Measuring spatial accessibility and equity to healthcare services using fuzzy inference system

Sabokbar, H A Faraji; Mohammadi, Hossein; Tahmasbi, Siamak; Rafii, Y; Hosseini, ‌Ali

doi:10.1016/j.apgeog.2021.102584

Cited by 11 publications

(5 citation statements)

References 81 publications

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“…The results and discussion present a comparative analysis of the metrics of service distributions, availability, waiting time, allocation time, and failures. In this analysis, the requests vary from 100 to 1100 and the average time out varies from 300 s to 3000 s. The comparison is performed between SSF [13] and PSTBA [18].…”

Section: Resultsmentioning

confidence: 99%

“…The introduced CSCP increases the accuracy of sleep disorder detection and enhances the efficiency range in diagnosis. Sabokbar et al [13] proposed a fuzzy inference system (FIS) based spatial model for accessibility measurement in healthcare centers. The main aim of the proposed model is to evaluate and address the healthcare services provided to patients.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities

Kaliappan

Gnanamurthy²,

Yahya

et al. 2023

Sustainability

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Smart healthcare using the cloud and the Internet of Things (IoT) allows for remote patient monitoring, real-time data collection, improved data security, and cost-effective storage and analysis of healthcare data. This paper proposes an information-centric dissemination scheme (ICDS) for smart healthcare services in smart cities. The proposed scheme addresses the time sensitiveness of healthcare data and aims to ensure consistent dissemination. The ICDS uses decision-tree learning to classify requests based on time-sensitive features, allowing prioritization of access. The scheme also involves segregating sensitive information and distributing digital health data within the classified time to retain time sensitiveness and prioritize access. The learning is then modified for the leaves based on data significance and minimum resources to reduce waiting times and improve availability.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities

Kaliappan

Gnanamurthy²,

Yahya

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…It combines land utilization and transportation systems, reflecting the potential of people to participate in spatially dispersed activities [ 13 ]. In recent years, studies on accessibility have covered urban transport [ 14 ], social employment [ 15 ], tourism resources [ 16 ], and medical services [ 17 ], revealing interdisciplinary features. There are many measurement methods [ 18 ], such as simple linear (Euclidean) distance models [ 19 ], gravity-based models reflecting the distance from the origin through a weight decay function [ 20 ], and utility-based models [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Study on the spatial accessibility and influencing factors of traditional villages in the Central Plains Urban Agglomeration of China

Zhang,

Li,

Zhao

et al. 2024

Preprint

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Traditional villages, as important carriers of rural tangible and intangible cultural heritage, have immeasurable economic and cultural value. However, complex geographical characteristics cause the spatial connection between traditional villages and the outside world to be inconvenient, and rapid urbanization ignores their development. Thus, the activation and utilization of traditional villages was difficult. Studying the spatial accessibility of traditional villages is important for development and utilization because of its theoretical and practical significance. A total of 696 traditional villages in the Central Plains Urban Agglomeration (CPUA) of China were taken as the research object, and kernel density estimation and the nearest neighbor index based on ArcGIS were used to analyze their spatial distribution characteristics. Then, the spatial accessibility time costs were calculated via the Amap API. Finally, the influence of each influencing factor was analyzed quantitatively via PCCs, OLS regression and GWR. The results showed that (1) the spatial distribution of traditional villages was clustered, and there was a high-value core-cluster area in the north and two subhigh-value areas in the central and southern parts. (2) The spatial accessibility level of traditional villages exhibited a “core-edge” pattern, decreasing from the core area to the edge area. (3) Factors that exhibited spatial heterogeneity. Influence from the largest to the smallest: slope, density of river, density of road, GDP, density of population, elevation. In addition, the elevation and density of rivers had the greatest impact intensity in the northeast, the slope had the greatest impact in the north, and the density of roads, GDP, and population had the greatest impact at the edge far from the core area.

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“…The layout of service facilities for older people needs to be gradually matched with the development of the city and the needs of its citizens [5]. Much research has been conducted on the layout of service facilities for older people [6][7][8]. The first type of study is the arrangement of land sites for service facilities for older people in urban planning.…”

Section: Introductionmentioning

confidence: 99%

“…The second type of study aims to evaluate and optimize the allocation efficiency of existing service facilities for older people [13,14]. The studies cover various aspects, including the geographical environment and spatial factors affecting the balanced layout of service facilities for older people [15,16], the distribution characteristics of service facilities for older people and the coupling relationship between facilities and people [17,18], the accessibility of facilities, and the spatial coordination and continuity of service facilities [8,19,20]. The third category of studies focuses on modeling and forecasting the sizes and types of future service facilities for older people [21,22], mainly based on the natural growth rate of the local population [23,24].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Layout of Service Facilities for Older People Based on POI Data and Machine Learning: Guangzhou City as an Example

Feng,

Tang,

Zou

2024

Land

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Population aging is a global issue. China is facing the same challenge, especially in its megacities, with more than 10 million permanent urban residents. These densely populated cities urgently need the scientific planning and optimization of the layout of service facilities for older people. Taking Guangzhou, a megacity in China, as an example, this study uses point-of-interest (POI) data and the ID3 machine learning decision tree algorithm to train a site selection model for service facilities for older people. The model can help to select appropriate locations for new service facilities for older people more scientifically and accurately, and it can provide targeted suggestions to optimize the layout of the service facilities for older people in Guangzhou. First, Guangzhou city is divided into 29,793 grids of 500 m × 500 m based on the range of activities of older people, and 985 grids are found to contain service facilities for older people. Then, the POI data of the grid are fed into the ID3 algorithm for training to obtain a prediction model for the selection of sites for service facilities for older people. The effective prediction rate of the model reaches 87.54%. Then, we apply the site selection model to predict the whole city of Guangzhou, and 4534 grids are suitable for service facilities for older people. In addition, considering the degree of concentration of the elderly population in each street, we further filter out 1066 priority grids as the final site selection. Finally, taking into account the situation of the streets in different districts, we propose several strategies to optimize the layout of the construction of service facilities for older people.

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Measuring spatial accessibility and equity to healthcare services using fuzzy inference system

Cited by 11 publications

References 81 publications

Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities

Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities

Study on the spatial accessibility and influencing factors of traditional villages in the Central Plains Urban Agglomeration of China

Optimizing the Layout of Service Facilities for Older People Based on POI Data and Machine Learning: Guangzhou City as an Example

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