Interesting sequential patterns in human movement trajectories can provide valuable knowledge for urban management, planning, and location-based business. Existing methods for mining such patterns, however, tend not to consider the reduced likeliness of trips with increasing travel cost. Consequently, it is difficult to differentiate the patterns emerging from people’s specific travel interests from those simply due to travel convenience. To solve this problem, this article presents Geo-SigSPM for mining geographically interesting and statistically significant sequential patterns from trajectories. Here, ‘geographically interesting’ patterns are those more frequent than their expected frequencies which consider both the travel cost and non-redundancy of any place in the patterns. To achieve this, Geo-SigSPM formulates the expected frequencies of the patterns based on doubly-constrained human mobility models and the frequencies of their subsequences. A set of statistical tests is also developed to evaluate the identified interesting patterns. Experiments with synthetic and Foursquare check-in datasets demonstrate the efficacy of Geo-SigSPM in discovering geographically interesting patterns, controlling the spurious pattern rate, and discovering patterns that better reflect people’s specific travel interests than the conventional frequency-based pattern mining approach. Geo-SigSPM is a promising solution to improving relevant decision-making when people’s travel preference beyond travel cost is concerned.

Various methods have been proposed to detect the base locations of individuals, with their geo-tagged social media data. However, a common challenge relating to base-location detection methods (BDMs) is that, the rare availability of ground-truth data impedes the method assessment of accuracy and robustness, thus undermining research validity and reliability. To address this challenge, we collect users’ information from unstructured online content, and evaluate both the performance and robustness of BDMs. The evaluation consists of two tasks: the detection of base locations and also the differentiation between local residents and tourists. The results show BDMs can achieve high accuracies in base-location detection but tend to overestimate the number of tourists. Evaluation conducted in this study, also shows that BDMs’ accuracy is subject to the intensity of user’s activities and number of countries visited by the user but are insensitive to user’s gender. Temporally, BDMs perform better during weekends and summertime than during other periods, but the best performances appear with datasets that cover the whole time periods (whole day, week, and year). To the best of knowledge, this study is the first work to evaluate the performance and robustness of BDMs at individual level.

Wenzhong Shia , Pengfei Chenab* , Xiaoqi Shenc & Jianxiao Liuaa Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, Chinab School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou, Guangdong, Chinac School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaWenzhong Shi is the Head and Chair Professor of Geographical Information Science and Remote Sensing in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University. His research interests include GIScience and remote sensing, focusing on uncertainties and quality control of spatial data, satellite images and LiDAR data, 3D modelling, and human dynamics.Pengfei Chen works as an assistant professor in School of Geospatial Engineering and Science at Sun Yat-Sen University. His research interests include human mobility modeling, geospatial artificial intelligence and spatial uncertainty.Xiaoqi Shen is a PhD candidate in School of Environment Science and Spatial Informatics at China University of Mining and Technology. His research interest includes human mobility analysis, geographical data mining, and geospatial artificial intelligence.Jianxiao Liu currently is a research assistant in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University. His research interests include urban and rural planning, human mobility, and geographical data mining.CONTACT Pengfei Chen [email protected] movement uncertainty is of profound significance in promoting effective trajectory analysis and mining. However, classic uncertainty models are limited by rigid assumptions on moving speed and distance, which ignores the stochastic nature of individual’s travel behaviour. This study introduces a novel method using adaptive ellipses to represent the movement uncertainty in a planar space under the framework of time geography. Two models are established by considering different error sources in trajectory data. The first model captures the uncertainty caused by sampling error, and the second one further, takes the measurement error into account. The Minkowski distance metric is adopted to determine the size of uncertainty ellipses, in which the Minkowski parameter is optimized for each segment in the raw trajectory on the basis of the geometric characteristics extracted. Compared with state-of-the-art methods on five real-life trajectory datasets, the proposed method is proved to produce more effective uncertain regions, which significantly reduce the redundant uncertain area, while retaining at a comparative level of actual movement coverage. Given the heterogeneity of human mobility patterns, this study provides a robust and applicable solution for adaptively modelling individual’s movement uncertainty, which is expected to benefit trajectory-related applications in various scenarios.

Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check-ins and regional desirability have not been revealed and clearly modelled yet. To solve the problem, a novel neural network model ‘RegNet’ is proposed. The user check-in history is input into a neural network encoder structure firstly for redundancy reduction and feature learning. The encoded representation is then fed into a hidden-layer structure and the regional desirability is predicted. The proposed RegNet is data-driven and can adaptively model the unknown mappings from input to output, without presumed bias and prior knowledge. We conduct experiments with real-world datasets and demonstrate RegNet outperforms state-of-the-art methods in terms of ranking quality and prediction accuracy of rating. Additionally, we also examine how the structure of encoder affects RegNet performance and suggest on choosing proper sizes of encoded representation. This work demonstrates the effectiveness of data-driven methods in modelling the hidden unknown relationships and achieving a better performance over traditional empirical methods.

The majority of spatial data provided as Volunteered Geographic Information (VGI) are roads and other linear map features. Such data have been widely used in routing and navigation, road network update, emergency response, urban planning and more. Due to the lack of cartographic standards and issues with volunteer credibility, the quality of VGI linear features remains a concern and could seriously hinder the broad application of VGI data. This research proposes a comprehensive quality assessment framework for VGI linear features which adopts factor analysis to integrate two novel quality metrics with six other commonly used metrics, and further examines the spatial autocorrelation and semantic correlation of VGI linear feature quality. The OpenStreetMap road network of Allegheny County, Pennsylvania (USA) was selected as an example to test the proposed framework. Our results suggest that the proposed metrics, Box-counting dimension difference and Link accuracy are feasible for detecting quality issues and are important supplements to the common quality metrics. The findings also show that significant spatial autocorrelation exists in spatial completeness, positional accuracy, and logical consistency. Road type such as Tertiary, Residential, Service and Link has been proven to be a typical indicator of the different quality elements for VGI linear features.

Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.

An increasing number of social media users are becoming used to disseminate activities through geotagged posts. The massive available geotagged posts enable collections of users’ footprints over time and offer effective opportunities for mobility prediction. Using geotagged posts for spatio-temporal prediction of future location, however, is challenging. Previous studies either focus on next-place prediction or rely on dense data sources such as GPS data. Introduced in this article is a novel method for future location prediction of individuals based on geotagged social media data. This method employs the hierarchical density-based clustering algorithm with adaptive parameter selection to identify the regions frequently visited by a social media user. A multi-feature weighted Bayesian model is then developed to forecast users’ spatio-temporal locations by combining multiple factors affecting human mobility patterns. Further, an updating strategy is designed to efficiently adjust, over time, the proposed model to the dynamics in users’ mobility patterns. Based on two real-life datasets, the proposed approach outperforms a state-of-the-art method in prediction accuracy by up to 5.34% and 3.30%. Tests show prediction reliability is high with quality predictions, but low in the identification of erroneous locations.

Attractive regions can be detected and recommended by investigating users’ online footprints. However, social media data suffers from short noisy text and lack of a-priori knowledge, impeding the usefulness of traditional semantic modelling methods. Another challenge is the need for an effective strategy for the selection/recommendation of candidate regions. To address these challenges, we propose a comprehensive workflow which combines semantic and location information of social media data to recommend thematic urban regions to users with specific interests. This workflow is novel in: (1) developing a data-driven geographic topic modelling method which utilizes the co-occurrence patterns of self-explanatory semantic information to detect semantic communities; (2) proposing a new recommendation strategy with the consideration of region’s spatial scale. The workflow was implemented using a real-world dataset and evaluation conducted at three different levels: semantic representativeness, topic identification and recommendation desirability. The evaluation showed that the semantic communities detected were internally consistent and externally differentiable and that the recommended regions had a high degree of desirability. The work has demonstrated the effectiveness of self-explanatory semantic information for geographic topic modelling and highlighted the importance of including region spatial scale into the model for an effective region recommending strategy.

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.

In the field of digital terrain analysis (DTA), the principle and method of uncertainty in surface area calculation (SAC) have not been deeply developed and need to be further studied. This paper considers the uncertainty of data sources from the digital elevation model (DEM) and SAC in DTA to perform the following investigations: (a) truncation error (TE) modeling and analysis, (b) modeling and analysis of SAC propagation error (PE) by using Monte-Carlo simulation techniques and spatial autocorrelation error to simulate DEM uncertainty. The simulation experiments show that (a) without the introduction of the DEM error, higher DEM resolution and lower terrain complexity lead to smaller TE and absolute error (AE); (b) with the introduction of the DEM error, the DEM resolution and terrain complexity influence the AE and standard deviation (SD) of the SAC, but the trends by which the two values change may be not consistent; and (c) the spatial distribution of the introduced random error determines the size and degree of the deviation between the calculated result and the true value of the surface area. This study provides insights regarding the principle and method of uncertainty in SACs in geographic information science (GIScience) and provides guidance to quantify SAC uncertainty.

Spatial association rule mining (SARM) is an important data mining task for understanding implicit and sophisticated interactions in spatial data. The usefulness of SARM results, represented as sets of rules, depends on their reliability: the abundance of rules, control over the risk of spurious rules, and accuracy of rule interestingness measure (RIM) values. This study presents crisp-fuzzy SARM, a novel SARM method that can enhance the reliability of resultant rules. The method firstly prunes dubious rules using statistically sound tests and crisp supports for the patterns involved, and then evaluates RIMs of accepted rules using fuzzy supports. For the RIM evaluation stage, the study also proposes a Gaussian-curve-based fuzzy data discretization model for SARM with improved design for spatial semantics. The proposed techniques were evaluated by both synthetic and real-world data. The synthetic data was generated with predesigned rules and RIM values, thus the reliability of SARM results could be confidently and quantitatively evaluated. The proposed techniques showed high efficacy in enhancing the reliability of SARM results in all three aspects. The abundance of resultant rules was improved by 50% or more compared with using conventional fuzzy SARM. Minimal risk of spurious rules was guaranteed by statistically sound tests. The probability that the entire result contained any spurious rules was below 1%. The RIM values also avoided large positive errors committed by crisp SARM, which typically exceeded 50% for representative RIMs. The real-world case study on New York City points of interest reconfirms the improved reliability of crisp-fuzzy SARM results, and demonstrates that such improvement is critical for practical spatial data analytics and decision support.

For modeling the topological relations between spatial objects, the concepts of a bound on the intersection of the boundary and interior, and the boundary and exterior are defined in this paper based on the newly developed computational fuzzy topology. Furthermore, the qualitative measures for the intersections are specified based on the α‐cut induced fuzzy topology, which are (Aα∧∂A)(x)<1−α and ((Ac)α∧∂A)(x)<1−α. In other words, the intersection of the interior and boundary or boundary and exterior are always bounded by 1−α, where α is a value of a level cutting. Specifically, the following areas are covered: (a) the homeomorphic invariants of the fuzzy topology; (b) a definition of the connectivity of the newly developed fuzzy topology; (c) a model of the fuzzy topological relations between simple fuzzy regions in GIS; and (d) the quantitative values of topological relations can be calculated.

A digital elevation model (DEM), which is used to represent a terrain surface, is normally constructed by applying an interpolation method on given sample elevation points. Interpolation methods can be classified into two classes: linear methods, which have a low time cost and are suitable for terrains where there is little change in elevation, and nonlinear methods, which normally consume comparatively more time and are more suitable for terrains where there are frequent changes in elevation. A hybrid interpolation method, which involves both a linear method and a nonlinear method of interpolation, is proposed in this paper. The proposed method aims to integrate the advantages of both linear and nonlinear interpolation methods for the refinement of regular grid DEM. Here, the bilinear is identified as the linear method, and the bi‐cubic is taken to be the nonlinear interpolation method. The hybrid method is an integration of a linear model and nonlinear interpolation model with a parameter that defines the weights for each of the models. The parameter is dependent on the complexity of the terrain, for which a DEM is to be interpolated. The experimental results in this study demonstrate that the hybrid method is effective for interpolating DEMs for various types of terrain.

Multi‐resolution terrain models are an efficient approach to improve the speed of three‐dimensional (3D) visualizations, especially for terrain visualization in Geographical Information Systems (GIS). As a further development to existing algorithms and models, a new model is proposed for the construction of multi‐resolution terrain models in a 3D GIS. The new model represents multi‐resolution terrains using two major methods for terrain representation: Triangulated Irregular Network (TIN) and regular grid (Grid). In this paper, first, the concepts and formal definitions of the new model are presented. Second, the methodology for constructing multi‐resolution terrain models based on the new model is proposed. Third, the error of multi‐resolution terrain models is analysed, and a set of rules is proposed to retain the important features (e.g. boundaries of man‐made objects) within the multi‐resolution terrain models. Finally, several experiments are undertaken to test the performance of the new model. The experimental results demonstrate that the new model can be applied to construct multi‐resolution terrain models with good performance in terms of time cost and maintenance of the important features. Furthermore, a comparison with previous algorithms/models shows that the speed of rendering for 3D walking/flying through has been greatly improved by applying the new model.

In this paper, a new error model to describe circular curve features in GIS is presented. In the model, the error of a circular curve feature can be described by two methods. In the first method, the error is described by the root mean square error in the normal direction of the circular curve. In the second method, the error is indicated by the maximum distance between the curve feature and the error ellipse. The two methods are tested through case studies, and the results are analyzed.

Error propagation of buffer analysis in a vector-based geographical information system (GIS) is studied with the use of statistics and numerical analyses. In this paper, such factors as the error of commission, the error of omission, the discrepant area and the normalized discrepant area are proposed as the error indicators of buffer analysis. Analytical expressions for the error indicators are developed as multiple integrals. A numerical integration method is recommended to find an approximation to the analytical expression.

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.

This paper presents a model for describing the positional error of line segments in geographical information systems (GIS). The model is based on stochastic process theory with the assumptions that the errors of the endpoints of a line segment follow two-dimensional normal distributions. The distribution and density functions of the line segments are derived statistically. The uncertainty information matrix of line segments is derived to indicate the error of an arbitrary point on the line segment. This model covers the cases where two-end points are correlated to each other and points on the line segment are stochastically continuous to each other. The model is a more generic error band model than those previously developed and is called the G-Band model.

This paper presents a development of the extended Cellular Automata (CA), a Voronoi-based CA, to model dynamic interactions among spatial objects. Cellular automata are efficient models for representing dynamic spatial interactions. A complex global spatial pattern is generated by a set of simple local transition rules. However, its original definition for a two-dimensional array limits its application to raster spatial data only. This paper presents a newly developed Voronoi-based CA in which the CA is extended by using the Voronoi spatial model as its spatial framework. The Voronoi spatial model offers a ready solution to handling neighbourhood relations among spatial objects dynamically. By implementing this model, we have demonstrated that the Voronoi-based CA can model local interactions among spatial objects to generate complex global patterns. The Voronoi-based CA can further model interactions among point, line and polygon objects with irregular shapes and sizes in a dynamic system. Each of these objects possesses its own set of attributes, transition rules and neighbourhood relationships. The Voronoi-based CA models spatial interactions among real entities, such as shops, residential areas, industries and cities. Compared to the original CA, the Voronoi-based CA is a more natural and efficient representation of human knowledge over space.

This paper describes a newly developed statistical approach for modelling positional error of geometric features in GIS. The generic statistical models for N-dimensional features are firstly derived. The models for one- and twodimensional features are then developed as the specific cases of the generic models. In each dimension, the GIS features are classified as points, line segments and line features. Because of the errors, features stored in GIS may not correspond with their actual location in the real world. The true location of a GIS feature is only known within a certain area around the represented location in GIS. This newly developed approach can be used to provide a statistical description of such areas. For one-, two- and N-dimensional GIS features, they are defined as confidence intervals, confidence regions and confidence spaces respectively. The areas are related to the positional errors of the composite points of the features and to the predefined confidence level. The models are derived based on the assumptions that the errors of the composite points are independent and follow multi-dimensional normal distributions.