Mobile Phone Data Research Articles

Travel self-containment of new towns in a polycentric city: insights from Shanghai with mobile phone data

ABSTRACT Travel self-containment has been a main issue in new town planning since the mid-twentieth century, but its assessment has long been a challenge, especially in a polycentric city where networked structure complicates travel behavior. Traditional methods, such as census investigations can hardly trace multidirectional commuting patterns of large samples nor disclose non-commuting patterns. This paper fills the void by using billions of passive mobile phone data by which the interval of stay in a particular location and its spatiotemporal regularity can be identified to capture phone users’ travel routes. Shanghai, a polycentric city that evolved under the longstanding development of the ‘One City, Nine Towns’ polycentric urban spatial plan, is selected to assess various types of travel. The results disclose the disparity of the inward, intra, lateral and reverse journeys of commuting and non-commuting routes and reveal how the specificity of new towns, e.g. distance to the central city and metro lines, matters for such disparity.

Mobile Phone Data Transforming Casework in Asylum Decision-making. Insights from the Danish Case

Mobile Phone Data Transforming Casework in Asylum Decision-making. Insights from the Danish Case

Contextual Enrichment of Crowds from Mobile Phone Data through Multimodal Geo-Social Media Analysis

Contextual Enrichment of Crowds from Mobile Phone Data through Multimodal Geo-Social Media Analysis

Scalability challenges of machine learning models for estimating walking and cycling volumes in large networks

This study explores the scalability of machine learning models for estimating walking and cycling volumes across the extensive New South Wales (NSW) Six Cities Region in Australia using mobile phone and crowdsourced data. Previous research has focused on localized applications, missing the complexities of larger networks. The research addresses this gap by identifying unique challenges such as the scarcity and representativeness of observed count data, gaps in the crowdsourced and mobile phone data, and inconsistencies in link-level volume estimates. We propose and demonstrate the application of strategies like enhancing geographical diversity of observed count data and employing an extensive cross-validation approach in model training and testing. By leveraging various auxiliary datasets, the study demonstrates the effectiveness of these strategies in improving model performance. These findings provide valuable insights for transportation modelers, policymakers, and urban planners, offering a robust framework for supporting sustainable transportation infrastructure and policies with advanced data-driven methodologies.

Assessing spatial configuration of barrier-free facilities from the perspective of age-friendliness: A case Study of Beijing, China

Enhancing urban inclusivity is a crucial task for sustainable urban development. One key challenge is to create seamless and accessible urban space that caters to the mobility needs of people, especially the elderly. Addressing this pressing challenge requires a comprehensive overview of barrier-free facilities (BFFs) configuration within cities. In this study, we propose an integrated framework for assessing the BFFs in Beijing by leveraging open BFFs data and mobile phone data. The proposed framework encompasses considerations of spatial distribution, accessibility, and supply-demand patterns of BFFs. Our findings reveal the significant agglomeration and heterogeneity of BFFs in Beijing, as well as serious spatial and social inequalities. Furthermore, the supply-demand relationship of BBFs in Beijing is relatively good, with 77.8% of supply-demand balanced areas. Overall, our study provides a holistic understanding of the BFFs within Beijing, offering valuable insights for informed decision-making in inclusive city planning and development.

Activity-travel pattern inference based on multi-source big data

ABSTRACT We provide a comprehensive review of the literature on inferring activity-travel patterns (ATP) using multi-source big data; the increasing number of publications over time on this subject, demonstrates the importance of big data in this task. Our aims are to identify the advantages and research gaps in ATP inference and to promote further developments in this field. We clarify the fundamental concepts (i.e. ATP and its components), commonly used data sources, and inference processes employed in ATP inference studies. Emphasis is placed on two prominent big data sources: mobile phone data and smart card data. We outline the various approaches involved in the inference process, and we highlight existing shortcomings in data sources, ATP inference methodologies, and result validation. Based on the review, it is evident that future research should address several limitations in ATP inference. Firstly, it is necessary to improve the comprehensive understanding of ATP and understand the interrelationships among its different components. Secondly, it is necessary to integrate different data sources and leverage their respective strengths to gain deeper insights into activity-travel behaviour. Lastly, further investigation into emerging technologies such as artificial intelligence in ATP inference is warranted to improve inference accuracy. The findings of this study could provide valuable insights for policy makers, enabling them to gain a deeper understanding of activity-travel choice behaviour and develop more effective policies related to transportation system.

Suburban Autonomy? The Case of Inter‐Suburban Commuting Outside Prague

AbstractThis paper aims to map daily mobility to work and services within the suburban zone of Prague and to explain temporal changes in the structuration of the suburban zone during the day. This involves the identification of local centres outside Prague's administrative boundaries, defining their catchment areas, and exploring the temporal variability of daily mobility. Using mobile phone data, the paper identifies the main flows of commuting to work and mobility for services or leisure activities. The focus on two distinct timeframes presented in two synthetic maps compares the mobility patterns of these activities. Empirical findings identify 53 local centres of inter‐suburban mobility and delimitate their commuting areas. Noticeable variation in mobility patterns throughout the day was observed. Beyond empirical insights relevant to Prague and similar Central and Eastern European cities, the paper contributes to theoretical knowledge by introducing a hierarchical classification of mobility relationships within metropolitan areas.

Effects of Human Mobility on the Spread of Disease-Transmitting Mosquitoes in Spain: Insights from Mobile Phone Data

Effects of Human Mobility on the Spread of Disease-Transmitting Mosquitoes in Spain: Insights from Mobile Phone Data

Travel Mode Identification for Non-Uniform Passive Mobile Phone Data

Travel Mode Identification for Non-Uniform Passive Mobile Phone Data

Adjusting mobile phone data to account for children's travel and the impact on measles dynamics in Zambia.

Models of measles transmission can be used to identify areas of high risk to tailor immunization strategies. Estimates of spatial connectivity can be derived from data such as mobile phone records, however it is not clear how this maps to the movement of children who are more likely to be infected. Using travel surveys across two districts in Zambia and national mobile phone data, we compared estimates of out-of-district travel for the population captured in the mobile phone data and child-specific travel from travel surveys. We then evaluated the impact of unadjusted and adjusted connectivity measures on simulated measles virus introduction events across Zambia. The number of trips made by children from the travel survey was three to five times lower than the general population estimates from mobile phone data. This decreased the percentage of districts with measles virus introduction events from 78% when using unadjusted data to 51% - 64% following adjustment. Failure to account for age-specific heterogeneities in travel estimated from mobile phone data resulted in overestimating subnational areas at high risk of introduction events, which could divert mitigation efforts to districts that are at lower risk.

The role of everyday mobility in adaptation to air pollution hazard: A mixed-method approach combining big and traditional data

The empirical study aims to examine how residents perceive and respond to air pollution in their daily lives, whether they use mobility as an adaptation strategy to avoid or mitigate their exposure, and how socioeconomic and demographic factors modify such responses in mobility. To this end, this study conducts an analysis in the city of Chengdu using a mixed-method approach combining surveys and large-scale mobile phone data. It is found that most at-risk individuals take protective measures, and some choose to change mobility patterns to protect themselves from exposure to air pollution. Regression results suggest that engagement with air quality information and the perceived effectiveness of protective measures are the most important predictors of human mobility changes in response to air pollution. The use of mobility as an adaptation strategy occurs despite the availability of in-situ strategies in general, while low-cost and effective in-situ adaptation choices and high-cost mobility strategies are considered as substitutes. Using changes in origin–destination trips in Sichuan generated from 5,393,739 cellphone users in Chengdu, this study reveals that an increase in the difference of the air quality index at origin versus at destination is associated with more trips from the origin to the destination, and travelers are more sensitive to air quality at origin that drives them to escape from the polluted areas. The findings suggest the (re)production of inequality and marginalization of some population groups in hazard adaptation.

Comparing the impacts of built environment across different objective life neighborhoods on the out-of-home leisure activities of employed people using massive mobile phone data

An adequately-designed built environment can prompt willingness of employed people to carry out out-of-home leisure activities. However, since much of the literature relies on static or perceived residential neighborhoods which may have substantial deviations from realistic environmental exposure, some associations between out-of-home leisure activities and built environment may be biased. In this study, we develop three kinds of objective life neighborhood measurements (residential space, job-housing space, and activity space) using massive mobile phone data, to distinguish the differentiated impacts of built environment across different objective life neighborhoods on the out-of-home leisure activities. According to the results, the activity space makes the exploratory models far more reasonable and persuasive than other life neighborhoods, especially in urban development areas, and the benefits of activity space cannot be attained solely by expanding the distance parameters of residential space or job-housing space. Furthermore, the results demonstrate the relative importance of built environment for out-of-home leisure activities, especially the factors of population density, proportion of urban green space, and metro coverage rate, may be underestimated if the ordinary life neighborhoods are applied. This study provides some references for urban planners to formulate effective environmental intervention strategies to maintain active and healthy lifestyles of employed people.

Tensor Decomposition for Spatiotemporal Mobility Pattern Learning with Mobile Phone Data

Detecting urban mobility patterns is crucial for policymakers in urban and transport planning. Mobile phone data have been increasingly deployed to measure the spatiotemporal variations in human mobility. This work applied non-negative Tucker decomposition (NTD) to mobile phone-based origin–destination (O-D) matrices to explore mobility patterns’ latent spatial and temporal relationships in the province of Liège, Belgium. Four [Formula: see text] traffic tensors have been built for one regular weekday, one regular weekend day, one holiday weekday, and one holiday weekend day, respectively. The proposed method inferred spatial clusters and temporal patterns while interpreting the correlation between spatial clusters and temporal patterns through geographical visualization. As a result, we found the similarity of O-D and destination–origin (D-O) patterns and the symmetry for the trips of the temporal patterns with evening peak and morning peaks on the weekday. Moreover, we investigated the attraction of different spatial clusters with two temporal patterns on a regular weekday and validated the reconstructed demand using population counts and commuting matrices. Finally, the differences in spatial and temporal interactions have been addressed in detail.

An integrated approach to analyze equitable access to food stores under disasters from human mobility patterns.

Limited access to food stores is often linked to higher health risks and lower community resilience. Socially vulnerable populations experience persistent disparities in equitable food store access. However, little research has been done to examine how people's access to food stores is affected by natural disasters. Previous studies mainly focus on examining potential access using the travel distance to the nearest food store, which often falls short of capturing the actual access of people. Therefore, to fill this gap, this paper incorporates human mobility patterns into the measure of actual access, leveraging large-scale mobile phone data. Specifically, we propose a novel enhanced two-step floating catchment area method with travel preferences (E2SFCA-TP) to measure accessibility, which extends the traditional E2SFCA model by integrating actual human mobility behaviors. We then analyze people's actual access to grocery and convenience stores across both space and time under the devastating winter storm Uri in Harris County, Texas. Our results highlight the value of using human mobility patterns to better reflect people's actual access behaviors. The proposed E2SFCA-TP measure is more capable of capturing mobility variations in people's access, compared with the traditional E2SFCA measure. This paper provides insights into food store access across space and time, which could aid decision making in resource allocation to enhance accessibility and mitigate the risk of food insecurity in underservedareas.

Revealing multi-scale spatial synergy of mega-city region from a human mobility perspective

ABSTRACT Spatial synergy is strengthened integration and connection between cities in a mega-city region, transcending administrative boundaries. The central flow theory suggests that the mega-city regions are formed by the interconnected flows of people across cities, making the spatial synergy can be measured by assessing the aggregation and intensity of flows and interactions between cities and regions. Human mobility data, such as mobile phone data and social media check-ins, enable the tracking of human movements, thus facilitating the transition of central flow theory from theoretical constructs to empirical research. To this end, this study presents an alternative data-driven framework to reveal the multi-scale spatial synergy of mega-city regions from a human mobility perspective. It uncovers homogeneously spatial communities with high inter-city integration using community detection. Strong internal spatial connections of 2.13 billion mobility are filtered using network backbone extraction. An experiment in the Pearl River Delta (PRD), China, demonstrates a multi-scale and multi-core hierarchical spatial synergy in the PRD region. The detailed findings are as follows: (1) Three cities attract the majority of human mobility. Mobility distance is short in urban centers and long in suburban areas. (2) The spatial integration pattern shows the detected communities reveal the hierarchical integration pattern with three main integrated regions: Guangzhou-Foshan-Zhaoqing, Shenzhen-Dongguan-Huizhou, and Zhuhai-Zhongshan-Jiangmen. (3) The spatial connection pattern illustrates the close ties of 9 cities and three core cities, including Guangzhou, Shenzhen, and Foshan. These results provide a human-centric understanding of urban synergy and deeper insights into central flow theory, which supports cooperative development in mega-city regions.

The Structural and Functional Development of an Urban Network System from the Perspective of Flow Space: A Case Study of Nanjing

Abstract: Globalization and informatization have exerted far-reaching impacts on the spatial connection and development of urban systems. This study, concerning the network of an urban system based on the space of flows, supplements the insufficiency on the micro-level in macro-urban network research. Taking Nanjing as an example, this study explores the characteristics of the network of the urban system from the perspective of people flow, refining the granularity of the analysis to the township- and street-level spatial units using mobile phone data. The findings are as follows: (1) There is a multicenter layered network pattern, with the main urban area being the core of the network, while Dongshan Street and Moling Street, as secondary centers, form a joint development pattern with the main urban area. (2) The spatial differentiation is significant. The spatial heterogeneity of “centralization in the central region, delayering in the north, and hierarchization in the south” is obvious. The net people inflow nodes are mainly concentrated in the main urban area and its surroundings, while the net outflow nodes are mostly located on the edge of the city. Moreover, the nodes to the south of Yangtze River are advantageous in urban resource control. (3) The phenomenon of “double shadow circle” appears in the ring of the main city and the ring of the municipal area. Moreover, the northern district experiences a serious outflow of population. (4) The effect of policy intervention is beginning to show. Improved levels of development of street and township units such as Jiangbei New District show the positive influence of national strategy on regional development.

Using mobile phone data to capture residential segregation and its association with travel mobility

AbstractThis study measures residential segregation and investigates its association with travel mobility by using mobile phone data from Shenzhen, China. It considers residential segregation in terms of income level and migrant group; and travel mobility in terms of travel frequency, activity space, and travel distance. Unlike previous research on residential segregation and travel mobility, our research uses mobile phone data to produce empirical evidence. The extent of segregation between different migrant and income‐level groups is measured using the location quotient and Getis‐Ord index. This enables us to develop a linear regression model with which to investigate the associations of residential segregation with travel mobility. The study results show that the segregation of middle‐ and low‐income groups and migrants from Southwestern China and Jiangxi is negatively associated with travel mobility among those in the suburbs; meanwhile, for groups segregated in the city center, there is a positive association with travel mobility. These findings suggest that residential segregation is especially adverse for the travel mobility of disadvantaged groups and those living in the suburbs. Accordingly, the paper presents policy recommendations that would enhance travel mobility by alleviating the problems associated with the residential segregation of socially disadvantaged groups living in the suburbs.

Optimizing urban park locations with addressing environmental justice in park access and utilization by using dynamic demographic features derived from mobile phone data

Urban parks play a critical role in improving the quality of life of local residents and creating sustainable and resilient urban environments. As urban parks are essential public services, it is important that they be accessible, sufficient, and of high quality so all city residents can access and benefit from them to advocate environmental justice. To make park services more equitable, it is crucial to evaluate the distribution and quality of existing urban parks and consider potential locations for new urban parks, in terms of both park access and utilization. By adopting a new type of dynamic population data based on mobile phone-based tracking, we assessed urban park access by vulnerable populations, particularly economically marginalized older adults, and employed dynamic demographic features and neighboring environmental features of existing urban parks and park candidate sites to estimate park utilization. Ultimately, we developed an advanced multi-objective location optimization model considering park access, utilization, and construction cost. The three objectives of maximizing utilization of the candidate sites when urban parks are established, maximizing the number of vulnerable people who can be covered by the urban parks, and minimizing the total establishment cost for the urban parks were optimized in locating urban parks among candidate sites. The proposed methodology was validated with an application to Ulsan Metropolitan City in the Republic of Korea.

Contextualising the investigation work in China: the facilitators and barriers in using mobile phone evidence

ABSTRACT Despite the huge quantity of potentially relevant data on crimes, victims, and offenders generated by and associated with technologies such as smartphones, evidence suggests these are rarely used in court proceedings. Moreover, while empirical findings suggest that techniques for analysing digital trace evidence have outstripped the abilities of investigators to interpret it, there is a lack of research on the specific challenges and potential solutions for upskilling police officers to handle digital evidence. This study seeks to identify facilitators and barriers for investigators interpreting mobile phone evidence generated during criminal investigations in China. Based on interviews with 29 frontline investigators regarding their working experience and perceptions, findings illustrate that although Chinese legislation grants investigators access to and legitimacy in retaining mobile phone data and applying it as admissible evidence, and despite the acknowledged importance of this data, investigators often lack the capability to interpret it. The lack of functional techniques for efficiently reviewing and analysing data is identified as a barrier. But more importantly, unfamiliarity with technological innovations in criminal activities hinders investigators’ capabilities to understand the generation and meaning of mobile phone data within the context of the investigation.

Characterizing last-mile freight transportation using mobile phone data: The case of Santiago, Chile

Understanding the mobility of surface freight transportation is relevant in urban planning and for developing public policies. Literature shows that most previous efforts on this topic rely on surveys and limited data. In contrast to other works, in this paper, we present an innovative methodology for characterizing last-mile freight transportation that uses a novel and copious data source: mobile phone data, which provides a broader scope. Our methodology involves calibrating supervised machine-learning models that allow us to link cell phones with truck drivers. In this endeavor, we construct several input variables that track mobile phone’s daily movement patterns, including traveled distances, interactions with highway networks, and land use variables. We test our approach by conducting a case study in Santiago, Chile, for which we analyze mobility patterns and logistics indicators disaggregated by day, hour, and zoning. For this case, we show that all supervised models performed well regarding AUC, which can be attributed to the high granularity and handling of the data. However, we chose to use NGBoost in all subsequent experiments, as it provided slightly better results on our validation data. Our work has several implications for practice. For instance, our results can support decision-makers and policymakers in identifying critical areas where urban logistics centers and transportation interventions are needed. Finally, several research lines stem from our work, which include assessing the impact of incorporating geospatial information and the measurement of logistics sprawl over time.