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1.
Addressing location uncertainties in GPS‐based activity monitoring: A methodological framework 
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下载免费PDF全文 Location uncertainty has been a major barrier in information mining from location data. Although the development of electronic and telecommunication equipment has led to an increased amount and refined resolution of data about individuals’ spatio‐temporal trajectories, the potential of such data, especially in the context of environmental health studies, has not been fully realized due to the lack of methodology that addresses location uncertainties. This article describes a methodological framework for deriving information about people's continuous activities from individual‐collected Global Positioning System (GPS) data, which is vital for a variety of environmental health studies. This framework is composed of two major methods that address critical issues at different stages of GPS data processing: (1) a fuzzy classification method for distinguishing activity patterns; and (2) a scale‐adaptive method for refining activity locations and outdoor/indoor environments. Evaluation of this framework based on smartphone‐collected GPS data indicates that it is robust to location errors and is able to generate useful information about individuals’ life trajectories. 相似文献
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GPS空间位置精度因子模糊分析 总被引：2，自引：0，他引：2
高精度测量是GPS的重要特性，但由于各种因素的存在，所有GPS测量信息都存在着一定的误差，这说明GPS的读数并不能准确代表目标的真实位置。本文提出一种为提高GPS测量精度的模糊分析，比较本方法与简单的点平均，并提供了在S／A存在与不存在不同时间的真实试验数据，证明了本方法能比简单的点平均提供更精确的位置信息。 相似文献
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The accuracy of GPS positioning based on pseudorange measurements under signal degradation environments is limited by poor satellite geometry and signal distortions due to diffraction and multipath. As a result, the GPS position solutions could become unreliable. Those deteriorated solutions should be identified and not used for navigation. For that purpose, methods for reliable identification of deteriorated GPS positioning solutions from a navigation receiver should be developed. In this paper, a fuzzy inference system is proposed to classify the quality of GPS positioning solutions. The input for the system includes the signal quality evaluated by the difference between the measured and expected carrier-to-noise density ratio (C/N0) and the satellites geometry strength evaluated by the dilution of precision (DOP) number. The proposed fuzzy inference system is developed based on the human knowledge and understanding of the problem under consideration and is further optimized using data acquired from the field. The test results indicate that the proposed method can be used for reliable identification of deteriorated GPS position solutions affected by signal degradations. 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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Anastasia Petrenko Anton Sizo Winchel Qian A. Dylan Knowles Amin Tavassolian Kevin Stanley Scott Bell 《Transactions in GIS》2014,18(3):351-369
The popularization of tracking devices, such as GPS, accelerometers and smartphones, have made it possible to detect, record, and analyze new patterns of human movement and behavior. However, employing GPS alone for indoor localization is not always possible due to the system's inability to determine location inside buildings or in places of signal occlusion. In this context, the application of local wireless networks for determining position is a promising alternative solution, although they still suffer from a number of limitations due to energy and IT‐resources. Our research outlines the potential for employing indoor wireless network positioning and sensor‐based systems to improve the collection of tracking data indoors. By applying various methods of GIScience we developed a methodology that can be applicable for diverse human indoor mobility analysis. To show the advantage of the proposed method, we present the result of an experiment that included mobility analysis of 37 participants. We tracked their movements on a university campus over the course of 41 days and demonstrated that their movement behavior can be successfully studied with our proposed method. 相似文献
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Smartphones can be operated in a 3G network environment at any time or location, and they also cost less than existing photogrammetric UAV systems, providing high-resolution images and 3D location and attitude data from a variety of built-in sensors. This study aims to assess the feasibility of using a smartphone as the payload for a photogrammetric UAV system. To carry out the assessment, a smartphone-based photogrammetric UAV system was developed and utilized to obtain image, location, and attitude data under both static and dynamic conditions. The accuracy of the location and attitude data obtained and sent by this system was then evaluated. The smartphone images were converted into ortho-images via image triangulation, which was carried out both with and without consideration of the interior orientation (IO) parameters determined by camera calibration. In the static experiment, when the IO parameters were taken into account, the triangulation results were less than 1.28 pixels (RMSE) for all smartphone types, an improvement of at least 47% compared with the case when IO parameters were not taken into account. In the dynamic experiment, on the other hand, the accuracy of smartphone image triangulation was not significantly improved by considering IO parameters. This was because the electronic rolling shutter within the complementary metal-oxide semiconductor (CMOS) sensor built into the smartphone and the actuator for the voice coil motor (VCM)-type auto-focusing affected by the vibration and the speed of the UAV, which is likely to have a negative effect on image-based digital elevation model (DEM) generation. However, considering that these results were obtained using a single smartphone, this suggests that a smartphone is not only feasible as the payload for a photogrammetric UAV system but it may also play a useful role when installed in existing UAV systems. 相似文献
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Existing predictive mapping methods usually require a large number of field samples with good representativeness as input to build reliable predictive models. In mapping practice, however, we often face situations when only small sample data are available. In this article, we present a semi‐supervised machine learning approach for predictive mapping in which the natural aggregation (clustering) patterns of environmental covariate data are used to supplement limited samples in prediction. This approach was applied to two soil mapping case studies. Compared with field sample only approaches (decision trees, logistic regression, and support vector machines), maps using the proposed approach can better capture the spatial variation of soil types and achieve higher accuracy with limited samples. A cross validation shows further that the proposed approach is less sensitive to the specific field sample set used and thus more robust when field sample data are small. 相似文献
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随着全球卫星导航系统(GNSS)的发展和移动通信技术的进步,用户对位置服务(LBS)提出了更高的要求. 本文采用市面上常见的两部Android智能手机采集GNSS数据,对Android智能手机伪距单点定位(SPP)和单频精密单点定位(PPP)算法进行研究,分析了在不同条件下智能手机的SPP、单频PPP定位性能. 结果表明:在使用多普勒平滑伪距和信噪比随机模型的基础上,Android智能手机GPS单系统的SPP定位精度可达3 m,GPS、Galileo、GLONASS、北斗卫星导航系统(BDS)四系统定位精度可达亚米级. 在单频PPP静态定位中,在GPS单系统下,定位精度仅能达到米级,且收敛时间较长;在GPS、Galileo、GLONASS、BDS四系统下,定位精度可达亚米级,且平面方向可在40 min内收敛. 在单频PPP动态定位中,手机的定位精度仅能达到米级. 相似文献
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Traffic collisions have been well acknowledged as a significant threat to public health, closely related to human driving errors. This study introduces an innovative approach to investigate spatiotemporal distributions of individualized driving errors and to characterize hazardous driving scenes, in which drivers are more prone to make driving mistakes. We first create a multi‐feature‐fusion framework to extract driving errors using smartphone sensors. Then, the detected errors are geo‐statistically analyzed with road networks and driving trajectories to identify driving error hotspots. We next construct a “scenic tuple” for representing the occurrence of driving errors. Finally, the individualized hazardous driving scenes are extracted by mining a long‐term collection of scenic tuples. Results demonstrate that our proposed approach can effectively identify driving errors. Additionally, the spatiotemporal patterns of driving mistakes can be identified from the individualized hazardous driving scenes, which has the potential to aid in reducing driving risks. 相似文献
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针对室内定位行人航位推算中步态探测算法步数识别准确率不高、同步控制不精确及位置估计偏差较大等问题,提出了一种面向智能手机平端活动的改进有限状态机步态探测算法。通过设定有限状态对应步行过程合加速度变化趋势,利用相邻合加速度差值和上/下坡次数阈值实现步数识别和步态周期估计。在211 m走廊内由2名实验人员分别平端智能手机开展实验,结果表明,改进算法的步数识别准确率为100%,每一步的平均识别时间提前了0.004 s,平均位置误差为0.384 m,相比于自相关分析和加速度差分有限状态机算法,识别准确率、同步控制精度和位置估计精度分别至少提高了0.7%、60%和21.15%。改进算法在步数识别、同步控制及位置估计方面优于现有算法。 相似文献
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智能手机凭借其普遍性、便携性和低成本等优势,已成为大众用户导航与位置服务的主流终端载体,其多频多系统GNSS（global navigation satellite system）观测值的开放进一步激发了手机高精度定位的研究。然而,受限于消费级GNSS器件性能,手机卫星观测值呈现出信号衰减严重、伪距噪声大、粗差周跳多等问题;并且受城市复杂环境影响,手机GNSS定位的连续性、可靠性也难以保证。提出一种城市场景手机GNSS/ MEMS（micro-electro mechanical system）融合的车载高精度定位方案。首先,构建了速度约束的GNSS差分定位模型;然后,通过手机内置MEMS与车辆运动约束,在挑战环境下进行GNSS/MEMS融合精密定位。实验结果表明,在开阔和树荫场景下,速度约束方法可达到分米至米级定位精度,相比于常规方法分别提升了35.2%和78.9%;在高架场景下,GNSS/MEMS融合定位的精度和连续性均提升显著;在隧道场景下,MEMS推算位置累积误差约为2.5%。实验结果初步表明,手机GNSS具备开阔环境下的车道级定位能力,手机GNSS/MEMS融合可提升城市复杂环境下车载定位的精度和连续可用性。 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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Identifying Environmental and Human Factors Associated With Tick Bites using Volunteered Reports and Frequent Pattern Mining 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 Irene Garcia‐Martí Raul Zurita‐Milla Arno Swart Kees C. van den Wijngaard Arnold J.H. van Vliet Sita Bennema Margriet Harms 《Transactions in GIS》2017,21(2):277-299
Tick populations and tick‐borne diseases like Lyme borreliosis have been steadily increasing since the mid‐1990s. Realizing the threat that ticks pose to public health, two Dutch citizen science projects have collected tick bite reports since 2006. This unique volunteered geographical dataset, which currently has nearly 35,000 reports, was used to identify environmental and other circumstantial factors associated with tick bites. For this, we first enriched the tick bite reports with temperature, precipitation, vegetation and volunteered data associated with the location of the tick bite. Using this enriched dataset, we then derived a series of features to characterize the environmental and volunteer‐related conditions in which each tick bite occurred. Next, we discretized these features using the Jenks Natural Breaks algorithm and, after that, we mined frequent environmental patterns associated with tick bites using the AprioriClose algorithm. Finally, we checked that these patterns are specifically associated with the tick bites by comparing them with the frequent patterns mined from pseudo‐random locations. The frequent patterns were visualized using heat maps and ring maps and two representative patterns associated with tick bites were projected into geographic space to study their spatio‐temporal distribution. Our results show that factors linked to human activity are more relevant to model tick bites than seasonal accumulations of temperature, vegetation or precipitation. In particular, the number of warm and dry days per season are present in a significant number of patterns and the majority of tick bites are produced within a distance of half a kilometer of a forest, recreational or built‐up area. The study of patterns in the time‐series revealed that there are several persistent patterns consistently occurring each year and the validation process showed that the volunteer tick bites collection is capturing environmental conditions associated with tick bites, suggesting that these reports have a high scientific value. These results support the creation of a Dutch tick bite risk map that, in turn, will open the door to the design of public health interventions to reduce the incidence of Lyme disease. 相似文献
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ABSTRACTIntersections are the critical parts where different traffic flows converge and change directions, forming “bottlenecks” and “clog points” in urban traffic. Intersection travel time is an important parameter for public route planning, traffic management, and engineering optimization. Based on low-frequency spatial-temporal Global Positioning System (GPS) trace data, this article presents a novel method for estimating intersection travel time. The proposed method first analyzes the different travel patterns of vehicles through an intersection, then determines the range of an intersection dynamically and reasonably, and obtains traffic flow speed and delay at the intersection under different travel patterns using a fuzzy fitting approach. Finally, the average intersection travel time is estimated from traffic flow speed and delay and intersection range in different travel patterns. Wuhan road network data and GPS trace data from taxicabs were tested in the experiments and the results show that the proposed method can improve the accuracy of travel time estimation at city intersections. 相似文献
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Spatial co‐location pattern mining aims to discover a collection of Boolean spatial features, which are frequently located in close geographic proximity to each other. Existing methods for identifying spatial co‐location patterns usually require users to specify two thresholds, i.e. the prevalence threshold for measuring the prevalence of candidate co‐location patterns and distance threshold to search the spatial co‐location patterns. However, these two thresholds are difficult to determine in practice, and improper thresholds may lead to the misidentification of useful patterns and the incorrect reporting of meaningless patterns. The multi‐scale approach proposed in this study overcomes this limitation. Initially, the prevalence of candidate co‐location patterns is measured statistically by using a significance test, and a non‐parametric model is developed to construct the null distribution of features with the consideration of spatial auto‐correlation. Next, the spatial co‐location patterns are explored at multi‐scales instead of single scale (or distance threshold) discovery. The validity of the co‐location patterns is evaluated based on the concept of lifetime. Experiments on both synthetic and ecological datasets show that spatial co‐location patterns are discovered correctly and completely by using the proposed method; on the other hand, the subjectivity in discovery of spatial co‐location patterns is reduced significantly. 相似文献
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桥梁施工控制网的质量是工程施工总体质量的基础。探讨了GPS控制网的方案设计,精度确定及外业布设流程与方法。重点研究了GPS坐标基准的选择与确定,内业数据处理。针对高程数据异常拟合模型参数估计,提出了两步抗差估计GPS高程拟合方法,有效地保证了似大地水准拟合模型的可靠性。 相似文献
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The discovery of zones and people's movement patterns supports a better understanding of modern cities and enables a more comprehensive strategy for urban planning. This article proposes a modified method based on previous research to simultaneously discover people's zones and movement patterns, called movement patterns between functional zones (MPFZ). The method attempts to take full advantage of taxi GPS data to identify MPFZs by merging the movement traces satisfying the merging conditions. Considering movement directions, movement numbers and the adjacent constraints that consist of spatial relationship and attribute features, the merging conditions limit the movement traces to be merged. The new MPFZs are discovered by an iteration process and are measured by the following three evaluation indices: v‐value, a‐value and c‐value, which represent coverage, accuracy and their trade‐off. Using a real‐world taxi dataset of Beijing, 24 new MPFZs are discovered, which have higher v‐, a‐ and c‐values than the unmerged MPFZs. The results of the real‐world dataset experiment show that the proposed approach is effective and efficient. The proposed method can also be applied to other types of transportation data and regions by adjusting the dataset utilized and controlling the iteration process. 相似文献
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GPS坐标转换方法对于GPS空间定位系统至关重要。目前已有很多方法被提出用于转换GPS坐标，但效果并不是很显著。究其原因，是因为大多数都存在模型误差和投影误差。针对目前方法的不足，本文利用深度学习对非结构化数据处理的优势，提出了一种基于卷积神经网络（CNN）的GPS坐标转换方法。该方法将GPS数据转化为非结构化图片数据，以其作为CNN的输入层来训练GPS坐标转换模型，这样能够最小化满足对数据的预处理要求，无监督地从数据中学习出有效特征。试验结果表明，该方法与传统坐标转换方法相比，具有更高的转换精度。 相似文献