Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate, and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the eight articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to infectious diseases simulation, risk prediction, response policy design, mobility analysis, and case diagnosis. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness. © 2022 held by the owner/author(s).

A Systematic Review of COVID-19 Geographical Research: Machine Learning and Bibliometric Approach

The rampant COVID-19 pandemic swept the globe rapidly in 2020, causing a tremendous impact on human health and the global economy. This pandemic has stimulated an explosive increase of related studies in various disciplines, including geography, which has contributed to pandemic mitigation with a unique spatiotemporal perspective. Reviewing relevant research has implications for understanding the contribution of geography to COVID-19 research. The sheer volume of publications, however, makes the review work more challenging. Here we use the support vector machine and term frequency-inverse document frequency algorithm to identify geographical studies and bibliometrics to discover primary research themes, accelerating the systematic review of COVID-19 geographical research. We confirmed 1,171 geographical papers about COVID-19 published from 1 January 2020 to 31 December 2021, of which a large proportion are in the areas of geographic information systems (GIS) and human geography. We identified four main research themes—the spread of the pandemic, social management, public behavior, and impacts of the pandemic—embodying the contribution of geography. Our findings show the feasibility of machine learning methods in reviewing large-scale literature and highlight the value of geography in the fight against COVID-19. This review could provide references for decision makers to formulate policies combined with spatial thinking and for scholars to find future research directions in which they can strengthen collaboration with geographers.Alternate :2020年, 新冠肺炎流行病迅速席卷全球, 对人类健康和全球经济造成了巨大影响。这次流行病激发了各个学科研究的爆炸性增长。其中, 地理学研究以独特的时空角度, 为流行病治理做出了贡献。对有关研究进行综述, 有助于理解地理学对新冠肺炎研究的贡献。然而, 海量的文献使得这个综述更具挑战性。为了加快对新冠肺炎地理研究的系统性综述, 我们利用支持向量机和词频-反文档频率算法寻找文献中的地理学研究, 利用文献计量学发掘主要研究题目。本文确认了2020年1月1日至2021年12月31日发表的1,171篇新冠肺炎地理学论文, 其中多数文章属于地理信息系统和人文地理学领域。确定了体现地理学贡献的四个主要研究题目：流行病传播、社会管理、公众行为和流行病影响。研究结果表明了利用机器学习方法去开展海量文献综述的可行性, 强调了地理学在抗击新冠肺炎的价值。该文献综述有助于决策者制定具备空间思维的政策, 也有助于学者们寻求加强与地理学者合作的未来研究方向。

Geo-information communication technology (Geo-ICT) framework to prevent spread of corona virus disease (COVID-19)

Health is an important part of human life. The awareness about the quality health care plays a major role in the human life. The present Corona virus Disease (COVID-19) is infectious and fast spreading. In a country like India, prevention of the infection is still the best option. The use of Geo-Information Communication Technology (Geo-ICT) framework can help in the prevention of spread of the disease. The use of geo-spatial technologies simplifies the complex data to improve decision making. In this manuscript, an attempt is made to design a geo-spatial framework to capture data, store data in centralized geo-spatial data bank and use the data to alert the citizens in near real time for COVID-19 clusters using mobile map interface. The solution will support citizens in protecting themselves from infection. The paper also discusses the methods of data moderation and data dissemination to the mobile app users. We conclude that the present study is an effort towards enabling the information dissemination process for quick and reliable mitigation measures.

Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate, and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the eight articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to infectious diseases simulation, risk prediction, response policy design, mobility analysis, and case diagnosis. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.

Mortality due to noninfectious lower respiratory diseases: A spatiotemporal, cross-sectional study.

Background and Aims: Geography plays an important role in the incidence of respiratory diseases. The aim of this study was to investigate the epidemiology and geographical distribution of death due to noninfectious lower respiratory diseases (NILRDs). Methods: Data related to all death due to NILRD in Kerman Province between 2012 and 2018 were extracted from the National Mortality Registry. The underlying causes of death were extracted from the registry based on the assigned codes from ICD-10 (International Classification of Diseases 10th Revision) classification. The existence of spatial clusters and outliers was evaluated using local indicators of spatial association statistics. Results: The frequency of death due to NILRD was 8005 persons during the 7 years of the study. The main cause of death was chronic lower respiratory disease (54.2%). Other causes of death were, respectively, lung diseases due to external agents (1.09%), other respiratory diseases mainly affecting the interstitium (1.16%), other diseases of pleura (0.57%), and other diseases of the respiratory system (42.13%). The age- and sex-adjusted mortality rates due to NILRD in the north and center of the province increased significantly from 2012 to 2018. Also, the results of cluster analysis identified northern regions as the clustered areas of NILRD. Conclusions: Our findings showed a significant increase in mortality due to NILRD in Kerman Province during the 7 years of the study. To reduce this type of death, health policymakers should have environmental health plans and basic solutions, such as a warning system to reduce the commuting on highly air-polluted days and to control pollutants, especially in the industrial areas of the north of this province.

Re-evaluating the merits of decentralization as a core strategy for effective delivery of drug-resistant tuberculosis care in Pakistan.

Decentralized, person-centred models of care delivery for drug-resistant tuberculosis (DR-TB) continue to be under-resourced in high-burden TB countries. The implementation of such models-made increasingly urgent by the COVID-19 pandemic-are key to addressing gaps in DR-TB care. We abstracted data of rifampicin-resistant (RR)/multidrug-resistant tuberculosis (MDR-TB) patients initiated on treatment at 11 facilities between 2010 and 2017 in Sindh and Balochistan provinces of Pakistan. We analysed trends in treatment outcomes relating to programme expansion to peri-urban and rural areas and estimated driving distance from patient residence to treatment facility. Among the 5586 RR/MDR-TB patients in the analysis, overall treatment success decreased from 82% to 66% between 2010 and 2017, as the programme expanded. The adjusted risk ratio for unfavourable outcomes was 1.013 (95% confidence interval 1.005-1.021) for every 20 km of driving distance. Our analysis suggests that expanding DR-TB care to centralized hubs added to increased unfavourable outcomes for people accessing care in peri-urban and rural districts. We propose that as enrolments increase, expanding DR-TB services close to or within affected communities is essential.

A GIS-aided cellular automata system for monitoring and estimating graph-based spread of epidemics.

In this study, we introduce an application of a Cellular Automata (CA)-based system for monitoring and estimating the spread of epidemics in real world, considering the example of a Greek city. The proposed system combines cellular structure and graph representation to approach the connections among the area's parts more realistically. The original design of the model is attributed to a classical SIR (Susceptible-Infected-Recovered) mathematical model. Aiming to upgrade the application's effectiveness, we have enriched the model with parameters that advances its functionality to become self-adjusting and more efficient of approaching real situations. Thus, disease-related parameters have been introduced, while human interventions such as vaccination have been represented in algorithmic manner. The model incorporates actual geographical data (GIS, geographic information system) to upgrade its response. A methodology that allows the representation of any area with given population distribution and geographical data in a graph associated with the corresponding CA model for epidemic simulation has been developed. To validate the efficient operation of the proposed model and methodology of data display, the city of Eleftheroupoli, in Eastern Macedonia-Thrace, Greece, was selected as a testing platform (Eleftheroupoli, Kavala). Tests have been performed at both macroscopic and microscopic levels, and the results confirmed the successful operation of the system and verified the correctness of the proposed methodology.

FROM QGIS TO PYTHON: COMPARISON OF FREE AND OPEN TOOLS FOR STATISTICAL ANALYSIS OF CULTURAL HERITAGE AND DATA REPRESENTATION

The onset of the Covid pandemic in 2020 changed the approach to work, research, and study. This period has been a wake-up call for public administrations, the private sector, and the academic community, to digitise their data. In Italy, digital and information technologies for the protection and enhancement of cultural heritage, which were an imperative for more than a decade, have been accelerated. This paper aims to collect and to process openly available data on patrimony from OpenStreetMap and the Lombardian Geoportal. The study is divided into two phases: a simple statistical analysis of cultural heritage in Monza is obtained, and the results are presented graphically. Firstly, built-in tools and Python Console of QGIS are evaluated, to filter attributes and add geometrical values to the downloaded material. Secondly, plug-in DataPlotly and an online coding application named Replit are assessed. The results are presented and compared in terms of their flexibility, quality of visual representation, customisation, and simplicity of use. Tools developed through and for QGIS are easy to use and available to everyone. Additionally, coding applications can be integrated for more refined results. This approach fosters interdisciplinarity, bridges the gap between professionals and non-expert users of GIS, and opens a range of opportunities for future collaborations. The citizen, as a mapper, can be involved in the administrative decision-making process, contributing with data collected in situ. Collaboration between these two sides can potentially produce the better for evaluating the contemporary built environment and its undividable part of cultural heritage.

TOURISM, NATURAL PROTECTED AREAS AND OPEN SOURCE GEOSPATIAL TECHNOLOGIES

The Covid-19 outbreak has greatly impacted society behaviours fostering proximity tourism and valorising the social role of peri-urban natural protected areas as key locations for outdoor activities. FOSS and FOSS4G can play a critical role to support the value creation for these sites. This work evaluates its application in the context of two different protected areas for the creation of 3D digital products, the monitoring of touristic fluxes and the conduction of parks management activities. To this aim three solutions that copes with the mentioned aspects are presented and gaps, weakness and limitations evaluated. The investigated solutions consists in: the data workflow from survey to 3D rendering using Blender and GIS plugin;the touristic fluxes monitoring system based on a machine learning algorithm for image recognition from captured video data streams and istSOS;and finally the park assets management system which is based on PostGIS and OpenLayers.

Application of geographic information system in the control of COVID-19 epidemic. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Since the outbreak of coronavirus disease 2019 (COVID-19) epidemic, the geographic information system (GIS) has played an important role in explaining the epidemic distribution, characteristics of regional transmission, risk assessment, and early prediction and warning, which greatly helped the disease control and prevention. In this study, the application of GIS in COVID-19 prevention and control was reviewed, hoping to provide reference for future improvement in the prevention and control measures.

Urban Particulate Matter Hazard Mapping and Monitoring Site Selection in Nablus, Palestine

Few air pollution studies have been applied in the State of Palestine and all showed an increase in particulate matter concentrations above WHO guidelines. However, there is no clear methodology for selecting monitoring locations. In this study, a methodology based on GIS and locally calibrated low-cost sensors was tested. A GIS-based weighted overlay summation process for the potential sources of air pollution (factories, quarries, and traffic), taking into account the influence of altitude and climate, was used to obtain an air pollution hazard map for Nablus, Palestine. To test the methodology, eight locally calibrated PM sensors (AirUs) were deployed to measure PM2.5 concentrations for 55 days from 7 January to 2 March 2022. The results of the hazard map showed that 82% of Nablus is exposed to a high and medium risk of PM pollution. Sensors’ readings showed a good match between the hazard intensity and PM concentrations. It also shows an elevated PM2.5 concentrations above WHO guidelines in all areas. In summary, the overall average for PM2.5 in the Nablus was 48 µg/m3. This may indicate the effectiveness of mapping methodology and the use of low-cost, locally calibrated sensors in characterizing air quality status to identify the potential remediation options.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.

Agri-food trade in 2020

The year 2020 marked one of the biggest recessions in global economic activity and world trade. During this period, the EU economy contracted by 6% and its international trade followed a similar downward trend - EU exports of goods decreased by 9% and imports by 12%, compared to 2019. By contrast, EU international trade in agri-food reported a slight growth. Over the course of 2020, the value of EU agri-food exports increased to 184.3 billion (a growth of 1.4% compared to 2019), while the value of imports rose to 122.2 billion (a growth of 0.5%). As a result, the EU further reinforced its leading position among the world's biggest exporters. On the import side, the EU has become the third largest importer after the US and China. The contraction in global trade was accompanied by increasing prices of food, including commodities as evidenced by the increases reflected in the FAO Global Price Index. The EU exports a wide range of products from all parts of the value chain which demonstrates the competitiveness of the EU agri-food sector in a variety of product classes ranging from commodities to highly processed food industry products. EU imports, on the other hand, are clearly dominated by basic agricultural food and feed products, which represent about 75% of all imports. Looking at product categories, exports of pig meat and wheat strongly contributed to the increase in EU overall agri-food exports. Conversely, spirits and liqueurs as well as wine are among the sectors that experienced a difficult period for a number of reasons (e.g. the COVID pandemic, US retaliatory tariffs). The growth in EU agri-food imports was mainly driven by increases in import values for oilseeds, other than soya beans;fatty acids and waxes, palm oil, fruit including tropical fruit, and soya beans. China, Switzerland and the Middle East and North Africa (MENA) region were the major growth destinations for EU agri-food exports in 2020. The value of EU exports fell most to the United States, Turkey, Singapore and Japan. In terms of imports, Canada grew significantly as a source for the EU imports. By contrast, EU imports declined most in value from the United Kingdom, Ukraine and the United States. In 2020, the UK has become the EU's most important partner in agri-food trade, with a share of 23% in total EU agri-food exports and 13% in total imports. With EU exports and imports both decreasing, its trade surplus with the US increased by 2% when compared to 2019, as falls on the imports side were stronger. China became the top destination for US agri-food exports. EU agri-food exports to China were primarily driven by continued record high sales of pig meat which increased by 74%. Pig meat and meat offal - the latter mainly comprised of products originating from pigs - accounted for over 40% of EU exports to China in 2020, demonstrating the importance of this market for the pig meat sector. Brazil's exports to China continued to increase in 2020, absorbing 35% of its total agri-food exports. Combined agri-food exports from Brazil to the EU and the US now account for half of Brazilian exports to China. In 2020, Brazil supplied 50% of extra-EU demand for soya beans and 40% for oilcakes. Wheat continued to be the leading EU export product to Africa with a 23% share of the EU's total export basket, whereas cocoa beans dominate in the EU imports from Africa, with the same share of 23%. Most African countries benefit from duty-free, quota-free access to the EU market under the "Everything But Arms" scheme and for many of them Economic Partnership Agreements (EPAs) or other trade agreements with the EU are applied, encouraging regional cooperation and trade. In 2020, the EU applied 45 free trade agreements (FTAs) with 77 partners. The share of agri-food trade under preferential agreements is also expanding and in 2021, it accounted for 31% and 41% of total EU agri-food exports and imports, respectively. The value of EU agri-food trade under preferential agreements expanded more in relative terms compared to total EU agri-food trade. EU agri-food ex

Pandemic tilts local agriculture and communities to behavior changes

Typical agricultural distribution systems and venues temporarily ceased during the COVID-19 pandemic. Commodities were turned under in fields, dumped, or left behind for wildlife to pilfer while food banks ran out of produce. Residents did not know where to get fresh produce and wholesale producers lacked the ability to instantly shift their business model and distribution methods to meet local needs. Advisory board networks helped connect components of the food system: wholesale producers;retail outlets;and consumers to locations where produce was available. I connected volunteer gleaners with wholesale producers to gather produce for distribution to food banks around Manatee County. The Bradenton Downtown Farmer's Market started a Community Supported Agriculture (CSA) venue to accommodate wholesale producers with a retail venue. The county (Geographic Information System) (GIS) team and I created an interactive map of local agriculture commodities such as vegetables, fruit and vegetable crops, aquaculture, beef and dairy products, and nursery plants. The volunteer group organized over 60 gleaning events and harvested over 72,000 pounds of produce for food insecure residents. Based on the farmer's market model, two producers created CSA markets, in addition to their restaurant venue. As a result of the pandemic, four wholesale producers expanded distribution to local retail venues. The GIS map included over 100 Manatee County agriculture producers, searchable by location and commodity for markets, nurseries, aquaculture, beef and dairy, fruits, and vegetables. Key players in the food system who initially connected during the pandemic lockdown were able to provide beneficial opportunities for everyone. The GIS map has increased awareness of local agricultural production in Manatee County. Some wholesale producers adopted alternative marketing venues to rebound from pandemic deficits and prepare for future market changes. Gleaning events continue to provide local fruits and vegetables to food insecure residents, allowing them to enjoy healthy, accessible produce. Socially, many new connections and relationships have been fostered between the farming and residential communities.

Quantification and mapping of domestic plastic waste using GIS/GPS approach at the city of Guayaquil

A significant contributor to the waste stream is the domestic single-use plastic used in households, being the final disposal in most cases the local landfill. There is a significant opportunity to promote resource recovery and efficiency through the introduction of circular economy strategies. However, the knowledge and management of post-consumer plastic waste in the country is poor, and there is a lack of an efficient collection and sorting system. In this context, spatial information on domestic plastic waste generation (DPWG) is essential for recycling decision-making. The integration of Geographic Information Systems (GIS) and the Global Positioning System (GPS) shows an opportunity to collect, mapping, and analyse spatial DPWG issues. Thus, this paper had a double objective. The first was to assess the evolution of eight different types of plastic waste in the city's households and their daily per capita generation between 2019 and 2021. The second objective was to provide a complete geo-referenced information on the quantities and typologies of domestic plastic waste (DPW) produced in Guayaquil and analyse how the flows have shifted throughout the years. The results showed that PET is the most generated, recording 97.76% and 100.00 % of the households who generate this type of plastic for 2019 and 2021, respectively, with an average of 13.08 and 15.13 g/day/c. Following, we had HDPE, PP and PVC occupying the second, third and fourth place for 2019 with 5.86, 3.05, 2.54 g/day/c, respectively. On the other hand, for 2021, PP (7.43 g/day/c), HDPE (5.92 g/day/c), and LDPE (3.99 g/day/c) occupied the second, third and fourth, respectively. According to the spatial maps, the DPW increment is in most of the popular zones. These popular zones are neighborhoods with a considerable quantity of population and limited basic services. Most of these people live in extreme poverty, being a possible relation between the COVID-19 lockdown and the increasement of DPW. © 2022 Elsevier B.V.. All rights reserved.

An Exploration of Features Impacting Respiratory Diseases in Urban Areas.

Air pollution exposure has become ubiquitous and is increasingly detrimental to human health. Small Particulate matter (PM) is one of the most harmful forms of air pollution. It can easily infiltrate the lungs and trigger several respiratory diseases, especially in vulnerable populations such as children and elderly people. In this work, we start by leveraging a retrospective study of 416 children suffering from respiratory diseases. The study revealed that asthma prevalence was the most common among several respiratory diseases, and that most patients suffering from those diseases live in areas of high traffic, noise, and greenness. This paved the way to the construction of the MOREAIR dataset by combining feature abstraction and micro-level scale data collection. Unlike existing data sets, MOREAIR is rich in context-specific components, as it includes 52 temporal or geographical features, in addition to air-quality measurements. The use of Random Forest uncovered the most important features for the understanding of air-quality distribution in Moroccan urban areas. By linking the medical data and the MOREAIR dataset, we observed that the patients included in the medical study come mostly from neighborhoods that are characterized by either high average or high variations of pollution levels.

HappyRec: Evaluation of a "happy Spot" Recommendation System Aimed at Improving Mental Well-Being

In recent years, the number of people with depression has increased due to the COVID-19 pandemic. One of the activities which has been found to help in improving mental well-being and reducing stress is an exercise called the "three good things". In this exercise, participants are asked to write down and reflect on three good things that happened to them each day every night before they sleep. However, for the people who suffer from conditions such as low self-esteem and depression, it is not easy for them to be aware of good things or small moments of happiness in their daily lives. To address this issue, we propose a happiness spot recommendation system to provide suggestions for nearby spots which might improve their positive affect using Google Maps. To verify the validity of our proposed method, we asked nine subjects to evaluate the proposed system and reported the happiness ranking accuracy of the recommended spots. © 2021 IEEE.

Development of a Web GIS for small-scale detection and analysis of COVID-19 (SARS-CoV-2) cases based on volunteered geographic information for the city of Cologne, Germany, in July/August 2020.

BACKGROUND: Various applications have been developed worldwide to contain and to combat the coronavirus disease-19 (COVID-19) pandemic. In this context, spatial information is always of great significance. The aim of this study is to describe the development of a Web GIS based on open source products for the collection and analysis of COVID-19 cases and its feasibility in terms of technical implementation and data protection. METHODS: With the help of this Web GIS, data on this issue were collected voluntarily from the Cologne area. Using house perimeters as a data basis, it was possible to check, in conjunction with the Official Topographic Cartographic Information System object type catalog, whether buildings with certain functions, for example residential building with trade and services, have been visited more frequently by infected persons than other types of buildings. In this context, data protection and ethical and legal issues were considered. RESULTS: The results of this study show that the development of a Web GIS for the generation and evaluation of volunteered geographic information (VGI) with the help of open source software is possible. Furthermore, there are numerous data protection and ethical and legal aspects to consider, which not only affect VGI per se but also affect IT security. CONCLUSIONS: From a data protection perspective, more attention needs to be paid to the intervention and post-processing of data. In addition, official data must always be used as a reference for the actual spatial consideration of the number of infections. However, VGI provides added value at a small-scale level, so that valid information can also be reliably derived in the context of health issues. The creation of guidelines for the consideration of data protection, ethical aspects, and legal requirements in the context of VGI-based applications must also be considered. Trial registration The article does not report the results of a health care intervention for human participants.

Outdoor recreation and nature's contribution to well-being in a pandemic situation - Case Turku, Finland.

Urban green infrastructure provides a range of experiences for people and various health benefits that support human well-being. To increase urban resilience, exceptional situations, such as the COVID-19 pandemic, are important to learn from. This study aims to understand how the residents in Turku, a middle-sized city in Finland, perceived their outdoor recreation changed and how nature contributed to their subjective well-being during the early phases of the COVID-19. Sites of outdoor recreation and associated ecosystem service benefits were gathered through a map-based survey. In addition, the contribution of nature on subjective well-being was measured through Likert scale statements and the perceived changes in outdoor recreation behaviour were measured through self-reported number of days and from responses to open survey questions. Data was analysed through quantitative, qualitative and spatial methods. The results show that nearly half of the respondents increased outdoor recreation and the majority of outdoor recreation sites were visited more or as often as before the pandemic. The spatial analysis revealed that the most often visited recreation sites were near forests, semi-natural areas and housing areas as well as relatively close to respondent's residence. Respondents had various reasons for changes in outdoor recreation behaviour. For some a shift to working remotely and changes in everyday routines led to spending time outdoors more often and for some spending less while others avoided recreation in crowded areas due to social distancing. The results also indicate that people's opportunities to adapt to the pandemic conditions differ greatly. The nature's contribution to subjective well-being during COVID-19 was important regardless of respondent's outdoor recreation behaviour. Our study highlights that urban planning should respond to different needs for outdoor recreation in order to widely, and in a just way, promote the well-being benefits of urban nature during a pandemic, and to increase the resilience of the city and its residents. Participatory mapping can capture the variety in resident's values and identify key recreation sites of multiple ecosystem service benefits.

A spatial-epidemiological dataset of subjects infected by SARS-CoV-2 during the first wave of the pandemic in Mashhad, second-most populous city in Iran.

OBJECTIVE: In March 2020, Iran tackled the first national wave of COVID-19 that was particularly felt in Mashhad, Iran's second-most populous city. Accordingly, we performed a spatio-temporal study in this city to investigate the epidemiological aspects of the disease in an urban area and now wish to release a comprehensive dataset resulting from this study. DATA DESCRIPTION: These data include two data files and a help file. Data file 1: "COVID-19_Patients_Data" contains the patient sex and age + time from symptoms onset to hospital admission; hospitalization time; co-morbidities; manifest symptoms; exposure up to 14 days before admission; disease severity; diagnosis (with or without RT-PCR assay); and outcome (recovery vs. death). The data covers 4000 COVID-19 patients diagnosed between 14 Feb 2020 and 11 May 2020 in Khorasan-Razavi Province. Data file 2: "COVID-19_Spatiotemporal_Data" is a digital map of census tract divisions of Mashhad, the capital of the province, and their population by gender along with the number of COVID-19 cases and deaths including the calculated rates per 100,000 persons. This dataset can be a valuable resource for epidemiologists and health policymakers to identify potential risk factors, control and prevent pandemics, and optimally allocate health resources.