Exploring the relationship between mobile positioning data and wastewater flows: evidence from five Swiss catchments.

Mobile location data are a major form of Big Data that hold many possibilities for study and insight into human behaviour. This research used mobile location data to investigate the differences in the activity patterns of tourists in Maui, Hawai’i. Mobile data used in this study were app-based location data collected as a stream of mobile phone locations with a timestamp. Tourists were clustered using K-Means based on time spent at attraction types. Different travel experiences were analyzed based on traveler’s accommodation choices, the average distance travelled from accommodation to attraction, and vacation length, which all varied significantly between the tourist clusters. This work provided a new use for K-means clustering with mobile location data to provide insightful information to marketing professionals and tourism management bodies.

Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.

As the most immediately impacted population, survivors’ mobility and distribution characteristics are closely linked to earthquake emergency response. Accurate population distribution and mobility data are vital foundational resources for post-disaster decision-making. With the help of mobile phone location data within the earthquake-stricken area, we explore novel rapid assessment approaches to identifying perceived impact area.The widespread adoption of smart mobile devices in China has led to the installation of numerous third-party applications. These applications rely on push notification services enabled by Push Software Development Kits (SDKs) provided by mobile service providers. These SDKs, compliant with data security standards, collection scopes, and transmission protocols, utilize built-in functional modules to periodically collect user-authorized geolocation data. This data encompasses device identifiers, GPS coordinates, WiFi signatures, cellular base station logs, and network connectivity metadata. After encryption, these multi-source data streams inputs are aggregated into structured mobile location datasets. They can provide high-resolution insights into population mobility patterns during disasters.At 9:05 (Beijing time) on 7 January 2025, an earthquake with magnitude Ms 6.8 hit Dingri County, Tibet Autonomous Region, which killed 126 people. We collected a mobile phone location dataset covering a 150 km radius from the epicenter, spanning 38 hours from 9:00 on 6 January to 1:00 on 8 January. It comprises 146,123 records. Each record includes four mobile location-based indicators, namely (1) Active Base Stations (base stations scanned and periodically reported by mobile devices), (2) Active Wi-Fi Hotspots (Wi-Fi hotspots scanned and periodically reported by mobile devices), (3) Mobile Devices (mobile devices that obtain services through multiple positioning methods) and (4) Wi-Fi-Connected Devices (mobile devices connected to Wi-Fi hotspots). Utilizing natural neighbor interpolation and Thiessen polygon interpolation methods, we analyzes changes in four mobile location-based indicators and their spatiotemporal distribution characteristics before and after the earthquake, summarizing crowd movement patterns and communication behaviors after the Dingri earthquake. The results indicate an uneven distribution of population and differing dynamics in mobile phone signal activity. This reflects different behavioral patterns and the potential perceived extent of the earthquake. Within 50 km of the epicenter, all four indicators showed varying degrees of decline post-earthquake, while areas beyond 100 km exhibited short-term surges, reflecting differentiated behavioral responses based on seismic impact severity. In areas experiencing strong shaking, risk avoidance behavior predominated, while in areas where shaking was noticeable but less severe, communication behavior was more prominent. Mobile data decline zones showed high spatial correlation with intensity VIII+ regions, proving their effectiveness as rapid indicators for identifying strongly affected areas. Notably, mobile location data enabled accurate identification of strongly affected zones within 30 min post-earthquake.The research establishes a theoretical-technical framework supporting three critical post-disaster applications: (1) dynamic population distribution sensing, (2) behavioral pattern analysis of affected populations, and (3) rapid evaluation of seismic perception zones.

Researchers face the trade-off between publishing mobility data along with their papers while protecting the privacy of the individuals. In addition to the anonymization process, other techniques, such as spatial discretization and location concealing or removal, are applied to achieve these dual objectives. The primary research question is whether concealing the observation area is an adequate form of protection or whether human mobility patterns in urban areas are inherently revealing of location. The characteristics of the mobility data, such as the number of activity records in a given spatial unit, can reveal the silhouette of the urban landscape, which can be used to infer the identity of the city in question. The presented locating method was tested on multiple cities using different open datasets and coarser spatial discretization units. While publishing mobility data is essential for research, concealing the observation area is insufficient to prevent the identification of the urban area. Instead of obscuring the observation area, noise should be added to the trajectories to mitigate privacy risks regarding the individuals.

We introduce mobile positioning based data sources in tourism studies using the case study of tourism in Tartu, Estonia. Mobile positioning data is a promising source for tourism geography as it is one easiest and most cost effective sources for investigating the flows of tourists with relatively good spatial and temporal coverage. Mobile positioning data allows one to link the digital track of visitors with visited events and locations retrospectively. The data also has potential for the development of real-time monitoring tools for tourism planning and management, as it has been tested in Estonia with “Positium Tourism Barometer”. The biggest problem with positioning data is privacy and surveillance, and those issues needs to be addressed and discussed very carefully.

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.

Mobile location data are increasingly relevant subjects of research, used to understand mobility patterns, track an individual's mode of transportation, optimize route planning, manage humanitarian aid, and mitigate pandemics. Recently, mobile location data have also been used to investigate social issues such as economic segregation. In this study, we propose a method to mine mobile point-of-interest (POI) visit data for socioeconomic analysis. The method has four steps: 1) infer each individual's economic status based on their visits to POIs with price level information; 2) compute the degree to which a pair of individuals visit the same venues; 3) calculate the physical distance between a pair of individuals based on the locations of the POIs they visit; 4) analyze how behavioral similarity and physical distance are linked to economic statuses. Compared with preexisting methods, our method highlights the distinction between physical proximity and actual POI visits, providing new insights. It also requires less data, needing only POI visit information rather than precise tracking data. Experimental results using a large mobile POI visit dataset reveal an interesting contrast: while people tend to visit POIs similar to other people of similar economic statuses, they are on average physically closest to those of the highest economic status.

Simulation of the interaction between an urban drainage system (UDS) and surface flow is a challenging issue. Conventional rainfall–runoff models are only able to simulate the flow in UDSs without considering the interaction between surface runoff and UDS. In this study, SWMM (storm water management model), as a well‐known urban drainage simulation model, and a two‐dimensional overland flow model developed based on the cellular automata approach, have been coupled for UDS simulation. In the proposed model, sewer flows are one‐dimensionally simulated using the SWMM model and at the flooded manholes, the surcharge hydrographs are determined. Then the two‐dimensional overland flow model is used for routing the surcharged flows. The coupled model could consider the complex interaction between the drainage system and the overland flow. The proposed model has been used in a study area located in Tehran, capital of Iran. The results show that almost 40% of surcharged flow returns back to the UDS, which caused 1.5% increase in the UDS's outflow volume. This can greatly affect the UDS performance downstream of the study area. The implementation results show the model's capability in simulation of flood behaviour as well as the urban inundated area with acceptable accuracy and much less computational effort. Copyright © 2017 John Wiley & Sons, Ltd.

This paper proposes a technique for improving the accuracy of mobile device movement trajectory reconstruction using passive mobile positioning data. The major sources of uncertainty in trajectory reconstruction are imprecise cell shape data and ‘ping-pong’ effects caused by cell handovers. We used a novel technique for improved ‘ping-pong’ effect suppression by compensating for some cell shape distortions based on temporal cell-to-cell transit statistics. The results were evaluated by estimating traffic flow using trajectory reconstruction. The proposed technique improved the accuracy of results compared to ‘ping-pong’ suppression algorithms found in the literature.

This special issue of the European Journal of Transport and Infrastructure Research (EJTIR) comprises three papers addressing some key issues in transport geographical research that use location tracking data. Preliminary versions of those papers were presented at the Mobile Ghent 2013 Conference. This event is part of a series of biannually held conferences on location-based services (LBS) that initially started in Tartu, Estonia. This editorial briefly points to the growing academic relevance of mobile phone tracking data and location-based services, describes the history and aim of the Mobile Ghent conference and gives a preview of the selected contributions.

The spatial concentration of the human activity is a crucial indication of socioeconomic vitality. Accurately mapping activity volumes is fundamental to support the regional sustainable development. Current approaches rely on mobile positioning data, which record information about human daily activity but are inaccessible in most cities due to privacy and data sharing concerns. Alternative methods are needed to provide more generalized predictions on extensive areas while maintaining low cost. This study demonstrates how remote sensing imagery can be used through an end-to-end deep learning framework for reliable estimates of human activity volumes. The neighbor effect, representing the inherent nature of spatial autocorrelation in the volumes, is incorporated to improve the network. The proposed model exhibits strong predictive power and demonstrates great explainability of physical environment on variations of activity volumes. Landscape interpretations based on hierarchical features provide both object-based and region-based insights into the coevolvement of landscape and human activity. Our findings indicate the possibility of extensively predicting activity volumes, especially in areas with limited access to mobile data, and provide support for the promising framework to better comprehend broad aspects of the human society from observable physical environments.

Telecommunications data are being explored by many countries as a new source of data that can be incorporated into their national statistical systems. In particular, “mobile positioning data” are increasingly being used to study population movements and population distributions. However, the legal, ethical, and technical complexities of working with this type of data often pose many barriers, which can prevent the data from being used at the times when it is most urgently needed. We demonstrate how having a robust public–private partnership framework, a privacy-preserving technical setup, and a communications strategy already in place, prior to an emergency, can enable governments to harness the advantages of telecommunications data at the times when it is most valuable. However, even once these foundations are in place, the challenges of competing priorities, managing expectations, and maintaining communication with data consumers during a pandemic mean that the potential of the data is not automatically translated into direct impact. This highlights the importance of sensitisation exercises, targeted at potential data users, to make clear the potential and limitations of the data, as well as the importance of being able to maintain direct communication with data users. The views expressed in this work belong solely to the authors and should not be interpreted as the views of their institutions.

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

Abstract. Contributions to social capital, risk awareness, and preparedness constitute the parameters against which applications of digital technologies in the field of disaster risk management should be tested. We propose here an evaluation of four of these: mobile positioning data, social media crowdsourcing, drones, and satellite imaging, with an additional focus on acceptability and feasibility. The assessment is carried out through a survey disseminated among stakeholders. The frame of the analysis also grants the opportunity to investigate to what extent different methodologies to aggregate and evaluate the results, i.e., the Criteria Importance Through Criteria Correlation (CRITIC) model, the (Euclidean)-distance Criteria Importance Through Criteria Correlation (dCRITIC) model, the entropy model, the mean weight model, and the standard deviation model, may influence the preference of one technology over the others. We find that the different assumptions on which these methodologies rely deliver diverging results. We therefore recommend that future research adopt a sensitivity analysis that considers multiple and alternatives methods to evaluate survey results.

This study identifies actual retail catchment areas using an evidence-based method using mobile location data, moving beyond traditional isochrone-based models. By employing geofencing, geohash techniques, and analysing over 117 million data points from 1.6 million users in Auckland, we map actual catchment areas of two shopping malls. Using DBSCAN clustering and the concave hull method, we identify catchment areas based on visitors’ home-like locations, revealing significant deviations from traditional boundaries. These insights enhance understanding of consumer travel patterns and motivations, enabling more targeted retail strategies.