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While aerial photography is associated with vertical objectivity and spatial abstractions, street-level imagery appears less political in its orientation to the particularities of place. I contest this assumption, showing how the aggregation of street-level imagery into “big datasets” allows for the algorithmic sorting of places by their street-lev...
Context in source publication
Context 1
... can see a Coca-Cola billboard and street signage in the mid-ground, and you can locate yourself in Kiev, Ukraine, with the aid of a small Google Map in the top-left corner. Compare this with a satellite view of Kiev (Figure 2). On the one hand, the street-level image reveals more visual information: you get a general sense of what the people of Kiev look like, what they wear and, from this, know something about the temperature and weather (at least on the day the image was captured), how many lanes are on this busy road, and a building under construction in the background. ...
Citations
... The sensor project highlights that vulnerable populations often emerge as testing grounds for new technologies relating to the intersection of class-based racial and citizenship dynamics (Benjamin, 2019). Understandably, these areas emerge as an excellent context for sensors that supposedly help to efficiently fight vandalism, which in turnfollowing ideas of the broken window theory (Shapiro, 2018) potentially leads to more serious crime. ...
Proponents of digital transformation in welfare provision argue that digital technologies can take over tedious tasks and free resources to provide better care for those in need. Digital technologies, however, are often developed in line with a logic of control and dispositions around surveillance and efficiency which challenge careful engagements. In this conceptual article, we explore emerging tensions in digital welfare arrangements and propose an analytical framework to illuminate interrelations between care and control in values, infrastructures, and work related to the provision of welfare services. Illustrating the application of this framework with three empirical vignettes, we discuss how digital welfare technologies shape relations between state care and control. Considering theories of care in relation to the digital welfare state, we give a nuanced perspective on the contingencies of the digital transformation and add to the literature concerned with social justice by attending to everyday lived experiences in-between control and care.
... Extending this latter denition of materials to maps avoids any a priori assumption that they do mediate practices-a stark contrast with Power et al. (2012) and Shapiro (2017) discussed in the previous chapterwhilst remaining sensitive to their anchoring potential as internal elements of practice that might mediate. at is, Schatzki's approach would risk media-centricism on starting a priori with the assumption that practices are arranged around maps. ...
... (Bolter & Grusin, 1999, p. 83) Relating this to digital maps and their ability to provide 'views' that shift cartographic representation from primarily topographic static representation of paper-based maps to the slippy, spreadable, and dynamically navigable photo-realist (indexical) street-level imagery that veraciously mimics the visual aesthetic of lived experience, e.g., Google's Street View or Bing's StreetSide, could be labelled 'immediacy' (Bolter & Grusin, 1999). Likewise, when Power et al. (2012) and Shapiro (2017) express concern over Google Street View's potential to stigmatise place identities, they both rely on an underlying assumption of digital maps' immediacy. In this, Bolter and Grusin (1999) and Power et al. (2012) reify media by marginalising users as passive, locating media eects as an attribute of the medium itself, and thus imbue media with agency. ...
Examining how digital maps feature in people’s everyday lives and its relation to wider set of social consequences requires an appropriate framework. This chapter develops such a framework by drawing on a lineage of practice theory derived from Giddens’ structuration theory, incorporating concepts from digital geographies, digital sociology, experimental psychology, and various overlapping sub-disciplines within Internet studies (i.e., critical data studies, platform capitalism, mobile media studies). It explains why practice theory makes such a suitable base for exploring the social consequences of digital media, and how it differs from other approaches in science and technology studies and media studies, before outlining a few central tenets. Next, it charts the main theories and concepts of first- and second-wave practice theories before discussing their shared ontological base, and relevance for examining how new practices are formed and stabilised, and thus, how new technologies (such as digital maps) become integrated into everyday life. Throughout, the chapter develops a practice-orientated digital sociological framework, connecting it with digital sociology. The chapter also explains how the framework might be adapted to study engagement with a broad range of digital media.
... Mc Quire characterizes Google Maps in this context as 'one map to rule them all' (2019). However, cartographers point also to evolving fragmentations of space that are based on filtering mechanisms geared towards probable individualized consumer behaviour (Shapiro, 2018). Some aspects of the Google map as representation or text are critical from a 'traditional' cartographic perspective: ...
The study grasps the transformation of agency in the context of mundane production and use of mapping apps. It is asked theoretically and empirically, who produces and maintains Google Maps as cartographic infrastructure and how is this kind of agency being reflected and acted upon from a lifeworld perspective. Firstly, findings are compiled from various interdisciplinary studies on digital cartography, platform capitalism and digital infrastructures. Since these studies focus on agency almost exclusively from a structural perspective, a qualitative study is conducted to explore the use of Google Maps in everyday life. 20 interviews with German users show that digitalization and datafication profoundly change the dynamics of how agency is perceived and reflected. It can be understood as a form of extension of agency because it is deeply rooted in the entanglement of Google Maps’ infrastructure and city dwellers everyday practices.
... Geomedia, in much of the literature, focuses on applications, such as Google Street View (Shapiro, 2018), Google Maps (McQuire, 2019), Yelp, Foursquare (Frith & Kalin, 2016;Wilken & Humphreys, 2019), Facebook Places (Wilken, 2014), and other similar applications (de Souza e Silva & Frith, 2010). In these applications, the geolocation of users or the abstraction of the material into immaterial space is fundamental to their algorithmic logic. ...
The impact of gentrification in cities is well established. The continuous evolution in geolocation and social media is intensifying the contest between competing stakeholder claims to authenticity about gentrifying places. In this article, we examine the way that different geolocative social media define a struggle over the rights to authenticity in a rapidly gentrifying neighborhood in Brisbane, Australia. Local voices are often submerged by the voices of commercial imperative, particularly when the rent gap in gentrifying neighborhoods begins to attract abstract capital with a vested interest in commodifying local culture. We use Instagram and Facebook to critically examine how the hegemonic influence of social media can construct a gentrifying neighborhood in immaterial space and argue that these constructions work to eradicate the complex array of communities that comprise this neighborhood in material space.
... In addition to GSV, other digital platforms have launched street-level panoramic imagery products, such as Apple Look Around (some US and international cities), Microsoft Bing StreetSide (US and some European cities), Baidu Total View and Tencent Street View (Chinese cities), Kakao/Daum Road View and Naver Street View (South Korea), and Yandex (Russia and some Eastern European countries), as well as the corporate crowdsourcing platforms KartaView (formerly OpenStreetCam) and Mapillary (acquired by Facebook) [35]. These street-level images have great potential for researchers as a large repository of panoramic images as a source of urban big data [36]. GSV has been used successfully to assess urban trees on streets and highways [14] and even to assess the state of tree health [37]. ...
Resilient cities incorporate a social, ecological, and technological systems perspective
through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have compared and combined two of the most common data sources for updating urban tree inventories:
Airborne Laser Scanning (ALS) point clouds combined with aerial orthophotographs, and street-level imagery from Google Street View (GSV). Depending on the data source, different methodologies were used to identify the trees. In the first stage, the use of individual tree detection techniques in ALS point clouds was compared with the detection of objects (trees) on street level images using computer vision (CV) techniques. In both cases, a high success rate or recall (number of true positive with respect to all detectable trees) was obtained, where between 85.07% and 86.42% of the trees were well-identified, although many false positives (FPs) or trees that did not exist or that had been confused with other objects were always identified. In order to reduce these errors or FPs, a second
stage was designed, where FP debugging was performed through two methodologies: (a) based on the automatic checking of all possible trees with street level images, and (b) through a machine learning binary classification model trained with spectral data from orthophotographs. After this second stage, the recall decreased to about 75% (between 71.43 and 78.18 depending on the procedure used) but most of the false positives were eliminated. The results obtained with both data sources were robust and accurate. We can conclude that the results obtained with the different methodologies are very similar, where the main difference resides in the access to the starting information. While the
use of street-level images only allows for the detection of trees growing in trafficable streets and is a source of information that is usually paid for, the use of ALS and aerial orthophotographs allows for the location of trees anywhere in the city, including public and private parks and gardens, and in many countries, these data are freely available.
... Neoliberal governance by objectives, specifically, is a case in which quantitative indicators play a central role (Bruno et al. 2014a, b;Thévenot 2011). Particularly around basic social institutions like health care (Ruckstein and Schüll 2017), transport and traffic control (Shapiro 2018) or trade (Davis et al. 2012), state and market actors may converge around quantitative techniques that mix political and economic objectives (e.g., Mennicken and Espeland 2019). Some warn that such techniques give rise to technologies of surveillance, valuation and ranking (van Dijk 2014) that combine the most draconian parts of states and markets in a hybrid of quantified governance. ...
... Practices of capture relate to the objects of quantification in various ways, with different implications for the phenomena which are quantified (Pink and Lanzeni 2018). In some cases, lived phenomena may seem to naturally afford quantification, for example, quantities of goods that are easily measured or discrete objects whose countability does not require extraction from entangled webs of other objects (cf., Shapiro 2018). Other aspects of social life may be made amenable to quantification only after high levels of processing, manipulation, or abstraction, such as the case with psychological variables like well-being (Alexandrova 2012) or sociological concepts such as class (Desrosières 1993). ...
Social practices of quantification, or the production and communication of numbers, have been recognized as important foundations of organizational knowledge, as well as sources of power. With the advent of increasingly sophisticated digital tools to capture and extract numerical data from social life, however, there is a pressing need to understand the ethical stakes of quantification. The current study examines quantification from an ethical lens, to frame and promote a research agenda around the ethics of quantification. After a brief overview of quantification research and its uses in state and market organization, I discuss quantification in terms of three core subprocesses—capture, specification, and appropriation, illustrating and identifying ethical concerns around each process. Linking these processes to the performative effects of measures, I present a working model of quantification from which the discussion builds ideas for developing a research agenda around quantification.
... Despite experimental and meta-analysis evidence supporting relationships with health outcomes, studies have criticized the potential role that objective neighborhood disorder might play [2,6,13]. This de-emphasis might be due to assertions that objective physical disorder, as a measure of broken windows theory, has contributed to over-policing communities of color [16,17], as well as empirical evidence for attenuated associations between objective and perceived neighborhood disorder with adjustment for sociodemographic covariates (i.e., gender, individual-level socioeconomic factors, neighborhood racial-ethnic composition) and neighborhood social cohesion or collective efficacy [6,13,14]. However, more recent evidence supports conceptualization of observed neighborhood disorder as a product of discrimination and disinvestment of individuals and communities of color [18,19]. ...
Background
Mounting evidence supports associations between objective neighborhood disorder, perceived neighborhood disorder, and health, yet alternative explanations involving socioeconomic and neighborhood social cohesion have been understudied. We tested pathways between objective and perceived neighborhood disorder, perceived neighborhood social cohesion, and socioeconomic factors within a longitudinal cohort.
Methods
Demographic and socioeconomic information before diagnosis was obtained at interviews conducted approximately 10 months post-diagnosis from participants in the Women’s Circle of Health Follow-up Study – a cohort of breast cancer survivors self-identifying as African American or Black women ( n = 310). Neighborhood perceptions were obtained during follow-up interviews conducted approximately 24 months after diagnosis. Objective neighborhood disorder was from 9 items audited across 23,276 locations using Google Street View and scored to estimate disorder values at each participant’s residential address at diagnosis. Census tract socioeconomic and demographic composition covariates were from the 2010 U.S. Census and American Community Survey. Pathways to perceived neighborhood disorder were built using structural equation modelling. Model fit was assessed from the comparative fit index and root mean square error approximation and associations were reported as standardized coefficients and 95% confidence intervals.
Results
Higher perceived neighborhood disorder was associated with higher objective neighborhood disorder (β = 0.20, 95% CI: 0.06, 0.33), lower neighborhood social cohesion, and lower individual-level socioeconomic factors (final model root mean square error approximation 0.043 (90% CI: 0.013, 0.068)). Perceived neighborhood social cohesion was associated with individual-level socioeconomic factors and objective neighborhood disorder (β = − 0.11, 95% CI: − 0.24, 0.02).
Conclusion
Objective neighborhood disorder might be related to perceived disorder directly and indirectly through perceptions of neighborhood social cohesion.
... Accessing this massive data resource can provide a unique tool in developing efficient automated pavement damage detection systems. GSV has been successfully used in many research fields [33][34][35]. ...
In this study, the utility of using Google Street View (GSV) for evaluating the quality of pavement is investigated. A convolutional neural network (CNN) is developed to perform image classification on GSV pavement images. Pavement images are extracted from GSV and then divided into smaller image patches to form data sets. Each image patch is visually classified into different categories of pavement cracks based on the standard practice. A comparative study of pavement quality assessment is conducted between the results of the CNN classified image patches obtained from GSV and those from a sophisticated commercial visual inspection company. The result of the comparison indicates the feasibility and effectiveness of using GSV images for pavement evaluation. The trained network is then tested on a new data set. This study shows that the designed CNN helps classify the pavement images into different defined crack categories.
... Viewers of these platforms can navigate between images captured at regularly defined intervals along streets, virtually experiencing a diversity of urban streetscapes, built environments, and human activity from an eye-level, 360 • perspective. While the interactive, immersive potential of streetlevel imagery platforms draws in users for informational, educational, and experiential purposes, researchers around the world are drawn to their vast repositories of panoramic imagery as a source of urban big data [4]. This paper provides the first comprehensive, global, state-of-the-art review of literature on the use of panoramic street-level imagery for data-driven urban research. ...
The release of Google Street View in 2007 inspired several new panoramic street-level imagery platforms including Apple Look Around, Bing StreetSide, Baidu Total View, Tencent Street View, Naver Street View, and Yandex Panorama. The ever-increasing global capture of cities in 360° provides considerable new opportunities for data-driven urban research. This paper provides the first comprehensive, state-of-the-art review on the use of street-level imagery for urban analysis in five research areas: built environment and land use; health and wellbeing; natural environment; urban modelling and demographic surveillance; and area quality and reputation. Panoramic street-level imagery provides advantages in comparison to remotely sensed imagery and conventional urban data sources, whether manual, automated, or machine learning data extraction techniques are applied. Key advantages include low-cost, rapid, high-resolution, and wide-scale data capture, enhanced safety through remote presence, and a unique pedestrian/vehicle point of view for analyzing cities at the scale and perspective in which they are experienced. However, several limitations are evident, including limited ability to capture attribute information, unreliability for temporal analyses, limited use for depth and distance analyses, and the role of corporations as image-data gatekeepers. Findings provide detailed insight for those interested in using panoramic street-level imagery for urban research.
... Regarding the third form, obsolescence is unique in the sense that representation is spatially present and unfragmented; yet it is temporally outdated, which may or may not be understood by viewers. While people typically construct an understanding of places through a "multitude of present and past discursive and physical layers" (Graham, 2010: 422), the purported realism of immersive geolocated imagery ascribes it an undeserved finality (Shapiro, 2017;Cinnamon, 2019). ...
... (respondent, Quay area) Feedback from the respondents suggests that increased visibility through 360°representation could positively affect perceptions of local areas, although as the quote above indicates, some respondents specifically queried whether visibility would necessarily lead to positive representations of place. Street-view imagery exists at the intersection of two powerful ways of knowing and representing the world, cartography and photography, and yet there has been limited criticism of the performative power of GSV imagery, at least compared to the critique of Google's Maps and Earth platforms (Shapiro, 2017;Gilge, 2016). 5 Through differential visibility, GSV has a persuasive role in shaping how urban places are imagined-the visual, immersive nature of the imagery affords it potent discursive authority (Elwood and Leszczynski, 2011: 7). ...
... Although respondents were concerned specifically with the link between representation and reputation, a further risk of DIY street-view production that should be considered is the use of the imagery for spatial profiling (Dodge, 2018). Here, a distinction between 360°imagery as visual representation or quantitative data is useful (Hoelzl and Marie, 2014;Shapiro, 2017;Cinnamon, 2019). Beyond mere visual representation, recent developments in data extraction from imagery (using machine learning and manual techniques) illustrates how 360°images could be a powerful source of data for profiling areas (and their residents) according to the risk of crime, insecurity, and public disorder (Zhang et al., 2019;Marco et al., 2017). ...
Google Street View (GSV) is the de facto platform for street-level visual representation in most settings; however, its coverage is highly uneven due to a range of political, legal, technological, and economic factors. GSV's spatiotemporal disparities are most evident within cities, and this advances a distorted urban imaginary of absences, fragments, and obsolescences. This paper traces key developments in 360°imaging poised to expand the production and consumption of street-level imagery, including new actors, platforms, technologies, and data production approaches. Then, engaging with consumer-grade imaging technologies and the notion of do-it-yourself urbanism, this paper develops a DIY street view approach as one new mode of producing street-level imagery. Drawing on the findings of a pilot study, the paper considers key practical issues for street-view production, the benefits and risks of DIY approaches in relation to corporate and crowdsourced imagery initiatives, and the politics of urban representation in 360°. Findings suggest that the DIY approach offers the potential for a more "careful curation" of space in 360°street-level representations; however, there are considerations specific to this "third way" that require further attention.
