The vulnerability of places in the Covid-19 pandemic. An analysis model based on spatial interaction

The article presents the results of an empirical research that addresses the issue of place vulnerability in the first three waves of the Covid-19 pandemic in Italy, discussing the spatial factors involved in the spread of the virus. Through a re-reading of the literature on epidemic spatial diffusion, the usefulness of the principle of potential interaction for a strategic geography of public health is verified, assuming a public geography perspective. With reference to the different impact probabilities, the theoretically possible types of spatial interaction are discussed, then the model elaborated is tested by means of experimentation in territories that differ in terms of interaction and spread modes. The results of the application to the territories of two Italian regions (Tuscany and Molise) characterised by a different socio-territorial organisation and differentiated diffusion dynamics, show how the variety of the territorial configurations present in Italy, characterised by the pervasiveness of interaction and by a multiple articulation of short and long networks, conditions epidemic diffusion. The analysis arrives at unexpected conclusions concerning the criticality of peripheral areas, often erroneously perceived as less vulnerable. © 2023 Pacini Editore. All rights reserved.

COVID-19 and Minimizing Micro-Spatial Interactions

COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts. © 2022 held by the owner/author(s).

COVID-19 and Minimizing Micro-Spatial Interactions

COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts.

Life-Cycle Spatial Strategy for Multidimensional Health-Oriented Medical Care Community—From the Perspective of Sustainable Marketing

The aging population and the increasing number of sub-healthy people in all age groups in China have brought huge opportunities for related industries. From the perspective of marketing and consumer psychology, there is a great demand for health care properties, especially those that provide long-term medical care. Against this situation, almost all the leading real estate companies have entered this field and tried to occupy more market shares through different products and brand marketing sustainably. In this context, it is urgent to explore a comprehensive community model combining medical and nursing care that covers all stages of life, so as to promote the health of diverse populations. In China, existing research on the growth of medical care communities for sustainable needs started relatively late, and insufficient attention has been paid to the supply–demand linkage among psychological demand, health behavior, spatial bearing, and service supply. Taking Wuzhishan city for example, we deduce the Medical-Care Maslow’s Hierarchy of Needs System according to classical theories. Based on motivation theory and marketing strategy, a theoretical model of Health demand-behavior-facilities and Spatial Interaction (HBSI) mediated by healthy behavior is constructed. Then, expert group decision making processes and the Fuzzy Delphi Method (DFM) were used to screen 67 spatial impact factors of 14 categories in five dimensions, including life safety, physical health, mental health, social adaptation and resilience recovery, which fit users’ multi-dimensional health needs. Finally, to provide a spatial strategy reference for the construction of sustainable and adaptive medical caring communities, spatial planning strategies and guidelines are offered based on correlation analysis, so as to fit the changeable market pattern, meet the psychological expectations and life-cycle caring needs of consumers.

Spatial Humanities and Geo-computation for Social Sciences:Advances and Applications

Humanities and Social Sciences (HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics (including RS: Remote Sensing;GIS: Geographical Information System;GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences (SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover (LUCC), Specially Protected Natural Areas (SPNA) analysis, editing behavior analysis of Volunteered Geographic Information (VGI), electricity consumption anomaly detection, First and Last Mile Problem (FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.

Modeling Transmission Dynamics and Risk Assessment for COVID-19 in Namibia Using Geospatial Technologies.

The SARS-CoV-2 infections continue to increase in Namibia and globally. Assessing and mapping the COVID-19 risk zones and modeling the response of COVID-19 using different scenarios are very vital to help decision-makers to estimate the immediate number of resources needed and plan for future interventions of COVID-19 in the area of interest. This study is aimed to identify and map COVID-19 risk zones and to model future COVID-19 response of Namibia using geospatial technologies. Population density, current COVID-19 infections, and spatial interaction index were used as proxy data to identify the different COVID-19 risk zones of Namibia. COVID-19 Hospital Impact Model for Epidemics (CHIME) V1.1.5 tool was used to model future COVID-19 responses with mobility restrictions. Weights were assigned for each thematic layer and thematic layer classes using the Analytical Hierarchy Process (AHP) tool. Suitably ArcGIS overlay analysis was conducted to produce risk zones. Current COVID-19 infection and spatial mobility index were found to be the dominant and sensitive factors for risk zoning in Namibia. Six different COVID-19 risk zones were identified in the study area, namely highest, higher, high, low, lower, and lowest. Modeling result revealed that mobility reduction by 30% within the country had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases and delay to the peak number. The research output could help policy-makers to estimate the immediate number of resources needed and plan for future interventions of COVID-19 in Namibia, especially to assess the potential positive effects of mobility restriction.

Reimagining “Space”: An Approach to Increase Participants’ Social Aspects During Online Conferencing

Online conferencing has become a new normal after the COVID-19 pandemic. However, existing systems like ZOOM fall short of facilitating informal social aspects like chats, talks, discussions, dialogues, gatherings, secrets, or even gossip or quarrel, which often occur spontaneously during physical meetings. In this study, we design and prototype a novel system considering key spatial features that influence social interactions offline. The proposed system consists of three typical meeting modes: square mode for free social, room mode for split group discussion, stage mode for speech and presentation. Through Wizard-of-Oz testing with 10 participants, we summarize the design features that contribute to the richness of ambiance, the flexibility of distance, the serendipity of interaction of online conferences, and the effect of these aspects on social interaction. Together with the limitations and suggestions for future work, we hope this paper can inspire the design of spatial interaction on screen, with the aim to improve informal social aspects of online conferencing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spatial differentiation and determinants of COVID-19 in Indonesia.

BACKGROUND: The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors. METHODS: The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020. RESULTS: Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali. CONCLUSION: Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.

Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement.

BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. METHODS: We modeled infection risk from seating and fine-grained movement patterns - boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. RESULTS: Our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95-100%, while cloth masks would have reduced it by only 40-80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. CONCLUSIONS: Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.

The post-pandemic city: speculation through simulation

Although we can explain the way centripetal and centrifugal forces determine the form and function of contemporary cities, our abilities to predict their futures are severely limited. The pandemic has led to changes in locational and travel behaviour as well as regulated lockdowns with respect to where people work, live and social distance from one another. This makes it impossible to predict a ‘new normal’ reflecting ways we are able to control and manage the pandemic. As we have little data pertaining to this future, to engage in informed discussion, we develop a hypothetical city organised around theories of spatial interaction, urban hierarchy, density, and heterogeneity of movement. We propose a symmetric square grid of locations, simulate the interactions using gravitational models, and then lock it down. We release the lockdown in the transition to a new normal, assuming different parameter values controlling the effects of distance, illustrating the difficulty of generating highly decentralised city forms. We apply the model to London, locking down the metropolis, and exploring seven functional forms that provide us with a sample of different city shapes and densities. Our approach provides a framework for speculating about the future using what we call ‘computable thought experiments’. © 2022 The Author

Resident travel path planning based on risk assessment of epidemic-related areas

Aiming at the problem of residents' need to travel during the epidemic, this paper designs a risk assessment method based on the spatial interaction of space-time objects in the epidemic-related area, and evaluates the epidemic risk in the associated area based on an improved gravity model and a one-dimensional steady-state water quality migration model. Based on the Floyd algorithm and according to the residents' choice preferences, an optimal path solution model that simultaneously considers the epidemic risk factor and travel distance is established and the algorithm is verified. An actual road network in Shenyang City was used for example verification, and the difference between the path guidance during the regular period and the epidemic period was compared. The results show that the improved Floyd algorithm can effectively avoid epidemic-related areas. As residents increase their preference for risk avoidance, the length of the guide path will gradually increase, but the risk of infection will gradually decrease. © 2021 SPIE.

A dynamic microsimulation model for epidemics.

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.