An address regional tessellation method for spatial subdivision and geocoding in digital earth system

With the increased use of locational information, spatial location referencing and coding methods have become much more important to the mining of both geographical and nongeographical data in digital earth system. Unfortunately, current methods of geocoding, based on reverse lookup of coordinates for a given address, have proven too lossy with respect to administrative and socioeconomic data. This paper proposes a spatial subdivision and geocoding model based on spatial address regional tessellation (SART). Given a hierarchical address object definition, and based on the ‘region of influence’ characteristics of an address, SART creates multiresolution spatial subdivisions by irregular and continuous address regions. This model reflects most of the geographical features and many of the social and economic implications for a given address. It also better reflects the way people understand addresses and spatial locations. We also propose an appropriate method of geocoding for standard addresses (SART-GC). The codes generated by this method can record address footprints, hierarchical relationships, and spatial scales in a single data structure. Finally, by applying our methods to the Shibei District of Qingdao, we demonstrate the suitability of SART-GC for multi-scale spatial information representation in digital earth systems.     

Addressing location uncertainties in GPS‐based activity monitoring: A methodological framework

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.     

A deep learning approach for rooftop geocoding

Geocoding has become a routine task for many research investigations to conduct spatial analysis. However, the output quality of geocoding systems is found to impact the conclusions of subsequent studies that employ this workflow. The published development of geocoding systems has been limited to the same set of interpolation methods and reference data sets for quite some time. We introduce a novel geocoding approach utilizing object detection on remotely sensed imagery based on a deep learning framework to generate rooftop geocoding output. This allows geocoding systems to use and output exact building locations without employing typical geocoding interpolation methods or being completely limited by the availability of reference data sets. The utility of the proposed approach is demonstrated over a sample of 22,481 addresses resulting in significant spatial error reduction and match rates comparable to typical geocoding methods. For different land‐use types, our approach performs better on low‐density residential and commercial addresses than on high‐density residential addresses. With appropriate model setup and training, the proposed approach can be extended to search different object locations and to generate new address and point‐of‐interest reference data sets.     

顾及时空语义的疑犯位置时空预测

预测疑犯的社会活动行踪,对案件嫌疑人的排查以及犯罪行为的主动预防具有重大意义。当前研究主要依据疑犯的历史系列作案位置预测其住址或未来犯罪位置,缺少对其复杂社会活动位置的转移过程进行建模,也没有考虑位置数据稀疏性对预测性能产生的影响。为此,提出了融合时空语义的位置时空预测（spa-tio-temporal semantics location prediction,SSLP）模型。首先,利用疑犯在不同语义时段和语义位置上的分布邻近性提取目标疑犯的相似疑犯群体;其次,结合该群体的轨迹数据和位置语义信息,基于核密度平滑方法估算出涉及未记录位置的转移频次及其时态访问概率;最后,采用贝叶斯模型实现疑犯个体的时空预测。实验结果表明,基于W市2013年1月至6月间158名疑犯的17 539个位置记录数据,SSLP模型在top-k距离偏离度和top-k精确率上优于其他流行方法40%~50%,对疑犯位置数据稀疏性具有优异的适应能力。     

Multi‐Scale Qualitative Location: A Topology‐Based Model

Qualitative locations describe spatial objects by relating the spatial objects to a frame of reference (e.g. a regional partition in this study) with qualitative relations. Existing models only formalize spatial objects, frames of reference, and their relations at one scale, thus limiting their applicability in representing location changes of spatial objects across scales. A topology‐based, multi‐scale qualitative location model is proposed to represent the associations of multiple representations of the same objects with respect to the frames of reference at different levels. Multi‐scale regional partitions are first presented to be the frames of reference at multiple levels of scale. Multi‐scale locations are then formalized to relate multiple representations of the same objects to the multiple frames of reference by topological relations. Since spatial objects, frames of reference, and topological relations in qualitative locations are scale dependent, scale transformation approaches are presented to derive possible coarse locations from detailed locations by incorporating polygon merging, polygon‐to‐line and polygon‐to‐point operators.     

使用参照物的射电望远镜中心坐标测量方法

为满足新建射电望远镜在单站或多站联合进行的深空探测或射电天文观测对中心坐标精确测定的要求,提出一种利用已知精确中心坐标的望远镜作为参照物,测量地平式射电望远镜中心点坐标的方法和测量数据处理方法。这一方法对场地和设备的要求较低,能够得到毫米级或亚毫米级的位置精度。尤其适合对天线阵列的中心位置进行测量。对国家天文台密云观测站的40 m射电望远镜进行了中心坐标测量,位置均方根误差为2.312 mm,满足了后续的观测工作对其位置的需求。     

NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages

Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models.     

Similarity Measurements on Multi‐Scale Qualitative Locations

Qualitative representation of spatial locations and their similarity measurements are essential for the analysis of linguistic term‐based data. Existing methods have focused on the similarities of spatial relations and spatial scenes but have not considered the variations in geometrical representations and relations over scales. This study developed some new measures to assess the similarities of both single‐ and multi‐scale qualitative locations. Region‐ and cell‐based models were used to formalize qualitative locations of spatial objects with respect to multi‐scale frames of reference. The similarities were assessed by integrating the similarities of frames and qualitative relations. The frame similarity measures how two objects are compared considering the common elements that they occupy in the reference frames. Moreover, the similarity of qualitative relation measures how two relations relate two objects to the corresponding elements in the frames. The location similarities at a single level integrate the similarities of the frames and qualitative relations, whereas the location similarities at multiple scales incorporate the variations in qualitative locations over scales. These methods were used to assess location similarities concerning residential areas, roads, and lakes. The results indicated that the location‐based measurements can disclose the distributions of the similarities and that the cell‐based model is more accurate than the region‐based model.     

A Clustering‐Based Approach to the Capacitated Facility Location Problem1

This research develops a clustering‐based location‐allocation method to the Capacitated Facility Location Problem (CFLP), which provides an approximate optimal solution to determine the location and coverage of a set of facilities to serve the demands of a large number of locations. The allocation is constrained by facility capacities – different facilities may have different capacities and the overall capacity may be inadequate to satisfy the total demands. This research transforms this special location‐allocation problem into a clustering model. The proposed approach has two parts: (1) the allocation of demands to facilities considering capacity constraints while minimizing the cost; and (2) the iterative optimization of facility locations using an adapted K‐means clustering method. The quality of a location‐allocation solution is measured using an objective function, which is the demand‐weighted distance from demand locations to their assigned facilities. The clustering‐based method is evaluated against an adapted Genetic Algorithm (GA) alternative, which integrates the allocation component as described above but uses GA operations to search for ‘optimal’ facility locations. Experiments and evaluations are carried out with various data sets (including both synthetic and real data).     

An innovative GPS trajectory data based model for geographic recommendation service

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.     

Improvement of a location-aware recommender system using volunteered geographic information

Abstract

Recommender systems (RS), as supportive tools, filter information from a massive amount of data based on the determined preferences. Most of the RS require information about the context of users such as their locations. In such cases, location-aware recommender systems (LARS) can be employed to suggest more personalized items to the users. The most current research projects on LARS focus on the development of algorithms, evaluation methods and applications. However, the role of up-to-date spatial databases in LARS is not a well-researched area. The up-to-date spatial information would potentially improve the accuracy of items which are recommended by LARS. Volunteered geographic information (VGI) could be a low-cost source of up-to-date spatial information for LARS. This article proposes an approach to enrich spatial databases of LARS by VGI. Since not all records of VGI are fitted for use in LARS, a mechanism is developed to identify useful information. Some VGI data sets refer to existing spatial data in the database while other VGI data sets are shared for the first time. Therefore, the proposed method assessed the quality of VGI with reference source (for VGI which is existed in the database) and VGI without reference source (for VGI which is shared for the first time). To demonstrate the feasibility of the proposed approach, a mobile application has been developed to recommend suitable restaurants to the users based on their geospatial locations. The evaluation of the method indicates that VGI can potentially enhance the functionality of the LARS in predicting the users’ interests.     

Forest parameter estimation in the Pol-InSAR context employing the multiplicative-additive speckle noise model

This paper addresses the problem of speckle noise on single baseline polarimetric SAR interferometry (Pol-InSAR) on the basis of the multiplicative-additive speckle noise model. Considering this speckle noise model, a novel filtering technique is defined and studied in terms of simulated and experimental Pol-InSAR data. As demonstrated, the use of the multiplicative-additive speckle noise model does not lead to a corruption of the useful information but to an improvement of its estimation. The performance of the algorithm is analyzed in terms of the physical parameters retrieved from the filtered data, that in this work correspond to the forest height and the ground phase. In case of experimental data, the retrieved forest height is compared and validated against Lidar ground truth measurements.     

结合地名知识和位置信息的中文地名匹配方法

针对中文地名数据中存在的“同名异地”“异地同名”等歧义问题,本文提出了一种结合地名知识和位置信息的地名匹配方法。首先基于特征词的规则匹配模式对地名名称结构进行了解析。然后结合地名的位置信息实现中文地名匹配;同时,基于地名的行政区划信息和实体信息关联多源地名数据,构建地名知识库。最后以兴趣点数据为例,基于名称相似度和位置相似度匹配地名数据中的地名信息,并以知识图谱的形式进行存储和表达。     

DGPS在资源环境遥感中的应用方法研究

为了能够利用较低配置的GPS设备，方便有效地完成与资源环境遥感有关的地物定位测量任务，利用伪距DGPS设备进行了如下试验研究：基站参考坐标对定位精度的影响，基于大地测量三角控制点和WGS84到北京54坐标系转换模型进行基站参考坐标差分测量导算的方法和精度，在导算出的控制点处建立基站进行试验区样地定位测量的方法及所能达到的精度，试验结果表明，在具备测区至少一个三角控制点和坐标转换参数条件下，可以采用首先从已恬三角点导算出一个位于方便位置处的点的大地坐标，然后在该点所能达到的在当地坐标系中的相对定位精度远远小于一个像元大小，可以推广应用到资源环境遥感的各个应用领域，在不具备基站参考坐标和坐标系转换参数的条件下，可以基于差分测量结果之图形要素绝对位置图形转换到已经和当地坐标系配准的遥感影上去，采用这种办法，在差分处理时不用考虑基站的精确WGS84地心参考坐标，也不用考虑坐标系的转换参数，利用其站单点绝对定位结果作为参考坐标进行差分就可，可以节省不少人力和物力，试验研究及结论为如何利用较低配置的伪距DGPS设备，简捷，快速，精确地完成资源环境遥感领域地物定位测量任务提供了有益参考。     

Calibrating the dynamic Huff model for business analysis using location big data

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.     

基于地名树的最佳空间尺度新闻事件地点提取方法

从新闻纯文本数据中识别地名信息并确定对应的最佳空间尺度与事件所属地点,是准确抽取新闻事件发生地点的关键。针对上述目标,提出了基于隶属关系地名树的最佳空间尺度新闻事件地点提取方法。在完成地名实体识别和歧义消除的文本数据预处理工作的基础上,提出了一种顾及新闻结构的方法消除语义干扰等噪声的影响;通过引入虚父节点构建合理准确的隶属关系地名树,结合最小包围盒的概念实现了最佳空间尺度的选取,使用地名实体权重和实体相关性完成了事件地候选集推荐排序,从而合理定位事件发生地。实验证明,所提出的新闻文本地理信息抽取方法可以较高的准确率获取新闻所对应的最佳空间尺度和相应的事件地点。讨论和解决了新闻文本地理信息抽取涉及的空间尺度问题,使得新闻文本中抽取的地理信息具有更好的可用性和可解释性,在丰富地理信息数据来源的同时,可实现数量呈几何级增长的网络新闻自动地域划分,有助于人们对各类事件空间态势的关注与认知。     

BDS/GPS/GLONASS RTK定位算法研究

实现了BDS/GPS/GLONASS三系统组合RTK定位算法,介绍了BDS/GPS/GLONASS三系统组合RTK数学模型,解决了多模融合导航定位时空基准统一问题,并针对附加模糊度参数的卡尔曼滤波函数模型,提出了一种确定实时动态定位中卡尔曼滤波参数的方法。编制了BDS/GPS/GLONASS RTK定位程序,并对28 m超短基线及31 km短基线实测数据进行了解算。对比分析了BDS、GPS、GLONASS、BDS/GPS、BDS/GLONASS、GPS/GLONASS、BDS/GPS/GLONASS七种模式下的定位结果。     

相对轨道根数的重轨InSAR卫星基线分析与控制

星载重轨干涉SAR卫星在高程测绘和形变测量中有着全天时全天候和大范围的优势,其中干涉基线是决定干涉性能的重要指标,而卫星重访轨道对干涉基线起着决定性的作用。通过对现有高分三号干涉数据轨道参数的分析,发现干涉基线相比国外先进卫星过长且稳定性有待提高。本文通过对相对轨道根数和机动控制的分析,得到满足重轨干涉SAR系统要求的稳定基线。以第一次观测的轨道为参考轨道,基于在摄动情况下重复观测轨道与参考轨道的相对轨道根数,计算得到重轨干涉基线的变化规律,并对不同纬度的观测目标进行了样例分析。在基线变化规律的基础上,利用机动速度和相对轨道根数的关系,进一步计算得到满足基线状态需求的机动控制方法。通过实际数据分析,给出了相对轨道根数变化对初始理想构型的影响,验证了重轨干涉基线变化规律符合本文的分析,并利用仿真样例给出了使得重轨干涉基线达到预期要求的机动控制方案。实际数据和仿真实验表明该模型能够通过可长时间观测并准确获得的轨道根数直接计算基线状态,并能从干涉基线需求出发,快速准确的得出对卫星的控制策略。     

Effects of reverse geocoding on OpenStreetMap tag quality assessment

OpenStreetMap (OSM), a widely-used open-source geographic information system platform, provides a vast geographic dataset in which users contribute both geometric information (nodes, ways, and relations) and semantic information (tags). This method of voluntary contributions is governed by the collective effort of the users. It is widely acknowledged that the quantity of tag information is substantial, but its quality is often poor. Researchers are therefore trying to assess the quality of the tags and enhance the data through various integration experiments. This article investigates the validity of the tags for geographical objects in metropolitan areas using municipal data and a reverse geocoding technique. The proposed method evaluates the data quality and the matching process carried out by reverse geocoding, using municipal points of interest as a reference. The accuracy of the tag and address information and road network centrality metrics were assessed for the OSM objects that were matched to the locations of interest. The tags were found to match the points of interest with an accuracy of 88%. Furthermore, the tag values were categorized and analyzed based on their similarity. It is concluded that in metropolitan settings where centers of interest are closely located, the accuracy of tags and addresses tends to decrease.