Measuring Spatial Accessibility to Services within Indices of Multiple Deprivation: Implications of Applying an Enhanced two-Step Floating Catchment Area (E2SFCA) Approach

Spatial accessibility to general hospitals is an important indicator of the convenience and ability of residents to obtain medical services. Therefore, developing a model for measuring accessibility to general hospitals by multiple transportation modes is necessary. In this study, considering that the increase in travel time will reduce the attractiveness of general hospitals, we used the Two-Step Floating Catchment Area with the Gaussian attenuation function, in which the supply was presented by capacity of hospitals (i.e., number of beds), and the demand was presented by population in each grid derived with social media data mapping real-time locations of active users. The Gaussian Two-Step Floating Catchment Area (Ga2SFCA) simulates the attenuation tendency of the general hospital service capabilities over transit time. To obtain a highly precise understanding of accessibility to hospitals, transit time on Baidu Maps’ navigation service was used as the impedance condition, and the study area was divided into 1 square kilometer grids as the basic unit of research. Taking Nanjing city as a case study, it is found that the accessibility distribution shape changes from a multi-centered circular pattern to a multi-peak distribution, as the time threshold increases. By comparing the accessibility among 11 districts varying from main urban area to suburbs, the accessibility to general hospitals in Nanjing is significantly regionally unbalanced in both travel modes. By calculating and mapping the Modal Accessibility Gap (MAG) of the two travel modes, different modes of transportation resulted in different general hospital accessibility distributions. Generally, private car is superior in access to general hospitals to public transit in most areas. In the central area, public traffic may not contribute to the access to medical services as much as we thought, rather it plays a role in areas far from hospitals along metro lines and bus routes.

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Multi-Mode Two-Step Floating Catchment Area (2SFCA) Method to Measure the Potential Spatial Accessibility of Healthcare Services

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8050236 ◽

2019 ◽

Vol 8 (5) ◽

pp. 236 ◽

Cited By ~ 2

Author(s):

Jianhua Ni ◽

Ming Liang ◽

Yan Lin ◽

Yanlan Wu ◽

Chen Wang

Keyword(s):

Travel Behavior ◽

Urban Areas ◽

Catchment Area ◽

Methodological Approach ◽

Open Data ◽

Healthcare Services ◽

Spatial Accessibility ◽

Urban Residents ◽

Transportation Modes ◽

Multi Mode

While great progress in the development of a methodological approach to measure the accessibility of healthcare services has been made, the exclusion of the complex multi-mode travel behavior of urban residents and a rough calculation of travel costs from the origin to the destination limit its potential for making a detailed assessment, especially in urban areas. In this paper, we aim to describe and implement an enhanced method that enables the integration of multiple transportation modes into a two-step floating catchment area (2SFCA) method to estimate accessibility. We used a travel-mode choice survey, based on distance sections, to determine the complex multi-mode travel behavior of urban residents. Taking Nanjing as a study area, we proposed complete door-to-door approaches to determine every aspect of basic transportation modes. Additionally, we processed open data to implement an accurate computing of the origin-destination (OD) time cost. We applied the enhanced method to estimate the accessibility of residents to hospitals and compared it with three single-mode 2SFCA methods. The results showed that the proposed method effectively identified more accessibility details and provided more realistic accessibility values.

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Physical Activity and Sedentary Behavior Clustering: Segmentation to Optimize Active Lifestyles

Journal of Physical Activity and Health ◽

10.1123/jpah.2015-0307 ◽

2016 ◽

Vol 13 (9) ◽

pp. 921-928 ◽

Cited By ~ 8

Author(s):

Stephen Zwolinsky ◽

James McKenna ◽

Andy Pringle ◽

Paul Widdop ◽

Claire Griffiths ◽

...

Keyword(s):

Public Health ◽

Physical Activity ◽

Sedentary Behavior ◽

Hierarchical Cluster ◽

Optimal Number ◽

Multiple Deprivation ◽

Highly Active ◽

Active Lifestyles ◽

Univariate Analyses ◽

Indices Of Multiple Deprivation

Background:Increasingly the health impacts of physical inactivity are being distinguished from those of sedentary behavior. Nevertheless, deleterious health prognoses occur when these behaviors combine, making it a Public Health priority to establish the numbers and salient identifying factors of people who live with this injurious combination.Methods:Using an observational between-subjects design, a nonprobability sample of 22,836 participants provided data on total daily activity. A 2-step hierarchical cluster analysis identified the optimal number of clusters and the subset of distinguishing variables. Univariate analyses assessed significant cluster differences.Results:High levels of sitting clustered with low physical activity. The Ambulatory & Active cluster (n = 6254) sat for 2.5 to 5 h·d−1 and were highly active. They were significantly younger, included a greater proportion of males and reported low Indices of Multiple Deprivation compared with other clusters. Conversely, the Sedentary & Low Active cluster (n = 6286) achieved ≤60 MET·min·wk−1 of physical activity and sat for ≥8 h·d−1. They were the oldest cluster, housed the largest proportion of females and reported moderate Indices of Multiple Deprivation.Conclusions:Public Health systems may benefit from developing policy and interventions that do more to limit sedentary behavior and encourage light intensity activity in its place.

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Using hierarchical clustering to explore patterns of deprivation among English local authorities

Journal of Public Health ◽

10.1093/pubmed/fdz182 ◽

2019 ◽

Vol 42 (4) ◽

pp. 772-777

Author(s):

Steven L Senior

Keyword(s):

Hierarchical Clustering ◽

Demographic Characteristics ◽

Homogeneous Group ◽

Statistical Testing ◽

Clustering Methods ◽

Local Authorities ◽

Multiple Deprivation ◽

Hierarchical Clustering Methods ◽

Different Levels ◽

Indices Of Multiple Deprivation

Abstract Background The English Indices of Multiple Deprivation (IMD) is widely used as a measure of deprivation. However, similarly ranked areas can differ substantially in the underlying domains of deprivation. These domains contain a richer set of data that might be useful for classifying local authorities. Clustering methods offer a set of techniques to identify groups of areas with similar patterns of deprivation. Methods Hierarchical agglomerative (i.e. bottom-up) clustering methods were applied to domain scores for 152 upper tier local authorities. Advances in statistical testing allow clusters to be identified that are unlikely to have arisen from random partitioning of a homogeneous group. The resulting clusters are described in terms of their subdomain scores and basic geographic and demographic characteristics. Results Five statistically significant clusters of local authorities were identified. These clusters only partially reflect different levels of overall deprivation. In particular, two clusters share similar overall IMD scores but have contrasting patterns of deprivation. Conclusion Hierarchical clustering methods identify five distinct clusters that do not correspond closely to quintiles of deprivation. This approach may help to distinguish between places that face similar underlying challenges, and places that appear similar in terms of overall deprivation scores, but that face different challenges.

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