Impervious surface is an important factor for the ecological performance of the built environment, in particular for the water balance. Therefore, the rainwater drainage infrastructure of new housing developments is planned according to the expected amount of impervious surface and the resulting surface runoff. Drainage infrastructure could be overwhelmed, however, due to small, dispersed, and often overlooked increases in impervious surface cover, a process we refer to as “hidden urbanization.” There is some evidence that impervious surface cover in housing areas has increased significantly over decades, but is there also a gap between planning and implementation? In order to find out, we compared eight development plans (i.e., the legally binding documents that steer building in Germany) of low-density (single-family) housing with the actual status-quo extracted from 2016 orthophotos. All sites are located in Lower Saxony, Germany; four are close to major urban centers and four are in small municipalities. We then modeled the local water balance for the plans and status-quo and compared results. All sites but one showed a relative increase between 8 and 56% of impervious surface, comparing plans with status-quo. For all sites with an increase of impervious cover, infiltration rates decreased by 4–19%, evaporation rates increased by 0.2–1% and surface runoff increased by 4–18%. In general, the more impervious surface, the stronger the effect. Our results point to a gap between planning and implementation and they underline the environmental consequences, illustrated by effects on the water balance. In order to prevent “hidden urbanization,” we suggest that more emphasis should be put on integrated design of housing areas and monitoring of impervious surface cover.

The COVID-19 pandemic has spread rapidly all over the world, affecting many countries to varying degrees. In this study, an in-depth analysis of the factors influencing the spread of COVID-19 is offered mainly through big data in the European Union (EU) context. In doing so, the data of the first wave of the pandemic are assessed. Afterward, we evaluate the impacts of the COVID-19 spread in specific countries and regions. Based on the existing literature, mobility is recognized as a significant direct factor affecting disease transmission. The same applies to the case of COVID-19. However, compared with the analysis of mobility itself, this paper explores more profound reasons that affect mobility, ranging from policy and economy to geographical and transportation factors. Specifically, this paper studies nine EU countries based on their population density and the degree of impact of the epidemic in the first six months (February to July 2020) of the pandemic. Our study aims to illustrate how policies, economies, and geographical locations (including transportation factors) directly or indirectly affect the spread of the novel coronavirus by applying the SEIR model to analyze all selected countries’ big data. The key findings of this research are: (1) the timeliness of relevant policies and the effectiveness of government implementation indirectly limit the spread of the epidemic by reducing population mobility; (2) a better medical level would contribute to detect, isolate, and treat patients, and help control the epidemic; and (3) the large land borders and developed transportation between countries exacerbate the spread of the COVID-19. The paper contributes to ongoing research on COVID-19 by addressing the above points.

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of ‘Urban population ratio’, ‘Retail sales of consumer goods’, ‘Number of tourists’, ‘Tourism Income’, ‘Ratio of the elderly population (> 60 year old) in this city’, ‘population density’, ‘Mobility scale (move in/inbound) during the spring festival’, ‘Ratio of Population and Health facilities’, ‘Jobless rate (%)’, ‘The straight-line distance from original epicenter Wuhan to this city’, ‘urban per capita GDP’, and ‘the prevalence of the COVID-19’. The study’s results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic.

Many years, urbanisation research has largely focused the development of urban agglomerations and megacity regions, whereas less attention was paid on the development of medium-sized cities, small towns, villages, or rural areas. Yet many interrelations and spatial linkages between urban and rural areas exist. In this paper, we present a novel method called ‘TOPOI’ for the integrated analysis and description of settlement units in an urban–rural setting. The TOPOI-method enhances the understanding of the built environment by clustering and describing settlement units of similar characteristics with view to their physical form, function, and connectivity. The method is built on known planning parameters, but does not limit the analysis of settlement units to their administrative boundaries. Based on 11 indicators, 13 TOPOI-classes were identified in two exemplary study regions revealing new insights into urban–rural settlement types. This allows a better understanding of urban–rural linkages and therefore opens up new pathways for a more sustainable development.

The spread of COVID-19 at a large scale and at a rapid pace indicates the lack of social distancing measures at multiple levels. The individuals are not to be blamed, nor should we assume the early measures were ineffective or not implemented. It is all down to the multiplicity of transmission factors that made the situation more complicated than initially anticipated. Therefore, in facing the COVID-19 pandemic, this overview paper discusses the importance of space in social distancing measures. The methods used to investigate this study are literature review and case study. Many scholarly works have already provided us with evidence-based models that suggest the influential role of social distancing measures in preventing COVID-19 community spread. To further elaborate on this important topic, the aim here is to look at the role of space not only at the individual level but at larger scales of communities, cities, regions, etc. The analysis helps better management of cities during the pandemics such as COVID-19. By reflecting on some of the ongoing research on social distancing, the study concludes with the role of space at multiple scales and how it is central to the practice of social distancing. We need to be more reflective and responsive to achieve earlier control and containment of the disease and the outbreak at the macro level.

Urban areas around the world are increasingly facing environmental challenges such as water scarcity, water pollution, and water-related disasters, which demands sustainable design solutions for cities. Efforts to introduce sustainable methods in architectural education are noteworthy since the early 1990s. However, Water Sensitive Design (WSD) has not been fully integrated to architectural education. WSD is an interdisciplinary approach that considers the water cycle as the primary element of design strategies, integrating the site’s ecological and social aspects to structure water management. The main objective of this study is to identify cases introducing WSD in an architecture design studio revealing its pedagogical approaches, comparing and discussing with a WSD-focused design studio. This study adapts on an exploratory and descriptive research, analyzing the literature on the topic of WSD in architectural education and documenting a graduate-level architectural design studio that proposes the development of water-oriented masterplan. The results suggest that WSD, as interdisciplinary method, can be incorporated into the design studio as the topic due to its tangible tools and strategies towards water. It also fits the proposal of a design studio to integrate knowledge from diverse disciplines. This unique study presents a comprehensive WSD introduction in an architectural design case and indicative pedagogical methods, contributing to the development of an approach for future related works.