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The objective of this study was to assess the effectiveness and practicability of an activity index combining acceleration and location data for automated estrus detection in dairy cows. By using a wearable neck tag, measurements of acceleration and location were gathered from 22 multiparous cows monitored incessantly for 6 days to derive activity records of each cow. The maximum-minimum distance clustering (MMDC) method was used to divide hourly activity data into low, medium, high, and intensity level groups. The weighted sum of the proportions of the low, medium, high, and intensity activities in an hour constituted the activity level. The activity index was defined as the ratio of the variation in hourly activity level compared to the same time period during the previous three days. Furthermore, whether the cow was in estrus was judged above a set threshold. The study showed that the power consumption and communication effects of the neck tags were acceptable for indoor-housing conditions. For the two consecutive time periods, the activity-index-based detection algorithm achieved 90.91% for accuracy, 100% for precision, 100% for specificity, 83.33% for recall, 90.91% for F1 score, and 0.82 for Kappa coefficient. On the basis of these results, it can be concluded that the combination of acceleration and location in the activity index can promote estrus detection in dairy cows.

In this paper, a new 3D visualization technical method was developed for hydraulic fractures using micro-seismic monitoring. This technical method consists of four steps: i. interpret the geologic hydraulic fracture model based on seismic source location data from micro-seismic monitoring; ii. develop a hydraulic fracture indication model, relying on the 3D spatial freeze-frame of micro-seismic monitoring sources from hydraulic fracturing; iii. construct a hydraulic fracture density model using the intensity from the micro-seismic monitoring; and iv. implement a 3D visualization of the hydraulic fractures, relying on the spatial constraints of the density model, the hydraulic fracture indication model, and the properties of the hydraulic fractures. This proposed technical method was used to produce 3D visualizations of the hydraulic fractures in well X in the Jiao reservoir, China, and the 3D visualizations of the distribution, development, extent and cutting relationships of hydraulic fractures were successfully realized. The results show that this technical method can be used as a practical and reliable approach to characterize hydraulic fractures.

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. The proposed method was tested with 34 traffic congestion cases in the Knoxville,Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

Many factors can impact the location data of microseismic events, including natural fractures, rock lithology, in situ stress, and hydraulic-fracturing parameters. The distribution of microseismic events generally tends toward highly brittle areas or areas with brittle minerals. Moreover, location data of microseismic events lack effective evaluation methods. Therefore, we have developed a method to use lithologic information and prestack seismic data to explain the distribution of well Tian Xing microseismic events. We have analyzed the brittleness of the target formation through the well logs and core. We inverted the Young’s modulus and Poisson’s ratio based on simultaneous amplitude variation with offset inversion by the prestack seismic data. We then computed the 3D brittleness index (BI) property volume by Grieser and Rickman’s method. In addition, the microseismic event distribution and BI map were then combined to show the internal relationship between the two results. We found that the well logs and core analysis demonstrated that the target formation has high brittleness. Generally, areas with more natural fractures have a higher probability of inducing hydraulic fractures. However, the analysis results show that the BI has an impact on the distribution of hydraulic fractures. Therefore, BI explains the reason for the distribution of almost all events in the northeast of the perforation. These observations also supported the concept that microseismic events preferentially grow toward more brittle areas.

Location-based services (LBSs) provide enhanced functionality and convenience of ubiquitous computing, but they open up new vulnerabilities that can be utilized to violate the users’ privacy. The leakage of private location data in the LBS context has drawn significant attention from academics and industry due to its importance, leading to numerous research efforts aiming to confront the related challenges. However, to the best of our knowledge, none of relevant studies have performed a qualitative and quantitative comparison and analysis of the complex topic of designing countermeasures and discussed the viability of their use with different kinds of services and the potential elements that could be deployed to meet new challenges. Accordingly, the purpose of this survey is to examine the privacy-preserving techniques in LBSs. We categorize and provide an inside-out review of the existing techniques. Performing a retrospective analysis of several typical studies in each category, we summarize their basic principles and recent advances. Additionally, we highlight the use of privacy-preserving techniques in LBSs for enabling new research opportunities. Providing an up-to-date and comprehensive overview of existing studies, this survey may further stimulate new research efforts into this promising field.

An increasing amount of users’ locations are aggregated, and the statistical results about the collected data are further released to support mobile applications, such as point-of-interest recommendation and smart transportation. However, such statistical results cause users’ membership privacy leakage. Unfortunately, most studies concerning data aggregation focused on privacy preservation and various attacks rather than the membership inference attacks. Moreover, literature about membership inference attacks mainly aimed at machine learning models and gene sequences rather than the locations in data aggregation. More importantly, these work concerning membership inference attacks assumed that adversaries know the exact data of victims, which is always impossible in practical scenarios. To this end, we propose LocMIA, a more invasive attack system that allows adversaries to launch membership inference attacks against aggregated location data without reliance on any prior knowledge of the locations of victims. The main idea of LocMIA is to train a binary classifier to infer whether a specific victim’s location data is involved in the aggregation group, based solely on the data aggregation’s output (i.e., the statistical results). Finally, experimental results on a real-world check-in data set prove the devastating privacy leaks caused by the proposed LocMIA.

Urban development in many cities worldwide presents a dispersed development with urban sprawl and excessive land use. Compact city development facilitates an efficient use of land that aims to reduce excessive land consumption and commuting. However, the ignorance of the relationships between land use and the compactness of employment activities has resulted in excessive commuting. The separation of jobs and housing due to functional zoning has caused criticism in compact city development. This research aims to examine the compactness of workers’ employment activities and its relationship with land use to enrich our knowledge on compact city development. The relationship between urban and employment activity compactness is examined through cellphone location data in Shanghai, China. A set of indicators, including intrazonal employment ratio and interzonal commuting distance, are examined to measure the compactness of employment activities. Land-use compactness is measured through building density, land-use diversity, and accessibility. The effect of land-use compactness on employment activity compactness is analyzed through structural equation modeling. A high proportion of residential land use reduces the intrazonal employment ratio and increases the average interzonal commuting distance. A high proportion of industrial and commercial land use increases the intrazonal employment ratio. These are the spatial characteristics to improve urban compactness. This study contributes to decision-making for compact development, which is of great significance to promoting compact activities. It can also provide spatial planning policies that facilitate workers’ access to local employment opportunities with short travel distances through land use planning for mixed land use.

The advent of GPS technologies generates location data-streams and accentuates the importance of developing practical tools that can process and analyze the vast amounts of location data at a given...

A claimed benefit of real-time information (RTI) apps in public transit systems is the reduction of waiting time by allowing passengers to appropriately time their arrivals at transit stops. Although previous research investigated the overall impact of RTI on waiting time, few studies examine the mechanisms underlying these claims, and variations in its effectiveness over time and space. In this paper, we theorize and validate the sources of RTI-based users’ waiting time penalties: reclaimed delay (bus drivers compensating for being behind schedule) and discontinuity delay (an artifact of the update frequency of RTI). We compare two RTI-based strategies – the greedy strategy used by popular trip planning apps and a prudent strategy with an insurance buffer – with non-RTI benchmarks of arbitrary arrival and following the schedule. Using real-time bus location data from a medium-sized US city, we calculate the empirical waiting times and risk of missing a bus for each trip planning strategy. We find that the best RTI strategy, a prudent tactic with an optimized insurance time buffer, performs roughly the same as the simple, follow-the-schedule tactic that does not use RTI. However, relative performance varies over time and space. Moreover, the greedy tactic in common transit apps is the worst strategy, even worse than showing up at a bus stop arbitrarily. These results suggest limitations on claims that RTI reduces public transit waiting times.

金沢大学理工研究域地球社会基盤学系, How many base travel groups (models) are necessary for clarifying the long-term day-to-day dynamics of intercity travel? In the past, several travel purposes (e.g., sightseeing, business, etc.) have been assumed. However, mobile-phone location data enables us to answer the above question because of their detailed time-series information. In this study, we propose a method for deriving the basic travel groups necessary for clarifying the time-series changes by applying nonnegative tensor factorization (NTF). This method is applied to the time-series data of several long-distance travelers to the Ishikawa prefecture, to where the Hokuriku High-speed rail (HSR) has been newly extended. Based on this, the number of base travel groups necessary for predicting the effect of the new HSR is estimated as twelve, which is greater than the number used in the previous demand forecasting models. The estimated groups include components that appear to correspond to different travel purposes (e.g., sightseeing, business, and homecoming), as in previous surveys. These results confirm that the methodology proposed in this study can clearly extract groups with different elasticities, due to the traffic service. The HSR effect can be clarified by dividing it into several characteristics and detailed components. In addition, if multiple HSR effects are analyzed, a more accurate demand-forecasting model for the new HSR service can be proposed., Embargo Period 12 months

Linking user accounts belonging to the same user across different platforms with location data has received significant attention, due to the popularization of GPS-enabled devices and the wide range of applications benefiting from user account linkage (e.g., cross-platform user profiling and recommendation). Different from most existing studies which only focus on user account linkage across two platforms, we propose a novel model ULMP (i.e., user account linkage across multiple platforms), with the goal of effectively and efficiently linking user accounts across multiple platforms with location data. Despite of the practical significance brought by successful user linkage across multiple platforms, this task is very challenging compared with the ones across two platforms. The major challenge lies in the fact that the number of user combinations shows an explosive growth with the increase of the number of platforms. To tackle the problem, a novel method GTkNN is first proposed to prune the search space by efficiently retrieving top-k candidate user accounts indexed with well-designed spatial and temporal index structures. Then, in the pruned space, a match score based on kernel density estimation combining both spatial and temporal information is designed to retrieve the linked user accounts. The extensive experiments conducted on four real-world datasets demonstrate the superiority of the proposed model ULMP in terms of both effectiveness and efficiency compared with the state-of-art methods.

The emergence of urban Big Data creates new opportunities for a deeper understanding of transportation within cities, revealing patterns and dynamics that were previously hidden. Public transit agencies are collecting and publishing high-resolution schedule and real-time vehicle location data to help users schedule trips and navigate the system. We can use these data to generate new insights into public transit delays, a major source of user dissatisfaction. Leveraging open General Transit Feed Specification (GTFS) and administrative Automatic Passenger Counter (APC) data, we develop two measures to assess the risk of missing bus route transfers and the consequent time penalties due to delays. Risk of Missing Transfers (RoMT) measures the empirical probability of missed transfers, and Average Total Time Penalty (ATTP) shows overall time loss compared to the schedule. We apply these measures to data from the Central Ohio Transit Authority (COTA), a public transit agency serving the Columbus, Ohio, USA metropolitan area. We aggregate, visualise and analyse these measures at different spatial and temporal resolutions, revealing patterns that demonstrate the heterogeneous impacts of bus delays. We also simulate the impacts of dedicated bus lanes reducing missing risk and time penalties. Results demonstrate the effectiveness of measures based on high-resolution schedule and real-time vehicle location data to assess the impacts of delays and to guide planning and decision making that can improve on-time performance.

Location data of construction resources are important in understanding on the context of a construction site, yet most sites still rely on people’s observations to localize their resources. Among then various localization technologies, radio frequency identification (RFID) is considered as a good solution. However, RFID either provides limited location data when fixed receivers are used, or it requires considerable manpower for scanning the tagged resources when hand-held receivers are used. These requirements result in inefficiency and impractical demands on time and cost, particularly in the case of complex or large-scale sites. This study attempted to overcome the limitations by proposing an integrated unmanned aerial vehicle-RFID (UAV-RFID) platform to replace the considerable manpower with the UAV and to enable identifying tags on a site. It applies deep learning algorithms to localize an RFID tag position within an acceptable range of accuracy, thereby demonstrating the feasibility of the integrated platform for construction resource localization.

Archaeologists are tasked with balancing a call to open data and the need to maintain confidentiality of sensitive archaeological site locations. Low-resolution mapping and data aggregation are the methods most commonly used to hide site locations; however, we understand little of the effectiveness of these practices. Trends in geomasking, obscuring observed geographic points, to anonymize public health data are suggested as a source of methods for sharing archaeological site data. Archaeologists have available to them a number of geomasking methods that balance open data and site security in different ways. Low-resolution mapping at several scales and random direction with fixed radius, random perturbation donut, and Gaussian donut techniques are tested on a set of archaeological site locations. Random perturbation donuts resulted in the best balance between obscuring archaeological locations and conveying observed spatial patterning. Researchers should carefully consider how they convey archaeological location data, as commonly used low-resolution scales may not provide the desired level of obscurity. Researchers should also be explicit as to how and why their methods of site visualization are chosen.

Automatically collected behavioral data on the location of users of mobile phones offer an unprecedented opportunity to measure mobilization in mass protests, while simultaneously expanding the range of researchable questions. Location data not only improve estimation of the number and composition of participants in large demonstrations. Thanks to high spatial and temporal resolution they also reveal when, where, and with whom different sociopolitical sectors join a protest campaign. This article compares the features and advantages of this type of data with other methods of measuring who participates in street protests. The steps in preparing a usable data set are explained with reference to a six-week campaign of mass mobilization in Israel in 2011. Findings based on the Israeli data set illustrate a wide range of potential applications, pertaining to both the determinants and consequences of protest participation. Limitations of mobile location data and the privacy issues it raises are also discussed.

Logistics facilities dedicated to e-commerce operations have recently emerged in parallel to the rapid growth of the B2C e-commerce sector. This study aims to explore the spatial organisation and locational characteristics of such e-commerce logistics, by relating them with urban form. The study performs a spatial analysis with geo-referenced location data of Amazon logistics facilities in Japan and the United Kingdom (UK). Amazon expanded fulfilment centres at many locations as part of its logistics system. The findings determine the spatial distribution pattern of logistics with urban form and describe locational characteristics at facility locations in both countries.

Understanding city residents' collective geotagged behaviors (CGTBs) in response to hazards and emergency events is important in disaster mitigation and emergency response. It is a challenge, if not impossible, to directly observe CGTBs during a real-time matter. This study used the number of location requests (NLR) data generated by smartphone users for a variety of purposes such as map navigation, car hailing, and food delivery to infer the dynamics of CGTBs in response to rainstorms in eight Chinese cities. We examined rainstorms, flooding, and NLR anomalies, as well as the associations among them, in eight selected cities across mainland China. The time series NLR clearly reflects cities' general diurnal rhythm, and the total NLR is moderately correlated with the total city population. Anomalies of the NLR were identified at both the city and grid scale using the Seasonal Hybrid Extreme Studentized Deviate (S-H-ESD) method. Analysis results demonstrated that the NLR anomalies at the city and grid levels are well associated with rainstorms, indicating that city residents request more location-based services (e.g., map navigation, car hailing, food delivery, etc.) when there is a rainstorm. However, the sensitivity of the city residents' collective geotagged behaviors in response to rainstorms varies in different cities as shown by different peak rainfall intensity thresholds. Significant high peak rainfall intensity tends to trigger city flooding, which leads to increased location-based requests as shown by positive anomalies in the time series NLR.

After an earthquake, rapidly assessing the affected areas, particularly the meizoseismal area, is crucial to emergency rescue work. Currently, many methods exist for delimiting the range of the area affected by an earthquake. The main method for estimating this area is to use an empirical model of the seismic intensity attenuation relationship. In contrast, this article uses the change rate (CR) of the phones’ location data in an area after the earthquake to infer the extent of the meizoseismal area. This study selects phone location data from eight earthquakes for experimentation and analysis. The results show a correlation between the phone location data and the actual distribution of the meizoseismal area based on intensity maps that were issued by the China Earthquake Administration. According to the results, the extent of the meizoseismal area can be estimated by the magnitude, the focal depth, the CR, the time of the earthquake, and the distance to the epicenter. The magnitude, CR, and focal depth are dominant and are positively correlated with the intensity. Based on the aforementioned parameters, a calculation model for delimiting the extent of the meizoseismal area based on phone location data can be constructed. The accuracy of the correlation coefficient of fitting result is 0.7826, and the R2 value is 0.6125. The results demonstrate that phone location data can be used to quickly delimit the meizoseismal area and can provide a basis for earthquake emergency rescue work.

Timely responses to emergencies are critical for urban disaster and emergency management, particularly in densely populated mega-cities. Researchers and personnel involved in urban emergency management nowadays rely on computers to carry out complex evacuation planning. Agent-based modeling, which supports the representation of interactions among individuals and between individuals and their environments, has become a major approach to simulating evacuations wherein spatial–temporal dynamics and individual conditions need attention, such as congestion in urban areas. However, the development of optimal evacuation plans based upon agent-based evacuation simulations can be very time-consuming. In this study, to shorten the computation time to provide a timely response in an efficient way, we develop a knowledge database to store evacuation plans for typical population distributions generated by mobile phone location data. Subsequently, we use the prepared knowledge database (offline) to accelerate real-time (online) processes in searching for near-optimal evacuation plans. Our experimental result demonstrates that the evacuation plans generated with a knowledge database always outperform those that are generated without a knowledge database. Specifically, the knowledge database can reduce the computation time by an average of 96.76%, with an average fitness value improvement of 21.86%. This result confirms the effectiveness of our proposed approach in improving agent-based evacuation planning. With the rapid development of human sensor data collection and analysis, the estimation of a more accurate population distribution will become easier in future. Thus, we believe that the proposed approach of developing a knowledge database based on population distribution patterns will provide a more feasible alternative solution for evacuation planning in the practice of urban emergency management.

Western Japan was hit by heavy rain from June 8 to July 28, 2018. Record-breaking rain caused nearly all rivers to flood in Hiroshima and other areas. Over 200 people died following this disaster. Authorities attempted to understand why evacuation was not conducted swiftly enough to stop these deaths. They mentioned that normalcy bias and cognitive dissonance are two primary causes of significant damage [1]. Moreover, an effective alert system is necessary to ensure that evacuation behaviors and procedures are incited at the appropriate time. To understand the factors that influence people’s behavior, we estimated the probability of irregular behavior by unit changes in external condition. We chose 500 m mesh as a unit of analysis to consider individual singularity and classified 3 classes of mesh to identify abnormal behavior. We verified that as the number of residents in each mesh increases, the likelihood of a person in that region to exhibit normalcy bias increases as well. Owing to data, the accuracy of this method is somewhat low. However, several implications may still be drawn from our results, such as the demand for an adequate alert system. Using the results of people’s mobility and disaster risk information, approaches to dangerous situations such as the examined case may be improved in the future.

Location-based services are raising remarkable convenience to our daily life while seriously threatening the location privacy of individuals. Differential privacy provides a promising privacy definition for location data. It is enforced by injecting random noise into each location such that the level of privacy and utility provided by this sanitization when querying an LBS is quantified and controlled. However, the primitive differential privacy overlooks data errors, which constantly exist in real-life location data, thereby potentially deviating a specified indistinguishability. Therefore, we determine the impact of data errors on the indistinguishability to address the abovementioned issue. Then, we design an equivalent mechanism to enforce differential privacy and analyze its privacy and utility. Extensive experimental evaluation on real-world datasets demonstrates that our proposed equivalent mechanism consistently outperforms several state-of-the-art mechanisms in data utility at the same privacy level.

As users increasingly rely on online social networks for their communication\ud activities, personal location data processing through\ud such networks poses significant risks to users’ privacy. Location\ud tracks can be mined with other shared information to extract rich\ud personal profiles. To protect users’ privacy, online social networks\ud face the challenge of ensuring transparent communication to\ud users of how their data are processed, and explicitly obtaining\ud users’ informed consent for the use of this data. In this paper, we\ud explore the complex nature of the location disclosure problem and\ud its risks to personal privacy. We evaluate, with an experiment\ud involving 715 participants, the contributing factors to the perception\ud of such risks with scenarios that mimic (a) realistic modes of\ud interaction, where users are not fully aware of the extent of their\ud location-related data being processed, and (b) with devised scenarios\ud that deliberately inform users of the data they are sharing\ud and its visibility to others. The results are used to represent the\ud users’ perception of privacy risks when sharing their location\ud information online and to derive a possible model of privacy\ud risks associated with this sharing behaviour. Such a model can\ud inform the design of privacy-aware online social networks to\ud improve users’ trust and to ensure compliance with legal frameworks\ud for personal privacy.

In view of the location privacy problem of participants in mobile crowd sensing, this paper proposes a method to protect the location of participants based on local differential privacy preference. First of all, the map is discretized and mapped from two-dimensional space to one-dimensional space by means of MHC, which can guarantee the spatial correlation, and the map is segmented based on the density of participants using genetic algorithm; Then, according to the personal privacy needs of current location, two different local differential privacy perturbation methods, RAPPOR and k-RR, are chosen by participants; Next, the chosen local differential privacy is used to perturb the location of each participant in the region after segmentation, and the perturbed location data are sent to the data collection server to protect the participants’ locations. Finally, the simulation experiments are carried out and show that map density segmentation can reduce the privacy cost, and the method proposed in this paper is superior to the method using k-anonymous and differential privacy and the method using Hilbert and differential privacy in terms of running time and average relative error, and prove that the execution time is lower and the data availability is improved.

During a trip, tourists are mostly dependent on their mobile phone to select their next points of interest (POI). A mobile application that recommends POIs such as tourist attractions or restaurants is based on the user's location data such as check-in history. This article recommends a novel approach to leverage the check-in data captured by location-based social networks (LBSNs) with an aim to improve POI recommendations through personalized explanations. The proposed algorithm generates a user's motivation profile, and its applicability is presented by analyzing a dataset extracted from a popular LBSN. A between-subject experiment (N = 182) is conducted that shows explanations generated using a user's motivation profile increase transparency, which leads to intent to use the LBSN. Perceived usefulness of the LBSN also increases intent to use. The study indicates that when suggesting a POI, recommender system developers include explanations based on user's motivation behavior profile.

Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by...

Paratransit services have been established to mitigate poor accessibility in Asian developing cities. However, their potential role as a connective mode for mass transit systems is often overlooked. This study verifies the hypothesis that, due to the lack of guidance or planning by the government, paratransit services have been driven by market mechanisms to concentrate in areas closer to mass transit stations and other centers of economic activity, including markets. We tested this hypothesis with a negative binomial regression using data on motorcycle taxi services in Bangkok, as a case study. Motorcycle taxis mostly operate as a group, known as a win in Thai, waiting for passengers at certain places, including mass transit stations. Location data on 821 wins were randomly collected from 144 grid zones (= 144 km2) through Google Maps between July 24 and August 24, 2017. Win density was defined as the number of wins in each one-square-kilometer zone. The results showed that win density increased by 4.4% and 25.1% in zones that were 1 km closer to transit stations and markets, respectively, all else being held constant; this confirmed the hypothesis. Policy implications are discussed along with the recent penetration of ride-hailing services in the paratransit market.

The modern outdoor advertising industry is introducing new technologies to attract attention and enhance effective reach. However, displaying the pertinent advertisements (ads) and maximizing their coverage are still a challenge, as little is known about the interests of passersby. In this paper, we attempted to fill this gap by 1) identifying the individuals’ interests extracted from the mobile phone internet usage data, and 2) analyzing the individuals’ mobility patterns acquired from the mobile phone location data. Based on these data, the problem of distributing the outdoor ads to the maximum reach of the potentially interested users was further formulated as a maximal covering location problem (MCLP). Specifically, a continuous space maximal coverage model, the MCLP-complementary coverage (MCLP-CC), was utilized to search for the optimal locations for a given category of advertisements. Finally, the proposed methodology was applied to Wuxue, a city in central China, as a case study. The calculation results show that when setting the same number of places for outdoor advertising, our approach achieves an average of 69% improvement compared with the current dominant ads placement approach (selecting the most crowded location) in reaching the potential target audiences. An average of 15% improvement compared with the classic MCLP model. Overall, this paper demonstrates the value of the mobile phone data for interest-driven outdoor advertising and its potential applications in other mobile services.

This paper presents the design and implementation of a location data collecting system only using the barometer sensor on the smartphone. It is energy efficient because of using low-power barometer sensor readings to infer the location, and it protects user privacy by providing the non-sensitive location data. To design such a location data collecting system, we make some key technical contributions: 1) a curve fitting-based solution to remove the barometer sensor reading noise caused by weather change; 2) a deep learning algorithm to detect user moving activities based on the restricted Boltzmann machine, and; 3) a clustering-based extraction algorithm for signatures of different locations. The field studies show that the SELoc provides user daily locations with an accuracy of 85%; meanwhile, the average energy consumption is only about 22% compared with GPS.

Road semantic segmentation is unique and difficult. Road extraction from remote sensing imagery often produce fragmented road segments leading to road network disconnection due to the occlusion of trees, buildings, shadows, cloud, etc. In this paper, we propose a novel fusion network (FuNet) with fusion of remote sensing imagery and location data, which plays an important role of location data in road connectivity reasoning. A universal iteration reinforcement (IteR) module is embedded into FuNet to enhance the ability of network learning. We designed the IteR formula to repeatedly integrate original information and prediction information and designed the reinforcement loss function to control the accuracy of road prediction output. Another contribution of this paper is the use of histogram equalization data pre-processing to enhance image contrast and improve the accuracy by nearly 1%. We take the excellent D-LinkNet as the backbone network, designing experiments based on the open dataset. The experiment result shows that our method improves over the compared advanced road extraction methods, which not only increases the accuracy of road extraction, but also improves the road topological connectivity.

The availability of large amounts of data has driven the fast development of many research fields such as computer vision. However, the sharing of location data has been limited due to the concern of data privacy. Cellular location data contains user location information which reflects human mobility patterns. Therefore, in this study, we propose a novel Generative Adversarial Network (GAN) and apply the model to generate cellular location data as a case study for location-based sensing applications. The key insight of this study is that individual mobility correlates with user-specific information, e.g., age, gender. Therefore, to better capture the underlying pattern of human mobility, we design a soft-label conditional GAN which utilizes user-specific information to generate individual movement trajectories. This work plans to train a generator on a large real-world cellular location dataset and evaluate the synthetic data in terms of both utility and privacy.

In this paper, we aim to reveal the relationship between the number of people infected with COVID-19 in each ward in Tokyo and the changes in human mobility behavior using demographic information (population density, number of restaurants, etc.) and mobility data collected from GPS data of residents in Tokyo's 23 wards. The results confirmed that changes in human mobility behavior extracted from mobility data in each ward were an important feature related to the number of people infected by COVID-19 on the previous day's difference. These results suggest that the transition of the number of infected people in COVID-19 is largely due to human mobility behavior.

A geo-marketplace allows users to be paid for their location data. Users concerned about privacy may want to charge more for data that pinpoints their location accurately, but may charge less for data that is more vague. A buyer would prefer to minimize data costs, but may have to spend more to get the necessary level of accuracy. We call this interplay between privacy, utility, and price \emph{spatial privacy pricing}. We formalize the issues mathematically with an example problem of a buyer deciding whether or not to open a restaurant by purchasing location data to determine if the potential number of customers is sufficient to open. The problem is expressed as a sequential decision making problem, where the buyer first makes a series of decisions about which data to buy and concludes with a decision about opening the restaurant or not. We present two algorithms to solve this problem, including experiments that show they perform better than baselines., 10 pages, SIGSPATIAL'20

In the case of high-rise buildings with more than five floors, there is a greater risk of a large number of casualties due to the spread of large-scale fires when a fire occurs. To overcome this problem, the indoor navigation system was implemented using the IoT devices installed in the building and AR technology to guide the evacuation route reflecting the fire situation. It describes the member and function of the system and mentions the parts that need to be supplemented and how to solve them.

In this paper, we describe a LoRa (from &ldquo, Long Range&rdquo, )-based, linear sensor network we have developed for transmitting location information of personnel and equipment in an underground mine. The system is intended to be used during emergencies when existing communications infrastructure has failed. Linear networks comprise a sequence of relays that forward data to a common destination, the headend. Relays forward location information transmitted from tags carried by personnel or equipment. Relays will usually be put in place as investigators or rescuers enter the mine. LoRa is used both by the tags to communicate to the relays and by the relays to forward messages to the headend. We have implemented and tested this system, and have carried out simulations and analyses to determine its scalability, reliability and fairness. The need for robustness and reliability has led us to use flooding rather than unicast communication. We also use message sequence numbers and time-to-live fields to prevent broadcast storms. Contention is managed using a simplified Carrier Sense Multiple Access (CSMA) scheme. We also address fairness. When the network is under load messages may be dropped by relays making messages generated more hops from the headend more likely to be dropped than messages nearer the headend. We explore the relationship between unfairness, traffic load and number of relays. We also observe that a network of larger numbers of lightly loaded relays performs more effectively than smaller numbers of heavily loaded relays.

This paper addresses how to enhance post-tour activities with digital mobile devices. Walkers sometimes use GPS loggers to record, review, and share their walking stories. However, the raw trajectory data recorded can be difficult to browse through on a map. To resolve this, based on our observations, we illustrated chunks of messy trajectories, inaccurate indoor points, and unstable trajectory lines as problems encountered when mapping raw trajectory data. Then, we proposed a new framework that focuses on GPS horizontal accuracy to find indoor location points and on an accelerometer to find stopping points. We also applied the Douglas-Peucker algorithm to make the trajectory cleaner and then integrated the extracted points with the clean trajectory line. An implementation test demonstrated that the detection of the indoor location points and outdoor stopping points was done correctly, and the proposed system gave users a better experience for their review. The paper contributes to the research on cleaning up map-based displays of raw trajectory data by using not only location data but also other spatial information that smartphones can collect.

This paper presents GeoPact, an assembly of technological objects that materialises location-aware smart contracts using internet of things and digital ledger technologies. Such contracts may facilitate the creation of distributed systems and services for transport and logistics that are locally constructed and adaptable, thus supporting specific community needs and sustainable objectives. However the technological infrastructures that underpin these systems are complex, making it difficult to engage publics in design processes. GeoPact grounds infrastructure in relatable physical activities, that are linked with holistic views of the system, and creates new experiences for public engagement. In these activities participants were invited to roleplay as couriers, and to progress through delivery scenarios which were governed by smart contracts. Participants and spectators were then encouraged to discuss their reactions, concerns and ideas. This paper illustrates the GeoPact assembly and reflects on our engagement activities.

An improved three-frame-difference algorithm is proposed. The algorithm contains two sub-algorithm‐‐‐‐double-layer three-frame-difference algorithm, and history location data statistics and analysis algorithm. Double-layer three-frame-difference algorithm can fill the incomplete parts, and the history location data statistics and analysis algorithm can eliminate noise. Two examples containing one target (with size about 10*40 pixels) and two targets (with size about 80*160 pixels) respectively are chosen. Results of them prove that the improved three-frame-difference algorithm can resolve the problems of traditional one-layer three-frame-difference algorithm, and get the accurate results.

Location-based services are widely used today. The huge growth of location-based services is enabled by GPS capable mobile devices. Moreover, these devices can sense several types of signals over the air using different radio frequency technologies (e.g., Wi-Fi, Bluetooth, cellular signals, etc.). They receive broadcast messages from transmitting entities (e.g., network access points, cellular phone towers). Broadcast messages carry the information needed in case a mobile device chooses to establish communication. We believe that these signals can be utilized to validate GPS data, which, in many cases, can be tampered with. In this paper, we propose a cheat-proof system to validate reported GPS data and find if this data is authentic or tampered with. This is done by utilizing time-sensitive tamper-proof location validation data broadcasted by Wi-Fi access points in the infrastructure. Then, we present an experiment to test the proposed system.

“IT IS POSSIBLE TO STOP THE EPIDEMIC ” That’s the message splashed atop a website built by a University of Oxford team this week to share new research on the spread of the novel coronavirus Below that hopeful statement comes a big caveat: To stop the virus’ spread, health officials need to find and isolate the contacts of infected people—lots of them—and fast Such contact tracing is a mainstay of infectious disease control But the Oxford team is one of several now advocating for a new approach: tapping into cellphone location data to track the spread of infection and warn people who may have been exposed

Zhenjiang Dagang Ferry, located in the middle and upper reaches of the Yangtze River Jiangsu section, is full of different types of ships. The steam ferry crosses the waterway and frequently meets with the passing ships, inducing water collision accidents. This article, taking cross-river ferrys and normal navigation ships as research subjects, proposes an algorithm for water navigation collision avoidance based on position data. First, by analyzing the dynamic coordinate of the ferry and navigation vessel, their navigation trajectories are predicted respectively. Second, the time when they arrive at the possible collision point is calculated, the type of traffic conflict is determined, the drive’s collision avoidance response time is considered, and a warning criterion for early warning is established. Finally, the proposed warning algorithm is analyzed to check to what extent the warning can help the ship avoid the collision. Sixteen sets of data showing the navigation of the ships are selected to compare the effect of the warning algorithm with the radar warning method. The research results show that the application of the algorithm can avoid unnecessary alarm, false alarm, and alarm failure the radar warnings have, improving the accuracy and helping avoid the collision accidents.

Despite the growing popularity of human mobility studies that collect GPS location data, the problem of determining the minimum required length of GPS monitoring has not been addressed in the current statistical literature. In this paper we tackle this problem by laying out a theoretical framework for assessing the temporal stability of human mobility based on GPS location data. We define several measures of the temporal dynamics of human spatiotemporal trajectories based on the average velocity process, and on activity distributions in a spatial observation window. We demonstrate the use of our methods with data that comprise the GPS locations of 185 individuals over the course of 18 months. Our empirical results suggest that GPS monitoring should be performed over periods of time that are significantly longer than what has been previously suggested. Furthermore, we argue that GPS study designs should take into account demographic groups. KEYWORDS: Density estimation; global positioning systems (GPS); human mobility; spatiotemporal trajectories; temporal dynamics, 19 pages, 4 figures

Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.

This chapter uses Mobile Kukan Toukei™ (mobile spatial statistics) to collect the location data of mobile phone users in order to count the number of visitors at specific tourist destinations and examine their characteristics. Mobile Kukan Toukei is statistical population data created by an operational data of mobile phone networks. It is possible to estimate the population structure of a region by gender, age, and residence using this service of the company. The locations and characteristics of the individuals obtained herein are derived through a non-identification process, aggregation processing, and concealment processing. Therefore, it is impossible to identify specific individuals. This chapter attempts to identify the number of visitors in different periods and their characteristics based on the location data of mobile phone users collected by the mobile phone company. In addition, it also attempts to demonstrate an alternative method to more accurately infer the number of visitors in specific areas.

Location-based games (LBGs) are becoming increasingly popular. These games use player’s physical location as a game mechanic, and many of the games are played in real time. This study investigates the affordances that three popular LBGs, Ingress, Pokemon GO and The Walking Dead: Our World, provide for users to collect location data from other players. To this end, the game mechanics of the games and the end user privacy policies are analyzed and compared together. The results reveal several privacy concerns which are not currently adequately addressed in the privacy policies of the games. As LBGs are becoming increasingly complex, the risk of unwanted player data collection opportunities rises, combating which is predicted to be a major design challenge for LBG developers in the future.

Continuous authentication systems are considered as a promising solution to secure access to mobile devices. Their main benefit is the improvement of the users' experience when they use the services or applications of their mobile device. Specifically, continuous authentication avoids having to remember or possess any key to access an application or service that requires authentication. In this sense, having the user authenticated permanently increases the security of the device. It also allows the user interaction with applications to be much more fluid, simple, and satisfactory. This chapter proposes a new continuous authentication system for mobile devices. The system acquires data from the device sensors and the GPS location to create a dataset that represents the user's profile or normal behaviour. Then, the proposed system uses Machine Learning algorithms based on anomaly detection to perform user identification in real time. Several experiments have been carried out to demonstrate the performance and usefulness of the proposed solution.

The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate context-enriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliably preprocesses raw smartphone-based location data in such a manner that relevant behavioral phenotypes of interest can be derived.

A simulation tool that supports developers to build scenarios automatically in multiple simulation platforms is proposed. As an essential part of this simulator, this study proposed an activity schedule generator to mimic the daily life of elderly people living alone. This generator outperforms existing methods of activity schedule planning in three aspects: 1) it is adaptive to the layout of a simulated smart house; 2) there is no unspecified time in the timeline of generated schedules; and 3) it generates stable, but not tedious schedules for a number of days. A real-time location data generator is proposed to convert generated schedules to simulated real-time location data of the resident, and a proposed interface converts these simulated location data to simulated records of virtual passive infrared (PIR) sensors, which can be used to optimize placement of PIR sensors in a smart house.

Traditional mobility prediction literature focuses primarily on improved methods to extract latent patterns from individual-specific movement data. When such predictions are incorrect, we ascribe it to 'random' or 'unpredictable' changes in a user's movement behavior. Our hypothesis, however, is that such apparently-random deviations from daily movement patterns can, in fact, of ten be anticipated. In particular, we develop a methodology for predicting Likelihood of Future Non-Conformance (LFNC), based on two central hypotheses: (a) the likelihood of future deviations in movement behavior is positively correlated to the intensity of such trajectory deviations observed in the user's recent past, and (b) the likelihood of such future deviations increases if the user's strong-ties have also recently exhibited such non-conformant movement behavior. We use extensive longitudinal indoor location data (spanning 4+ months) from an urban university campus to validate these hypotheses, and then show that these features can be used to build an accurate non-conformance predictor: it can predict non-conformant mobility behavior two hours in advance with an AUC ≥ 0.85, significantly outperforming the baseline. We also show that this prediction methodology holds for a representative outdoor public-transport based mobility dataset. Finally, we use a real-world mobile crowd-sourcing application to show the practical impact of such non-conformance: failure to identify such likely anomalous movement behavior causes workers to suffer a noticeable drop in task completion rates and reduces the spatial spread of successfully completed tasks.

Snapping photos or videos on a smartphone makes recording visual memories convenient, but what isn't captured may still be meaningful in retrospect. In this paper, digital mementos are automatically generated for participants using the Rewind system, which assists the recall of location-based minutiae. Rewind is a video-like medium describing people's daily excursions using a sequence of street-level images determined by self-tracked location data. The Rewinds are color-processed to reflect seasonal, time-of-day and weather characteristics. Through two user studies with a combined 40 users, Rewinds were shown to be used as memory artifacts, and are especially meaningful in the many situations when photos or videos are not available. The small cues in Rewinds evoke longer fragments of memory tied to nostalgic routines or significant events, and the sequence of images provide a first-person perspective of remembrance for users. While they are more generic and can possess inaccuracies, Rewinds give people anchors for memories that feel like their own that are used to craft a narrative for that day. Rewind strikes a balance between automatic logging and manually-curated memories by personalizing it in meaningful visuals and consolidating an otherwise overwhelming amount of data.

Three-dimensional (3D) uncertainty representations help to avoid ambiguity in the interpretation of utility data. Existing data models and 3D-solutions do not, however, facilitate this adequately yet. They store uncertainties only by means of textual attributes or require stochastic data and expert input to visualize uncertainties. Such data is difficult to obtain in practice. We address this issue by proposing an approach that integrates multiple available utility location datasets to represent geographical uncertainties. To this end, we identified four parameters that practitioners use to store location data – i.e. surveyed, standard, estimated and unknown; and used these to extend the existing CityGML Utility Network ADE model. Next, we calculated and visualized 3D uncertainty buffer shapes for three scenarios that were based on real data of a city district in the Netherlands. The approach may eventually enable engineers to avoid design errors and support utility localization in the field.