基于二分网络的北京公交线路布局的空间依赖性

公交网络运行系统的结构性缺陷常常致使其成为城市交通拥堵的罪魁祸首。传统公交网络空间研究皆将其视为一个独立的交通运输方式,忽视了城市道路网形态与结构的内生作用。本文基于二分网络的思想,通过构建公交线路—城市道路的空间依赖矩阵,引入系列空间依赖度测度指标从局域、全局两个尺度探讨了北京市的公交线路的空间依赖度及依赖的核心空间,并通过社团识别对北京市公交线路的依赖空间进行了划分。结果发现：① 局域依赖度方面,北京市公交线路布局高度依赖度少数城市主干道和城—郊区快速干道,其空间依赖格局形成了以市中心为核心,以城—郊区快速干道为通道的中心—外围的廊道扩散格局;② 全局依赖度方面,北京市公交线路空间布局结构失衡,比较脆弱,受城市道路路面路况影响较大,同时以四环为模糊边界的市中心区域成为北京市公交线路依赖的核心空间;③ 依赖空间划分上,北京市公交线路布局与城市地域空间呈现良好的对应性,朝阳、海淀、三环内（东城和西城）是公交线路布局规模最密的3个区域。     

合肥市物流空间区位特征与形成机理

以合肥市111个专业大市场、852家物流企业、38个物流节点为样本,结合合肥市2014年交通网络、工业园要素地图,采用GIS技术,探讨合肥市物流空间特征及形成机理。研究结果显示:(1)陆路交通网络对物流空间区位选择影响显著,而内河港口、机场对其影响不显著;(2)城区物流空间呈单中心等级扩散格局,中心城区与近郊区为物流企业、专业大市场布局热点,远郊区主要布局规模(大中型)物流节点,且无明显物流节点体系分层现象;(3)物流空间与工业布局不协同,区域差异明显,瑶海区、庐阳区物流空间集聚,而包河区与蜀山区无明显集聚现象。     

公路交通可达性视角下山东省区域空间重构

《山东半岛蓝色经济区发展规划》、《济南都市圈发展规划》、《黄河三角洲高效生态经济区发展规划》和《鲁南经济带发展规划》是涉及山东省域范围的四个重要区域规划。四个规划所覆盖的区域范围重叠现象严重,这将导致重叠区域发展方向不明,进而会影响到区域的整体发展。明确区域规划合理范围,统筹协调区域发展成为山东省区域发展亟须解决的问题之一。本文以公路交通可达性和城市综合实力为切入点,计算出了山东省17个地级市的经济腹地,并遵循一定的原则对山东省区域发展进行空间重构,提出了"两核、四区、五轴"的区域空间发展结构。     

Empirical determination of geometric parameters for selective omission in a road network

Selective omission in a road network is a necessary operation for road network generalization. Most existing selective omission approaches involve one or two geometric parameters at a specific scale to determine which roads should be retained or eliminated. This study proposes an approach for determining the empirical threshold for such a parameter. The idea of the proposed approach is to first subdivide a large road network, and then to use appropriate threshold(s) obtained from one or several subdivisions to infer an appropriate threshold for the large one. A series of experiments was carried out to validate the proposed approach. Specifically, the road network data for New Zealand and Hong Kong at different scales (ranging from 1:50,000 to 1:250,000) were used as the experimental data, and subdivided according to different modes (i.e. administrative boundary data, a regular grid of different sizes, different update years, and different road network patterns). Not only geometric parameters, but also structural and hybrid parameters of existing selective omission approaches were involved in the testing. The experimental results show that although the most appropriate thresholds obtained from different subdivisions are not always the same, in most cases, the appropriate threshold ranges often overlap, especially for geometric parameters, and they also overlap with those obtained from the large road network data. This finding is consistent with the use of different subdivision modes, which verifies the effectiveness of the proposed approach. Several issues involving the use of the proposed approach are also addressed.     

A metaheuristic approach for efficient and effective sketch-to-metric map alignment

Sketching as a natural mode for human communication and creative processes presents opportunities for improving human–computer interaction in geospatial information systems. However, to use a sketch map as user input, it must be localized within the underlying spatial data set of the information system, the base metric map. This can be achieved by a matching process called qualitative map alignment in which qualitative spatial representations of the two input maps are used to establish correspondences between each sketched object and one or more objects in the metric map. The challenge is that, to the best of our knowledge, no method for matching qualitative spatial representations suggested so far is applicable in realistic scenarios due to excessively long runtimes, incorrect algorithm design or the inability to use more than one spatial aspect at a time. We address these challenges with a metaheuristic algorithm which uses novel data structures to match qualitative spatial representations of a pair of maps. We present the design, data structures and performance evaluation of the algorithm using real-world sketch and metric maps as well as on synthetic data. Our algorithm is novel in two main aspects. Firstly, it employs a novel system of matrices known as local compatibility matrices, which facilitate the computation of estimates for the future size of a partial alignment and allow several types of constraints to be used at the same time. Secondly, the heuristic it computes has a higher accuracy than the state-of-the-art heuristic for this task, yet requires less computation. Our algorithm is also a general method for matching labelled graphs, a special case of which is the one involving complete graphs whose edges are labelled with spatial relations. The results of our evaluation demonstrate practical runtime performance and high solution quality.     

A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data

Matching road networks is an essential step for data enrichment and data quality assessment, among other processes. Conventionally, road networks from two datasets are matched using a line-based approach that checks for the similarity of properties of line segments. In this article, a polygon-based approach is proposed to match the OpenStreetMap road network with authority data. The algorithm first extracts urban blocks that are central elements of urban planning and are represented by polygons surrounded by their surrounding streets, and it then assigns road lines to edges of urban blocks by checking their topologies. In the matching process, polygons of urban blocks are matched in the first step by checking for overlapping areas. In the second step, edges of a matched urban block pair are further matched with each other. Road lines that are assigned to the same matched pair of urban block edges are then matched with each other. The computational cost is substantially reduced because the proposed approach matches polygons instead of road lines, and thus, the process of matching is accelerated. Experiments on Heidelberg and Shanghai datasets show that the proposed approach achieves good and robust matching results, with a precision higher than 96% and a F1-score better than 90%.     

Prediction of uniaxial compressive strength of sandstones using petrography-based models

The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo–convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.     

An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps

The main purpose of this study is to highlight the conceptual differences of produced susceptibility models by applying different sampling strategies: from all landslide area with depletion and accumulation zones and from a zone which almost represents pre-failure conditions. Variations on accuracy and precision values of the models constructed considering different algorithms were also investigated. For this purpose, two most popular techniques, logistic regression analysis and back-propagation artificial neural networks were taken into account. The town Ispir and its close vicinity (Northeastern part of Turkey), suffered from landsliding for many years was selected as the application site of this study. As a result, it is revealed that the back-propagation artificial neural network algorithms overreact to the samplings in which the presence (1) data were taken from the landslide masses. When the generalization capacities of the models are taken into consideration, these reactions cause imprecise results, even though the area under curve (AUC) values are very high (0.915 < AUC < 0.949). On the other hand, the susceptibility maps, based on the samplings in which the presence (1) data were taken from a zone which almost represents pre-failure conditions constitute more realistic susceptibility evaluations. However, considering the spatial texture of the final susceptibility values, the maps produced using the outputs of the back-propagation artificial neural networks could be interpreted as highly optimistic, while of those generated using the resultant probabilities of the logistic regression equations might be evaluated as pessimistic. Consequently, it is evident that, there are still some needs for further investigations with more realistic validations and data to find out the appropriate accuracy and precision levels in such kind of landslide susceptibility studies.     

The social networks of British and Indian expatriate scientists in Boston

This paper argues that although the social networks of highly skilled migrants are important, British and Indian scientists in Boston tend not to use expatriate social networks. I demonstrate that wider differences in cultural norms to the indigenous population, homogeneity within a migrant group and length of time spent in the US are not the only factors that affect the participation of highly skilled migrants in expatriate social networks. Other factors such as the size of an expatriate group, the annual flow of migrants to a region as well as the propensity of expatriates to participate in indigenous social networks will also affect the extent to which they participate in these networks.     

Estimation of Hardgrove grindability index of Turkish coals by neural networks

In this research, different techniques for the estimation of coal HGI values are studied. Data from 163 sub-bituminous coals from Turkey are used by featuring 11 coal parameters, which include proximate analysis, group maceral analysis and rank. Non-linear regression and neural network techniques are used for predicting the HGI values for the specified coal parameters. Results indicate that a hybrid network which is a combination of 4 separate neural networks gave the most accurate HGI prediction and all of the neural network models outperformed non-linear regression in the estimation process.