Urban land use classification and mapping are critical for urban planning, resource management, and environmental monitoring. Existing remote sensing techniques often lack precision in complex urban environments due to the absence of ground-level details. Unlike aerial perspectives, street view images provide a ground-level view that captures more human and social activities relevant to land use in complex urban scenes. Existing street view-based methods primarily rely on supervised classification, which is challenged by the scarcity of high-quality labeled data and the difficulty of generalizing across diverse urban landscapes. This study introduces an unsupervised contrastive clustering model for street view images with a built-in geographical prior, to enhance clustering performance. When combined with a simple visual assignment of the clusters, our approach offers a flexible and customizable solution to land use mapping, tailored to the specific needs of urban planners. We experimentally show that our method can generate land use maps from geotagged street view image datasets of two cities. As our methodology relies on the universal spatial coherence of geospatial data ("Tobler's law"), it can be adapted to various settings where street view images are available, to enable scalable, unsupervised land use mapping and updating. The code is available at https://github.com/lin102/CCGP.

Navigating complex environments is crucial for human life, yet understanding the cognitive processes involved in its wayfinding component remains challenging. One theoretical model that explains these processes is Downs and Stea’s four-step model. Our study builds on this model to empirically analyze its steps, focusing particularly on the monitoring step. Machine learning models were trained on gaze behavior and head/body movement data from over 300 routes walked by 56 participants in a real-world outdoor study, predicting three of these wayfinding steps: self-localization, route planning, and goal recognition. Applying this trained model to the respective monitoring segment of the same routes suggests that monitoring includes micro-versions of these three steps, indicating it operates as a recursive process rather than a distinct cognitive step. By bridging theoretical frameworks with empirical evidence, these findings enhance our understanding of spatial cognition and can inform the design of navigational tools and urban spaces.

When predicting observations across space and time, the spatial layout of errors impacts a model’s real-world utility. For instance, in bike sharing demand prediction, error patterns translate to relocation costs. However, commonly used error metrics in GeoAI evaluate predictions point-wise, neglecting effects such as spatial heterogeneity, autocorrelation, and the Modifiable Areal Unit Problem. We put forward Optimal Transport (OT) as a spatial evaluation metric and loss function. The proposed framework, called GeOT, assesses the performance of prediction models by quantifying the transport costs associated with their prediction errors. Through experiments on real and synthetic data, we demonstrate that (1) the spatial distribution of prediction errors relates to real-world costs in many applications, (2) OT captures these spatial costs more accurately than existing metrics, and (3) OT enhances comparability across spatial and temporal scales. Finally, we advocate for leveraging OT as a loss function in neural networks to improve the spatial accuracy of predictions. Experiments with bike sharing, charging station, and traffic datasets show that spatial costs are significantly reduced with only marginal changes to non-spatial error metrics. Thus, this approach not only offers a spatially explicit tool for model evaluation and selection but also integrates spatial considerations into model training.

Decisions with spatial visualizations are often made under uncertainty and high time pressure. However, missing or improper representation of uncertainty can hamper the decision-making process. This paper investigates the impact of uncertainty visualization on cognitive load in the context of a safety-critical, time-sensitive decision-making task with a transportation system map. In a controlled experiment (n = 40) with a dual-task paradigm, we compared three different uncertainty visualization techniques and a baseline for different levels of time pressure. Cognitive load was measured using psycho-physiological metrics based on eye tracking and galvanic skin response, as well as self-reported. The results reveal significant differences in cognitive load among different visualization types, with line uncertainty representation techniques leading to lower cognitive load under both low and high-time pressure scenarios (α<0.05).

Accurate traffic speed forecasting is a prerequisite for anticipating future traffic status and increasing the resilience of intelligent transportation systems. However, most studies ignore the involvement of context information ubiquitously distributed over the urban environment to boost speed prediction. The diversity and complexity of context information also hinder incorporating it into traffic forecasting. Therefore, this study proposes a multimodal context-based graph convolutional neural network (MCGCN) model to fuse context data into traffic speed prediction, including spatial and temporal contexts. The proposed model comprises three modules, ie (a) hierarchical spatial embedding to learn spatial representations by organizing spatial contexts from different dimensions, (b) multivariate temporal modeling to learn temporal representations by capturing dependencies of multivariate temporal contexts and (c) attention-based multimodal fusion to integrate traffic speed with the spatial and temporal context representations for multi-step speed prediction. We conduct extensive experiments in Singapore. Compared to the baseline model (spatial-temporal graph convolutional network, STGCN), our results demonstrate the importance of multimodal contexts with the mean-absolute-error improvement of 0.29 km/h, 0.45 km/h and 0.89 km/h in 30-min, 60-min and 120-min speed prediction, respectively. We also explore how different contexts affect traffic speed forecasting, providing references for stakeholders to understand the relationship between context information and transportation systems.

Predicting the next visited location of an individual is a key problem in human mobility analysis, as it is required for the personalization and optimization of sustainable transport options. Here, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes, which are behaviour dimensions often overlooked in previous work. In particular, the prediction of the next travel mode is designed as an auxiliary task to help guide the network's learning. For evaluation, we apply this approach to two large-scale and long-term GPS tracking datasets involving more than 600 individuals. Our experiments show that the proposed method significantly outperforms other state-of-the-art next location prediction methods by a large margin (8.05% and 5.60% relative increase in F1-score for the two datasets, respectively). We conduct an extensive ablation study that quantifies the influence of considering temporal features, travel mode information, and the auxiliary task on the prediction results. Moreover, we experimentally determine the performance upper bound when including the next mode prediction in our model. Finally, our analysis indicates that the performance of location prediction varies significantly with the chosen next travel mode by the individual. These results show potential for a more systematic consideration of additional dimensions of travel behaviour in human mobility prediction tasks. The source code of our model and experiments is available at https://github.com/mie-lab/location-mode-prediction.

Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. However, such deep neural networks are difficult to interpret and lack robustness, which slows the deployment of these AI-powered algorithms in practice. To improve their usability, increasing research efforts have been devoted to developing interpretable and robust machine learning methods, among which the causal inference approach recently gained traction as it provides interpretable and actionable information. Moreover, most of these methods are developed for image or sequential data which do not satisfy specific requirements of mobility data analysis. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness, summarizes recent developments in using causal inference for improving the interpretability and robustness of machine learning methods, and highlights opportunities in developing causally-enabled machine learning models tailored for mobility analysis. This research direction will make AI in the transportation sector more interpretable and reliable, thus contributing to safer, more efficient, and more sustainable future transportation systems.

Though GPS-based human trajectory data have been commonly used in travel surveys and human mobility studies, missing data or data gaps that are intrinsically relevant to research reliability remain a critical and challenging issue. This study proposes a novel framework for imputing data gaps based on frequent-pattern mining and time geography, which allows for considering spatio-temporal travel restrictions during imputation by evaluating the spatio-temporal topology relations between the space-time prisms of gaps and corresponding frequent activities or trips. For the validation, the proposed framework is applied to raw GPS trajectories that were collected from 139 participants in Switzerland. In the case study, the temporal and spatio-temporal gaps are artificially generated by randomly choosing activities and trips from the trajectory data. Through comparing the mobility indicators (i.e. duration and distance) calculated from raw data, imputed data, and data with gaps, we quantitatively evaluate the performance of the proposed method in terms of Pearson correlation coefficients and deviation. We further compare the framework with the shortest path interpolation method based on the generated spatio-temporal gaps. The comparison results demonstrate the performance and advantage of the proposed method in imputing gaps from GPS-based human movement data.

Andrew Frank, who retired in September 2016 from the chair of geoinformation position at the Vienna University of Technology that he had held for a quarter century, has had anextraordinary influenc...

Currently, systems that let people search for opportunities to fulfill their spatio-temporal needs are built according to the conceptual model of service provider and consumer: After the providers make their needs publicly available, consumers use a specifically tailored query engine to find fitting offers. E.g., in carpooling, someone wants to fill an empty seat and to share costs (and publishes this offer), while another person wants to travel the same route. This model prevents the consuming side from making their needs available to the service providers and makes it hard to generalize, as query engines require rigid (often domain-specific) properties. Addressing this problem, we propose a generic model for publishing and processing complementary spatio-temporal needs. Our model uses a simulator to assess how well the collaboration between different entities would approximate their goals. To reuse existing concepts and embed the model into the emerging Semantic Web, everything is modeled in accordance with Linked Data principles.

Location-based services (LBS) provide more useful, intelligent assistance to users by adapting to their geographic context. For some services that context goes beyond a location and includes further spatial parameters, such as the user’s orientation or field of view. Here, we introduce Gaze-Informed LBS (GAIN-LBS), a novel type of LBS that takes into account the user’s viewing direction. Such a system could, for instance, provide audio information about the specific building a tourist is looking at from a vantage point. To determine the viewing direction relative to the environment, we record the gaze direction relative to the user’s head with a mobile eye tracker. Image data from the tracker’s forward-looking camera serve as input to determine the orientation of the head w.r.t. the surrounding scene, using computer vision methods that allow one to estimate the relative transformation between the camera and a known view of the scene in real-time and without the need for artificial markers or additional sensors. We focus on how to map the point of regard of a user to a reference system, for which the objects of interest are known in advance. In an experimental validation on three real city panoramas, we confirm that the approach can cope with head movements of varying speed, including fast rotations up to to 63 degrees per second. We further demonstrate the feasibility of GAIN-LBS for tourist assistance with a proof-of-concept experiment in which a tourist explores a city panorama, where the approach achieved a recall that reaches over 99%. Finally, a GAIN-LBS can provide objective and qualitative ways of examining the gaze of a user based on what the user is currently looking at.

Travel activities are embodied as people’s needs to be physically present at certain locations. The development of Information and Communication Technologies (ICTs, such as mobile phones) has introduced new data sources for modeling human activities. Based on the scattered spatiotemporal points provided in mobile phone datasets, it is feasible to study the patterns (e.g., the scale, shape, and regularity) of human activities. In this paper, we propose methods for analyzing the distribution of human activity space from both individual and urban perspectives based on mobile phone data. The Weibull distribution is utilized to model three predefined measurements of activity space (radius, shape index, and entropy). The correlation between demographic factors (age and gender) and the usage of urban space is also tested to reveal underlying patterns. The results of this research will enhance the understanding of human activities in different urban systems and demographic groups, as well as providing novel methods to expand the important and widely applicable area of geographic knowledge discovery in the age of instant access.

This paper proposes a methodology for using mobile telephone-based sensor data for detecting spatial and temporal differences in everyday activities in cities. Mobile telephone-based sensor data has great applicability in developing urban monitoring tools and smart city solutions. The paper outlines methods for delineating indicator points of temporal events referenced as ‘midnight’, ‘morning start’, ‘midday’, and ‘duration of day’, which represent the mobile telephone usage of residents (what we call social time) rather than solar or standard time. Density maps by time quartiles were also utilized to test the versatility of this methodology and to analyze the spatial differences in cities. The methodology was tested with data from cities of Harbin (China), Paris (France), and Tallinn (Estonia). Results show that the developed methods have potential for measuring the distribution of temporal activities in cities and monitoring urban changes with georeferenced mobile phone data.

The rapid development of information and communication technologies (ICTs) has provided rich data sources for analyzing, modeling, and interpreting human mobility patterns. This paper contributes to this research area by developing the Spatio-temporal Edit Distance measure, an extended algorithm to determine the similarity between user trajectories based on call detailed records (CDRs). We improve the traditional Edit Distance algorithm by incorporating both spatial and temporal information into the cost functions. The extended algorithm can preserve both space and time information from string-formatted CDR data. The novel method is applied to a large data set from Northeast China in order to test its effectiveness. Three types of analyses are presented for scenarios with and without the effect of time: (1) Edit Distance with spatial information; (2) Edit Distance with time as a factor in the cost function; and (3) Edit Distance with time as a constraint in partitioning trajectories. The outcomes of this research contribute to both methodological and empirical perspectives. The extended algorithm performs well for measuring low-resolution tracking information in CDRs, as well as facilitating the interpretation of user mobility patterns in the age of instant access.

The spatio-temporal characteristics of eye movements vary according to the activity the user of a cartographic map is performing. In this paper, we use these eye movement characteristics to automatically detect the map user's activity, an approach with great potential in gaze-assistive map interfaces. A dataset of 587 eye movement recordings from 17 participants was used to train and cross-validate a support vector machine (SVM) classifier over 229 features. The classifier can distinguish 6 common map activities with an accuracy of approx. 78%.

Exploratory search, in which a user investigates complex concepts, is cumbersome with today's search engines. We present a new exploratory search approach that generates interactive visualizations of query concepts using thematic cartography (e.g. choropleth maps, heat maps). We show how the approach can be applied broadly across both geographic and non-geographic contexts through explicit spatialization, a novel method that leverages any figure or diagram -- from a periodic table, to a parliamentary seating chart, to a world map -- as a spatial search environment. We enable this capability by introducing explanatory semantic relatedness measures. These measures extend frequently-used semantic relatedness measures to not only estimate the degree of relatedness between two concepts, but also generate human-readable explanations for their estimates by mining Wikipedia's text, hyperlinks, and category structure. We implement our approach in a system called Atlasify, evaluate its key components, and present several use cases.

Mobile intention recognition is the problem of inferring a mobile user's intentions from her behavior in geographic space. Such behavior is constrained in space and time. Current approaches, however, have difficulties to handle temporal constraints. We therefore propose using the framework of time geography to formalize and visualize both spatial and temporal constraints for the mobile intention recognition problem. A new rule language is introduced which allows for modeling intentions with spatial and temporal constraints. A location-based game application demonstrates that interpreting a user's spatio-temporal behavior sequence in terms of intentions reduces ambiguity compared to mobile intention recognition without temporal constraints.

An abundance of geospatial information is flourishing on the Internet but mining and disseminating these data is a daunting task. With anything published on the web available to the public it has become a grand repository of volunteered geographic information (VGI). Internet users often provide location information for videos, pictures, travel destinations, or other events. All of these data can be gathered by a web crawling geospatial agent that later performs geospatial data mining. The discovered geoinformation can be stored, analyzed, queried, and visualized as the agent creates a data repository of what it discovered. This paper presents the design and prototypical implementation of the GEDMWA (Geospatial Exploratory Data Mining Web Agent). It reads webpage data and follows links to acquire knowledge in order to add value to geoinformation usable in a GIS. The agent creates a database of webpage text, mines it for location information, and then converts it to proper geospatial data format. The data is quickly visualized and analyzed after GEDMWA converts it into proper GIS and virtual globe formats. This provides diverse user communities a tool that utilizes a variety of distributed sources to discover additional knowledge about their fields of interest.

Ad-hoc shared-ride trip planning is a planning task on a non-deterministic transportation network. We propose to solve this task in a mobile geosensor network, which consists of transportation clients and hosts. In a mobile geosensor network the communication costs are a critical factor. Trip planning agents need communication to collect knowledge about the current network, and any way to limit this need reduces the costs of a solution. This paper introduces a theoretical model based on time geography, where clients, as trip planning agents, can actively identify relevant transportation hosts before communication starts, and hosts can identify whether their route is relevant for a specific planning task before responding to any request. This model reduces the communication costs significantly, which is at first derived theoretically, and then confirmed by an example.

Agent-based systems are used to simulate the behaviour of people in various spatial environments. Designers of such systems need to give attention to ontological and epistemological concerns during the early stages of development to assure that the agent-based system is built upon a sound foundation. Only then is it possible to model the agent's behaviour in a cognitively plausible way. This paper describes an ecological approach to model ontology and epistemology for agent-based wayfinding simulation. The ontology of the wayfinding environment is based on a subdivision into medium, substances, and surfaces. The epistemological model uses the concept of affordances, which we divide into physical, social-institutional, and mental affordances. Ontology and epistemology are both grounded in people's descriptions of their wayfinding experiences. A case study of wayfinding in airports is used to demonstrate the applicability of the method for designing a simulation with a cognizing agent.