.6600317 1
0.4%
126.660929 1
0.4%
126.669188 1
0.4%
126.671149 1
0.4%
126.679844 1
0.4%
126.680532 1
0.4%
126.681509 1
0.4%
126.685111 2
0.8%
126.693565 1
0.4%
ValueCountFrequency (%)
126.858057 1
0.4%
126.856059 2
0.8%
126.85544 1
0.4%
126.854154 1
0.4%
126.853983 1
0.4%
126.852175 1
0.4%
126.851824 1
0.4%
126.85115 1
0.4%
126.850407 2
0.8%
126.850092 1
0.4%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing3
Missing (%)1.2%
Memory size2.1 KiB
Minimum2023-12-31 00:00:00
Maximum2023-12-31 00:00:00
2024-05-04T07:41:11.176283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:41:11.464332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-05-04T07:40:59.239583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:52.346917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:54.213705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:55.718046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:57.282576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:59.598616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:52.780285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:54.501767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:55.962119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:57.656115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:59.977084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:53.080112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:54.766834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:56.314909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:57.993289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:41:00.376761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:53.424864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:55.081364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:56.608873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:58.354887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:41:00.797235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:53.799618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:55.425928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:56.998510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:40:58.840921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:41:11.725650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번재산피해(천원)피해면적화재요인위도경도
연번1.0000.0000.0000.3630.0000.000
재산피해(천원)0.0001.0000.4200.4820.0000.000
피해면적0.0000.4201.0000.0000.4090.441
화재요인0.3630.4820.0001.0000.1700.000
위도0.0000.0000.4090.1701.0000.741
경도0.0000.0000.4410.0000.7411.000
2024-05-04T07:41:12.051092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번재산피해(천원)피해면적위도경도화재요인
연번1.0000.1140.1090.024-0.0110.127
재산피해(천원)0.1141.0000.4160.1680.0040.200
피해면적0.1090.4161.0000.064-0.1350.000
위도0.0240.1680.0641.0000.6380.052
경도-0.0110.004-0.1350.6381.0000.000
화재요인0.1270.2000.0000.0520.0001.000

Missing values

2024-05-04T07:41:01.333162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:41:01.811465image/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.
2024-05-04T07:41:02.269533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번출동위치재산피해(천원)피해면적화재요인출동일자위도경도데이터기준일자
01광주광역시 광산구 임곡동 178825500.0부주의(불씨,불꽃,화원방치)2023-01-0935.251487126.7431552023-12-31
12광주광역시 광산구 월곡1동 614-112053.0부주의(기기(전기, 기계 등) 사용.설치부주의)2023-01-1235.165206126.8140762023-12-31
23광주광역시 광산구 비아동 56-45940.0교통사고(교통사고)2023-01-1335.222998126.8326332023-12-31
34광주광역시 광산구 유계동 238-4535.0기계적 요인(과열, 과부하)2023-01-1535.101219126.782262023-12-31
45광주광역시 광산구 평동 1279720.0부주의(담배꽁초)2023-01-2035.108269126.752662023-12-31
56광주광역시 광산구 평동 10716830.0부주의(쓰레기 소각)2023-01-2135.14045126.7790392023-12-31
67광주광역시 광산구 신가동 95-166000.0전기적 요인(절연열화에 의한 단락)2023-01-2535.176603126.8368262023-12-31
78광주광역시 광산구 첨단2동 69739600.0미상(미상)2023-01-2735.213028126.8518242023-12-31
89광주광역시 광산구 하남동 2-669300.0부주의(담배꽁초)2023-01-2735.21811126.8069462023-12-31
910광주광역시 광산구 임곡동 2234080.0기계적 요인(과열, 과부하)2023-01-2835.194976126.7822082023-12-31
연번출동위치재산피해(천원)피해면적화재요인출동일자위도경도데이터기준일자
246247광주광역시 광산구 어룡동 50027500.0미상(미상)2023-12-2335.167956126.7383332023-12-31
247248광주광역시 광산구 비아동 353-16885115.0미상(미상)2023-12-2335.210324126.8195072023-12-31
248249광주광역시 광산구 우산동 1598-31100.0부주의(가연물 근접방치)2023-12-2735.160128126.808622023-12-31
249250광주광역시 광산구 도산동 920-91070.0부주의(담배꽁초)2023-12-2835.133901126.7937622023-12-31
250251광주광역시 광산구 우산동 1612-111550.0기계적 요인(오일,연료누설)2023-12-2935.162182126.8035062023-12-31
251252광주광역시 광산구 수완동 9631143422.0화학적 요인(자연발화)2023-12-3035.192829126.824272023-12-31
252253광주광역시 광산구 신가동 1017-34560.0기계적 요인(자동제어 실패)2023-12-3135.184973126.8378282023-12-31
253<NA><NA><NA><NA><NA><NA><NA><NA><NA>
254<NA><NA><NA><NA><NA><NA><NA><NA><NA>
255<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

연번출동위치재산피해(천원)피해면적화재요인출동일자위도경도데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>3