IV. Results
4.2 Descriptive statistics and correlation results
This section investigates the spatial characteristics of crash risk complaints, statistical characteristics of each variable, and correlation between variables.
Below graphs show the distribution of crash and crash risk complaints for the given period (aggregated value, 2008-2015).
Figure 17. Distribution of crash risk complaints (left) and normalized one with pop + emp (right)
Figure 18. Distribution of crash (left) and normalized one with pop + emp (right)
Right maps show the distribution of normalized crash and crash risk complaints. Number of complaints and crash are divided by sum of population and number of employees. Left maps which are not normalized with exposure show similar distribution pattern, while the normalized ones are not.
When the exposure is considered, number of crashes show almost opposite pattern. Comparing the normalized ones, it is hard to expect that there is high correlation between crash and crash risk complaints.
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Although it is the result of aggregating 8 years, there are 150 dong-districts that the number of total crash risk complaints is zero. Mean is estimated as 7.8, which means that the number of complaints raised in each dong is less than one in a year. There are 335 dong-districts corresponding to it. In areas where crash risk complaints are actively registered, mean value is calculated as 27.
Figure 19. Histogram of crash risk complaints (left) and crash (right)
Table 3 shows the descriptive statistics of all variables. For the dependent variable, total number of crash (crash), death crash (death), crashes occurred between cars (car_car), pedestrian-car crash (pedestrian), signal violation crash (v_signal) and pedestrian protection violation crash (v_pedestrian) for eight years (2008-2015) are used. The maximum value of crash is 6,343 and it is counted in Yeoksam-dong, Gangnam-gu. The lowest number of crash is occurred in Suha-dong, Jung-gu, which has only two blocks. As a matter of district-size, crosswalk density, the ratio of intersection which crossing is installed compared to the total intersection, recorded 100% in Suha-dong.
27 Table 3. Descriptive statistics of variables
Variables Unit Mean S.D. Min Max
Dependent variables (crashes)
crash Count 641.6 964.3 2 6364
death Count 7.4 10.6 0 58
car-car Count 510.8 771.5 2 5218
pedestrian Count 188.1 291.1 0 1757
v_signal Count 93.3 143.8 0 854
Complaints
total complaints Count 7.8 15.3 0 132
facility Count 3.1 6.3 0 61
visibility Count 2.8 6.5 0 62
signal Count 0.9 1.8 0 14
children Count 2.0 4.5 0 37
Exposure
pop + emp Person 28981.2 44726.4 165.0 285920.2
population density person/km2 22409.4 17028.8 2056.9 175111.2 employment density person/km2 27657.6 44358.0 1376.4 678608.9
male % % 50.0 3.7 43.6 62.7
children % % 10.0 3.2 3.7 19.6
senior % % 14.4 3.3 8.2 23.6
Built environment - facilities
road density km/km2 0.029 0.010 0.003 0.065
crosswalk density % 22.6 14.2 0 100
school density km2/km2 0.010 0.016 0 0.140
busstop density count/km2 37.1 30.9 0 239.9
subway density count/km2 8.3 21.0 0 165.8
building density km2/km2 0.4 0.1 0 1.3
parkinglot density count/km2 4.3 11.7 0 174.4
Built environment - landuse
residential area % 24.4 24.8 0 100
commercial area % 48.2 34.6 0 100
industrial area % 1.5 7.2 0 68.4
green area % 16.2 22.8 0 99.8
HHI Relative scale 5864.7 2451.9 2443.7 10000
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Meanwhile, the number of crashes occurred in 8 years recorded 2 as minimum value and 6,364 as maximum value. The mean value 641.6 shows that about 88 crashes are occurred in one district per year. This result can be considered as reasonable because there are too many dong-districts which have too small area. Therefore, if we assume that the frequency of the crash is relatively proportional with the area, then the number of crash occurrence can be under-estimated considering the district area.
Therefore, we need to classify the dong according to the area and conduct the analysis respectively. To consider the difference between dong-scale, we divided dong with the standard 1𝑘𝑚2. The distribution of the classified dongs is shown in figure 20. 139 dongs have over 11𝑘𝑚2 area and the 302 dongs have under 11𝑘𝑚2. Below table 4 shows the descriptive statistics of variables by the area classification. It shows that there are big differences in the number of crashes and also in the crash risk complaints. In case of the small area group (under 1𝑘𝑚2), the mean values of crash risk complaints recorded 2.4 for 8 years. However, big area group (over 1𝑘𝑚2) record 19.4 which means that average 2 complaints are steadily raised in those districts.
Table 4. Descriptive statistics of variables by area classification
area: under 𝟏𝒌𝒎𝟐 (n=302) area: over 𝟏𝒌𝒎𝟐 (n=139)
Variable Mean S.D. Min Max Variable Mean S.D. Min Max
crash 196.2 212.1 2 1231 crash 1609.1 1220.6 54 6364
crash risk
complaints 2.4 4.9 0 37 crash risk
complaints 19.4 22.3 0 132
pop + emp 7441.5 8089.1 165.0 50246 pop + emp 75779.7 54902.0 7100.1 285920 pop density 18649 18169 2056 175111 pop density 30579 10313 8447 70672 emp density 35073 51509 2123 678608 emp density 11546.0 10237.4 1376.4 64572.3
male % 50.3 4.4 43.6 62.7 male % 49.1 1.3 45.8 56.2
children % 9.1 3.0 3.7 16.5 children % 11.9 2.6 4.7 19.6
senior % 15.2 3.5 8.4 23.6 senior % 12.7 2.2 8.2 19.5
road density 0.031 0.011 0.003 0.065 road density 0.025 0.008 0.010 0.048 crosswalk
density 23.9 15.5 0 100 crosswalk
density 19.7 10.6 3.8 73.7
school density 0.010 0.019 0 0.139 school density 0.011 0.007 0 0.040 Bus stop
density 39.8 36.2 0 239.9 Bus stop
density 31.2 11.5 8.4 74.2
subway
density 10.6 25.0 0 165.8 subway
density 3.3 3.0 0 17.4
Parking lot
density 5.4 14.0 0 174.4 Parking lot
density 1.7 1.5 0 6.7
building
density 0.4 0.1 0 1.3 building
density 0.3 0.1 0.005 0.5
residential % 20.6 26.8 0 100 residential % 32.4 17.3 0.2 87.5
commercial
% 57.7 35.6 0 100 commercial
% 27.6 20.5 0 89.5
industrial % 1.3 6.9 0 62.9 industrial % 1.8 7.9 0 68.4
green % 9.7 18.5 0 98.8 green % 30.3 24.8 0.548 99.8
HHI 6622.8 2467.0 2443.7 10000 HHI 4217.7 1379.8 2545.8 99589.0
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Figure 20. Area classification
Table 5 shows the results of correlation between dependent variables and independent variables. First, crash and crash risk complaints are strongly correlated. Second, exposures – pop+emp, pop density – are negatively correlated with crash. Children% shows positive correlation with crash, while senior%
and male% showed the opposite direction. Third, for the vehicle exposure – road density – is negatively correlated with the number of crash. Forth, in case of the facility variables, crosswalk density and parking lot density show the negative correlation with crash, and other built environment variables have weak correlation with crash. Fifth, in case of the landuse variables, proportion of the residential area and green & openspace area are positively correlated with the crash. Commercial % and HHI show negative correlation.
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Table 5. Correlation results between crash & crash risk complaints and independent variables crash Correlation between crash and complaints
crash risk complaints 0.7005 * Normalized crash
pop + emp 0.9362 * Normalized crash risk complaints 0.0483 pop density 0.2802 *
emp density -0.1619 *
senior% -0.3626 *
male% -0.1035 *
children% 0.2399 *
road density -0.1820 * crosswalk density -0.1124 *
school density 0.0117 bus stop density -0.0858 subway density -0.0934
building density -0.1751 * area: over 1𝒌𝒎𝟐 Normalized crash parking lot density -0.1108 * Normalized crash risk complaints 0.1702 *
residential % 0.1758 * commercial % -0.1918 *
industrial % 0.0266 area: under 1𝒌𝒎𝟐 Normalized crash
green open % 0.1305 * Normalized crash risk complaints 0.0348
HHI -0.3991 *
*: p<0.05
Furthermore, to figure out pure relationship between crash and complaints without the exposure, normalized crash and complaints are also analyzed. When the population and employment exposure are considered, the correlation between the crash and complaints dropped sharply and it recorded 0.0483.
Therefore, it can be said that the high correlation figure (0.7005) between the crash and crash risk complaints is originated from exposure. Also, we conducted the correlation test between crash and complaints according to the area groups. First group which have the districts over 1𝑘𝑚2 show the significant correlation between normalized crash and normalized crash risk complaints. Meanwhile, in case of the second group, it shows the similar correlation record with the whole one. The correlation table results suggest that there is no big relationship between the number of crash and the number of crash risk complaints. Exposure (population and number of employees) explains the most of the correlation between the crash and complaints.
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