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The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one 'self' diagnoses and prescribes over the counter medicines for treatment. The self-care movement has important policy implications, perceived to relieve the National Health Service (NHS) burden, increasing patient subsistence and freeing resources for more serious ailments. However, there has been little research exploring how self-medication behaviours vary between population groups due to a lack of available data. The aim of our study is to evaluate how high street retailer loyalty card data can help inform our understanding of how individuals self-medicate in England. Transaction level loyalty card data was acquired from a national high street retailer for England for 2012-2014. We calculated the proportion of loyalty card customers (n ~ 10 million) within Lower Super Output Areas who purchased the following medicines: 'coughs and colds', 'Hayfever', 'pain relief' and 'sun preps'. Machine learning was used to explore how 50 sociodemographic and health accessibility features were associated towards explaining purchasing of each product group. Random Forests are used as a baseline and Gradient Boosting as our final model. Our results showed that pain relief was the most common medicine purchased. There was little difference in purchasing behaviours by sex other than for sun preps. The gradient boosting models demonstrated that socioeconomic status of areas, as well as air pollution, were important predictors of each medicine. Our study adds to the self-medication literature through demonstrating the usefulness of loyalty card records for producing insights about how self-medication varies at the national level. Big data offer novel insights that add to and address issues that traditional studies are unable to consider. New forms of data through data linkage may offer opportunities to improve current public health decision making surrounding at risk population groups within self-medication behaviours.

When conducting research within a framework of Geographic Information Science (GISc), the scientific validity of this work can be argued as highly dependent upon the extent to which the methods employed are reproducible, and that, in the strictest sense, can only be fully achieved by implementing transparent workflows that utilize both open source software and openly available data. After considering the scientific implications of non-reproducible methods, we provide a review of both open source Geographic Information Systems (GIS) and openly available data, before describing an integrated model for Open GISc. We conclude with a critical review of this embryonic paradigm, with directions for future development in supporting spatial data infrastructure.

In 2011 the Home Office released the police.uk website, which provided a high-resolution map of recent crime data for the national extents of England, Wales and Northern Ireland. Through this free service, crimes were represented as points plotted on top of a Google map, visible down to a street level of resolution. However, in order to maintain confidentiality and to comply with data disclosure legislation, individual-level crimes were aggregated into points that represented clusters of events that were located over a series of streets. However, with aggregation the representation of crimes as points becomes problematic, engendering spurious precision over where crimes occurred. Given obvious public sensitivity to such information, there are social imperatives for appropriate representation of crime data, and as such, in this paper we present a method of translating the ‘point’ crime events into a new representational form that is tied to street network geography; presenting these results in an alternate national crime mapping portal http://www.policestreets.co.uk

and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end,this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.tgis_1197 283..298

Active travel for the home to school commute is an ideal opportunity to improve pupil׳s physical activity levels. Many studies have looked at how pupils travel to school and the motivating factors behind these decisions. This paper applies an innovative methodology to model each pupil’s individual route to school and then evaluates how different policy changes could increase the uptake of active travel. The changes are quantified in terms of the proportion using active travel, CO2 emissions and criterion distances: a method of measuring how far pupils are willing to travel using a certain mode of transport. Findings suggest that the greatest reduction in CO2 and increase in health benefits can be made by encouraging more primary school pupils to use active travel and targeting schools with existing low levels of active travel.