Introduction: For the past 2400 years, the spatial relationship between health and location has been a concern for researchers. Studies have been conducted for decades to understand such a relationship, which has led to the identification of a number of healthcare planning issues. Geographic Information Systems (GIS) technology has contributed to addressing such issues by applying analytical approaches at the level of epidemiological surveillance and evaluating the spatial inequality of access to healthcare. Consequently, the importance of reviewing healthcare planning issues and recognition of the role of GIS are integral to relevant studies. Such research will contribute to increasing the understanding of how to apply analytical approaches for dealing with healthcare planning issues using GIS. Methods: This paper aims to provide an examination of healthcare planning issues and focuses on reviewing the potential of GIS in dealing with such issues by applying analytical approaches. The method of a typical literature review was used through collecting data from various studies selected based on temporal and descriptive considerations. Results: Researchers have focused on developing and applying analytical approaches using GIS to support two important aspects of healthcare planning: first, epidemic surveillance and modeling, despite a lack of health information and its management, and, second, evaluating the spatial inequality of access to healthcare in order to determine the optimum distribution of health resources. Conclusion: GIS is an effective tool to support spatial decision-making in public health through applying the evolving analytical approaches to dealing with healthcare planning issues. This requires a literature review before preparing relevant studies, particularly because of the continuous development of GIS technologies.

Geographic information systems (GIS) can be used to map the geographical distribution of the prevalence of disease, trends in disease transmission, and to spatially model environmental aspects of disease occurrence. The aim of this study is to discuss a GIS application created to produce mapping and cluster modeling of three diseases in Jeddah, Saudi Arabia: diabetes, asthma, and hypertension. Data about these diseases were obtained from health centers’ registered patient records. These data were spatially evaluated using several spatial–statistical analytical models, including kernel and hotspot models. These models were created to explore and display the disparate patterns of the selected diseases and to illustrate areas of high concentration, and may be invaluable in understanding local patterns of diseases and their geographical associations.

The main objective of this paper is to use Geographical Information Systems (GIS) for identifying spatial accessibility to health centers in Jeddah City, Saudi Arabia based on the drive-time analysis technique. A geo-database was created that includes the location of health centers, population distribution, and road networks. ArcGIS Network Analyst and overlay analyses were selected as the analysis tools for this paper. The results of the paper indicate that health centers that are located in the northern districts of Jeddah City have fewer registered patients. There are several areas of Jeddah City that have low accessibility to health centers because they fall outside the 30 min drive-time service area. These are located mainly in western, central, and northern city districts. Local health planners in Jeddah City can use the created application to allocate additional health centers in these less-served districts.

The issue of reducing spatial disparities in access to healthcare is one of the most important healthcare planning issues that policy makers and planners investigate and consider as a key focus until present time. A healthcare system that meets the requirements of availability and affordability will be useless if the spatial accessibility to healthcare is not provided to all equally. Therefore, this study aims to identify and analyze spatial disparities in access to healthcare centers in Jeddah, Saudi Arabia. The two-step floating catchment area (2SFCA) method was used to measure spatial accessibility of healthcare centers based on the travel time threshold (i.e., 30-min drive time in this study). The GIS technology was used to execute the 2SFCA method. A geodatabase, which includes the population districts, locations of healthcare centers, and road network, was created. Some procedures were performed within the road network database to set the travel time that is considered as an essential step to compute the origin–destination (OD) cost matrix. The OD matrix was later used as the source for calculating provider-to-population ratios and the spatial accessibility scores for population districts. The results of the study revealed spatial disparities in access to healthcare centers in Jeddah city. The majority of the Jeddah population (i.e., 97.51%) have accessibility to healthcare centers, but with disparate levels. The central districts have a higher access score compared to the rest of the city’s districts. Most districts that do not have accessibility to healthcare centers are concentrated in the southeast of the city. The results can help local health planners improve spatial equity in access to healthcare centers through giving the less-served districts a priority when allocating future healthcare centers in Jeddah city.

Using geographical information systems for defining the accessibility to health care facilities in Jeddah City, Saudi Arabia

Measuring performance of Sustainable Urban Transport is an important effort to face the challenges of future trends. This study used Geographic Information System (GIS) application for modelling the performance of Sustainable Urban Transport (SUT) in the Jakarta city Region (JCR). The GIS applications include identifying the existing performance of SUT in Jakarta city, exploring the relationships between indicators of SUT, and producing a prediction model of SUT. Research methods used in this study were GIS techniques with geo-reference, classifications, polygon to raster, re-classifications, sum-weighted, ordinary least square (OLS), exploratory regression, and geographic weighted regression (GWR). The results revealed that the SUT model have more capability in measuring the performance of SUTs spatially and simply. This model is to visualize the effect of the indicator on the SUT performance and its influence, respectively. The results of this study also discovered that the JCR’s Sustainable Urban Transport Performance was in medium level. The outputs of this modelling were useful for evaluating the level of SUT performances in the city based on districts area. Overall, this study provides valuable information on the SUT performance of the JCR, also highlights some important challenges faced in the future of SUT program development.