Popular Location-based Services Research Articles

‘Informed’ consent in popular location based services and digital sovereignty

ABSTRACT In many countries, informed consent is required before a service provider can collect personal data from a user. For location-based services (LBS), this applies in particular to personal location information, which can enable deep inferences about a person. In this paper, we present a systematic analysis of how informed consent for the collection of personal location information is obtained in 40 popular LBS on each of the two largest app stores. Two independent raters assessed the content, structure and design of the dialogues shown by apps to obtain consent from users. Based on their assessment, we identified common approaches used across and within different app categories and platforms, including the frequent use of ‘dark patterns’. We highlight key issues arising from these common designs, discuss specific gaps in the procedure of obtaining informed consent and propose improvements to that procedure. In addition, we consider current practice in the context of enabling digital sovereignty with respect to personal location information. Our findings can shape the design and evaluation of informed consent procedures for future LBS in research and practice.

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.

A Deviate-Based Prioritizing Technique for Regression Testing of Mobile Navigation Service

Mobile navigation service is an important and popular location-based services, which help to recommend routes for mobile users to their destinations. Modern mobile navigation service provides various navigation strategies and considers many complex situations, which make validation and verification of mobile navigation service becomes very difficult. In this paper, we present an approach to prioritize test cases for regression testing of mobile navigation Service. The approach is based on the assumption that there may be a failure if the user's actual route deviates from the navigation recommended route. In this paper, we analyze the mass mobile navigation logs, compare the recommended routes with the user travel routes, identify the deviation of the intersection point, and then priority regression test data by deviation. To evaluate our approach, we conduct a case study on a popular navigation software. By compare proposed prioritizing test approach with random test approach, the approach helps to improve test efficiency.

Spatial analysis of users-generated ratings of yelp venues

BackgroundWith popular location-based services on smart phones, users are willing to leave comments on the business venues (e.g., restaurants, shops, bars, etc.) that they visited. Reviews of users on Yelp venues somewhat indicate satisfaction of customers with services of those venues. Those reviews could be used to reflect service quality of business venues. Geo-localized venues could tell researchers where and how good a business venue is.MethodsIn terms of a spatial analysis of venues’ ratings, this paper explored geographic patterns of ratings of Yelp business venues in a city-wide region. Specifically, we identified clusters of high and low ratings and explored spatial patterns of clusters of high ratings for different venue categories (i.e., restaurants, fast foods and bars).ResultsIn this study, we undertook an analysis of Yelp ratings in Phoenix, USA. The empirical results indicate that spatial clusters of high ratings tend to be differently distributed between different categories of Yelp venues. More specifically, bars within or near the city centre are likely to get high ratings. Moreover, although hot spots and cold spots of restaurants and fast foods both tend to be randomly distributed over space, spatial distribution of restaurants’ ratings tends to be more similar to that of bars’ ratings.ConclusionMapping Yelp’s business venues with ratings provides a new way to understand spatial patterns of service quality of business or public venues at a large spatial scale.

EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data

With the pervasiveness of smart phones, location-based services (LBS) have received considerable attention and become more popular and vital recently. However, the use of LBS also poses a potential threat to user's location privacy. In this paper, aiming at spatial range query, a popular LBS providing information about points of interest (POIs) within a given distance, we present an efficient and privacy-preserving location-based query solution, called EPLQ. Specifically, to achieve privacy-preserving spatial range query, we propose the first predicate-only encryption scheme for inner product range (IPRE), which can be used to detect whether a position is within a given circular area in a privacy-preserving way. To reduce query latency, we further design a privacy-preserving tree index structure in EPLQ. Detailed security analysis confirms the security properties of EPLQ. In addition, extensive experiments are conducted, and the results demonstrate that EPLQ is very efficient in privacy-preserving spatial range query over outsourced encrypted data. In particular, for a mobile LBS user using an Android phone, around 0.9 s is needed to generate a query, and it also only requires a commodity workstation, which plays the role of the cloud in our experiments, a few seconds to search POIs.

Privacy quantification model based on the Bayes conditional risk in Location-Based Services

The widespread use of Location-Based Services (LBSs), which allows untrusted service providers to collect large quantities of information regarding users' locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms (LPPMs) have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary's estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.

Considering Affective Responses towards Environments for Enhancing Location Based Services

Abstract. A number of studies in the field of environmental psychology show that humans perceive and evaluate their surroundings affectively. Some places are experienced as unsafe, while some others as attractive and interesting. Experiences from daily life show that many of our daily behaviours and decision-making are often influenced by this kind of affective responses towards environments. Location based services (LBS) are often designed to assist and support people’s behaviours and decision-making in space. In order to provide services with high usefulness (usability and utility), LBS should consider these kinds of affective responses towards environments. This paper reports on the results of a research project, which studies how people’s affective responses towards environments can be modelled and acquired, as well as how LBS can benefit by considering these affective responses. As one of the most popular LBS applications, mobile pedestrian navigation systems are used as an example for illustration.

A context-aware scheme for privacy-preserving location-based services

We address issues related to privacy protection in location-based services (LBSs). Most existing privacy-preserving LBS techniques either require a trusted third-party (anonymizer) or use cryptographic protocols that are computationally and communicationally expensive. Our design of privacy-preserving techniques is principled on not requiring a trusted third-party while being highly efficient in terms of time and space complexities. The problem has two interesting and challenging characteristics: First, the degree of privacy protection and LBS accuracy depends on the context, such as population and road density, around a user’s location. Second, an adversary may violate a user’s location privacy in two ways: (i) based on the user’s location information contained in the LBS query payload and (ii) by inferring a user’s geographical location based on the device’s IP address. To address these challenges, we introduce CAP, a context-aware privacy-preserving LBS system with integrated protection for both data privacy and communication anonymity. We have implemented CAP and integrated it with Google Maps, a popular LBS system. Theoretical analysis and experimental results validate CAP’s effectiveness on privacy protection, LBS accuracy, and communication QoS (Quality-of-Service).

Collective intelligence-based route recommendation for assisting pedestrian wayfinding in the era of Web 2.0

Mobile pedestrian navigation systems are one of the most popular Location-Based Services. In the era of Web 2.0, current mobile navigation systems often suffer from the following problems: the lack of social navigation support (utilising other people's experiences) and the challenge of making user-generated content (UGC) useful. In this article, some collective intelligence-based route recommendation methods are designed to address these problems. The proposed methods can make use of UGC (reflecting other users’ navigation experiences), and provide users with the least complex and the length-complexity-optimised (LCO) routes. Some simulations using the street network of the first district of Vienna (Austria) are designed to evaluate the proposed methods. The results show that compared to the shortest route, the collective intelligence-based routes (i.e. the least complex and the LCO routes) have a significant improvement of the route quality (with lower complexity ratings), thereby more effectively supporting users’ navigation tasks (more chances of reaching the destination, fewer mistakes made and shorter distance travelled).

What Have You Done with Location-Based Services Lately?

This issue's Applications column revisits location-based applications and how far we've come since the original mobile device location-detection research started about 10 years ago. The authors discuss a number of popular location-based services and the speed at which such services have become available and commonplace. They also raise some issues about further technical challenges in privacy that result from such widespread use.