Table 1.1 Public and private goods from an economic perspective
Table 2.1 Used public and private data sources
Table 2.2 Fuel consumption for different modes of transportation (Davis, Diegel, and Boundy 2009)
Table 2.3 Recorded distances by waste type and corresponding GHG emissions (tons of CO₂ equivalent emissions per ton of material) based on EPA WARM, assuming one-way transportation with a fully loaded garbage truck. Note: shortest distances correspond to sensors destroyed prematurely.
Table A.1 Overview of the investigated groups
Table 6.1 Table 6.1 Citizens Connect: average response time by the city in days for closed issues (N=849)

List of Illustrations

Figure I.1 Waste trackers reporting from a landfill in Arlington, Oregon. U.S. Geological Survey, State USDA Farm Service Agency, reproduced under Google Maps fair-use policy.
Figure 1.1 Statistical Exhibits in the Municipal Parade by the Employees of the City of New York, May 17, 1913. “Many very large charts, curves and other statistical displays were mounted on wagons in such manner that interpretation was possible from either side of the street. The Health Department, in particular, made excellent use of graphic methods, showing in most convincing manner how the death rate is being reduced by modern methods of sanitation and nursing” (Brinton 1914, 342).
Figure 2.1 Volunteer with donated objects and sensors before tagging. Photo: Christophe Chung, courtesy MIT Senseable City Lab, 2009.
Figure 2.2 Tests of different materials for the protective enclosure of sensors, from left to right: epoxy foam (eventually used in the experiment), rubber, and epoxy resin. Photo: Jennifer Dunham, courtesy MIT Senseable City Lab, 2009.
Figure 2.3 The collected traces overlaid with the locations of waste-processing facilities from the EPA FRS database. Landfills are drawn in yellow, recycling facilities in blue. Visualization by the author, courtesy MIT Senseable City Lab, 2010.
Figure 2.4 Logarithmic scatter plot of recorded transportation distance separated by waste category. On the vertical, logarithmic axis three distinct clusters associated with different waste streams can be identified at the distances of 10km, ~600km, and >1,500km.
Figure 2.5 Mean durations of removal by waste type, with confidence intervals from bootstrapping⁶ (red).
Figure 2.6 A printer cartridge at the Seattle-Tacoma International Airport. Web application: David Lee. U.S. Geological Survey, USDA Farm Service Agency, reproduced under Google Maps fair-use policy.
Figure 2.7 Network visualization of the material streams and facilities visited by the tracked items (collected in different ZIP code areas, gray in the center). Visualization by the author, courtesy MIT Senseable City Lab, 2010.
Figure 2.8 Visualized traces of two printer cartridges, traveling from Seattle to the California–Mexico border via two different routes: truck through California (red) and train via Chicago (blue). The transport GHG emissions associated with the blue track are lower than the red one. Visualization by the author, courtesy MIT Senseable City Lab, 2010.
Figure 2.9 Website for reviewing traces used in the volunteer study (Lee et al. 2014). Courtesy MIT Senseable City Lab, 2010.
Figure II.1 View of the COOPAMARE facility, located under viaduct Paul VI in Pinheiros/São Paulo, November 2011, photo by the author.
Figure 4.1 Hand-written material price list at the Associação de Catadores O Verde é a Nossa Vida facility. Recife, June 2013, photo by the author.
Figure 4.2 Collected GPS traces of COOPAMARE collection routes. In blue, the collection truck routes, in orange, the manual cart collection routes. São Paulo, November 2011. DigitalGlobe, reproduced under Google Maps fair-use policy; courtesy MIT Senseable City Lab 2011.
Figure 4.3 Visited collection points by COOPAMARE during one week of collection. Yellow traces correspond to manual collection, blue traces to truck collection. Dots represent different clients, their size indicating the frequency of visits. Courtesy MIT Senseable City Lab 2011.
Figure 4.4 Screenshot of Android application for recording trajectories and documenting collected material. Courtesy Julian Contreras, David Lee, MIT Senseable City Lab 2013.
Figure 4.5 Online map for viewing trajectories and recorded objects. Web application: David Lee, MIT Senseable City Lab 2013.
Figure III.1 Street sign in Cambridge, MA, requesting citizen participation in reporting maintenance problems with urban infrastructure.
Figure 5.1 Map showing the spatial distribution of citizen complaints via New York’s 311 helpline with respect to three different complaint types, mapped to the RGB color channels: noise (green), graffiti (red), and litter (blue). The resulting color allows an estimation of the proportional distribution of each complaint type in different parts of the city. Visualization by the author.
Figure 6.1 Most frequently used service categories in 569 U.S. cities using the Open311 standard, May 2015.
Figure 6.2 Latent topics (selection) within the general “Other” category, Boston CitizensConnect, probabilistic topic models, figure by the author.
Figure 6.3 Screenshot of two smartphone civic-issue trackers used in Boston, both 2011 versions. Left: SeeClickFix (notice buttons “neighbors” and “my profile”), right: CitizensConnect.
Figure 6.4 Screenshot of the CitizensConnect website used to browse reports in its version from 2010.
Figure 6.5 Relative differences between reports submitted by CitizensConnect (CC) and SeeClickFix (SCF) users based on a randomly drawn sample of reports.
Figure C.1 Know Your Rights mural in Bushwick, Brooklyn by artist Dasic Fernández. Screenshot from Google Street View, reproduced under Google Maps fair-use policy.

Guide

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