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Spraycan: a PPGIS for capturing imprecise notions of place

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>12/2014
<mark>Journal</mark>Applied Geography
Volume55
Number of pages9
Pages (from-to)229-237
Publication statusPublished
Original languageEnglish

Abstract

IIt is increasingly common for participatory data to be collected from the public via a PPGIS, in order to gain a greater understanding both of the world around us, and people's experience of it. The place-based thoughts and feelings of an individual do not, however, always fit well with the space-based points and polygons into which they are typically reduced in such a system, which can limit the effectiveness of many traditional PPGIS' for data collection. In order to address this issue, an alternative PPGIS platform is presented, which allows the collection, storage and analysis of spatial data from the public without the requirement to artificially reduce these data to geographic primitives. The ‘Spraycan’ platform allows participants to create spatial representations on a map using a familiar airbrush-style interface, permitting the creation of vague regions and boundaries that better reflect the places to which they refer. Data are stored within a ‘multi-point-and-attribute’ data structure whereby each individual ‘dot’ of ‘paint’ created using the airbrush is stored as a discrete geographical object, and is joined to all other associated ‘dots’, as well as a variety of other demographic and contextual data, including free-text data in which the participants may add context to their spray patterns. The flexibility of this data structure provides a wide and extensible variety of analytical choices for the researcher, as is shown here by an example study demonstrating the use of the Spraycan for decision-support, as well as a number of real-world ‘in the wild’ applications. It is hoped that this work will open up the agenda for alternative PPGIS interfaces, in order to further explore the possibilities associated with the capture and analysis of ‘fuzzy’ place-based data.