Environment and Planning B: Planning and Design

Nowadays, the transformations of metropolises into smart cities is a crucial factor in improving the living conditions of the inhabitants. The goal of the smart city concept is modern urban management using technical tools that offer state-of-the-art technologies, considering the applicable ecological standards while saving resources and achieving the expected results. The purpose of this article is to identify the areas of research analysed in the international literature in the field of smart cities. The bibliometric analysis was carried out to achieve the purpose. The analysis covered publications on smart cities published in Scopus and Web of Science databases from January 2009 to May 2019. Based on the bibliometric analysis, a bibliometric map was developed using the mapping technique VOS — the visualisation of similarities. Original clusters were created using the VOSviewer software. The bibliometric map visualises the results of the analysis that targeted the word coexistence.

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Smart city concept in the light of the literature review

City governments are embracing data-driven and algorithmic planning to tackle urban problems. Data-driven analytics have an unprecedented capacity to call urban futures into being. At the same time, they can depoliticize planning decisions. I argue that this shift calls urban studies scholars to investigate geographies of algorithmic violence?a repetitive and standardized form of violence that contributes to the racialization of space and spatialization of poverty. This article examines this broader phenomenon through the case of a proprietary market value assessment that is being used to guide development in cities across the United States. The assessment employs an algorithm that helps city officials make critical decisions about which neighborhoods to target for investment, disinvestment and public service upgrades or disconnections. I argue that the racial, infrastructural, and epistemological violence associated with this evaluation can potentially lead to a new kind of municipal redlining. The article brings insights from critical race theory into conversation with critical scholarship on algorithms by analyzing how algorithmic violence works through data-driven planning technologies to depoliticize and leverage power while further entrenching racism and inequality.

Geographies of Algorithmic Violence: Redlining the Smart City

This research received funding from the Wenner‐Gren Foundation for Anthropological Research, the Social Science Research Council DPDF Program, and a Fulbright US Student Award.

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Contesting Digital Futures: Urban Politics, Alternative Economies, and the Movement for Technological Sovereignty in Barcelona

Digitality is deeply implicated in sociospatial processes of exclusion, adverse incorporation, impoverishment and enrichment. Theorizing digital practices of life and thriving is politically and ep...

Digital geographies, feminist relationality, Black and queer code studies: Thriving otherwise:

In this paper, we critically explore the interplay of algorithms and civic participation in visions of a city governed by equation, sensor and tweet. We begin by discussing the rhetoric surrounding techno-enabled paths to participatory democracy. This leads to us interrogating how the city is impacted by a discourse that promises to harness social/human capital through data science. We move to a praxis level and examine the motivations of local planners to adopt and increasingly automate forms of VGI as a form of citizen engagement. We ground theory and praxis with a report on the uneven impacts of algorithmic civic participation underway in the Canadian city of Toronto.

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Data-Driven Participation: Algorithms, Cities, Citizens, and Corporate Control

This paper presents a crowd sensing system (CSS) that captures geospatial social media topics and allows the review of results. Using Web-resources derived from social media platforms, the CSS uses...

A crowd sensing system identifying geotopics and community interests from user-generated content

GIScience 2016 Short Paper Proceedings Crowd Sensing System for Public Participation M. Tenney 1 , R. Sieber 1 , G.B. Hall 2 1 McGill University, Montreal, QC, Canada Email: matthew.tenney@mail.mcgill.ca, renee.sieber@mcgill.ca 2 Esri Canada, Toronto, ON, Canada Email: bhall@esri.ca Abstract We propose a crowd sensing system to capture certain dynamics of public participation in a city. Crowd sensing systems (CSS) attempt to capture the opinions of local publics from web- resources. We define our CSS using a spatially-situated social network graph where users along with different variables, such as time, location, social interaction, service usage, and human activities can be studied and used to identify experts or influential citizens who are relevant to municipal affairs. 1. Introduction In the fields of engineering and the computational sciences, the term crowd sensing represents a popular area of research (Cardone et al. 2013). Similar to citizen sensing, urban sensing and participatory sensing, we broadly define crowd sensing systems (CSS) as being an integrated hardware and software architecture designed to collect user-generated content for a specified topic, issue or theme. In this paper, we introduce a portion of our conceptual CSS, which describes several social and spatial interactions within a local population (i.e., connections between individuals and locations of communities-of-interest), establishes place- based topics across a city from user-generated content (e.g., geotagged posts from social media), and identifies various forms of activity across specific geographies (e.g., patterns of urban travel). The CSS combines methods of natural language processing, spatial analysis, and graph theory to create a data structure with possible value when used to inform local decision makers. Our work builds on smart city initiatives, data-science and Web 2.0 literature that seek to revise traditional forms of public participation (Cardone et al. 2013). In particular, these difficulties can be assuaged by integrating data-driven techniques that automatically extract “similar” information (i.e., topical pubic opinions) from user-generated content. 2. Crowd Sensing Systems as Tripartite Network Public participation in municipal affairs is often seen as a product of stakeholders and interest groups, which are spatially distributed across a city. Choosing to model public participation digitally, requires representing relationships between structured and unstructured content, deriving explicit and implicit social interactions, and inferring frames of context through shared interests and co-location. Like other social networks, our CSS network graph (G) contains nodes and edges G = (N, E). The graph is further divided into three subgraphs containing unique node and edge types defined as U = (Un, Ue), C = (Cn, Ce) and T = (Tn, Te), where Un, Ue are nodes and edges of the user profiles in subgraph U; user-generated content forms the subgraph C consisting of nodes Cn and edges Ce; and a “geotopics” subgraph T contains spatially located nodes Tn with temporally weighed edges Te. These connected subgraphs as shown in Figure 1, can form spatially-situated networks constructed from what we call Social Signals (SocSigs, see below). SocSigs information contained in each of the connected sub-graphs include a user-network U (i.e., social-network among

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Matthew Tenney

Papers

Data-Driven Participation: Algorithms, Cities, Citizens, and Corporate Control

A crowd sensing system identifying geotopics and community interests from user-generated content

Crowd Sensing System for Public Participation