Individual Location Data Research Articles

People Flow Trend Estimation Approach and Quantitative Explanation Based on the Scene Level Deep Learning of Street View Images

People flow trend estimation is crucial to traffic and urban safety planning and management. However, owing to privacy concerns, the collection of individual location data for people flow statistical analysis is difficult; thus, an alternative approach is urgently needed. Furthermore, the trend in people flow is reflected in streetscape factors, yet the relationship between them remains unclear in the existing literature. To address this, we propose an end-to-end deep-learning approach that combines street view images and human subjective score of each street view. For a more detailed people flow study, estimation and analysis were implemented using different time and movement patterns. Consequently, we achieved a 78% accuracy on the test set. We also implemented the gradient-weighted class activation mapping deep learning visualization and L1 based statistical methods and proposed a quantitative analysis approach to understand the land scape elements and subjective feeling of street view and to identify the effective elements for the people flow estimation based on a gradient impact method. In summary, this study provides a novel end-to-end people flow trend estimation approach and sheds light on the relationship between streetscape, human subjective feeling, and people flow trend, thereby making an important contribution to the evaluation of existing urban development.

Geo-Ellipse-Indistinguishability: Community-Aware Location Privacy Protection for Directional Distribution

Directional distribution analysis has long served as a fundamental functionality in abstracting dispersion and orientation of spatial datasets. Spatial datasets that describe sensitive information of individuals such as health status and home addresses must be used and shared cautiously to protect individuals&#x0027; privacy. There is an inherent tension between the need of accurate directional distribution result and the requirement of individuals&#x0027; location privacy. Plenty of excellent location privacy protection approaches such as geo-indistinguishability can provide strong protection for locations but considerably at the expense of statistical quality of subsequent directional distribution analysis. In this paper, to protect individual location data for directional distribution, we define the geographic feature of community with covariance matrix and then propose a <i>geo-ellipse-indistinguishability</i> privacy notion incorporating this covariance matrix. As an instantiation of metric differential privacy, <i>geo-ellipse-indistinguishability</i> guarantees pairwise inputs cannot be distinguishable with the level proportional to privacy budget and Mahalanobis distance between them, given a randomized output. We also present elliptical privacy mechanisms to achieve this privacy definition on the basis of gamma distribution and multivariate normal distribution. We finally evaluate the empirical utility of the proposed mechanism in New York home addresses database. Our experiments demonstrate that under the same privacy level, our proposed elliptical approach can achieve significantly higher directional distribution utility than circular noise function based method.

Applying contextual integrity to digital contact tracing and automated triage for hospitals during COVID-19

To control and minimise the spread of COVID-19, various technological solutions have been proposed. In this research, we focus on digital contact tracing and automated triage for hospitals. We conducted an online survey in Flanders (N = 1708) to investigate the perceived appropriateness of these systems based on the Contextual Integrity framework, as developed by Nissenbaum [1]. For digital contact tracing, significant differences were found between the appropriateness of using various types of data for different goals. Precise individual location data (i.e. GPS) was considered to be least appropriate and much less appropriate than proximity data (i.e. Bluetooth) or coarser location data (i.e. GSM). Goals for digital contact tracing with a high individual impact were considered to be less appropriate than goals with a low individual or societal impact. In addition, the data showed that respondents would find the usage of digital contact tracing to be less appropriate after the pandemic, underlining the temporality of this technological solution. For automated triage, the results indicated that gender is perceived to be significantly less appropriate than the other types of data, including age, to determine the priority of treatment.

Privacy-Preserving Travel Time Prediction With Uncertainty Using GPS Trace Data

The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of geo-indistinguishability-based sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time prediction with satisfactory accuracy at reasonably small privacy costs.

The use of mobile phone data in transport planning

The paper offers a historical review of the emergent use of individual location data from mobile phones in the field of transport planning that occurred over the past two decades. It identifies four key areas of development: 1) providing traffic information, 2) replacing the traditional data for transport planning, 3) enhancing our understanding of travel behaviour, and 4) validating transport models. It assesses the appropriate roles for mobile phone data in transport planning and suggests that the extraction of traffic information, the construction of origin-destination data, and transit route design have particularly practical implications for cities in the developing countries.

The use of mobile phone data in transport planning

The paper offers a historical review of the emergent use of individual location data from mobile phones in the field of transport planning that occurred over the past two decades. It identifies four key areas of development: 1) providing traffic information, 2) replacing the traditional data for transport planning, 3) enhancing our understanding of travel behaviour, and 4) validating transport models. It assesses the appropriate roles for mobile phone data in transport planning and suggests that the extraction of traffic information, the construction of origin-destination data, and transit route design have particularly practical implications for cities in the developing countries.

Quantifying personal exposure to air pollution from smartphone-based location data.

Personal exposure assessment is a challenging task that requires both measurements of the state of the environment as well as the individual's movements. In this paper, we show how location data collected by smartphone applications can be exploited to quantify the personal exposure of a large group of people to air pollution. A Bayesian approach that blends air quality monitoring data with individual location data is proposed to assess the individual exposure over time, under uncertainty ofboth the pollutant level and the individual location. A comparison with personal exposure obtained assuming fixed locations for the individuals is also provided. Location data collected by the Earthquake Network research project are employed to quantify the dynamic personal exposure to fine particulate matter of around 2500 people living in Santiago (Chile) over a 4-month period. For around 30% of individuals, the personal exposure based on people movements emerges significantly different over the static exposure. On the basis of this result and thanks to a simulation study, we claim thateven when the individual location is known with nonnegligible error, thishelps to better assess personal exposure to air pollution. The approach is flexible and can be adopted to quantify the personal exposure based on any location-aware smartphone application.

K Privacy: Towards improving privacy strength while preserving utility

Future autonomous vehicles will generate, collect, aggregate and consume significant volumes of data as key gateway devices in emerging Internet of Things scenarios. While vehicles are widely accepted as one of the most challenging mobility contexts in which to achieve effective data communications, less attention has been paid to the privacy of data emerging from these vehicles. The quality and usability of such privatized data will lie at the heart of future safe and efficient transportation solutions.In this paper, we present the K Privacy mechanism. K Privacy is to our knowledge the first such mechanism that enables data creators to submit multiple contradictory responses to a query, whilst preserving utility measured as the absolute error from the actual original data. The functionalities are achieved in both a scalable and secure fashion. For instance, individual location data can be obfuscated while preserving utility, thereby enabling the scheme to transparently integrate with existing systems (e.g. Waze). A new cryptographic primitive Function Secret Sharing is used to achieve non-attributable writes and we show an order of magnitude improvement from the default implementation.

Active or inactive: infer private user information in location-based social network

Private user information can be compromised while revealing individual location data in widely used location-based social networks (LBSNs). In order to reveal the risk of location privacy faced by users, we demonstrate a method, which transforms social networks into Bayesian networks, to infer private information through the location data and relationships among users in LBSNs, such as Gowalla, regardless of whether users are active or inactive. Location data from active users can be easily used to infer private information like consumption level. For example, people who frequently appear in expensive restaurants are likely to rank the high consumption level. Those inactive users, who share sparse location data, reveal their private information through their active friends whose private information is easily divulged. Our experimental results show that friends have a high probability of having been to the same places. Combining with relationship data, the possibility of revealing private information is dramatically improved.

Agent-based Movement Analysis and Location Prediction in Cellular Networks

The huge quantity of individual location data recorded by mobile phones or mobile networks inspires research questions regarding new and intelligible services on behalf of mobile users. Creating a system for location prediction and following user habits is a sizeable and challenging task. We present a system for movement analysis and location prediction of individual mobile users. The goal of movement analysis is to determine which locations are significant to the user and to classify locations according to user-specific regularities. The analysis is performed by multi-agent system in which individual agent performs a chosen set of functions. The prediction system consists of two predictors which work in parallel, based on the type of user-specific locations. Prediction system is intended to estimate the future location of a user device and provide it to user-authorized applications and services. The proposed system is evaluated using real movement data recorded as part of the MIT Reality Mining Project.

Sensing 'Socio-Spatio-Temporal' Activity Patterns from Location Sharing Services Data

Context: Online social networks and the Web 2.0 technologies embedded in these sites are creating an environment in which individuals can communicate and share information in ways that were previously not possible. Such websites are providing an unprecedented and growing collection of data on individual behavior that is very rich in detail. This includes information on with whom, when and where people interact, and more generally, what their activity patterns look like in time and space, and even what their sentiment or preferences are at specific moments and locations. Knowledge Gaps: There is a burgeoning body of literature that draws upon social media data and more broadly information collected via mobile communications devices (e.g., cell phone trajectories) to model and understand particular aspects of human behavior, including mobility patterns and social and spatio-temporal interaction. Yet, very little of this research has examined how and to what extent the spatio-temporal activity patterns revealed by these new forms of data vary across metropolitan areas, especially after controlling for relevant city-specific characteristics such as the size, density, composition or demographic profile of a city. While we recognize that the study of space-time activity patterns itself is not new, there are some gaps in the literature that should be noted. First, most analyses have been confined to a select set of cities – i.e., those that have conducted travel diaries or activity-based surveys. Due to inconsistencies in the format and type of information collected from such surveys, comparative analyses are problematic. Second, few studies have looked explicitly at the simultaneous integration of space, time and social (inclusive of cyber socialization) interaction, and the complex mobility patterns that arise from this behavior. Lastly, unlike location sharing services data, the information provided by travel behavior surveys tend to capture only mobility patterns arising from the primary residents of a city and not the behavior of transient visitors to that location. Study Objectives: The primary objectives of this study are to 1). understand how and to what extent location sharing services data approximate regional spatio-temporal activity patterns 2). develop a set of network-based metrics for characterizing the centrality and disorder of such activities in a region, and, 3). conduct a cross-city comparison using these metrics and related indicators of mobility.Data: To carry out the proposed research, we intend to use location services data collected over a five month period in 2010-11 (Cheng et al., 2011). This data provides information on user check-ins, or more specifically, where individuals indicate they are at different times of the day and week. Additional details on each individual’s status within the social networks that they belong are also included in the dataset. Methodology: The study methodology draws heavily on techniques from social network analysis, although concepts form landscape ecology, physics and geography are also utilized to capture different aspects of regional activity patterns. To gain an understanding of the types of activities that location sharing services data capture, we first conduct a correlation analysis using sector-based establishment data from the U.S. Census County Business Patterns. Correlations are examined at the zip code level. Second, using a space-time bipartite network topology, we derive a set of measures that characterize the centrality and disorder (entropy) of activities in a region, and that further can be decomposed to examine the spatial distribution of these characteristics. With individual location data aggregated to grid cells and summarized according to regular time intervals, we apply the technique to two U.S. metropolitan areas: Atlanta and Chicago.Significance: Implications for travel demand forecasting, epidemiological and information diffusion modelling and abnormal crowd detection (e.g., through “burstiness” analysis) will be drawn from the study.

Temporal and spatial interactions between an obligate predator, the Eurasian lynx (Lynx lynx), and a facultative scavenger, the wolverine (Gulo gulo)

Interspecific interactions between sympatric carnivores can be important for the behaviour and demography of involved species. We studied spatial and temporal interactions between an obligate predator, the Eurasian lynx ( Lynx lynx (L., 1758)), and a facultative scavenger, the wolverine ( Gulo gulo (L., 1758)). Wolverines are known to utilize lynx-killed reindeer ( Rangifer tarandus tarandus (L., 1758)) and may benefit from being sympatric with lynx if interference competition is low. We used individual location data from 9 lynx and 17 wolverines to analyse interaction between inter- and intra-specific dyads (n = 195). We found no spatial segregation between lynx and wolverines and we observed no attraction or avoidance between individuals of the two species, independent of proportion of home-range overlap. This opposed our prediction that wolverines will show direct or delayed attraction to lynx. Wolverines may still benefit by scavenging lynx-killed reindeer while avoiding direct encounters with the lynx. Within species, we found attraction between males and females, increasing with proportion of overlap for lynx. Attraction was also found between consexual lynx, while consexual wolverines showed little home-range overlap (7%–9%) and neutral temporal interaction, indicating territoriality. Individual space use may be more influenced by conspecific interactions than by other species.

Improving Analytical Procedures: A Case of Using Disaggregate Multilocation Data

According to SAS No. 56, Analytical Procedures, the use of disaggregate, individual location data can improve the effectiveness of analytical procedures used in multilocation audits. Using a case-study approach, we investigate whether improvements in the accuracy and precision of account balance expectations can be obtained by using disaggregate, individual location data in a large, multilocation company. Specifically, we examine two issues: (1) whether the summation of individual location expectations generates more accurate and precise expectations of company-wide account balances than expectations based on company-wide data only and (2) whether the accuracy and precision of analytical procedures is enhanced by including peer location observations of the account balance in individual location expectation models. We find that for the multilocation company examined in this case study the summation of individual location account balance expectations is not more accurate or precise than an expectation derived from aggregate models unless the individual location models include peer location observations of the account balance. When the individual location models include the same account observations from other peer locations within the company, the company-wide account balance expectations developed from disaggregate models are more accurate and precise (less variable) than expectations developed using aggregate, company-wide data only. The results from this case study indicate that when auditors are generating expectations of company-wide balances, disaggregate models incorporating peer location account observations provide account balance expectations that are both more accurate and more precise than company-wide, aggregate models. Given the limitations of a case-study approach, future research should be directed at establishing the generalizability of these findings.

Improving the Accuracy and Precision of Analytical Procedures Using Multilocation Data

According to SAS No. 56, Analytical Procedures, the use of disaggregate, individual location data can improve the effectiveness of analytical procedures used in multilocation audits. Using disaggregate data obtained from a multilocation company, we examine two issues: 1) whether the accuracy and precision of analytical procedures is enhanced by including contemporaneous observations of the account balance in the prediction model, and 2) whether expectations developed using disaggregate data are more accurate and precise than expectations based on aggregate data only. We find that the contemporaneous models are consistently more accurate and precise and that the summation of individual location account balance forecasts based on disaggregate, contemporaneous models are superior to expectations developed using aggregate, company-wide data only. The results indicate that when auditors are generating expectations of company-wide balances, that disaggregate, contemporaneous models will provide predictions that are both more accurate and more precise than company-wide, aggregate models.

Topographical corneal edema. An update.

Measurement of the topographical corneal edema response has been previously restricted by the use of optical pachometry and small sample sizes. This study uses ultrasound pachometry to measure changes in the corneal thickness of 23 subjects at 9 locations during 2 hours' wear of a thick soft contact lens. The data are analysed using both averaged and individual location data. The averaged location analysis supports the current paradigm that less edema occurs in the peripheral cornea than in the central cornea during contact lens wear. However, the individual location analysis reveals a more complex corneal edema response in which the greatest amount of edema following 2 hours' contact lens wear occurred in the central and mid-peripheral temporal cornea, in comparison with the peripheral nasal and peripheral inferior cornea which showed the least edema. The edema measured in the peripheral temporal and peripheral superior cornea following 2 hours' contact lens wear was not significantly different from that measured in the central cornea. The edema measured in the central cornea following 2 hours' contact lens wear was not significantly different from that found at any mid-peripheral location. This study demonstrates that the corneal edema response associated with 2 hours' wear of a thick soft contact lens is a complex dynamic topographical process and cannot be simply modelled using averaged data from small samples.