Dissenting poses

Abstract

In the spring of 2014, an unprecedented wave of police raids swept over every lower-class (sha‘abi) neighborhood across Morocco. Dubbed “Operation Tcharmil,” the raids targeted young, lower-class men that matched viral online images in which track-suit-wearing teens boastfully displayed status objects and white weapons. Drawing on the theoretical apparatus of the “affective turn,” in this article I unpack the structural and historical factors that shaped both popular reactions and policing actions toward the sudden, online visibility of a politically and economically disenfranchised group. I situate this episode within current debates about the entanglement of neoliberal disciplinary regimes and the reproduction of particular social orders, and argue that attention to such outbursts can help us revitalize and rethink existing notions of class.

The Implementation of the Smart City Process—Researchers’ Knowledge in Detecting Transport System Defects

“…After the city was mapped, then came the collection of other data sources (e.g., sociodemographic and socio-economic data, heat-maps from Strava [16], data collection points, data of road accidents, etc.). The visualization of heat-maps accidents can be found in Figure 6.…”

Outdoor cycling activity affected by COVID-19 related epidemic-control-decisions

“…In this study, we quantify the effects of governmental epidemic-control-decisions on outdoor cycling activities in Germany. For this, we use publicly available data of cycling behaviour from Strava [28], a popular fitness tracking app. Germany's regulations for social distancing epidemic-control-decisions/13285808, doi: https:// doi.org/10.6084/m9.figshare.13285808.v1.…”

Mobile Services for Green Living

“…Also, the methodological approach helped to enhance descriptive statistics, spatially allocate cycling facilities, and simplify the complexity of cycling patterns as expected on a flow map (Claudel et al, 2015). Our research complements the existing techniques applied in transport geography such as feature matching or density estimation (Basiri et al, 2016), popular in cycling analysis and used for the Strava (2018b) or Wikiloc Outdoor (2018) applications. The result of our analysis (see Figure 5.2) provided insights into participants' commuter behaviour emphasising the spatial representation of crowdsourced bicycle trips and the frictions identified.…”

“…Such an approach seemed more effective at attracting new users willing to generate cycling data than other web-based tools like OpenCycleMap, the cycling version of OpenStreetMap (OpenStreetMap Contributors, 2017;Haklay and Weber, 2008). Some of these applications emphasise social interaction and cycling performance, such as Strava (Strava, 2018b) or Endomondo (Under Armour, 2018), while others directly focus on crowdsourced data collection such as Wikiloc (Wikiloc Outdoor, 2018). Location-based services are a key component of current cycling applications which allowed researchers to explore visualisation and analysis of cycling data mostly with the tools used for analysing motorised transportation (Claudel et al, 2015;Meier, 2015;Adrienko and Adrienko, 2011).…”

“…Concerning geometry, we identified how trip origins and destinations grouped, and the common orientation of recorded trips. To extend existing visualisation tools for cycling, such as the Strava (2018b) or Wikiloc Outdoor (2018), we chose gender and participants using cycling applications to explore the differences in the spatial distribution of trips.…”