Mobile Location Data Research Articles

Sustainable Destination Management Using Visitors’ Movements: Applying Mobile Positioning Data

Understanding visitors’ movements is crucial to achieving the goals of sustainable destination management while dealing with the environmental, social, and cultural impact of tourism. This study examines the movement patterns of visitors within Taeanhaean National Park (TNP) by adopting a destination measurement approach from a longitudinal perspective. The spatial distribution of the visitors’ activities and movement patterns was obtained by using mobile positioning data (MPD) and we applied the theoretical concept of tourism destinations, which considers geographical, temporal, and compositional dimensions. Given the destination attributes and conservation values, the MPD analysis proved suitable as an aid for park managers to allocate resources efficiently and define the characteristics of park and recreation facilities. This analysis has extended our knowledge of visitors’ patterns at a large marine national park by increasing consistency and high resolution of real-time spatio-temporal data for longer periods and better representing the study population. The study results will allow park managers to implement destination management planning focused on influencing spatial distributions of visitors’ movements in a specific environment.

Intermunicipal travel networks of Mexico during the COVID-19 pandemic

Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society’s local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020–2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects

It is crucial to develop spatiotemporal analysis tools to mitigate risks during a pandemic. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings an interactive tool that is capable of crossing information about mobility patterns, geolocation characteristics and epidemiologic variables. To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, and epidemiologic indicators can be analyzed through multiple coordinated views. The proposal is to enable users to understand how different locations commute citizens, monitor risk over time, and understand what locations need more assistance, considering different layers of visualization, such as clusters and individual locations. The main novelty is the interactive way to construct the mobility network that defines the social distancing level and the way that risks are managed, since many different geolocation characteristics can be considered and visualized, such as socioeconomic indicators of a location, the economic importance of a set of locations, and the connection of important neighborhoods of a city with other cities. The proposed tool was built and verified by experts assembled to give scientific recommendations to the city administration of Recife, the capital city of Pernambuco. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and contamination risk, where the practical insights can also be used to tighten and relax mitigation measures in other phases of a pandemic.

Impact of COVID-19 on Public Transit Accessibility and Ridership.

COVID-19 has radically transformed urban travel behavior throughout the world. Agencies have had to provide adequate service while navigating a rapidly changing environment with reduced revenue. As COVID-19-related restrictions are lifted, transit agencies are concerned about their ability to adapt to changes in ridership behavior and public transit usage. To aid their becoming more adaptive to sudden or persistent shifts in ridership, we addressed three questions: To what degree has COVID-19 affected fixed-line public transit ridership and what is the relationship between reduced demand and -vehicle trips? How has COVID-19 changed ridership patterns and are they expected to persist after restrictions are lifted? Are there disparities in ridership changes across socioeconomic groups and mobility-impaired riders? Focusing on Nashville and Chattanooga, TN, ridership demand and vehicle trips were compared with anonymized mobile location data to study the relationship between mobility patterns and transit usage. Correlation analysis and multiple linear regression were used to investigate the relationship between socioeconomic indicators and changes in transit ridership, and an analysis of changes in paratransit demand before and during COVID-19. Ridership initially dropped by 66% and 65% over the first month of the pandemic for Nashville and Chattanooga, respectively. Cellular mobility patterns in Chattanooga indicated that foot traffic recovered to a greater degree than transit ridership between mid-April and the last week in June, 2020. Education-level had a statistically significant impact on changes in fixed-line bus transit, and the distribution of changes in demand for paratransit services were similar to those of fixed-line bus transit.

Understanding the Bias of Mobile Location Data Across Spatial Scales and Over Time: A Comprehensive Analysis of SafeGraph Data in the United States

Understanding the Bias of Mobile Location Data Across Spatial Scales and Over Time: A Comprehensive Analysis of SafeGraph Data in the United States

Capturing Unobserved Tourists: Challenges and Opportunities of Processing Mobile Positioning Data in Tourism Research

Even though tourism (both domestic and international) is one of the main triggering factors of human mobility worldwide, some of its forms are unexplored. This can be partly linked with the lack of reliable data and obstacles related to data processing and interpretation. Mobile Positioning Data (MPD) allows us to identify various forms of tourism that are undetectable through traditional data sources such as accommodation statistics. Using MPD, not only same-day tourists but also the real time mobility patterns of tourists among various destinations can be revealed, and even hidden (i.e., unobserved) forms of tourism can be detected. However, despite the obvious benefits of such data, very few comprehensive studies exist to date on the processing, and interpretation of MPD in tourism research. In this paper, a case study is presented on the challenges and opportunities of processing MPD from raw to good quality researchable data offering a baseline tool for MPD-based research in the field of tourism. With the methodology introduced in this paper, it is possible to provide a more accurate picture of tourist flows regarding unobserved tourists, including same-day visitors.

Identifying Tourist Spatial and Temporal Patterns Using GPS and Sequence Alignment Method

This study identified the spatial and temporal patterns of national park visitors by combining global positioning system (GPS) technologies and surveys using the sequence alignment method (SAM) based on the concept of space–time path of time geography. Data were collected from a sample of 344 GPS tracks and questionnaires obtained from Gyeryongsan National Park in South Korea. A complex random stratified sampling method was employed to represent a national sample based on mobile positioning data. The result of the SAM analysis revealed that the derived visitor typologies constituted three groups from 83 unique paths extracted from 341 sequences. Significant intergroup differences were found for the paths based on average travel distance, average visit duration, and average travel speed. The main theoretical contribution of this study is in expanding the application of the theory by providing methodological improvements using SAM and GPS analysis of the theoretical basis of time geography.

Huddling with families after disaster: Human resilience and social disparity.

Disasters, from hurricanes to pandemics, tremendously impact human lives and behaviors. Physical closeness to family post-disaster plays a critical role in mental healing and societal sustainability. Nonetheless, little is known about whether and how family colocation alters after a disaster, a topic of immense importance to a post-disaster society. We analyze 1 billion records of population-scale, granular, individual-level mobile location data to quantify family colocation, and examine the magnitude, dynamics, and socioeconomic heterogeneity of the shift in family colocation from the pre- to post-disaster period. Leveraging Hurricane Florence as a natural experiment, and Geographic Information System (GIS), machine learning, and statistical methods to investigate the shift across the landfall (treated) city of Wilmington, three partially treated cites on the hurricane’s path, and two control cities off the path, we uncover dramatic (18.9%), widespread (even among the partially treated cities), and enduring (over at least 3 months) escalations in family colocation. These findings reveal the powerful psychological and behavioral impacts of the disaster upon the broader populations, and simultaneously remarkable human resilience via behavioral adaptations during disastrous times. Importantly, the disaster created a gap across socioeconomic groups non-existent beforehand, with the disadvantaged displaying weaker lifts in family colocation. This sheds important lights on policy making and policy communication to promote sustainable family colocation, healthy coping strategies against traumatic experiences, social parity, and societal recovery.

Is social capital associated with individual social responsibility? The case of social distancing during the Covid-19 pandemic.

Individual social responsibility is essential to achieving the sustainable development goals of the society, yet there has been very little research on whether and how social and cultural factors influence individual social responsibility. Using the Covid-19 pandemic as our empirical context, this research examines the relationship between social capital and individual social distancing behaviors during the pandemic. Social distancing is a form of socially responsible behavior because it is critical in mitigating the spread of the Covid-19 virus. By exploiting daily mobile GPS location data, we provide strong evidence for the divergent relationships between the two constituents of social capital–civic norms and social networks–and social distancing behaviors. While civic norms are positively associated with social distancing, social networks have a negative association with social distancing. These results are consistent with a nuanced view of social capital: civic norms facilitate cooperation and self-sacrifice for the common good, whereas social networks increase individual embeddedness and hence inertia in maintaining social interactions, resulting in opposite effects on social distancing. Our results contribute to the research at the intersection of social capital and individual social responsibility by highlighting the nuanced effects of social capital on individual responses to the pandemic and provide valuable insights for policymakers and businesses in disaster management.

Evaluation of emerging technological opportunities for improving risk awareness and resilience of vulnerable people in disasters

The main contribution of this research is the evaluation of emerging technological opportunities for improving risk awareness and resilience of vulnerable people in disasters. The evaluation considered a survey, end-user evaluation, and co-creative workshops on technologies and was targeted to estimate the innovation potential, usefulness, importance, applicability, risks for vulnerable people, and ethical acceptability.The capabilities of the Mobile Positioning Data tool (1) to increase risk awareness in rescue planning and emergency management in cyber-hazard situations and (2) to help in locating and evacuating tourists and other vulnerable people in disasters was evaluated. The capabilities of the Trasim tool to help in crisis communications training for improving preparedness and tackling mis/disinformation through simulated responses was evaluated. The evaluation indicated that there are a lot of innovation potential and useful technical enablers for improving disaster risk awareness and resilience, but there are also ethical challenges, and risks for misuse.The digital divide between people in the unequal distribution of skills and access to technological means and tools remains an essential future challenge, especially with vulnerable people in a crisis. Issues of fairness and inclusivity need great attention in the application of these technologies in crises or disasters in order not to overlook such vulnerable people. The failure in critical infrastructures (e.g., energy and communications) seems to increase risks and exacerbate the situation of vulnerable people in crises. However, the potential of technological opportunities for improving operation in disasters is so essential that significant investment in research and development is recommended.

The geography of connectivity: a review of mobile positioning data for economic geography

Connectivity between and within places is one of the cornerstones of geography. However, the data and methodologies used to capture connectivity are limited due to the difficulty in gathering and analysing detailed observations in time and space. Mobile phone data potentially offer a rich and unprecedented source of data, which is exhaustive in time and space following movements and communication activities of individuals. This approach to study the connectivity patterns of societies is still rather unexplored in economic geography. However, a substantial body of work in related fields provides methodological and theoretical foundations, which warrant an in-depth review to make it applicable in economic geography. This paper reviews and discusses the state-of-the-art in the analysis of mobile phone and positioning data, with a focus on call detail records. It identifies methodological challenges, elaborates on key findings for geography, and provides an outline for future research on the geography of connectivity.

Mobile Positioning Data Based-Application for Medan Tourism Objects

Tourism Object is a fundamental component in the tourism industry and one of the reasons visitors travel. The State of Indonesia is one of the various countries whose number of tourist objects is very large and varied. One of these attractions is natural tourism, for example, the sea, beaches, rivers, lakes, and mountains, as well as for the building objects including historical heritage sites, forts, museums and so on. The method used in this study uses qualitative research by applying the method of mobile positioning data to help the manager of tourist attractions promote the location of tourist attractions. The mobile positioning system model can be used to understand and determine the mobile position based on location coordinates. This mobile positioning system uses GPS technology, a tool to get or generate the coordinates of the position of the online map technology that Google provides to show the position that has been stored in the system. The research results from this Tourism Object application can detect the user's location according to the mobile positioning data method. This Android-based Tourist Object application can display a list of tourist objects according to the area occupied by the user. This tourist attraction application can add new tourist objects by admin. It is hoped that in the future this application can be run on the iOS system and can add images to new tourist attractions.

Functional regions as a platform to define integrated transport system zones: The use of population flows data

The rapid expansion of cars as the main transport mode over the last few decades has caused many negative effects mainly in urban areas. Many countries across the world thus want to attract passengers back to public transport. One of the most important and useful tools is supporting public transport by creating integrated transport systems (ITS). ITS represent innovative solution for organising urban and regional public transport. ITS are based on the integration of individual transport modes into one interconnected and coordinated system. However, the key question is how they are spatially defined and how they are organised internally. This is a relatively complicated issue as natural population flows, tariff systems, as well as the specifics of the transport infrastructure of the area, must be reflected.The main aim of the article is to propose the optimal delimitation and internal structure of the integrated transport system in the South Bohemian Region of the Czech Republic. To design the optimal delimitation, we use an innovative method based on utilizing location data from mobile phones, which reliably reflect the population flows. To delimitate the zones of a new integrated transport system we use the CURDS regionalisation algorithm and Smart's interaction measure. We found out substantial disproportion between the current organisation of the public transport system and regular population flows based on mobile positioning data. We suggest adjusting and developing a new integrated transport system which will reflect the current organisation of regular population flows. Finally, we test four versions of micro-regions as a sophisticated basis to define and internally structure the new ITS in the South Bohemian Region.

Influence of Built Environment on Subway Trip Origin and Destination: Insights Based on Mobile Positioning Data

An accurate understanding of the relationship between subway trips and the built environment is crucial to meet people’s travel demands and promote the coordinated development of urban land. However, existing literature mainly examines this relationship at the station level, ignoring the variations between the surrounding areas of the station. Therefore, the present study aims to explore the influencing factors and spatial variations of subway trips at the grid level. First, a method was proposed to extract the subway trips using mobile positioning data in Chengdu, China. Then, two geographically weighted regression (GWR) models were adopted to examine the spatially varying relationships between the subway trip origin and destination and selected explanatory variables. The results show that the hotel, company, residential, tourist, bus, subway, road density, and transfer stations variables positively affect trip origin and destination. However, distance to the nearest subway station has a negative impact. Besides, the goodness of fit of the GWR model is better than that of the global regression model, indicating that the influence of the built environment on trip origin and destination varies across space. This study can guide planning departments and transportation agencies to implement target policies and create a convenient travel environment at the micro-level.

Using mobile data to evaluate unobserved tourist overnight stays

Tourist overnight stays are difficult to estimate properly due to the substantial gaps in the official data and the multiple types of accommodation. This trend has been accentuated with the emergence of new forms of accommodation resulting from the development of information and communication technologies and collaborative economies. Several studies have highlighted the importance of quantifying these accommodations because of their social, economic and environmental consequences for tourism destinations. This article explores the use of mobile data analysis as a complementary approach to official statistics to quantify the tourist overnight stays in the Walloon region. The mobile data highlight several aspects of tourism that are ignored by official statistics and raise questions about the underestimation and the current definition of a tourist. However, important limitations to this approach have been identified and are discussed. • Official statistics greatly underestimate tourist overnight stays. • The traditional definition of a tourist is no longer adapted to new forms of tourism. • Mobile data analysis provides a complementary approach for measuring tourism activity. • The paper proposes a methodology for locating visitors' overnight stays using mobile data analysis. • Knowledge of the local context is necessary for monitoring tourism activity with mobile positioning data.

Leveraging tourist trajectory data for effective destination planning and management: A new heuristic approach

Understanding tourist movements provides insights for destination planning, service design and marketing. The key challenge is to develop a tool that can capture the value in the tourist mobility data. This study presents a new heuristic approach that combines adaptive spatial clustering with frequent pattern mining to improve the performance and efficiency of trajectory data analytics. The aim is to fully leverage the semantic information in the tourist data and the duration that tourists stay at an attraction. Anonymous mobile positioning data from 741 tourists to one of China's leading destinations are used to illustrate the application of the new analytical approach. The results reveal a four-level destination spatial structure ranging from core to peripheral areas. The findings provide practical implications for facilitating intra-destination cooperation and optimizing destination resource allocation and service design.

Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder

BackgroundIt is essential to differentiate bipolar disorder (BD) from unipolar disorder (UD) as the course of illness and treatment guidelines differ between the two disorders. Measurements of activity and mobility could assist in this discrimination. Aims: 1) To investigate differences in smartphone-based location data between BD and UD, and 2) to investigate the sensitivity, specificity, and AUC of combined location data in classifying BD and UD. MethodsPatients with BD and UD completed smartphone-based self-assessments of mood for six months, along with same-time passively collected smartphone data on location reflecting mobility patterns, routine and location entropy (chaos). A total of 65 patients with BD and 75 patients with UD were included. ResultsA total of 2594 (patients with BD) and 2088 (patients with UD) observations of smartphone-based location data were available. During a depressive state, compared with patients with UD, patients with BD had statistically significantly lower mobility (e.g., total duration of moves per day (eB 0.74, 95% CI 0.57; 0.97, p = 0.027)). In classification models during a depressive state, patients with BD versus patients with UD, there was a sensitivity of 0.70 (SD 0.07), a specificity of 0.77 (SD 0.07), and an AUC of 0.79 (SD 0.03). LimitationsThe relative low symptom severity in the present study may have contributed to the magnitude of the AUC. ConclusionMobility patterns derived from mobile location data is a promising digital diagnostic marker in discriminating between patients with BD and UD.

Clustering Methods Based on Stay Points and Grid Density for Hotspot Detection

With the widespread use of GPS equipment, a large amount of mobile location data is recorded, and urban hotspot areas extracted from GPS data can be applied to location-based services, such as tourist recommendations and point of interest positioning. It can also provide decision support for the analysis of population migration distribution and land use and planning. However, taxi GPS location data has a large amount of data and sparse points. How to avoid the influence of noise and efficiently detect hotspots in cities have become urgent problems to be solved. This paper proposes a clustering algorithm based on stay points and grid density. Firstly, a filtering pre-processing algorithm using stay points classification and stay points thresholds is proposed, so the influence of stop points is avoided. Then, the data space is divided into rectangular grid cells; each grid cell is determined to be a dense or non-dense grid according to the defined density threshold, and the cluster boundary points and noise points are judged in the non-dense grid cells to avoid normal sampling points being treated as noise. Finally, the associated dense grids are connected into clusters. The sampling points mapped to the grid cells are the elements in the clusters. Our method is more efficient than the DBSCAN algorithm because the grid cells are calculated. The superiority of the proposed algorithm in terms of clustering accuracy and time efficiency is verified in the real data set compared to traditional algorithms.

Challenges and Opportunities of Emerging Data Sources to Estimate Network-Wide Bike Counts

Emerging sources of mobile location data such as Strava and other phone-based apps may provide useful information for assessing bicycle activity on each link of a network. Despite their potential to complement traditional bike count programs, the representativeness and suitability of these emerging sources for producing bicycle volume estimates remain unclear. This study investigates the challenges and opportunities by fusing Strava data with short-term and permanent conventional count program data to produce bicycle volume estimations using clustering and nonparametric modeling. Analysis indicates that the concentration of permanent counters at high bicycle volume locations presents a significant challenge to produce network-wide daily volume estimations even though Strava data demonstrate potential in mitigating the estimation bias at lower-volume sites. Despite the contribution of Strava to develop reliable and spatially and temporally transferable bicycle volume estimations, significant challenges remain to rely on Strava counts alone to characterize network-level activities due to sampling bias and spatial representations. This study will help planners discern and assess the challenges and opportunities of using emerging data in bicycle planning.

Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling

ABSTRACT Population spatialization is widely used for spatially downscaling census population data to finer-scale. The core idea of modern population spatialization is to establish the association between ancillary data and population at the administrative-unit-level (AU-level) and transfer it to generate the gridded population. However, the statistical characteristic of attributes at the pixel-level differs from that at the AU-level, thus leading to prediction bias via the cross-scale modeling (i.e. scale mismatch problem). In addition, integrating multi-source data simply as covariates may underutilize spatial semantics, and lead to incorrect population disaggregation; while neglecting the spatial autocorrelation of population generates excessively heterogeneous population distribution that contradicts to real-world situation. To address the scale mismatch in downscaling, this paper proposes a Cross-Scale Feature Construction (CSFC) method. More specifically, by grading pixel-level attributes, we construct the feature vector of pixel grade proportions to narrow the scale differences in feature representation between AU-level and pixel-level. Meanwhile, fine-grained building patch and mobile positioning data are utilized to adjust the population weighting layer generated from POI-density-based regression modeling. Spatial filtering is furtherly adopted to model the spatial autocorrelation effect of population and reduce the heterogeneity in population caused by pixel-level attribute discretization. Through the comparison with traditional feature construction method and the ablation experiments, the results demonstrate significant accuracy improvements in population spatialization and verify the effectiveness of weight correction steps. Furthermore, accuracy comparisons with WorldPop and GPW datasets quantitatively illustrate the advantages of the proposed method in fine-scale population spatialization.