5 research outputs found
A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions
Background: This paper analyses the relationship between public perceptions of access to general practitioners
(GPs) surgeries and hospitals against health status, car ownership and geographic distance. In so doing it explores
the different dimensions associated with facility access and accessibility.
Methods: Data on difficulties experienced in accessing health services, respondent health status and car ownership
were collected through an attitudes survey. Road distances to the nearest service were calculated for each
respondent using a GIS. Difficulty was related to geographic distance, health status and car ownership using
logistic generalized linear models. A Geographically Weighted Regression (GWR) was used to explore the spatial
non-stationarity in the results.
Results: Respondent long term illness, reported bad health and non-car ownership were found to be significant
predictors of difficulty in accessing GPs and hospitals. Geographic distance was not a significant predictor of
difficulty in accessing hospitals but was for GPs. GWR identified the spatial (local) variation in these global
relationships indicating locations where the predictive strength of the independent variables was higher or lower
than the global trend. The impacts of bad health and non-car ownership on the difficulties experienced in
accessing health services varied spatially across the study area, whilst the impacts of geographic distance did not.
Conclusions: Difficulty in accessing different health facilities was found to be significantly related to health status and car ownership, whilst the impact of geographic distance depends on the service in question. GWR showed
how these relationships were varied across the study area. This study demonstrates that the notion of access is a
multi-dimensional concept, whose composition varies with location, according to the facility being considered and
the health and socio-economic status of the individual concerned
The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions
Spatial data are used in many scientific domains including analyses of Ecosystem Services (ES) and Natural Capital (NC), with results used to inform planning and policy. However, the data spatial scale (or support) has a fundamental impact on analysis outputs and, thus, process understanding and inference. The Modifiable Areal Unit Problem (MAUP) describes the effects of scale on analyses of spatial data and outputs, but it has been ignored in much environmental research, including evaluations of land use with respect to ES and NC. This paper illustrates the MAUP through an ES optimisation problem. The results show that MAUP effects are unpredictable and nonlinear, with discontinuities specific to the spatial properties of the case study. Four key recommendations are as follows: (1) The MAUP should always be tested for in ES evaluations. This is commonly performed in socio-economic analyses. (2) Spatial aggregation scales should be matched to process granularity by identifying the aggregation scale at which processes are considered to be stable (stationary) with respect to variances, covariances, and other moments. (3) Aggregation scales should be evaluated along with the scale of decision making (e.g., agricultural field, farm holding, and catchment). (4) Researchers in ES and related disciplines should up-skill themselves in spatial analysis and core paradigms related to scale to overcome the scale blindness commonly found in much research
A modified grouping genetic algorithm to select ambulance site locations
This article describes the development and application of a modified grouping genetic algorithm (GGA) used to identify sets of optimal ambulance locations. The GGA was modified to consider a special case with only two groups, and the reproduction and mutation schemes were modified to operate more efficiently. It was applied to a case study locating ambulances from a fixed set of alternative locations. The sites were evaluated using data of emergency medical services (EMS) calls summarised over census areas and weighted by network distance. Census areas serviced by the same selected location defined ambulance catchments. The results indicated alternative sites for ambulances to be located, with average EMS response times improved by 1 min 14 s, and showed the impacts of having different numbers of ambulances in current locations and in new locations. The algorithmic developments associated with the modified GGA and the advantages of using census areas as spatial units to summarise data are discussed
A modified grouping genetic algorithm to select ambulance site locations
This paper describes the development and application of a modified Grouping Genetic Algorithm (GGA) used to identify sets of optimal ambulance locations. The GGA was
modified to consider a special case with only two groups, and the reproduction and mutation schemes were modified to operate more efficiently. It was applied to a case
study locating ambulances from a fixed set of alternative locations. The sites were evaluated using data of emergency medical services (EMS) calls summarised over census areas and weighted by network distance. Census areas serviced by the same selected location defined ambulance catchments. The results indicated alternative sites for ambulances to be located, with average EMS response times improved by 1
minute 14 seconds and showed the impacts of having different numbers of ambulances in current locations and in new locations. The algorithmic developments associated with the modified GGA and the advantages of using census areas as spatial units to summarise data are discussed