Tokenization is a fundamental process that feeds data into artificial intelligence (AI) models by breaking data into discrete, learnable units known as tokens. For instance, tokenizing text supports GPT in understanding semantic and contextual relationships within language. However, geospatial tokenization, the process of transforming spatial-temporal data into discrete tokens for geographic modeling, remains significantly underexplored, which has constrained the spatial reasoning capabilities of AI models. This article presents SPOK, a geospatial tokenization approach that explicitly encodes spatial relationships into spatial tokens. Using urban space as an example, SPOK defines parcels delimitated by road links as the basic spatial tokens and employs dynamic location referencing to encode their relative spatial relationships. By embedding these spatial relationships into high-dimensional vectors, SPOK effectively queries and models interactions among spatial tokens in latent space, thereby enhancing spatial reasoning in urban environments. We evaluate SPOK through dynamic mobility flow prediction and the results demonstrate that SPOK can infer origin-destination (OD) patterns without prior spatial interaction data, outperforming baselines with up to 20% reduction in RMSE and 14% increase in R2. By offering how spatial relationships can be tokenized, this study would like to call for attention on geospatial tokenization to develop geospatial foundation models and GeoAI.

Graph theory effectively explains urban structures via street–street connectivity. However, systematic comparisons of street structures across cities remain challenging. This study employs graph convolutional networks (GCNs) to analyze street network structures. A two-branch GCN was used as the backbone to extract comparable features among street networks. The proposed approach was used to examine the structures of different urban road networks in a case study of polycentricity prediction across 298 Chinese cities. The model transformed approximately 4.5-million street segments into natural streets to create urban street graphs, which were subsequently analyzed to extract local and global embeddings. The extracted embeddings – with a portion labeled with a known urban polycentricity score – were used to predict the score for each city through a single-layer perceptron (SLP) model. Our results show consistency between the predicted polycentricity scores based on the derived street embeddings and those based on the population. Thus, the proposed GCN-based method can effectively predict the complexity and interconnection of street networks in different cities. This innovative integration of GCNs into urban studies demonstrates that deep learning techniques can analyze and comprehend the intricate patterns of street networks on a large scale.

Understanding population distribution has excellent applications for planning and provision of municipal services. This study aims to explore the space-time structure of population distribution with area-level mobile phone data. We discuss a kind of Bayesian hierarchical models, fitted by Markov chain Monte Carlo simulation, that combines the overall spatial pattern and temporal trends as well as the departures from these stable components. We carry out an empirical study in Shenzhen, China, using the area-level mobile phone users in 24 hours. The results indicate that the estimation of the overall spatial pattern is not deteriorated when using a sophisticated spatio-temporal model. The temporal trend exhibits a reasonable fluctuation during the study period. Then we apply two rules to detect areas showing unstable trends of population fluctuation based on the posterior probabilities of the space-time interactions. We also include the population statistics and indices for mixed-use to explore the spatial pattern of population fluctuation. Our findings confirm that the Bayesian spatio-temporal model can enhance the understanding of the space-time variability of population distribution using mobile phone data. Further research should examine the spatial nonstationary effects of explanatory factors on mobile phone-based population fluctuation.

Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips.

This work proposes Winglets, an enhancement to the classic scatterplot to better perceptually pronounce multiple classes by improving the perception of association and uncertainty of points to their related cluster. Designed as a pair of dual-sided strokes belonging to a data point, Winglets leverage the Gestalt principle of Closure to shape the perception of the form of the clusters, rather than use an explicit divisive encoding. Through a subtle design of two dominant attributes, length and orientation, Winglets enable viewers to perform a mental completion of the clusters. A controlled user study was conducted to examine the efficiency of Winglets in perceiving the cluster association and the uncertainty of certain points. The results show Winglets form a more prominent association of points into clusters and improve the perception of associating uncertainty.

Sampling fraction is crucial to sampling-related studies and applications, especially in the big data era when most data are neither originally designed nor controllable in the data collection process. A common concern among researchers is ‘what’s the modelling accuracy when using a sample?’. Taking intra-city human mobility as the study objective, this study utilizes a simple and direct method to analyse the influences of various sampling fractions on modelling accuracy. Five common intra-city human mobility indicators (travel distance, travel time, travel frequency, radius of gyration and movement entropy) are evaluated considering mean value, median and probability distribution. Experimental results demonstrate that the representativeness of each considered indicator converges to 1 in its own unique rate and variances. The minimum required sampling fractions to satisfy specific accuracies differ for various indicators and evaluation measures. To further investigate how related factors influence the modelling accuracy of sampling fractions, additional experiments are conducted considering multiple sampling methods, study scopes, and data sources. Several interesting general findings are observed. This study provides a reference for other sampling-based applications.

Understanding urban functions and their relationships with human activities has great implications for smart and sustainable urban development. In this study, we present a novel approach to uncovering urban functions by aggregating human activities inferred from mobile phone positioning and social media data. First, the homes and workplaces (of travelers) are estimated from mobile phone positioning data to annotate the activities conducted at these locations. The remaining activities (such as shopping, schooling, transportation, recreation and entertainment) are labeled using a hidden Markov model with social knowledge learned from social media check-in data over a lengthy period. By aggregating identified human activities, hourly urban functions are inferred, and the diurnal dynamics of those functions are revealed. An empirical analysis was conducted for the case of Shenzhen, China. The results indicate that the proposed approach can capture citywide dynamics of both human activities and urban functions. It also suggests that although many urban areas have been officially labeled with a single land-use type, they may provide different functions over time depending on the types and range of human activities. The study demonstrates that combining different data on human activities could yield an improved understanding of urban functions, which would benefit short-term urban decision-making and long-term urban policy making.

Although mixed use is an emerging strategy that has been widely accepted in urban planning for promoting neighbourhood vibrancy, there is no consensus on how to quantitatively measure the mix and the effects of mixed use on neighbourhood vibrancy. Shannon entropy, the most commonly used diversity measurement in assessing mixed use, has been found to be inadequate in measuring the multifaceted, multidimensional characteristics of mixed use. And lack of data also makes it difficult to find the relationship between mixed use and neighbourhood vibrancy. However, the recent availability of new sources including mobile phone data and Point of Interest (POI) data have made it possible to develop new indices of mixed use and neighbourhood vibrancy to analyse their relationships. Taking advantage of these emerging new data sources, this study used the numbers of mobile phone users in a 24-hour period as a proxy of neighbourhood vibrancy and used POIs from a navigation database to develop a series of mixed-use indicators that can better reflect the multifaceted, multidimensional characteristics of mixed-use neighbourhoods. The Hill numbers, a unified form of diversity measurement used in ecological literature that includes richness, entropy, and the Simpson index, are used to measure the degrees of mixed use. Using such fine-grained data sets and the Hill numbers allowed us to obtain better insights into the relationship between mixed use and neighbourhood vibrancy. Four models varying in POI measurements that reflect different dimensions of mixed use were presented. The results showed that either POI density or entropy can explain approximately 1% of neighbourhood vibrancy, while POI richness contributes significantly in improving neighbourhood vibrancy. The results also revealed that the entropy has limitations as a measure for representing mixed use and demonstrated the necessity of adopting a set of more appropriate measurements for mixed use. Increasing the number of POIs has limited power to improve neighbourhood vibrancy compared with encouraging the mixing of complementary POIs. These exploratory findings may be useful for adjusting mixed-use assessments and to help guide urban planning and neighbourhood design.

Multilevel road networks such as grade-separated interchanges and elevated roads have been increasingly used to solve traffic congestion in large cities. When navigating a vehicle in a multilevel road network, identifying the location of the vehicle in different road levels is of equal importance to identifying its planar location, particularly for overlapping and parallel roads. Although they can be represented and visualized in the existing navigation system, at present, it is difficult to guide a vehicle through such a multilevel road network because the existing vehicle positioning system uses consumer-grade GPS, and the transport geographic information system (GIS-T) database is mainly 2-D-based. The location of a vehicle on diffrent road levels in multilevel road networks is often overlooked. This paper examines the deficiency of existing approaches in supporting vehicle navigation in multilevel road networks with consumer-grade GPS. It proposes to use an angle difference method that compares the vehicle pitch angle with the inclination angles of different road levels calculated from road elevations stored in the proposed GIS-T database to snap the vehicle to the appropriate road level when the vehicle is entering or exiting a multilevel road network. The angle difference method is implemented based on consumer-grade assisted GPS (A-GPS) and onboard vehicle pitch angle measurement with smartphone. Experiment results prove that the angle difference method have high accuracy in determining the road level when the vehicle is driving in a parallel multilevel road network.

Data collected from mobile phone have potential knowledge to provide background information of a mobile phone user, such as work location, home location, job occupation, income, consumption and even lifestyle etc., which are quite valuable to many location-aware applications. In the existing research, there is relatively few commercial software or application systems to fully meet the requirements of effectively mining these personal behavioral characteristics. In the paper, we propose approaches to analyzing personal activity characteristics and mining behavioral regularity from mobile phone location information, automatically generating some semantic labels by integrating mobile phone log data with map data and web data, and location prediction for personalized advertising services. We use actual mobile phone data to perform the functions for discovering background information and demonstrate effectiveness of our approaches.

Lane-based road network information, such as lane geometry, destination, lane changing, and turning information, is important in vehicle navigation, driving assistance system, and autonomous driving. Such information, when available, is mainly input manually. However, manual methods for creating and updating data are not only costly but also time-consuming, labor-intensive, and prone to long delays. This paper proposes a hierarchical polygonization method for automatic generation and updating of lane-level road network data for navigation from a road marking database that is managed by government transport department created by digitizing or extraction from aerial images. The proposed method extends the hierarchy of a road structure from ‘road–carriageway–lane’ to ‘road–carriageway–lane–basic lane’. Basic lane polygons are constructed from longitudinal road markings, and their associated navigational attributes, such as turning information and speed limit, are obtained from transverse road markings by a feature-in-polygon overlay approach. A hierarchical road network model and detailed algorithms are also illustrated in this paper. The proposed method can accelerate the process of generating and updating lane-level navigation information and can be an important component of a road marking information system for road management.

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.