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Utilization of GeoAI Applications in the Health Sector: A Review Anastasia Amponsah; Philia Latue; Heinrich Rakuasa
Journal of Health Science and Medical Therapy Том 1 № 02 (2023): September 2023
Publisher : Pt. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jhsmt.v1i02.240

Abstract

This research describes the use of GeoAI, a geospatial data-based artificial intelligence, to improve the understanding and management of health in a global context. GeoAI enables the integration of geographic data such as maps, satellite images, and environmental information with artificial intelligence technology to analyze disease spread, health risk factors, and health resource management more accurately. This research uses a descriptive qualitative approach. The type of research used is a literature study. The literature review database used is by searching on Google Scholar, Scopus, and Google Book. The results of this study show that the basic concept of GeoAI involves more accurate spatial analysis, disease spread monitoring, disease outbreak prediction, and more efficient health resource management. However, challenges such as access to adequate data, lack of understanding among health professionals, and data privacy and security issues need to be addressed for GeoAI to be effectively implemented. In conclusion, GeoAI has great potential in improving public health and addressing global health challenges, but requires careful steps in its implementation.
Mapping of Landslide Prone Areas in Huamual Sub-District, Seram Bangian Barat Regency, Indonesia Theochrasia Latue; Philia Latue; Heinrich Rakuasa; Glendy Somae; Abdul Muin
Jurnal Riset Multidisiplin dan Inovasi Teknologi Том 1 № 02 (2023): September 2023
Publisher : Pt. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jimat.v1i02.239

Abstract

This research aims to map landslide-prone areas in Huamual Sub-district, West Seram Regency, Indonesia. Through the collection and analysis of geospatial data, including characteristics of slope, land elevation, geology, rainfall, land cover and distance from active faults, this study successfully identified areas with high potential landslide risk. The results showed that the area in low landslide class has an area of 5,076.67 ha, the area in medium class has an area of 20,979.79 ha and the area in high landslide prone class has an area of 7,430.88 ha. The results of this study provide an important contribution in landslide risk mitigation planning, through identification of zones that need special attention, safer spatial planning, and more effective early warning system. This research provides a strong scientific basis for the government and other stakeholders to take appropriate preventive measures, so as to improve public safety and protect important assets from potential landslide hazards in Huamual Sub-district area.
Role of Geographers in the Analysis and Modeling of the Spread of Communicable Diseases (Malaria & COVID-19) in Ambon City: A Spatial Approach for Epidemiological Analysis Theochrasia Latue; Philia Latue; Sandy Liwan; Susan Manakane; Heinrich Rakuasa
International Journal of Multidisciplinary Approach Research and Science Том 1 № 03 (2023): International Journal of Multidisciplinary Approach Research and Science
Publisher : Pt. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/ijmars.v1i03.198

Abstract

This study explores the role of geographers in analyzing the distribution of infectious diseases (malaria and COVID-19) in Ambon City with a spatial approach in epidemiological analysis. The method used in this review is a comparative descriptive study with a qualitative approach using secondary data from relevant sources. This research will review the role of geographers in analyzing and modeling the distribution of infectious diseases (malaria & COVID-19) in Ambon City from previous research. This research integrates geographic and health data to understand the pattern of spread and environmental factors that influence disease. Through case mapping, environmental factor analysis, and modeling of future trends, this research illustrates the important contribution of geographers in infectious disease control and prevention efforts at the local level. Interdisciplinary collaboration plays a key role in this approach, which ultimately supports more informed and effective decision-making in addressing these health challenges.