Matthew TenneyDepartment of GeographyMcGill University

Introduction:

This project will focus on the use of geoweb content for “dashboard governance” initiatives within municipal operations and public administration (c.f., Mattern 2014; Kitchin et al. 2015b, 2015a). Big data and smart city initiatives are fundamentally altering many of the traditional concepts that have guided our understanding of civic engagement (Macintosh 2008; Gil de Zúñiga et al. 2010). Namely, searching for new modes of connectivity between citizens, ideas, and space to replace face-to-face and direct political action of communities within the city. As decision makers increasingly accept a recasting of “citizens as sensors” and “governance through algorithms” we are pressed to identify both the potential and pitfalls of mandating forms of digital participation (i.e., e-participation) across different geographic contexts (Batty et al. 2012; Caquard 2014; Shelton, Zook, and Wiig 2014). Therefore, I propose an investigation to what I term coded engagement – where the will, opinion, and influence of a public are both produced and collected through digital data (e.g., social media and user-generated content) and technologies (e.g., smart phones and personal computers) as a form of civic engagement. In turn, coded engagement stands to be both the fields and the harvest of digital participation, used to fuel a movement towards data-driven policy formation (c.f., Kitchin and Lauriault 2014; Kitchin 2014a, 2014b).

Research Justification:

The scene is set as “social scientists are inundated by social media data”, and are being drawn by incentives to make such data “synthesized and analyzed to conceptualize, comprehend and solve real-world problems” (Hartter et al. 2013). One of these real-world problems is to make dumb cities “smart” that allows for a panoptic view over their operations and population (Bail 2014; Andrejevic 2014; Crampton 2014). One of the goals in smart city projects (c.f., Kitchin 2014a) is to create a sort of Amazon “recommendation system” for choices in governance that fuses technology and public administration via the real-time sensing of public sentiment. eParticipation claims to improve the quality of opinion formation between citizens whileproviding a greater transparency in the democratic process (cf. Dalakiouridou et al. 2012). A smart city futurealso promotes an understanding of the city as a complex systems where both physical and social operations can: 1) be reduced to the calculation of variables that represent actualities of human existence and lived geographies (Mattern 2015), 2) the system can then be optimized through these derived indicators (i.e., data) and a series of algorithmic tweaks (Hollands 2014), 3) that in turn will inform city officials and policy-formation to better serves its public (i.e., coded engagement) (c.f., Klauser et al. 2014). This is in line with technocratic solutions where digital technologies tidy the messiness of democracy (Agar 2003) and solve the plight of social capital amongst communities (Putnam 1995; Kavanaugh and Patterson 2002; Subbian et al. 2014). It is also necessary to consider the social context of undertaking a research project on coded engagement; being currently one of a cultural receptivity to data-driven methods and algorithmic modes of assessment that have allowed the reality of dashboard governance to “...diffuse into the zeitgeist” (Mattern 2015). Therefore, understanding coded engagement is imperative so to establish practical paths towards “smart citizens living in a sensored city” instead of being forced into the corollary.

Challenges of coded engagement are in part captured by critiques against big data (c.f., boyd and Crawford 2012; Obole and Welsh 2012) and data science more generally (c.f., Frické 2014). Coded engagement could also be seen as an attempt to explore the duplicitous nature of digital (social) data and merger of code/space (Dodge, Kitchin, and Zook 2009) for application in real-world problems. Batty (2013, 2015) describe one such problem regarding the use of this data in the practical challenges of data integration and finding a means to represent connectivity itself between on- and off-line behaviors through non-explicit ties in the data (i.e., between data models and ontologies). Crampton et. al. (2013), further note that connecting digital data to the reality of social conditions within geographic space require one to look beyond the “geotag” and integrate varied techniques of analysis to heterogeneous datasets.

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Another challenge to coded engagement is how to connect harvested population data from our online profiles to our offline lives. Miller and Goodchild (2014) see this problem as an inverse to “classic sampling problem where we identify a question and collect data to answer that question. Instead, we collect the data and determine what questions we can answer.” However, there is a much more nuanced complexity within coded engagement that necessitate a distinction between data representations (i.e., the digital self vs. actual self or “multiple-selves”) while accommodating discrepancies and blurred lines between: space and place, the public and private self, as well explicating the latent assumptions behind connecting behavior to the activities and desires of people (Zook, Graham, and Shelton 2011; Elwood and Leszczynski 2013; Wilson 2014). Furthermore, variations in geography, namely distributions in participation and composition of local populations, require a deeper understanding between the local “who” and “how many” to understand coded engagement in cities of varying size, culture, and location.

Thus, a primary goal for this research project will be to seek methods for the infusion of data that can contextualize (spatial) data into meaningful (geographic) information about the opinions of local populations.In other words, this project will pursue the combination and development of computational techniques for the creation of coherent datasets that could represent digital forms of civic engagement to be then evaluated under a critical-theory perspective.

Theoretical and Methodological Approach:

This project will embrace a critical-computational perspective that explores affordances of geoweb content (Halpern and Gibbs 2013; Hogan 2015) to both produce and identify communities of civic interest and place (Zook and Graham 2007; Stephens and Squire 2012; Graham, Zook, and Boulton 2013). Echoing Shelton et. al. (2014), most of the intended use of content found under the big data umbrella would be best served under critical-quantitative approaches developed within geography and GIScience over the past 20 years (c.f., Kwan 2004; Elwood 2006; Kwan and Schwanen 2009). Furthermore, the study of coded engagement mandates an interrogation of what is gained and lost when productions of political behavior are distilled into algorithmically derived indicators (Warf and Sui 2010; Wilson 2011) and when digital inequities privilege some perspectives over others (Shelton, Poorthuis, and Zook 2015; Zook, Graham, and Stephens 2012; Haklay 2013)

A computational approach is also needed in researching coded engagement for two primary reasons. First, using data-driven techniques behind smart city initiatives provide an “inside-out” vantage point on their applicability for representing aspects of connectivity and engagement (Pentland 2013, 2014). Meaning by using these techniques one can develop more constructive critiques on data-driven science with specific reference to aspects of: their challenge the methodologies of social science (Obole and Welsh 2012; Boman 2014); forcing an acceptance to digital representations of citizens, community and civic engagement (Dutil etal. 2008; Mandarano et al. 2010); and to demonstrate plausible affects on the operations of municipal government and public administration (Andersen et al. 2010; Im et al. 2014). Second, evaluating the current capabilities of data-driven approaches stands to benefit from a more open, but cautiously skeptical (e.g., Graham and Shelton 2013) perspective of more data oriented geography; that avoid over-generalized critiques against the nebulous of terms like 'data science', 'big data', and the 'smart city' (e.g., Wyly 2014). Meaning suspending judgment until the evidence justifies a decision regarding the potential of mobilizing coded engagement or data-driven techniques for understanding “urban socio-spatial processes”; while adhering to contextualized theory and methodology that avoids rampant hyperbolic claims and unfounded hubris (Shelton, Poorthuis, and Zook 2015; Gartner 2014).

Theoretical Prospectus

As stated, this project will rely on combining both a computational approach to collecting, analyzing, and using data for the purposes of coding engagement, but will also keep a critical eye on the potential implications to affect both the citizens and cities that pursue such goals. In order to connect the digital content and lived geographies – certain tenets of activity theory (c.f., Barab, Schatz, and Scheckler 2004;

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Huang and Gartner 2009), approaches of behavioral geography methodology (c.f., Argent and Walmsley 2009), and geo-computation (c.f., Sui 2014; Abramson et al. 2014) will be utilized. Activity theory, alongsidesituationalist theories like those developed by Guy Debord (1994) and relational geography (e.g., Pooler 1976; Allen 2011; Malpas 2012), provide a theoretical base that can be extended for filling missing contextual aspects between behavior and intent through both social and geographic settings. The merger of psychology, disaggregated approaches to studying human behavior, and general mixed-method approaches that stem from behavioral geography will also contribute to the theoretical backing of activity theory. By avoiding reliance on assumptions of simplicity, rationality, and uniqueness (Barnes 2009), work on the human perception of and motivations for contributing to the digital data universe could bridge the gap between data-driven approaches and existing theories of geography for the study of coded engagement.

Methodological Prospectus

In order to collect, process, and analyze potentially massive amounts of digital data a broadening of spatial analysis techniques (Anselin and Rey 2010; Lingel and Bishop 2014). By broadening spatial analysis techniques, I mean including aspects of computational social science and methods of data-science, which will form the “algorithmic backbone” of research on coded engagement. For example, natural language processing (NLP) (e.g., topic modeling and text processing) provide one possible means to establish a form of connectivity between datum points (people) in geographic space that may not be explicitly georeferenced (Stock et al. 2013; Adams and McKenzie 2013). Furthermore, the establishment of social relationships from content similarity provide new means to “go beyond the geotag” and form communities of interest from content instead of prescribed digital interactions (e.g., likes, followers, and friends). By addressing the problem of ink propagation (c.f., Chen et al. 2013; Lü and Zhou 2011), search for new ways to form “connections” between actors, places, and content where they are not explicitly made through normative environments or actual functionalities of social media platforms can be integrated to geographic research (Golbeck 2013; Bode et al. 2014). Exploring aspects of connectivity between these dynamic entities, throughspatial networks, would also open the use of modified social network analysis (SNA) (Adalı et al. 2014; Berendt et al. 2010). SNA being a set of analytical tools well-known to be useful in measuring community participation and social capital (Rouse et al. 2007; de Souza et al. 2010; Stephens and Poorthuis In Press). Social capital in SNA is often measured through graph properties such as strong and weak ties, centrality, and node clustering – that are often compared to aspects of social influence and trust within social relationships (Freeman 1978; Kavanaugh et al. 2005; Golbeck 2009; Cheong and Lee 2010). The combination of SNA and spatial analysis could also reveal how internal and external factors influence a communities capacity to engage through the geoweb due to bias of access and representations in geographic contexts (adams et al. 2012; Amblard 2013).

The use of open datasets provided by many municipal governments will also be used to find emergent communities of interest in coded engagement (Kassen 2013; Arribas-Bel 2014; Light et al. 2014). Thus, the combination of heterogeneous datasets, both traditional spatial data (e.g., road networks, census data) and aspatial data (e.g., social networks and textual content), provide a practical and feasible opportunityto develop more local forms of spatial analysis that are sensitive to aspects of social productions in space andtime (Jiang 2011; Vertesi 2014).

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