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People's daily travels are structured and can be expressed as networks. Few studies explore how people organize their daily travels and which behavioral principles result in the choices of specific network types. In this study, we first reconstruct location networks and activity networks for numerous individuals from high-resolution mobile phone positioning data and define frequent networks as motifs. The results suggest that 99.9% of people's travels can be characterized by a limited set of location-based motifs and activity-based motifs. The results further reveal that the least effort principle governs the preferred motif choices through quantifying the rank-frequency properties. The scaling properties of distance characteristically impact motifs, and their scaling differences by node numbers and motif types coincide with the popularities of motifs, verifying the self-adaptions in motif choices; that is, although individuals travel with unique propensities, they always tend to choose the motif with the lowest consumption that satisfies their demand., Competing Interests: The authors have declared that no competing interests exist.

The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities. [ABSTRACT FROM AUTHOR]

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China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China's hotspot regions, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to 2017 were used to derive forest, and landscape metrics and geographic information system (GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in 1987–2017; meanwhile, the forest loss area of the GBA reached 4040.6 km2, of which 25.60% (1034.42 km2) was converted to urban land; 2) the percentages of forest loss to urban land in Dongguan (19.14%), Guangzhou (18.35%) and Shenzhen (15.81%) were higher than those in other cities; 3) the forest became increasingly fragmented from 1987–2007, and then the fragmentation decreased from 2007 to 2017); 4) the landscape responses to forest changes varied with the scale; and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong. Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation. [ABSTRACT FROM AUTHOR]

Lane-level road network updating is crucial for urban traffic applications that use geographic information systems contributing to, for example, intelligent driving, route planning and traffic control. Researchers have developed various algorithms to update road networks using sensor data, such as high-definition images or GPS data; however, approaches that involve change detection for road networks at lane level using GPS data are less common. This paper presents a novel method for automatic change detection of lane-level road networks based on GPS trajectories of vehicles. The proposed method includes two steps: map matching at lane level and lane-level change recognition. To integrate the most up-to-date GPS data with a lane-level road network, this research uses a fuzzy logic road network matching method. The proposed map-matching method starts with a confirmation of candidate lane-level road segments that use error ellipses derived from the GPS data, and then computes the membership degree between GPS data and candidate lane-level segments. The GPS trajectory data is classified into successful or unsuccessful matches using a set of defuzzification rules. Any topological and geometrical changes to road networks are detected by analysing the two kinds of matching results and comparing their relationships with the original road network. Change detection results for road networks in Wuhan, China using collected GPS trajectories show that these methods can be successfully applied to detect lane-level road changes including added lanes, closed lanes and lane-changing and turning rules, while achieving a robust detection precision of above 80%. [ABSTRACT FROM PUBLISHER]

Landmarks provide the most predominant navigation cue for pedestrian navigation. Very few navigation data models in the geographical information science and transportation communities support modeling of landmarks and use of landmark-based route instructions for pedestrian navigation services. This article proposes a landmark-based pedestrian navigation data model to fill this gap. This data model can model landmarks in several pedestrian navigation scenarios (buildings, open spaces, multimodal transportation systems, and urban streets). This article implements the proposed model in the ArcGIS software environment and demonstrates two typical pedestrian navigation scenarios: (1) a multimodal pedestrian navigation environment involving bus lines, parks, and indoor spaces and (2) a subway system in a metropolitan environment. These two scenarios illustrate the feasibility of the proposed data model in real-world environments. Further improvements of this model could lead to more intuitive and user-friendly landmark-based pedestrian navigation services than the functions supported by current map-based navigation systems. [ABSTRACT FROM AUTHOR]

The interpolation of link-based traffic data is an important topic for transportation researchers and engineers. In recent years the kriging method has been used in traffic data interpolation from the viewpoint of spatial analysis. This method has shown promising results, especially for a large-scale road network. However, existing studies using the Euclidean distance metric, which is widely used in traditional kriging, fail to accurately describe the spatial distance in a road network. In this article we introduce road network distance to describe spatial distance between road links, and we propose an improved distance metric called approximate road network distance (ARND), based on the isometric embedding theory, for solving the problem of the invalid spatial covariance function in kriging caused by the non-Euclidean distance metric. An improved Isomap algorithm is also proposed for obtaining the ARND metric. This study is tested on a large-scale urban road network with sparse road-link travel speeds derived from approximately 1200 ‘floating cars’ (GPS-enabled taxis). Comparison was conducted on both the Euclidean distance metric and the ARND metric. The validation results show that the use of the ARND metric can obtain better interpolation accuracy in different time periods and urban regions with different road network structures. Therefore, we conclude that the improved distance metric has the ability for improving kriging interpolation accuracy for link-based traffic data within real situations, providing more reliable basic traffic data for various traffic applications. [ABSTRACT FROM AUTHOR]

Abstract: The concept of accessibility has been attracting increasing attention from transportation planners and researchers. The goal of “smart growth” is to promote accessibility rather than mobility. Accessibility mapping is a highly effective tool to support smart growth planning. Studies on accessibility mapping and analysis have proliferated over the last decade as Geographic Information Systems (GIS) has been widely adopted to produce accessibility maps. Nevertheless, a major negative aspect of these efforts is the lack of dynamic access measurement. Traffic conditions change significantly over space and time, leading to non-stationary travel time estimation which is central to accessibility measurement. In this paper, we propose a dynamic accessibility measure based on real-time travel speeds that are derived from floating car data (FCD). We introduce a novel integrated access measure to compute accessibility to Points of Interest (POIs) constrained by road networks. POI accessibility measures are obtained by means of both Network Kernel Density Estimation (NKDE) and Network Linear Density Estimation (NLDE). Therefore, we are able to capture temporal dynamics by taking into account speed variability using FCD during peak and off-peak hours on weekdays and weekends. We demonstrate that the proposed approach can capture the spatio-temporal dynamics of accessibility using FCD for Wuhan city in China. Empirical studies were conducted to examine the impacts of density estimation parameters (search bandwidths) and POI weights on the integrated accessibility. We find that search bandwidth and POI weights would impose profound influence on final accessibility values. Experiments also show that NKDE and NLDE would produce significantly different estimation results. [Copyright &y& Elsevier]

Floating car data (FCD) is an important source of traffic data for the design and management of an intelligent transportation system, and the efficiency and accuracy of FCD map-matching influences the application of FCD. Most map-matching algorithms are very time-consuming because many links need to be evaluated as to whether they are the correct link for the travelling floating car, especially in urban areas where most FCD are collected. In fact, the highest degree of map-matching a link for an FCD is not always the correct link, so the use of candidate links is proposed here. In this study, candidate links are defined as those that fall within the FCD error. We propose a novel algorithm for quickly identifying accurate candidate links on the basis of a vector to raster road map conversion approach. First, a road buffer is constructed based on the size of the confidence region by taking into account the position error of the FCD and the width of the link. Second, all link buffers are converted to a raster map with resolution in metres, each pixel position of the raster map corresponding to a geographical coordinate. A pixel can have one or more values, which represent the link ID whether the link buffer covers the pixel; so a spatial index map between geographical coordinate and link ID is built in this way. Then, candidate links are calculated rapidly in the next three steps: (1) Selecting a link ID from the spatial index map that, according to the FCD positioning fix coordinate, must fall within the confidence region of the FCD. The number is very small and this step is very fast because it does not include a traverse process. (2) Getting data for selected link from the road networks vector map by the link ID. This step uses a binary search and is very fast. (3) Calculating the degree of map-matching of these links to identify candidate links of the FCD. This algorithm is far less time-consuming than earlier approaches. The algorithm was tested using more than 13 million real-world FCD collected in Wuhan City, China, and the performance was shown to be satisfactory. [ABSTRACT FROM AUTHOR]

The concept of accessibility needs to take into account factors like individual needs, preferences, and abilities as well as transportation, land use, and finances. This article proposes a sensitive space-time accessibility indicator from a regional perspective. This indicator, representing cumulative available activity time with a regional context, is different from both traditional accessibility measures (e.g., gravity-based accessibility, cumulative accessibility, utility-based accessibility, and so on) and previous concepts of individual space-time accessibility. The major advantage of this indicator is its sensitivity to the location of the anchor point, the transportation network, the distribution of activity sites over the whole region, and certain individual needs such as minimum activity times and activity time budgets. Several examples are worked to demonstrate these sensitivities and an innovative accessibility pattern which was previously unknown. This indicator should be helpful for planning agencies to assess urban environmental patterns and for business owners to evaluate potential business locations. [ABSTRACT FROM AUTHOR]

The concept of a digital elevation model (DEM) can be used for a digital representation of any single-valued surface such as a terrain relief model (digital terrain model, DTM). DEMs are widely used in remote sensing, geographical information systems (GIS), and virtual reality. Estimating the accuracy of a DEM is an essential issue in the acquisition of spatial data, particularly for applications that require a highly accurate DEM, such as engineering applications. The accuracy of a DEM is subject to many factors such as the number of sampling points, the spatial distributions of the sampling points, the methods used for interpolating surface elevations, the propagated error from the source data, and other factors. Of these factors, this study will focus on estimating the mean elevation error in a DEM surface that is caused by errors of component nodes in a triangulated irregular network (TIN). This paper will present a newly derived mathematical formula, with the details of the procedure used to derive this formula, to study the relationship between the errors at the TIN nodes and the propagated mean elevation error of a DEM surface that is linearly constructed from the TIN. We have verified the analytical formula by numerical simulation. The experimental results confirm the theoretical derivation of the formula. [ABSTRACT FROM AUTHOR]

Abstract: It is essential to generate multi-resolution triangulated irregular network (TIN) models dynamically and efficiently in three-dimensional (3D) visualization, virtual reality (VR), and geographic information systems (GIS), because the data processed is multiple in scale and large in volume. This paper proposes a new approach, which extends the iterative edge collapses and vertex splits algorithm, to dynamically generate multi-resolution TIN models. Moreover, a new method was proposed to improve the efficiency of vertex topological relationships storing in multi-resolution TIN models. And a set of rules were defined to improve the validity judgment of vertex splits and edge collapses. To evaluate the performance of the proposed approach, the root mean square error (RMSE) of the elevation of vertex and the quality of the shapes of triangles were measured to evaluate the quality of a generated multi-resolution TIN model. The experimental results demonstrate that the proposed approach can generate multi-resolution TIN models with a higher accuracy and better time performance. [Copyright &y& Elsevier]

Developing a three-dimensional (3D) data model for Geographic Information Systems (GIS) is an essential and complex issue. 3D modelling in GIS is becoming ever more important for the development of cyber cities and digital earth, which have recently become feasible. A competent 3D model forms an efficient foundation for 3D visualization, query and spatial analysis. As a development of the existing 3D models, this study proposes particular improvements in handling complex 3D objects. We present an object-oriented data model for handling complex 3D objects in GIS. First, the conceptual data model is developed based on the principle of object-oriented (OO) data modelling. This model is designed based on the following three basic geometric elements: node, segment and triangle. Accordingly, the abstract geometric objects are defined: including points, lines, surfaces and volumes. Second, the corresponding 3D logical model is designed based on the defined abstract objects and the relationships between them. Third, a formal representation of the 3D spatial objects is described in detail. Fourth, a prototype 3D GIS is developed based on the proposed 3D data model. Finally, we describe the results of an experimental study to reconstruct 3D objects using this 3D GIS and a comparison with the performance of other 3D data models. The proposed model is able to handle complex objects, such as complex buildings and TV towers, which is an essential functionality for building large-scale cyber cities, such as for Hong Kong. The proposed data model proves to be very efficient, particularly in visualization and rendering. The experimental results show which the data volume of the proposed model is compacted and the visualization speed for 3D objects is improved, compared with the existing models. [ABSTRACT FROM AUTHOR]

The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, Geographic Information System (GIS) techniques, and geographical detector method to characterize the spatiotemporal patterns of urban expansion in the GBA and investigate their driving factors during 1986–2017 on regional and city scales. The results showed that: the GBA experienced an unprecedented urban expansion over the past 32 years. The total urban area expanded from 652.74 km2 to 8137.09 km2 from 1986 to 2017 (approximately 13 times). The annual growth rate during 1986–2017 was 8.20% and the annual growth rate from 1986 to 1990 was the highest (16.89%). Guangzhou, Foshan, Dongguan, and Shenzhen experienced the highest urban expansion rate, with the annual increase of urban areas in 51.51, 45.54, 36.76, and 23.26 km2 y−1, respectively, during 1986–2017. Gross Domestic Product (GDP), income, road length, and population were the most important driving factors of the urban expansions in the GBA. We also found the driving factors of the urban expansions varied with spatial and temporal scales, suggesting the general understanding from the regional level may not reveal detailed urban dynamics. Detailed urban management and planning policies should be made considering the spatial and internal heterogeneity. These findings can enhance the comprehensive understanding of this bay area and help policymakers to promote sustainable development in the future. [ABSTRACT FROM AUTHOR]

The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the major bay areas in the world. However, the spatiotemporal characteristics and rationalities of urban expansions within this region over a relatively long period of time are not well-understood. This study explored the spatiotemporal evolution of 11 cities within the GBA in 1987–2017 by integrating remote sensing, landscape analysis, and geographic information system (GIS) techniques, and further evaluated the rationalities of their expansion using the urban area population elastic coefficient (UPEC) and the urban area gross domestic product (GDP) elastic coefficient (UGEC). The results showed the following: (1) Guangzhou, Shenzhen, Foshan, Dongguan, Zhongshan, and Zhuhai experienced unprecedented urbanization compared with the other cities, and from 1987 to 2017, their urban areas expanded by 10.12, 11.48, 14.21, 24.90, 37.07, and 30.15 times, respectively; (2) several expansion patterns were observed in the 11 cities, including a mononuclear polygon radiation pattern (Guangzhou and Foshan), a double-nucleated polygon pattern (Macau and Zhongshan), and a multi-nuclear urbanization pattern (Shenzhen, Hong Kong, Dongguan, Jiangmen, Huizhou, Zhaoqing, and Zhuhai); (3) with regard to the proportion of area, the edge-expansion and outlying growth types were the predominant types for all 11 cities, and the infilling growth type was the one of the important types during 2007–2017 for Shenzhen, Hong Kong, Dongguan, Zhongshan, and Foshan; (4) the expansion of most cities took on an urban-to-rural landscape gradient, especially for Guangzhou, Shenzhen, Foshan, Zhongshan, Dongguan, and Zhuhai; and (5) the rationalities of expansion in several time periods were rational for Guangzhou (1997–2007), Hong Kong (2007–2017), Foshan (1987–2007), Huizhou (1987–1997), and Dongguan (1997–2007), and the rationalities of expansion in the other cities and time periods were found to be irrational. These findings may help policy- and decision-makers to maintain the sustainable development of the Guangdong–Hong Kong–Macau Greater Bay Area. [ABSTRACT FROM AUTHOR]

Abstract: The spatial patterns of road networks play an important role in spatial recognition, urban structure analysis, spatial data matching, and network analysis. This paper proposes and implements a method for identifying the spatial patterns of road networks. The method first builds a node-edge topology, generates polygons from these edges, and then calculates a set of parameters, specifically consistent arrangement, shape similarity, and a grid shape index of the polygons. Finally, the method identifies spatial patterns via a multi-criteria decision method. Experimental testing of the method showed that it is a very competent feasible means to detect spatial patterns in road networks. [Copyright &y& Elsevier]

Abstract: The large-scale implementation of High-Speed Rail (HSR) network in China not only offers a new option for travelers’ mode choice, but also may influence, or even generate, the redistribution of demographic and economic activities. As has been observed over the past several years in other countries, the impact of HSR spans a wide range. However, few quantitative studies have been conducted to measure this impact. As a new attempt, this study uses accessibility analysis for quantifying the impact of China’s HSR network. Weighted average travel times and travel costs, contour measures, and potential accessibility are employed as indicators of accessibility at the macro or national level. Forty-nine major cities in the HSR network are used in the accessibility analysis. Accessibility quantification and spatial distribution analysis for the study cities are performed on a Geographical Information System (GIS) platform. Accessibilities associated with varying availabilities of HSR, conventional rail, and airline are estimated and compared. The selected indicators and computational methods are found effective in evaluating the accessibility impacts of HSR from different conceptualization strategies and perspectives. They also offer complementary information on accessibility capacity of the study cities created by the HSR network. [Copyright &y& Elsevier]