By utilizing today's web-based technologies, people can act as sensors and share their perceptions, emotions and observations in a variety of data forms, such as images, videos, texts, Global Positioning System (GPS) trajectories and maps. These forms are collectively called user-generated content (UGC). These data are in different domains and have a multi-modality nature. Although recent efforts have probed the acquisition of local knowledge by using single-domain UGC data in specific applications, such efforts have not thus far presented a model considering multi-domain UGC specifically to enrich road network data. This article aims at presenting such a model wherein, with the help of each data domain of UGC, one aspect of people knowledge about the road segment is obtained. These different aspects of knowledge are integrated using a Skyline operator to support multi-criteria route finding. We name this model ERSBU (enriching road segments based on UGC). In ERSBU, road segments are basic spatial units, and their subjective properties have been extracted by using available UGC. The scenic score for each road segment was computed by using geo-tagged Panoramio photos. The accessibility level of each road segment to different facilities was calculated based on data captured from Wikimapia and OpenStreetMap. Moreover, for measuring the movement popularity of each road segment, Wikiloc and Everytrail GPS trajectories were utilized. For the implementation of the ERSBU model, Tehran region 6 was considered the case study area. The Evaluation of the results proved that road segments that achieved a high score based on knowledge extracted from UGC also mostly gained top scores by analyzing traditional maps. ERSBU allows users to accomplish more-qualitative path finding by considering the multi-view characteristics of road segments.
A novel geostatistical modeling approach is developed to model nonlinear multivariate spatial dependence using nonlinear principal component analysis (NLPCA) and pair-copulas. In spatial studies, multivariate measurements are frequently collected at each location. The dependence between such measurements can be complex. In this article, a multivariate geostatistical model is developed that can capture both nonlinear spatial dependence across locations and nonlinear dependence between measurements at a particular location. Nonlinear multivariate dependence between spatial variables is removed using NLPCA. Subsequently, a pair-copula based model is fitted to each transformed variable to model the univariate nonlinear spatial dependencies. NLPCA and pair-copulas, within the proposed model, are compared with stepwise conditional transformation (SCT) and conventional kriging. The results show that, for the two case studies presented, the proposed model that utilizes NLPCA and pair-copulas reproduces nonlinear multivariate structures and univariate distributions better than existing methods based on SCT and kriging.
In the context of modeling regional freight the four-stage model is a popular choice. The first stage of the model, freight generation and attraction, however, suffers from three shortcomings: first of all, it does not take spatial dependencies among regions into account, thus potentially yielding biased estimates. Second, there is no clear consensus in the literature as to the choice of explanatory variables. Second, sectoral employment and gross value added are used to explain freight generation, whereas some recent publications emphasize the importance of variables which measure the amount of logistical activity in a region. Third, there is a lack of consensus regarding the functional form of the explanatory variables. Multiple recent studies emphasize nonlinear influences of selected variables. This article addresses these shortcomings by using a spatial variant of the classic freight generation and attraction models combined with a penalized spline framework to model the explanatory variables in a semiparametric fashion. Moreover, a Bayesian estimation approach is used, coupled with a penalized Normal inverse-Gamma prior structure, to introduce uncertainty regarding the choice and functional form of explanatory variables. The performance of the model is assessed on a real-world example of freight generation and attraction of 258 European NUTS-2 level regions, covering 25 European countries.
Spatial heterogeneity has been regarded as an important issue in space–time prediction. Although some statistical methods of space–time predictions have been proposed to address spatial heterogeneity, the linear assumption makes it difficult for these methods to predict geographical processes accurately because geographical processes always involve complicated nonlinear characteristics. An extreme learning machine (ELM) has the advantage of approximating nonlinear relationships with a rapid learning speed and excellent generalization performance. However, determining how to incorporate spatial heterogeneity into an ELM to predict space–time data is an urgent problem. For this purpose, a new method called geographically weighted ELM (GWELM) is proposed to address spatial heterogeneity based on an ELM in this article. GWELM is essentially a locally varying ELM in which the parameters are regarded as functions of spatial locations, and geographically weighted least squares is applied to estimate the parameters in a local model. The proposed method is used to analyze two groups of different data sets, and the results demonstrate that the GWELM method is superior to the comparative method, which is also developed to address spatial heterogeneity.
Basin-wide sediment transport affects estimates of basin sediment yield, which is a fundamental scientific issue in drainage basin studies. Many studies have been conducted to examine erosion and deposition rates in drainage networks. In this study, we proposed a new approach using grain-size standard deviation model of sedimentary samples from different geomorphological units for numerical analysis and paleo-climate interpretation in the Shiyang River drainage basin, arid China. 1043 sedimentary samples were obtained from the upper reaches, the midstream alluvial plain and the terminal lake area; chronological frames were established based on 58 radiocarbon ages. Grain-size standard deviation model was introduced to examine sediment components according to grain-size and transport forces. In addition, transient paleo-climate simulations, including the Community Climate System Model version 3 and the Kiel models, were synthesized, as well as the results from PMIP 3.0 project, to detect the long-term climate backgrounds. Totally, we found four major common components, including fine particulates (<2 μm), fine silt (2–20 μm), sandy silt (20–200 μm), coarse sand (>200 μm), from basin-wide sedimentary samples. The fine particulates and fine silt components exist in all the sedimentary facies, showing long-term airborne aerosol changes and its transport by suspended load. There are some differences in ranges of sandy silt and coarse sand components, due to lake and river hydrodynamics, as well as the distance with the Gobi Desert. Paleo-climate simulations have shown that the strong Asian summer monsoon during the transition of the Last Deglaciation and Holocene was conducive to erosion and transport of basin-wide suspended load, also enhancing sediment sorting effects due to strong lake hydrodynamics. Our findings provide a new approach in research of long-term basin-wide sediment transport processes.
Location-based social media make it possible to understand social and geographic aspects of human activities. However, previous studies have mostly examined these two aspects separately without looking at how they are linked. The study aims to connect two aspects by investigating whether there is any correlation between social connections and users' check-in locations from a socio-geographic perspective. We constructed three types of networks: a people–people network, a location–location network, and a city–city network from former location-based social media Brightkite and Gowalla in the U.S., based on users' check-in locations and their friendships. We adopted some complexity science methods such as power-law detection and head/tail breaks classification method for analysis and visualization. Head/tail breaks recursively partitions data into a few large things in the head and many small things in the tail. By analyzing check-in locations, we found that users' check-in patterns are heterogeneous at both the individual and collective levels. We also discovered that users' first or most frequent check-in locations can be the representatives of users' spatial information. The constructed networks based on these locations are very heterogeneous, as indicated by the high ht-index. Most importantly, the node degree of the networks correlates highly with the population at locations (mostly with R2 being 0.7) or cities (above 0.9). This correlation indicates that the geographic distributions of the social media users relate highly to their online social connections.