Jessica Wardlaw

Modelling catchment areas for secondary care providers: a case study

Hospitals need to understand patient flows in an increasingly competitive health economy. New initiatives like Patient Choice and the Darzi Review further increase this demand. Essential to understanding patient flows are demographic and geographic profiles of health care service providers, known as ‘catchment areas’ and ‘catchment populations’. This information helps Primary Care Trusts (PCTs) to review how their populations are accessing services, measure inequalities and commission services; likewise it assists Secondary Care Providers (SCPs) to measure and assess potential gains in market share, redesign services, evaluate admission thresholds and plan financial budgets. Unlike PCTs, SCPs do not operate within fixed geographic boundaries. Traditionally, SCPs have used administrative boundaries or arbitrary drive times to model catchment areas. Neither approach satisfactorily represents current patient flows. Furthermore, these techniques are time-consuming and can be challenging for healthcare managers to exploit. This paper presents three different approaches to define catchment areas, each more detailed than the previous method. The first approach ‘First Past the Post’ defines catchment areas by allocating a dominant SCP to each Census Output Area (OA). The SCP with the highest proportion of activity within each OA is considered the dominant SCP. The second approach ‘Proportional Flow’ allocates activity proportionally to each OA. This approach allows for cross-boundary flows to be captured in a catchment area. The third and final approach uses a gravity model to define a catchment area, which incorporates drive or travel time into the analysis. Comparing approaches helps healthcare providers to understand whether using more traditional and simplistic approaches to define catchment areas and populations achieves the same or similar results as complex mathematical modelling. This paper has demonstrated, using a case study of Manchester, that when estimating the catchment area of a planned new hospital, the extra level of detail provided by the gravity model may prove necessary. However, in virtually all other applications, the Proportional Flow method produced the optimal model for catchment populations in Manchester, based on several criteria: it produced the smallest RMS error; it addressed cross-boundary flows; the data used to create the catchment was readily available to SCPs; and it was simpler to reproduce than the gravity model method. Further work is needed to address how the Proportional Flow method can be used to reflect service redesign and handle OAs with zero or low activity. A next step should be the rolling out of the method across England and looking at further drill downs of data such as catchment by Healthcare Resource Group (HRG) rather than specialty level.

Understanding the influence of specific Web GIS attributes in the formation of non-experts’ trust perceptions

The Web has facilitated wider access to spatial information and allowed non-experts to view, use, access and build maps using Web GIS technology. There is a significant number of Web GIS applications which are open to the wider public including people without any GIS knowledge. The complexity of Web GIS interfaces, the risk and uncertainty they incorporate and the limited knowledge of non-experts in spatial data handling and GIS operations influence the perceived trustworthiness of these systems. Previous scholars, such as Monmonier (1996) recognised the importance of trust in map design, however there has been no research into how trust can be improved for Web GIS applications. Three studies were conducted in order to investigate what elements influence non-experts’ trust perceptions and how specific Web GIS trustee attributes should be designed in order to improve trust. The results not only demonstrate the importance of further trust research in the Web GIS context, but also show that trust can be improved through interface design, which is an ethical need in order to support non-experts’ trust assessments.

Task Workflow Design and its impact on performance and volunteers' subjective preference in Virtual Citizen Science

Virtual citizen science platforms allow non-scientists to take part in scientific research across a range of disciplines. What they ask of volunteers varies considerably in terms of task type, variety, user judgement required and user freedom, which has received little direct investigation. A study was performed with the Planet Four: Craters project to investigate the effect of task workflow design on both volunteer experience and the scientific results they produce. Participants feedback through questionnaire responses indicated a preference for interfaces providing greater autonomy and variety, with free-text responses suggesting that autonomy was the more important. This did not translate into improved performance however, with the most autonomous interface not resulting in significantly better performance in data volume, agreement or accuracy compared to other less autonomous interfaces. The interface with the least number of task types, variety and autonomy resulted in the greatest data coverage. Agreement, both between participants and with the expert equivalent, was significantly improved when the interface most directly afforded tasks that captured the required underlying data (i.e. crater position or diameter). The implications for the designers of virtual citizen science platforms is that they have a balancing act to perform, weighing up the importance of user satisfaction, the data needs of the science case and the resources that can be committed both in terms of time and data reduction. Citizen science participants prefer task designs with greater variety and autonomy.This does not translate into better performance for either data quality or quantity.Simpler task designs led to a greater volume of data being produced.Interfaces that afforded the most direct capture of data led to better quality data.Trade-offs exist between data volume and accuracy and between preference and productivity.

Comparing experts and novices in Martian surface feature change detection and identification

Change detection in satellite images is a key concern of the Earth Observation field for environmental and climate change monitoring. Satellite images also provide important clues to both the past and present surface conditions of other planets, which cannot be validated on the ground. With the volume of satellite imagery continuing to grow, the inadequacy of computerised solutions to manage and process imagery to the required professional standard is of critical concern. Whilst studies find the crowd sourcing approach suitable for the counting of impact craters in single images, images of higher resolution contain a much wider range of features, and the performance of novices in identifying more complex features and detecting change, remains unknown. n nThis paper presents a first step towards understanding whether novices can identify and annotate changes in different geomorphological features. A website was developed to enable visitors to flick between two images of the same location on Mars taken at different times and classify 1) if a surface feature changed and if so, 2) what feature had changed from a pre-defined list of six. Planetary scientists provided “expert” data against which classifications made by novices could be compared when the project subsequently went public. n nWhilst no significant difference was found in images identified with surface changes by expert and novices, results exhibited differences in consensus within and between experts and novices when asked to classify the type of change. Experts demonstrated higher levels of agreement in classification of changes as dust devil tracks, slope streaks and impact craters than other features, whilst the consensus of novices was consistent across feature types; furthermore, the level of consensus amongst regardless of feature type. These trends are secondary to the low levels of consensus found, regardless of feature type or classifier expertise. These findings demand the attention of researchers who want to use crowd-sourcing for similar scientific purposes, particularly for the supervised training of computer algorithms, and inform the scope and design of future projects.

Citizen science for observing and understanding the Earth

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges.