Overview

Dataset statistics

Number of variables9
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory79.9 B

Variable types

Categorical4
Text2
Numeric3

Dataset

Description*대구광역시 북구 교통사고 다발지역 현황(무단횡단, 자전거)
Author도로교통공단
URLhttps://www.data.go.kr/data/15094142/fileData.do

Alerts

중심점_경도 is highly overall correlated with 중심점_위도High correlation
중심점_위도 is highly overall correlated with 중심점_경도High correlation
부상자수 is highly overall correlated with 사고건수High correlation
사고건수 is highly overall correlated with 부상자수High correlation
사망자수 is highly imbalanced (68.1%)Imbalance
중심점_경도 has unique valuesUnique
중심점_위도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:54:25.402475
Analysis finished2023-12-12 17:54:27.027140
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대상사고
Categorical

Distinct2
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size492.0 B
자전거
38 
무단횡단
7 

Length

Max length4
Median length3
Mean length3.1555556
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자전거
2nd row자전거
3rd row자전거
4th row자전거
5th row자전거

Common Values

ValueCountFrequency (%)
자전거 38
84.4%
무단횡단 7
 
15.6%

Length

2023-12-13T02:54:27.122172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:54:27.268028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자전거 38
84.4%
무단횡단 7
 
15.6%

대상사고년도
Categorical

Distinct4
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size492.0 B
2017년
24 
2019년
11 
2018년
9 
2016년
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row2017년
2nd row2017년
3rd row2017년
4th row2017년
5th row2017년

Common Values

ValueCountFrequency (%)
2017년 24
53.3%
2019년 11
24.4%
2018년 9
 
20.0%
2016년 1
 
2.2%

Length

2023-12-13T02:54:27.409304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:54:27.544874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017년 24
53.3%
2019년 11
24.4%
2018년 9
 
20.0%
2016년 1
 
2.2%

시군구
Text

Distinct24
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-13T02:54:27.707928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.0888889
Min length8

Characters and Unicode

Total characters409
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)26.7%

Sample

1st row대구광역시 북구1
2nd row대구광역시 북구2
3rd row대구광역시 북구3
4th row대구광역시 북구4
5th row대구광역시 북구5
ValueCountFrequency (%)
대구광역시 38
42.2%
대구 7
 
7.8%
북구9 3
 
3.3%
북구6 3
 
3.3%
북구4 3
 
3.3%
북구5 3
 
3.3%
북구3 3
 
3.3%
북구7 3
 
3.3%
북구8 3
 
3.3%
북구2 3
 
3.3%
Other values (16) 21
23.3%
2023-12-13T02:54:28.063919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 90
22.0%
대 52
12.7%
45
11.0%
북 45
11.0%
광 38
9.3%
역 38
9.3%
시 38
9.3%
1 19
 
4.6%
2 6
 
1.5%
강 5
 
1.2%
Other values (9) 33
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 308
75.3%
Decimal Number 56
 
13.7%
Space Separator 45
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19
33.9%
2 6
 
10.7%
4 5
 
8.9%
3 5
 
8.9%
8 4
 
7.1%
6 4
 
7.1%
7 4
 
7.1%
5 4
 
7.1%
9 3
 
5.4%
0 2
 
3.6%
Other Letter
ValueCountFrequency (%)
구 90
29.2%
대 52
16.9%
북 45
14.6%
광 38
12.3%
역 38
12.3%
시 38
12.3%
강 5
 
1.6%
부 2
 
0.6%
Space Separator
ValueCountFrequency (%)
45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 308
75.3%
Common 101
 
24.7%

Most frequent character per script

Common
ValueCountFrequency (%)
45
44.6%
1 19
18.8%
2 6
 
5.9%
4 5
 
5.0%
3 5
 
5.0%
8 4
 
4.0%
6 4
 
4.0%
7 4
 
4.0%
5 4
 
4.0%
9 3
 
3.0%
Hangul
ValueCountFrequency (%)
구 90
29.2%
대 52
16.9%
북 45
14.6%
광 38
12.3%
역 38
12.3%
시 38
12.3%
강 5
 
1.6%
부 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 308
75.3%
ASCII 101
 
24.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 90
29.2%
대 52
16.9%
북 45
14.6%
광 38
12.3%
역 38
12.3%
시 38
12.3%
강 5
 
1.6%
부 2
 
0.6%
ASCII
ValueCountFrequency (%)
45
44.6%
1 19
18.8%
2 6
 
5.9%
4 5
 
5.0%
3 5
 
5.0%
8 4
 
4.0%
6 4
 
4.0%
7 4
 
4.0%
5 4
 
4.0%
9 3
 
3.0%

다발지점명
Text

Distinct37
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-13T02:54:28.311450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length30
Mean length24.311111
Min length20

Characters and Unicode

Total characters1094
Distinct characters119
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)66.7%

Sample

1st row대구광역시 북구 노원동1가(원대오거리 부근)
2nd row대구광역시 북구 침산동(성덕빌딩 부근)
3rd row대구광역시 북구 노원동1가(오봉오거리 부근)
4th row대구광역시 북구 노원동3가(팔달교 부근)
5th row대구광역시 북구 고성동3가(대구광역시립북부도서관입구 부근)
ValueCountFrequency (%)
대구광역시 45
24.1%
부근 45
24.1%
북구 45
24.1%
태전동(태전삼거리 3
 
1.6%
침산동(오봉오거리 2
 
1.1%
침산동(침산네거리 2
 
1.1%
노원동1가(원대오거리 2
 
1.1%
대현동(경대교사거리 2
 
1.1%
읍내동(거동교 2
 
1.1%
태전동(대구병원 2
 
1.1%
Other values (37) 37
19.8%
2023-12-13T02:54:28.707590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
142
 
13.0%
구 101
 
9.2%
대 55
 
5.0%
동 52
 
4.8%
시 48
 
4.4%
역 47
 
4.3%
북 47
 
4.3%
부 46
 
4.2%
광 46
 
4.2%
) 45
 
4.1%
Other values (109) 465
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 828
75.7%
Space Separator 142
 
13.0%
Close Punctuation 45
 
4.1%
Open Punctuation 45
 
4.1%
Decimal Number 22
 
2.0%
Uppercase Letter 12
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 101
 
12.2%
대 55
 
6.6%
동 52
 
6.3%
시 48
 
5.8%
역 47
 
5.7%
북 47
 
5.7%
부 46
 
5.6%
광 46
 
5.6%
근 45
 
5.4%
거 21
 
2.5%
Other values (90) 320
38.6%
Uppercase Letter
ValueCountFrequency (%)
S 3
25.0%
K 2
16.7%
G 1
 
8.3%
P 1
 
8.3%
L 1
 
8.3%
A 1
 
8.3%
B 1
 
8.3%
C 1
 
8.3%
T 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
2 8
36.4%
1 4
18.2%
3 4
18.2%
8 2
 
9.1%
4 2
 
9.1%
9 1
 
4.5%
6 1
 
4.5%
Space Separator
ValueCountFrequency (%)
142
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 828
75.7%
Common 254
 
23.2%
Latin 12
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 101
 
12.2%
대 55
 
6.6%
동 52
 
6.3%
시 48
 
5.8%
역 47
 
5.7%
북 47
 
5.7%
부 46
 
5.6%
광 46
 
5.6%
근 45
 
5.4%
거 21
 
2.5%
Other values (90) 320
38.6%
Common
ValueCountFrequency (%)
142
55.9%
) 45
 
17.7%
( 45
 
17.7%
2 8
 
3.1%
1 4
 
1.6%
3 4
 
1.6%
8 2
 
0.8%
4 2
 
0.8%
9 1
 
0.4%
6 1
 
0.4%
Latin
ValueCountFrequency (%)
S 3
25.0%
K 2
16.7%
G 1
 
8.3%
P 1
 
8.3%
L 1
 
8.3%
A 1
 
8.3%
B 1
 
8.3%
C 1
 
8.3%
T 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 828
75.7%
ASCII 266
 
24.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
142
53.4%
) 45
 
16.9%
( 45
 
16.9%
2 8
 
3.0%
1 4
 
1.5%
3 4
 
1.5%
S 3
 
1.1%
K 2
 
0.8%
8 2
 
0.8%
4 2
 
0.8%
Other values (9) 9
 
3.4%
Hangul
ValueCountFrequency (%)
구 101
 
12.2%
대 55
 
6.6%
동 52
 
6.3%
시 48
 
5.8%
역 47
 
5.7%
북 47
 
5.7%
부 46
 
5.6%
광 46
 
5.6%
근 45
 
5.4%
거 21
 
2.5%
Other values (90) 320
38.6%

중심점_경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.57286
Minimum128.54193
Maximum128.62521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T02:54:28.880682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.54193
5-th percentile128.54663
Q1128.54885
median128.5757
Q3128.59008
95-th percentile128.60576
Maximum128.62521
Range0.0832824
Interquartile range (IQR)0.0412373

Descriptive statistics

Standard deviation0.022401209
Coefficient of variation (CV)0.00017422968
Kurtosis-0.97229287
Mean128.57286
Median Absolute Deviation (MAD)0.0210594
Skewness0.32791299
Sum5785.7788
Variance0.00050181415
MonotonicityNot monotonic
2023-12-13T02:54:29.021271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
128.5758684 1
 
2.2%
128.5778442 1
 
2.2%
128.5484038 1
 
2.2%
128.5822366 1
 
2.2%
128.5981207 1
 
2.2%
128.5756973 1
 
2.2%
128.580807 1
 
2.2%
128.5615235 1
 
2.2%
128.5915901 1
 
2.2%
128.5900849 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
128.5419307 1
2.2%
128.5465563 1
2.2%
128.5465896 1
2.2%
128.5467675 1
2.2%
128.5470273 1
2.2%
128.5471909 1
2.2%
128.547239 1
2.2%
128.5482813 1
2.2%
128.5484038 1
2.2%
128.5485099 1
2.2%
ValueCountFrequency (%)
128.6252131 1
2.2%
128.6152851 1
2.2%
128.606588 1
2.2%
128.6024506 1
2.2%
128.6022584 1
2.2%
128.5996275 1
2.2%
128.5981207 1
2.2%
128.5975631 1
2.2%
128.5934994 1
2.2%
128.591752 1
2.2%

중심점_위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.904856
Minimum35.876004
Maximum35.946802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T02:54:29.181975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.876004
5-th percentile35.881685
Q135.887684
median35.894686
Q335.920872
95-th percentile35.945644
Maximum35.946802
Range0.07079753
Interquartile range (IQR)0.03318722

Descriptive statistics

Standard deviation0.021723895
Coefficient of variation (CV)0.00060504059
Kurtosis-0.98519717
Mean35.904856
Median Absolute Deviation (MAD)0.0104858
Skewness0.65056564
Sum1615.7185
Variance0.00047192762
MonotonicityNot monotonic
2023-12-13T02:54:29.340889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
35.88768448 1
 
2.2%
35.89421726 1
 
2.2%
35.91340025 1
 
2.2%
35.8863508 1
 
2.2%
35.887046 1
 
2.2%
35.8877186 1
 
2.2%
35.88140377 1
 
2.2%
35.93148037 1
 
2.2%
35.88807152 1
 
2.2%
35.88047969 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
35.87600438 1
2.2%
35.88047969 1
2.2%
35.88140377 1
2.2%
35.88280746 1
2.2%
35.8842006 1
2.2%
35.88428203 1
2.2%
35.88506673 1
2.2%
35.8863508 1
2.2%
35.88652032 1
2.2%
35.887046 1
2.2%
ValueCountFrequency (%)
35.94680191 1
2.2%
35.9463787 1
2.2%
35.94633275 1
2.2%
35.94289143 1
2.2%
35.94176089 1
2.2%
35.93370576 1
2.2%
35.93319755 1
2.2%
35.93165211 1
2.2%
35.93148037 1
2.2%
35.92549159 1
2.2%

사고건수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size492.0 B
4
17 
5
13 
6
10 
7
4 
10
 
1

Length

Max length2
Median length1
Mean length1.0222222
Min length1

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row10
2nd row7
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
4 17
37.8%
5 13
28.9%
6 10
22.2%
7 4
 
8.9%
10 1
 
2.2%

Length

2023-12-13T02:54:29.535958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:54:29.654520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 17
37.8%
5 13
28.9%
6 10
22.2%
7 4
 
8.9%
10 1
 
2.2%

사망자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
0
41 
1
 
3
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row0
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 41
91.1%
1 3
 
6.7%
2 1
 
2.2%

Length

2023-12-13T02:54:29.804938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:54:29.911821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 41
91.1%
1 3
 
6.7%
2 1
 
2.2%

부상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2222222
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-13T02:54:30.007803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median5
Q36
95-th percentile7
Maximum10
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3632996
Coefficient of variation (CV)0.26105737
Kurtosis1.7385865
Mean5.2222222
Median Absolute Deviation (MAD)1
Skewness1.0418186
Sum235
Variance1.8585859
MonotonicityNot monotonic
2023-12-13T02:54:30.140335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 17
37.8%
6 10
22.2%
5 9
20.0%
7 7
15.6%
10 1
 
2.2%
3 1
 
2.2%
ValueCountFrequency (%)
3 1
 
2.2%
4 17
37.8%
5 9
20.0%
6 10
22.2%
7 7
15.6%
10 1
 
2.2%
ValueCountFrequency (%)
10 1
 
2.2%
7 7
15.6%
6 10
22.2%
5 9
20.0%
4 17
37.8%
3 1
 
2.2%

Interactions

2023-12-13T02:54:26.362544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:25.833919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:26.120884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:26.470784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:25.947037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:26.199829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:26.592871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:26.037226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:26.283026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:54:30.249432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상사고대상사고년도시군구다발지점명중심점_경도중심점_위도사고건수사망자수부상자수
대상사고1.0000.6191.0001.0000.0000.0000.0000.0000.000
대상사고년도0.6191.0000.0000.0000.0000.4080.0000.0000.000
시군구1.0000.0001.0000.9160.6220.7630.0000.0000.000
다발지점명1.0000.0000.9161.0001.0001.0000.0001.0000.000
중심점_경도0.0000.0000.6221.0001.0000.6940.0430.0000.000
중심점_위도0.0000.4080.7631.0000.6941.0000.0000.6040.000
사고건수0.0000.0000.0000.0000.0430.0001.0000.0000.862
사망자수0.0000.0000.0001.0000.0000.6040.0001.0000.592
부상자수0.0000.0000.0000.0000.0000.0000.8620.5921.000
2023-12-13T02:54:30.419191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상사고년도사고건수사망자수대상사고
대상사고년도1.0000.0000.0000.418
사고건수0.0001.0000.0000.000
사망자수0.0000.0001.0000.000
대상사고0.4180.0000.0001.000
2023-12-13T02:54:30.564052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중심점_경도중심점_위도부상자수대상사고대상사고년도사고건수사망자수
중심점_경도1.000-0.6680.0440.0000.0000.0000.000
중심점_위도-0.6681.000-0.2050.0000.2470.0000.298
부상자수0.044-0.2051.0000.0000.0000.7680.283
대상사고0.0000.0000.0001.0000.4180.0000.000
대상사고년도0.0000.2470.0000.4181.0000.0000.000
사고건수0.0000.0000.7680.0000.0001.0000.000
사망자수0.0000.2980.2830.0000.0000.0001.000

Missing values

2023-12-13T02:54:26.775850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:54:26.966861image/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

대상사고대상사고년도시군구다발지점명중심점_경도중심점_위도사고건수사망자수부상자수
0자전거2017년대구광역시 북구1대구광역시 북구 노원동1가(원대오거리 부근)128.57586835.88768410010
1자전거2017년대구광역시 북구2대구광역시 북구 침산동(성덕빌딩 부근)128.59349935.887727707
2자전거2017년대구광역시 북구3대구광역시 북구 노원동1가(오봉오거리 부근)128.58244535.889756624
3자전거2017년대구광역시 북구4대구광역시 북구 노원동3가(팔달교 부근)128.55132235.894686616
4자전거2017년대구광역시 북구5대구광역시 북구 고성동3가(대구광역시립북부도서관입구 부근)128.5857335.884201606
5자전거2017년대구광역시 북구6대구광역시 북구 침산동(삼익아파트 입구 부근)128.59756335.885067606
6자전거2017년대구광역시 북구7대구광역시 북구 태전동(SK엔크린 한일주유소 부근)128.54702735.924292606
7자전거2017년대구광역시 북구8대구광역시 북구 칠성동2가(칠성시장역 부근)128.60658835.876004607
8자전거2017년대구광역시 북구9대구광역시 북구 태전동(대구병원 부근)128.54854235.913502507
9자전거2017년대구광역시 북구10대구광역시 북구 태전동(칠곡네거리 부근)128.5485135.933198505
대상사고대상사고년도시군구다발지점명중심점_경도중심점_위도사고건수사망자수부상자수
35자전거2019년대구광역시 북구9대구광역시 북구 침산동(오봉오거리 부근)128.58297535.88972404
36자전거2019년대구광역시 북구10대구광역시 북구 태전동(태전삼거리 부근)128.54655635.920872404
37자전거2019년대구광역시 북구11대구광역시 북구 팔달동(작원길1 부근)128.54884835.897484404
38무단횡단2016년대구 대구강북1대구광역시 북구 읍내동(칠곡지하차도 부근)128.54828135.931652606
39무단횡단2017년대구 대구북부1대구광역시 북구 노원동3가(노원성당 부근)128.56701335.892313404
40무단횡단2017년대구 대구북부2대구광역시 북구 고성동3가(침산새마을금고 고성동지점 부근)128.58569835.884282404
41무단횡단2017년대구 대구강북1대구광역시 북구 매천동(춘하추동김치 앞교차로 부근)128.54193135.904044506
42무단횡단2017년대구 대구강북2대구광역시 북구 구암동(튼튼한한의원 부근)128.56200535.941761505
43무단횡단2017년대구 대구강북3대구광역시 북구 태전동(매천고네거리 부근)128.5465935.91366404
44무단횡단2017년대구 대구강북4대구광역시 북구 태전동(운전면허시험장교차로 부근)128.54723935.925492404