Accessibility and site suitability for healthcare services using GIS-based hybrid decision-making approach: a study in Murshidabad, India

Investigating geographical accessibility and site suitability of medical laboratories in Kermanshah-Iran

Introduction

One of the major challenges in developing countries is the inappropriate spatial distribution of medical laboratory centers (MLCs) which can lead to injustice in access to health services. This study aimed to investigate the accessibility to and site suitability of MLCs in Kermanshah Metropolis by GIS.

Materials and methods

In this cross-sectional study, data were collected from the Iran Statistical Center and Deputy of Treatment of Kermanshah University of Medical Sciences. We used Arc/GIS 10.6 software, AHP technique, and network analysis tools to determine the access status of Kermanshah citizens to MLCs in 2019 and site selection for MLCs. The layers used in this study included population density, city development trends, compatible and incompatible land uses, pathways, land slope, river area, and access radius.

Results

About 70% of households had inappropriate access to all MLCs in walking scenario. This ratio was 31.26% for 5 min, 9.58% for 10 min, and 6.09% for 15 min driving. Comparisons between public and private MLCs showed that in walking scenario, 88% of households had improper access to public and 80% to private MLCs. Based on 5 and 10 min of driving, 57 and 19% of households had inappropriate access to public MLCs, and 45 and 17% to private MLCs, respectively. Also, with 15 min of driving, 8% of households had improper access to public and 18% to private MLCs. Findings showed that scores provided for population density criteria were (0.298), distance from existing laboratories (0.213), proximity to pathways (0.175), consistent land use (0.129), city development trend (0.087), distance from riverfront (0.053), distance from incompatible land uses (0.015), and land slope (0.03). The final model was obtained by overlaying the layers. The model showed a 9-degree range from very bad to very good in Kermanshah city for the construction of laboratory centers (CR<0.01).

Conclusion

The site selection model showed that the location of the proposed centers can be in the north and outskirts of the city to facilitate citizens' access to the MLCs. These results emphasize the justice in the spatial distribution of MLCs for the benefit of deprived populations as a global value.

A GIS-Based Method for Identification of Blindness in Former Site Selection of Sewage Treatment Plants and Exploration of Optimal Siting Areas: A Case Study in Liao River Basin

With regard to environmental facilities, blindness and the subjectivity of site selection lead to serious economic, engineering and social problems. A proper siting proposal often poses a challenge to local governments, as multiple factors should be considered, such as costs, construction conditions and social impact. How to make the optimal siting decision has become a topical issue in academic circles. In order to enrich the framework of site selection models, this study combined GIS, AHP and Remote Sensing (RS) technologies to conduct siting suitability analysis of sewage treatment plants, and it was first applied in the Liao River basin in Jilin Province in China. The enriched model is able to reveal blindness in the former site selection of sewage treatment plants and explore optimal siting areas, involving an effective quantification method for summer dominant wind direction and urban stream direction. In a case study, it was found that local governments need to be cautious of the distance of sites from rivers and residential areas and the impact of these sites on downwind and downstream residents. Additionally, siting suitability has obvious regional characteristics, and its distribution varies significantly between towns. Huaide Town shows the largest optimal siting areas and can be given priority for the construction of new sewage treatment plants. This paper developed a more scientific approach to site selection, and the outcome can provide a robust reference for local governments.

Exploring COVID-19 incidence hotspot in Metropolitan area of Pakistan using geo-statistical approach: a study of Lahore city

Globally, COVID-19 is a top level public health concern. This paper is an attempt to identify and assess COVID-19 incidence hotspots in Metropolitan area of Pakistan using geo-statistical approach. The study is based on secondary data. The COVID-19 confirmed cases record (15/03/2020 to 15/04/2020) of entire Metropolitan area is obtained from hospitals and National Institute of Health website. Point-level geo-coding technique was applied on patient’s record and the relative location was converted into absolute location. Getis-Ord Gi* statistical model was applied in ArcGIS 10.3 to calculate Z-score and P values for each point location representing the COVID-19 incidence intensity. Then inverse distance weighted technique of spatial interpolation was applied on Z-score and spatial clusters of crime were geo-visualized in the form hotspot and cold spot. Spatially, more than 50% of land area of Allama Iqbal, Samanabad, Gulburg and Cantonment is covered by very high incidence zone which is surrounded by high incidence zone whereas Ravi, Shalimar, and north of Wagha and Aziz Bhatti towns are located in very low incidence zone. This study provides a suitable methodological framework for identification and analysis infectious disease hotspots. The study can also facilitate health and related authorities to fight war against COVID-19. Similarly, it can help policy makers to manage the movement of travelers and restrict social interaction.

Risk and space: modelling the accessibility of stroke centers using day- & nighttime population distribution and different transportation scenarios

Purpose

Rapid accessibility of (intensive) medical care can make the difference between life and death. Initial care in case of strokes is highly dependent on the location of the patient and the traffic situation for supply vehicles. In this methodologically oriented paper we want to determine the inequivalence of the risks in this respect.

Methods

Using GIS we calculate the driving time between Stroke Units in the district of Münster, Germany for the population distribution at day- & nighttime. Eight different speed scenarios are considered. In order to gain the highest possible spatial resolution, we disaggregate reported population counts from administrative units with respect to a variety of factors onto building level.

Results

The overall accessibility of urban areas is better than in less urban districts using the base scenario. In that scenario 6.5% of the population at daytime and 6.8% at nighttime cannot be reached within a 30-min limit for the first care. Assuming a worse traffic situation, which is realistic at daytime, 18.1% of the population fail the proposed limit.

Conclusions

In general, we reveal inequivalence of the risks in case of a stroke depending on locations and times of the day. The ability to drive at high average speeds is a crucial factor in emergency care. Further important factors are the different population distribution at day and night and the locations of health care facilities. With the increasing centralization of hospital locations, rural residents in particular will face a worse accessibility situation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-021-00284-y.

Sanitary landfill site selection by integrating AHP and FTOPSIS with GIS: a case study of Memari Municipality, India

Sanitary landfill is still considered as one of the most significant and least expensive methods of waste disposal. It is essential to consider environmental impacts while selecting a suitable landfill site. Thus, the site selection for sanitary landfill is a complex and time-consuming task needing an assessment of multiple criteria. In the present study, a decision support system (DSS) was prepared for selecting a landfill site in a growing urban region. This study involved two steps of analysis. The first step of analysis involved the application of spatial data to prepare the thematic maps and derive their weight. The second step employed a fuzzy multicriteria decision-making (FMCDM) technique for prioritizing the identified landfill sites. Thus, initially, the analytic hierarchy process (AHP) was used for weighting the selected criteria, while the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) was applied for addressing the uncertainty associated with decision-making and prioritizing the most suitable site. A case study was conducted in the city of Memari Municipality. The main goal of this study was the initial evaluation and acquisition of landfill candidate sites by utilizing GIS and the following decision criteria: (1) environmental criteria consisting of surface water, groundwater, land elevation, land use land cover, distance from urban residence and buildup, and distance from sensitive places; and (2) socioeconomic criteria including distance from the road, population density, and land value. For preparing the final suitability map, the integration of GIS layers and AHP was used. On output, 7 suitable landfill sites were identified which were further ranked using FTOPSIS based on expert's views. Finally, candidate site-7 and site-2 were selected as the most suitable for proposing new landfill sites in Memari Municipality. The results from this study showed that the integration of GIS with the MCDM technique can be highly applied for site suitability. The present study will be helpful to local planners and municipal authorities for proposing a planning protocol and suitable sites for sanitary landfill in the near future.

Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique

The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value < 0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score (> 3 and > 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19.