厦漳泉城市群小时交通圈研究

本文基于2014年厦漳泉城市群交通网络数据和调查数据,采用问卷调查确定城内交通时间及交通时间满意度时间阈值,应用凸壳理论划分城际理论小时交通圈,继而利用城内交通时间修正城际理论小时交通圈,生成城际实际小时交通圈,并结合百度地图划分基于交通时间满意度的城内小时交通圈。研究结果表明：①城际理论小时交通圈均超过本市域范围,而厦门、漳州和泉州各城市城际实际小时交通圈面积相较于城际理论小时交通圈大幅缩小,缩小幅度分别为91.7%、82.9%、83.9%,覆盖区域主要为各自市域范围;②受访者城内交通时间为31~61 min;而使用公交车通常需60 min以上到达汽车站、90 min以上到达动车站,处于不满意甚至很不满意的时间阈值内,其中非常不满意占主导地位;③67.71%的受访者对城际交通时间表示很满意或满意,但69.1%受访者对城内交通时间表示很不满意或不满意。厦漳泉城市群城内交通系统有待改善,建议应完善厦漳泉交通网络尤其是城内交通网络。     

The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data

Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by the temporal interval between consecutive records, determines how well such data can describe human activities and influence the values of human mobility indicators. However, systematic investigations of how the TSI affects human mobility indicators remain scarce, and characterizing those relationships is a fundamental research question for many related studies. This study uses a mobile phone location dataset containing 19,370 intensively sampled individual trajectories (TSI < 5 minutes) to systematically assess the impacts of the TSI on four typical mobility indicators that describe human mobility patterns from different aspects, which are movement entropy, radius of gyration, eccentricity, and daily travel frequency. We find that different TSIs have complex impacts on the values of different mobility indicators. Specifically, (1) coarser TSIs tend to underestimate the values of the four selected indicators with different degrees; (2) the degrees of underestimation vary significantly among users for eccentricity and daily travel frequency but exhibit high inter-user consistency for radius of gyration and movement entropy. The above findings can help better understand the variations among human mobility studies.     

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。     

A proportional odds model of human mobility and migration patterns

The modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.     

Environmental quality indicators: where to from here?

Environmental or land quality indicators are being developed internationally as a means whereby the ‘State of the Environment’ (SoE) can be assessed and trends monitored. In Australia, the use of indicators in SoE reporting is in its early stages of development. Indicators have been developed, in relation to agriculture, for flora and fauna, soil, chemical contamination, surface waters and groundwater. Internationally, the World Bank has listed ‘indicators of pressure’ on the land, ‘indicators of state and impact’, and ‘indicators of response’ for each of seven major issues of land degradation. Late in 1996, an ‘indicators of catchment health’ workshop in Australia identified a relatively small number of key indicators relating to farm productivity, soil health, water quality and ‘landscape integrity’, later adding social indicators, and also distinguishing between indicators at farm and catchment scales. A broad range of other indicators was also considered by various participants in the workshop, which concluded by supporting a nationally co‐ordinated effort and the establishment of a national steering committee. Although most indicators are static measures, those from which predictions can be made require a greater emphasis on the functioning of the system. This implies that indicators are only one aspect of system modelling, and that environmental or land assessment needs to progress further than the search for quality indicators. Since this involves both biophysical and human processes, geographers should be playing a central role.     

地理学时空数据分析方法

随着地理空间观测数据的多年积累,地球环境、社会和健康数据监测能力的增强,地理信息系统和计算机网络的发展,时空数据集大量生成,时空数据分析实践呈现快速增长。本文对此进行了分析和归纳,总结了时空数据分析的7类主要方法,包括：时空数据可视化,目的是通过视觉启发假设和选择分析模型;空间统计指标的时序分析,反映空间格局随时间变化;时空变化指标,体现时空变化的综合统计量;时空格局和异常探测,揭示时空过程的不变和变化部分;时空插值,以获得未抽样点的数值;时空回归,建立因变量和解释变量之间的统计关系;时空过程建模,建立时空过程的机理数学模型;时空演化树,利用空间数据重建时空演化路径。通过简述这些方法的基本原理、输入输出、适用条件以及软件实现,为时空数据分析提供工具和方法手段。     

北京城市街区尺度对居民交通评价的影响

居民交通出行质量是影响城市宜居性和可持续发展的重大问题,国内越来越多的地理和规划学者开始关注城市建成环境对交通出行方式的影响,尤其是街区尺度成为相关政策的重点,但细化讨论建成环境对居民交通感知的研究尚不多见。本文以北京为例,结合街区尺度等建成环境客观数据与居民交通评价调查主观数据,采用有序多分类逻辑模型探讨街区尺度特征对居民主观交通便捷性评价和交通安全性评价的影响。研究表明：小街区的模式下交叉路口多,易于设置公交站点,有利于提高居民交通便捷性评价,并且避免了大马路带来的交通隐患,对居民的交通安全性评价也有提升作用;容积率、区位、公交站点等对交通评价具有促进作用。最后分析表明,交通评价还与居民自身属性有关,交通建设需充分考虑不同居民的需求及其分布特征。本文的结论可为推行街区制提供理论支持。     

A new algorithm to find raster-based least-cost paths using cut and fill operations

We developed a least-cost path analysis algorithm that satisfies a slope threshold condition in hilly terrain. The new algorithm uses an expanding moving-window to explore a combination of cells that satisfy an elevation threshold condition and then supplements this by executing cut and fill operations when there are obstacle cells between source and destination cells. Cut and fill factors regarding the difference in the actual elevation and revised elevation are considered and a least-cost path is analyzed after calculating the accumulated travel cost to the destination point. After applying the developed algorithm to synthetic and real-world data, the least accumulated travel cost from the source point can then be calculated for all cells on the raster surface by considering various slope thresholds, moving-window sizes and raster data resolutions. This algorithm can be implemented as a useful tool in GIS software as well as engineering design software utilized in the construction and mining industries.     

Guest Editorial Latest developments in GIS/LIS

Abstract

Significant achievements have taken place in Geographical/Land Information Systems (GIS/LIS). An attempt is made to give an overview of the state of the art as well as a vision of progress still to be accomplished. Key issues are discussed in various GIS/LIS activities. A generic analysis shows that most of the applications are environment-oriented. The various GIS/LIS topical issues are discussed in the order of an ascending graph, including data capture, data structure and modelling, data quality and standards, institutions, ethics, education, visualization and interface, spatial decision support systems, and applications. Finally a brief assessment of the role and nature of GIS is made. It is noted, in particular, that there are great discrepancies in the spread and success of GIS/LIS at the international level. This is true between developed and developing countries, but the case can also be made between western industrialized countries. It is also observed that there is an increasing number of professions involved in GIS/LIS activities and an increasing collaboration between those professions. Finally, it is observed that GIS/LIS are used predominantly as a tool to manage and keep records, although there are signs that GIS/LIS are also leading the way towards a new spatial science. This is only a 1993 ‘snap shot’, and is expected that many of the statements have only a limited life, considering the rapid changes in both theory and technology.     

A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

ABSTRACT

Short-term traffic forecasting on large street networks is significant in transportation and urban management, such as real-time route guidance and congestion alleviation. Nevertheless, it is very challenging to obtain high prediction accuracy with reasonable computational cost due to the complex spatial dependency on the traffic network and the time-varying traffic patterns. To address these issues, this paper develops a residual graph convolution long short-term memory (RGC-LSTM) model for spatial-temporal data forecasting considering the network topology. This model integrates a new graph convolution operator for spatial modelling on networks and a residual LSTM structure for temporal modelling considering multiple periodicities. The proposed model has few parameters, low computational complexity, and a fast convergence rate. The framework is evaluated on both the 10-min traffic speed data from Shanghai, China and the 5-min Caltrans Performance Measurement System (PeMS) traffic flow data. Experiments show the advantages of the proposed approach over various state-of-the-art baselines, as well as consistent performance across different datasets.     

Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping

Sampling efforts are constrained by limited availability of resources. Therefore, methods to reduce the number of samples, while still achieving reasonable accuracy are needed. Land-surface segmentation (LSS) has proven a powerful technique to partition digital elevation models (DEMs) and their derivatives into relatively homogeneous areas, which can be further employed as support in soil sampling. Though topography is one of the main soil forming factors, a robust assessment of the potential of this technique to digital soil mapping (DSM) is still missing. In this study, we aimed at evaluating the potential of LSS in stratifying a landscape into relatively homogeneous areas, which can be used as strata for guiding the selection of sampling points in DSM. The experiments were carried out in two study areas where soil samples were available. Land-surface derivatives were derived from DEMs and segmented with a tool based on the multiresolution segmentation algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment from the existing sample data, based on which predictions of soil classes/sub-orders and properties, i.e. soil texture and A-horizon thickness, were made. Results were compared with predictions based on simple random sampling (SRS) and conditioned Latin hypercube (cLHS). The segmentation-based sampling (SBS) scheme performed better than SRS and cLHS schemes in predicting the A-horizon thickness, soil texture fractions and soil classes, showing a high potential of LSS in stratifying a landscape for the purposes of DSM. The novelty of this study is in the way strata are constructed, rather than in the sampling design itself. Further research is needed to demonstrate the value of a SBS design for practical use. The analyses presented here further highlight the importance of considering locally adaptive techniques in optimization of sampling schemes and predictions of soil properties.     

人类移动性与健康研究中的时间问题

随着地理信息科学的发展,地理学围绕着社会热点问题不断向其他学科交叉渗透,同时学科的交叉也为地理学研究本身注入了热量。空间一直是地理学研究中的基本问题,而时间与空间密不可分,从时间维度研究人类移动性及其丰富内涵具有重要意义。本文在总结人类移动性热点问题的基础上,重点从时间维度探讨了可达性、出行体验与幸福感、人类健康与疾病等问题的研究内容和方法,并从低收入者社区医疗保健可达性研究和阿帕拉契亚乡村地区吸烟者影响因素研究等案例进行详细阐释,最后对人类移动性与健康研究的发展方向进行了展望。     

中国城市人力资本水平与人口集聚对创新产出的影响

基于中国2007—2012年287个地级及以上城市的创新投入要素和专利申请量的面板数据（未含港澳台地区数据）,采用面板固定效应模型和面板分位数回归模型,揭示人力资本水平提升对区域创新产出的影响机制及其区域异质性,以及人口空间集聚对人才创新驱动作用的调节机制。结果表明：① 平均而言,人力资本水平越高的城市,创新产出水平越高,但人口集聚程度与创新产出水平并不存在显著的关联;② 人力资本水平提升对创新的驱动作用存在区域差异,在创新等级越低的城市中其作用效果越强;③ 人口空间集聚强化了人力资本水平对创新的驱动作用,城市规模扩大促进了知识的溢出;④ 人口空间集聚调节作用的显现需要达到一定的创新基础门槛,城市创新等级越高,调节作用越强。因此,各地方政府应当结合当地发展的实际情况,制定适宜的人才培育和人才引进策略,合理引导人才流动。     

A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks

Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns.     

Thematic land-cover map assimilation and synthesis: the case of locating potential bioenergy feedstock in eastern Ontario,Canada

Insufficient spatial coverage of existing land-cover data is a common limitation to timely and effective spatial analysis. Achieving spatial completeness of land-cover data is the most challenging for large study areas which straddle ecological or administrative boundaries, and where individuals and agencies lack access to, and the means to process, raw data from which to derive spatially complete land-cover maps. In many cases, various sources of secondary data are available, so that land-cover map assimilation and synthesis can resolve this research problem. The following paper develops a reliable and repeatable framework for assimilating and synthesizing pre-classified data sets. Assimilation is achieved through data reformatting and map legend reconciliation in the context of a specific application. Individual maps are assessed for accuracy at various geographic scales and levels of thematic precision, with an emphasis on the ‘area of overlap’, in order to extract information that guides the synthesis process. The quality of the synthesized land-cover data set is evaluated using advanced accuracy assessment methods, including a measure describing the ‘magnitude of disagreement’. This method is applied to derive a seamless thematic map of the land cover of eastern Ontario from two disparate map series. The importance of assessing data quality throughout the process using multiple reference data sets is highlighted, and limitations of the method are discussed.     

基于地理加权回归的中心城区共享单车出行特征及影响因素研究

移动信息通讯技术的发展和全面渗透融入社会生活,导致高度压缩的时间、空间和距离,重塑了城市居民行为活动模式。共享单车作为“互联网+共享”的新型出行方式改善了居民的出行方式。但既有成果对共享单车的研究尚不充分。使用摩拜单车1周的骑行数据,剖析广州中心城区共享单车出行轨迹的时空间分布特征,基于地理加权回归方法进一步探究建成环境的功能密度因素对共享单车出行的影响及程度。研究发现,共享单车在工作日和休息日的出行均具有明显的早晚高峰特征。公共交通站点POI密度、功能混合度、机动车道密度等因素的边际作用表现出显著的空间不稳定性。研究能为共享单车企业提升运营水平和政府优化慢行交通环境提供参考。     

An inductive modelling procedure based on Bayes' theorem for analysis of pattern in spatial data

Abstract

This paper describes an inductive modelling procedure integrated with a geographical information system for analysis of pattern within spatial data. The aim of the modelling procedure is to predict the distribution within one data set by combining a number of other data sets. Data set combination is carried out using Bayes’ theorem. Inputs to the theorem, in the form of conditional probabilities, are derived from an inductive learning process in which attributes of the data set to be modelled are compared with attributes of a variety of predictor data sets. This process is carried out on random subsets of the data to generate error bounds on inputs for analysis of error propagation associated with the use of Bayes’ theorem to combine data sets in the GIS. The statistical significance of model inputs is calculated as part of the inductive learning process. Use of the modelling procedure is illustrated through the analysis of the winter habitat relationships of red deer in Grampian Region, north-east Scotland. The distribution of red deer in Deer Management Group areas in Gordon and in Kincardine and Deeside Districts is used to develop a model which predicts the distribution throughout Grampian Region; this is tested against red deer distribution in Moray District. Habitat data sets used for constructing the model are accumulated frost and altitude, obtained from maps, and land cover, derived from satellite imagery. Errors resulting from the use of Bayes’ theorem to combine data sets within the GIS and introduced in generalizing output from 50 m pixel to 1 km grid squares resolution are analysed and presented in a series of maps. This analysis of error trains is an integral part of the implemented analytical procedure and provides support to the interpretation of the results of modelling. Potential applications of the modelling procedure are discussed.     

国外城市系统智能体模型的科学计量分析

智能体模型用于自下而上模拟城市系统.当前综述性研究多关注其原理和缺陷等,而对研究内容演化的梳理尚不够细致.故运用主路径和冲积图分析,基于文献引用网络和关键词共现网络,梳理了国外城市系统智能体模型的研究脉络.结果 表明,土地利用是核心研究领域,居住隔离、城市增长和交通等亦是重要应用主题;元胞自动机、网络分析等方法在200...     

Mining online footprints to predict user’s next location

Social media applications are widely deployed in mobile platforms equipped with built-in GPS tracking devices, and these devices have led to an unprecedented collection of geolocated data (geo-tags). Geo-tags, along with place names, offer new opportunities to explore the trajectory and mobility patterns of social media users. However, trajectory data captured by social media are sparsely and irregularly spaced and therefore have varying degrees of resolution in both space and time. Previous studies on next location prediction are mostly applicable for detecting the upcoming location of a moving object using dense GPS trajectories where locations are recorded at regular time intervals (e.g., 1 minute). Additionally, point features are commonly used to represent the locations of visits, but using point features cannot capture the variability of human mobility. This article introduces a new methodology to predict an individual’s next location based on sparse footprints accumulated over a long time period using social networks, and uses polygons to represent the location corresponding to the physical activity area of individuals. First, the density-based spatial clustering algorithm is employed to discover the most representative activity zones that an individual frequently visits on a daily basis, and a polygon-based region is then derived for each representative activity zone. A sparse mobility Markov chain model considering both the movements and online behaviors of the social media user is trained and used to predict the user’s next location. Initial experiments with a group of Washington DC Twitter users demonstrate that the proposed methodology successfully discovers the activity regions and predicts the user’s next location with accuracy approaching 78.94%.     

Artificial neural networks and cluster analysis in landslide susceptibility zonation

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.