Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering.

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.

Citizen participation through volunteered geographic information as equipment for a smart city to monitor urban decay.

One of the issues brought on by rapid urbanization is the loss of livability owing to poor waste management, a lack of resources, air pollution, traffic congestion, and outdated buildings and public infrastructure. The idea of a smart city has arisen as one of the potential solutions to this concern. In this study, we have demonstrated how citizen science helps immensely to the growth of a smart city by crowdsourcing data about urban decay. Tehran, the capital of Iran, which is home to the history of many people's social lives, has begun losing some of its vitality. Municipalities have plans for dealing with urban decline and revival, but we indicate that volunteered geographic information (VGI) would be more effective in turning such ideas into practice. According to our research, there are three different types of organizations active in urban renewal: municipalities, civil and urban planning agencies, and organizations that support tourism and cultural heritage. As a result, we provided a system design that outlines the duties that each institution has in relation to urban renewal. In addition, we suggested a service broker with the capacity to publish VGI in order to offer interoperability related to geographic data and services based on spatial data infrastructure (SDI). Beyond that, various types of urban decay, necessary management procedures, the sequence of information in system components, and the volunteer's behavior confronting the system were all specified in the form of an object-oriented design. A prototype system based on state-of-the-art in open-source technologies was developed to test our hypothesis. The VGI system enabled us to monitor urban decay events in real time using the management dashboard. We have shown that volunteers can distinguish between urban declines and typical instances like a binary classifier in the quality control method proposed in this study. Performance was 90% based on the findings of the quality control. A running VGI system would start beneficence after 3 months and make a profit of 150% after a year, based on our estimates.

Hospital site selection using fuzzy AHP and its derivatives.

Environmental managers are commonly faced with sophisticated decisions, such as choosing the location of a new facility subject to multiple conflicting criteria. This paper considers the specific problem of creating a well-distributed network of hospitals that delivers its services to the target population with minimal time, pollution and cost. We develop a Multi-Criteria Decision Analysis process that combines Geographical Information System (GIS) analysis with the Fuzzy Analytical Hierarchy Process (FAHP), and use this process to determine the optimum site for a new hospital in the Tehran urban area. The GIS was used to calculate and classify governing criteria, while FAHP was used to evaluate the decision factors and their impacts on alternative sites. Three methods were used to estimate the total weights and priorities of the candidate sites: fuzzy extent analysis, center-of-area defuzzification, and the alpha-cut method. The three methods yield identical priorities for the five alternatives considered. Fuzzy extent analysis provides less discriminating power, but is simpler to implement and compute than the other two methods. The alpha-cut method is more complicated, but integrates the uncertainty and overall attitude of the decision-maker. The usefulness of the new hospital site is evaluated by computing an accessibility index for each pixel in the GIS, defined as the ratio of population density to travel time. With the addition of a new hospital at the optimum site, this index improved over about 6.5 percent of the geographical area.