Transactions in Urban Data,Science,and Technology

In recent years, pandemics have become one of the most significant challenges due to their huge socio-economic impacts. Fortunately, smart technologies have provided new ideas to fight against them. Many studies have focused on analyzing particular technologies applied in pandemics, but few have systematically discussed the difference and the relationship among multiple perspectives. China is well represented in the development of technologies and pandemic responses. Therefore, this paper uses China’s response to COVID-19 as an empirical study to systematically review the application of smart technologies and build a case base from multiple perspectives. A total of 1,102 cases from 14 technologies were collected from January 2020 to June 2020 after screening, and a series of analyses were conducted in terms of types, scales, stages, and targets. The result shows various subjects participated in pandemic responses using smart technologies. General technologies such as Big Data and Mobile Internet are most widely used. Besides, most technologies are used on the country or district/city scales and focus on the prevention and control of pandemics. There are significant differences in the penetration of technologies among different perspectives. We hope to provide a reference for applying smart technologies against pandemics in the future.

Despite many studies on trip inference using call detail record (CDR) data, a fundamental understanding of their limitations is lacking. In particular, because of the sparse nature of CDR data, users may travel to a location without being revealed in the data, which we refer to as a hidden visit. The existence of hidden visits hinders our ability to extract reliable information about human mobility and travel behavior from CDR data. In this study, we propose a data fusion approach to obtain labeled data for statistical inference of hidden visits. In the absence of complementary data, this can be accomplished by extracting labeled observations from more granular cellular data access records, and extracting features from voice call and text messaging records. The proposed approach is demonstrated using a real-world CDR dataset of 3 million users from a large Chinese city. Different machine learning models are used to infer whether a hidden visit exists during an observed displacement. The test results show significant improvement over the naive no-hidden-visit rule, which is an implicit assumption adopted by most existing studies. Based on the proposed model, we estimate that over 10% of the displacements extracted from CDR data involve hidden visits.

The severe population ageing has rapidly increased the demand for urban elderly care services in most countries. As a novel urban elderly care mode, community-embedded elderly care facilities integrate various functions and allow older urban adults to enjoy comprehensive care services in a familiar environment at an acceptable cost. Therefore, it is widely recognised as an effective way to resolve the contradiction between the increasing demand and limited supply capacity of elderly care services in large cities. However, spatial analysis of elderly care facilities in previous studies were focused on static characters, ignoring the evolution process. The traditional static analysis methods might be one-sided for the spatial analysis of community-embedded elderly care facilities, considering their highly dynamic development. This study considers Beijing as a case study and establishes a novel spatiotemporal analytical framework, augmented by big data, to analyse the spatial distribution of the local community-embedded elderly care facilities (elderly stations) from a dynamic view. The multi-source data regarding elderly stations, the elderly population and basic geographic information of Beijing were extracted and integrated into the analysis. On this basis, the proposed framework was applied to examine the distribution tendency, evolution trend and accessibility of elderly stations from 2017 to 2020. The results reveal a significant cluster development characteristic of elderly stations. Although the density of elderly stations in the downtown area is much higher than that in the urban periphery, the elderly stations might still be unable to satisfy the enormous elderly care demand in Xicheng and Dongcheng districts. Moreover, the imbalance between the urban centre and peripheries and the spatial mismatch between the elderly stations and population were identified. The research output could support the planning practice of elderly stations for relevant departments.