空间运动对象与时空数据类型研究

当前时空数据模型多以描述空间实体的离散变化为主。该文中对空间运动对象在抽象层次的无限连续空间、离散层次的有限离散空间上的数据类型进行分析和定义，将其分别划分为时间类型、空间类型和时态类型来研究，并提出支持空间运动对象的表示方法和操作方式。该方法既能表示空间实体的连续运动，也能表示其离散变化，为空间运动对象时空数据模型的建立奠定了基础。     

Discovering relative motion patterns in groups of moving point objects

Technological advances in position‐aware devices are leading to a wealth of data documenting motion. The integration of spatio‐temporal data‐mining techniques in GIScience is an important research field to overcome the limitations of static Geographic Information Systems with respect to the emerging volumes of data describing dynamics. This paper presents a generic geographic knowledge discovery approach for exploring the motion of moving point objects, the prime modelling construct to represent GPS tracked animals, people, or vehicles. The approach is based on the concept of geospatial lifelines and presents a formalism for describing different types of lifeline patterns that are generalizable for many application domains. Such lifeline patterns allow the identification and quantification of remarkable individual motion behaviour, events of distinct group motion behaviour, so as to relate the motion of individuals to groups. An application prototype featuring novel data‐mining algorithms has been implemented and tested with two case studies: tracked soccer players and data points representing political entities moving in an abstract ideological space. In both case studies, a set of non‐trivial and meaningful motion patterns could be identified, for instance highlighting the characteristic ‘offside trap’ behaviour in the first case and identifying trendsetting districts anticipating a political transformation in the latter case.     

人类活动研究的新支点：Wi-Fi数据的特点、研究现状与应用前景

把握人类活动的时空特征是地理学研究中探究人地关系、提升人类福祉的重要基础和核心内容,日益普及的Wi-Fi网络能够为此提供可靠的数据支持。为明确Wi-Fi数据融入地理学研究的切入点和发展方向,论文通过与GPS、手机信令、蓝牙等位置感应数据的比较,认为Wi-Fi数据具有更高的采样精度和更强的采样代表性,能够获取个体在室内外各类城市空间的连续活动轨迹,支撑精细尺度下的人类活动研究。通过系统梳理人群活动状态监测、个体间的社会关系识别、建筑物的功能识别和降低隐私泄露风险等方面的研究进展,认为Wi-Fi数据将会在基于实时动态人口数据的城市功能设施规划、融合多源数据的人地关系探究、以居民福祉为导向的宜居城市建设等方面具有应用前景,有望成为地理学研究人类活动的新支点。     

人类活动轨迹的分类、模式和应用研究综述

各种传感器的应用与发展,如车载GPS、手机、公交卡、银行卡等,记录了人类的活动轨迹。这些海量的人类活动轨迹数据中蕴含着人类行为的时空分布模式。通过对这些轨迹的研究可以挖掘个体轨迹模式,理解人类动力学特征,进而为对轨迹预测、城市规划、交通监测等提供支持。因此,研究各类传感器记录的人类活动轨迹数据成为当前的研究热点。本文对人类活动轨迹的获取与表达方式进行剖析,并将人类的活动轨迹按照采样方式和驱动因素的不同分为基于时间间隔采样、基于位置采样和基于事件触发采样等3类轨迹数据。由于各类轨迹数据均由起始点、锚点和一般节点等构成,因而将轨迹模式挖掘的研究按照锚点、出行范围、形状模式、OD流模式、时间模式等进行组织,研究成果揭示人类活动轨迹在时间、空间的从聚模式、周期性等特点。在此基础上,将人类活动轨迹在城市研究中的应用,按照用户轨迹预测、城市动态景观、城市交通模拟与监控、城市功能单元识别以及城市中其他方面的研究应用进行系统综述,认为人类活动模式挖掘是城市规划、城市交通、公共安全等方面应用的基础。     

滑动平均差检测法的提出及验证

时间序列突变点的检测研究是当前气候变化问题的关键环节,其方法和成果也较多,传统的检测方法因统计量结构不合理、假设条件不符合实际以及受主观因素影响等,结果常常缺乏可信度。本文提出一种新的时间序列突变点的检测方法——滑动平均差检测法,将该方法分别用理想时间序列和实际观测序列进行应用检验,并与现有常用的4种突变检验方法比较,发现滑动平均差法具有：① 结构简单、物理意义明确;② 检测突变点更精确;③ 能同时检测出所有突变点的突变位置和其突变强度3个明显的优势。综合利用滑动平均差检测法和传统的4种方法,对黄河流域几个重点水文控制站实测输沙量资料进行突变分析,结果表明：黄河上游输沙量在1986年发生突变,黄河中游输沙量在1979年和1996年发生突变,华县站和潼关站输沙量在1979年、1996年和2003年发生突变,造成输沙量突变的主要因素为水利工程建设和大规模水土保持措施等人类活动。     

Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement

The population distribution grid at fine scales better reflects the distribution of residents and plays an important role in investigating urban systems. The recent years have witnessed a growing trend of applying the nighttime light data to the estimation of population at micro levels. However, using the nighttime light data alone to estimate population may cause the overestimation problem due to excessively high light radiance in specific types of areas such as commercial zones and transportation hubs. In dealing with this issue, this study used taxi trajectory data that delineate people’s movements, and explored the utility of integrating the nighttime light and taxi trajectory data in the estimation of population in Shanghai at the spatial resolution of 500 m. First, the initial population distribution grid was generated based on the NPP-VIIRS nighttime light data. Then, a calibration grid was created with taxi trajectory data, whereby the initial population grid was optimized. The accuracy of the resultant population grid was assessed by comparing it with the refined survey data. The result indicates that the final population distribution grid performed better than the initial population grid, which reflects the effectiveness of the proposed calibration process.     

Multi-level method for discovery of regional co-location patterns

Regional co-location patterns represent subsets of feature types that are frequently located together in sub-regions in a study area. These sub-regions are unknown a priori, and instances of these co-location patterns are usually unevenly distributed across a study area. Regional co-location patterns remain challenging to discover. This study developed a multi-level method to identify regional co-location patterns in two steps. First, global co-location patterns were detected, and other non-prevalent co-location patterns were identified as candidates for regional co-location patterns. Second, an adaptive spatial clustering method was applied to detect the sub-regions where regional co-location patterns are prevalent. To improve computational efficiency, an overlap method was developed to deduce the sub-regions of (k + 1)-size co-location patterns from the sub-regions of k-size co-location patterns. Experiments based on both synthetic and ecological data sets showed that the proposed method is effective in the detection of regional co-location patterns.     

沙漠地质记录和人类活动遗迹数据汇交规范化

数据汇交是实现数据共享的前提,是实现科学数据的持续累积、科学整合和有效管理可持续发展的重要基础.分析了沙漠地质记录和人类活动遗迹数据汇交规范化存在的问题,强调了制定沙漠地质记录和人类活动遗迹数据汇交规范的必要性,根据沙漠地质记录和人类活动遗迹资料的数据特性,对数据分类并建立不同专题数据库,因地制宜采纳国际和国内现行的规...     

A data model for moving regions of fixed shape in databases

Moving object databases are designed to store and process spatial and temporal object data. An especially useful moving object type is a moving region, which consists of one or more moving polygons suitable for modeling the spread of forest fires, the movement of clouds, spread of diseases and many other real-world phenomena. Previous implementations usually allow a changing shape of the region during the movement; however, the necessary restrictions on this model result in an inaccurate interpolation of rotating objects. In this paper, we present an alternative approach for moving and rotating regions of fixed shape, called Fixed Moving Regions, which provide a significantly better model for a wide range of applications like modeling the movement of oil tankers, icebergs and other rigid structures. Furthermore, we describe and implement several useful operations on this new object type to enable a database system to solve many real-world problems, as for example collision tests, projections and intersections, much more accurate than with other models. Based on this research, we also implemented a library for easy integration into moving objects database systems, as for example the DBMS Secondo (1) (2) developed at the FernUniversität in Hagen.     

A regionalization method for clustering and partitioning based on trajectories from NLP perspective

ABSTRACT

Regionalization attempts to group units into a few subsets to partition the entire area. The results represent the underlying spatial structure and facilitate decision-making. Massive amounts of trajectories produced in the urban space provide a new opportunity for regionalization from human mobility. This paper proposes and applies a novel regionalization method to cluster similar areal units and visualize the spatial structure by considering all trajectories in an area into a word embedding model. In this model, nodes in a trajectory are regarded as words in a sentence, and nodes can be clustered in the feature space. The result depicts the underlying socio-economic structure at multiple spatial scales. To our knowledge, this is the first regionalization method from trajectories with natural language processing technology. A case study of mobile phone trajectory data in Beijing is used to validate our method, and then we evaluate its performance by predicting the next location of an individual’s trajectory. The case study indicates that the method is fast, flexible and scalable to large trajectory datasets, and moreover, represents the structure of trajectory more effectively.     

Deferred decentralized movement pattern mining for geosensor networks

This article presents an algorithm for decentralized (in-network) data mining of the movement pattern flock among mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to ‘graze’ over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors' most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge.     

社会感知视角下基于“头尾划分”的游憩热点识别新方法

作为游憩空间的核心研究对象,游憩热点在研判居民游憩偏好、落实城市游憩规划等方面具有重要作用。该文从“社会感知”视角,结合“头尾划分”理论,提出基于时空轨迹数据识别城市游憩热点的新方法。首先基于个人轨迹数据识别停留点,然后基于“头尾划分”理论划定游憩停留点簇群,最后整合停留点簇群并赋值于邻近道路形成游憩热点;以广州市老城区为例进行实证分析,并与两种既有点数据聚类方法进行对比。结果显示:1)广州市老城区的游憩热点整体集中于中、西部,热点类型可分为蓝绿空间、历史风貌、商业街区、批发市场和新兴网红场所5类;2)相比DBSCAN和CFSFDP算法,该文方法在识别游憩热点数量、捕捉游憩热点核心、明确游憩热点边界和运行简洁程度等方面表现更好,有利于后续游憩热点的深度分析与整合。研究成果可为城市游憩热点的识别提供新思路,也可为国土空间规划语境下游憩系统的重塑与提升提供重要分析基础。     

GIS and remote sensing integration for environmental applications

Abstract

The combined use of remotely-sensed images and vector GIS data has received considerable interest in recent years. This review article discusses the historical move towards closer integration of remote sensing and GIS technologies and the requirements of integrated software systems to enable remotely-sensed data to be combined with vector datasets for maximum effect. The benefits of integration to users of both GIS and remote sensing for environmental applications are reviewed and some thoughts are given on terminology and future directions in this field.     

A comprehensive methodology for discovering semantic relationships among geospatial vocabularies using oceanographic data discovery as an example

It is challenging to find relevant data for research and development purposes in the geospatial big data era. One long-standing problem in data discovery is locating, assimilating and utilizing the semantic context for a given query. Most research in the geospatial domain has approached this problem in one of two ways: building a domain-specific ontology manually or discovering automatically, semantic relationships using metadata and machine learning techniques. The former relies on rich expert knowledge but is static, costly and labor intensive, whereas the second is automatic and prone to noise. An emerging trend in information science takes advantage of large-scale user search histories, which are dynamic but subject to user- and crawler-generated noise. Leveraging the benefits of these three approaches and avoiding their weaknesses, a novel methodology is proposed to (1) discover vocabulary-based semantic relationships from user search histories and clickstreams, (2) refine the similarity calculation methods from existing ontologies and (3) integrate the results of ontology, metadata, user search history and clickstream analysis to better determine their semantic relationships. An accuracy assessment by domain experts for the similarity values indicates an 83% overall accuracy for the top 10 related terms over randomly selected sample queries. This research functions as an example for building vocabulary-based semantic relationships for different geographical domains to improve various aspects of data discovery, including the accuracy of the vocabulary relationships of commonly used search terms.     

基于手机信令数据的城内迁居活跃度识别方法研究——以武汉市为例

中国城市的快速发展加速了城市内部人口的居住迁移,精细测度居民迁居行为及其空间分异特征,对于从人的行为视角来分析城市居住空间结构演进过程具有现实意义。本文以武汉都市发展区为例,基于手机信令大数据,提出净活跃度指标和总活跃度指标来量化描述迁居活跃度,并依据两个指标的分类及组合,将居民居住地划分为高迁入型活跃区、平稳型活跃区、高迁出型活跃区、高迁入型非活跃区、平稳型非活跃区与高迁出型非活跃区六种空间类型,进而探讨居民迁居的空间分布特征。结果表明：① 武汉都市发展区内部人口迁移总体呈现出从主城区向近郊区逐步迁移的趋势,且主城区人口总活跃度相较更高。② 主城区以高迁出型活跃区和高迁入型活跃区为主,近郊区则以平稳型非活跃区为主。③ 不同空间类型内的居住类型存在差异：高迁入型活跃区内以新建小区、高校住区、学区房、城中村为主;平稳型活跃区以农村居住地、园区周边住宅为主;高迁出型活跃区以老旧小区、园区周边住宅、城中村、农村居住地为主;非活跃区则以农村居住地为主。本文提出了一种基于时序手机信令大数据的居民迁居活跃度评价指标体系,并以实证研究证明其对于居民迁居地空间类型划分的有效性,研究结果可为相关规划决策部门掌控城市内部的人口迁移特征提供数据支撑、为城市不同区域针对性的进行公共资源配置提供参考依据。     

天山北坡人类活动强度与地表温度的时空关联性

人类活动强度与地表温度的时空关联研究对于充分认识气候变化的成因和机制、积极适应气候变化、合理开发及保护生态环境等均具有重大意义。本文以地处西北干旱区且对气候变化极为敏感的天山北坡为研究区,基于MODIS数据反演地表温度,以夜间灯光数据、人口分布数据及土地利用数据等共同表征人类活动强度,分析2000—2018年人类活动强度与地表温度的演变特征,并进一步探究二者的时空关联性。研究结果显示：① 2000年以来天山北坡平均人类活动强度（0.11）较低,整体呈阶梯式缓慢上升趋势（0.0024 a^-1）,其中人类活动强度较建设用地和人口规模增加滞后1~2 a。② 天山北坡年均地温为7.18 ℃且呈显著升温态势,变化率（0.02 ℃·a^-1）约为全球的2.33倍,春季显著增温（0.068 ℃·a^-1）对整体升温的贡献最大;受高程和植被覆盖度等下垫面性状的显著影响,研究区地温空间上呈南低北高特征。③ 天山北坡人类活动区人类活动强度与地表温度显著正相关,呈东强西弱分布特征,其空间分异与相关性的表达受到人类活动范围、表现形式及土地利用变化等因素的综合影响,农林种植、城市绿化和植树造林等与植被相关的人为干预能够有效减弱人类活动造成的地表增温。本文不仅为人类活动强度的精细刻画提出了新思路,更可为区域人地协调和统筹发展等提供科学参考。     

基于居民时空行为的社区日常活动空间测度及活力影响因素研究——以南京市沙洲、南苑街道为例

新时期人本导向的社区生活圈规划转型关注居民的日常活动对社区空间的利用情况,从而反映出居民对社区公共服务设施的使用需求.但目前研究较少关注不同类型居民的差异化时空行为特征,不能有效应对社区居民的差异化需求.论文结合行为地理学的理论,利用手机信令数据,基于居民出行活动的频率和密度测度其核心活动空间和弹性活动空间,分析不同类...