ISPRS International Journal of Geo-Information

The deployment volume of urban surveillance cameras has reached hundreds of thousands or even millions with the advancement of intelligent transportation systems (ITSs), indicating an enormous scale. However, the number of small-field-of-view surveillance cameras in large-scale traffic areas is insufficient to achieve full coverage of urban traffic zones. In the fields of ITSs, this study proposes a traffic information-based driving route inference method to clarify target vehicles’ paths in zones with monitoring blind spots and enhance the collaborative capability between surveillance cameras and traffic networks. First, this study maps traffic roads containing monitoring blind spots and their topologies into Bayesian network (BN) structures. The influencing factors of the target vehicle path can be analyzed, extracted, and quantified by the known data in a traffic network. A weight analysis method is utilized to estimate the weight coefficients of the influencing factors on the basis of the traditional BN model, thereby realizing the driving routes based on traffic networks. This study conducted experiments in Xinbei District, Changzhou City, and Jiangsu Province, China. Experimental results verify that the proposed method can accurately infer and reconstruct driving routes with monitoring blind zones. This method can provide theoretical support for analyzing driving directions at complex traffic intersections and enabling driving route inference in traffic network areas with monitoring blind spots.

With the deepening of population ageing, the spatial planning of an elderly care service system faces unprecedented challenges. Building an elderly care service network that aligns with the pace of population ageing has become increasingly important and urgent. Based on annual longitudinal data on older adults’ health status and care service utilization from Japan’s Long-Term Care Insurance (LTCI) system, this study quantifies the relationship between changes in health status and elderly care service demand using a discrete time homogeneous Markov model and Poisson regression analysis. Subsequently, Geographic Information System (GIS) techniques are applied to conduct spatial analysis of the urban built environment to identify living service centres for older adults. Indicators including distance, supply–demand balance, and service capacity are then integrated through multi-objective clustering optimization to construct a multi-level elderly care service network system, achieving a quantitative linkage between elderly health status and spatial demand-oriented planning. Finally, the proposed integrated framework, which combines health status transitions, service demand estimation, and spatial allocation, is applied to Qinhuai district in Nanjing, China, generating practical policy recommendations that promote the integration of healthy ageing and precision service delivery.

Implementing effective geospatial data discovery mechanisms in geoportals is crucial for facilitating easy access to geospatial data and services. Despite existing efforts to formulate geoportal design requirements, understanding end-user issues beyond a single geoportal in the context of geodata discovery is still lacking. To address this gap, this study reports on a usability study conducted in Germany and Namibia, with the aim of examining issues faced by users during geodata search and discovery. The study employed a mixed-method approach combining Retrospective Think-Aloud (RTA) interviews and structured questionnaires. The results reveal key usability issues, including inefficient search mechanisms, inefficient presentation of search results, lack of user guidance, inefficient map interactions, and inefficient metadata descriptions. Additionally, the study revealed a difference in user perceptions regarding user experience aspects between the two user groups. The findings are of interest to the designers of geoportals in the context of open data reuse and spatial data infrastructure.