Potential applications of geospatial information systems for planning and managing aged care services in Australia

[Abstract]: This paper discusses the potential applications of Geospatial Information Technology (GITs) to assist in planning and managing aged care programs in Australia. Aged care is complex due to the numbers of participants at all levels of including planning of services, investing in capacity, funding, providing services, auditing, monitoring quality, and in accessing and using facilities and services. There is a vast array of data spread across the entities that are joined to aged care. The decision-making process for investment in capacity and service provision might be aided by technology including GIT. This is also expected to assist in managing and analysing the vast amount of demographic, geographic, socio-economic and behavioral data that might indicate current and future demand for services the aged and frail-aged population. Mapping spatio-temporal changes in near real time can assist in the successful planning and management of aged care programs. Accurate information on the location of aged care services centres and mapping the special needs of clients and their service needs may assist in monitoring access to services and assist in identifying areas where there are logistic challenges for accessing services to meet needs. GIT can also identifying migrations of aged people and of the cohorts of the population who are likely to be the next wave of clients for aged care services. GITs include remote sensing, geographic information systems (GIS) and global positioning systems (GPS) technologies, which can be used to develop a user friendly digital system for monitoring, evaluating and planning aged care and community care in Australia. Whilst remote sensing data can provide current spatiotemporal inventory of features such as locations of carer services, infrastructure, on a consistent and continuous coordinate system, a GIS can assist in storing, cross analysing, modeling and mapping of spatial data pertaining to the needs of the older people. GITs can assist in the development of a single one-stop digital database which will prove a better model for managing aged care in Australia. GIT will also be a component of technologies such as activity monitors to provide tracking functionality. This will assist in tracking dementia sufferers who may be prone to wandering and be exposed to risk

Adaptable Spatial Agent-Based Facility Location for Healthcare Coverage

Lack of access to healthcare is responsible for the world’s poverty, mortality and morbidity. Public healthcare facilities (HCFs) are expected to be located such that they can be reached within reasonable distances of the patients’ locations, while at the same time providing complete service coverage. However, complete service coverage is generally hampered by resource availability. Therefore, the Maximal Covering Location Problem (MCLP), seeks to locate HCFs such that as much population as possible is covered within a desired service distance. A consideration to the population not covered introduces a distance constraint that is greater than the desired service distance, beyond which no population should be. Existing approaches to the MCLP exogenously set the number of HCFs and the distance parameters, with further assumption of equal access to HCFs, infinite or equal capacity of HCFs and data availability. These models tackle the real-world system as static and do not address its intrinsic complexity that is characterised by unstable and diverse geographic, demographic and socio-economic factors that influence the spatial distribution of population and HCFs, resource management, the number of HCFs and proximity to HCFs. Static analysis incurs more expenditure in the analytical and decision-making process for every additional complexity and heterogeneity. This thesis is focused on addressing these limitations and simplifying the computationally intensive problems. A novel adaptable and flexible simulation-based meta-heuristic approach is employed to determine suitable locations for public HCFs by integrating Geographic Information Systems (GIS) with Agent-Based Models (ABM). Intelligent, adaptable and autonomous spatial and non-spatial agents are utilized to interact with each other and the geographic environment, while taking independent decisions governed by spatial rules, such as •containment, •adjacency, •proximity and •connectivity. Three concepts are introduced: assess the coverage of existing HCFs using travel-time along the road network and determine the different average values of the service distance; endogenously determine the number and suitable locations of HCFs by integrating capacity and locational suitability constraints for maximizing coverage within the prevailing service distance; endogenously determine the distance constraint as the maximum distance between the population not covered within the desired service distance and its closest facility. The models’ validations on existing algorithms produce comparable and better results. With confirmed transferability, the thesis is applied to Lagos State, Nigeria in a disaggregated analysis that reflects spatial heterogeneity, to provide improved service coverage for healthcare. The assessment of the existing health service coverage and spatial distribution reveals disparate accessibility and insufficiency of the HCFs whose locations do not factor in the spatial distribution of the population. Through the application of the simulation-based approach, a cost-effective complete health service coverage is achieved with new HCFs. The spatial pattern and autocorrelation analysis reveal the influence of population distribution and geographic phenomenon on HCF location. The relationship of selected HCFs with other spatial features indicates agents’ compliant with spatial association. This approach proves to be a better alternative in resource constrained systems. The adaptability and flexibility meet the global health coverage agenda, the desires of the decision maker and the population, in the support for public health service coverage. In addition, a general theory of the system for a better-informed decision and analytical knowledge is obtained

A GIS-Based Spatial Decision Support System for Facility Location Planning in Nigeria

Abstract Public facilities are to be located optimally in the interest of society. In Nigeria, public facilities' locations are largely influenced by administrative constraints and politics, rather than efficiency and equity. This practice limits access, most especially, in rural communities where the population is dispersed.  Studies on efficiency and equity in access to public health facilities focused on urban centres. The aim of this study, therefore, is to advance the understanding of the application of the spatial decision support system (SDSS) to evaluate efficiency and equity in access to public facilities in rural regions. The study used Ogun State, Nigeria as a case.  The data used include the population and coordinates of the location of the settlements, coordinates of the location of health facilities and the transport networks. This study showed that 38.5% of settlements do not have access to primary care and the application of the p-median model showed that the efficiency of the existing location of health facilities can be improved by 40.6%.  Application of the maximal covering location model showed that the existing maximum travel distance of 26.3km can be reduced. It can be reduced for the sake of equity to 9.9 km. This study demonstrated ways to develop evaluative tools for analyzing the distribution of public facilities in Nigeria. It is suggested that planners in rural regions of other developing countries can adopt these techniques and tools to make their location decisions more logical. Keywords: Geographic Information System, Public Health Facilities, Spatial Decision Support System, Location Efficiency, Location Equity. &nbsp

Using the Global Positioning System (GPS) in household surveys for better economics and better policy

Distance and location are important determinants of many choices that economists study. While these variables can sometimes be obtained from secondary data, economists often rely on information that is self-reported by respondents in surveys. These self-reports are used especially for the distance from households or community centers to various features such as roads, markets, schools, clinics and other public services. There is growing evidence that self-reported distance is measured with error and that these errors are correlated with outcomes of interest. In contrast to self-reports, the Global Positioning System (GPS) can determine almost exact location (typically within 15 meters). The falling cost of GPS receivers (typically below US$100) makes it increasingly feasible for field surveys to use GPS as a better method of measuring location and distance. In this paper we review four ways that GPS can lead to better economics and better policy: (i) through constructing instrumental variables that can be used to understand the causal impact of policies, (ii) by helping to understand policy externalities and spillovers, (iii) through better understanding of the access to services, and (iv) by improving the collection of household survey data. We also discuss several pitfalls and unresolved problems with using GPS in household surveys

Designing a GIS Web Base for Locating Health Care Locations in KSA using Google Earth

This study focuses on the development of a web-based GIS for public healthcare system using GIS web base application choosing Google Earth as a sample . The development of this system is motivated to provide opportunities for the healthcare workers to gain access to vital information that can aid him/her in the location of viable hospitals for the patients to fully enjoy available enhanced  healthcare services. Currently, three major problems still exist in the healthcare geographic applications. This relate to health mapping methods, reusability of health applications, and interoperability issues. To handle these problems, we design a Web based GIS for Public healthcare system to support health data sharing and representation. The developed model makes it possible to locate the nearest hospitals as well as the services they rendered.   With Google Earth, you'll be able to see math concepts in a different light.  They're worth exploring and understanding fully.  In Google Earth you'll be able to interact with the concepts and see how they're evident in real life. These lessons are sure to be more interesting than just another page in a math text. Keywords: Web-based GIS, Public healthcare, Decision Support System. Google Earth ,ArcGS10.3,GP

Incremental planning of the location of public health facilities in a rural region

Some people in rural areas are often excluded from using health facilities in developing nations due to political interference in facility location decision-making. Limited attention has been paid in the literature to promoting inclusiveness in public facilities usage in developing nations. Therefore, this study was designed to examine the access to Primary Health Centres (PHCs) in the Yewa region,  Nigeria. Data on the 509 settlements and 91 PHCs in the Yewa region were obtained from government directories. The p-median Location-Allocation model was used for data analyses. The study showed that the number of PHCs increased and access to them improved in the Yewa region between 1991 and 2019. It was also shown that inclusiveness in facilities could be promoted by optimally adding new PHCs. The study assessed the effectiveness of past locational decisions, similar to other studies in Bangladesh and India, and revealed that the military administration performed better than the civilian administration in facility location decision-making between 1991 and 2019. The study showed how new facilities could be optimally located to improve access and inclusiveness in public usage

A study into Healthcare Service Location Problems, Location and Allocation in the Inanda area

Inanda is a predominantly rural area located on the northern coast of the province of KwaZulu Natal, South Africa. It is bordered by the areas of Phoenix, Verulam and Tongaat. In the context of healthcare accessibility in the Inanda area, the research aimed at investigating the problem in service location planning. This was done by investigating level of accessibility to existing healthcare facilities available to the residents of Inanda. Following the classification of accessibility problems, recommendations were made on where the facility locations can be improved or expanded to provide better accessibility in terms of location-allocation. Literature that has been reviewed focused on geographic location, GIS and accessibility measures, spatial accessibility, models used to test accessibility, service location planning and accessibility measures and metrics so as to provide a background and precedent for the service location planning carried out in the research. The research aimed to confirm that accessibility to the healthcare facilities is indeed a problem and to propose alternative strategies to overcome the accessibility problems identified. The access to healthcare service locations is dependent on a number of factors. Some of these factors include travel time and distance, available capacity at facilities, existing road network, and provision or lack thereof of an efficient public transport system. This accessibility to the health service locations was assessed by using available GIS information on healthcare facilities and using accessibility analysis to identify problems in terms of the services location as well as additional location-allocation of current and additional facilities. The analysis was based on the assumption that all service locations have unlimited capacity. Flowmap was used as the tool to analyse the GIS data and conduct various accessibility models. The different models were Expansion Model Analysis, Relocation Model Analysis, Catchment Area and Clinic Allocation Analysis, Catchment Profile, Market share of Supply Locations, Regular Proximity Count, Average Distance in Competition, Proximity Count in Competition, Lowest Mean Trip Cost Alternate, Second Best Catchment Distance and Pareto Cover Set. The results of the research showed that while the locations of the existing healthcare facilities are not ideal, most are accessible to the majority of the Inanda residents. The information on actual capacity available at each of the locations was not available at the time of the research being carried out and would be worthwhile to research in the future