The precise recognition of urban fringes is vital to monitor urban sprawl and map urban management planning. The spatial clustering method is a prevalent way to identify urban fringes due to its objectivity and convenience. However, previous studies had problems with ignoring spatial heterogeneity, which could overestimate or underestimate the recognition results. Nighttime light can reflect the transitional urban–rural regions’ regional spatial characteristics and can be used to identify urban fringes. Accordingly, a new model has been established for urban fringe identification by combining spatial continuous wavelet transform (SCWT) and dual spatial clustering. Then, Nanjing City, China, as a case study, is employed to validate the model through the NPP/VIIRS nighttime light data. The identification of mutated points across the urban–rural gradient is conducted by utilizing the SCWT. By using dual spatial clustering in the urban fringe identification, it transmits the mutation points’ spatial patterns to the homogeneous spatially neighboring clusters effectively, which measures the similarity between mutation points regarding spatial and attribute domains. A comparison of the identified results by various spatial clustering approaches revealed that our method could be more suitable for the impacts of mutation points’ local spatial patterns on different density values over the whole density surface, thus leading to more accurate spatial boundaries featured by differentiating actual differences of mutation points between adjacent clusters.

Walkability is becoming increasingly important in urban planning, public health, and environmental protection. Traditional assessment tools like streetscape images and semantic segmentation focus on objective factors, while questionnaires as the main tool for perceived walkability are limited by cost and scale. This study introduces a new method using the Multimodal Contrastive Learning Model, CLIP, to assess perceived walkability by analysing both tangible and subjective factors such as safety and attractiveness. The method compares perceived with physical walkability by scoring street view images with a customized scale. Initial results indicate CLIP can identify pedestrian-friendly streetscapes that might score low on physical metrics. While its accuracy needs more evaluation, CLIP offers a cost-effective alternative without needing extensive labelled datasets. This method can be combined with objective pedestrian assessment methods to serve as reference information for various industries such as real estate, transportation planning, and tourism.

The Common Crawl (CC) corpus is the largest open web crawl dataset containing 9.5+ petabytes of data captured since 2008. The dataset is instrumental in training large language models, and as such it has been studied for (un)desirable content, and distilled for smaller, domain-specific datasets. However, to our knowledge, no research has been dedicated to using CC as a source of annotated geospatial data. In this paper, we introduce an efficient pipeline to extract annotated user-generated tracks from GPX files found in CC, and the resulting multimodal dataset with 1,416 pairings of human-written descriptions and MultiLineString vector data from the 6 most recent CC releases. The dataset can be used to study people's outdoor activity patterns, the way people talk about their outdoor experiences, as well as for developing trajectory generation or track annotation models, or for various other problems in place of synthetically generated routes. Our reproducible code is available on GitHub: https://github.com/ilyankou/cc-gpx.

Global positioning system (GPS) trajectories recorded by mobile phones or action cameras offer valuable insights into sustainable mobility, as they provide fine-scale spatial and temporal characteristics of individual travel. However, the high volume, noise, and lack of semantic information in this data poses challenges for storage, analysis, and applications. To address these issues, we propose an end-to-end pipeline named CycleTrajectory for processing high-sampling rate GPS trajectory data from cyclists' action cameras, leveraging OpenStreetMap (OSM) for semantic enrichment. The methodology includes (1) Data Preparation, which includes filtration, noise removal, and resampling; (2) Map Matching, which accurately aligns GPS points with road segments using the OSRM API; (3) OSM Data integration to enrich trajectories with road infrastructure details; and (4) Variable Calculation to derive metrics like distance, speed, and infrastructure usage. Validation of the map matching results shows an error rate of 5.64%, indicating the reliability of this pipeline. This approach enhances efficient GPS data preparation and facilitates a deeper understanding of cycling behavior and the cycling environment.

This study examines cycling near misses in London using naturalistic data collection methods. Sixty commute cyclists were equipped with helmet-mounted 360-degree cameras and GPS devices to record their journeys over two weeks. Participants verbally reported near misses as they occurred, with 94 events recorded across 317 h of footage. Close passes were the most common near miss type (25 events), followed by near left/right hooks (22 events) and pulling out incidents (17 events). Most near misses occurred during peak hours and on roads without cycling infrastructure. Poisson regression modelling revealed that higher cycling speeds, time spent on shared lanes, and not having previous solo crashes were associated with fewer near misses, while time spent on 30mph roads and morning peak cycling increased near miss rates. The findings suggest that cycling infrastructure plays a protective role and highlight the need for targeted interventions to address close passes and junction conflicts.

Large language models (LLMs) exhibit emerging geospatial capabilities, stemming from their pre-training on vast unlabelled text datasets that are often derived from the Common Crawl (CC) corpus. However, the geospatial content within CC remains largely unexplored, impacting our understanding of LLMs' spatial reasoning. This paper investigates the prevalence of geospatial data in recent Common Crawl releases using Gemini 1.5, a powerful language model. By analyzing a sample of documents and manually revising the results, we estimate that 18.7% of web documents in CC contain geospatial information such as coordinates and addresses; this percentage varies only slightly between Enlgish- and non-English-language documents. Our findings provide quantitative insights into the nature and extent of geospatial data in CC, and complement existing studies on geospatial knowledge and biases of LLMs.