Alexis J. Comber

A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions

BackgroundThis 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.MethodsData 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.ResultsRespondent 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.ConclusionsDifficulty 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.

What is land cover

Much geographic information is an interpretation of reality and it is possible for multiple interpretations to coexist. This is unproblematic for the research community but, as the numbers of users increase through initiatives resulting in data integration on an unprecedented scale, such as E-science and GRID, issues of information meaning and conceptualisation become more important. We explore these issues through the mapping of land cover and the variety of conceptions of land-cover features that may be held by actors in the creation, distribution, and use of the information. Current metadata do not report the wider meaning of the information categories in terms of the decisions that were made and by whom in specifying class conceptualisations.

Comparing the Quality of Crowdsourced Data Contributed by Expert and Non-Experts

There is currently a lack of in-situ environmental data for the calibration and validation of remotely sensed products and for the development and verification of models. Crowdsourcing is increasingly being seen as one potentially powerful way of increasing the supply of in-situ data but there are a number of concerns over the subsequent use of the data, in particular over data quality. This paper examined crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation to determine whether there were significant differences in quality between the answers provided by experts and non-experts in the domain of remote sensing and therefore the extent to which crowdsourced data describing human impact and land cover can be used in further scientific research. The results showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than non-experts in identifying the land cover type. This suggests the need to create training materials with more examples in those areas where difficulties in identification were encountered, and to offer some method for contributors to reflect on the information they contribute, perhaps by feeding back the evaluations of their contributed data or by making additional training materials available. Accuracies were also found to be higher when the volunteers were more consistent in their responses at a given location and when they indicated higher confidence, which suggests that these additional pieces of information could be used in the development of robust measures of quality in the future.

Using control data to determine the reliability of volunteered geographic information about land cover

There is much interest in using volunteered geographic information (VGI) in formal scientific analyses. This analysis uses VGI describing land cover that was captured using a web-based interface, linked to Google Earth. A number of control points, for which the land cover had been determined by experts allowed measures of the reliability of each volunteer in relation to each land cover class to be calculated. Geographically weighted kernels were used to estimate surfaces of volunteered land cover information accuracy and then to develop spatially distributed correspondences between the volunteer land cover class and land cover from 3 contemporary global datasets (GLC-2000, GlobCover and MODIS v.5). Specifically, a geographically weighted approach calculated local confusion matrices (correspondences) at each location in a central African study area and generated spatial distributions of users, producers, portmanteau, and partial portmanteau accuracies. These were used to evaluate the global datasets and to infer which of them was ‘best’ at describing Tree cover at each location in the study area. The resulting maps show where specific global datasets are recommended for analyses requiring Tree cover information. The methods presented in this research suggest that some of the concerns about the quality of VGI can be addressed through careful data collection, the use of control points to evaluate volunteer performance and spatially explicit analyses. A research agenda for the use and analysis of VGI about land cover is outlined.

Integrating land-cover data with different ontologies: identifying change from inconsistency

Spatially coincident land-cover information frequently varies due to technological and political variations. This is especially problematic for time-series analyses. We present an approach using expert expressions of how the semantics of different datasets relate to integrating temporal time series land-cover information where the classification classes have fundamentally changed. We use land-cover mapping in the UK (LCMGB and LCM2000) as example data sets because of the extensive object-based meta-data in the LCM2000. Inconsistencies between the two datasets can arise from random, gross and systematic error and from an actual change in land cover. Locales of possible land-cover change are inferred by comparing characterizations derived from the semantic relations and meta-data. Field visits showed errors of omission to be 21% and errors of commission to be 28%, despite the accuracy limitations of the land-cover information when compared with the field survey component of the Countryside Survey 2000.

Actor–network theory: a suitable framework to understand how land cover mapping projects develop?

Abstract The Countryside Surveys of 1990 and 2000 are introduced and their methodological or ontological differences described. Actor–network theory examines the processes by which individual scientific claims are supported, debated and constructed by determining the interactions, connections and activities of the actors involved. The actors and their networks for the Countryside Surveys of 1990 and 2000 are compared. Such an analysis provides a description of why science evolves. Changes in the commissioning context behind scientific results and the process by which empirical facts are established are clearly illustrated. This type of analysis goes beyond the technological developments that would be revealed if only the scientific elements were examined. This type of analysis provides a useful tool to those seeking to reconcile ontological and semantic differences between scientific data.

Assessment of a Semantic Statistical Approach to Detecting Land Cover Change Using Inconsistent Data Sets

A semantic, statistical approach to reconciling data with different ontologies is introduced. It was applied to UK land cover datasets from 1990 and 2000 in order to identify land cover change. The approach combined expression of expert opinion about how the semantics of the two datasets relate with spectral homogeneity metadata. A sample of the changes identified was assessed by field validation. Change was identified in 41 percent of the visited parcels, and all of the false positives were found to be due to classification error in either dataset. Thus, the approach reliably identifies inconsistency between two datasets, and the results indicate the suitability of uncertainty formalisms. The inclusion of extensive objectlevel metadata by the data producers greatly facilitates practical solutions to problems of data interoperability.

The separation of land cover from land use using data primitives

The common confusion of land cover and land use in many data sets is problematic for many data integration activities. This article proposes an approach for the separation of land cover and land use based on data primitives. Data primitives are those dimensions that describe at the most fundamental level the processes under investigation. In this case, they provide building blocks that allow land cover and land use to be separated. A series of data primitives were identified from the literature and applied to US National Land Cover Dataset (2001). Mapped outputs, separating the concepts of form (land cover) and function (land use), show the degree of land use, the degree of land cover and locations where the concepts of use and cover are confused. The separation of land cover and land use facilitates the integration of land data for environmental modelling and planning activities. The work promotes the need for land use and land cover to be maintained as distinct concepts in data collection activities.

A Comparison of Fuzzy AHP and Ideal Point Methods for Evaluating Land Suitability

This article compares two fuzzy approaches to land suitability evaluations, Analytical Hierarchy Process (AHP) and Ideal Point. The methods were evaluated using a case study which models the opportunities for wheat production under irrigation conditions in the north-western region of Jeffara Plain, Libya. A number of relevant soil and landscape criteria were identified through a review of the literature and their weights specified as a result of discussions with local experts. The results of the Fuzzy AHP showed that the majority of the study area has membership values to the set of suitability between 0.40 and 0.50, while the results of the Ideal Point approach revealed most of the study area to have membership values between 0.30 and 0.40. While the Fuzzy AHP and Ideal Point approaches accommodate the continuous nature of many soil properties and produce more intuitive distributions of land suitabilities values, the Fuzzy AHP approach was found to be better than Fuzzy Ideal Point. This was due to the latter’s tendency to be biased towards positive and negative ideal values.

Accurate Attribute Mapping from Volunteered Geographic Information: Issues of Volunteer Quantity and Quality

Crowdsourcing is a popular means of acquiring data, but the use of such data is limited by concerns with its quality. This is evident within cartography and geographical sciences more generally, with the quality of volunteered geographic information (VGI) recognized as a major challenge to address if the full potential of citizen sensing in mapping applications is to be realized. Here, a means to characterize the quality of volunteers, based only on the data they contribute, was used to explore issues connected with the quantity and quality of volunteers for attribute mapping. The focus was on data in the form of annotations or class labels provided by volunteers who visually interpreted an attribute, land cover, from a series of satellite sensor images. A latent class model was found to be able to provide accurate characterisations of the quality of volunteers in terms of the accuracy of their labelling, irrespective of the number of cases that they labelled. The accuracy with which a volunteer could be characterized tended to increase with the number of volunteers contributing but was typically good at all but small numbers of volunteers. Moreover, the ability to characterize volunteers in terms of the quality of their labelling could be used constructively. For example, volunteers could be ranked in terms of quality which could then be used to select a sub-set as input to a subsequent mapping task. This was particularly important as an identified subset of volunteers could undertake a task more accurately than when part of a larger group of volunteers. The results highlight that both the quantity and quality of volunteers need consideration and that the use of VGI may be enhanced through information on the quality of the volunteers derived entirely from the data provided without any additional information.