Mobile Location Data Research Articles

Population displacement after earthquakes: benchmarking predictions based on housing damage

In the aftermath of an earthquake, the number of residents whose housing was destroyed is often used to approximate the number of people displaced (i.e., rendered homeless) after the event. While this metric can provide rapid situational awareness regarding potential long-term housing needs, more recent research highlights the importance of additional factors beyond housing damage within the scope of household displacement and return (e.g., utility disruption, tenure, place attachment). This study benchmarks population displacement estimates using this simplified conventional approach (i.e., only considering housing destruction) through three scenario models for recent earthquakes in Haiti, Japan, and Nepal. These model predictions are compared with officially reported values and alternate mobile location data-based estimates from the literature. The results highlight the promise of scenario models to realistically estimate population displacement and potential long-term housing needs after earthquakes, but also highlight a large range of uncertainty in the predicted values. Furthermore, purely basing displacement estimates on housing damage offers no view on how the displaced population counts vary with time as compared to more comprehensive models that include other factors influencing population return or alternative approaches, such as using mobile location data.

Measuring daily tourism mobility spillover at the intra-metropolis level with mobile positioning data

ABSTRACT Although tourism mobility spillover continues to be a key indicator for tourism management, more innovative research must be conducted at the micro level and high sampling frequency. Against the backdrop of an increasing number of global cities, in this paper, we evaluate the daily tourism mobility spillover inside a worldwide city of China: Shanghai. Based on the Granger causal network model and an original mobile positioning dataset, we analyse the causal relationship between local tourism flows and the spillover effects of tourism mobility within Shanghai. By categorising tourists into ‘local tourists from Shanghai’ and ‘tourists from out of Shanghai’, we reveal a significant causal relationship between Shanghai districts and flows generated by ‘tourists from out of Shanghai’. The analysis of the causal network structure also reveals key districts and points of interest that significantly contribute to congestion in tourism mobility and Shanghai's dynamics. This econometric approach offers policymakers a valuable tool to monitor mobility drivers and optimise flows within the city.

A novel approach to improve population mapping considering facility-based service capacity and land livability

Social sensing data, including points-of-interest (POI) and mobile position, are important data sources for population mapping. However, existing studies using POI data disregard the heterogeneity in facility size among the same type of POI and urban–rural differences. Moreover, mobile position data face biased issues. This study presents a hybrid model that considers facility-based service capacity (FSC) and land livability to map fine-scale population distributions. Based on extracting the FSC index by integrating POI, mobile phone positioning (MPP), and road network data, the district-level census population was disaggregated into 100-m grids using a random forest model. Subsequently, a regression equation was developed from the land use data to correct the estimated residuals. The results showed that the hybrid model exhibited considerably smaller errors than those of the POI density-based method and direct mapping of MPP (RMSE = 5,393.31, 7,348.91, and 7,824.76, respectively), effectively reducing population misallocation in extreme-density regions. Compared to WorldPop and LandScan datasets and a comparative model, our method reduced the RMSE by 30–60%, proving the effectiveness of integrating various social sensing and remote sensing data for improving population mapping. This study improves on an existing method as a thought-provoking step toward advancement in fine-scale population mapping research.

Do hypothetical and actual service distances align for urban fringe parks? An investigation of Shanghai using big data

In China's transition from rapid urbanization to high-quality development, urban peripheral parks are increasingly crucial for ecological services and quality of life. However, a disparity persists between their intended function and actual usage, a common occurrence in megacities of China. This study aims to identify the actual service distance and accessibility of parks in fringe areas, using the southwestern sector of Shanghai's main urban fringe as an example. By utilizing mobile location data, it explores the multilevel service attributes of urban fringe parks. Analyzing spatial characteristics from the perspectives of clustering distances and life circle service capabilities, it concludes that: 1) fringe parks exhibit lower service capacities compared to central ones, mainly providing routine services; 2) fixed service range models are inadequate, especially for fringe parks; and 3) community and district-level parks have comparable service distances, highlighting the need to enhance district-level parks. This research aims to optimize park site selection and functions in urban peripheries, providing insights for sustainable planning.

Research on rescue priority based on high spatiotemporal resolution mobile positioning data

Estimate the key rescue areas of earthquake accurately, which is of great significance for deploying rescue forces and implementing rescue activities in post-earthquake scientifically.This paper based on the idea of first zoning, then classification, and then prioritizing rescue, taking the core area of Tangshan City as the study area, based on urban road data and mobile positioning data, combined with GIS methods to achieve street level rescue zoning, k-means clustering analysis is used to classify rescue sectors, and personnel burial model is used to conduct rescue priority classification.The results indicate that rescue priority is closely related to the time of earthquake occurrence. When the earthquake occurs between 18pm and 7pm in the next day, the number of priority rescue sector at level I and II is the highest. When the earthquake occurs between 8am and 11am on weekends, the number of priority rescue sector in residential areas increases, while the number of priority rescue zone decreases in workspace areas. This study provides refined rescue zoning and priority grading in the early stages of disaster relief with the absence of disaster information, which will help to assist in decision-making for professional force dispatch.

Non-Linear Impact of Economic Performance on Social Equity in Rail Transit Station Areas

Rail transit station areas (RSAs) are heralded as a transformative approach to urban planning, emphasizing the integration of transportation, housing, and commercial development to foster sustainable and inclusive cities. This study presents a comprehensive exploration of the interplay between transit-oriented development (TOD) economic performance and social equity in RSAs, employing advanced methodologies, like eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAPs), to decipher the complex relationships between TOD characteristics and social equity outcomes. Focused on Dalian’s urban center, this study integrates diverse datasets, including mobile location, geospatial, and economic price data, to construct a nuanced analysis framework within the NPE (node–place–economic) model. The results indicate that economic factors significantly impact overall social equity, particularly influencing key variables, such as weekday and weekend commuter population densities. Local explanatory plots reveal that economic performance variables associated with transportation development exhibit a broad non-linear impact on social equity in RSAs. This study advances equitable urban development through TOD by stressing the importance of factoring in multiple variables in RSA planning. This approach is vital for creating synergistic effects, fostering equitable spatial planning, and has both theoretical and practical benefits for improving residents’ well-being.

Untapping the potential of mobile location data: The opportunities and challenges for retail analytics

Smartphone technology has created a burgeoning mobile location data (MLD) marketplace. MLD has been adopted by many retailers to track consumer behaviours at a granular level across space and time. This paper provides a critical empirical case study approach to generate spatiotemporal MLD-based insights using raw smartphone data at five super-regional shopping centres. The analysis and visualization of MLD-based metrics provide the basis to rethink how we approach retail analytics and location decision-making. Many challenges need to be addressed to leverage the full potential of MLD, including data bias, spatial and temporal accuracy and significant consumer privacy concerns.

Stereoscopic imaging of volatile organic compounds distribution in the region and tracing emission sources of volatile organic compounds using a novel movable single-photon ionization time-of-flight mass spectrometer.

This paper presents a newly developed high-performance mobile single-photon ionization time-of-flight mass spectrometry (M-SPI-TOFMS) system for on-line analysis and stereoscopic monitoring of complex gas mixtures. The system is designed for stereoscopic imaging to map the distribution of volatile organic compounds (VOCs) and trace their emission sources in urban areas and industrial parks. It mainly consists of a SPI-TOFMS instrument, a customized commercial vehicle, a meteorological five-parameter monitor with GPS, a high-power generator, and an uninterruptible power supply. The SPI technique, using a 118 nm VUV lamp, can ionize compounds with an ionization potential below 10.78 eV. Mass spectra obtained using this technique show the profiles of various VOCs and some inorganic compounds. The VOCs composition information and mobile location data are simultaneously sent to the GIS software. In GIS software, this data is used for real-time stereoscopic imaging of VOC distribution and precise tracking of VOC movement. The system can achieve a spatial data resolution of 0.69 mm at 25 km/h due to the microsecond detection speed of the M-SPI-TOFMS instrument. The laboratory test provides a rapid overview characterization of benzene, toluene, and xylene. The M-SPI-TOFMS has limits of detection and mass resolution of 33.7 pptv and 1060, respectively. Several field applications were carried out using M-SPI-TOFMS at various locations to identify VOC sources near different factories. The M-SPI-TOFMS system has a navigation monitoring speed of 25 km/h with a time resolution of 1 s. The widespread use of this system will provide accurate data to support environmental management departments in formulating VOCs pollution control policies and improving control efficiency.

Rapid determination of seismic influence field based on mobile communication big data-A case study of the Luding Ms 6.8 earthquake in Sichuan, China.

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal-spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

When spatial interpolation matters: Seeking an appropriate data transformation from the mobile network for population estimates

Analyses utilizing mobile positioning data rarely provide an exact method of data transformation to target spatial units. A common reason is likely the fact that researchers have already worked with spatially aggregated data prepared by the mobile operator or processing company. The article demonstrates the critical importance of employing an appropriate method to transform data from the mobile network into target spatial units, ensuring the precision and accuracy of the results. By evaluating ten different methods of data transformation from the mobile network topology to a population grid of 1 × 1 km, the optimal transformation has been sought. The most promising results were obtained through the methods using auxiliary information. While a dasymetric transformation utilizing building volume as the ancillary layer proved to be the most accurate, the utilization of free data from the Global Human Settlement Layer project also exhibits encouraging potential. Frequently used interpolation methods such as point-to-polygon (the user's location is considered to be the same as the base transceiver station's position.) or areal weighting are in fact the least appropriate methods of data transformation at a subregional level.

Application of the local colocation quotient method in jobs-housing balance measurement based on mobile phone data: A case study of Nanjing City

The issue of jobs-housing balance concerns the sustainable development of cities and the well-being of residents. Conventional measurement approaches, however, often fall short due to the zoning problem (as a subproblem of the modifiable areal unit problem), leading to inconsistent and inaccurate results depending on the spatial partitioning scheme applied. This paper discusses the application and advantages of the local colocation quotient method in jobs-housing balance measurement. A case study of Nanjing, China, is selected, and mobile location data are used to obtain the jobs and housing locations of workers. Then, the adjusted jobs-workers ratio and the local colocation quotient values that reflect the degree of jobs-housing balance are calculated and compared by category. The results show that on the one hand, due to the zoning effect, when points are aggregated into spatial units, some points with different spatial characteristics are masked by the dominant value of the units; on the other hand, the local colocation quotient method can solve the zoning problem and obtain more fine-scale and accurate results, thus providing a new analytical tool and perspective for this field.

Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States.

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.

An assessment of potential improvements in social capital, risk awareness, and preparedness from digital technologies

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.

Use mobile location data to infer airport catchment areas and calibrate Huff gravity model in the New York metropolitan area

Mobile location data have emerged as a pivotal asset for analyzing travelers' spatial behaviors and movement patterns. In the context of air travel, the data empower researchers to gain empirical insights into travelers' choices of airports. This study employs mobile location data to scrutinize the market shares and infer catchment areas of three primary hub airports within the New York Metropolitan Area. Our study, together with Teixeira and Derudder (2021), helps contribute to a better understanding of competitive airport dynamics in the New York Metropolitan Area. In addition, the mobile location data allow us to calibrate the two key components of the Huff Gravity Model, which is frequently used in existing studies focusing on airport competition and catchment areas in Multiple Airport Regions (MARs). Our investigation underscores that the application of the Huff Model should not follow a uniform approach across different scenarios. The dynamics of airport competition and ground access alternatives exhibit unique characteristics within each MAR. Furthermore, our study unveils inherent quality challenges associated with mobile location data. Future studies intending to incorporate mobile location data are advised to conduct preliminary assessments of data quality before embarking on empirical analyses.

Digital twins, big data governance, and sustainable tourism

The rapid adoption of digital technologies has revolutionized business operations and introduced emerging concepts such as Digital Twin (DT) technology, which has the potential to predict system responses before they occur, making it an attractive option for smart and sustainable tourism. However, implementing DT software systems poses significant challenges, including compliance with regulations and effective communication among stakeholders, and concerns surrounding security, privacy, and trust with the use of big data. To address these challenges, this paper proposes a documentation framework for architectural decisions (DFAD) that applies the concept of big data governance to the digital system. The framework aims to ensure accountability, transparency, and trustworthiness while adhering to rules and regulations. To demonstrate its applicability, a case study and three case scenarios on the potential use of Mobile Positioning Data (MPD) in Indonesia for DT technology in smart and sustainable tourism were examined. The paper highlights the benefits of DFAD in shaping stakeholder communication and human–machine interactions while leveraging the potential of MPD to measure tourism statistics by Statistics Indonesia since 2016. Not only the documentation framework promotes compliance with regulations, but it also facilitates effective communication among stakeholders and enhances trust and transparency in the use of big data in DT technology for smart and sustainable tourism. This paper emphasizes the importance of effective big data governance and its potential to promote sustainable tourism practices. The multidisciplinarity approach on political science, software engineering, tourism, and official statistics provides an opportunity for academic contribution and decision-making processes.

Analysing the Relationship between Proximity to Transit Stations and Local Living Patterns: A Study of Human Mobility within a 15 Min Walking Distance through Mobile Location Data

Urban planning and transportation policies are vital to creating sustainable and liveable cities. Transit-orientated development (TOD) has emerged as a prominent approach that emphasises the establishment of neighbourhoods with convenient access to public transportation, thereby promoting car-free lifestyles. This research investigates the connection between proximity to transit stations and local living habits in Auckland, New Zealand, which is a car-dependent city aiming to transition to a sustainable TOD model. We use geolocational data from mobile phones to measure the daily mobility patterns of residents living within a 15 min walking distance of various transit stations. Employing ordinary least squares (OLS) regression, we analyse the correlation between residents’ average travel distances and individual mobility, considering different station contexts. We aim to determine whether individuals living near transit stations are more inclined to participate in local activities and make a higher proportion of short-distance trips. The results illustrate that approximately 54% of the residents show dominant localised mobility patterns. Living near a station is significantly associated with shorter annual travel distances, although this trend varies by area. Notably, only about 16 of the 34 stations studied indicate that their local residents predominantly engage in ‘local’ travel patterns. Rural stations show less correlation, likely due to poor infrastructure and limited walkability. This study underscores the vital role of proximity to transit stations in promoting sustainable mobility. It serves as a foundational guide for urban planners and designers to make informed decisions that improve the built environment and optimise land use.

The technical approach of using mobile positioning data to support urban population size monitoring

This paper summarizes the methods and approaches of using mobile positioning data to estimate and monitor urban population size. It starts with the necessity of using big data to monitor urban population size in territorial spatial planning. Then it elaborates on the difference between the definition of "population size" reflected by mobile positioning data and the common concept of urban population size, and the necessity of verifying the logic and measurement of sample expansion at four levels. Finally, taking Wuhan city as a case, this paper proposes the technical approach of monitoring the size of urban permanent population through multi-source data verification. The study finds that when it comes to monitoring urban population size, mobile positioning data have the advantage of monitoring short-period changes in population size and spatial distribution, yet special attention must be paid to the three technical links of definition, sample expansion and verification.

Mobile positioning-based population statistics in crisis management: An Estonian case study

Key to strong disaster management practices is to have a clear understanding of people's whereabouts in disaster areas. One novel method for retrieving this information near real time is mobile positioning data. The goals of this research were to develop a methodology for identifying de facto populations and population groups in potential disaster areas, evaluate their spatio-temporal variation, and assess the applicability of the associated statistics in disaster management. Population groups – residents and temporary residents, workers, foreign and domestic tourists, regular and transit visitors – were identified based on Estonian historical mobile positioning data, where the details of stays and trips are provided based on longitudinal mobility behaviour.The main findings included that population changes follow clear week-hourly rhythms for de facto populations, residents and workers, but not for tourists and temporary residents. There are distinctive and identifiable geographical differences in how population group presence changes over time. Rescue workers asserted that the methodology of population statistics enabled them to assess population presence and composition in areas more precisely than registry-based approaches. This should evacuation planning, resource allocation and communication activities while preparing for and responding to disasters.

Assessing the Impact of the COVID-19 Pandemic on Population Heat in Urban Green Spaces in China

Using mobile positioning data, the study assessed the impact of the COVID-19 pandemic on population heat in urban green spaces (UGSs) in Beijing, Shanghai, and Qingdao, China. The results were summarized as follows. The range of the spatial distribution of population heat in the three cities expanded in 2020 compared with that in 2019. Moreover, the shape of the hourly curve of population heat in UGSs changed from an inverted U-shape to a flat shape due to the impact of the COVID-19 pandemic. Restricted–non-fee-paying UGSs were least affected by the COVID-19 pandemic. Last, regression analysis revealed a significant influence of the change in population heat in hotel accommodation within UGS service areas on the change of population heat in UGSs. These findings provided new insights into the responses of recreational populations to policy decisions, risk perception of an infectious disease, and resilience to crises of different types of UGSs.

Measuring visitor flows using mobile positioning data in three Hungarian second-tier cities

PurposeThe research analyses the spatial-temporal behaviour of international visitors using mobile positioning data (MPD) in three second-tier cities in Hungary: Szeged, Debrecen and Pécs. The purpose of this study is to identify the origins of visitors, length of stay, seasonal concentrations and mobility between cities.Design/methodology/approachA large volume of data generated by foreign mobile phone users was processed and analysed for six consecutive months in 2018. The movements of around 3.5 million foreigners visiting Hungary were captured.FindingsThe data showed significant differences in tourist turnover, mobility patterns and flows indicating different levels of tourism activity and potential in the three cities. This included day trips, cross-border tourism, seasonal fluctuations and activity-driven demand (e.g. special events and cultural tourism).Research limitations/implicationsMPD cannot fully explain or predict drivers of visitor behaviour, such as purpose of visit, the precise activities that they undertake there and the experiences that they gain. It can, however, provide information on spatial and temporal flows, patterns and concentrations of visitors.Practical implicationsThe investigated cities should follow distinct tourism development policies to attract more tourists and strengthen the links with their neighbouring destinations at the same time as differentiating themselves through marketing as competitive alternative destinations.Social implicationsAugmenting tourism has socio-economic implications for residents and communities. The development of cultural tourism and local event-based tourism must incorporate community needs.Originality/valueThe paper analyses the extent to which MPD can provide insights into visitors’ spatio-temporal mobility and flows in relatively under-visited second-tier cities and it highlights opportunities and gaps for big data research in an urban context.