Spatial Accessibility and Equity Evaluation of Medical Facilities Based on Improved 2SFCA: A Case Study in Xi'an, China.

Accurate evaluation of the accessibility of medical facilities is a prerequisite for the reasonable allocation of medical resources in a city. The accessibility of medical facilities depends not only on the distance to the supply and demand points, but also on the time spent in the process, and the supply capacity of the supply points. Taking Xi'an City of Shaanxi Province as an example, this paper comprehensively considers the facility supply capacity and introduces the selection probability function based on the two-step floating catchment area (2SFCA) method. In addition, in order to approximate the residents' acceptance of different types of hospitals for long-distance medical treatment in real situations, different levels of search radius were set for the different levels of hospitals, and ArcGIS was used to measure the accessibility and evaluate the spatial layout of medical facilities in the main urban area of Xi'an. The results show that there is a significant difference in the accessibility of medical facilities in the main urban area of Xi'an, and the accessibility tends to decrease gradually from the central city to the periphery. The inequity in the allocation of medical facilities in the main urban area of Xi'an is more obvious, with about 81.64% of people having access to 54.88% of medical resources. The accessibility evaluation model established by the improved 2SFCA method can obtain more accurate and objective evaluation results. This study can provide a reference basis for urban medical facilities' planning and rational spatial layout.

Spatial Pattern and Formation Mechanism of Rural Tourism Resources in China: Evidence from 1470 National Leisure Villages

Rural tourism development has been an essential driving force behind China’s promotion of integrated urban–rural development, sustainable rural development and rural revitalization in the new era. This study included 1470 villages on the national list of beautiful leisure villages in China (BLVCs) from 2010 to 2021. We explored the distribution characteristics and influencing factors based on mathematical statistics and spatial analysis in ArcGIS to provide a theoretical reference for promoting the development of leisure village agriculture and rural tourism. The results show that the distribution of BLVC presents a clustered state, showing a distribution pattern of a dual core, seven centers and multiple scattered points. BLVCs are mainly distributed in areas with flat terrain and sufficient water resources, which are conducive to agricultural production and life. Having convenient transportation and rich tourism resources aids the promotion of rural tourism development. The resulting gap in regional development is balanced to some extent by government support. The research results provide a reference value for future rural spatial optimization and sustainable development. This paper summarizes the law of rural development and clarifies the factors influencing the development of rural tourism, and it provides the Chinese experience as a model for a rural renaissance empowered by rural tourism.

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

Background: Unequal housing access resulted in more than 150 million homeless people worldwide, with mil-lions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research. Methods: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research. Results: Our findings indicated a geographic clustering of case study locations- 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters. Conclusion: Most studies were restricted in analyzing and visualizing the patterns and disease clusters;however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.

Multi-Criteria Decision-Making System for Wind Farm Site-Selection Using Geographic Information System (GIS): Case Study of Semnan Province, Iran

Selecting the best place for constructing a renewable power plant is a vital issue that can be considered a site-selection problem. Various factors are involved in selecting the best location for a renewable power plant. Therefore, it categorizes as a multi-criteria decision-making (MCDM) problem. In this study, the site selection of a wind power plant is investigated in a central province of Iran, Semnan. The main criteria for classifying various parts of the province were selected and pairwise compared using experts’ opinions in this field. Furthermore, multiple restrictions were applied according to local and constitutional rules and regulations. The Analytic Hierarchy Process (AHP) was used to weigh the criteria, and according to obtained weights, wind speed, and slope were the essential criteria. Moreover, a geographic information system (GIS) is used to apply the weighted criteria and restrictions. The province’s area is classified into nine classes according to the results. Based on the restrictions, 36.2% of the total area was unsuitable, mainly located in the north part of the province. Furthermore, 2.68% (2618 km2) and 4.98% (4857 km2) of the total area are the ninth and eightieth classes, respectively, which are the best locations for constructing a wind farm. The results show that, although the wind speed and slope are the most essential criteria, the distance from power facilities and communication routes has an extreme impact on the initial costs and final results. The results of this study are reliable and can help to develop the wind farm industry in the central part of Iran.

Modelling of Air Pollution Dispersion in Thiruvananthapuram Corporation

Air is a crucial element of the earth’s ecosystem, and even minor changes in its composition can have a wide range of effects on the survival of creatures on earth. Deterioration of air quality is an important issue faced by many cities in India. Modelling of air pollution is a numerical method for describing the causal relationship between emissions, meteorology, atmospheric concentrations and deposition. The current study prepared annual and monthly air pollution dispersion maps at sensitive areas of Thiruvananthapuram Municipal Corporation, which is the administrative spot in the city of Thiruvananthapuram, the capital of Kerala. ADMS-Urban model was used in conjunction with GIS to produce the dispersion maps. The study has demonstrated a methodology for the development of emission inventory, dispersion modelling and mapping. Dispersion modelling and trend analysis were used to investigate the concentration of the pollutants and their intensity of dispersion in relation to meteorological conditions in the study area such as wind speed, wind direction, temperature and humidity. The present study calculates emission concentration of nitrogen dioxide (NO2), sulphur dioxide (SO2), suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM), from various monitoring stations and industries within the study area from the year 2016–2020. It was found that concentration of pollutants lie within the Central Pollution Control Board limits. Also, trend analysis of pollutant concentration was done separately for the year 2020 and there was a significant reduction (>50%) in pollution concentration due to the lockdown scenario created by COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Technological application for the post-pandemic reopening of COVID 19 in educational institutions based on a geospatial model

Building Information Modeling (BIM) and Geographic Information Systems (GIS) technologies are computational tools that allow better management of inhabited physical spaces. In educational institutions, for example, it is possible to generate real-time monitoring of the people who enter, recording their journey permanently within the facilities. This will allow greater control over biosafety and protection measures such as social distancing, reservation of workspaces, and registration in smart forms regarding the health conditions of each person. The purpose of this project is to design an application that allows the management of the physical spaces of Block A-Candelaria headquarters of the University of La Salle through the use of GIS tools in the framework of the post-COVID-19 reopening;With this pilot of the interconnection and visualization of geographic information carried out in one of the busiest buildings of the university (block A), the concept of 'digital twins' is addressed and web and mobile applications of the ArcGIS Online platform are developed integrating ArcGIS Indoors as a new work extension for virtual post-pandemic monitoring and control environments. © 2021 IEEE.

GIS Based emergency management framework for large-scale events: A case study of the torch relay activity

Due to the high popular concentration of large-scale events, once an emergency (like a stampede) occurs, it will often cause severe casualties. Moreover, since the widespread of the COVID-19, the prevention of the novel coronavirus should also be considered during mass gatherings. How to reduce the probability and potential consequence of emergencies is of great significance. This research designs an emergency management framework using ArcGIS-based geographic information technology for large-scale events. To verify the effectiveness of our framework, we take the Winter Olympic torch relay in university as an example. The paper is mainly divided into two parts, emergency resource allocation and the emergency prevention model. The former part focuses on the site selection of emergency sentries and emergency hospitals during the torch relay. In the latter part, an emergency prevention model is designed for two significant emergencies: stampede and epidemic. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

Integration of multiple geospatial applications and intelligence for responding to COVID-19 in Ghana.

OBJECTIVE: We describe the use of integrated geospatial applications for the provision of access to timely and accurate data on samples, visualisation of Spatio-temporal patterns of cases and effective communication between field sample collectors, testing laboratories, Regional Health directors and Government Decision Makers. DESIGN: This study describes how an integrated geospatial platform based on case location and intelligence was developed and used for effective COVID-19 response during the initial stages of COVID-19 in Ghana. DATA SOURCE: Collector for ArcGIS, ArcGIS Survey123. MAIN OUTCOME MEASURE: successful development and deployment of integrated geospatial applications and analytics. RESULTS: The Collector for ArcGIS app was customised to collect COVID-19 positive cases location information. Survey 123 was introduced as a COVID-19 contact tracing application to digitise the case-based forms and provide real-time results from the laboratories to GHS and other stakeholders. The laboratory backend allowed the testing laboratories access to specific information about each patient (sample) collected by the fieldworkers. The regional supervisors' backend web application provided accessing test results for confidentiality and timely communication of results. CONCLUSION: Geospatial platforms were successfully established in Ghana to provide timely results to Regional Health Directors and Government decision-makers. This helped to improve the timeliness of response and contact tracing at the district level. FUNDING: The development and deployment of the application, COVID-19 pandemic response and writing workshop by the Ghana Field Epidemiology and Laboratory Training Programme (GFELTP) was supported with funding from President Malaria Initiative - CDC, and Korea International Cooperation Agency (on CDC CoAg 6NU2GGH001876) through AFENET and the United States Agency for International Development (USAID) through Results for Development (R4D).

Modeling the effectiveness of social distancing interventions on the epidemic curve of coronavirus disease in Ethiopia.

SARS-CoV-2 infections are now spreading across the world. Different measures were used by governments around the world to combat the spread of COVID-19. The efficacy of social distancing approaches in reducing the spread of COVID-19 in Ethiopia was investigated using geospatial technologies and the CHIME model. The COVID-19 response was predicted, measured, and compared after 25%, 75%, and 95% social distancing interventions in Ethiopia. Social distancing strategies flatten and delay the epidemic curve, according to the model findings. The model result shows that most new events and hospitalizations were avoided when social distancing measures were in effect. Social distancing can provide a critical time for increasing healthcare capability, and the research findings could assist policymakers in estimating the immediate number of resources required and planning for potential COVID-19 initiatives in Ethiopia.

Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans.

Several studies on spatial patterns of COVID-19 show huge differences depending on the country or region under study, although there is some agreement that socioeconomic factors affect these phenomena. The aim of this paper is to increase the knowledge of the socio-spatial behavior of coronavirus and implementing a geospatial methodology and digital system called SITAR (Fast Action Territorial Information System, by its Spanish acronym). We analyze as a study case a region of Spain called Cantabria, geocoding a daily series of microdata coronavirus records provided by the health authorities (Government of Cantabria-Spain) with the permission of Medicines Ethics Committee from Cantabria (CEIm, June 2020). Geocoding allows us to provide a new point layer based on the microdata table that includes cases with a positive result in a COVID-19 test. Regarding general methodology, our research is based on Geographical Information Technologies using Environmental Systems Research Institute (ESRI) Technologies. This tool is a global reference for spatial COVID-19 research, probably due to the world-renowned COVID-19 dashboard implemented by the Johns Hopkins University team. In our analysis, we found that the spatial distribution of COVID-19 in urban locations presents a not random distribution with clustered patterns and density matters in the spread of the COVID-19 pandemic. As a result, large metropolitan areas or districts with a higher number of persons tightly linked together through economic, social, and commuting relationships are the most vulnerable to pandemic outbreaks, particularly in our case study. Furthermore, public health and geoprevention plans should avoid the idea of economic or territorial stigmatizations. We hold the idea that SITAR in particular and Geographic Information Technologies in general contribute to strategic spatial information and relevant results with a necessary multi-scalar perspective to control the pandemic.