Meixu Chen

A high-frequency city, born in the era of smart sensors and geo-big data, epitomises a self-organised urban form that sustains vitality, balances functions, and resilience, while confronting urban science with unprecedented complexity and heterogeneity. Differences in scale, morphology, and function across cities accumulate, reinforcing this rapidly evolving data landscape and shaping a dynamic, complex high-frequency urban system. To address this challenge, this study innovatively develops a framework for structural simplification and dimensionality reduction that combines perceptual hashing with the Hilbert curve, emphasising methodological novelty in bridging computational efficiency and spatial semantics. Through key feature extraction, spatial order mapping, and dimensional-size transformation, the framework distils essential information and progressively projects complex high-dimensional data onto two- and one-dimensional spaces. At its core, the framework introduces two novel forms of representation – Urban QR codes and Urban barcodes – that enable compact presentation and efficient comparison of urban spatial features. Validation using comprehensive nighttime light imagery shows that the framework not only preserves the core characteristics of spatial patterns during dimensionality reduction but also achieves high consistency with existing clustering studies. Processed imagery further demonstrates potential as a structural supplement for multimodal data integration, while the framework itself opens new avenues for cross-scale comparison and multi-city analysis of high-dimensional, high-frequency urban data.

Satellite imagery is often used to study and monitor Earth surface changes. The open availability and extensive temporal coverage of Landsat imagery has enabled changes in temperature, wind, vegetation and ice melting speed for a period of up to 46 years. Yet, the use of satellite imagery to study cities has remained underutilised, partly due to the lack of a methodological approach to capture features and changes in the urban environment. This notebook offers a framework based on Python tools to demonstrate how to batch-download high-resolution satellite imagery; and enable the extraction, analysis and visualisation of features of the built environment to capture long-term urban changes.

Urban environments play a crucial role in the design, planning, and management of cities. Recently, as the urban population expands, the ways in which humans interact with their surroundings has evolved, presenting a dynamic distribution in space and time locally and frequently. Therefore, how to better understand the local urban environment and differentiate varying preferences for urban areas has been a big challenge for policymakers. This study leverages geotagged Flickr photographs to quantify characteristics of varying urban areas and exploit the dynamics of areas where more people assemble. An advanced image recognition model is used to extract features from large numbers of images in Inner London within the period 2013–2015. After the integration of characteristics, a series of visualisation techniques are utilised to explore the characteristic differences and their dynamics. We find that urban areas with higher population densities cover more iconic landmarks and leisure zones, while others are more related to daily life scenes. The dynamic results demonstrate that season determines human preferences for travel modes and activity modes. Our study expands the previous literature on the integration of image recognition method and urban perception analytics and provides new insights for stakeholders, who can use these findings as vital evidence for decision making.