Tag Archives: locational data

New Geothink graduate: Dr. Harrison Smith

Dr. Harrison Smith recently completed his PhD at the University of Toronto’s Faculty of Information under the supervision of David J. Phillips and co-supervised by Geothink co-applicant Dr. Leslie Shade (University of Toronto). In this article, he tells us how his research examined the impact of location data in marketing. Dr. Smith’s next endeavour is a post doctoral research position at Newcastle University’s Global Urban Research Unit in the UK under the direction of Roger Burrows and Steve Graham.

By Harrison Smith

My dissertation, “The Mobile Distinction: Economies of Intimacy in the Field of Location Based Marketing”, examines the cultural and economic significance of location data in new kinds of marketing applications. When you survey existing research on location-based media, you tend to see a focus on user-centric studies that examines how these new interfaces can produce new kinds of intimacies and affective relationships between people and places. While certainly important, I argue there is a gap in our understanding of the political economy of locative media, and in turn the geo-spatial web, particularly with respect to how audiences are commodified and classified into specific segments through location data. I hypothesized that marketers are using location data to measure consumer lifestyles and tastes in ways that are similar to geodemographic classification. Traditionally, audiences are segmented by postal codes; in my dissertation, I sought to understand how location data can be used in a similar way to measure and classify lifestyles along particular hierarchies of cultural and economic worth. This allows us to theorize a broader political and cultural economy of the geo-spatial web, and questions certain dominant beliefs concerning the relationship between interactive cartography, big data, and power, particularly as urban environments are increasingly mediated by mobile for a variety of civic and commercial applications.

I focused specifically on the emergence of location based marketing using Pierre Bourdieu’s conceptual framework of habitus, capital, and field. I gathered my data through qualitative interviews with mobile and location-based marketers, participant observations of marketing conferences, as well as document analysis of mobile and location based marketing literature.

I asked two basic questions:

  1. What is the political economy of location data in mobile and location-based marketing?
  2. What are the underlying values, beliefs, philosophies of location data in the field of location based marketing?

These two questions are complimentary because the economic value of location data is contingent upon how marketers can successfully imbricate audiences into new fields of cultural production by appealing to specific logics of consumer lifestyles and practices through mobile media. Put differently, I discovered that the potential success of location based marketing depends on audience consent to participate and interact with marketers. This is important because it reveals a deeper level of understanding about geo-locative media and data that is structured by social, cultural, and economic relationships between consumers and institutional forces such as marketers.

I was particularly interested in understanding the specific values and philosophies that marketers are trying to enact in order to reveal how location data can inform geodemographic classifications using new kinds of metrics. I discovered that marketers employ numerous strategies for collecting location data from audiences that extend beyond GPS sensing. Sometimes, audiences may not even realize this is happening on an everyday basis because of the numerous methods it is possible to collect or infer location data from smartphones without our knowledge. For example, in some cases, location data is not actually collected by marketers themselves, but instead harvested from third party advertising exchanges during routine advertisement requests. When that happens, location data can be used to measure the efficacy of advertising. Third parties analyze the extent to which mobile advertising can drive audiences into particular stores, effectively offering a mobile measurement for audience conversion rates, namely by driving audiences into particular locations.

Furthermore, this can also be done through the passive collection of MAC (media access control) addresses, which are unique identifiers for hardware that are broadcast by smartphones on regular intervals. This is interesting because it represents a non-intrusive method for collecting location data. It is also worth considering how this kind of location data could also be used by non-commercial institutions, such as urban planners. In fact, there are many examples in which public spaces such as parks are now layered with sensors that collect location data from visitors, and can measure who they are, where they came from, and what other places they visited.

However, this is not an inevitable trend in the future of smart cities, as I argue that the capacity for collecting location data depends on the production of consent or the negotiation of resistance. A lot of work and investment must be done to convince large brands and individual stores of the value of targeting consumers in this way. The smartphone is a very personal, intimate device, and there may be resistance from consumers to letting marketers track them all the time, with ubiquitous access to their location history, or the ability to send targeted push notifications to mobile audiences in specific locations. This necessarily brings up important ethical questions around surveillance and privacy, as well as the kinds of lifestyles and consumer practices that are encouraged through mobile media. In my own interviews, many marketers side-stepped the issue of privacy by focusing instead on the inherent value exchange of data for various kinds of rewards or distinctions.

We will definitely see many different conversations emerge around how location data intersects with our values and attitudes towards surveillance in increasingly automated urban environments. In an interdisciplinary context such as Geothink, this will allow us to ask better questions concerning the value of location data, and be more critical on these issues.

I would like to thank my supervisory committee, which includes David Phillips, Leslie Shade, and Ronda McEwan. I also want to thank Geothink, particularly for the friendships I have developed on the team, and which has helped me appreciate the broader significance of my research.

Geothink Student Twitter Chat on Location and Privacy on the Geoweb

Laura Garcia, PhD student at the University of Ottawa under Prof. Elizabeth Judge (University of Ottawa), recently conducted a Spanish language Twitter chat with students at Los Andes University.

Discussion revolved around privacy issues especially in location-based services on the Geoweb 2.0. Using the hashtag #locationmine, participants discussed how location is both ‘mine’ in the sense of being very personal and private information and a mine of data to be exploited. Protecting privacy requires education, laws, regulation, and maybe even changes to technologies (such as the creation of standards). We are in the midst of changes in the technological landscape that are already having an effect on the amount of privacy internet users can realistically have, and this will continue into the future. Not only is technology changing, our habits are also changing as well, resulting in many agreeing to terms of use without a proper examination or thought over the details. Locational privacy must be debated and defined as a response to changes in the ecosystem, to enable proper regulation and protection of rights.

Laura presented the discussants with five conclusions:

  1. One of the most important elements of the right to privacy is for the user to have control over the information shared and who has access this information
  2. It is not easy to find and/or remove the collection of geographic information made automatically by some technologies and companies. Therefore, in these cases the user does not have control over the collection of their locational information
  3. It is important for the users of the Geoweb to take an active role in the protection of their privacy
  4. Better regulations are needed. These need to be mandatory and unambiguous
  5. Civil society needs to advocate for its own rights and demand corporate social responsibility

View the chat transcript below.

Spotlight on Recent Publications: Interrogating the Nature of Geosocial Data with Stéphane Roche


London Olympic wayfinding beacon (Photo courtesy of www.mudarchitecture.com).

By Drew Bush

In two articles published this January, Geothink researcher Stéphane Roche and his doctoral student Teriitutea Quesnot argue that not all geosocial data is equivalent, and that better data on the social significance of a landmark could greatly enhance our understanding of human wayfinding behavior. A Professor of Geomatics at University of Laval, Roche’s research over the past five years has focused on how new forms of digital spatiality affect spatial reasoning skills, and the capacity of individuals to engage in the city.

Entitled “Measure of Landmark Semantic Salience through Geosocial Data Streams,” the first paper was published by Roche in the ISPRS International Journal of Geo-Information. The authors write that a lot of research “in wayfinding is done in order to enable individuals to reach as quickly as possible a desired destination, to help people with disabilities by designing cognitively appropriate orientation signs, and reduce the fact of being lost.”

Previous researchers in the field of geo-cognition have tried to characterize the salience of landmarks in human wayfinding behaviour. Most have classified differing landmarks by visual, structural and semantic cues. However, the social dimensions of a landmark, such as how they are practised or recognized by individuals or groups, had been excluded from its semantic salience (or often reduced to historical or cultural cues), according to the authors.

Instead, the authors follow in a tradition of research which utilizes text mining from the web to understand how places are expressed by Internet users rather than relying on how they are visually perceived. Such an approach has been made possible by social media and mobile communications technology that has resulted in vast user-generated databases that constitute “the most appropriate VGI data for the detection of global semantic landmarks.”

In conducting their research, the authors examined world famous landmarks and detected semantic landmarks in the cities of Vienna and Paris using data from Foursquare API v2 and Facebook API v2.1. from September 29, 2014 to November 15, 2014.

In a second paper entitled “Platial or Locational Data? Toward the Characterization of Social Location Sharing,” the authors expanded on this theme in arguing that not all geosocial data is equal. The paper was presented at 48th Hawaii International Conference on Systems Sciences this past January.

Some data, which the authors consider “platial,” relates more to users experiences of a given place while “spatial” data is tied to the actual coordinates of a place. In the context of geosocial data, spatial data might mean the exact location of the Eiffel tower while palatial could refer to a person passing by the Eiffel tower or taking a photo of it from another location.

Because each can potentially represent a very different kind of data point, they must be treated differently. As the authors write, “With the objective of a better understanding of urban dynamics, lots of research projects focused on the combination of geosocial data harvested from different social media platforms. Those analyses were mainly realized on a traditional GIS, which is a tool that does not take into account the platial component of spatial data. Yet, with the advent of Social Location Sharing, the inconvenience of relying on a classic GIS is that a large part of VGI is now more palatial than locational.”

Find links to each article along with their abstracts below.

Measure of Landmark Semantic Salience through Geosocial Data Streams


Research in the area of spatial cognition demonstrated that references to landmarks are essential in the communication and the interpretation of wayfinding instructions for human being. In order to detect landmarks, a model for the assessment of their salience has been previously developed by Raubal and Winter. According to their model, landmark salience is divided into three categories: visual, structural, and semantic. Several solutions have been proposed to automatically detect landmarks on the basis of these categories. Due to a lack of relevant data, semantic salience has been frequently reduced to objects’ historical and cultural significance. Social dimension (i.e., the way an object is practiced and recognized by a person or a group of people) is systematically excluded from the measure of landmark semantic salience even though it represents an important component. Since the advent of mobile Internet and smartphones, the production of geolocated content from social web platforms—also described as geosocial data—became commonplace. Actually, these data allow us to have a better understanding of the local geographic knowledge. Therefore, we argue that geosocial data, especially Social Location Sharing datasets, represent a reliable source of information to precisely measure landmark semantic salience in urban area.

Platial or Locational Data? Toward the Characterization of Social Location Sharing


Sharing “location” information on social media became commonplace since the advent of smartphones. Location-based social networks introduced a derivative form of Volunteered Geographic Information (VGI) known as Social Location Sharing (SLS). It consists of claiming “I am/was at that Place”. Since SLS represents a singular form of place-based (i.e. platial) communication, we argue that SLS data are more platial than locational. According to our data classification of VGI, locational data (e.g. a geotagged tweet which geographic dimension is limited to its coordinate information) are a reduced form of platial data (e.g. a Swarm check-in). Therefore, we believe these two kinds of data should not be analyzed on the same spatial level. This distinction needs to be clarified because a large part of geosocial data (i.e. spatial data published from social media) tends to be analyzed on the basis of a locational equivalence and not on a platial one.

If you have thoughts or questions about the article, get in touch with Drew Bush, Geothink’s digital journalist, at drew.bush@mail.mcgill.ca.