Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people’s social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people’s interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.     

Spatial representation in the social interaction potential metric: an analysis of scale and parameter sensitivity

The social interaction potential (SIP) metric measures urban structural constraints on social interaction opportunities of a metropolitan region based on the time geographic concept of joint accessibility. Previous implementations of the metric used an interaction surface based on census tracts and the locations of their centroids. This has been shown to be a shortcoming, as the metric strongly depends on the scale of the zoning system in the region, making it difficult to compare the SIP metric between metropolitan regions. This research explores the role of spatial representation in the SIP metric and identifies a suitable grid-based representation that allows for comparison between regions while retaining cost-effectiveness with respect to computational burden. We also report on findings from an extensive sensitivity analysis investigating the SIP metric’s input parameters such as a travel flow congestion factor and the length of the allowable time budget for social activities. The results provide new insights on the role of the modifiable areal unit problem in the computation of time geographic measures of accessibility.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Curve matching approaches to waveform classification: a case study using ICESat data

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback–Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov–Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.     

Risk Prediction of Tunnel Water or Mud Inrush Based on Disaster Forewarning Grading

Water or mud inrush can not only hamper the normal construction of tunnel, but also cause severe casualties and property losses. Through the cause analysis and statistical theory, a total of 9 predictors based on 3 factors, the engineering geology, hydrogeology and construction, were put forward to control the happening of water or mud inrush. Based on the classification principles of forewarning, 3 alert levels, red, orange and yellow, were established. Using the AHP–TOPSIS evaluation theory, the risk prediction model of water or mud inrush was built based on the classification of disaster forewarning. The model was used in the diversion project of Fujian Long Jin–xi and achieved good effects.     

Experimental Evaluation of Construction Waste and Ground Granulated Blast Furnace Slag as Alternative Soil Stabilisers

In this study, the effect of ground granulated blast furnace slag (GGBFS) and recycled construction waste (CW) on bentonite clay stabilisation were investigated. The unconfined compressive strength (UCS) of specimens was evaluated with different combinations of GGBFS and CW over various curing periods. A series of micro analysis tests consisting of scanning electron microscope, energy dispersive spectrometer and X-ray diffraction were also conducted to determine the microstructural arrangement and mineralogical effect of the stabilisation treatment. The UCS results showed an increment in strength after introduction of GGBFS and CW and the longer curing period produced more pronounced results. The optimum additive ratio was calculated as 5 % of slag and 20 % of construction waste under all curing conditions. The micro analytical results also indicated formation of structural bonds between admixtures and bentonite in stabilised specimens, as slag crystals and bentonite particles were observed to occupy the cavities and vesicles on the construction waste grains. However, the experimental data shows that the strength improvement is not significant with the addition of only construction waste.     

Laboratory Estimation of Rock Joint Stiffness and Frictional Parameters

Numerical modeling of complex rock engineering problems involves the use of various input parameters which control usefulness of the output results. Hence, it is of utmost importance to select the right range of input physical and mechanical parameters based on laboratory or field estimation, and engineering judgment. Joint normal and shear stiffnesses are two popular input parameters to describe discontinuities in rock, which do not have specific guidelines for their estimation in literature. This study attempts to provide simple methods to estimate joint normal and shear stiffnesses in the laboratory using the uniaxial compression and small-scale direct shear tests. Samples have been prepared using rocks procured from different depths, geographical locations and formations. The study uses a mixture of relatively smooth natural joints and saw-cut joints in the various rock samples tested. The results indicate acceptable levels of uncertainty in the calculation of the stiffness parameters and provide a database of good first estimates and empirical relations which can be used for calculating values for joint stiffnesses when laboratory estimation is not possible. Joint basic friction angles have also been estimated as by-products in the small scale direct shear tests.     

Investigation of Cycle Time Segments of Dragline Operation in Surface Coal Mine: A Statistical Approach

Dragline is highly capital intensive equipment to procure, operate and maintain in any surface mining operation. Given this, every second of operation of this capital intensive equipment is absolutely important. Improvement of even a single second in the total cycle time has a tremendous bearing on the overall performance of this equipment. In this light, the present paper is an endeavour to critically analyze the cycle time of dragline operations in a major surface coal mine in India. Rigorous statistical analysis has been performed on individual cycle time segments, of complete dragline cycle. The segmental cycle times have been found to be statistically significant and appear to be best represented by lognormal, normal and beta distributions. Furthermore, the mean time of the statistical distribution for segmental cycle time of dragline has revealed the dependence of cycle time on cut geometry and depth. Results have been illustrated in the form of figures, graphs and tables.     

Behavior of Shallow Strip Footing on Twin Voids

This paper numerically examines the bearing capacity and failure mechanism of a shallow strip foundation constructed above twin voids. The voids may refer to caves, caverns, underground aqueduct or tunnels due to water seepage, chemical reaction or deliberately excavated in soil deposit. The ability of numerical model to accurately predict the system behavior is evaluated by performing verification analyses on existing researches. Subsequently, a parametric study carried out to reveal the influence of size of footing/voids and their location (i.e. depth, spacing, eccentricity) on the bearing capacity of footing. To clarify the failure mechanism, the distribution of shear strain in the soil for different scenarios is assessed. The parametric study provided a new framework to determine the bearing capacity and the mode of failure for footings on voids. Based on the results, a criterion can be issued to avoid collapse of footing/voids regarding the shape, location and size of voids. The results can also be used to design construction of a footing on existing voids while the acquired failure mechanisms can be appointed to develop analytical solutions for this problem. Results demonstrated that a critical depth for voids and a critical distance between them exist where the influence on the ultimate bearing capacity of footing disappears.