The impact of social segregation on human mobility in developing and industrialized regions

Transportation and communication networks form the fabric of industrialized nations. The roll-out of such infrastructure in such regions can play a major role in supporting, or deterring, a regions’ ability to thrive economically and socially. Likewise, citizens’ use of these networks can tell us much about the region, including insight on how ideas and diseases may be spreading, or how to most effectively augment services, such as health care and education [1].

Existing studies of mobile phone data have given us insight on numerous aspects of human mobility [2‐6]. However, these studies tend to focus on regions with the highest mobile phone coverage, which also happens to be in more stable, mature, and developed regions. Thus, the models produced based on this data might not be as appropriate for developing regions with a substantially different patterns of social interactions and human mobility. However, these highly industrialized and wealthy regions represent less than one-third of the world’s population, with the remaining two-thirds living in developing and less economically mature regions. Accurate models for developing regions are critical as these regions are facing the most rapid demographic and economic shifts worldwide, and are in even greater need of such models to help inform policy makers, urban planners, and service providers. Yet, little work has been done to assess the appropriateness of models conceptualized for industrialized regions for use in developing regions.

Obtaining a comprehensive and accurate dataset of the telecommunications activity in developing regions can be extremely difficult due to security and privacy considerations, limited coverage by any single provider, and the need for a rigorous data capture methodology and infrastructure. The Data4Development (D4D) dataset [7] provided a unique opportunity by collecting data throughout the Ivory Coast and releasing it specifically for research purposes, so that developing regions could also be analyzed in greater detail. The contrast of long-standing cultural and linguistic diversity with relatively recent and rapid urbanization offered researchers a unique opportunity to understand the communication and mobility patterns and needs of a developing nation during key phases of its transformation.

Our study brought the D4D data from Ivory Coast together with mobility data from an industrialized nation (Portugal) in order to assess the ability of human mobility models developed for industrialized regions to accurately model developing regions. We focus on the comparison of these two countries at very different stages in their industrialization and in their levels of cultural and linguistic diversity, and sheds new light on the applicability of metrics and models conceptualized for industrialized regions to developing regions. Our results demonstrate the importance of considering cultural and linguistic diversity in the construction of new models to address the challenges of developing regions. The insights gained from our study have important applications to policymaking, urban planning, and the services deployments that are transforming Ivory Coast and many other developing countries.

In the following sections, we provide additional details on the data used in this study, the results derived, and the conclusions drawn.

Leveraging mobile phone data to elucidate and quantify many aspects of human life is growing in popularity. For example, mobile phone data has been used to gain insights from a diversity of cultures, ranging from university students to professionals in the US, Finland, and Africa [8]. Targeted cultural patterns included pace of life, reaction to outlier events, and social support, as opposed to the mobility focus of our study. Eagle et al used mobile phone communication logs and top up records to conduct a comprehensive comparison of urban and rural life within a small country, as opposed to across countries as targeted by our research [9]. Mobile phone data has also been used to study the seasonal consumption patterns of tourists in Estonia [10].

As part of the D4D competition, researchers studied a wide range of topics ranging from social behaviors, economics, health, transportation, and mobility. Several studies [11‐13] considered mobility patterns for the purpose of improving the planning or efficiency of the transportation systems. While some articles [14, 15] targeted mobility within the largest city, Abidjan, and others considered mobility across the country, none tackled the challenge of assessing mobility models created for mature and industrialized nations on the developing nation of Ivory Coast. Additionally, ours is the first study to have considered the linguistic and cultural barriers and affinities that we have shown to be significantly stronger in the developing nation of Ivory Coast, in comparison the more industrialized nation of Portugal. Thus our study represents a novel and important contribution to understanding the challenges of creating globally applicable mobility models.

We used four datasets to assess and compare the human mobility patterns in the Ivory Coast and Portugal. The first dataset, D1 was provided by Orange Telecom as SET2, via the Data for Development (D4D) Challenge [7]. This dataset was based on anonymized Call Detail Records (CDRs) of 2.5 billion calls and SMS exchanges between 5 million users December 1, 2011 until April 28, 2012 (150 days).

SET2 contains consecutive call activities of each subscriber over the study period. Each record in this dataset represents a single connection to an antenna and contains the following fields: timestamp, anonymized ID of the user, and the antenna ID they connected to. To further anonymize this data, the original dataset was subsampled to the calls of 50K randomly sampled individuals for each of 2-week periods in the dataset. The geographical positions of the antenna for D1 was also provided and visualized in Figure 1B according to the density of antennae in every region. Black lines overlaid on the country represent the corresponding first-level administrative boundaries (19 Ivorian régions and 20 Portuguese districts). Records without antenna IDs were removed; 107 antennae had no calls and 128 antennae had no population movements.

The second dataset, D2, provided 400 million anonymized CDRs across Portugal for the time period of January 1, 2006 to December 31, 2007. D2 was also provided by Orange Telecom with 2000 antennae distributed across Portugal, and the same data fields as in D1.

Datasets D3 and D4 provided a high-resolution population density data for Ivory Coast [16] and Portugal [17], respectively. To map the population data to the antennae, we created a Voronoi tessellation [18] of each country based on the antennae location. For the 12 locations that had 2-3 antennas in a single location, those 2-3 antennas were collapsed into a single Voronoi cell. Each antenna was assigned the total population within the corresponding Voronoi cell. Figure 1A provides a logarithmic scale population density distribution map using the data from D3 and D4. The population maps were created as an interpolation of the population density at each antenna.

Although used widely for human mobility studies, mobile phone data provides only a proxy for human mobility, for example, callers are tracked only to the spatial resolution of the antenna (which may be up to 70 km, depending on tower height and terrain), and usually only when the phone is in use while not everyone uses a mobile phone while traveling. However, even in developing regions, mobile phone penetration is high. Ivory Coast has 85% mobile phone coverage, with Orange Telecom (the provider of the data for Ivory Coast and Portugal used in this study) being the top mobile phone provider having a market share of 42.5%. In general mobile phone penetration in Portugal is almost absolute, while the total number of phone accounts is even higher than the total number of people - 142% of the country population owns a mobile device, with Orange Telecom at 19% of market share.

We first performed a bulk mobility pattern analysis based on D1 and D2 by plotting the probability density function P ( Δ r ) of the individual travel distances (or jump sizes) Δr in a trace of agglomerated de-identified callers over a period of two weeks, for Ivory Coast (left plot, solid black line) and Portugal (right plot, solid black line), as shown in Figure 2A. The distributions were qualitatively similar to each other except that at the administrative level, the distributions in Ivory Coast are much more scattered than those observed in Portugal, suggesting greater regional variance. We fit the density function to a truncated power law of the form P ( Δ r ) = ( Δ r + Δ r 0 ) − β exp ( − Δ r / κ ), as described in [2], where Δ r 0, β, and κ are the fit constants. While the two distributions had similar cutoff distance (κ Portugal = 106 ± 10 km; κ Ivory = 122 ± 5 km), the two countries have slightly different power law coefficients (β Portugal = 1.37 ± 0.06; β Ivory = 1.62 ± 0.03). This higher coefficient in Ivory Coast indicates that the likelihood of displacement generally decays faster with distance in comparison to Portugal.

We also investigated regional differences in the mobility patterns. In both cases of Ivory Coast and Portugal, we identified the first level administrative boundaries as the highest country-defined level of partitioning. For Ivory Coast these are called ‘régions,’ while in Portugal they are referred to as ‘districts.’ We partitioned the mobility data by the different level-one administrative regions and overlaid the same density functions specific for each administrative region on the same plots above. Different administrative regions are identified by different scatter marker types and colors. We observed the same truncated power law behavior across the different regions, but the Ivory Coast regions exhibited significantly greater diversity than similarly defined regions in Portugal. This would indicate that in Ivory Coast the likelihood that people migrate and commute with respect to distance is much more dependent on what part of the country they are in, as opposed to in Portugal where the different administrative regions show very little diversity from each other.

Another important metric for assessing mobility patterns is the radius of gyration. As defined in [2], the radius of gyration for each caller is the characteristic distance traveled by each caller when observed up to time t, and is computed as the probability distribution function of the mean squared variance of the center of mass of each user’s set of catchment locations. The results are plotted in Figure 2B, with t= the period of data collection for each of the datasets.

The distributions plotted in Figure 2B and show that the bulk mobility data from Ivory Coast adheres well to the scale-free framework proposed in [2]. The similarity in the bulk mobility characteristics between Ivory Coast and Portugal serves to strengthen the argument that we can make valid comparisons between the two datasets, as described in the sections below.

Daily commuting patterns are a critical component of any region’s mobility requirements. Displacement is defined as movement from one cell tower to another cell tower between two consecutive calls, and is a key marker for assessing mobility. To focus on daily commuting patterns, we excluded data collected during weekends, and computed the fraction of inter-call events that were accompanied by displacements in a moving 40-minute window of time for Ivory Coast and Portugal. We averaged the fraction of displacement for each 40-minute window across 45 weekdays to get a 24-hour temporal profile of the probability of displacement during a workday.

The first and probably the most significant difference is the absolute difference in the probability of displacement, which can be seen in Figure 2C. We observe that in Portugal, in a given period, people are much more mobile compared to their counterparts in Ivory Coast.

Both countries exhibit a commuting pattern; there is a sharp rise in the probability of displacement around 7-9 a.m. The evening decline is not as sharp, suggesting that people leave work at different times in the evening.

Significant quantitative differences between the countries can also be seen throughout the day. In Portugal, people in Lisbon and across the nation exhibited similar likelihood to commute during the busiest hours. However, a significantly higher percentage of people in Abidjan were mobile than across the nation. Additionally, while displacement levels in Abidjan and across Ivory Coast were similar during the lowest period (4-7 a.m.). Displacement for the same period is significantly higher for Portugal than for Lisbon, and is likely an indicator of more significant numbers of suburban commuters in Portugal than in Ivory Coast.

Figure 2D provides a comparison of the mean migration distances between the 2 countries for the same period. Here again, the average distance traveled is significantly less in Ivory Coast and its capital city, than in Portugal. In the country-wide data, we observe a sharp increase in the mean inter-event displacement distance near the morning peak commute (around 5-9 a.m.) in both Ivory Coast and Portugal. However, the spike in distance encountered in Lisbon during morning commute does not occur in Abidjan. This difference may be indicative of people both living and working in close proximity in Abidjan, as opposed to commuting in from outside or across the city as is often the case in developed regions with more comprehensive public transport facilities.

We examined the country-specific commuting pattern more closely by looking at how the distance commuted may affect the daily behaviors. The observed distances traveled were binned (0-1 km, 1-5 km, 5-10 km, 10-20 km, and 20-50 km), and the daily temporal profile of the probability of displacement was computed for each bin for the two countries, as shown in Figure 2E.

The temporal profiles for both Ivory Coast and Portugal show a bimodal pattern in Figure 2E. For Ivory Coast the morning peak is around 7 a.m. and the evening peak is around 8 p.m., as opposed to roughly 10 a.m. and 6 p.m. for Portugal. Note that the peaks for Portugal are much sharper than those of Ivory Coast. Overall these differences indicate a shorter prime commuting period for Portugal, which is likely indicative of the ability for commuters to travel more efficiently to their destinations.

Large networks, such as the telecommunications or transportation networks of a nation, often exhibit community structure, i.e., the organization of vertices into clusters with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Identifying the community structure in such networks has many applications, such as better placement and provisioning of services. Recently, this type of community structure analysis has been performed on land-line communications in Great Britain [19], mobile connections in Belgium [20], United States [21], and various other countries across Europe, Asia, and Africa [22, 23]. While there is research investigating the impact of physical human mobility on the space-independent community structure [21] there has been a lack of similar research for developing nations.

Network modularity [24] is a measure of the strength of the division of a network into clusters. Networks with high modularity have dense connections between nodes within clusters, and sparse connections between nodes in different clusters. Modularity is computed as the fraction of edges that fall within a cluster, minus the expected such fraction if the edges were distributed at random with respect to the node strength distribution. The value of modularity lies in the range [ − 1 , 1 ], and is positive if the edges within groups exceeds the number expected on the basis of chance.

The definition of network modularity is:

where A i j is the weight of the link from i to j, k i is the sum of the weights from node i, c i is the community that node i was assigned to, m = 1 2 ∑ i j A i j, and δ ( c i , c j ) is 1 if c i = c j and 0 otherwise.

High modularity in mobility networks may point to an efficient organization of residences, employment, and services all in close proximity, or it may point to restrictive policies or infrastructures that limit free movement across communities. We were interested in the community structure of developing nations, such as Ivory Coast, especially in comparison to more developed nations, such as Portugal.

We used datasets D1 and D2 to build the human mobility networks and identify the community structure of antennae within the Ivory Coast and Portugal. We set nodes to the locations of each cell tower, and edges to the total number of migrations of all people that placed two consecutive calls between the two nodes within a time window of 24 hours. We tested the following Community Detection algorithms: Louvain [25], Le Martelot [26], Newman [27], Infomap [28], and a new method of community detection suggested in [29]. We computed the modularity of community structures identified by each of these methods. The method described in [29] provided the highest modularity, and was subsequently chosen to be used for this part of the study. Figure 1C graphically compares the communities identified (in color) with their first level administrative boundaries (outlined in black).

An especially interesting difference in the communities identified for Ivory Coast and those identified for Portugal was the similarity between identified communities and the official administrative boundaries of the nations. We calculated 7 different clustering coefficients, each representing the qualitative similarity between the two different partitions of each region. While the communities identified for Portugal exhibited high similarity with the 20 official administrative boundaries (districts), this was not the case for the Ivory Coast’s 19 official administrative boundaries (régions). As shown in Table 1, communities identified for Portugal show significantly higher similarity (as much as 28% higher clustering coefficients) to administrative boundaries, in comparison to that of the Ivory Coast.

While this significant difference in community and official boundary alignments may be attributable to the layout of infrastructure along official boundaries, we began to question whether there might be more fundamental differences. Previous studies have shown that other factors, such as geographical features, can play an important role in how communities are formed and services are sought [19, 37]. However, little has been done to investigate the direct impact of culture and language on human mobility.

Ivory Coast represents an especially interesting context to investigate cultural and linguistic influences on mobility within a single nation. The Ivory Coast is a nation made up of more than 60 distinct tribes, classified into 5 principle regions [38]. The official language is French, although many of the local languages are widely used, including Baoulré, Dioula, Dan, Anyin and Cebaara Senufo, and an estimated 65 languages are spoken in the country.

Intuitively, these cultural and linguistic differences are likely to influence mobility patterns in the region. However, it is also known that as regions become more industrialized, cultural ways are often blended or lost altogether. Portugal represents an interesting context for the latter, as Portuguese is the single national language of Portugal, and any tribal boundaries pre-date Roman times.

Due to the vast differences seen in the network community structure to administrative-defined boundaries, our goal in the next section was to understand if tribal structure of the Ivory Coast could be the influential factor of the mobility patterns in the region.

Since the communities detected in Section IV exhibited low similarity to administrative boundaries, we began to investigate the impact of certain factors present in the Ivory Coast that may be attributable to such a substantial difference. Since people and their behaviors throughout a large portion of Africa are still impacted by their tribal affiliations while any tribal boundaries in Portugal predate Roman times, studying the tribal boundary impact in Ivory Coast represented a perfect example of this. We generated digital shape files for each of the eight distinct tribal regions in Ivory Coast [39]. Such tribal maps do have a level of uncertainty associated with them due to migrations over time; however, we used the most recent versions of tribal boundaries available.

We then modified the community detection approach to use the tribal boundaries as the Level 1 boundaries and subsequently ran a hierarchical community detection using the Louvain method [25] in each of these Level 1 tribal partitions in order to produce the sub-communities inside different tribal regions which we will refer to as sub-tribal communities. The Louvain method provided the closest final number of partitions to the administratively defined subprefectures of the algorithms that we tested, and was thus chosen as the most appropriate for current purposes. By doing so, we were able to generate sub-tribal communities while also conserving the physical shape of each tribal region. Figure 3A demonstrates this by showing the official prefecture and subprefecture boundaries (left) and the tribal and sub-tribal communities (right) that were created using this approach. The larger first level partitions (prefecture and tribal) are indicated as a single color, while the smaller secondary partitions (subprefecture and sub-tribal) are indicated as black lines within their respective first level partition. The number of subprefectures and sub-tribal regions are very similar (approximately 250 and 220 respectively) and thus can be used as a valid comparison against each other.

As a first measure of impact of tribes on mobility, we aggregated the mobility network between communities. Figure 3B provides a plot of the mobility network with each node representing a sub-tribal community, and each edge is colored on a logarithmic scale to reflect the number of migrations between the connected nodes in the mobility network. Nodes are colored with the same logarithmic scheme and represent the sum of all migrations into this location. The intra-tribal community mobility network is plotted separately from the inter-tribal community mobility network, in order to facilitate comparison. The intra-tribal network plots only those edges between communities in the same tribe. The inter-tribal network plots only edges between communities of differing tribes.

This diagram provides a first insight into tribal influences on mobility. Note that the number of intra-tribal migrations (as indicated by the color coding of edges) dwarfs the number of inter-tribal migrations. Additionally, the inter-tribal migrations are largely dominated by connections to the largest city, Abidjan.

The impact of the tribal borders versus administrative borders in Ivory Coast on human mobility could be also directly demonstrated by measuring the strength of normalized mobility fluxes at different geographical distances in case they cross or do not cross those borders. Figure 4B provides a zoomed in version of the statistically significant data points of Figure 4A and shows that for any given distance, the flux between intra-Administrative and intra-Tribal regions is consistently higher than the flux between inter-Administrative and inter-Tribal regions. One can then conclude that the fluxes crossing the tribal border are much weaker on average compared to fluxes at the same geographical distance but crossing an administrative border instead.

We also quantified the strength of these partitioning ties by computing the network modularity of the sub-tribal communities versus that of the administrative boundaries. The network modularity of the sub-tribal communities was 0.6548, in comparison to a network modularity of 0.6158 for the administrative boundaries. Given that the number of regions for both partitions (sub-tribal and sub-prefecture) is very similar, this increase of network modularity corresponding to sub-tribal communities by the definition of network modularity shows again that mobility patterns have a stronger connection in a sub-tribal country partitioning compared to that of an administrative partitioning.

In previous sections, we demonstrated the issues arising from using mobility models, such as the Gravity and Radiation models, on regions where the partitioning scheme, such as Ivory Coast administrative boundaries, does not reflect the regional affinities and border strengths these models assume. We illustrated techniques to create more appropriate partitioning schemes, such as the tribal communities, and demonstrated the ability to achieve higher accuracy mobility modeling using this improved partitioning.

However, it may not always be possible to simply re-draw borders. Instead, tools are needed to assess the efficacy of an existing partitioning scheme, in terms of the affinities within the identified borders and the strength of the borders.

In this section, we propose two novel techniques to address the above challenge. Firstly, we present a metric to determine whether affinities exist within borders that may impact the accuracy of mobility modeling. We test our metric on the tribal and administrative boundaries used in the previous sections. Secondly, we propose a technique to assess the strength of existing borders, and we demonstrate our technique on the existing administrative borders of Portugal and Ivory Coast. Our techniques use the same mobility data used in previous sections and can be performed on any regional partitioning scheme, and thus provide valuable tools to mobility researchers and to urban planners.

Africa is a continent that has been shaped by human migration over tens of thousands of years. Indeed, migrations within and beyond the African borders have recently been shown as influencing all civilizations as we know them. However, until recently, there has been a dearth of data on the forms and patterns of migration within the nations of Africa. Moreover, much of the mobility research is based on theories that have emerged from highly industrialized nations and lack validation in the context of developing environments.

Our study has demonstrated that many of these conceptions are not necessarily applicable in the African context. We have made these differences clear by comparing our findings in Ivory Coast to one such industrialized nation, Portugal. For example, we have shown that the probability of displacement during normal commuting hours in Portugal is often nearly double that of Ivory Coast for the same time of day. Similarly, average distances traveled by commuters in Portugal is nearly double that of commuters in Ivory Coast.

While differences in the likelihood of travel and average distance travel can be attributed to quantitative differences in infrastructural support for mobility this already strongly affects the whole mobility picture leading to a number of quantitative dissimilarities. Our study shows evidence of more fundamental differences in infrastructural support for mobility, such as tribal, cultural, and lingual differences. In addition, we demonstrate that the similarity between administrative boundaries and communities detected in mobile phone data is markedly lower in Ivory Coast than in Portugal.

By identifying the tribal influence on mobility in the Ivory Coast, we were able to illuminate further differences in mobility patterns. For example, we were able to show intra-tribal migrations were much more frequent than that of inter-tribal migrations over the same distance and therefore are under or overestimated by the models. Taking this into account by exploiting our tribally aligned communities for the mobility models drastically improves modeling of human mobility in Ivory Coast. We validated this higher accuracy by computing the MAPE across all data points for both models, and found a 20% to 50% higher error for the models using administrative boundaries. We also validated our results by computing the distribution of migrations by distance migrated and found that by using this sub-tribal method of spatial units definition in modeling human mobility we were able to improve the accuracy of the models so drastically, that the Ivory Coast performed even better than its developed country-counterpart, Portugal.

We propose novel techniques for assessing the strength of borders within a regional partitioning scheme, and for assessing the impact of inappropriate partitioning on model accuracy. Our results offer improved insights on why models developed for mature and stable regions may not translate well to developing regions, and provide tools for urban planners and data scientists to address these deficiencies.

We believe that the findings of this study demonstrate important differences that exist between developing and industrialized regions. Using these two countries as an example, we are motivated to further explore these differences by considering more countries and areas with diverse cultural, economical and social backgrounds.