Modelling spatio-temporal distribution of urban population - A high-resolution model for German cities

The advent of big data has aided understanding of the driving forces of human mobility, which is beneficial for many fields, such as mobility prediction, urban planning, and traffic management. However, the data sources used in many studies, such as mobile phone location and geo-tagged social media data, are sparsely sampled in the temporal scale. An individual’s records can be distributed over a few hours a day, or a week, or over just a few hours a month. Thus, the representativeness of sparse mobile phone location data in characterizing human mobility requires analysis before using data to derive human mobility patterns. This paper investigates this important issue through an approach that uses subscriber mobile phone location data collected by a major carrier in Shenzhen, China. A dataset of over 5 million mobile phone subscribers that covers 24 h a day is used as a benchmark to test the representativeness of mobile phone location data on human mobility indicators, such as total travel distance, movement entropy, and radius of gyration. This study divides this dataset by hour, using 2- to 23-h segments to evaluate the representativeness due to the availability of mobile phone location data. The results show that different numbers of hourly segments affect estimations of human mobility indicators and can cause overestimations or underestimations from the individual perspective. On average, the total travel distance and movement entropy tend to be underestimated. The underestimation coefficient results for estimation of total travel distance are approximately linear, declining as the number of time segments increases, and the underestimation coefficient results for estimating movement entropy decline logarithmically as the time segments increase, whereas the radius of gyration tends to be more ambiguous due to the loss of isolated locations. This paper suggests that researchers should carefully interpret results derived from this type of sparse data in the era of big data.

Human mobility analysis is a multidisciplinary research subject that has attracted a growing interest over the last decade. A substantial amount of such recent studies is driven by the availability of original sources of real-world information about individual movement patterns. An important task in the analysis of mobility data is reliably distinguishing between the stop locations and movement phases that compose the trajectories of the monitored subjects. The problem is especially challenging when mobility is inferred from mobile phone location data: here, oscillations in the association of mobile devices to base stations lead to apparent user mobility even in absence of actual movement. In this paper, we leverage a unique dataset of spatiotemporal individual trajectories that allows capturing both the user and network operator perspectives in mobile phone location data, and investigate the oscillation phenomenon. We present probabilistic and machine learning approaches for detecting oscillations in mobile phone location data, and a filtering technique for removing those. Our analyses and comparison with state-of-the-art approaches demonstrate the superiority of our solution, both in terms of removed oscillations and of error with respect to ground-truth trajectories.

The increasing use of mobile phone location (MPL) data in mobility research has provided many insights into people's travel behaviors. Despite these achievements, the spatial distribution of MPL data positioning uncertainties and their influence mechanism are rarely discussed. In this research, we investigate the influence of geographical determinants on the spatial distribution of the positioning uncertainties in MPL data. First, we discuss the spatial distribution trends in the positioning uncertainties of MPL data. Then we apply multiple linear regression and geographical detector (GeoDetector) models to explore the influence mechanism on the spatial distribution of the positioning uncertainties. By applying these methods to MPL data sets from a major operator in Nanjing city, we find a spatial aggregation phenomenon in the positioning uncertainties. Elevation contributes most to the spatial distribution of the positioning uncertainties. Furthermore, the influencing power of geographical factors on the spatial distribution of positioning uncertainties is nonlinearly enhanced after an interaction.

Mobile phone location data for disasters: A review from natural hazards and epidemics

The Department for Transport (DfT) in the UK is evaluating the process of obtaining traffic statistics such as those collected in the National Travel Survey and the traffic census. Alongside traditional methods, such as surveys and loop data, mobile phone data can be used to support and complete the existing methods. As well as considering the potential of ITS to compile traffic statistics, the DfT is also interested in its applicability to the measurement of road freight (HGV and LGV) activity, particularly origins and destinations data. In 1998, TRL was commissioned by the Highways Agency to conduct research into the feasibility of using mobile phone location data to obtain traffic information, in particular origin-destination (OD) information which would provide the core data required by many transportation simulation models including SATURN and SISTM. The results of the research are described in several documents, including White and Quick (2000) and White and Wells (2002). The research found that it was feasible to obtain OD information from mobile phone location data, as well as other forms of traffic information such as journey times and speeds. Routeing information was also extractable. TRL developed an algorithm to analyse anonymous billing data, kindly provided by BTCellnet, now O/sub 2/. The paper provides an update on the previous work and focuses on the use of mobile phone data for the Department for Transport's requirements.

The paper presents initial steps into the research of commuting patterns and functional regions using mobile phone location data. The main aim is to introduce and discuss the potential of mobile phone location data as an alternative data sources to censuses for mapping commuting flows and subsequent functional regionalization. A set of analytical maps covering various aspects of regular daily movements of population and functional regionalization is provided. Estonia is serving as a pilot laboratory for analyses based on commuting flows derived from mobile phone location data. The maps give to reader a synthetic overview of contemporary settlement system in Estonia and introduce the potential of mobile phone location data for research in this field.

Identifying stops is a primary step in acquiring activity‐related information from mobile phone location data to understand the activity patterns of individuals. However, signal jumps in mobile phone location data may create “fake moves,” which will generate fake activity patterns of “stops‐and‐moves.” These “fake moves” share similar spatiotemporal features with real short‐distance moves, and the stops and moves of trajectories (SMoT), which is the most extensively used stop identification model, often fails to distinguish them when the dataset has coarse temporal resolution. This study proposes the stops, moves, and uncertainties of trajectories (SMUoT) model to address this issue by introducing uncertain segment analysis to distinguish “fake moves” and real short‐distance moves. A real mobile phone location dataset collected in Shenzhen, China is used to evaluate the performance of SMUoT. We find that SMUoT improves the performance (i.e., 15 and 19% increase in accuracy and recall rate for a one‐hour temporal resolution dataset, respectively) of stop identification and exhibits high robustness to parameter settings. With a better reliability of “stops‐and‐moves” pattern identification, the proposed SMUoT can benefit various individual activity‐related research based on mobile phone location data for many fields, such as urban planning, traffic analysis, and emergency management.

BackgroundDuring the second wave of COVID-19 in August 2020, the Tokyo Metropolitan Government implemented public health and social measures to reduce on-site dining. Assessing the associations between human behavior, infection, and social measures is essential to understand achievable reductions in cases and identify the factors driving changes in social dynamics.ObjectiveThe aim of this study was to investigate the association between nighttime population volumes, the COVID-19 epidemic, and the implementation of public health and social measures in Tokyo.MethodsWe used mobile phone location data to estimate populations between 10 PM and midnight in seven Tokyo metropolitan areas. Mobile phone trajectories were used to distinguish and extract on-site dining from stay-at-work and stay-at-home behaviors. Numbers of new cases and symptom onsets were obtained. Weekly mobility and infection data from March 1 to November 14, 2020, were analyzed using a vector autoregression model.ResultsAn increase in the number of symptom onsets was observed 1 week after the nighttime population volume increased (coefficient=0.60, 95% CI 0.28 to 0.92). The effective reproduction number significantly increased 3 weeks after the nighttime population volume increased (coefficient=1.30, 95% CI 0.72 to 1.89). The nighttime population volume increased significantly following reports of decreasing numbers of confirmed cases (coefficient=–0.44, 95% CI –0.73 to –0.15). Implementation of social measures to restaurants and bars was not significantly associated with nighttime population volume (coefficient=0.004, 95% CI –0.07 to 0.08).ConclusionsThe nighttime population started to increase after decreasing incidence of COVID-19 was announced. Considering time lags between infection and behavior changes, social measures should be planned in advance of the surge of an epidemic, sufficiently informed by mobility data.

Estimation of urban crowd flux based on mobile phone location data: A case study of Beijing, China

Recently, the frequency and intensity of weather-related disasters are increasing and are becoming more ubiquitous, often devastating vulnerable rural areas. To prepare for speedy and effective first response, we need a flood detection method that works much faster and is able to cover a wider area compared to conventional methods that use CCTV cameras and low cost sensors, which are costly to distribute ubiquitously in all areas with possible flood threats. With the spread of mobile phones, we are able to obtain real time anonymized location information of individuals in a ubiquitous, low cost, and a continuous manner from users that have agreed to provide their location data for disaster relief purposes. Here we propose a novel method that infers flooded areas in real time by detecting anomalous behaviors of individuals using mobile phone location data. We are motivated in applying our method to rural areas that are costly to cover using cameras and sensors. To overcome the sparseness of mobile phone location signals in such rural areas, our method combines mobile phone location data with terrain information including the digital elevation model and river trajectory data. We evaluated our method using real world data from 2 severe floods in the rural parts of Japan and verified that our method is more accurate and has numerous advantages compared to conventional methods. This work presents the potential use of mobile phone data as a complementary, if not an alternative method for flood detection especially in rural areas.

The aim of the pilot study is to examine the possibilities of mobile phone location data in geographical research of the everyday life and individual spatial mobility of the population. Developing and testing a new research instrument thus represent the key aims of the pilot study. The proposed technique is 'tried out' on a group of young people living or working in Prague. Their daily activities and spatial mobility are explored and discussed against the everyday and geographical context of the young people´s lives. Theoretically the study draws on the strong tradition of time geography as well as on the new geography of everyday life. Methodologically the research combines two different types of data sources and the relevant analytical tools. First, mobile phone location data are used to record the daily trajectories of the participants.Second, deep interpretative interviews are carried out to understandthe reasons and motives behind the recorded daily trajectories. Despite a few technical obstacles in mobile phone location data processing, the pilot study proved the very promising potential of this source, especially in combination with interviews, when studying the patterns of the everyday life and individual spatial mobility of an urban population.

This paper evaluates the role of ‘temporality’ in defining functional regions. Functional regions are viewed as relatively closed in terms of selected population flows (or more generally concerning spatial interactions). They are usually defined by the daily commuting to work and are therefore commonly referred to as local labour market areas or travel-to-work areas. Using mobile phone location data, however, it is possible to work with population flows in a broader temporal and spatial context. Then we can talk about the temporality alternatives of functional regions depending on whether we base them on regular daily population flows, irregular daily population flows (which, according to the data analysis, are irregular from an individual’s point of view but regular from a spatial unit’s point of view between which they take place) or weekend population flows. Thus, several functional region’s versions can be defined for a single regional system, where the different population movement’s rhythm lengths movements limit their length and also determine their hierarchy. All functional regions’ temporal alternatives according to mobile phone location data are defined based on data from the Czech Republic.

Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data

Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the recovery process of cities, where inter-city dependencies and intra-city coupling of social and physical systems may affect the outcomes in unforeseen ways. Despite the large implications of understanding the recovery processes of cities after disasters for many domains including critical services, disaster management, and public health, little work have been performed to unravel this complexity. Rather, works are limited to analyzing and modeling cities as independent entities, and have largely neglected the effect that inter-city connectivity may have on the recovery of each city. Large scale mobility data (e.g. mobile phone data, social media data) have enabled us to observe human mobility patterns within and across cities with high spatial and temporal granularity. In this paper, we investigate how inter-city dependencies in both physical as well as social forms contribute to the recovery performances of cities after disasters, through a case study of the population recovery patterns of 78 Puerto Rican counties after Hurricane Maria using mobile phone location data. Various network metrics are used to quantify the types of inter-city dependencies that play an important role for effective post-disaster recovery. We find that inter-city social connectivity, which is measured by pre-disaster mobility patterns, is crucial for quicker recovery after Hurricane Maria. More specifically, counties that had more influx and outflux of people prior to the hurricane, were able to recover faster. Our findings highlight the importance of fostering the social connectivity between cities to prepare effectively for future disasters. This paper introduces a new perspective in the community resilience literature, where we take into account the inter-city dependencies in the recovery process rather than analyzing each community as independent entities.

With increasing awareness of the privacy risks posed by mobile phone location data, researchers need ways to use mobility data while offering stronger privacy guarantees to the individuals included in this data. A promising approach to this challenge is the creation of privacy-preserving mobility insights from decentralized location data using Local Differential Privacy (LDP). However, mobility data generated with LDP, based on the introduction of noise by individual mobile devices, is limited by the volume of noise required to achieve individual privacy. In this paper, we provide a fully reproducible model of the accuracy of mobility networks generated with LDP compared to mobility network data generated with more traditional privacy mechanisms: Central Differential Privacy (CDP) and K-anonymity. Using a simulated mobile phone mobility dataset informed by real-world travel patterns in the USA, we explore the trade-off between privacy and data utility provided by different parameters in a federated system with LDP. We also explore the impact of spatial and temporal aggregation on data accuracy, showing that long-standing considerations regarding the appropriate units of analysis for geographic data play a key role in determining the utility of federated mobility data with LDP. Our paper facilitates an in-depth understanding of the trade-offs between privacy and data utility entailed by the future adoption of a federated approach which uses LDP to generate insights from decentralized mobility data.