Computational Science and Its Applications – ICCSA 2021

In 2021 ISTAT presented the Report on Equitable and Sustainable Well-being (BES 2020), consisting of a system of indicators that follow the significant changes that have characterized the Italian society in the last 10 years. With the integration of new indicators, realized in coherence with the fundamental lines of the Next Generation EU, there has been an enrichment of information on the country system concerning health aspects, education and training, and economic well-being. The 20 Italian regions, the 2 autonomous provinces of Bolzano and Trento, the 3 territorial divisions North, Center, South and the total of Italy constituting a set of 26 territorial units, have been described each with a set of 36 numerical indicators, concerning the areas of Health, Education and Training, Economic Wellbeing. These areas are the most suitable for highlighting regional imbalances in Italy. In this paper has been analyzed the input data matrix, made up of 26 rows, one for each of the territorial units, and of 36 columns, the number of descriptors used for each territorial unit, by means of a factor analysis, using the principal components method, in order to construct a regional taxonomy characterized by those of the 36 indicators that are most correlated with each of the factors that have emerged. Moreover, starting from the coordinates calculated for each of the 26 territorial units in the factor space, a cluster analysis of the 26 territorial units was carried out, using the connected graph method, in order to highlight the territorial similarities and differences in Italy.

Whether for work, for leisure or for moving goods necessary for health, people and industry, mobility is a fundamental aspect of economic and social life of each community. Unfortunately, it is not without costs for the society: gas emissions, air, water and noise pollution, road crashes, congestions, are examples which affect health and environment. For these reasons the most severe challenge for the transportation sector is to become more sustainable, resilient and safe. All can be possible only betting on green mobility and cutting-edge digital technologies.According to this vision, the “Smart Urban Mobility Management” (SUMMa) project has been proposed, with the aim to develop, for the Artena Municipality, new service structures for modernizing and managing transportation systems, by means of digital technologies and 5G communication networks. In particular the proposed digital platform, thanks to enhanced Mobile Broadband, massive Machine Type Communications and Mobile Edge Computing, can support Artificial Intelligence algorithms for recognition image and interpretation of field data, collected by drones or H-D cameras. In real time, Origin-Destination flows, individual trips and environmental data can be update and traffic, congestion and health information can be shared with the citizens.In this paper, the general architecture of the SUMMa platform has been described and preliminary findings of the pilot project in the Municipality of Artena have been presented.

The communities’ interest in urban forestry is growing, recently also in order to face the COVID-19 pandemic crisis. Although the multiple benefits (ecosystem services) that forestry provides in cities are recognized by the international community, the issue of economic evaluation of each service in the context of urban renewal processes is still little debated.This paper describes the Benefit Transfer Method (BTM) as a framework for estimating the total economic value of urban forests. This is done with the aim of outlining an economic model to support decision-making processes. The model is tested on a set of Italian cities. Research perspectives are in the conclusions.

The present study is part of a wider research line focused on the analysis of the effects caused by the pandemic of the Coronavirus disease (Covid-19) on the Italian residential property market. The paper aims to propose a methodology for the assessment of the effects of this abnormal event on the housing price mechanisms. The research could be an operational support for the Public Administration and private investors in their decision making processes. In particular, with reference to the city of Rome (Italy), two datasets of residential properties have been collected and processed through an econometric technique, in order to identify the variations occurred in terms of market appreciation for specific housing factors. The outputs highlight changes in market demand concerning a preference for outdoor spaces, both condominiums and private (terraces and balconies) and for properties located in peripheral areas of the city.

With reference to a study sample related to the city of Milan (Northern Italy), the present research intends to identify the impact of the most influencing factors on the residential rents. In particular, in the analysis two hundred and twenty housing properties rented in the second half of 2019 have been collected and the most relevant intrinsic and extrinsic factors in the bargaining phases between the lessors and the potential lessees have been selected. Through the implementation of an econometric technique the investigation of the different functional relationships between the explanatory factors considered and the housing rents has been carried out. The present research could represent a valid reference for the private operators in the investment decisions phases and for the Public Administrations to monitor housing rent dynamics and to provide essential implications for fair housing policies.

The Organization for Economic Co-operation and Development (OECD) in the last Going for Growth report (2021) urged the Italian government to redefine the tax on the first home ownership and to review the cadastral rates. This could represent an opportunity to re-discuss the objectives of the property taxation, as a part of the general tax reform announced by the Italian government. The aim of this paper is to perform some preliminary analyses on the fiscal inequality related to luxury properties in six different Italian cities at municipal level. The proposed methodological approach is based on three steps and can be also applied to other urban contexts. A stratified sampling of data from real estate advertisements provide the basis for the calculation of the cadastral values and a set of innovative fiscal inequality indicators. Descriptive statistics and regression analyses are performed to study the relations between property prices, cadastral values and the inequality level expressed by the proposed indicators. Findings show that cadastral values are not related to property prices and that the fiscal inequality level is significant in the analysed cities, even if with some differences that highlight a chaotic fiscal situation. The regression results also highlight the random relationship between tax rates and property prices and thus suggest that redistributive policies are necessary in the Italian context. The outputs of these first exploratory analyses represent a good starting point that deserves to be carried on and developed to test the proposed methodological approach. In particular, further researches could be focused on other housing segments, such as economic properties and tenements, and the analyses could be improved by considering the different urban areas and the related sub-markets.

The European Union’s environmental policies actively promote the transition to a low-carbon society and to sustainable energy systems that improve people’s quality of life and do not negatively impact the natural environment. To achieve these goals, the European Union funded several programs to pilot energy efficiency measures for buildings and districts and, lately, launched the European Green Deal. The results of these experimentations have shown that often the economic feasibility of retrofitting interventions is not achieved without public grants. This contribution aims to analyze the influence of financial parameters on the profitability of projects of energy efficient districts. The study is based on the data from the demo sites of the CITyFiED program (Soma in Turkey and Laguna de Duero in Spain) that are reworked according to new several alternative scenarios, differentiated by cost financing and amount of public grants.

This paper is the first part of a broader work that illustrates a multidisciplinary research activity carried out as part of an institutional collaboration, between the Mediterranea University of Reggio Calabria and 18 Sicilian municipalities, located in an area called “Jonian Valleys of Peloritani”. The collaboration is finalized in order to outline perspectives and strategies for the material and immaterial progress of the area. The research activities are at initial stages, this paper illustrates the methodological aspects.

The COVID-19 pandemic in Italy, as in many countries around the world, has imposed rigid restrictions on outdoor activities, resulting in forced confinement. The new condition requires an analysis and a rethinking of the way of life and of the new pre- and post-pandemic needs related to the use of domestic spaces, necessary to work, study or carry out other daily activities.Politecnico di Milano and Politecnico di Torino, with the collaboration of the institute of studies and research Scenari Immobiliari, have launched a survey for exploring the new needs and preferences of residents. These needs, which arose in conjunction with the pandemic, concern not only the desire to readapt their homes, but also to change them. In order to investigate these preferences, a questionnaire was developed using the Best to Worst Scaling (BWS).The items consider both modifications of the internal distribution and interventions on the efficiency of domestic appliances and systems components. The study aims to highlight how the spread of the pandemic has changed housing needs and how physical space affects people’s well-being.

The present research analyzes the main methods implemented for the assessment of the market value of residential properties in Italian judicial procedures. This value represents the reference for the “starting price” in the subsequent property auctions. An Italian study sample of 514 residential properties assessed by technicians in judicial procedures between November 2020 and March 2021 has been collected.The analysis shows that almost 58% of the studied dataset have used indirect sources (e.g. quotations published by public and private entities/operators) for the market value assessment, whereas about 27% do not specify the approach and/or the data elaborated for the evaluation. Only 15.4% have implemented approaches provided by the International Valuation Standards (IVS): in particular, 4.9% have used a market approach method, 1.4% have applied the direct capitalization method, 9.1% have combined different approaches for checking the results obtained through an IVS method or for considering an arithmetical average of the outputs assessed.

Housing is often considered a crucial element in determining the level of income and social well-being, in recognition of the ways in which housing shortages, or the use of poor-quality housing, are statistically linked to income levels and can negatively affect on people’s well-being (Rolfe et alii, 2020). So, planning social housing isn’t just a question of houses, but it claims for a deep understanding of people living conditions, social and economic dynamics. That’s why is necessary to integrate different statistical data to develop a model of social and economic living conditions of people to address better and context-based housing policies. The paper analyses methods and tools to integrate appropriate statistical data to guide housing policies in the case of the city of Taranto, selecting those most useful for determining supply and demand to guide urban planning in subsequent participatory and implementation paths. Planning social housing not just to improve physical spaces, but to interpret the needs of living.

The natural disasters that hit the Italian Apennines with increasing frequency, earthquakes, landslides and floods, cause enormous damage to people and things, modifying economies and social contexts, already affected by the scarcity and antiquity of infrastructures and the abandonment of some territories, located in particular in the inner areas of the country. In these territories there is a significant social, historical, economic, environmental and landscape capital of Italy that everyone knows and loves. The need emerges to increase infrastructural resilience, carrying out significant extraordinary maintenance interventions, promoting the technological development of monitoring activities and infrastructures, prevention activities, civil protection and public rescue. Resilience, however, is a broader concept than the physical ability to overcome disasters, as the ongoing pandemic crisis has shown. This includes, for example, the ability of the urban system to respond to unforeseen seismic events or health problems; the solidity of the network of public spaces and services to support communities and their ability to effectively deal with sudden crises. In the event of catastrophic events, it is precisely the peripheral urban contexts of the Inner Areas that are most exposed to “Risks of isolation”, as shown by the seismic events of 2016, where the secondary infrastructural network was heavily affected, limiting mobility of residents in an unsustainable way and sentencing them to further forms of isolation. The work explores the experimental methodologies capable of planning substantial changes to the structure of cities and minor urban areas (both with reference to damaged buildings and to the infrastructural network) that reconstruction can allow, making it a unique opportunity to renew and re-organize the territory.

From urban regeneration to social regeneration up to culture-led regeneration, the concept of urban regeneration evolves from the idea of the physical transformation of cities to a more complex vision of changing able to improve the inhabitants’ quality of life. At the same time, the social dimension of the recognised impacts, from a factor juxtaposed to the regenerative processes, becomes central to build new models of “impact economy” with long-term sustainable effects. In this change of perspective, the driver is the repositioning of culture, the community's centrality and involvement, and the reuse of abandoned cultural heritage spaces. In urban regeneration processes, evaluation has thus assumed a decisive role in guiding strategic choices, empowering the communities involved, supporting decision-makers and attracting new funding. Starting from the keywords “urban regeneration” and “social impact”, the paper integrates the literature review with bibliometric maps through the VOSviewer tool to investigate the role of evaluation in a broader framework to feed the contemporary debate on the impacts of urban regeneration.

In real estate property valuation, the availability of comparables is crucial. The reliability of the valuation of the market value depends on the number and on the accuracy of data that a professional can rely on. International standards suggest using historical prices as comparable since they are real transactions of sale/rent of a property that actually happened in a specific market. However, in the Italian real estate market, historical transaction prices are not available for professionals, and they have to base their valuations, primarily, on the asking prices enclosed in the selling advertisements. Asking prices can change in the future as they are subject to negotiation. Besides, sell ads always contain incomplete data or even wrong information. In this research, we employ Artificial Neural Networks to estimate how much offer prices and selling advertisements are misleading in property valuation in Italy. We, in a way, assess the opacity of the Italian real estate market, and we designate the major sources of error. The present work is a first step towards developing a model fitted for estimating data accuracy used generally in real estate estimates, namely, asking prices.

The Ecosystem Services provided by food encompass a vast amount of material and immaterial benefits to human beings and shared values linked to creativity, self-fulfilment, recreation, sociality, culture, and mutual learning, which are at the basis of a modern and eco-instructed community. In Italy, agri-food no-profit sector or small-medium enterprises can empower a community to cope with resource depletion, waste production, biodiversity loss, and climate change by sparking sustainable urban and rural practices preserving the current ecosystem services and generating new ones. Within the ecological economics, the integrated assessments’ contribution becomes relevant when the potentials of sustainable agri-food practices and values – which local communities assign to the related Ecosystem Services – have to be estimated to better inform Decision Makers in empowering policy and planning fostering maintenance and regulation of these services in rural and urban contexts. The contribution's purpose was to propose a methodological approach for assessing creative ecosystem services within an agri-food cultural value chain with the Fuzzy Analytic Hierarchy Process (F-AHP) multi-criteria method. The overall results have allowed obtaining a global ranking of the preferable scenarios linked to a Cultural Creative Enterprise (CCE) located in Foggia, Apulia (Italy). The research follow-up addresses the co-creation of creative ecosystem services in synergy with local stakeholders and beneficiaries, generating new job opportunities, awareness and innovation through an advanced form of shared responsibility.

The worrying levels of land take and insufficient ecosystem services related to phenomena of uncontrolled urban expansion in the cities force many countries far away from achieving Sustainable Development Goals of Agenda 2030. It is necessary to promote strategies which lead to effective and efficient measures in the perspective of sustainable development.The work aim is to propose an evaluation protocol useful to support public and private subjects for sustainable practices in urban contexts. Depending on the types of use and land cover envisaged, the implementation of the proposed methodological framework allows for the definition of a Composite Indicator (CI) to measure the urban environmental and economic sustainability level. The proposed CI expresses qualitatively and quantitatively the socio-economic and environmental impact (trade-off) that single initiative generates in the reference context in terms of ecosystem services as a function of the land use change between ex-ante and ex-post phases of the urban transformation process.Multi-parameter methodological approach by a sequence of logical-operational phases that lead to the definition of the CI is proposed. Steps of the proposed method are characterized by the algebraic structures typical of the Benefit of Doubt Approach (BDA) and Goal programming principles. Testing of the proposed protocol for CI construction is in progress.

During the last two decades, the European Union regulation has paid more and more attention to the issue of energy efficiency, especially as far as the building sector is concerned. Lately, the adoption of the European Green Deal (EGD) has further fueled interest in the topic. The building industry is expected to step forward towards decarbonization, as well as to mobilize towards a clean and circular economy, so supporting the pursuit of climate neutrality. In this position paper, we aim to discuss three major issues at stake, which are crucial to increase the renovation rate of the building stock and, hence, to the success of the EGD. The first key issue is whether a cost premium has to be expected due to retrofit projects. The second key issue is whether or not the large swing currently experienced in the price premium for building energy efficiency undermines stakeholders’ confidence. The third key issue is whether we need tailored evaluation methodologies for energy-efficient building portfolios.

This paper proposes a logical-deductive model for the estimate of forced sale value, in support of individuals involved in real estate auctions. This value is estimated by starting from market value, considering an appropriate discount/premium coefficient which, due to obtainable yields and associated risk, guarantees transaction admissibility for the investor in terms of convenience. The model borrows from Ellwood logic as applied to the real estate sector, integrated through the evaluation approach of investment risk inherent in Real Options Analysis. Applying this model to one hundred and forty cities in which the Italian courts are based allowed for comparison of the discount/premium coefficients determined by the model with those determined by sector operators. The results of this application underlined the speculative behavior of market operators, who mainly focus on obtaining better discounts than those admissible, while at the same time supplying useful indications on the territorial contexts where the difference between hammer price and admissible value can be maximized.

The second post world-war period has been widely characterized by urbanization phenomena related to the urban rent formation dynamics. The scarcity of public financial resources and the growing privatization of the benefits generated by several territorial initiatives have highlighted the need for negotiation tools capable of ensuring a fair redistribution of the rent gap deriving from urban transformation interventions. For this reason, the institution of the “extraordinary urbanization contribution” in the 2014 in Italy represents a significant regulatory support, that legitimizes public administrations in acquiring a share of the private extra-profit. The implementation of this national legislative provision has not been yet sufficiently applied at the local level, due to the lack of a univocal and codified methodology. The aim of this work consists in defining a decision support model that can be adopted in the negotiation phases between public and private subjects, in order to determine the most convenient financial conditions that ensure the law provisions. In particular, by applying the computational logic of Operational Research, the model is able to determine the amount of the main urban planning parameters that affect the balance sheets of the public-private initiatives.

The establishment of real estate funds has made it possible to attract greater local and foreign capital in the context of the enhancement and reuse of the Italian public real estate assets. The process of optimal allocation of the financial resources available in a real estate portfolio, however, is often opaque and linked to multiple factors. The aim of this research is to define an asset allocation model capable of supporting the decision-making processes of public and private investors in the context of the creation of optimized property portfolios. By adopting the logic and principles of goal programming, the model is able to identify the best combination of properties in the portfolio by optimally managing the available financial resources of a generic institutional investor. The ability of the proposed model to be flexible and implementable in any geographical context constitutes one of the main advantages for public and private investors.

In the present research the risks matrix related to a transformation intervention to be carried out through the Project Financing (PF) operational tool, has been developed. With reference to the expansion and management of the cemetery of a small town located in Southern Italy, the identification and allocation of the risks among the parties involved – private investor and Public Administration – have been implemented. Furthermore, the verification of the feasibility by the Public Administration in the use of the PF operational tool has been performed, by analyzing the results obtained by the project proposer subject in financial terms. The risk assessment constitutes a support tool for the public Administration in the decision-making processes aimed to evaluate the PF proposals by the proposer subject in order to ensure an appropriate and detailed investigation on the Public Private Partnerships mechanism and to avoid complications and contingencies that could lead to initiative failure. In this sense, the present analysis allows to evaluate the advantages for public and private subjects to use the PF mechanism in bridging the existing gulf between the scarce public resources and the investment demand of expansion and/or redevelopment of urban cemeteries.

The strong socio-spatial implications of the covid-19 pandemic and the physical distancing measures have emphasised the fundamental role played by socio-digital networks in sharing and urbanising information. Worldwide, the emergence of shared survival needs led to the self-organisation of local communities in care infrastructures for mutual aid, psychological support, sharing experiences and storytelling, often based on crowd-sourcing and crowd-mapping platforms. From the (post-)pandemic urban planning perspective, the growth of this phenomenon has implied the call for spatial research to reconsider ICTs and collaborative spatial information systems as strategic tools to support vulnerable communities and public engagement in collective matters. This contribution aims to define an open-source database to be used and implemented by citizens and social operators to provision territorial care systems and mutual aid initiatives. The adopted methodological approach proposes a citizen-led spatial information system, where updated information, perceptions and preferences can be spatialised and collected, building a crowd-sourced and sharable system of territorial knowledge, useful for collective actions and the development of sustainable and effective strategies in emergency conditions. The obtained results refer to data about mutualism in public space, collected during the pandemic period in the Ponticelli district, the eastern periphery of Naples, Italy. They are to be framed within the broader on-going European HERA research project “PuSH: Public Space in European Social Housing”.

This paper presents the follow-up phase of a broader work that illustrates a multidisciplinary research activity carried out as part of an institutional collaboration between the Mediterranea University of Reggio Calabria and 18 Sicilian municipalities, located in an area called “Jonian Valleys of Peloritani”, Italy. The collaboration is finalized in order to outline perspectives and strategies for the material and immaterial progress of the area. The research activities are at initial stages, this paper illustrates the first results of the cognitive phase relating to the case study.

The COVID-19 pandemic required the implementation of restrictions in the transport sector, changing the movement habits of users and, at the same time, highlighting the importance of creating a flexible transport system aimed at guaranteeing an uninterrupted supply of goods and people in any event. In the last decade, the pace at which sustainable and resilient mobility strategies were developed grew significantly due to the spread of technologies (such as ITS systems) and digital platforms related to Mobility-as-a-Service (MaaS). These strategies might improve the overall efficiency of the transport system and reduce dependence on private cars in urban area. Moreover, the development of the demand responsive transport (DRT) is one of the solutions for planning end-to-end travel according to the transport needs. Actually DRT, such as the other shared modes, appears more convenient and capable to turn private car superfluous, reducing traffic congestion and noise and atmospheric pollution levels. After a review of the state of the art in literature, this paper shows the results of a descriptive and a preliminary analysis of DRT services in Italy. In particular, in the studied areas, the authors detected the type of service given and its national displacement, the category of users, the kind of stops, the fleet of vehicles and the presence of shared mobility services. Finally, the authors assessed the possibility to include these services within the Maas platforms suggesting some possible strategies for optimizing the implementation of intelligent mobility systems at Italian national level.

Since the spread of the COVID-19 pandemic in March 2020, the transport system has suffered a profound reduction in demand worldwide. In order to understand which factors played a crucial role in reducing transport demand, an online questionnaire survey was conducted exploring one of the regions of Italy, i.e. Sicily, which was severely affected by the pandemic and was characterized by the greatest transport disruption among the other Italian regions. The survey was answered by 700 respondents who were frequent PT commuters before the outbreak of the pandemic. It collected sociodemographic information, highlighted the public transport demand figures before and after the lockdown period in Sicily and assessed the acceptance rates of the national recommendations which aimed to prevent contagion and encourage the use of PT. The survey data were analyzed using descriptive and cluster analysis techniques. During the examined periods, the results demonstrated three (3) clusters of PT commuters in Sicily and associated them with certain sociodemographic characteristics and acceptance rates of the national PT recommendations. Our findings lay the basis for public transport service improvements which could help local authorities to cope with such extreme conditions and perform a successful restart of operations.

Local Public Transport (LPT) plays a key role in the economic system of any country. The Covid-19 outbreak and subsequent lockdowns led to an unprecedented economic crisis in this sector. Governments had to block all unnecessary travel and keep only essential services moving, reducing public transport services (air, train, bus, metro, etc.) by more than 90%. Despite the economic aids provided by Governments, many LPT companies are facing a crisis that they are unlikely to be able to overcome. This for two reasons: skepticism of passengers in the use of public transport, and therefore reduced revenue from ticket sales, and additional costs for the vehicles’ sanitization Focusing on the Italian case, and especially on Sicily, the paper aims to analyze the standard costs of the LPT road service and assess the cost increase in which companies have to incur in order to meet the guidelines imposed by Government and encourage citizens to use public transport. The paper relies on the results of a direct investigation on clients’ attitudes through a questionnaire submitted to a population travelling to and from the city of Enna in the centre of Sicily.

The advent of e-commerce has surged during the recent COVID-19 pandemic period. Already in 2019, many habits of users concerning online shopping platforms have changed: the age of those buying online has risen, the average income has fallen and the contagion has reached all urban and rural areas, even the smaller towns and villages. Since March 2020, when increased restrictions were applied, an increase in shopping of various kinds in several European and non-European countries is recorded. Many operators had to modify their logistics and change their interfaces. In short, the emergency has given a huge cultural boost to e-commerce. Tens of thousands of small retailers found themselves in the position of not being able to welcome consumers into their shops or being forced to create e-shops in a very short period of time and thus they identified home delivery as an opportunity not to be missed. Remote payment was the first, but not the only, effect. The present paper analyses the correlation between online shopping habits and socio-demographic characteristics in 27 European countries. The results were obtained from a descriptive statistical evaluation and the correlation between the variables was defined by the chi-square calculation. The results lay the foundations for the definition of the change in transport demand by home-purchase motivation and allow to define some considerations on the emission patterns that characterise transport demand.

The global pandemic of COVID-19 has a significant impact on the public transportation (PT) sector and users’ perception. This study aimed to assess the public transportation users’ perceptions, needs, and preferences during the COVID-19 pandemic in Amman, Jordan. A cross-sectional correlation design was used to recruit 510 participants between March 2020 - October 2020. We used an online survey that included the users’ sociodemographic data sheet, frequency of using PT and other traveling modes, and travelers’ perception towards PT before and after the pandemic. This study indicated a dramatic decrease in using PT during the pandemic, reaching 50%, and a reduction in the frequency of PT daily usage from 16% to 4%, however, all other traveling modes experienced an increase. In addition, 50% of the participants indicated that social distancing was the most important criterion to choose the mode of traveling during the pandemic, while equally 22% of the participants considered the duration and cost before traveling as an important criterion. A significant correlation between the frequency of using PT during the pandemic and anxiety was observed. However, the frequency of using PT or walking indicated a significant correlation with education levels only before the pandemic. Besides, 50% and 40% of the participants reported that neither public transportation nor walking infrastructure conditions were appropriate, respectively. The study yielded important empirical data that can be employed in PT planning and understanding users’ preferences during this pandemic and for anticipated coming waves.

Better landscape designs demand better landscape analysis and vice versa. At regional scale, this feedback demands precision and integration among multiple scales. This work prospects the potential of information modelling to improve Geodesign in decision making and collaborative design through increasing feedback. As a participant of the International Geodesign Collaboration (IGC) workshop “Trees for Metropolitan Regions”, this research has been developed for the Metropolitan Region of Fortaleza (MRF), the 6th largest in Brazil, located on the Northeast coast, and aimed at increasing green areas for Carbon Credit (CC) purposes. This methodological research achieved a script for subsequently information modelling application, which will improve precision in the achievement and evaluation of pre-established goals.Six methodological steps accomplished a selection of data for latter parametric modelling: 1. Reading enrichment and definition of the planning concept; 2. Division of the metropolitan region into Landscape Units (LU); 3. Selection of squared cut out samples for each LU; 4. Proposition of innovative solutions; 5. Design for CC increment through conservation, expansion, and creation of green areas; 6. Evaluation of CC enhancement and feedback. For the fifth step, three future scenarios have been designed, in 2035 and 2050: the traditional, the late adopter and the early adopter. Replicating designed green areas from samples to their respective LU resulted in a total CC increase of 33.19% for the traditional scenario, 44.29% for the late adopter and 87.71% for the early adopter, all for 2050, which goal has been established by the IGC at 30%. Information modelling, using the identified parameters in this work, may review, accelerate and improve these results. This improvement will be enhanced when precision of sample recognition is increased to define smaller cut out squares. With opportunities of resources for data processing and design, information modelling allows the design of samples on microscale to feedback regional planning, which leads to adaptive goals and to develop a valuable tool for integration among multiple scales through Landscape Information Modeling (LIM).

This paper is the result of a case study based on the geodesign proposal (Steinitz 2012) adapted to the metropolitan reality of Belém, Pará, Brazil. Composed of seven municipalities and with about 1.7 million inhabitants, this metropolitan region is the second most populous in the Brazilian Amazon. Paradoxically, even in the midst of high rainfall, a dense hydrographic network and an abundance of water resources, Belém shows significant vulnerability in terms of the quality and quantity of water accessed by its population. According to the Sanitation Panel, 39.8% of the population in the Metropolitan Region of Belém (MRB), does not have access to drinking water (Brasil 2018). In turn, 90.1% do not have sewage collection and treatment, further aggravating the water crisis, socioeconomic and health inequality. This reality requires more effective urban planning with regard to the vulnerabilities presented. In this scenario, geodesign represented an interesting methodological approach, primarily for highlighting the importance of the water issue in Belém and also for sketching propositional scenarios, in a collaborative way and based on Geographic Information System (GIS).

As the world has become more connected, the scale of cities would be no different. Thus, it became necessary to expand the solutions to meet the planning expectations related to the urban environment in response to contemporary challenges. At the same time, the advent of new information and communication technologies, combined with the popularization of mobile devices, created opportunities to increase the involvement of ordinary citizens in activities of geolocalized data generation and maintenance - Volunteered Geographic Information. Parallel to this scenario of collective data generation, Geodesign framework emerges to support decision making, based on the generation of critical awareness and the co-creation of ideas. In this context, this article reports the experience of a methodological experiment developed in the scope of the project “Geodesign Brazil: Trees for Metropolitan Regions” through the case study of Rio de Janeiro Metropolitan Region, in which workshops were held using the collaborative and digital platforms GISColab and Vicon SAGA. For four weeks a group of collaborators gathered into virtual meeting platforms to apply Geodesign methodological procedures, performed into the following steps: (1) analyzing and enriching the local knowledge base with geolocalized annotations; (2) propose projects considering non-adopter, early adopter, and late adopter scenarios for 2035 and 2050; (3) evaluate their impacts over the UN Sustainable Development Goals. The study showed that inclusive and democratic methodologies supported by platforms encourage discussion, and support decisions on the importance of conscious urban and environmental planning.

Territorial planning is undergoing significant transformations that establish as working conditions the use of web-based geospatial technologies, the use of methods for sharing decisions and listening to citizens and the wide use of mechanisms to facilitate understanding due to geovisualization. Among these resources, the methods of planning by Geodesign stand out, which were adapted in the proposal of the web-based platform GISColab, elaborated with the purpose of favoring the shared planning by co-creation. The platform extensively applies the resources of Spatial Data Infrastructures (SDI’s) through protocols established by OGC (Open Geospatial Consortium) for consumption of information via WMS (Web Map Service) or WFS (Web Feature Service), allowing in both cases the increment in performances by WPS (Web Processing Service). The article discusses the differences in information consumption and illustrates them through two case studies: Trees for Metropolitan Regions, in which the accesses to data were built by WFS and support to decisions were based on WPS; and Geodesign at Participatory Budgeting where accesses were via WMS. The conclusions indicate the importance of public data being, in fact, of public access and consumption, demonstrating the positive impact of alignment with data exchange protocols. The GISColab platform, developed according to these principles, supports participatory planning, expressively communicative and in transformative-learning actions.

Trees are central in the Nature-based Solutions for promoting simultaneously quality of life and biodiversity while providing mitigation and adaptive ecosystem services in the cities. Based on the Geodesign framework using the GISColab Platform, the impact of decision-making scenarios on tree-cover changes, as well as the consequences it will have for carbon sequestration, was evaluated for 2020, 2035 and 2050 in the Metropolitan Area of São Paulo (MASP). This metropolitan area is one of the largest urban conglomerates in the world with more than 22 million people. It lies on the Atlantic Rainforest Biome, a tropical moist broadleaf forest regarded as a world hotspot of biodiversity. First, a diagnostic of the current conditions was elaborated using available layers of geospatial data from the MASP. Then the future tree cover was discussed according to three scenarios: i) the non-adopters that represent the business as usual; ii) the late-adopters that develop innovative actions from 2035; and iii) early-adopters that undertake innovative interventions of urban greening from 2020. The vegetation cover was estimated to be reduced by 4% considering the current non-adopter scenario by 2050. On the other hand, vegetation cover has the potential to increase 30% in 2050, once there is an early adoption of innovative interventions, promoting various ecosystem services and co-benefits that support the quality of life and the biodiversity in the MASP, while fostering the carbon credit in the city through vegetation carbon sink. This article points to possible pathways required to attain desired afforestation goals in the MASP following the Geodesign framework. This framework proved to be effective even though it was based only on remote meetings, imposed by the social distancing during the pandemic of COVID-19.

The main objective of this article is to present and discuss the experience of a Geodesign workshop aimed at the Metropolitan Region of Campinas, SP. It was intended to debate the potential of the Geodesign method to reflect on the characteristics of the territory and propose alternatives for the proper use and occupation of land in the region from the perspective of sustainability. The workshop offered support for the participants to co-create alternatives for the sustainable planning of the territory and to develop the potential of the area. Its main characteristic was the elaboration of proposals based on the sustainability triad: Environmental, Economic and Social. The results showed that the methodology favors the preparation of proposals for adequate land use, allowing for an evolutionary process of co-creation of ideas, as the activities were developed in an evolutionary way, that is, the proposals were created for the 2035 and 2050 scenarios, no innovations, few innovations and many innovations. After the preparation and presentation of the proposals, the groups analyzed them for the 2050 scenario, showing that this step is essential for the critical analysis of the ideas, allowing to verify which systems were fully contemplated, which were moderately covered and which were not adequately covered. Thus, it is suggested that the evaluation stage precedes the adequacy and finalization of the proposals.

The experience is part of a broader one, Geodesign Brazil: Trees for metropolitan regions, composed of a set of workshops that were held in twelve Brazilian metropolitan areas, that in Amapa was conducted by technicians of two planning state departments. The workshop aimed to develop dialogs and proposals for alternative futures to the metropolitan region, targeting the years of 2035 and 2050. The goal was to discuss ten main topics: vegetation, hydrography, housing, transportation, institutions, trade and industry, agriculture, energy, tourism and culture, and carbon credit. The GISColab platform was used as a tool for registering opinions, alerts, ideas and voting of designs for each scenario. The workshop was developed over four stages: reading enrichment and note creation; creation of proposals with that continued the existing planning; creation of proposals with some innovations; creation of proposals with many innovations and a final voting. The experience pointed to an active participation of the actors in the discussion process, but a limitation in changing from analysis to proposals, mainly accepting innovative ideas, a fact possibly related to the wide technical experience of the participants in public agencies, who acted during the meetings in the same way that they do in their professional practices: discussing the difficulties and consequences of implementing innovations. However, as a result, when comparing the first designs to the last ones, it was possible to observe improvements in performance and an adherence to a new way of planning.

The covid-19 pandemic has resumed old discussions about the virtual environments´ different functionalities needed to subsidize online activities synchronously (in real-time) or asynchronyously (not in real-time). This article discusses the inclusion of features in the webgis to ensure that their activities can be promoted in a totally asynchronous way, especially when they aim at popular participation. The discussion was developed within the Geodesign Brazil project, which promoted 12 similar workshops, between March and April 2021, each in a metropolitan region of Brazilian capitals. The project focused on the use of Geodesign supported by Giscolab (Brazilian online platform for Geodesign) to identify problems and create territorial proposals on 10 themes (water infrastructure, agriculture, green infrastructure, energy infrastructure, transport infrastructure, industry and commerce, institutional, residential, tourism and culture, carbon storage). Specifically, this article reports the experience that took place in the Recife metropolitan region’s workshop, capital of Pernambuco, state of Northeast Brazil. Since it was decided to apply asynchronous dynamics, adjustments and additions of resources were necessary to make it viable, mostly to ensure users’ interest, participation and linkage to the project. The asynchronous mode in webgis is a challenge, as it requires resources for greater clarity in the definition of activities; forms of feedback and personification of users’ paths and to incentivize the users to complete the activities proposed.

Spreading of automatically generated clickbaits, fake news, and fake reviews undermines the veracity of the internet as a credible source of information. We investigate the problem of recognizing automatically generated short texts by exploring different Deep Learning models. To improve the classification results, we use text augmentation techniques and classifier hyperparameter optimization. For word embedding and vectorization we use Glove and RoBERTa. We compare the performance of dense neural network, convolutional neural network, gated recurrent network, and hierarchical attention network. The experiments on the TweepFake dataset achieved an 89.7% accuracy.

The explosive growth of data generated by the widespread use of IoT devices and the increasing realizations of IoT applications that require real-time responses have made it difficult for traditional cloud computing or edge computing to keep up with the tasks processing demands and/or near real-time response requirements of applications. We employ the strategy of computation offloading to nearby edge nodes to meet these requirements on time. In this research, we developed an efficient offload broker mechanism using deep reinforcement learning to perform optimal task allocation and computation offloading on this platform. Experiments show that the model learns the policies for offloading tasks to the optimal nodes appropriately. These promising results will enlighten more computation offloading issues to improve the efficiency of the model and its deployment in edge computing environment.

Context: According to the WHO, suicide is the 2nd leading cause of death for young people aged 15 to 28, which often may have been the result of a depressive or anxiety disorder. Objective: To construct and evaluated a Chatbot that dialogues with college young people, with the purpose of disseminating mental health and ameliorate both depressive and anxious symptoms. Method: Our Chatbot is based on Rumination-focused Cognitive Behavioral Therapy (RFCBT), which focuses on observing the interaction between thought feelings and actions. For evaluation, an experiment that reached 105 young people was carried out. The bot’s dialogues were constructed from psychological literature on RFCBT and tests were performed based on the PHQ-4 protocol and rumination tests. It is worth mentioning that the whole experiment was analyzed and authorized by the Ethics Committee. Results: The experiment results statistical significance was confirmed and obtained through T-test, for the rumination tests, and Wilcoxon Test, for the PHQ-4, obtaining p-values lower than 0.05. Conclusion: It has been shown that there was a change in symptoms after the use of Chatbot, as a result of mitigating the symptoms of evaluated mental disorders.

Price forecasting is a challenging and essential problem studied in different markets. Many researchers and institutions, academically and professionally, develop future price forecasting techniques. This study proposes a data collection and processing pipeline to forecast the next day’s price of a product in business to consumer (B2C) markets using the price data obtained from web crawlers, preprocessing steps, the deep features produced by the autoencoder, and the technical indicators. For this purpose, we use web crawlers to collect different airline companies’ ticket prices daily and create a price index. We apply the discrete wavelet transform (DWT) preprocessing method to denoise the price index data, calculate some technical indicators analytically, and extract the deep features of the price data via three different autoencoders, linear, stacked linear, and long short term memory (LSTM). An LSTM forecaster generates forecasts using deep and calculated features. Finally, we measure the effects of autoencoder types, and mentioned features on the forecasting performance. Our study shows that using LSTM autoencoder on denoised time series price data with technical indicators in B2C markets yields promising results.

During this prevailing generation of the digital world, mobile users are multiplying globally by leaps and bounds. A mobile banking system is an electronic channel for Electronic Banking (E-Banking) all over the world. The utility of mobile banking systems has become one of the innovations to transform financial institutions from the traditional to the digital world with all the banking services. However, financial institutions do not provide enhanced banking services and electronic cheques using the mobile banking system globally. This paper proposes a new contactless mobile banking system (C-MBS) that integrates enhanced banking services with novel functions like electronic cheques, registration of the user, and cancellation of the user account included in the model. This paper develops an extended finite state machine model with parameters, variables, and constraints for C-MBS. This paper also develops a verification model of C-MBS with system properties specified utilizing process meta language (PROMELA) and security properties applying linear temporal logic (LTL). A simple promela interpreter (SPIN) is employed to verify the verification model of C-MBS. SPIN verification results confirm that the proposed C-MBS model is free from deadlocks and errors. Hence, the financial institutions can implement this model as a secure enhanced mobile banking system in E-banking. Banking users can use the enhanced banking services remotely using C-MBS on mobile and will play a significant role towards a cashless society in the digital world.

This article presents the results of applying the method of the minimum area of alarm to the complex forecasting of earthquakes based on data of different types. Point fields of earthquake epicenters and time series of displacements of the earth’s surface, measured using GPS, were used for the prediction. Testing was carried out for earthquakes with a hypocenter depth of up to 60 km for two regions with different seismotectonics: Japan, the forecast time interval from 2016 to 2020, magnitudes $$m \ge 6$$ m ≥ 6 ; California, the forecast time interval from 2013 to 2020, magnitude $$m \ge 5.5$$ m ≥ 5.5 . Testing has shown the effectiveness of systematic earthquake forecasting using seismological and space geodesy data in combination.

In November of 2019, the company MEDS BV, based in the Netherlands but mainly active in the Americas, initiated experimental aerial remote sensing with airborne LiDAR imagery in the context of a private-public sector collaboration to enable identification of undocumented archaeological sites concealed beneath the high Andean tropical cloud forests in northern Peru’s Amazonas Region. Remote sensing fieldwork and post-processing application of Deep Learning methods by MEDS BV specialists, and subsequent analysis of DTM images by archaeologists yielded a remarkably detailed picture of a forest-covered, previously unstudied sector at the extensive archaeological complex of Kuelap called Imperio. At 3000 m above sea level, the Kuelap site complex consists of at least 12 sectors and two cliff cemeteries sprawling 900 hectares along a ridge top above the western banks of the Utcubamba River valley. Kuelap’s centerpiece and featured tourist attraction called “La Fortaleza” is a large settlement built atop a massive walled platform a long, prominent ridgetop. The Kuelap complex was probably the most populous locality in the Utcubamba River valley and is attributed to peoples that the Inka and Spaniards called “Chachapoya.” Early Spanish settlers left no known written descriptions of the site, nor useful descriptions regarding the region’s inhabitants. Consequently − and despite extensive archaeological studies − important questions concerning the political, economic, and religious roles of Kuelap in the region remain unresolved.The project reported here has primary and secondary objectives, both resulting from multiple stages of data gathering, processing, analysis, and interpretation. The primary goal was to capture high-resolution, three-dimensional georeferenced imagery of archaeological remains hidden beneath the region’s dense tropical montane forests and provide sufficient data for a rich preliminary description. This work responds to the urgent need to identify, characterize, and protect such cultural heritage from looting and destructive activities that accompany population growth and deforestation. The second objective emerged as an unexpected bonus, only because of the extraordinary success of the first. Successful imaging of surface details at Imperio provided an extraordinary opportunity to reevaluate previous interpretations of the site, and to offer an alternative novel hypothesis regarding Imperio’s history of occupation and particularly the site’s special functions. The imagery enabled identification of subtle surface features that we suggest could be overlooked and inadvertently destroyed during conventional ground-level mapping and documentation activities in such complex, overgrown terrain. Many such features are functional elements of a planned drainage system that warrants further study for long-term conservation planning.

Estimating salinity concentrations in coastal waters allows characterization of the spatial and temporal dynamics of the freshwater/saltwater interface. In Southeast Florida (USA) the saltwater interface is monitored and evaluated for potential impacts to public supply wellfields and biological communities. In this research, a closed-loop autoregressive neural network with exogenous inputs was developed to estimate salinity concentrations at a coastal water quality station (BISCC4) in Biscayne Bay, Florida. The neural network (ANN) is shown to successfully simulate hourly salinity concentrations for years 2015 through 2019. A statistical comparison of simulated concentrations versus observed data demonstrates that the ANN simulates salinity concentration values and trends within acceptable margin of errors (R2 = 0.59, K-G = 0.64, NSE = 0.33, d = 0.86, PBIAS = 1.5%, RSR = 0.82). In its current form, the ANN model performs better in simulating salinity concentrations and trends, than an existing hydrodynamic model. These results have the potential to be applied to other coastal locations in Biscayne Bay where freshwater inputs from inland streams and canals are affecting salinity concentrations.

The nexus of factors influencing the dissemination of the SARS-CoV-2 virus is so complex that identification of (some) determining factors of COVID-19 spatial diffusion is significantly hampered. COVID-19 characterize of specific dynamics and enormous volume of morbidity. The aim of the study is construction of the model of spatial dissemination of COVID-19 in Poland, identification of the main outbreak places and spatial heterogeneity of pandemic based on the spatial set of first twelve months morbidity data (in 2020 and 2021). The target (prototypical) model is intended rather as the supporting tool than replacement of the well-known and used SIR or SEIR (Susceptible – Exposed – Infected - Recovered) models in epidemiology. It also assumed that the target model could be used as a priori estimation tool of the spatial locations of infections outbreaks as well as evaluation of future volume of infections due to changing numbers of exposed and recovered persons related also to, newly, introduced and continuation coronavirus (COVID-19) vaccinations. One of the expected advantages of the construed model is its spatial aspect i.e. it will enable to evaluate the potential spatial differentiation of infected number of people within the set of observed spatial units i.e. counties in Poland.

The aim of this paper is to analyze the problem of the sustainability of the urban transport in Italian provinces. After defining what we mean for sustainable mobility we individuate some indicators to obtain a measure of it.The methodology used in this paper is the Multiple Factor Analysis (MFA). This method is applied to tables in which a set of individuals is described by a set of variables and the variables are organized into groups. We have applied the MFA to the choosen indicators for Italian cities in the year 2019. This method of analysis allows to identify two main dimensions that describe more than 58% of the variability of sustainability of transports in Italian cities.

Fires represents one of the main causes of environmental degradation and have an important negative impact on the landscape. Fires, in fact, strongly influenced ecological processes and compromise the ecosystems. Measurements of the post-fire damage levels over burned areas are important to quantify fire’s impact on landscapes. Remote sensing and geo-statistical approaches are useful tools for the monitoring and analysis of burned areas on a regional scale, because provides reliable and rapid diagnosis of burned areas. Spatial autocorrelation statistics, such as Moran’s I and Getis–Ord Local Gi index, were also used to measure and analyze dependency degree among spectral features of burned areas. This approach improves characterization of a burnt area and improves the estimate of the severity of the fire. This paper provides an application of fire severity studies describing post-fire spectral responses of fire affected vegetation to obtain a burned area map. The aim of this work is to implement a procedure, using ESA Sentinel 2 data and spatial autocorrelation statistics in a GIS open-source environment, a graphical model that analyzes the change detection of the potential burned area, as case of study Northern part of Apulia Region (Italy) was used. The burned area was delineated using the spectral indices calculated using Sentinel two images in the period July–August 2020 and using also the land use map of the area.

The processes of land transformation related to soil erosion and land degradation are complex phenomena that require an approach as detailed and multidisciplinary as possible. In some Mediterranean inland areas, these issues seem to be very connected to the dynamics of transformation and abandonment of agricultural areas. In order to carry out this preliminary investigation for the assessment of dynamics and relationships between processes and land cover, an approach based on GIS and remote sensing has been applied. The study started with implementation of the Revised Universal Soil Loss Equation (RUSLE) model to calculate soil erosion on a monthly and annual basis. The resulting data were then processed through a Getis-Ord local autocorrelation index in order to produce a persistent erosion map. All datasets created were correlated with the cover classes that need more attention, i.e., arable land and post-cultivation vegetation area. All the techniques and methodologies, have been applied in a rural area of the Basilicata Region (South Italy) using exclusively a Free and Open Source Software (FoSS) GIS approach as it guarantees the possibility to perform a series of complex analyses in a simple and effective way so that they can be implemented in environmental monitoring actions and plans.

European Commission in 2009 assessed that in the period 2015–2030 about 11% of agricultural land in the EU are under high potential risk of abandonment due to factors, which has strong and known environmental and socio-economic consequences. The diverse impacts of abandonment need to be addressed via a broader set of policy instruments to alleviate the negative effects or even - reverse the trends in the early stages of the process. The clear identification of abandoned agricultural land is fundamental for a correct mapping for the future management and monitoring of the territories. In this context, this study proposes an innovative method for the detection and mapping of abandoned arable land through the use of remote sensing techniques and geo-statistical analysis. The combined use of Sentinel 2 images and the Landsat constellation, the use of NDVI index and change detection analysis made it possible to identify the change in agricultural use and/or abandonment of land in the eastern part of the Basilicata region in the period 1990–2020. (Italy). All process has been developed integrating Remote Sensing and Geographic Information System (GIS), using open-source software.

Soil degradation is a phenomenon that describes the degradation of soil quality due to which agricultural land in particular is unproductive as a consequence of the loss of ability to produce crops and biomass. The causes are many but, especially in the inland areas of the Mediterranean regions, some dynamics related to agriculture have particularly influenced the grading process. Specifically, agricultural over exploitation with unsustainable practices and land abandonment are causing ecological alterations that require contextual analysis to assess the medium and long-term effects. The aim of this work is to investigate the role of some factors that make up the RUSLE index have in the detection and monitoring of potentially degraded areas.In particular, the areas cultivated with arable crops were chosen as the area to be analyzed, because the average annual rate of soil erosion (A factor in RUSLE equation) is high despite the presence of vegetation cover and shown evident problems due to the phenomenon of degradation. In order to identify the potential degraded areas, two factor of RUSLE index have been correlated: C factor that describes the vegetation cover of the soil and A factor which represent the amount of potential soil erosion. All methodologies have been applied in a rural area in the northern part of Basilicata Region (Italy) using GIS and remote sensing approaches, as allows the possibility to perform a series of a complex studies and can be efficiently implemented in environmental monitoring plans.