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Using Open Data to Derive Local Amenity Demand Patterns for Walkability Simulations and Amenity Utilization Analysis


Abstract and Figures

Understanding human behavior and preferences are important for urban planning and the design of walkable neighborhoods. However, it remains challenging to study human activity patterns because significant efforts are required to collect the relevant data, convert unstructured data into useful knowledge, and take into consideration different urban contexts. In the context of the heated discussion about urban walkability and amenities, as well as the need of identifying a feasible approach to analyze human activities, this paper proposes a simple and effective metric of the amenity demand patterns, which demonstrates the spatiotemporal distribution of human activities according to the activeness in urban amenities. Such metric has the potential to support the urban study about people, mobility, and built environment, as well as other relevant design thinking. Further, a case study illustrates the data and the new metric can be used in walkability simulations and amenity utilization analysis, thus informing the design decision-making process.
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Using Open Data to Derive Local Amenity Demand
Patterns for Walkability Simulations and Amenity
Utilization Analysis
Yang Yang1, Samitha Samaranayake2, Timur Dogan3
1,3Environmental Systems Lab, Cornell University 2School of Civil and Environ-
mental Engineering, Cornell University
Understanding human behavior and preferences are important for urban
planning and the design of walkable neighborhoods. However, it remains
challenging to study human activity patterns because significant efforts are
required to collect the relevant data, convert unstructured data into useful
knowledge, and take into consideration different urban contexts. In the context of
the heated discussion about urban walkability and amenities, as well as the need
of identifying a feasible approach to analyze human activities, this paper
proposes a simple and effective metric of the amenity demand patterns, which
demonstrates the spatiotemporal distribution of human activities according to the
activeness in urban amenities. Such metric has the potential to support the urban
study about people, mobility, and built environment, as well as other relevant
design thinking. Further, a case study illustrates the data and the new metric can
be used in walkability simulations and amenity utilization analysis, thus
informing the design decision-making process.
Keywords: Big Data, Urban Amenity, Walkability, Human Activity
Understanding human behavior is crucial for study-
ing mobility-related issues in urban planning and
design (Huang and Wong 2016). It not only sup-
ports decision-making in road network design, real
estate development, and architecture programming
but also relates to interdisciplinary topics such as
healthy and sustainable cities (Nieuwenhuijsen and
Khreis 2019). However, it remains challenging to
study human activities because significant efforts are
required to collect the relevant data, convert unstruc-
tured data into useful knowledge, and take into con-
sideration different urban contexts.
Large efforts have been made to empower ur-
ban study about human activities with data-based
methods. Some of them are used to describe the
aggregated commuting pattern and characterize the
built environment. For example, Grauwin et al (2015)
clustered mobile network signatures and used them
to characterize human dynamic behavior on the city
and local scale. Dashdorj and Sobolevsk y (2016) clas-
sified geographical areas based on their amenity dis-
tribution, such as working or shopping oriented ar-
eas and proved the similarity between such activity
Design - ALGORITHMIC AND PARAMETRIC 2 - Volume 2 - eCAADe 37 / SIGraDi 23 |665
categorical profiles and human activity timeline cat-
egories estimated through cell phone data records.
Frias-Martinez et al. (2012) identified urban land uses
and landmarks from geolocated Twitter data. Be-
sides, there are also more specific metrics proposed
to support planning decision-making from different
aspects. Yoshimura et al. (2016) uncovered cus-
tomers’ spatial distributions by analyzing their con-
secutive transactions, which can help improve spatial
arrangements of retail shops. Wang et al. (2015) de-
fined linked activity spaces among social groups by
analyzing call dataset, which may relate to demands
for amenities for social life. Despite new perspectives
in understanding the urban environment, none of
these researches have directly shown the way of im-
plementing the proposed methods in the planning
or design process, or how these data can practically
influence the decision making.
Meanwhile, current planning paradigms pro-
mote high density, walkable neighborhoods as one
solution for many challenges. Studies have shown
that walkable neighborhoods can significantly re-
duce traffic-related pollution and lower the risk for
chronic diseases (Frank et al. 2006; Lee and Buch-
ner 2008), promote economic growth and prosper-
ity (Claris and Scopelliti 2016), and foster an increase
in social capital and political participation (Leyden
2003). Walkable amenities, as one of the most im-
portant factors towards a walkable city, have also be-
come a heated topic in urban planning and design.
It has been associated with socioeconomic growth
(Clark et al. 2002; Zandiatashbar and Hamidi 2018),
urban environment (Carlino and Saiz 2019) and qual-
ity of life (Mulligan and Carruthers 2011; Carr et al.
In the context of the discussion about urban
walkability and amenities, as well as the need of iden-
tifying a feasible approach to analyze human activi-
ties, this paper proposes a simple and effective met-
ric of the amenity demand patterns, which demon-
strates the spatiotemporal distribution of human ac-
tivities according to the activeness in urban ameni-
ties. More specifically, we regard the utility of ameni-
ties as the indicator of human activity (e.g. going to
a restaurant, going to a cinema) because (1) urban
amenities basically refer to all the services, functions
and infrastructures that residents use in their daily
lives (Allen 2015), which makes it a viable measure of
what people usually do; (2) the amenity-based met-
ric explicitly specifies people’s destinations so that it
can be interpreted and employed in mobility-related
researches more effortlessly.
Also, this paper introduces a case study about
the way of utilizing this metric in the design pro-
cess. Based on Urbano (Dogan et al., 2018), an inde-
pendently researched and developed plugin toolbox
for Rhinoceros3D & Grasshopper, this metric proves
to be valuable input data that can help promote
mobility-aware urban design.
We consider Lower Manhattan as our test ground be-
cause it is one of the most walkable regions world-
wide with a high density of urban amenities. For
other cities, although details of data processing may
vary due to the difference in available open data, the
foundational information needed to derive the result
stays the same.
Data Sources
Figure 1
OSM has fewer
amenity data points
compared to
Google Maps. The
presented region is
the same as the
following case
study region. (a)
Cafe in OSM; (b)
Cafe in Google
Maps; (c) Pharmacy
in OSM; (d)
Pharmacy in
Google Maps.
The datasets are retrieved from NYC open data and
map data platforms, including Open Street Maps and
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Google Places API.
Firstly, the PLUTO dataset from NYC Department
of City Planning is an extensive land use dataset at
the tax lot level. I t contains fields about building floor
area of different land uses including commercial, res-
idential, office, retail, garage, storage, factory, and
other use. Secondly, the Overpass API of Open Street
Maps (OSM) provides geographical data of urban fab-
rics and building footprints. It also contains informa-
tion about urban amenities as nodes with metadata
telling its coordinates, name, amenity type, opening
hours, rating, etc. Although OSM data has reason-
ably high quality and is comparatively easy to obtain,
its amenity data is not as comprehensive as Google
Places data (Figure 1). Furthermore, Google’s data
provides a critical dataset called popular times, which
presents the utility of a specific place using a 24/7 ma-
trix with each popularity number of any given hour
relative to the typical peak popularity (100) for the
business for the week (Table 3). To infer occupant
density in the amenities we refer to ASHRAE Stan-
dard 62.1-2013: Ventilation for Acceptable Indoor
Air Quality that provides standardized occupancy as-
sumptions per architectural use case (ASHRAE 2015).
Data Processing
In general, the necessary information to derive the re-
sult consists of amenity type, location, capacity, and
temporal utility of each amenity in the studied re-
gion. The process is introduced here by steps (Figure
Step 1 is to map out the primary data, includ-
ing PLUTO data and all the amenity information from
OSM and Google Maps. The latter one is straightfor-
ward, while the first one needs additional processing.
In PLUTO data, The location of a lot is depicted by the
coordinate of a point near its center, expressed in the
New York-Long Island State Plane coordinate system.
Also, the commercial area refers to all the allocated
area for commercial use in a lot, including but not lim-
ited to office and retail use. Hence, we use commer-
cial area subtracting office area as the equivalence of
the total indoor amenity area in each tax lot and then
map them onto the lat-long coordinate system.
Figure 2
Diagram of data
processing steps
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Step 2 is to derive amenity areas in preparation for
calculating amenity capacities since there is no of-
ficial open data directly containing such informa-
tion. We divide the total amenity area on tax lot
level equally between all amenities in that lot. This
step introduces inaccuracy as amenities may differ
in size. Nonetheless, it is an approximate measure-
ment and can obtain desirable results when we apply
it to a larger region (e.g. the whole Lower Manhattan
Area) and introduce some descriptive statistics such
as the interquartile range (IQR) to filter the outliers
(Figure 3). The median value, perceived as the normal
amenity area of each type, can be assigned to all the
outliers as well as amenities with an unknown area in
the same region (Table 2).
Step 3 is to calculate amenity capacities based on
its type and area. The amenity capacity is the out-
come of multiplying the amenity area the standard-
ized occupant densities described by ASHRAE (2015).
With regard to the choice of study types (Table 1), we
first choose the most common amenity types in the
city so that there is sufficient data to retrieve from
maps platforms. Among them, we then select impor-
tant types of walkable amenities as reported by the
sample data sheet from Walk Score so that they are
valuable for the discussion.
Figure 3
Use the
interquartile range
(IQR) to analyze the
distribution of the
deriving amenity
area (sqm). (a)
Hardware stores; (b)
Pharmacies; (c)
Restaurants; (d)
Table 1
Study types of
amenities are
different datasets.
Table 2
General statistics
generated from
step 2 and 3 are
used to depict the
average area and
information of each
study type.
Step 4 is to explore temporal utility data from Google
popular times. For each amenity with available pop-
ular time data, we calculate the average activeness
of each hour in a week, which results in an array of
24-hour data expressing the specific utility patterns
of this place in a general day. Since the urban mobil-
ity pattern differs from weekday to weekend, we also
treat them separately (Table 3).
The final step is to combine all the previous re-
sults so as to convert the temporal utility pattern to
the user pattern. Under an assumption that the peak
activeness in amenity (100) is equivalent to the utility
of full capacity, the temporal amenity utility can be
directly transformed into the temporal user popula-
tion by multiplying its capacity. Ideally, the user pop-
ulation should be calculated for each amenity in the
studied region and then be summed up for each type
in order to derive the result. However, to limit the
computational burden, we instead use a sampling
method (300 samples for each type), derive average
statistics for each type, and finally multiply them with
the total number of amenities in each kind. The even-
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Table 3
A sample sheet of
the comprehensive
collected for one
amenity. An array of
24-hour activeness
in percent (%) is
independently for
weekdays and
expressing its
temporal utility
tual datasheet for amenity demand patterns is pre-
sented in Table 4.
Using Table 4, the amenity demand patterns can
be represented by a 24-hour timeline graph (Figure
4). The total height of the graph refers to the over-
all amount of activities in the studied region, which
peaks during the day and dips in the early morn-
ing. Each layer in the graph represents the demand
pattern of a particular amenity, some of which are
not quite consistent with the overall pattern due to
unique functionality. For example, banks and post of-
fices tend to stop service early in the afternoon, while
bar and pub become dominant activities during mid-
Intuitively, human activities demonstrate diverse
patterns in distinct spatial and temporal contexts. By
separating the weekday and weekend data and mak-
ing comparative graphs, it is shown that, during the
weekend, both daytime and nightlife are more active,
and the peak hour of overall activities shifts several
hours earlier. Regarding the spatial difference, we
conduct our deriving method for Lower Manhattan
and Downtown Paris and compare the results using
the same graph. It turns out that the urban pulse de -
picted by amenity demand patterns in the two cities
also have significant differences.
To examine the value of amenity demand data in de-
sign practice, this case study aims to use the derived
data to inform an urban mobility model and discuss
how it can influence the design decisions in urban de-
sign and program allocation.
Case Setup and Tool Description
Urbano ( is a tool that allows designers
to interactively modify the model according to ur-
ban mobility simulation results (Figure 5). It stream-
lines the workflow of importing data, model setup,
simulating iteratively and modifying designs in or-
der to promote the mobility-aware urban design pro-
cess. Moreover, it provides the ability to customize
people’s different preferences and demands for ur-
ban amenities. Taking advantage of these features
of Urbano, we embed into the initial model all the
relative data of driving the mobility simulation, in-
cluding amenity locations, building-level population,
amenity capacities, and amenity demand patterns in
weekdays of Lower Manhattan, along with the auto-
matically downloaded geographical metadata of ur-
Table 4
Sample datasheet
of amenity demand
patterns for
weekdays in Lower
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Figure 4
Timeline graph for
amenity demand
patterns reveals
differences in urban
pulse. (a) Weekday
pattern in Lower
Manhattan; (b)
Weekday pattern in
Paris; (c) Weekend
pattern in Lower
Manhattan; (d)
Weekend pattern in
ban networks. These inputs construct a contextual
model with abundant metadata that enables design-
ers to get a more profound perception of urban mo-
bility environment.
We select a rectangle area in Lower Manhattan
as the study region to carry out the general simula-
tion based on the 24-hour matrix of the generated
amenity demand patterns input. Additionally, we
choose a block in the center of this region to test how
a new project would affect the current condition in
the sense of pedestrians and amenities (Figure 6).
Figure 5
Framework of
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Figure 6
Study regions
Result and Analysis
There are 24 simulation results each of which repre-
sents the mobility state of an hour in a weekday (Fig-
ure 7). Though Urbano is able to calculate various
mobility metrics such as walk score and street utility,
we will only focus on the simulation of amenity us-
age, which is called “amenity hits”, referring to pedes-
trian hits that an amenity received in a round of sim-
ulation. This metric is mainly driven by the shortest-
path routing system of Urbano, with the building-
level population data deciding the population com-
ing from all origins and the data of amenity demand
patterns deciding the population going to all desti-
nations. It can measure local demands for different
amenities and help decide the appropriate program
under the context.
Furthermore, in the selected block as the test
site for the new project, we run the same simula-
tion with different program scenarios to explore their
variance in the performances, including self-interests
in receiving pedestrian hits and regional impact on
nearby businesses (Figure 8). Accordingly, we can
make decisions in the program selection with custom
In pursuit of a more explicit illustration of how
the derived data works, we plot each amenity’s ac-
tivity pattern from the simulation result and com-
pare them with the real data from the original Google
popular times (Figure 9). Regardless of the different
height, which represents the overall activeness of the
business and is totally driven by the routing simu-
lation and population data, the shapes of the lines
that are variant in different amenity types are indeed
determined by the amenity demand data derived in
the previous chapter. The comparison confirms that
the data is overall representative and can be con-
cerned as a workable proxy for measuring human ac-
tivities regarding amenity utilities, especially for the
types with less variance in the business pattern, such
as restaurants and banks. Nevertheless, this derived
data depicts a general pattern over the whole Lower
Manhattan region. If one would like to get a more
Figure 7
Simulation results
in a 24-hour matrix
for the selected
rectangle region in
Lower Manhattan
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Figure 8
Impacts and
Performances of
different scenarios
in the site context
precise simulation result resembling the truth, it will
be helpful to incorporate more detailed local meta-
data into the model, such as opening hours. How-
ever, this is usually not the case in early-stage site
analysis and urban design thinking.
Figure 9
Comparison of
amenity demand
patterns between
simulation and real
data. For each type,
we randomly select
5 samples. (a)Real
data; (b)Simulation
Scale of the study area
Caution is needed about choosing the scale of the
study area, both for deriving the amenity demand
patterns and for running the simulation as the case
study. Under the first circumstance, there is a risk
in misinterpreting the existing situation as implicit
needs, which is especially problematic if we only con-
sider a relatively small area. For example (Figure 10),
if we only compute the data from the case region,
the amenity demand patterns remain similar. But af-
ter downsizing the computing area into the quarter
of the region, the result starts to change drastically,
and some amenity types even start to have zero de-
mands, which is questionable. Under the latter cir-
cumstance, since there is a cut-off border for the se-
lected region and the simulation result near the bor-
der is less reliable, it is also inappropriate to use a too-
small study area.
Limitation in data sources
The accessibility and contents of open data largely
differ among countries and cities, and there certainly
exist other possibilities in processing data. For exam-
ple, the dataset like PLUTO may not be available out-
side of NYC, while other cities may provide data that
is not available for Manhattan as well (e.g. the city of
Melbourne provides an open dataset of seating ca-
pacity of cafes and restaurants). Besides, some data
has not been updated for years and may deviate from
672 |eCAADe 37 / SIGraDi 23 - Design - ALGORITHMIC AND PARAMETRIC 2 - Volume 2
the most recent fact. Moreover, the user-generated
data mostly relies on GPS on mobile phones (e.g.
Google popular times data), which has the problem
of coverage and bias.
While more efforts are being made to empower
the urban database in the era of big data and smart
city, the metric of amenity demand pattern and its
deriving method proposed in this paper should also
evolve with more advanced data in the future. How-
ever, intellectual merits will remain as it develops a
new way of quantifying and measuring people’s be-
havior in the city and links it closely to urban function
and built environment so that it is more useful to ur-
ban planning and design.
“Cities have the capability of providing something
for everybody, only because, and only when, they
are created by everybody” (Jacobs, 1992). Urban
planners and designers have long pursued a human-
centric urban environment. The accelerated techni-
cal development and increased access to urban data
are providing us a profound opportunity to better un-
derstand human preferences and behavior in cities.
This paper aims to propose a data-driven metric of
amenity demand patterns to help measure human
activities in the local context. Constructed from the
local data and user-generated contents, such metric
has the potential to support the urban study about
people, mobility, and built environment, as well as
other relevant design thinkings.
Also, under the bigger technological context of
extraordinary advancements in areas like big data
and smart city, and emerging paradigms about em-
powering planners and architects with computa-
tional techniques, this paper shows how amenity de-
mand data can be utilized and leveraged in active
mobility simulation workflows and how these simula-
tions can be used in the design decision-making pro-
cess to achieve “better” design outcomes.
Figure 10
Amenity demand
patterns of 6
sample types
derived from
different scales of
the study area
express significant
The authors would like to thank Nikhil Saraf for as-
sisting with the implementation and adaptation of
modeling workflows in Urbano. Further, the authors
would like to thank the Center for Transportation, En-
vironment, and Community Health (CTECH) for fund-
ing this research.
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674 |eCAADe 37 / SIGraDi 23 - Design - ALGORITHMIC AND PARAMETRIC 2 - Volume 2
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The pervasiveness of cell phones and mobile social media applications is generating vast amounts of geolocalized user-generated content. Since the addition of geotagging information, Twitter has become a valuable source for the study of human dynamics. Its analysis is shedding new light not only on understanding human behavior but also on modeling the way people live and interact in their urban environments. In this paper, we evaluate the use of geolocated tweets as a complementary source of information for urban planning applications. Our contributions are focussed in two urban planing areas: (1) a technique to automatically determine land uses in a specific urban area based on tweeting patterns, and (2) a technique to automatically identify urban points of interest as places with high activity of tweets. We apply our techniques in Manhattan (NYC) using 49 days of geolocated tweets and validate them using land use and landmark information provided by various NYC departments. Our results indicate that geolocated tweets are a powerful and dynamic data source to characterize urban environments.
Modern urban economic theory and policymakers are coming to see the provision of consumer‐leisure amenities as a way to attract population, especially the highly skilled and their employers. However, past studies have arguably only provided indirect evidence of the importance of leisure amenities for urban development. In this paper, we propose and validate the number of tourist trips and the number of crowdsourced picturesque locations as measures of consumer revealed preferences for local lifestyle amenities. Urban population growth in the 1990‐2010 period was about 10 percentage points (about one standard deviation) higher in a metro area that was perceived as twice more picturesque. This measure ties with low taxes as the most important predictor of urban population growth. “Beautiful cities” disproportionally attracted highly educated individuals and experienced faster housing price appreciation, especially in supply‐inelastic markets. In contrast to the generally declining trend of the American central city, neighborhoods that were close to central recreational districts have experienced economic growth, albeit at the cost of minority displacement. This article is protected by copyright. All rights reserved.
This volume brings together the world’s leading experts on urban and transport planning, environmental exposures, physical activity, health and health impact assessment to discuss challenges and solutions in cities. The book provides a conceptual framework and work program for actions and outlines future research needs. It presents the current evidence-base, the benefits of and numerous case studies on integrating health and the environment into urban development and transport planning. Within cities there is a considerable variation in the levels of environmental exposures such as ambient air pollution, noise, and temperature, green space availability and physical activity. Many of these exposures, and their adverse health impacts, are related to and are being exacerbated by urban and transport planning and policy. Emerging research suggests that urban and transport planning indicators such as road network, distance to major roads, traffic density, household density, industry, and natural and green space can explain a large proportion of the variability in environmental exposures and therefore represent important and highly modifiable factors. The urban environment is a complex interlinked system. Decision-makers need not only better data on the complexity of factors in environmental and developmental processes affecting human health, but also an enhanced understanding of the linkages between these factors and health effects to determine at which level to target their actions most effectively. In recent years, there also has been a shift from trying to change at the national level to more comprehensive and ambitious actions being developed and implemented at the regional and local levels. Cities have come to the forefront of providing solutions for environmental issues such as climate change, which has co-benefits for health, but yet need better knowledge for wider health-centric action. This book provides the latest and most up-to-date information and studies for academics and practitioners alike.
Understanding people’s consumption behavior while traveling between retail shops is essential for successful urban planning as well as determining an optimized location for an individual shop. Analyzing customer mobility and deducing their spatial distribution help not only to improve retail marketing strategies, but also to increase the attractiveness of the district through the appropriate commercial planning. For this purpose, we employ a large-scale and anonymized datasets of bank card transactions provided by one of the largest Spanish banks: BBVA. This unique dataset enables us to analyze the combination of visits to stores where customers make consecutive transactions in the city. We identify various patterns in the spatial distribution of customers. By comparing the number of transactions, the distributions and their respective properties such as the distance from the shop we reveal significant differences and similarities between the stores.
Individual activity patterns are influenced by a wide variety of factors. The more important ones include socioeconomic status (SES) and urban spatial structure. While most previous studies relied heavily on the expensive travel-diary type data, the feasibility of using social media data to support activity pattern analysis has not been evaluated. Despite the various appealing aspects of social media data, including low acquisition cost and relatively wide geographical and international coverage, these data also have many limitations, including the lack of background information of users, such as home locations and SES. A major objective of this study is to explore the extent that Twitter data can be used to support activity pattern analysis. We introduce an approach to determine users’ home and work locations in order to examine the activity patterns of individuals. To infer the SES of individuals, we incorporate the American Community Survey (ACS) data. Using Twitter data for Washington, DC, we analyzed the activity patterns of Twitter users with different SESs. The study clearly demonstrates that while SES is highly important, the urban spatial structure, particularly where jobs are mainly found and the geographical layout of the region, plays a critical role in affecting the variation in activity patterns between users from different communities.
The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportunity to study such location's contexts. This paper investigates relationships existing between human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification.
Studies of the city traditionally posit a division between a city’s economy and its culture, with culture subordinate in explanatory power to work. However, post–industrial and globalizing trends are dramatically elevating the importance of culture. Cultural activities are increasingly crucial to urban economic vitality. Models to explain the growth of cities from the era of industrial manufacturing are outmoded. Citizens in the postindustrial city increasingly make quality of life demands, treating their own urban location as if tourists, emphasizing aesthetic concerns. These practices impact considerations about the proper nature of amenities that post–industrial cities can sustain.