A new insight into land use classification based on aggregated mobile phone data

Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.     

Measuring place-based accessibility under travel time uncertainty

Travel time uncertainty has significant impacts on individual activity-travel scheduling, but at present these impacts have not been considered in most accessibility studies. In this paper, an accessibility evaluation framework is proposed for urban areas with uncertain travel times. A reliable space-time service region (RSTR) model is introduced to represent the space-time service region of a facility under travel time uncertainty. Based on the RSTR model, four reliable place-based accessibility measures are proposed to evaluate accessibility to urban services by incorporating the effects of travel time reliability. To demonstrate the applicability of the proposed framework, a case study using large-scale taxi tracking data is carried out. The results of the case study indicate that the proposed accessibility measures can evaluate large-scale place-based accessibility well in urban areas with uncertain travel times. Conventional place-based accessibility indicators ignoring travel time reliability can significantly overestimate the accessibility to urban services.     

基于IC卡综合换乘信息的公交乘客上车站点推算

现有研究在缺少公交车运营信息的情况下,利用公交线间正交换乘信息的方法来识别公交车持卡乘客的上车站点,但在实际运用中很多班次无正交换乘的乘客,容易导致公交乘客上车站点匹配困难。因此,本文提出了一种基于IC卡综合换乘信息的公交乘客上车站点推算方法,该方法充分利用IC卡数据中的综合换乘信息（公交线间的正交和非正交换乘信息、地铁和公交线间的换乘信息）与公交网数据确定班次行驶方向,对班次内的乘客进行分组,充分利用综合换乘信息确定基准组及其对应的上车站点;然后,通过最小偏差规则匹配待定组的上车站点,实现公交乘客上车站点的推估。利用换乘信息确定班次行驶方向弥补了现有研究无法确定班次行驶方向的不足,使公交乘客上车站点推算方法更科学;最后,基于2011年8月的“深圳通”IC卡数据进行实例分析,对比只利用正交换乘信息确定的基准组数量和利用综合换乘信息确定的基准组数量,结果表明：该方法可使确定上车站点的组数占总组数的比值提高,克服了只利用正交换乘信息推算上车站点方法中存在的单个班次无基准组从而无法进行站点匹配的困难。本文方法比只利用正交换乘信息匹配站点,具有更高的可行性与准确率。     

Clustering of temporal event processes

A temporal point process is a sequence of points, each representing the occurrence time of an event. Each temporal point process is related to the behavior of an entity. As a result, clustering of temporal point processes can help differentiate between entities, thereby revealing patterns of behaviors. This study proposes a hierarchical cluster method for clustering temporal point processes based on the discrete Fréchet (DF) distance. The DF cluster method is divided into four steps: (1) constructing a DF similarity matrix between temporal point processes; (2) constructing a complete linkage hierarchical tree based on the DF similarity matrix; (3) clustering the point processes with a threshold determined by locating the local maxima on the curve of the pseudo-F statistic (an index which measures the separability between clusters and the compactness in clusters); and (4) identifying inner patterns for each cluster formed by a series of dense intervals, each of which contains at least one event of all processes of the cluster. The contributions of the article are: (1) the proposed DF cluster method can cluster temporal point processes into different groups and (2) more importantly, it can identify the inner pattern of each cluster. Two synthetic data sets were created to illustrate the DF distance between temporal point process clusters (the first data set) and validate the proposed DF cluster method (the second data set), respectively. An experiment and a comparison with a method based on dynamic time warping show that DF cluster successfully identifies the preconfigured patterns in the second synthetic data set. The cluster method was then applied to a population migration history data set for the Northern Plains of the United States, revealing some interesting population migration patterns.     

A Voronoi neighborhood-based search heuristic for distance/capacity constrained very large vehicle routing problems

Local search heuristics for very large-scale vehicle routing problems (VRPs) have made remarkable advances in recent years. However, few local search heuristics have focused on the use of the spatial neighborhood in Voronoi diagrams to improve local searches. Based on the concept of a k-ring shaped Voronoi neighbor, we propose a Voronoi spatial neighborhood-based search heuristic and algorithm to solve very large-scale VRPs. In this algorithm, k-ring Voronoi neighbors of a customer are limited to building and updating local routings, and rearranging local routings with improper links. This algorithm was evaluated using four sets of benchmark tests for 200–8683 customers. Solutions were compared with specific examples in the literature, such as the one-depot VRP. This algorithm produced better solutions than some of the best-known benchmark VRP solutions and requires less computational time. The algorithm outperformed previous methods used to solve very large-scale, real-world distance constrained capacitated VRP.     

What about people in pedestrian navigation?

Pedestrian navigation has become an important theoretical and practical research topic in many disciplines such as cartography, geographical information science, global and indoor positioning, spatial behavior, psychology, sociology, and neuroscience. Many research studies view pedestrian navigation using process-oriented and goal-directed approaches. However, this paper revisits people’s needs in pedestrian navigation and classifies their needs as three layers: physical sense layer, physiological safety layer, and mental satisfaction layer according to Maslow’s theory. This paper introduces a people-centric framework for pedestrian navigation theory based on these three layers and discusses theoretical challenges for meeting each layer of people’s needs. These challenging theories may represent promising and valuable research and promote usage of pedestrian navigation systems or devices in the future.     

Extracting and analyzing semantic relatedness between cities using news articles

News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics as semantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can ‘read’ a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research.     

A Method for the Exploratory Analysis of Airline Networks

The airline industry in the United States has experienced significant changes in the spatial configuration of networks since the Airline Deregulation Act of 1978. Attempts have been made to study the structure of the hub-and-spoke network in the airline industry, but no efficient methodology has been available. This paper describes an exploratory approach to analyzing the spatial configuration of airline networks. Flight frequencies and numbers of passengers from published schedules of six U.S. domestic airlines (American, Continental, Delta, Northwest, United, and USAir) are used in this study. The analytic components in the exploratory system include the examination of network connectivity and network autocorrelation in a dynamic mapping environment. The results of the analyses show both the overall spatial patterns of airline networks and the hierarchical hubbing structures.     

A simple and direct method to analyse the influences of sampling fractions on modelling intra-city human mobility

Sampling fraction is crucial to sampling-related studies and applications, especially in the big data era when most data are neither originally designed nor controllable in the data collection process. A common concern among researchers is ‘what’s the modelling accuracy when using a sample?’. Taking intra-city human mobility as the study objective, this study utilizes a simple and direct method to analyse the influences of various sampling fractions on modelling accuracy. Five common intra-city human mobility indicators (travel distance, travel time, travel frequency, radius of gyration and movement entropy) are evaluated considering mean value, median and probability distribution. Experimental results demonstrate that the representativeness of each considered indicator converges to 1 in its own unique rate and variances. The minimum required sampling fractions to satisfy specific accuracies differ for various indicators and evaluation measures. To further investigate how related factors influence the modelling accuracy of sampling fractions, additional experiments are conducted considering multiple sampling methods, study scopes, and data sources. Several interesting general findings are observed. This study provides a reference for other sampling-based applications.     

Time geography in a hybrid physical–virtual world

Journal of Geographical Systems - Time geography was conceptualized in the 1960s when the technology was very different from what we have today. Conventional time-geographic concepts therefore were...