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Table 3. Basic information on Shanghai traffic monitoring stations
Station name Longitude Latitude Station type
Caoxi road roadside station 121.435 31.174 Roadside air station
Yan'an West road roadside station 121.414 31.318 Roadside air station
Gonghe new road roadside station 121.449 31.277 Roadside air station
Dongfang road roadside station 121.528 31.212 Roadside air station
National exhibition center 121.467 31.207 Roadside air station
Hongqiao airport station 121.352 31.208 Airport air station
Waigaoqiao port area - station 2 121.582 31.369 Port air station
Waigaoqiao port area - station 4 121.654 31.331 Port air station

Mobile phone signal data
The mobile phone signal dataset for Shanghai, covering the period from November 11 to November 30,
2019, was provided by JISMART (http://daas.smartsteps.com/, accessed on January 19, 2021). This dataset
includes information on time, location, and users. Initially, it comprised data on approximately 5.28 million
mobile phone users, which can be considered representative of Shanghai’s population of around
23.55 million, excluding the Chongming District .
[39]

Using data from the Seventh National Population Census as a reference, SPSS 26.0 was employed to
perform a correlation analysis and paired sample t-test to compare the spatial distribution, gender
composition, and age structure of the resident population in Shanghai, as inferred from the mobile signaling
data. These analyses aimed to verify whether significant differences exist between the two datasets and, in
turn, to assess the extent to which mobile signaling data can represent the demographic distribution of
Shanghai's resident population.

Regarding population distribution, the proportions of residents in each administrative district, as derived
from both the census and the mobile signaling data, are presented in Table 4. The Pearson correlation
coefficient was calculated at 0.998, indicating a strong linear relationship. Furthermore, the significance level
of the paired sample t-test was 0.999, which is much greater than the conventional threshold of 0.05,
suggesting that there is no statistically significant difference between the two datasets at the 0.05 level.
Overall, these results confirm that mobile signaling data can reliably represent the population distribution
across Shanghai’s various districts.

In analyzing the gender structure, the gender ratio, defined as the number of males per 100 females, was
used as the evaluation metric. Table 5 presents the gender ratios for each administrative district in Shanghai
based on data from the Seventh National Population Census and mobile signaling data. The Pearson
correlation coefficient between the two datasets was 0.794, indicating a strong positive correlation. The
paired sample t-test result was 0.374, greater than the 0.05 significance level. This suggests there is no
statistically significant difference in the mean gender ratios between the two datasets, supporting the
conclusion that mobile signaling data can effectively represent gender structure across different districts in
Shanghai.

Regarding age structure, a comparative analysis was conducted for three age groups (0-14, 15-64, and
65 years and older) across Shanghai’s administrative districts, as shown in Table 6. The Pearson correlation
coefficients for the three age groups are 0.518, 0.912, and 0.942, respectively - all above 0.5 - indicating