The Dark Web of Urban Data: Fitness Data Ecosystems, Urban Design and Privacy in the Modern City

Introduction

Using data to help shape the built environment is not a new phenomenon, references to environmental information can be found as far back as the first century BC in Vitruvius (Pollio & Morgan, 1960). Currently, however, we find ourselves in a maelstrom of data, thanks to the Internet of things (IoT) and the proliferation of digital infrastructure and connected devices. Virtually everything we do on mobile devices can be linked to the place and time it occurred. Simple Google searches will reveal dozens of city dashboards, which store and stream data from sensors deployed throughout cities across the world. Conceived as a utility, like many new utilities, they can initially be ad-hoc; provided by different often uncoordinated stakeholders (Graham, 2002), and the infrastructure that sits behind them is often contested. Some initiatives are driven from the top-down, funded by multinational corporations such as Intel and focused on monetizing the infrastructure. Others are bottom-up community initiatives, for example The Things Network create community sponsored Internet infrastructure. In different ways both approaches are interested in the creation of new value through data. Although there is a history of using information to inform decision making in regard to the built environment, I suggest we are entering a profoundly different epoch. One where the quantity, quality and granularity of information available on the built environment is unprecedented in human history.

The use of the term dark web in the title is allegorical, in actuality, the phrase most often refers to nefarious world wide web Internet content that cannot be found by search engines like Google (Chen, 2011, nor can it be accessed through normal web browsers like Internet Explorer or Firefox. Some parallels can be drawn with geospatial data; although it can be found on public websites, visualization is very limited and doing it effectively requires specific software such as QGIS or ArchGIS. The term dark web is invoked here referring to specific types of geospatial data (GPX files) being generated and posted online. A GPX file lists a series of Global Positioning System (GPS) points and times, which in this context refer to a particular fitness activity such as the route of a run or cycle. GPX (Global Position system eXchange format) data is typically contained within an extensible markup language (XML) schema. While this format is easily accessed by both human and machine it requires significant manipulation before it can be visualized or analyzed. Thus, simply finding and viewing one directly in a traditional web browser is of little or no use (Figure 1). There are web services that all the visualization of individual GPX files over a map, However, an early innovator in this space is Koordinates (koordinates.com) a New Zealand commercial start-up specializing in the management of geospatial data. Various sections of government, such as Land Information New Zealand use Koordinates to make their geospatial datasets easily visualized, searched and shared. Layers of data can be added to create a unique layering and visualization of this data. Still, Koordinates and the data it manages represents a small subset of the geospatial data currently in existence.

Figure 1.

GPX raw file data

This paper focuses on data creation from the fitness industry, where an abundance of devices, smartphone applications and digital infrastructures exist. GPX data is generated when people monitor a run or a cycle activity using a smartphone of watch. With specific or additional devices it is also possible to record bio-metric information such as heart rate, calories and blood oxygen levels. The nature of this data is by default public on websites like Strava or MapMyFitness, although it is not searchable in any particularly meaningful way. This pilot project is concerned with three things: First, to what extent this data be accessed? Second, are there correlations between this and pre-existing systems of urban data/surveillance? And third, are there methodologies for using this in a design context?