With the growing challenge of aging populations around the world, the study of the care services for older adults is an essential initiative to accommodate the particular needs of the disadvantaged communities and promote social equity. Based on open-source data and the geographic information system (GIS), this paper quantifies and visualizes the imbalance in the spatial distribution of elderly care facilities in 14,578 neighborhoods in downtown (seven districts) Shanghai, China. Eight types of elderly care facilities were obtained from Shanghai elderly care service platform, divided into two categories according to their service scale. With the introduction of the improved Gaussian 2-step floating catchment area method, the accessibility of two category facilities was calculated. Through the global autocorrelation analysis, it is found that the accessibility of elderly care facilities has the characteristics of spatial agglomeration. Local autocorrelation analysis indicates the cold and hot spots in the accessibility agglomeration state of the two types of facilities, by which we summarized the characteristics of their spatial heterogeneity. It is found that for Category−I, there is a large range of hot spots in Huangpu District. For Category−II, the hot-spot and cold-spot areas show staggered distribution, and the two categories of hot spot distribution show a negative correlation. We conclude that the two categories are not evenly distributed in the urban area, which will lead to the low efficiency of resource allocation of elderly care facilities and have a negative impact on social fairness. This research offers a systematic method to study urban access to care services for older adults as well as a new perspective on improving social fairness.

Assessing street walkability is a critical agenda in urban planning and multidisciplinary research, as it facilitates public health, community cohesion, and urban sustainability. Existing evaluation systems primarily focus on objective measurements, often neglecting subjective assessments and the diverse walking needs influenced by different urban spatial elements. This study addresses these gaps by constructing a comprehensive evaluation framework that integrates both subjective and objective dimensions, combining three neighbourhood indicators: Macro-Scale Index, Micro-Scale Index, and Street Walking Preferences Index. A normalization weighting method synthesizes these indicators into a comprehensive index. We applied this framework to assess the street environment within Beijing’s Fifth Ring Road. The empirical results demonstrate that: (1) The framework reliably reflects the distribution of walkability. (2) The three indicators show both similarities and differences, underscoring the need to consider the distinct roles of community and street-level elements and the interaction between subjective and objective dimensions. (3) In high-density cities with ring-road development patterns, the Macro-Scale Index closely aligns with the Comprehensive Index, demonstrating its accuracy in reflecting walkability. The proposed framework and findings offer new insights for street walkability research and theoretical support for developing more inclusive, sustainable and walkable cities.

Urban fringe areas, characterized by relatively larger community sizes and lower population densities compared to central areas, may lead to variations in walkability as well as gender differences, such as safety perception. While objective measurements have received considerable attention, further research is needed to comprehensively assess subjective perceptions of walking in the urban periphery. As a case study, we evaluated survey responses of community perceptions of “Imageability”, “Enclosure”, “Human scale”, “Complexity” and “Safety” of Shanghai’s five new towns, comparing these with responses from the central area in terms of gender difference, and analyzed influencing factors and prediction performance of machine learning (ML) models. We developed a TrueSkill-based rating system to dynamically collect audits of street view images (SVIs) from professional students and used the result to integrate with Geographic Information Systems (GIS), Computer Vision (CV), Clustering analysis, and ML algorithm for further investigation. Results show that most of the new towns’ communities are perceived as moderately walkable or higher, with the city center’s community exhibiting the best walkability perceptions in general. Male and female perceptions of the “Human scale” and the factors that affect it differ little, but there are significant disparities in the other four perceptions. The best-performing ML models were effective at variable explanations and generalizations, with Random Forest Regression (RFR) performing better on more perception predictions. Responses also suggest that certain street design factors, such as street openness, can positively influence walkability perceptions of women and could be prioritized in new town development and urban renewal for more inclusive and walkable cities.

Half the world’s population now lives in cities, and this figure is expected to reach 70% by 2050. To ensure future cities offer equity for multiple age groups, it is important to plan for spatially inclusive features such as pedestrian accessibility. This feature is strongly related to many emerging global challenges regarding health, an ageing population, and an inclusive society, and should be carefully considered when designing future cities to meet the mobility requirements of different groups of people, reduce reliance on cars, and encourage greater participation by all residents. Independent travel to public open spaces, particularly green spaces, is widely considered a key factor that affects human health and well-being and is considered a primary motivation for walking. At the same time, unfavourable steepness and restrictive access points to open spaces can limit accessibility and restrict the activities of older adults or people with mobility impairments. This paper introduces a novel open access proximity modelling web application, PedestrianCatch, that simulates pedestrian catchments for user-specified destinations utilising a crowd-source road network and open topographic data. Based on this tool, we offer a multi-criteria evaluation approach that considers travel speed, time, urban topography, and visualisation modes to accommodate various simulation needs for different urban scenarios. Two case studies are conducted to demonstrate the technical feasibility and flexibility using the proposed evaluation approach, and explain how new renewal strategies can be tested when designing a more inclusive neighbourhood. This evaluation tool is immediately relevant to urban designers, health planners, and disability communities, and will be increasingly relevant to the wider community as populations age, while the corresponding analysis approach has a huge potential to contribute to the pre-design and design process for developing more walkable and accessible communities for all.

The rapid expansion of online learning platforms presents both challenges and opportunities for educational institutions. Online learning analytics has emerged as a critical tool to derive insights from student learning behaviours, offering flexible educational access and promoting lifelong learning. This paper provides a comprehensive review of recent advancements in online learning analytics, with a focus on personalized and adaptive learning systems. We systematically analyze the co-occurrence of keywords, identifying key research clusters that highlight the evolution of methodologies, particularly in areas such as natural language processing and multimodal learning analytics. Furthermore, we explore contributions from leading authors, institutions, and countries, demonstrating the global collaboration driving this field. The paper emphasizes the implications of these advancements for educators and instructional designers, suggesting that integrating AI-driven, data-rich systems into educational environments will enhance personalized learning experiences. Our findings highlight emerging trends, including the use of sophisticated algorithms for predictive analytics, learner behavior modeling, and adaptive educational frameworks. These trends suggest future research opportunities to address the challenges of personalized education, aiming to inspire ongoing innovation in both the academic and practical domains of online learning analytics.

With population ageing being a notable demographic phenomenon, aging in place is an efficient model to accommodate the mounting aging needs. Based on the community scale, this study takes the 15-min community-life circle as the basic research unit to investigate the imbalanced distribution of pension resources and its influencing factors in downtown Shanghai. We obtained six types of elderly care facilities data from the Shanghai elderly care service platform and utilized the Gaussian 2-step Floating Catchment Area method to calculate the accessibility of 6-type elderly care facilities. Then, we used the Entropy Weight Method to calculate the comprehensive accessibility of elderly care facilities. The Getis–Ord Gi* method was adopted to analyze the overall distribution, identifying the well-developed and the under-developed areas. To explore the influencing factors of the distribution, this paper obtained multi-source data to construct a total of 17 indicators and established a Random Forest model to identify the feature importance. With the selected eight factors, the Geographically Weighted Regression (GWR) model was applied to study the spatial heterogeneity of influencing factors, and the model showed a good performance with the AdjR² being 0.8364. The findings of this research reveal the following: (1) The distribution of six types of elderly care facilities is extremely uneven, with obvious spatial aggregation characteristics. Amongst the seven administrative regions, Huangpu District has the best accessibility to pension resources, while the resources in the other six regions are highly inadequate. (2) Essential influencing factors of the comprehensive accessibility of community-based elderly care facilities are accessibility of nursing institutions (positive), hotel density (positive), catering density (negative), education density (positive) and medical density (negative), while “rents”, “plot ratio” and “building density” have little impact on comprehensive accessibility. (3) The results of GWR revealed that the eight indicators are heterogeneous in space, all of which have bidirectional effects on comprehensive accessibility. By investigating the spatial distribution patterns and influencing factors of pension resources in Shanghai, this research could further contribute to establishing a sound community-based elderly care service system that improves older adults’ quality of life and promotes social fairness and justice.