A Framework for Scaling Urban Transformative Resilience Through Utilizing Volunteered Geographic Information

Understanding transformative capacity to boost urban climate adaptation: A Semi-Systematic Literature Review

Transformative capacity (TC) is key for addressing climate change impacts. It refers to urban areas' ability for profound and intentional change to address current challenges and move towards a more desirable and resilient state. However, its varied applications across disciplines can lead to misunderstandings and implementation challenges. Thus, this Semi-Systematic Literature Review (SSLR) on TC within urban studies from 2016 to 2022 aims to overview and synthesise TC literature and its gaps to inform ongoing debates, intersecting it with climate-related research. The results show an increasing interest in TC within two fields of knowledge: resilience studies and transformative research. The review found TC as a catalyst for transformative actions, promoting sustainable pathways, enhancing resilience, and driving fundamental changes in urban climate adaptation. Finally, the prevailing literature gaps concern the TC concept's fragmentation, excessive research on governance features, and lack of joint research about TC and innovation.

Identifying Evacuation Needs and Resources Based on Volunteered Geographic Information: A Case of the Rainstorm in July 2021, Zhengzhou, China

Recently, global climate change has led to a high incidence of extreme weather and natural disasters. How to reduce its impact has become an important topic. However, the studies that both consider the disaster's real-time geographic information and environmental factors in severe rainstorms are still not enough. Volunteered geographic information (VGI) data that was generated during disasters offered possibilities for improving the emergency management abilities of decision-makers and the disaster self-rescue abilities of citizens. Through the case study of the extreme rainstorm disaster in Zhengzhou, China, in July 2021, this paper used machine learning to study VGI issued by residents. The vulnerable people and their demands were identified based on the SOS messages. The importance of various indicators was analyzed by combining open data from socio-economic and built-up environment elements. Potential safe areas with shelter resources in five administrative districts in the disaster-prone central area of Zhengzhou were identified based on these data. This study found that VGI can be a reliable data source for future disaster research. The characteristics of rainstorm hazards were concluded from the perspective of affected people and environmental indicators. The policy recommendations for disaster prevention in the context of public participation were also proposed.

A Meta-Overview and Bibliometric Analysis of Resilience in Spatial Planning - the Relevance of Place-Based Approaches

This study offers a literature review and bibliometric analysis aiming to enhance our understanding of the actual contribution of resilience approaches to spatial and territorial development and planning studies. Using citation link-based clustering and statistical text-mining techniques (in terms of prevalence of topics, over time, extraction of relevant terms, keywords frequencies), our study maps scientific domains that include the spatial dimension of resilience thinking. It offers a systematic assessment of modern approaches by connecting profoundly theoretical views to more instrumental and policy-oriented approaches. Firstly, the theoretical background of spatial resilience used in numerous studies in various fields is analysed from the viewpoint of the type of embedded resilience (engineering, ecological, social-ecological, economic, social etc.). Secondly, we review and discuss the significance of three main and consistent research directions in terms of different scales and political/institutional contexts that matter from the viewpoint of spatial and territorial planning. Our findings show that spatial resilience debates are far from being settled, as according to many scientists, resilience measurements are often based on technical-reductionist frameworks that cannot comprehensively reflect the complex systems and issues they address. Our conclusions highlight the necessity of a harmonized framework and integrated perspective on resilience in sustainable territorial planning and development, in both theoretical and empirical contexts.

A Fine-Grain Batching-Based Task Allocation Algorithm for Spatial Crowdsourcing

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing.