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1.
Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach 
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下载免费PDF全文 Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone‐measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone‐collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g. up to 96% in activity identification) using of this method. Two examples are provided in the Appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics. 相似文献
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Identifying stops from mobile phone location data by introducing uncertain segments 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 Zhiyuan Zhao Ling Yin Shih‐Lung Shaw Zhixiang Fang Xiping Yang Fan Zhang 《Transactions in GIS》2018,22(4):958-974
Identifying stops is a primary step in acquiring activity‐related information from mobile phone location data to understand the activity patterns of individuals. However, signal jumps in mobile phone location data may create “fake moves,” which will generate fake activity patterns of “stops‐and‐moves.” These “fake moves” share similar spatiotemporal features with real short‐distance moves, and the stops and moves of trajectories (SMoT), which is the most extensively used stop identification model, often fails to distinguish them when the dataset has coarse temporal resolution. This study proposes the stops, moves, and uncertainties of trajectories (SMUoT) model to address this issue by introducing uncertain segment analysis to distinguish “fake moves” and real short‐distance moves. A real mobile phone location dataset collected in Shenzhen, China is used to evaluate the performance of SMUoT. We find that SMUoT improves the performance (i.e., 15 and 19% increase in accuracy and recall rate for a one‐hour temporal resolution dataset, respectively) of stop identification and exhibits high robustness to parameter settings. With a better reliability of “stops‐and‐moves” pattern identification, the proposed SMUoT can benefit various individual activity‐related research based on mobile phone location data for many fields, such as urban planning, traffic analysis, and emergency management. 相似文献
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The implementation of social network applications on mobile platforms has significantly elevated the activity of mobile social networking. Mobile social networking offers a channel for recording an individual’s spatiotemporal behaviors when location-detecting capabilities of devices are enabled. It also facilitates the study of time geography on an individual level, which has previously suffered from a scarcity of georeferenced movement data. In this paper, we report on the use of georeferenced tweets to display and analyze the spatiotemporal patterns of daily user trajectories. For georeferenced tweets having both location information in longitude and latitude values and recorded creation time, we apply a space–time cube approach for visualization. Compared to the traditional methodologies for time geography studies such as the travel diary-based approach, the analytics using social media data present challenges broadly associated with those of Big Data, including the characteristics of high velocity, large volume, and heterogeneity. For this study, a batch processing system has been developed for extracting spatiotemporal information from each tweet and then creating trajectories of each individual mobile Twitter user. Using social media data in time geographic research has the benefits of study area flexibility, continuous observation and non-involvement with contributors. For example, during every 30-minute cycle, we collected tweets created by about 50,000 Twitter users living in a geographic region covering New York City to Washington, DC. Each tweet can indicate the exact location of its creator when the tweet was posted. Thus, the linked tweets show a Twitter users’ movement trajectory in space and time. This study explores using data intensive computing for processing Twitter data to generate spatiotemporal information that can recreate the space–time trajectories of their creators. 相似文献
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Enhancing data privacy with semantic trajectories: A raster‐based framework for GPS stop/move management 下载免费PDF全文
下载免费PDF全文 Tracking facilities on smartphones generate enormous amounts of GPS trajectories, which provide new opportunities to study movement patterns and improve transportation planning. Converting GPS trajectories into semantically meaningful trips is attracting increasing research effort with respect to the development of algorithms, frameworks, and software tools. There are, however, few works focused on designing new semantic enrichment functionalities taking privacy into account. This article presents a raster‐based framework which not only detects significant stop locations, segments GPS records into stop/move structures, and brings semantic insights to trips, but also provides possibilities to anonymize users’ movements and sensitive stay/move locations into raster cells/regions so that a multi‐level data sharing structure is achieved for a variety of data sharing purposes. 相似文献
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Mostafizur Rahaman Laskar Suman Chatterjee Aniruddha Das 《Journal of the Indian Society of Remote Sensing》2018,46(6):873-883
Air pollution is a major problem, conscious both for health and surroundings. This is a novel approach for the design & development of a system for the monitoring of different air pollutants especially at remote places where it is difficult to install any conventional air quality monitoring stations as well as for the cities. In this research work, a framework of Functional air quality index which is an indicator of susceptibility to respiratory illness has been built using the Bayesian neural network to provide the random real-time data about a location through wireless communication. The monitoring system is integrated with different types of sensors to measure the level of different air pollutants or air quality parameters such as Suspended particulate matters, (PM2.5), Nitrogen dioxide, Sulphur dioxide, Ozone which are directly associated with airways inflammatory diseases such as Asthma, Bronchitis, COPD. Each location in Map (GPS) can be updated automatically with fAQI to the user through mobile computing and satellite commutation. The user gets information about the neighborhood location with health-related information such as- whether a particular location is sensitive to respiratory diseases such as Bronchitis, asthma, COPD etc. due to suspended allergen/pollutants in the ambient air. This novel approach is designed with its’ own prototype and an application of Inter of Things in Health GIS for the benefit of humanity. 相似文献
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Despite their increasing popularity in human mobility studies, few studies have investigated the geo‐spatial quality of GPS‐enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter “active mobile phone data”). We focus on two key issues in active mobile phone data—systematic gaps in tracking records and positioning uncertainty—and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals. We evaluate the performance of the proposed imputation strategy by comparing raw versus imputed data with participants’ online survey responses. The results indicate that imputed data are superior to raw data in identifying individuals’ frequently visited places on a weekly basis. To assess the location accuracy of active mobile phone data, we investigate the spatial and temporal patterns of the positional uncertainty of each record and examine via Monte Carlo simulation how inaccurate location information might affect human mobility pattern indicators. Results suggest that the level of uncertainty varies as a function of time of day and the type of land use at which the position was determined, both of which are closely related to the location technology used to determine the location. Our study highlights the importance of understanding and addressing limitations of mobile phone derived positioning data prior to their use in human mobility studies. 相似文献
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Location‐Based Services (LBS) are the delivery of information to mobile devices customised to location and context of the user. Some research has focused on data content, while other studies have emphasised the role of multimedia communication. However, very few objective tests have been carried out concerning usage and behaviour. In this paper, a conceptual model and a LBS test environment are presented which aim to study the behaviour and interaction between environments, individuals and mobile devices. The test environment was created, for this purpose, with three main components: urban VR models that allow individuals to ‘walk around’ at street level, a mobile device as information source which simulates LBS applications, and software for recording participant actions and reactions within the test environment. Multi‐source data were collected regarding movement tracking, information accessed through mobile device and participant observations using a combination of automated and semi‐automated methods. A case study on pedestrian wayfinding in urban settings is presented. This illustrates how the test environment can be used for LBS applications and how the data collected can be analysed. The research presented in this paper provides a novel approach in studying and understanding the interaction between environments, individuals and mobile devices. 相似文献
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Using Spatially Explicit Simulated Data to Analyze Animal Interactions: A Case Study with Brown Hyenas in Northern Botswana 总被引：1，自引：0，他引：1
Jennifer A. Miller 《Transactions in GIS》2012,16(3):271-291
New developments in global positioning systems (GPS) and related satellite tracking technologies have facilitated the collection of highly accurate data on moving objects, far surpassing the ability to analyze them. Within geographic information science, ‘movement pattern analysis’ (MPA) has developed as a subfield that addresses concepts and theories used to explore the spatio‐temporal structure in data, although the methodological and analytical framework associated with MPA is new and still evolving. Interactions between individuals can be considered a second order property of movement and have been far less studied. The nature of interactions between individuals in a population is a fundamental aspect of a species' behavioral ecology and information on the frequency and duration of these interactions is vital to understanding mating and territorial behavior, resource use, and infectious disease epidemiology. The focus of this work was to explore how spatially explicit simulated data can be used to analyse dynamic interactions between individuals. Five different techniques that have been used to quantify dynamic interactions based on GPS data of pairs of individuals were utilised, and all were compared in the context of spatially explicit simulated data intended to represent biologically realistic null models for individual movement, and subsequently paired interactions. 相似文献
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Global positioning system-enabled vehicles provide an efficient way to obtain large quantities of movement data for individuals. However, the raw data usually lack activity information, which is highly valuable for a range of applications and services. This study provides a novel and practical framework for inferring the trip purposes of taxi passengers such that the semantics of taxi trajectory data can be enriched. The probability of points of interest to be visited is modeled by Bayes’ rules, which take both spatial and temporal constraints into consideration. Combining this approach with Monte Carlo simulations, we conduct a study on Shanghai taxi trajectory data. Our results closely approximate the residents’ travel survey data in Shanghai. Furthermore, we reveal the spatiotemporal characteristics of nine daily activity types based on inference results, including their temporal regularities, spatial dynamics, and distributions of trip lengths and directions. In the era of big data, we encounter the dilemma of “trajectory data rich but activity information poor” when investigating human movements from various data sources. This study presents a promising step toward mining abundant activity information from individuals’ trajectories. 相似文献
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Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user‐generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road‐type use of cyclists in urban environments. Since user‐generated data shows data‐deficiencies, we introduce tailored spatial data‐handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map‐matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road‐type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabrück (Germany), proving its feasibility and reliability in mining road‐type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning. 相似文献
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This article studies the analysis of moving object data collected by location‐aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so‐called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non‐spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real‐world public data case study, the article shows that trajectory queries are expressed more naturally on the graph‐based representation than over the relational alternative, and perform better in many typical cases. 相似文献
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Geographic services based on GPS trajectory data, such as location prediction and recommender services, have received increasing attention because of their potential social and commercial benefits. In this study, a Geographic Service Recommender Model (GSRM) is proposed, which loosely comprises three essential steps. Firstly, location sequences are obtained through a clustering operation on GPS locations. To improve efficiency, a programming model with a distributed algorithm is employed to accelerate the clustering. Secondly, in order to mine spatial and temporal information from the cluster trajectory, an algorithm (MiningMP) is designed. Last but not least, the next possible location to which the user will travel is predicted. An integrated framework of GSRM could then be constructed and provide the appropriate geographic recommendation service by considering location sequences as well as other related semantic information. Experiments were conducted based on real GPS trajectories from Microsoft Research Asia (182 users within a period of five years). The experimental results clearly demonstrate that our proposed GSRM model is effective and efficient at predicting locations and can provide users with personalized smart recommendation services in the following possible position with excellent performance in scalability, adaptability, and quality of service. 相似文献
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Mobility and spatial interaction data have become increasingly available due to the wide adoption of location‐aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article we focus on a special type of mobility data, i.e. origin‐destination pairs, and present a new approach to the discovery and understanding of spatio‐temporal patterns in the movements. Specifically, to extract information from complex connections among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is two‐fold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in origin‐destination movements. Second, the approach is scalable to large data sets and can summarize massive data to facilitate pattern extraction and understanding. 相似文献
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Integrated Traffic Management Systems (ITMS) need reliable, accurate, and real-time data. Travel time, speed, and delay are
three of the most important factors used in ITMS for monitoring, quantifying, and controlling congestion. GPS has recently
become available for civil applications. Because it provides real-time spatial and time measurements, it has an increasing
use in conducting different transportation studies. This article presents the application of GPS in collecting travel time,
speed, and delay information of 64 major roads in the state of Delaware. A comparative statistical analysis was performed
on data collected by GPS, with data collected simultaneously by the conventional method. The GPS data proved to be at least
as accurate as the data collected by the conventional method, and it was 50% more efficient in terms of manpower. Moreover,
the sample-size requirement was determined to maintain 95% confidence level throughout the controlled test. Benefiting from
the Geographic Information System's dynamic segmentation tool, our travel time, delay, and speed information were integrated
with other relevant traffic data. This was presented graphically on the Internet for public use. Statistical trend analysis
for the data collected in 1997, 1998, 1999, and 2000 are also presented and applications on the overall ITMS are discussed.
? 2002 Wiley Periodicals, Inc. 相似文献
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The Huff model has been widely used in location‐based business analysis to delineate a trade area containing a store’s potential customers. Calibrating the Huff model and its extensions requires empirical location visit data. Many studies rely on labor‐intensive surveys. With the increasing availability of mobile devices, users in location‐based platforms share rich multimedia information about their locations at a fine spatio‐temporal resolution, which offers opportunities for business intelligence. In this research, we present a time‐aware dynamic Huff model (T‐Huff) for location‐based market share analysis and calibrate this model using large‐scale store visit patterns based on mobile phone location data across the 10 most populated US cities. By comparing the hourly visit patterns of two types of stores, we demonstrate that the calibrated T‐Huff model is more accurate than the original Huff model in predicting the market share of different types of business (e.g., supermarkets versus department stores) over time. We also identify the regional variability where people in large metropolitan areas with a well‐developed transit system show less sensitivity to long‐distance visits. In addition, several socioeconomic and demographic factors (e.g., median household income) that potentially affect people’s visit decisions are examined and summarized. 相似文献
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随着无线通信技术、地理信息技术和GPS定位技术的发展以及当前社会经济发展的需要,GPS车辆定位监控系统已在智能交通、物流管理及特种车辆的监控调度等方面得到了广泛应用,并且带来了很大的经济效益和社会效益.随着嵌入式技术的不断研究和发展,嵌入式技术已应用到很多方面,其技术变得越来越成熟,其成本在不断降低.本文通过对GPRS特点简介,对GPRS车载定位网络服务系统结构及嵌入式GPS/GPRS车载定位的关键技术进行了研究. 相似文献
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Zhengcong Yin Daniel W. Goldberg Tracy A. Hammond Chong Zhang Andong Ma Xiao Li 《Transactions in GIS》2020,24(3):656-680
Reverse geocoding, which transforms machine‐readable GPS coordinates into human‐readable location information, is widely used in a variety of location‐based services and analysis. The output quality of reverse geocoding is critical because it can greatly impact these services provided to end‐users. We argue that the output of reverse geocoding should be spatially close to and topologically correct with respect to the input coordinates, contain multiple suggestions ranked by a uniform standard, and incorporate GPS uncertainties. However, existing reverse geocoding systems often fail to fulfill these aims. To further improve the reverse geocoding process, we propose a probabilistic framework that includes: (1) a new workflow that can adapt all existing address models and unitizes distance and topology relations among retrieved reference data for candidate selections; (2) an advanced scoring mechanism that quantifies characteristics of the entire workflow and orders candidates according to their likelihood of being the best candidate; and (3) a novel algorithm that derives statistical surfaces for input GPS uncertainties and propagates such uncertainties into final output lists. The efficiency of the proposed approaches is demonstrated through comparisons to the four commercial reverse geocoding systems and through human judgments. We envision that more advanced reverse geocoding output ranking algorithms specific to different application scenarios can be built upon this work. 相似文献