In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

Accurate and prompt traffic data are necessary for the successful management of major events. Computer vision techniques, such as convolutional neural network (CNN) applied on video monitoring data, can provide a cost-efficient and timely alternative to traditional data collection and analysis methods. This paper presents a framework designed to take videos as input and output traffic volume counts and intersection turning patterns. This framework comprises a CNN model and an object tracking algorithm to detect and track vehicles in the camera’s pixel view first. Homographic projection then maps vehicle spatial-temporal information (including unique ID, location, and timestamp) onto an orthogonal real-scale map, from which the traffic counts and turns are computed. Several video data are manually labeled and compared with the framework output. The following results show a robust traffic volume count accuracy up to 96.91%. Moreover, this work investigates the performance influencing factors including lighting condition (over a 24-h-period), pixel size, and camera angle. Based on the analysis, it is suggested to place cameras such that detection pixel size is above 2343 and the view angle is below 22°, for more accurate counts. Next, previous and current traffic reports after Texas A&M home football games are compared with the framework output. Results suggest that the proposed framework is able to reproduce traffic volume change trends for different traffic directions. Lastly, this work also contributes a new intersection turning pattern, i.e., counts for each ingress-egress edge pair, with its optimization technique which result in an accuracy between 43% and 72%.

Urban traffic congestion and crashes have been considered by city planners as critical challenges to the economic development of the city. Traffic signal coordination, which connects a series of signals along an arterial by various coordination methodologies, has been proved as one of the most cost-effective means of reducing traffic congestion. In this regard, Metropolitan Planning Organizations (MPO) or Transportation Management Centers (TMC) have included signal timing coordination in their strategic plans. Nevertheless, concerns on the safety effects of traffic signal coordination have been continuously raised by both transportation agencies and the public. This is mainly because signal coordination may increase the travel speed along an arterial, which increases the risk and severity of traffic collisions. To date, there is neither solid evidence from the field to support the concern, nor theoretical-level models to analyze this issue. This research aims to investigate the effects of traffic signal coordination on the safety performance of urban arterials through microsimulation modeling of two traffic operational conditions: free signal operation and coordinated signals, respectively. Three urban arterials in Reno, Nevada were selected as the simulation testbed and were coded in the PTV VISSIM software. The simulated trajectory data were analyzed by the Surrogate Safety Assessment Model (SSAM) to estimate the number of traffic conflicts. Sensitivity analyses were conducted for various traffic demand levels. Results show that under unsaturated conditions, traffic signal coordination could reduce the number of conflicts in comparison with the free signal operation condition. However, under oversaturated conditions, no significant difference was found between coordinated and free signal operations. Findings from this research indicate that traffic signal coordination has the potential to reduce the risk of crashes on urban arterials under unsaturated conditions.

The spatial pattern and mechanism of human flow are of great significance for urban planning, economic development, transportation planning and so on. In this study, we used cell phone location data to represent the human flow network in Guangdong Province, China, using the 21 cities in Guangdong as “nodes” and the human flow intensity among them as “edges”. Then we explored macro and micro features of the human flow network, by using the index of degree distribution, alter-based centrality and alter-based power, respectively. Finally, we proposed a human flow estimation model which integrates individual urban characteristics, intercity links, and differences to further analyze the affecting factors of human flow. We found that the human flow network in this region is significantly scale-free, with Guangzhou, Shenzhen, Foshan, and Dongguan being the most important cities. We also found that the newly proposed model can explain the human flow in the study area, with an R ² of 0.914. Analysis results show that the factors of employment in tertiary sector, intercity internet attention, intercity differences in the number of tertiary workers, differences in population size, and distance have significant impacts on the human flow. This study may provide insights into human activity mechanisms that can contribute to urban planning and management.

The p-center location problem in an area is an important yet very difficult problem in location science. The objective is to determine the location of p hubs within a service area so that the distance from any point in the area to its nearest hub is as small as possible. While effective heuristic methods exist for finding good feasible solutions, research work that probes the lower bound of the problem’s objective value is still limited. This paper presents an iterative solution framework along with two optimization-based heuristics for computing and improving the lower bound, which is at the core of the problem’s difficulty. One method obtains the lower bound via solving the discrete version of the Euclidean p-center problem, and the other via solving a relatively easier clustering problem. Both methods have been validated in various test cases, and their performances can serve as a benchmark for future methodological improvements.

The impact of climate extremes upon human settlements is expected to accelerate. There are distinct global trends for a continued rise in urban dwellers and associated infrastructure. This growth is occurring amidst the increasing risk of extreme heat, rainfall, and flooding. Therefore, it is critical that the urban development and architectural communities recognize climate impacts are expected to be experienced globally, but the cities and urban regions they help create are far more vulnerable to these extremes than nonurban regions. Designing resilient human settlements responding to climate change needs an integrated framework. The critical elements at play are climate extremes, economic growth, human mobility, and livability. Heightened public awareness of extreme weather crises and demands for a more moral climate landscape has promoted the discussion of urban climate change ethics. With the growing urgency for considering environmental justice, we need to consider a transparent, data-driven geospatial design approach that strives to balance environmental justice, climate, and economic development needs. Communities can greatly manage their vulnerabilities under climate extremes and enhance their resilience through appropriate design and planning towards long-term stability. A holistic picture of urban climate science is thus needed to be adopted by urban designers and planners as a principle to guide urban development strategy and environmental regulation in the context of a growingly interdependent world.

Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in Urban Science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of disease. These use cases not only demonstrate how our geographically-explicit synthetic population can be easily utilized for initializing agent populations which can explore a variety of urban problems, but also show how social networks can be integrated into such populations and large-scale simulations.

Linear cities where activity is spread out along a transportation line, aim to offer the highest levels of accessibility to their adjacent populations as well as to the countryside. These city forms are popular amongst architects and planners in envisioning ideal cities but they are difficult to implement as they involve strict controls on development which often ignore human behaviour associated with where we locate and how we move. We briefly explore the history of these ideas, noting the latest proposal to build a 170 km city called Neom in north west Saudi Arabia, a plan that has attracted considerable criticism for its apparent ignorance of how actual cities grow and evolve. We use a standard model of human mobility based on gravitational principles to define a set of equilibrium conditions that illustrate how a theoretical city on a line would, without any controls, successively adapt to such a new equilibrium. First, we represent the city on a line, showing how its population moves to an equilibrium along the line, and then we generalise this to a bigger two-dimensional space where the original line cutting across the grid evolves as populations maximise their accessibility over the entire space. In this two-dimensional world, we simulate different forms that reflect a balance of centralising versus decentralising forces, showing the power of such equilibria in destroying any idealised form. This approach informs our thinking about how far idealised future cities can depart from formal plans of the kind that the linear city imposes.

Investigating spatial accessibility of township to medical resources in provincial China is critical for policymakers to plan a more effective distribution of medical resources. However, accessibility of township to medical resources in provincial China has not been well studied. Accessibility of townships to hospitals in urban areas was calculated by Enhanced Two-step Floating Catchment Area (E2SFCA) by different age and urbanizing groups. Cold and hot spot analysis was used to recognize medical-shortage townships. The results showed that average percent of 65 + and 0–14 age groups in townships with below-average accessibility were 11.55% and 20.38%, higher than those in townships with above-average accessibility by 2 and 3.8 percentage points significantly, and when urbanization level fell from above 0.7 to 0.3–0.7 or below 0.3, accessibility declined by 27.39% or 51.32% significantly. There were 34 physiological medical-shortage townships with both significantly low accessibility and high percent of 65 + or 0–14 age group, and 13 economic medical-shortage townships with both significantly low accessibility and urbanization level. According to the results, spatial accessibility of children on the provincial or county boundaries in northern Anhui and elders and rural population in mountainous western and southern Anhui needed more attention from policymakers.

Mining hotel social sensing data and analyzing its spatial and temporal characteristics can provide decision support for hotel managers. Present research on this topic is limited to the overall hotel industry and text mining. Here, we first obtain POI and reviews for star-rated hotels in Nanchang from 2018 to 2021. Secondly, the hotel POI (Point of Interest) is combined with the sentiment value of customer reviews. Finally, comparative analysis and topic mining of Spatio-temporal aspects of text reviews of different star-rated hotels are conducted using sentiment analysis, spatial analysis, and thematic social network analysis. Results show that: (1) Hotel star rating and hotel review sentiment value are significantly positively correlated. The seasonal trends of different star rating hotel sentiment values are similar, but are highest in summer and lower in autumn; (2) The highest sentiment value is seen for friends’ outings and the lowest is for business trips; (3) Customer reviews of star-rated hotels focus on three aspects: facilities, service, and location. Three-star hotels focus on the stay experience, while four-star hotels focus on the breakfast situation. Exploring hotel social sensing data can intuitively illustrate hotel selection’s behavioral patterns and spatial-temporal characteristics. The methods of this study can expand the application of social sensing data in different fields, such as the tourism and restaurant industries.

Schools across the United States and around the world canceled in-person classes beginning in March 2020 to contain the spread of the COVID-19 virus, a public health emergency. Many empirical pieces of research have demonstrated that educational institutions aid students’ overall growth and studies have stressed the importance of prioritizing in-person learning to cultivate social values through education. Two years into the COVID-19 pandemic, policymakers and school administrators have been making plans to reopen schools. However, few scientific studies had been done to support planning classroom seating while complying with the social distancing policy. To ensure a safe return to campus, we designed a ‘community-safe’ method for classroom management that incorporates social distancing and computes seating capacity. In this paper, we present custom GIS tools developed for two types of classroom settings – classrooms with fixed seating and classrooms with movable seating. The fixed model tool is based on an optimized backtracking algorithm. Our flexible model tool can consider various classroom dimensions, fixtures, and a safe social distance. The tool is built on a python script that can be executed to calculate revised seating capacity to maintain a safe social distance for any defined space. We present a real-world implementation of the system at Eastern Michigan University, United States, where it was used to support campus reopening planning in 2020. Our proposed GIS-based technique could be applicable for seating planning in other indoor and outdoor settings.

Evidence has suggested that built environments are significantly associated with residents’ health and the conditions of built environments vary between neighborhoods. Recently, there have been remarkable technological advancements in using deep learning to detect built environments on fine spatial scale remotely sensed images. However, integrating the extracted built environment information by deep learning with geographic information systems (GIS) is still rare in existing literature. This method paper presents how we harnessed deep leaning techniques to extract built environments and then further utilized the extracted information as input data for analysis and visualization in a GIS environment. Informative guidelines on data collection with an unmanned aerial vehicle (UAV), greenspace extraction using a deep learning model (specifically U-Net for image segmentation), and mapping spatial distributions of greenspace and sidewalks in a GIS environment are offered. The novelty of this paper lies in the integration of deep learning into the GIS decision-making system to identify the spatial distribution of built environments at the neighborhood scale.

Crime changes have been reported as a result of human routine activity shifting due to containment policies, such as stay-at-home (SAH) mandates during the COVID-19 pandemic. However, the way in which the manifestation of crime in both space and time is affected by dynamic human activities has not been explored in depth in empirical studies. Here, we aim to quantitatively measure the spatio-temporal stratified associations between crime patterns and human activities in the context of an unstable period of the ever-changing socio-demographic backcloth. We propose an analytical framework to detect the stratified associations between dynamic human activities and crimes in urban areas. In a case study of San Francisco, United States, we first identify human activity zones (HAZs) based on the similarity of daily footfall signatures on census block groups (CBGs). Then, we examine the spatial associations between crime spatial distributions at the CBG-level and the HAZs using spatial stratified heterogeneity statistical measurements. Thirdly, we use different temporal observation scales around the effective date of the SAH mandate during the COVID-19 pandemic to investigate the dynamic nature of the associations. The results reveal that the spatial patterns of most crime types are statistically significantly associated with that of human activities zones. Property crime exhibits a higher stratified association than violent crime across all temporal scales. Further, the strongest association is obtained with the eight-week time span centred around the SAH order. These findings not only enhance our understanding of the relationships between urban crime and human activities, but also offer insights into that tailored crime intervention strategies need to consider human activity variables.

Real estate markets are complex both in terms of structure and dynamics: they are both influenced by and influence almost all aspects of the economy and are equally vulnerable to the shocks experienced by the broader economy. Therefore, understanding the extent and nature of the impact of large-scale disruptive events such as natural disasters and economic financial downturns on the real estate market is crucial to policy makers and market stakeholders. In addition to anticipating and preparing for long-term effects, it has become imperative for stakeholders to monitor and manage the short-term effects as well due to the emergence of ‘PropTech’ and ‘platform real estate’. In this work, we explore the use of online, real-time dashboards which have been used extensively in the context of urban management, policymaking, citizen engagement and disaster response as an appropriate tool for the purpose of monitoring real estate markets. We describe the process of designing, building, and maintaining an operational dashboard for monitoring the residential real estate market in Australia during the COVID-19 pandemic in 2020. We detail the techniques and methods used in creating the dashboard and critically evaluate their feasibility and usefulness. Finally, we identify the major challenges in the process, such as the spatial and temporal availability and veracity of the real estate market data, and we identify possible avenues for consistent, high-quality data; methodology; and outputs for further research.

Municipalities across the country have debated the safety effect of automatic red-light cameras (RLC) and their political and financial implications. Most empirical studies have used the Empirical Bayesian (EB) approach to assess the safe effects to facilitate policy debates. While popular, the EB method has several limitations in data requirement, reference site selection, and control of confounding factors. Moreover, empirical studies of the RLC deactivation effects are limited. This study fills these gaps using the Moran’s I statistic and the Geographically Weighted Negative Binomial Regression (GWNBR) approach for data in the City of Arlington, Texas. The results indicate that the total, injury, and angle crashes in Arlington are on the rise over the study period and that crashes are higher at RLC deactivation intersections than those at other intersections. The direct safety effect of removing RLCs is statistically significant. The spillover effect is observed but statistically insignificant. Speed limit plays an important role in road safety. The findings have significant implications for safety research and practices.

The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utilized a hybrid GIS- and remote sensing imagery-based framework to systematically compare and evaluate different machine and deep learning methods. The Convolution Neural Network (CNN) classifier outperforms in terms of accuracy, but it requires multi-pixel input, which reduces the output’s spatial resolution and creates a tradeoff between accuracy and spatial resolution. The Random Forest (RF) classifier performs best among the single-pixel classifiers. This study also shows that incorporating building height dataset improves the accuracy of the high- and mid-rise classes in the RF classifiers, whereas an imperviousness dataset improves the low-rise classes. The single-pass forward permutation test reveals that both auxiliary datasets dominate the classification accuracy in the RF classifier, while near-infrared and thermal infrared are the dominating features in the CNN classifier. These findings show that the conventional LCZ classification framework used in the World Urban Database and Access Portal Tools (WUDAPT) can be improved by adopting building height and imperviousness information. This framework can be easily applied to different cities to generate LCZ maps for urban models.

In this era of drastic global change, the Anthropocene, carbon neutrality and sustainable development have become common twenty-first century human challenges and goals. Large-scale urbanization is indicative of human activities and provides an important impetus for environmental changes; therefore, cities have become an important stage in which to promote a more sustainable future development of human society. However, current researchers study urbanization issues based on the perspectives and tools of their respective disciplines; therefore, a holistic and comprehensive understanding of urbanization is lacking due to the insufficient integration of multidisciplinary study perspectives. We explored the construction of interdisciplinary computable sustainable urbanization and introduces a conceptual framework for interdisciplinary urbanization, as scientific computing supports and integrates the natural sciences and humanities to simulate urban evolution and further observe, explain, and optimize human and environment interactions in urban areas. We advocated for the establishment of major international research programs and organizations in the field of sustainable urbanization, and the cultivation of talented young professionals with broad-ranging interdisciplinary interests. Expectantly, we hope a livable planet in the Anthropocene era could be created by developing sustainable urbanization and achieving carbon neutrality.

Identification of suitable landfill sites for urban wastes with ease and economic benefits in the metropolitan area is a complex task. Most of the developing countries consider wastelands outside of the urban areas are the ideal places to dispose of urban wastes. Landfill site selection is an essential planning procedure that helps to avoid environmental concerns such as water contamination, public health degradation caused by unsanitary landfills. So, employing a geographic information system (GIS) and multi-criteria decision analysis (MCDA), this study was carried out to find an appropriate planning waste dump site. Nine thematic layers were evaluated as key criteria, including elevation, slope, geology, lineament, land value, distance from river, roads, residence, and Land use and land cover (LULC) weights assigned using Analytical Hierarchical Process (AHP) method analysis. The relative relevance of each parameter was calculated using Saaty’s 1 to 9 priority scale. The consistency ratio was used to check the weighting of each parameter, allowing the efficiency of the chosen parameters to be justified. The overlay analysis of all parameters with aid of GIS provides suitable sites that were marked and refined after the comprehensive field visits were performed. According to the findings, in the study area, 35.61% area is very low suitable for landfilling, 32.64% area is low suitable, 19.37% area is moderate suitable, 8.90% area is highly suitable and certainly, 3.48% area is very high suitable by Natural breaks classification. The very high suitable site belongs to Dhadagoch, Gadheaganj, and its surroundings in the study area. Nevertheless, the present study can help urban planners and concerned authorities to better succeed in urban waste management in the Siliguri municipal corporation planning area.

Urban morphology and human mobility are two sides of the complex mixture of elements that implicitly define urban functionality. By leveraging the emerging availability of crowdsourced data, we aim for novel insights on how they relate to each other, which remains a substantial scientific challenge. Specifically, our study focuses on extracting spatial-temporal information from taxi trips in an attempt on grouping urban space based on human mobility, and subsequently assess its potential relationship with urban functional characteristics in terms of local points-of-interest (POI) distribution. Proposing a vector representation of urban areas, constructed via unsupervised machine learning on trip data’s temporal and geographic factors, the underlying idea is to define areas as “related” if they often act as destinations of similar departing regions at similar points in time, regardless of any other explicit information. Hidden relations are mapped within the generated vector space, whereby areas are represented as points and stronger/weaker relatedness is conveyed through relative distances. The mobility-related outcome is then compared with the POI-type distribution across the urban environment, to assess the functional consistency of mobility-based clusters of urban areas. Results indicate a meaningful relationship between spatial-temporal motion patterns and urban distributions of a diverse selection of POI-type categorizations, paving the way to ideally identify homogenous urban functional zones only based on the movement of people. Our data-driven approach is intended to complement traditional urban development studies on providing a novel perspective to urban activity modeling, standing out as a reference for mining information out of mobility and POI data types in the context of urban management and planning.

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.

A restless and dynamic intellectual landscape has taken hold in the field of spatial social network studies, given the increasingly attention towards fine-scale human dynamics in this urbanizing and mobile world. The measuring parameters of such dramatic growth of the literature include scientific outputs, domain categories, major journals, countries, institutions, and frequently used keywords. The research in the field has been characterized by fast development of relevant scholarly articles and growing collaboration among and across institutions. The Journal of Economic Geography, Annals of the Association of American Geographers, and Urban Studies ranked first, second, and third, respectively, according to average citations. The United States, United Kingdom, and China were the countries that yielded the most published studies in the field. The number of international collaborative studies published in non-native English-speaking countries (such as France, Italy, and the Netherlands) were higher than native English-speaking countries. Wuhan University, the University of Oxford, and Harvard University were the universities that published the most in the field. “Twitter”, “big data”, “networks”, “spatial analysis”, and “social capital” have been the major keywords over the past 20 years. At the same time, the keywords such as “social media”, “Twitter”, “big data”, “geography”, “China”, “human mobility”, “machine learning”, “GIS”, “location-based social networks”, “clustering”, “data mining”, and “location-based services” have attracted increasing attention in that same time frame, indicating the future research trends.

Recent advances in computing and immersive technologies have provided Meta (formerly Facebook) with the opportunity to leapfrog or expedite its way of thinking and devising a global computing platform called the “Metaverse”. This hypothetical 3D network of virtual spaces is increasingly shaping alternatives to the imaginaries of data-driven smart cities, as it represents ways of living in virtually inhabitable cities. At the heart of the Metaverse is a computational understanding of human users’ cognition, emotion, motivation, and behavior that reduces the experience of everyday life to logic and calculative rules and procedures. This implies that human users become more knowable and manageable and their behavior more predictable and controllable, thereby serving as passive data points feeding the AI and analytics system that they have no interchange with or influence on. This paper examines the forms, practices, and ethics of the Metaverse as a virtual form of data-driven smart cities, paying particular attention to: privacy, surveillance capitalism, dataveillance, geosurveillance, human health and wellness, and collective and cognitive echo-chambers. Achieving this aim will provide the answer to the main research question driving this study: What ethical implications will the Metaverse have on the experience of everyday life in post-pandemic urban society? In terms of methodology, this paper deploys a thorough review of the current status of the Metaverse, urban informatics, urban science, and data-driven smart cities literature, as well as trends, research, and developments. We argue that the Metaverse will do more harm than good to human users due to the massive misuse of the hyper-connectivity, datafication, algorithmization, and platformization underlying the associated global architecture of computer mediation. It follows that the Metaverse needs to be re-cast in ways that re-orientate in how users are conceived; recognize their human characteristics; and take into account the moral values and principles designed to realize the benefits of socially disruptive technologies while mitigating their pernicious effects. This paper contributes to the academic debates in the emerging field of data-driven smart urbanism by highlighting the ethical implications posed by the Metaverse as speculative fiction that illustrates the concerns raised by the pervasive and massive use of advanced technologies in data-driven smart cities. In doing so, it seeks to aid policy-makers in better understanding the pitfalls of the Metaverse and their repercussions upon the wellbeing of human users and the core values of urban society. It also stimulates prospective research and further critical perspectives on this timely topic.

During a natural disaster, mining messages from social media platforms can facilitate local agencies, rescue teams, humanitarian aid organizations, etc., to track the situational awareness of the public. However, for different stakeholders, the concerns about people’s situational awareness in a natural disaster event are different. Therefore, I developed a Twitter-based analytic framework to take perception-level situational awareness, humanitarian-level situational awareness, and action-level situational awareness into consideration. Specifically, perception-level situational awareness mainly reflects people’s perception of the ongoing natural disaster event (i.e., if people are discussing the disaster event). Decision-makers can rapidly have a big picture of severely impacted regions. Humanitarian-level situational awareness represents tweets that are associated with the humanitarian categories based on the definition from the United Nations Office for the Coordination of Humanitarian Affairs. The detection of humanitarian-level situational awareness can help response teams understand the specific situations and needs of local communities. In terms of the action-level situational awareness, I extracted noun-verb pairs in each tweet to explicitly represent the specific event described in a given tweet, so that the response teams can quickly act on the situation case by case. Moreover, to shed light on disaster resilience and social vulnerability, I further examined the demographic characteristics of three levels of situational awareness. I empirically demonstrated the analytic framework using geo-tagged tweets during 2018 Hurricane Michael.

The emerging phenomenon of platformization has given rise to what has been termed "platform society,“ a digitally connected world where platforms have penetrated the heart of urban societies—transforming social practices, disrupting social interactions and market relations, and affecting democratic processes. One of the recent manifestations of platformization is the Metaverse, a global platform whose data infrastructures, governance models, and economic processes are predicted to penetrate different urban sectors and spheres of urban life. The Metaverse is an idea of a hypothetical set of “parallel virtual worlds” that incarnate ways of living in believably virtual cities as an alternative to future data-driven smart cities. However, this idea has already raised concerns over what constitutes the global architecture of computer mediation underlying the Metaverse with regard to different forms of social life as well as social order. This study analyzes the core emerging trends enabling and driving data-driven smart cities and uses the outcome to devise a novel framework for the digital and computing processes underlying the Metaverse as a virtual form of data-driven smart cities. Further, it examines and discusses the risks and impacts of the Metaverse, paying particular attention to: platformization; the COVID-19 crisis and the ensuing non-spontaneous "normality" of social order; corporate-led technocratic governance; governmentality; privacy, security, and trust; and data governance. A thematic analysis approach is adopted to cope with the vast body of literature of various disciplinarities. The analysis identifies five digital and computing processes related to data-driven smart cities: digital instrumentation, digital hyper-connectivity, datafication, algorithmization, and platformization. The novelty of the framework derived based on thematic analysis lies in its essential processual digital and computing components and the way in which these are structured and integrated given their clear synergies as to enabling the functioning of the Metaverse towards potentially virtual cities. This study highlights how and why the identified digital and computing processes—as intricately interwoven with the entirety of urban ways of living—arouse contentions and controversies pertaining to society’ public values. As such, it provides new insights into understanding the complex interplay between the Metaverse as a form of science and technology and the other dimensions of society. Accordingly, it contributes to the scholarly debates in the field of Science, Technology, and Society (STS) by highlighting the societal and ethical implications of the platformization of urban societies through the Metaverse.

This study examines how far the level of knowledge on a new public transport mode in Lebanon might affect mode choice. Indeed, passenger mode choice is a major issue associated with the effectiveness of new transport projects, as their level of effectiveness and feasibility will depend on the number of new adopters. This investigation is performed by developing mode choice models based on data collected via a questionnaire-based survey. The models were used to compare preferences among private cars, current public transport modes and a newly proposed Bus Rapid Transit system. The driving factors are divided into two categories: economic and psychological. The results reveal that explicit evaluations of several factors on the proposed transport modes yields mode choices different from direct evaluation. Besides, the structure of the utility function reveals that economic driving factors prevail over the psychological aspects, which is the opposite of what is observed with direct mode assessment. Moreover, people’s expectations of the proposed Bus Rapid Transit were significantly positive in terms of usability in addition to operational and economic reliability. This study shows that people’s level of knowledge of previous transport modes and their perceptual expectations of new travel modes must be taken into consideration in the feasibility studies of any transport implementations in the developing countries where the public transport services are discouraged.

By using data collected from a self-administered survey, this study evaluates the variation of mental wellbeing between individuals and neighborhoods and its personal and neighborhood determinants in Fresno, California. It reveals the disparities of mental wellbeing, physical activity, and neighborhood environment between disadvantaged and non-disadvantaged neighborhoods in Fresno. Residents in disadvantaged neighborhoods report slightly lower levels of mental wellbeing and physical activity, significantly weaker neighborhood social capital, and much lower neighborhood environment quality. Our path analysis suggests that outdoor physical activity and perceived neighborhood social capital are the only two factors that influence mental wellbeing after controlling for personal socioeconomics and personality. Neither perceived nor objectively measured neighborhood environmental factors show significant and direct impacts on mental wellbeing. Neighborhood environment, however, shows indirect associations with mental wellbeing through their correlations with outdoor physical activity and perceived neighborhood social capital.

Collection and delivery points are an alternative to home delivery and represent an important opportunity to reduce delivery failures in urban areas. As online shopping has become increasingly popular, different accessibility modes such as walking, cycling, and driving are considered for the collection of parcels at collection and delivery points (CDPs). The primary objective of the present study was to assess the spatial variability and accessibility of CDPs in Nanjing City, China. The point of interest (POI) data of 1224 CDPs (including 424 China Post Stations and 800 Cainiao Stations), and population and gross domestic product data were employed for the spatial analysis. The results showed that China Post Stations and Cainiao Stations were distributed in Nanjing as clusters at α = 0.01. Both types (51.1% China Post Stations and 63.2% Cainiao Stations) of CDPs were aggregated in the high population density areas. Moreover, 28.0% of China Post Stations and 50.9% of Cainiao Stations were located in high GDP density areas. The overall spatial distribution of China Post Stations in population and GDP density areas was medium, while that of the Cainiao Stations was high. There was a significant correlation between the spatial distribution of the CDPs, population, and GDP. There were significant spatial accessibility differences to CDPs among different accessibility modes like walking, cycling, and driving. Walking and cycling mode accessibility to China Post Stations and Cainiao Stations were 13.8 and 25.3% and 9.2 and 28.9%, respectively while 71.8% of China Post Stations and 71.1% of Cainiao Stations were accessed by driving. The findings of this study would be beneficial for policymakers and practitioners to develop related policies, to assist companies in building up more sustainable urban logistics and a booming CDPs’ network in the future.

Population growth and affordable housing have boosted realty sector and urban sprawl in India. Understanding the interrelation between urbanization and local climate, though complex, is the need of the hour and the focus of this study. An analysis of the Expert Team on Climate Change Detection and Indices (ETCCDI) on temperature and precipitation was carried out, and it confirms the change in the local urban climate. A Clausius-Clapeyron (CC) scaling relationship has been developed between the range of daily maximum temperature and precipitation for finding precipitation intensity, which is influenced by a rise in maximum temperature. Land use and land cover change derived for the period 1970–2017 from Landsat images were used to understand the effect of urbanization on average daily temperature and extreme precipitation. Multivariate ENSO Index and Global Temperature Anomalies were taken as global physical drivers. Urbanization growth rate anomalies, annual mean temperature anomalies, and summer mean temperature anomalies were taken as local physical drivers that affect one-day extreme precipitation. 22 combinations of these physical drivers were used as covariates to develop extreme value models. Models were evaluated with the L-R test and AIC. It is found that global average temperature and urbanization, individually as well as in combination with local summer mean temperature, were found to be influencing local extreme precipitation. Changes in precipitation patterns have a direct impact on urban water management.

To gain a better understanding of online education status during and after the pandemic outbreak, this paper analyzed the data from a recent survey conducted in the state of Florida in May 2020. In particular, we focused on college students’ perception of productivity changes, benefits, challenges, and their overall preference for the future of online education. Our initial exploratory analysis showed that in most cases, students were not fully satisfied with the quality of the online education, and the majority of them suffered a plummet in their productivities. Despite the challenges, around 61% believed that they would prefer more frequent participation in online programs in the future (compared to the normal conditions before the pandemic). A structural equation model was developed to identify and assess the factors that contribute to their productivity and future preferences. The results showed that lack of sufficient communication with other students/ instructor as well as lack of required technology infrastructure significantly reduced students’ productivity. On the other hand, productivity was positively affected by perceived benefits such as flexibility and better time management. In addition, productivity played a mediating role for a number of socio-economic, demographic, and attitudinal attributes: including gender, income, technology attitudes, and home environment conflicts. Accordingly, females, high income groups, and those with home environment conflicts experienced lower productivity, which indirectly discouraged their preference for future online education. As expected, a latent pro-online education attitude increased both the productivity and the future online-education preference. Last but not the least, Gen-Xers were more likely to adopt online-education in the post pandemic conditions compared to their peers.

In modern era, the maintenance of public infrastructure often takes up a large share of financial budget for a city. The management of these urban assets is supported by a frequently updated inventory reflecting facility conditions. Traditional methods relying on inspection staff or sensors are faced with two main challenges: comprehensive and standardized data collection; quick and automatic assessment process. In this technical note, we introduce a unified method for condition assessment, purely based on street views and machine learning to develop perception quantification models with pairwise labeling datasets. In this way, the two problems could be solved with automatic and scalable processes, updatable algorithms, and affordable costs The method has been tested in the city of Ulaanbaatar, in which a benchmark covering the assessment of eight types of urban infrastructure (roadway, road curbs, road markings, road signs, sidewalks, catch basins, guardrails, and manholes) is demonstrated.

Since the Corona Virus Disease 2019 (COVID-19) swept the world, many countries face a problem that is a shortage of medical resources. The role of emergency medical facilities in response to the epidemic is beginning to arouse public attention, and the construction of the urban resilient emergency response framework has become the critical way to resist the epidemic. Today, China has controlled the domestically transmitted COVID-19 cases through multiple emergency medical facilities and inclusive patient admission criteria. Most of the existing literature focuses on case studies or characterizations of individual facilities. This paper constructs an evaluation system to measure urban hospital resilience from the spatial perspective and deciphered the layout patterns and regularities of emergency medical facilities in Wuhan, the city most affected by the epidemic in China. Findings indicate that the pattern of one center and two circles are a more compelling layout structure for urban emergency medical facilities in terms of accessibility and service coverage for residents. Meanwhile, the Fangcang shelter hospital has an extraordinary performance in terms of emergency response time, and it is a sustainable facility utilization approach in the post-epidemic era. This study bolsters areas of the research on the urban resilient emergency response framework. Moreover, the paper summarizes new medical facilities’ planning and location characteristics and hopes to provide policy-makers and urban planners with valuable empirical evidence.

Coastal areas have the most obvious ocean–land interaction and experience the most frequent human activities. As the development of coastal areas has a high degree of spatiotemporal variability, local governments bear direct responsibility for marine governance, yet accurately evaluating and analyzing local governments’ marine governance efficiency in coastal areas is challenging. This study constructs a spatiotemporal coupling coordination model to comprehensively evaluate local governments’ marine governance efficiency in six coastal cities in Liaoning Province from 2004 to 2019. A complex system was necessary to obtain the development level, discrete degree, and development speed of each subsystem. The construction of the evaluation index system was the foundation, and the construction of the spatiotemporal weight matrix was the key. The results show that overall, the local governments’ marine governance efficiency level is generally increasing, and the agglomeration effect is obvious. The efficiency of each cities’ economic, ecological, and social governance subsystem is in a process of continuous and dynamic change. The coupling and coordination degrees of the six governance systems have continuously improved and the spatial and temporal differences have decreased; each city shows different coupling and coordination degrees in each subsystem. Regarding the factors affecting comprehensive marine management, Liaoning’s coastal areas fail to attract foreign tourists; the discharge and treatment of industrial wastewater restricts ecological governance; and the reduction of fisheries hinders the social governance system’s efficiency. The results contribute to the understanding of costal cities’ marine governance and promote the sustainable development of coastal areas.

Understanding thermal gradients is essential for sustainability of built-up ecosystems, biodiversity conservation, and human health. Urbanized environments in the tropics have received little attention on underlying factors and processes governing thermal variability as compared to temperate environments, despite the worsening heat stress exposure from global warming. This study characterized near surface air temperature (NST) and land surface temperature (LST) profiles across Kenyatta University, main campus, located in the peri-urban using in situ traverse temperature measurements and satellite remote sensing methods respectively. The study sought to; (i) find out if the use of fixed and mobile temperature sensors in time-synchronized in situ traverses can yield statistically significant temperature gradients (ΔT) attributable to landscape features, (ii) find out how time of the day influences NST gradients, (iii) determine how NST clusters compare to LST values derived from analysis of ‘cloud-free’ Landsat 8 OLI (Operational Land Imager) satellite image, and (iv) determine how NST and LST values are related to biophysical properties of land cover features.. The Getis–Ord Gi* statistics of ΔT values indicate statistically significant clustering hot and cold spots, especially in the afternoon (3–5 PM). NST ‘hot spots’ and ‘cold spots’ coincide with hot and cold regions of Landsat-based LST map. Ordinary Least Square Regression (OLS) indicate statistically significant (p < 0.01) coefficients of MNDWI and NDBI explaining 15% of ΔT variation, and albedo, MNDWI, and NDBI explaining 46% of the variations in LST patterns. These findings demonstrate that under clear sky, late afternoon walking traverses records spatial variability in NST within tropical peri-urban environments during dry season. This study approach may be enhanced through collecting biophysical attributes and NST records simultaneously to improve reliability of regression models for urban thermal ecology.

Heart disease is the leading cause of death in the United States. A person who has type-2 diabetes is twice as likely to have heart disease than someone who doesn’t have diabetes. Therefore, analyzing factors associated with both diseases and their interrelationships is essential for cardiovascular disease control and public health. In this article, we propose a Multi-scale Geographically Weighted Regression (MGWR) approach to observe spatial variations of environmental and demographic risk factors such as alcohol consumption behavior, lack of physical activity, obesity rate, urbanization rate, and income from 2005 to 2015 in the United States. The MGWR model has applied to eight census divisions of the United States at the county level: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, and Mountain. Results illustrate that there are notable differences in the spatial variation of the risk factors behind these two diseases. In particular, obesity has been a leading factor that associate with diabetes in the east, south-central, and south Atlantic regions of the U.S. On the other hand, smoking and alcohol consumption was the primary concern in the northern part of the U.S., in 2005. In 2015, alcohol consumption levels decreased, but the smoking level remained the same in those regions, which showed a significant impact on diabetes in the neighboring regions. Between 2005 and 2015, lack of physical exercise has become a significant risk factor associated with diabetes in the Northeast and West parts of the U.S. The proposed MGWR produced high goodness to fit (R²) for most of the areas in the United States.

Forecasting travel demand is a classic problem in transportation planning. The models made for this purpose take the socioeconomic characteristics of a subset of a population to estimate the total demand, mainly using random utility models. However, with machine learning algorithms fast becoming key instruments in many transportation applications, the past decade has seen the rapid development of such models for travel demand forecasting. As these algorithms are independent of assumptions, have high pattern recognition ability, and often offer promising results, they can be effective alternatives to discrete choice models for forecasting trip patterns. This paper aimed to predict mandatory and non-mandatory trip patterns using a Deep Neural Network (DNN) algorithm. A dataset containing Metropolitan Washington Council of Government Transportation Planning Board (MWCGTPB) 2007–2008 survey data and a dataset containing traffic analysis zones’ characteristics (TAZ) were prepared to extract and predict these patterns. After the modeling phase, the models were evaluated based on accuracy and Cohen’s kappa coefficient. The estimates of mandatory and non-mandatory trips were found to have an accuracy of 70.87% and 50.02%, respectively. The results showed that a DNN could find the relationship between socioeconomic factors and trip patterns. This can be helpful for transportation planners when they are trying to predict travel demand.

Modern urban development urgently requires a new management concept and operational mechanism to encourage the exploration of frameworks for cognizing and studying urban characteristics. In the present study, modern cities are first understood from the perspective of their basic theoretical evolution. Each modern city is seen as a complex system of organic life forms. Urban information science propels modern urban research in the direction of rationality. This paper also presents the new characteristics of modern cities (and how they have changed) in relation to external structure and internal functions. It examines the generation of urban problems and governance adaptability. On this basis, this paper proposes a cognitive model for studying modern cities, integrating basic theoretical, methodological support, and governance systems. It discusses the basic rationale and core idea for constructing each of these three systems. The research aims to guide and implement modern urban construction and sustainable development in a more effective way.

Brazos Valley Food Bank (BVFB) is a non-profit organization in the Bryan-College Station area of Texas. It distributes food supplies through partner agencies and special programs to eradicate hunger in Brazos Valley. However, a big gap exists between the meals distributed by BVFB and the size of the food-insecure population. This research is motivated by BVFB’s desire to reach more people by recruiting more sustainable partner agencies. We used Geographic Information Systems (GIS) to map food desert areas lacking access to nutritious food. We combined expert knowledge with multi-criteria decision-making (MCDM) to address the challenges and time consumption of manually identifying sustainable partner agencies for local food delivery. We identified evaluation criteria for all agencies based on BVFB managers’ preferences using a qualitative approach, and then applied three quantitative decision-making models: the Weighted Sum Model (WSM), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Multi-criteria Optimization and Compromise Solution (VIKOR) models to obtain ranking results. We compared the quantitative models’ rankings to BVFB managers’ manual choices and discussed the impacts of our research. The key innovation of the research is to develop a mixed method by combining expert knowledge with mathematical decision models and GIS to support spatial decision making in food distribution. Although our results were specific to BVFB, these procedures can be applied to food banks in general. Future studies include finetuning our models to measure and address human biases, wider applications and more data collections.

Owing to the onset of the new media age, the idea of e-public participation has proven to be a great complement to the limitations of the conventional public participation approach. In this respect, location-based social networks (LBSN) data can prove to be a game shift in this digital era to offer an insight into the commuter perception of service delivery. The paper aims to investigate the potential of using Twitter data to assess commuters’ perceptions of the Delhi metro, India, by presenting a comprehensive methodology for extracting, processing, and interpreting the data. The study extracts Twitter data from the official handle of the Delhi metro, performs semantic and sentiment analysis to comprehend commuters’ concerns and assesses commuters’ sentiments on the predicted concerns. The paper outlines that the current depth of Twitter data is more inclined to instantaneous responses to grievances encountered. Moreover, the analysis presents that for the data extraction period, the topics ‘Ride Safety’ and ‘Crowding’ have the lowest scores, while ‘Personnel Attitude’ and ‘Customer Interface’ have the highest scores. Further, the paper highlights insights gleaned from Twitter data in addition to the aspects included in the conventional satisfaction survey. The paper concludes by outlining the opportunities and limitations of LBSN analytics for effective public transportation decision-making in India.

Urban vitality is an essential indicator of an area’s capacity to promote lively social and economic activities. Urban functional areas can play different roles throughout the day, and urban vitality may exhibit significant intraday temporal dynamics. However, few studies have evaluated the dynamic vitality throughout the day among various urban functional areas or explored how the built environment influences this attribute. To bridge this gap, we assessed the vitality dynamics in intensity, variability, and night ratio. We then examined the influencing factors of urban vitality in Central Shanghai using heatmap and point of interest (POI) data. We found significant differences in the intensity, variability, and night ratio of urban vitality among different urban functional areas. The difference in vitality intensity was more significant than the variability and night ratio between weekdays and weekends. The built environment significantly affected urban vitality, but its role differed among the various urban functional areas. Overall, describing urban vitality from a dynamic perspective could improve our understanding of the differences in attracting and maintaining human activities among different urban functional areas.

Climate change and sustainability are among the most widely used terms among policymakers and the scientific community in recent times. However, climate action or steps to sustainable growth in cities in the global south are mostly borrowed from general studies at a few large urban agglomerations in the developed world. There are very few modeling studies over south Asia to understand and quantify the impact of climate change and urbanization on even the most primary meteorological variable, such as temperature. Such quantifications are difficult to estimate due to the non-availability of relevant long-term observational datasets. In this modeling study, an attempt is made to understand the urban heat island (UHI), its transition, and the segregation of regional climate change effects and urbanization over the rapidly growing tier 2 tropical smart city Bhubaneswar in India. The model is able to simulate the UHI for both land surface temperature, called the SUHI, and 2-m air temperature, called UHI, reasonably well. Their magnitudes were ~ 5 and 2.5°C, respectively. It is estimated that nearly 60–70% of the overall air and 70–80% of the land surface temperature increase during nighttime over the city between the period 2004 and 2015 is due to urbanization, with the remaining due to the regional/non-local effects.

Amplified rates of urban convective systems pose a severe peril to the life and property of the inhabitants over urban regions, requiring a reliable urban weather forecasting system. However, the city scale's accurate rainfall forecast has constantly been a challenge, as they are significantly affected by land use/ land cover changes (LULCC). Therefore, an attempt has been made to improve the forecast of the severe convective event by employing the comprehensive urban LULC map using Local Climate Zone (LCZ) classification from the World Urban Database and Access Portal Tools (WUDAPT) over the tropical city of Bhubaneswar in the eastern coast of India. These LCZs denote specific land cover classes based on urban morphology characteristics. It can be used in the Advanced Research version of the Weather Research and Forecasting (ARW) model, which also encapsulates the Building Effect Parameterization (BEP) scheme. The BEP scheme considers the buildings' 3D structure and allows complex land–atmosphere interaction for an urban area. The temple city Bhubaneswar, the capital of eastern state Odisha, possesses significant rapid urbanization during the recent decade. The LCZs are generated at 500 m grids using supervised classification and are ingested into the ARW model. Two different LULC dataset, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and WUDAPT derived LCZs and initial, and boundary conditions from NCEP GFS 6-h interval are used for two pre-monsoon severe convective events of the year 2016. The results from WUDAPT based LCZ have shown an improvement in spatial variability and reduction in overall BIAS over MODIS LULC experiments. The WUDAPT based LCZ map enhances high-resolution forecast from ARW by incorporating the details of building height, terrain roughness, and urban fraction.

This study investigates and forecasts the effects of implementing a newly proposed Bus Rapid Transit (BRT) system in Lebanon on the urban land use evolution between the years 2019 and 2049. It contributes to the emerging scientific literature by proposing a technique intended to identify the potential urban land use impacts of BRT. The identification of these impacts as part of the feasibility study for the BRT is considered important for policymakers, local officials, and urban planners. The impacts are identified by conducting the Analytical Hierarchy Process, based on data collected via survey and interviews with real estate experts. The outcomes show that implementing a BRT service complemented with bus feeder services will: (i) reshape the urban fabric, in proximity to BRT routes and particularly around the stations, by triggering the Transit-Oriented Development and increasing the attractivity of urban development by 6 to 9% according to the distance from BRT route; and (ii) increase the attractivity of urban development projects by 11% in areas distant from the highway if these areas are characterized by high coverage of bus feeder services, low possibility of an increase in estate prices, and medium to high public acceptance of the proposed BRT.

In this article, we present the process and results of using quantum computing (QC) to solve the maximal covering location problem proposed by Church and ReVelle. With this contribution, we seek to lay the foundations for other urban and regional scientists to begin to consider quantum technologies. We obtained promising results, but it is clear that there is a need for more capable devices with more qubits and less susceptibility to electronic noise to solve instances that currently cannot be optimally solved by traditional solvers. We foresee that QC will be of common use in urban and regional science and its applications in the years to come.

This paper investigates whether temporary migrant workers still attract foreign direct investment (FDI) in China nowadays after they played a strong magnet role for FDI in the last century. This paper tests the hypothesis that foreign firms reduce investments to avoid urbanization diseconomies from temporary migrants when China is experiencing rapid urbanization in the 2000s, with the urbanization rate raised from 36% in 2000 to 59% in 2017. This research employs spatial statistics and analyses to examine the change in the spatial inequality of temporal migrant workers and FDI. This research also uses regression models to investigate whether temporary migrant workers still attract foreign direct investment (FDI) in China nowadays. Temporary migrants are increasingly concentrated in the Pearl River Delta, the Yangtze River Delta, and the Bohai Rim Region of the eastern region, and Chengdu in the western region. The results indicate that a one-person increase in temporary migrant workers is associated with 259 dollars decrease in FDI, suggesting that FDI might reduce with increased migrants to avoid urbanization diseconomies from these cities, helping policymakers develop urbanization and migration policies to optimize labor allocation and promote industrial upgrading, developing peripheral cities.

In light of the growing global environmental challenges, smart cities need to serve as testing workshops or labs to smartly tackle complex cross-sectional issues like jobs, seamless mobility, safety and security, sustained growth, while responding to the impending climate change too. This necessitates for developing a smart model or tool that integrates such varied but crucial climate concerns of a city into its direct decision-making and long-term planning. In this research, we conduct a literature review to have an overview of the state-of-the-affairs on urban climate planning in Asia-Pacific Cities covering China, Japan, India, Philippines, Singapore and Thailand. This is followed by an intensive theoretical understanding on the need of having a smart tool in urban climate action planning. This includes the study of recent urban climate metrics and tools, their different typologies based on key purpose, method, sectoral and geographical scope, and challenges and gaps in formulating smart urban climate tools. We then introduce the conceptual framework for integrated climate action planning (ICLAP) tool that transects spatial, statistical and bibliometric methods. We establish applicability of ICLAP in case of Indian cities by discerning climate vulnerabilities, GHG trends and relevant urban climate solutions. The paper eventually culminates with major scientific findings and policy recommendations, essentially underscoring more intensive and wider application of ICLAP like smart urban climate tools in local decision making and national urban policies duly supported by international scientific collaborations.

Floating population is an important group in the emerging urbanization process. This group promotes long-term settlement, which is a significant driving force increasing the urbanization level of countries. This study analyzed the changes in population mobility between Chinese cities and the willingness of the floating population to settle down. The analyses were based on data obtained from the China Migrants Dynamic Survey (CMDS) in 2017, and the China Seventh Census 2020. Spatial econometric models were constructed for in-depth research. The result showed that: ① the floating population migrated mainly from the central region to the surrounding cities, and their long-term settlement intention presented a spatial pattern of "high in the east, low in the west, and local concentration." ②the long-term settlement intention significantly negatively affected the urban floating population. City economic level, public service capacity, and environmental quality significantly positively or negatively influence the number of the floating population. For promoting more floating population to become urban residents, management of the group should be strengthened, construction level of the urban economy, society, and ecology improved, and the willingness of the group to settle for an extended time encouraged.

The severe acute respiratory syndrome coronavirus 2 (COVID-19) pandemic has brought a heavy burden and severe challenges to the global economy and society, forcing different countries and regions to take various preventive and control measures ranging from normal operations to partial or complete lockdowns. Taking Xi’an city as an example, based on multisource POI data for the government’s vegetable storage delivery points, logistics terminal outlets, designated medical institutions, communities, etc., this paper uses the Gaussian two-step floating catchment area method (2SFCA) and other spatial analysis methods to analyze the spatial pattern of emergency support points (ESPs) and express logistics terminals in different situations. It then discusses construction and optimization strategies for urban emergency support and delivery service systems. The conclusions are as follows. (1) The ESPs are supported by large-scale chain supermarkets and fresh supermarkets, which are positively related to the population distribution.The spatial distribution of express logistics terminals is imbalanced, dense in the middle while sparse at the edges. 90% of express terminals are located within a 500 m distance of communities, however, some terminals are shared, which restrict their ability to provide emergency support to surrounding residents. (2) In general, accessibility increases as the number of ESPs increases; under normal traffic, as the distance threshold increases, the available ESPs increase but accessibility slightly decreases; with a traffic lockdown, the travel distance of residents is limited, and as ESPs increase, accessibility and the number of POIs covered significantly increase. (3) The spatial accessibility of the ESPs has a “dumbbell-shaped” distribution, with highest accessibility in the north and south, higher around the second ring road, slightly lower in the center, and lowest near the third ring road at east and west. (4) With the goal of “opening up the logistics artery and unblocking the distribution microcirculation”, based on “ESPs + couriers + express logistics terminals + residents”, this paper proposes to build and optimize the urban emergency support and delivery service system to improve the comprehensive ability of the city to cope with uncertain risks.