Anas M. M. Awad

Background This study applied geographical standards (coverage distance) and demographic standards to investigate the capabilities of primary healthcare facilities to cover the largest area of the study area and the service area that can be accessed. This study aimed to determine the number of primary healthcare (PHC) centres required to ensure adequate healthcare coverage for the entire community. Methods Data was obtained by applying geographic information system (GIS) techniques, including proximity analysis, spatial overlay, and network analysis, to analyze primary care facilities using the demographic and geographic standards for primary care facilities. The study was conducted in Sleman District, Yogyakarta, Indonesia, using ArcGIS 10.5 and QGIS software for spatial analysis. Results PHC centers cover 79% of the study area according to the geographical standard. The study area needs 41 additional centers to cover the shortfall in service provision per the demographic and geographic standards. Conclusions A significant deficiency in the number of primary care centers found in the study area compared to the large population at the geographical and demographic standards level.

Background This study applied geographical standards (coverage distance) and demographic standards to investigate the capabilities of primary healthcare facilities to cover the largest area of the study area and the service area that can be accessed. This study was conducted to find out the sufficient number of primary healthcare (PHC) centers required to provide healthcare services to the entire community. Methods Data was obtained by applying geographic information system (GIS) techniques to analyze primary care facilities using the demographic and geographic standards for primary care facilities. Results PHC centers cover 79% of the study area according to the geographical standard. The study area needs 41 additional centers to cover the shortfall in service provision per the demographic and geographic standards. Conclusions A significant deficiency in the number of primary care centers found in the study area compared to the large population at the geographical and demographic standards level.

Background This study applied geographical standards (coverage distance) and demographic standards to investigate the capabilities of primary healthcare facilities to cover the largest area of the study area and the service area that can be accessed. This study was conducted to find out the sufficient number of primary healthcare (PHC) centers required to provide healthcare services to the entire community. Methods Data was obtained by applying geographic information system (GIS) techniques to analyze primary care facilities using the demographic and geographic standards for primary care facilities. Results PHC centers cover 79% of the study area according to the geographical standard. The study area needs 41 additional centers to cover the shortfall in service provision per the demographic and geographic standards. Conclusions A significant deficiency in the number of primary care centers found in the study area compared to the large population at the geographical and demographic standards level.

Background This study aims to develop a vulnerability map for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to assess the city’s vulnerability to COVID-19. Methods Six key factors influencing vulnerability were identified and their relative importance determined through the Analytic Hierarchy Process (AHP) pairwise comparison matrix. GIS was utilized to classify Surabaya’s vulnerability into five levels: very low, low, medium, high, and very high. Results The resulting vulnerability map provides essential insights for decision-makers, healthcare professionals, and disaster management teams. It enables strategic resource allocation, targeted interventions, and formulation of comprehensive response strategies tailored to specific needs of vulnerable districts. Conclusions Through these measures, Surabaya can enhance its resilience and preparedness, ensuring the well-being of its residents in the face of potential emergency outbreaks.

Background This study aims to develop a vulnerability map for Surabaya using GIS-based Multi-Criteria Decision Analysis (MCDA) to assess the city’s vulnerability to COVID-19. Methods Six key factors influencing vulnerability were identified and their relative importance determined through the Analytic Hierarchy Process (AHP) pairwise comparison matrix. GIS was utilized to classify Surabaya’s vulnerability into five levels: very low, low, medium, high, and very high. Results The resulting vulnerability map provides essential insights for decision-makers, healthcare professionals, and disaster management teams. It enables strategic resource allocation, targeted interventions, and formulation of comprehensive response strategies tailored to specific needs of vulnerable districts. Conclusions Through these measures, Surabaya can enhance its resilience and preparedness, ensuring the well-being of its residents in the face of potential emergency outbreaks.

Purpose: The rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.Design/methodology/approach: This paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.Findings: The results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.Practical implications: The results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.Originality/value: The proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.

Background This study applied geographical standards (coverage distance) and demographic standards to investigate the capabilities of primary healthcare facilities to cover the largest area of the study area and the service area that can be accessed. This study was conducted to find out the sufficient number of primary healthcare (PHC) centers required to provide healthcare services to the entire community. Methods Data was obtained by applying geographic information system (GIS) techniques to analyze primary care facilities using the demographic and geographic standards for primary care facilities. Results PHC centers cover 79% of the study area according to the geographical standard. The study area needs 41 additional centers to cover the shortfall in service provision per the demographic and geographic standards. Conclusions A significant deficiency in the number of primary care centers found in the study area compared to the large population at the geographical and demographic standards level.