5.2. Temporal Patterns of Human Convergence and Divergence
illustrates the temporal patterns of the average values of each cluster. Distinct temporal characteristics can be observed between the clusters.
Grid cells in C1 illustrate the high intensity of human convergence during most time slots, while C8 cells display divergence during most of the day, except during the morning commute (T6–T8) when the cells display high-intensity convergence. Grid cells in C2 show convergence from T6–T18 followed by high-intensity divergence from T19 until midnight (T23).
C3 and C4 have similar mobility patterns, with divergence mainly occurring from T6–T10 and convergence after T17. The major difference between these clusters is that the mobility intensity in C4 is significantly higher than that in C3. C3 also exhibits a clear convergence-divergence pattern from T11–T14.
Cluster C5 shows a distinct convergence pattern during the morning and evening commutes, which last approximately two time slots, and divergence in the remaining time slots of the day.
C7 shows an opposite human mobility pattern to that of C3, with convergence mainly occurring from T7–T9 and divergence after T17.
Compared to other clusters, there is no apparent temporal pattern in the grid cells of C6, and the mobility intensity is generally low.
The spatial distributions and mobility intensities of these human convergence and divergence patterns are associated with the spatial distribution of different land use types (e.g., residential, industrial, commercial, etc.) and the socioeconomic features of the geographical contexts [4
5.3. Spatial Distribution of Derived Clusters
We further analyzed the spatial distribution of the identified clusters by combining functional regions to gain better understanding of human convergence and divergence in the urban context. To simplify the maps, hollow cells were used to represent grid cells. In addition, we calculated the average percentages of different land uses in each cluster. We first calculated the proportion of each land use in each grid cell. Then, for grid cells belonging to a certain cluster, we calculated the average proportion of each land use. Table 4
lists the average percentages of the different land use types in each cluster.
shows the spatial distribution of C
1 and C
8. It is counterintuitive that some areas continue to converge (C
1) or diverge (C
8) during most time slots (Figure 6
). Most grid cells in these clusters are along the main roads of Shenzhen, and the average percentage of transportation land use in each grid cell in the two clusters is 15.2% and 18.4%, which are higher than the values in other clusters (Table 4
1 cells tend to be on the boundary between industrial and residential regions, with industrial and residential land use accounting for 31.3% and 30.3%, respectively, of all land use in the cells (Table 4
8 cells are mainly distributed along roads in industrial and downtown regions, and industrial and residential land use accounts for 41.7% and 16.5%, respectively, of land use in the cells. Thus, a large number of people flow into these regions during the morning commute (T7
). The regions include some important intra-urban traffic junctions, as well as several inter-urban transportation hubs connected to nearby cities, e.g., several high-speed intersections, two railway stations and Futian Port (which connects to Hong Kong). Therefore, it is likely that the human mobility patterns in C
1 and C
8 are related to urban transportation. A possible explanation for the continuous convergence and divergence is that our dataset does not include interactions with nearby cities and neglects outflow from the city and inflow from other cities through these grid cells; thus, there is continuous positive or negative netflow during the day. This indicates that these areas may be main hubs that are closely connected to regions outside the city. This observation provides a reference for urban planners to locate and optimize urban bus public transit, so that people can be easily transferred from these places. Therefore, it is likely that C
1 and C
8 are often located along main urban roads.
shows the spatial distributions of grid cells in clusters C
2 and C
2 grid cells are located in main commercial and industrial regions in the city, i.e., concentrated job locations that attract many people during the morning commute. The average commercial land use in this cluster is 11.6%, which is the maximum among all clusters (Table 4
). The commercial regions also include many shopping malls, restaurants, financial institutions and recreational venues (bars, karaoke, entertainment, etc.). Therefore, these locations also attract numerous people for shopping, meals, entertainment and other activities during the daytime, with high-intensity divergence after T19
. Grid cells in C
5 are mainly located near small business districts and workplaces inside residential regions, and the commercial, industrial and residential land uses are 3.4%, 31.1% and 40.1% in this cluster, respectively (Table 4
). Land use in residential regions is mixed and includes shopping malls, restaurants and recreational venues. Therefore, human mobility in these locations does not exhibit a consistent pattern, and the human mobility intensity is low. For example, these locations attract people for work during morning times, while people living in residential regions diverge to workplaces simultaneously. Thus, convergence and divergence both occur during the morning commute time (T6
). The convergence and divergence pattern in C
2 is likely to occur in main urban commercial regions, whereas it tends to occur near business districts and workplaces within residential regions in C
shows the spatial distributions of clusters C
3 and C
4. Grid cells in both clusters are mainly located in urban residential regions. The cells in C
3 are mainly located in the northern part of the city, while the cells in C
4 are located in the southern part of the city. As shown in Table 4
, residential land is dominant in C
3 and C
4, accounting for 50.4% and 67.6% of land use in the clusters, respectively. As discussed in Section 5.2
, there are also some human mobility differences between the clusters. For example, divergence lasts longer in C
4 than in C
3 during the morning (Figure 6
). The cluster differences may be caused by differences between economic development and human mobility space in the northern and southern parts of the region. The southern region is the core of the urban business district in Shenzhen, and the economy in the southern region is more developed than that of the northern region. The southern population density is also higher than that in the northern region. The more developed economy and high population density may be the underlying reasons for the cluster pattern differences. However, many immigrant workers live in the northern part of Shenzhen, and they tend to live near their workplaces to save commuting time [47
]. This short commute distance also makes it convenient for them to return home at noon for lunch or to take short breaks for activities, which may also contribute to the convergence-divergence pattern differences between T11
). Thus, the cells in C
3 and C
4 are likely located in urban residential regions, with C
3 mainly located in the northern part of the city and C
4 generally located in the southern part.
shows the spatial distribution of C
7. The grid cells in this cluster are mainly scattered across urban industrial regions. As shown in Table 4
, the percentage of industrial land in this cluster is 58.4%, which is the dominant land use; thus, a large number of people converge in these areas to engage in work during the morning commute and then diverge from these areas to return home or travel to other locations when they finish their daily work. Thus, the human convergence and divergence pattern in C
7 contrasts that in C
3, although human mobility in both clusters show typical daily travel patterns related to work. Therefore, the human mobility pattern in C7
is likely associated with urban industrial regions.
Based on the spatial distribution, grid cells in C
6 are not confined to a specific functional area, but scattered across different regions of Shenzhen (Figure 11
), including urban administrative, education, sports and tourism regions. People have the freedom to choose the timing at which they arrive and leave these regions; thus, no consistent temporal patterns are formed in the regions. We can see that the difference between residential land (27.9%) and industrial land (28.8%) is small (Table 4
). Many grid cells in this cluster are also located on the border of residential and industrial regions, so it is possible that a mixture of patterns occurs in these grid cells, e.g., during the morning commute, a grid cell containing industrial and residential land use would attract people to work, but people living in the grid cell may leave for work, resulting in an overall low netflow intensity. Some grid cells are also located in suburban areas with very low population densities, which may be another reason for the low intensity of human mobility.
The clusters identified in this study provide insight into the human dynamics at different locations in the city and potential land use characteristics associated with these different human mobility patterns. For example, C
1 and C
8 are likely located along main urban roads, whereas C
2 tends to be located in urban commercial regions. In residential-dominant regions, a geographical difference in human mobility can be identified between the northern and the southern parts of Shenzhen. Although the study area and dataset are different, our findings are similar to those of a study that explored the interdependence between land use and traffic patterns using GPS-enabled taxi data in Shanghai [27
]. In addition, these human mobility patterns are closely related to socioeconomic development and human activity areas [47
]. These findings provide preliminary knowledge about human convergence and divergence patterns in urban areas based on different land use information.
This knowledge can help urban planners and policy makers to improve the efficiency of urban operations. Additionally, it can be used as input in Markov or training models to predict real-time urban traffic flows [31
]. For example, when a new residential area is planned, human mobility patterns can be predicted based on its economic characteristics, thereby providing initial knowledge regarding the temporal travel demands of local residents. In addition, the findings can be used as a reference to estimate human convergence and divergence patterns using urban land use data in other cities without human tracking data. Conversely, urban land use information can be inferred based on these human mobility patterns [32
]. In addition, based on the temporal convergence and divergence patterns of human mobility in different urban regions, managers can optimize urban public bicycle dock locations or real-time bicycle schedules in convergent and divergent areas to maintain a balance between supply and demand [50
]. Similarly, taxi companies can allocate taxis in locations with high human convergence and divergence activities at specific times of a day [51
]. Therefore, these findings can be used to improve urban public transport efficiency, which helps promote intelligent urban mobility [52