面向活动地点推荐的个人时空可达性方法

如何在时空制约条件下合理安排个人活动与出行是现代社会中人们日常工作和生活的迫切需求。个人时空可达性研究以个人时空行为视角聚焦个人在时空条件下开展各种活动的自由度,长期以来一直受到人文地理学、社会学和交通工程学等领域的广泛关注。本文基于时间地理学理论,提出一种个人时空可达性方法,顾及活动地点开放时间、最短活动时长及个人活动偏好,实现个人时空可达性分析与评价。然后,利用城市餐饮类服务设施空间位置、营业时间、公众评级等多维时空属性信息及城市路网数据检验方法有效性。本文提出的个人时空可达性方法可为空间规划、时空行为研究提供方法支撑,同时,面向个性化活动地点推荐,可为个人智慧出行提供策略与指导,并且在公众位置信息服务及位置社交网络内容服务等方面具有良好的应用前景。     

Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns

Understanding the stability of urban flows is critical for urban transportation, urban planning and public health. However, few studies have measured the stability of aggregate human convergence or divergence patterns. We propose a spatiotemporal model for assessing the stability of human convergence and divergence patterns. A mobile phone location data set obtained from Shenzhen, China, was used to assess the stability of daily human convergence and divergence patterns at three different spatial scales, i.e. points (cell phone towers), lines (bus lines) and areas (traffic analysis zones [TAZs]). Our analysis results demonstrated that the proposed model can identify points and bus lines with time-dependent variations in stability, which is useful for delineating TAZs for transportation planning, or adjusting bus timetables and routes to meet the needs of bus riders. Comparisons of the results obtained from the proposed model and the widely used entropy measure indicated that the proposed model is suitable for assessing the differences in stability for various types of spatial analysis units, e.g. cell phone towers. Therefore, the proposed model is a useful alternative approach of measuring spatiotemporal stability of aggregate human convergence and divergence patterns, which can be derived from the space–time trajectories of moving objects.     

Spatiotemporal data model for network time geographic analysis in the era of big data

There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.     

A Space-Time GIS Approach to Exploring Large Individual-based Spatiotemporal Datasets

The increasing number of large individual-based spatiotemporal datasets in various research fields has challenged the GIS community to develop analysis tools that can efficiently help researchers explore the datasets in order to uncover useful information. Rooted in Hägerstrand's time geography, this study presents a generalized space-time path (GSTP) approach to facilitating visualization and exploration of spatiotemporal changes among individuals in a large dataset. The fundamental idea of this approach is to derive a small number of representative space-time paths (i.e. GSTPs) from the raw dataset by identifying spatial cluster centers of observed individuals at different time periods and connecting them according to their temporal sequence. A space-time GIS environment is developed to implement the GSTP concept. Different methods of handling temporal data aggregation and the creation of GSTPs are discussed in this article. Using a large individual-based migration history dataset, this study successfully develops an operational space-time GIS prototype in ESRI's ArcScene and ArcMap to provide a proof-of-concept study of this approach. This space-time GIS system demonstrates that the proposed GSTP approach can provide a useful exploratory analysis and geovisualization environment to help researchers effectively search for hidden patterns and trends in such datasets.     

Map-matching algorithm for large-scale low-frequency floating car data

Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance.     

Toward space-time buffering for spatiotemporal proximity analysis of movement data

Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.     

城市人群聚集消散时空模式探索分析——以深圳市为例

城市中人群的移动是带有目的性的,城市空间结构功能也存在差异,导致人群在城市中出现聚集或消散的现象,而且该现象会随着时间不断变化。本文基于海量的手机位置数据,以深圳市为例,采用自相关分析识别出城市中人群聚集与消散的区域,然后将这些区域一天中人群聚散组合成时间序列矩阵,采用自组织图聚类方法（SOM）进行聚类得到9种典型的人群聚集、消散时空模式,结合土地利用现状数据,分析解释了每种聚散模式最可能出现的土地利用组合。该研究从聚集和消散的角度探索了城市人群移动的时空模式,进一步帮助理解城市不同区域人群的移动模式以及与城市空间结构功能之间的关系,对城市规划、交通管理具有参考和指导意义。     

面向人类行为研究的时空GIS

人类时空行为是地理学、物理学、规划学、流行病学等多学科共同关注的研究主题。时空GIS面向地理时空数据的建模与分析需求,注重时间与空间的一体化表达,为人类行为特征分析与规律探索提供基础方法支撑。然而,现有时空GIS在人类行为时空过程表达以及人类行为与时空场境交互分析等方面存在不足。本文通过深度融合时间地理学理论,提出一种面向人类行为研究的时空GIS方法,以丰富与完善现有的时空GIS方法体系。在深入解读时间地理学中“情境”、“企划”等核心概念的基础上,本文分别从情境要素存在性动态表达、情境要素相关性动态理解及情境要素变化的动态感知等方面探讨拓展现有时空GIS方法的可行性。     

Measuring segregation: an activity space approach

While the literature clearly acknowledges that individuals may experience different levels of segregation across their various socio-geographical spaces, most measures of segregation are intended to be used in the residential space. Using spatially aggregated data to evaluate segregation in the residential space has been the norm and thus individual’s segregation experiences in other socio-geographical spaces are often de-emphasized or ignored. This paper attempts to provide a more comprehensive approach in evaluating segregation beyond the residential space. The entire activity spaces of individuals are taken into account with individuals serving as the building blocks of the analysis. The measurement principle is based upon the exposure dimension of segregation. The proposed measure reflects the exposure of individuals of a referenced group in a neighborhood to the populations of other groups that are found within the activity spaces of individuals in the referenced group. Using the travel diary data collected from the tri-county area in southeast Florida and the imputed racial–ethnic data, this paper demonstrates how the proposed segregation measurement approach goes beyond just measuring population distribution patterns in the residential space and can provide a more comprehensive evaluation of segregation by considering various socio-geographical spaces.