Urban flooding is a major concern in many cities around the world. Together with continuous urbanization, extreme weather events are likely to increase the magnitude and frequency of flood hazards and exposure in populated regions. This article examines the changing pathways of flood risk management (FRM) in Portland, Oregon; Seoul, South Korea; and Tokyo, Japan, which have different histories of land development and flood severity. We used city governance documents to identify how FRM strategies have changed in the study cities. Using a combined framework of social learning with an integrated social–ecological–technological systems (SETS) lens, we show what components of SETS have been emphasized and how FRM strategies have diversified over time. In response to historical flood events, these cities built hard infrastructure such as levees to reduce flood risks. The recent paradigm shift in urban FRM, such as the adoption of socioecological elements in SETS, including floodplain restoration, green infrastructure, and public education, is a response to making cities more resilient or transformative to the anticipated future extreme floods. The pathways that cities have taken and the main emphasis across SETS elements differ by city, however, suggesting that opportunities exist for learning from each city’s experience collectively to tackle global flooding issues.

Many countries have started to reopen economic activities after the COVID-19 pandemic; however, due to the disease’s long incubation period and high infectivity, social distancing remains an essential measure despite the start of vaccinations. It is therefore necessary to understand the changes in human mobility from the outbreak’s initiation to deceleration to control the disease’s transmission and revitalize the economy. This study suggests a methodology for investigating the changes in human mobility and their influencing factors using a case study of Seoul Metropolitan City, Korea. First, the changing patterns of human mobility were investigated based on mobile big data, including the acceleration and deceleration stages. The results showed that it varied by area and travel distance. Second, we clarified the influence of sociodemographic factors such as employee density and land use proportion on the change pattern of human mobility by applying a machine learning method. This finding implies that the effectiveness of policies such as social distancing can vary with sociodemographic factors. For example, areas with more real estate, public administration, and health care employees showed rapid recovery and faced a transmission risk by reopening economic activities. The suggested methodology can help understand human mobility and explore exit strategies.

Land-Use Mix (LUM) refers to the strategy of integrating complementary functions within a building or area. While LUM has become a dominant approach in urban planning, its actual benefits and vision for spatial planning remain unclear. To clarify this issue, this study discerns the spatial features of land-use patterns depending on the compatibilities among land-use categories. Accordingly, this study introduces three LUM measures – adjacency, intensity, and proximity – to identify differences in the spatial distribution of land-use categories. Based on these measures, a land-use allocation model is developed to specify spatial patterns satisfying the given compatibilities. This model is tested by applying the concept of the neighborhood unit on a case study of normative land-use patterns subject to specified compatibilities. The results describe spatial features of four compatibility sets, including a set exhibiting a compatibility conflict between the same land-use pair and LUM measures when, for example, a given land-use pair is compatible in terms of intensity but incompatible in terms of proximity. Understanding the spatial features of a normative land-use pattern that satisfies various possible compatibilities will facilitate the incorporation of the LUM approach into local planning guidance and zoning ordinances.

Monitoring pedestrian activities and understanding influential factors such as weather conditions and special events are important issues for urban planners who endeavor to foster a vital and sustainable urban environment. This study quantified pedestrian activity by developing pedestrian distribution indexes (PDIs) for the aspects of volume, space, and time, to clarify how PDIs were affected by variations in situational factors related to weather and special events. Accordingly, the PDIs were related by volume and spatial and temporal inequalities to capture pedestrian activities in designated neighborhoods/districts, based on pedestrian count data from multiple locations. The specific effects of weather and special events were investigated based on the PDIs. The results confirmed that the indexes can be used to distinguish between spatial and temporal pedestrian distributions, even when total numbers are equal, thus providing critical information for planners. Furthermore, weather conditions were shown to influence pedestrian quantities, as well as their spatial and temporal distributions. Indeed, temperature and wind speed exacted non-linear effects on quantity and spatial inequality. Additionally, pedestrian distributions varied according to event type. Most events increased pedestrian numbers and expanded their coverage within downtown areas. However, these effects were limited to specific time periods. Thus, this study contributes to incorporating pedestrian distributions into spatial planning efforts by quantifying and evaluating the impacts of special events.