Exploring urban social media Selfiecity and On Broadway .docx

Exploring urban social media:
Selfiecity and On Broadway
Lev Manovich
Abstract
User-generated visual media such as images and video shared
on Instagram, YouTube,
Sino Weibo, VK, Flickr and other popular social media services
open up amazing
opportunities for the study of contemporary visual culture and
urban environments. By
analyzing media shared by millions of users today, we can
understand what people
around the world imagine and create; how people represent
themselves and others; what
topics, styles and visual techniques are most popular and most
unique, and how these
topics and techniques differ between locations, genders, ages,
and many other
demographic characteristics. In a number of projects completed

between 2012 and 2015,
we analysed large number of images shared on Instagram by
people in urban areas. This
article discusses two of these projects: Selfiecity (2014) and On
Broadway (2015). In
Selfiecity, we compared patterns in self-representations using a
collection of “selfie”
photos shared on Instagram by people in five global cities. In
On Broadway, we focused
on a single street in NYC – part of Broadway running through
Manhattan for 13 miles –
and analysed images shared along Broadway on Instagram and
Twitter, Foursquare
check-ins, taxi rides, and selected economic and social
indicators using U.S. Census data.
The article presents our methods, findings, and unique
interactive interfaces for
explorations of the collected data we constructed for each
project.
User-generated visual media such as images and video shared
on Instagram, YouTube, Sino
Weibo, VK, Flickr and other popular social media services open
up amazing opportunities for

the study of contemporary visual culture. By analysing media
shared by millions of users today,
we can understand what people around the world imagine and
create; how people represent
themselves and others; what topics, styles and visual techniques
are most popular and most
unique, and how these topics and techniques differ between
locations, genders, ages, and many
other demographic characteristics.
In 2005 I coined the term “cultural analytics” to refer to the
“analysis of massive cultural data
sets and flows using computational and visualization
techniques” and 2007 we set a research lab
(Software Studies Initiative, softwarestudies.com) to begin
concrete research. Having developed
and tested our techniques and software tools on variety of
smaller datasets such as 4535 covers
of Time magazine from 1923 to 2009, in 2012 we started
working on social media data.
In a number of projects completed since then, we analysed large
number of images shared on
Instagram by people in urban areas. Starting with a general
comparison between 2.3 million
images shared by hundreds of thousands of people in 13 global

cities (Phototrails, 2013,
http://phototrails.net/ ), we consequently focused on more
specific types of images, filtered by
http://selfiecity.net/
http://on-broadway.nyc/
http://www.softwarestudies.com/
http://phototrails.net/
type of content (self-portraits in Selfiecity, 2014,
http://selfiecity.net) or a specific city area (13
miles of Broadway in Manhattan in On Broadway, 2015,
http://on-broadway.net).
Given that all users of Instagram app are presented the same
interface, same filters, and even
same square image size, how much variance between the cities
do we find? Are networked apps
and their tools such as Instagram creating a new global visual
language, an equivalent of visual
modernism a hundred years earlier? Does the ease of capturing,
editing and sharing photos lead
to more or less aesthetic diversity? Do software and networks
result in more repetition,
uniformity and visual social mimicry, as food, cats, selfies and
other popular subjects seem
appear to drown everything else?

Use of large samples of social media, and computational and
visualization tools allows us to
investigate such questions quantitatively. Our analysis in
Phototrails revealed strong similarity
between the cities in terms of basic visual characteristics – such
as tonality and colours of images
– and also the use of filters. But these findings were partly an
artefact of the method we used. We
disregarded the content of photos, the differences in
compositions and other aspects of
photographic aesthetics, the relative popularity of various photo
types and many other possible
dimensions of difference. Instead, we considered the photos
only as assemblages of colour
pixels.
Figure 1.
50,000 Instagram photos shared in Tokyo in 2012, organized by
brightness mean (distance to the center)
and hue mean (angle). http://phototrails.net/instagram-cities/
http://on-broadway.net/

http://phototrails.net/instagram-cities/
Figure 2.
Top: 50,000 Instagram images in NYC over a number of
consecutive days, organized by upload date and
time. Bottom: 50,000 Instagram images in Tokyo over a number
of consecutive days, organized by
upload date and time. Both samples are from early 2012.
To compensate for some of the limitations of this first project,
in 2013 we started a new project
Selfiecity (http://selfiecity.net). Rather than using an arbitrary
sample of social media images
with any content, we focused on only one kind – the popular
selfies (self-portraits captured with
mobile phone’s cameras). In the next part of this text I will
discuss how we assembled the selfie
dataset, our research methods, the presentation of the work via
visualizations and a website, and
some of our findings.
1. Selfiecity
Making Selfiecity
The Project Team. To work on Selfiecity, we assembled a large

multidisciplinary team.
The team includes media theorists, an art historian, data
scientists, visual designers and
programmers who work between New York, Germany and
California. The project was
coordinated by Manovich, while Moritz Stefaner was
responsible for creative direction and
visualizations.
The project presentation online combines Findings about the
demographics of people taking
selfies and their poses and expressions; a number of media
visualizations (Imageplots) which
assemble thousands of photos together; and an interactive
application (Selfiexploratory) which
allows visitors to explore the whole set of 3,200 selfie photos,
sorting and filtering it to find new
patterns. It addition, the website selfiecity.net also includes
three essays about the history of
photography and the selfie phenomenon, the functions of images
in social media, and media
visualization method.

Data Collection. The first stage in working on this project was
the creation of a selfie
dataset. This required many steps. When you browse Instagram,
at first it looks as though it
contains a large proportion of selfies. A closer examination
reveals that many of them are not
selfies, but photos taken by other people. For our project, we
wanted to use only single-person
‘true selfies’.
The team partnered with Gnip, a third party company which at
that time was the world’s largest
provider of social data (gnip.com). After developing software
that interfaces with the Gnip
service, in September 2013 we started to collect Instagram
photos in different locations. After
many tests, we focused on central areas in five cities located in
North America, Europe, Asia and
South America. The size of an area used for Instagram images
collection was the same in every
city.
We wanted to collect images and data under the same
conditions, so we selected a particular
week (5–11 December 2013) for the project. Listed below are

the numbers of photos shared on
Instagram inside the chosen areas of our five cities during this
week, according to Instagram data
provided by Gnip (sorted by size, and rounded to nearest
thousand):
New York City – 207,000
Bangkok – 162,000
Moscow – 140,000
Sao Paolo – 123,000
Berlin – 24,0000
Total: 656,000 photos.
For our next step, we randomly selected 140,000 photos (20,000
or 30,000 photos per city) from
the total of 656,000 photos. We then used Amazon Mechanical
Turk service to select selfie
photos from this set. Each of 140,000 photos was tagged by
between two and four Amazon
Mechanical Workers. We experimented with different forms of a
question the workers had to
answer, and found that the simplest form – “Does this photo
show a single selfie?” – produced
best results.

We then selected the top 1,000 photos for each city (i.e. photos
which at least two workers
tagged as a single-person selfie). We submitted these photos to
Mechanical Turk again, asking
the three ‘master workers’ not only to verify that a photo
showed a single selfie, but also to tag
gender and guess the age of a person.
As the final step, at least one member of the project team
examined all these photos manually.
While most photos were tagged correctly (apparently every
Mechanical Turk workers knew what
a selfie was), we found some mistakes. We wanted to keep the
data sets size the same to make
analysis and visualizations comparable, and therefore our final
set contains 640 selfie photos for
every city (eliminating the mistakes), for a total of 3,200
photos.
Computer analysis. This sample set of 3,200 selfie photos was
analysed using state-of-
the-art face analysis software rekognition.com. The software
analysed the faces in the photos,
generating over 20 measurements, including face size,

orientation, emotion, presence of glasses,
presence of smile, and whether eyes are closed or open, and
others.
We have used these measurements in two ways. We compared
the measured face characteristics
between cities, genders and ages. We also included some of the
measurements in the
Selfiexploratory interactive application, to allow website
visitors to filter the selfies database by
any combination of selected characteristics.
The software also estimated the gender and age of a person in
each photo. We found that both
gender and the age estimates were generally consistent with the
guesses of Mechanical Turk
workers.
Visualizing the selfie photos
Typically, a data visualization shows simple data such as
numbers. However, a single number
cannot fully everything a photo contains. “A single photo is not
a ‘data point’ but a whole world,
rich in meanings, emotions and visual patterns” (Moritz
Stefaner, artistic director and
visualization designer of Selfiecity). This is why showing all

photos in the visualizations (along
with the graphs or by themselves) is the key strategy of the
project. We call this approach “media
visualization.” As Moritz Stefaner explained “Showing the high
level patterns in the data – the
big picture – as well as the individual images has been an
important theme in our project. How
can we find summarizations of big data collections, which still
respect the individuals, and don’t
strip away all the interesting details? This has become a quite
central question to us, not only
with respect to selfies”.
Stefaner created a few different types of visualizations for the
project, described below.
Blended Video Montages
(http://vimeo.com/moritzstefaner/selfiecity-five-cities). Each
video
presents 640 selfies from each city. It runs through all the
images, but not in a simple sequence.
Instead, a few selfies are superimposed on the screen at a time,
with new ones fading on top of
the old ones. The faces are aligned with respect to eye position
and sorted by the head tilt angle.
This visual strategy is designed to create a tension between

individual selfie photos and patterns
across many images. We do not show each face by itself. But we
also do not superimpose all
faces together – which would only produce a generic face
template, the same for every city.
Instead, we show something else: a pattern and individual
details at the same time.
Imageplots. Manual inspection of photos one by one can reveal
many interesting details, but it is
difficult to quantify the patterns observed. We created
histogram-type visualizations that show
distributions of genders, ages and smiles in different cities.
Like normal data visualization, they
allow you to immediately see patterns expressed in the shapes
of the graphs. Bu, because these
http://vimeo.com/moritzstefaner/selfiecity-five-cities
graphs are composed of individual photos, they also provide a
different way to explore the
interplay between the particular and the general.
Selfiexploratory. This is the key part of the project. It is the
interactive visualization app, which
allows website visitors to explore the selfie dataset in many
ways. Visitors can filter the photos

by city, gender, age and a number of face measurements
extracted by face analysis software.
Figure 3.
Imageplot showing distribution of selfie photos in five cities
according to gender (vertical axis) and
degree of smile (horizontal axis). The degree of smile was
measured by face analysis software; it can take
any value between 0 (no smile) and 100 (strong smile).
http://selfiecity.net/#imageplots
http://selfiecity.net/#imageplots
Figure 4.
A screen shot from Selfiexploratory application. The user
selected some of the youngest selfies from our
data of 3200 selfies using Age graph (left column, second row).
(http://selfiecity.net/selfiexploratory/)
The application allows visitors to explore the photos using data
from both human judgements
and computer measurements – two ways of seeing the photos.
The gender and age graphs on the
left use human tags and guesses (from Amazon’s Mechanical
Turk workers). All other graphs to

the right use software face measurements. Whenever a selection
is made, the graphs are updated
in real time, and the bottom area displays all photos that match
the selection. The result is an
innovative, fluid method of browsing and spotting patterns in a
large media collection.
In addition to presenting the selfie dataset though
visualizations, videos and the interactive
selfiexploratory application, we also decided to present selected
findings in a more conventional
format as statistics. Out of a larger set of findings, we selected
and presented the following:
1) Depending on the city, only 3–5% of images we analysed
were actually selfies.
2) In every city we analysed, there were significantly more
female than male selfies (from 1.3
times as many in Bangkok to 1.9 times more in Berlin). Moscow
is a strong outlier – here, we
have 4.6 times more female than male selfies. (While we do not
have this data for other
countries, in the US the ratio of female to male Instagram users
is close to 1:1, according to a
Pew Internet survey).

3) Most people in our photos are pretty young (estimated
median age 23.7). Bangkok is the
youngest city (21.0), whereas New York City is the oldest
(25.3). Men’s average age is higher
than that of women in every city. Surprisingly, more older men
(30+) than women post selfies on
Instagram.
4) Computational face analysis revealed that you can find lots
of smiling faces in Bangkok (0.68
average smile score) and Sao Paulo (0.64). People taking selfies
in Moscow smile the least (only
0.53 on the smile score scale).
5) Women’s selfies have more expressive poses; for instance,
the average amount of head tilt is
50% higher than for men (12.3° vs. 8.2°). Sao Paulo is most
extreme – there, the average head
tilt for females is 16.9°!
These findings present only some of the patterns we found. In
general, reviewing all the patterns,
we discovered that each of our five cities is an outlier in a
unique way (on patterns, see Berry

2015, this volume). Depending on which dimension we choose,
one of the cities usually stands
out. However, when we combine many dimensions together,
Moscow and Bangkok stand out
from other cities.
Perhaps our overall most interesting finding is the following.
Even though people use same
photo app and service (Instagram) that also allows them to
easily see how others photographs
themselves around the world, selfie photos we analysed have
significant local specificity. The
types of poses change from city to city, and between genders
and ages. So while Instagram
maybe contributing to the emergence of a uniform “global
visual language,” at the same time it
still reveals cultural and social differences in how different
groups of people represent
themselves.
2. On Broadway
In Phototrails, we compared photos from 13 global cities,
without filtering them by type or
location. In Selfiecity, we filtered photos to only compare
single type photos (selfies) also across

multiple cities. For our next project On Broadway, we decided
to zoom in closer into the
universe of social media by focusing on the posts along a single
city street. At the same time, we
expanded our data sources, going beyond Instagram and adding
Twitter, Foursquare, Google
Street View, taxi pickups and drop-offs, and economic
indicators from US Census Bureau.
Figure 5.
Data and image layers used to create the interface to navigating
a city street in On Broadway project.
http://on-broadway.net
Figure 6.
Screenshot from On Broadway application, showing a zoomed-
in view centered on Time Square.
http://on-broadway.net
Figure 7.
Screenshot from On Broadway application, showing full

zoomed-out view – all 13 miles of Broadway in
Manhattan. http://on-broadway.net
Figure 8.
Interaction with On Broadway installation at Public Eye
exhibition in New York Public Library (2014-
2016). http://on-broadway.net
Representing The City
Modern writers, painters, photographers, filmmakers and digital
artists have created many
fascinating representations of the city life. Paintings of Paris
boulevards and cafés by Pissarro
and Renoir, photomontages by Berlin Dada artists, Broadway
Boogie-Woogie by Piet Mondrian,
Spider-Man comics (Stan Lee and Steve Ditko), Playtime by
Jacques Tati, and Locals & Tourists
data maps by Eric Fischer are some of the classic examples of
artists encountering the city. The
artwork that directly inspired our project is Every Building on
the Sunset Strip by Edward

Ruscha (1996). It is an artist book that unfolds to 25 feet (8.33
meters) to show continuous
photographic views of both sides of a 1.5-mile long section of
Sunset Boulevard.
Today, a city “talks” to us in data. Many cities make available
datasets and sponsor hackathons
to encourage creation of useful apps using their data. (For
example, NYC Mayor Office’s
sponsored NYC Open Data website offers over 1,200 datasets
covering everything from the trees
in the city to bike data.) Locals and tourists share massive
amounts of visual geo-coded media
using Twitter, Instagram and other networks. Services such as
Foursquare tell us where people
go and what kind of venues they frequent.
How can we represent the 21st century using such rich data and
image sources? Is there a
different way to visualize the city besides using graphs,
numbers, or maps?
Constructing Broadway
The first step in our project was to precisely define the area to

analyze, and assemble the data
form this area. Like a spine in a human body, Broadway runs
through the middle of Manhattan
Island curving along its way. We wanted to include a slightly
wider area than the street itself so
we can capture also the activities nearby. To define this area,
we selected points at 30-meter
intervals going through the center of Broadway, and defined
100-meter wide rectangles centered
on every point. The result is a spin-like shape that is 21,390
meters (13,5 miles) long and 100
meters wide.
We used the coordinates of this shape to filter Instagram,
Twitter, Foursquare, Google Street
View, taxi and economic data. In the following I describe the
details of our datasets.
Instagram. Using the services provided by Gnip, we downloaded
all geo-coded Instagram
images publicly shared in larger NYC area between February 26
and August 3, 2014. The dataset
contains 10,624,543 images, out of which 661,809 are from
Broadway area.

Twitter. As a part of Twitter Data Grant awarded to Software
Studies Initiative, we received all
publically shared tweets with images around the world during
2011-2014. We filtered this
dataset, leaving only tweets shared inside Broadway area during
the same time period as we used
for Instagram (158 days in 2014).
Foursquare. We downloaded Foursquare data for March 2009 -
March 2014 (1826 days)
through the Foursquare API. Overall, we counted 8,527,198
check-ins along Broadway.
Google Street View images. We experimented with our own
video and photo captures moving
along Broadway, but at the end our results did not look as good
as Google Street View images.
So we decided to include these images as another data source.
We wrote a script and used it to
download Google Street View images (one image for each of
our 713 points along Broadway),
looking in three directions: east, west and up. The first two
views show buildings on both sides

of the streets. The view up is particularly interesting, since it
shows the amount of sky visible
between buildings to Google wide angle lens. In Downtown and
Midtown areas, most of the
images in these views are taken by high-rise building, and only
a small part of the sky is visible.
But in the northern part of Broadway, buildings are lower, and
this is reflected in larger parts of
sky visible in the images.
Taxi. Chris Whong obtained 2013 taxi pickups and drop-offs
data from NYC Taxi and
Limousine Commission (TLC). He describes how he was able to
get the data here
http://chriswhong.com/open-data/foil_nyc_taxi/.) In 2013 there
have been 140 million trips in
Manhattan. Filtering this dataset using Broadway coordinates
left us with 22 million trips
(10,077,789 drop-off and 12,391,809 pickup locations).
Economic indicators. We used the latest data available
American Community Service (ACS).
It is a yearly survey of the sample of the US population by US
Census Bureau. ACS reports the

data summarized by census tracks. These are areas that are
much larger than 30 x 100 meter
rectangles we use to define Broadway area. Our Broadway
consists from 713 rectangles that
cross 73 larger US Census tracks. Because of these two
different scales, any Census population
statistics available will only approximately apply to the smaller
Broadway parts. Given this, we
decided to only use a single economic indicator from ACS -
estimated average household
income. This data was shown as one of the layers in the
application.
Navigating the Data Street, without Maps
We have spent months experimenting with different possible
ways to present all these data using
a visual interactive interface. The result of our explorations is a
visually rich image-centric
interface, where numbers play only a secondary role, and no
maps are used.
The project proposes a new visual metaphor for thinking about
the city: a vertical stack of image
and data layers. There are 13 such layers in the project, all
aligned to locations along Broadway.

As you move along the street, you see a selection of Instagram
photos from each area, left, right,
and top Google Street View images and extracted top colours
from these image sources. We also
show average numbers of taxi pickups and drop-offs, Twitter
posts with images, and average
family income for the parts of the city crossed by Broadway. To
help with navigation, we added
additional layers showing names of Manhattan neighbourhoods
crossed by Broadway, cross-
streets and landmarks.
This interactive interface is available online as part of the
project website (on-broadway.nyc).
We also showed it on a 46-inch interactive touch screen as part
of the exhibition Public Eye at
New Your Public Library (12/2014-1/2016). Since the
exhibition was free and open every day to
the public, with dozens of people inside at any given time, we
were able to see how ordinary
New Yorkers and city tourists were interacting with the
interface. It became clear that focusing

on the visual layers – Instagram photos and Google Street View
images – was the key in making
the interface meaningful and useful to the public. We saw many
times how visitors would
immediately navigate and zoom in a particular block of the city
meaningful to them: perhaps a
place where they were born, or lived for a long time.
This personalization of the “big data” was one of our main
goals. We wanted to let citizens see
how many types of urban data relate to each other, and let them
relate massive and sometime
abstract datasets to their personal experiences - places where
they live or visit.
Conclusion: Aesthetics vs. Politics of Big Data
Today companies, government agencies and other organizations
collect massive data about the
cities. This data is used in many ways invisible to us. At the
same time, as I already mentioned,
many cities make available some of their datasets and sponsor
competitions to encourage
creation of useful apps using this data.

But these two activities – collection of data, and release of the
data to the public - are not
symmetrical. The data released by cities only covers what city
administers and controls –parks
and streets, infrastructure repairs, parking tickets, etc. This is
the data about the city as an entity,
not about particular individuals or detailed patterns of their
activities. In contrast, the data
collected and analyzed by social media services, surveillance
camera networks, telecom
companies, banks, and their commercial clients (or government
agencies if they were able to get
access to parts of this data) is about the individuals: their
patterns of movement, communication
with other people, expressed opinions, financial transactions.
Some of the data from social media services is easily available
via API to anybody with a basic
knowledge of computer programming. This data is used in
numerous free and commercial apps.
(For example, when I use Buffer to schedule my posts to Twitter
and Facebook, Buffer interacts
with them via their APIs to place these posts at particular times
on my account pages). The same

data has already been used in hundreds of thousands of
computer science papers and conference
talks. Numerous students in computer and design science
classes also routinely download,
analyze and visualize social media data as part of their
assignments. But ordinary people are not
aware that the tweets, comments, images, and video they share
are easily accessible to anybody
via these free API tools. While articles in popular media often
note that individuals’ data is
collected, aggregated and used for variety of purposes,
including surveillance or customization of
advertising, they typically don’t explain that this data is also
available to individual researchers,
artists or students.
Artists can certainly play their role in “educating the public”
about the access and use of people
data. In our project websites, we have carefully explained how
we obtained the data for
Phototrails, Selfiecity and On Broadway, and how we used it.
But our main goal was “aesthetic

education” as opposed to “political education.”
“Big data” including visual social media is our new artistic
medium, and the projects discussed
here investigate its possibilities. In fact, we wanted to combine
aesthetic questions and research
questions: not only what we can learn from social media, but
how we use it to create aesthetic
representations and experiences? How should we imagine our
cities and ourselves in the era of
massive data collection and its algorithmic analysis? How can
visualizations of such data
combine bigger patterns and individual details? What
alternative interfaces for exploring and
relating to this data are possible, in addition to linearly
organized “walls”, maps, timelines, and
rectangular grids of images and video in Facebook, Twitter,
YouTube and other social media
service? In short: how we can see differently – not only the
world around us (this was the key
question of modern art) but also our new “data reality”?
Acknowledgements
Each of the projects described in this article was created by a
team:

Phototrails: Nadav Hochman, Lev Manovich, Jay Chow.
Selfiecity: Lev Manovich, Moritz Stefaner, Dominicus Baur,
Daniel Goddemeyer, Alise
Tifentale, Nadav Hochman, Jay Chow.
On Broadway: Daniel Goddemeyer, Moritz Stefaner, Dominikus
Baur, and Lev Manovich.
Contributors: Mehrdad Yazdani, Jay Chow, Nadav Hochman,
Brynn Shepherd and Leah
Meisterlin; PhD students at The Graduate Center, City
University of New York (CUNY):
Agustin Indaco (Economics), Michelle Morales (Computational
Linguistics), Emanuel Moss
(Anthropology), Alise Tifentale (Art History).
The development of Phototrails, Selfiecity and On Broadway
was supported by The Graduate
Center, City University of New York (CUY), California
Institute for Telecommunication and
Information (Calit2), and The Andrew W. Mellon Foundation.
We are grateful to Gnip for their
help with Instagram data collection. The part of this article

about Selfiecity project was adapted
from Alise Alise Tifentale and Lev Manovich, “Selfiecity:
Exploring Photography and Self-
Fashioning in Social Media,” Postdigital Aesthetics: Art,
Computation and Design, ed. David
Berry (Palgrave Macmillan, forthcoming).
ffirs.qxd 1/3/13 3:48 PM Page i
PROJECT
MANAGEMENT
ffirs.qxd 1/3/13 3:48 PM Page i
Dr. Kerzner’s 16 Points to Project
Management Maturity
1. Adopt a project management methodology and use it
consistently.
2. Implement a philosophy that drives the company toward

project
management maturity and communicate it to everyone.
3. Commit to developing effective plans at the beginning of
each project.
4. Minimize scope changes by committing to realistic
objectives.
5. Recognize that cost and schedule management are
inseparable.
6. Select the right person as the project manager.
7. Provide executives with project sponsor information, not
project
management information.
8. Strengthen involvement and support of line management.
9. Focus on deliverables rather than resources.
10. Cultivate effective communication, cooperation, and trust to
achieve
rapid project management maturity.
11. Share recognition for project success with the entire project
team and
line management.
12. Eliminate nonproductive meetings.
13. Focus on identifying and solving problems early, quickly,
and cost
effectively.

14. Measure progress periodically.
15. Use project management software as a tool—not as a
substitute for
effective planning or interpersonal skills.
16. Institute an all-employee training program with periodic
updates based
upon documented lessons learned.
ffirs.qxd 1/3/13 3:48 PM Page ii
P RO J E C T
MANAGEMENT
A Systems Approach to
Planning, Scheduling,
and Controlling
E L E V E N T H E D I T I O N
H A R O L D K E R Z N E R , P h . D .
Senior Executive Director for Project Management
The International Institute for Learning
New York, New York
ffirs.qxd 1/3/13 3:48 PM Page iii
Cover illustration: xiaoke ma/iStockphoto

This book is printed on acid-free paper.
Copyright © 2013 by John Wiley & Sons, Inc. All rights
reserved
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
No part of this publication may be reproduced, stored in a
retrieval system, or transmitted in any form or by any
means, electronic, mechanical, photocopying, recording,
scanning, or otherwise, except as permitted under
Section 107 or 108 of the 1976 United States Copyright Act,
without either the prior written permission of the Publisher, or
autho-
rization through payment of the appropriate per-copy fee to the
Copyright Clearance Center, 222 Rosewood Drive, Danvers,
MA
01923, (978) 750-8400, fax (978) 646-8600, or on the web at
www.copyright.com. Requests to the Publisher for permission
should
be addressed to the Permissions Department, John Wiley &
Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-
6011, fax
(201) 748-6008, or online at www.wiley.com/go/permissions.
Limit of Liability/Disclaimer of Warranty: While the publisher
and author have used their best efforts in preparing this book,
they

make no representations or warranties with the respect to the
accuracy or completeness of the contents of this book and
specifically
disclaim any implied warranties of merchantability or fitness
for a particular purpose. No warranty may be created or
extended by
sales representatives or written sales materials. The advice and
strategies contained herein may not be suitable for your
situation.
You should consult with a professional where appropriate.
Neither the publisher nor the author shall be liable for damages
arising
herefrom.
For general information about our other products and services,
please contact our Customer Care Department within the United
States at (800) 762-2974, outside the United States at (317)
572-3993 or fax (317) 572-4002.
Wiley publishes in a variety of print and electronic formats and
by print-on-demand. Some material included with standard print
versions of this book may not be included in e-books or in
print-on-demand. If this book refers to media such as a CD or
DVD that
is not included in the version you purchased, you may download
this material at http://booksupport.wiley.com. For more
informa-

tion about Wiley products, visit www.wiley.com.
Library of Congress Cataloging-in-Publication Data:
Kerzner, Harold.
Project management : a systems approach to planning,
scheduling, and controlling / Harold Kerzner, Ph. D. Senior
Executive
Director for Project Management, the International Institute for
Learning, New York, New York. — Eleventh edition.
pages cm
Includes bibliographical references and index.
ISBN 978-1-118-02227-6 (cloth); ISBN 978-1-118-41585-6
(ebk); ISBN 978-1-118-41855-0 (ebk); ISBN 978-1-118-43357-
7
(ebk); ISBN 978-1-118-48322-0 (ebk); ISBN 978-1-118-48323-
7 (ebk) 1. Project management. 2. Project management—Case
studies. I. Title.
HD69.P75K47 2013
658.4’04 —dc23
2012026239
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
ffirs.qxd 1/3/13 3:48 PM Page iv

http://www.copyright.com
http://www.wiley.com/go/permissions
http://www.wiley.com
http://booksupport.wiley.com
To
Dr. Herman Krier,
my Friend and Guru,
who taught me well the
meaning of the word “persistence”
ffirs.qxd 1/3/13 3:48 PM Page v
ffirs.qxd 1/3/13 3:48 PM Page vi
Contents
Preface xxiii
1 OVERVIEW 1
1.0 Introduction 1
1.1 Understanding Project Management 2
1.2 Defining Project Success 7
1.3 Success, Trade-Offs, and Competing Constraints 8
1.4 The Project Manager–Line Manager Interface 9

1.5 Defining the Project Manager’s Role 14
1.6 Defining the Functional Manager’s Role 15
1.7 Defining the Functional Employee’s Role 18
1.8 Defining the Executive’s Role 19
1.9 Working with Executives 19
1.10 Committee Sponsorship/Governance 20
1.11 The Project Manager as the Planning Agent 23
1.12 Project Champions 24
1.13 The Downside of Project Management 25
1.14 Project-Driven versus Non–Project-Driven Organizations
25
1.15 Marketing in the Project-Driven Organization 28
1.16 Classification of Projects 30
1.17 Location of the Project Manager 30
1.18 Differing Views of Project Management 32
1.19 Public-Sector Project Management 34
1.20 International Project Management 38
1.21 Concurrent Engineering: A Project Management Approach
38
1.22 Added Value 39
1.23 Studying Tips for the PMI® Project Management
Certification Exam 40
Problems 42
Case Study
Williams Machine Tool Company 44
vii
ftoc.qxd 1/3/13 3:50 PM Page vii
2 PROJECT MANAGEMENT GROWTH: CONCEPTS
AND DEFINITIONS 47

2.0 Introduction 47
2.1 General Systems Management 48
2.2 Project Management: 1945–1960 48
2.5 Resistance to Change 59
2.6 Systems, Programs, and Projects: A Definition 64
2.7 Product versus Project Management: A Definition 66
2.8 Maturity and Excellence: A Definition 68
2.9 Informal Project Management: A Definition 69
2.10 The Many Faces of Success 70
2.11 The Many Faces of Failure 73
2.12 The Stage-Gate Process 76
2.13 Project Life Cycles 78
2.14 Gate Review Meetings (Project Closure) 83
2.15 Engagement Project Management 84
2.16 Project Management Methodologies: A Definition 85
2.17 Enterprise Project Management Methodologies 87
2.18 Methodologies Can Fail 91
2.19 Organizational Change Management and Corporate
Cultures 94
2.20 Project Management Intellectual Property 100
2.21 Systems Thinking 101
Problems 107
Case Study
Creating a Methodology 108
3 ORGANIZATIONAL STRUCTURES 111
3.0 Introduction 111

3.1 Organizational Work Flow 113
3.2 Traditional (Classical) Organization 114
3.3 Developing Work Integration Positions 117
3.4 Line-Staff Organization (Project Coordinator) 121
3.5 Pure Product (Projectized) Organization 122
3.6 Matrix Organizational Form 125
3.7 Modification of Matrix Structures 132
3.8 The Strong, Weak, or Balanced Matrix 136
3.9 Center for Project Management Expertise 136
3.10 Matrix Layering 137
viii CONTENTS
ftoc.qxd 1/3/13 3:50 PM Page viii
3.11 Selecting the Organizational Form 138
3.12 Structuring the Small Company 143
3.13 Strategic Business Unit (SBU) Project Management 146
3.14 Transitional Management 147
3.15 Barriers to Implementing Project Management in Emerging
Markets 149
3.16 Seven Fallacies that Delay Project Management Maturity
156
Problems 161
Case Studies
Jones and Shephard Accountants, Inc. 166
Coronado Communications 168
4 ORGANIZING AND STAFFING THE PROJECT OFFICE
AND TEAM 171

4.1 The Staffing Environment 172
4.2 Selecting the Project Manager: An Executive Decision 174
4.3 Skill Requirements for Project and Program Managers 178
4.4 Special Cases in Project Manager Selection 184
4.5 Selecting the Wrong Project Manager 184
4.6 Next Generation Project Managers 188
4.7 Duties and Job Descriptions 189
4.8 The Organizational Staffing Process 193
4.9 The Project Office 199
4.10 The Functional Team 204
4.11 The Project Organizational Chart 205
4.12 Special Problems 208
4.13 Selecting the Project Management Implementation Team
210
4.14 Mistakes Made by Inexperienced Project Managers 213
Problems 216
5 MANAGEMENT FUNCTIONS 223
5.1 Controlling 225
5.2 Directing 225
5.3 Project Authority 230
5.4 Interpersonal Influences 237
5.5 Barriers to Project Team Development 240
5.6 Suggestions for Handling the Newly Formed Team 243
Contents ix
ftoc.qxd 1/3/13 3:50 PM Page ix

5.7 Team Building as an Ongoing Process 246
5.8 Dysfunctions of a Team 247
5.9 Leadership in a Project Environment 250
5.10 Life-Cycle Leadership 252
5.11 Value-Based Project Leadership 255
5.12 Organizational Impact 257
5.13 Employee–Manager Problems 259
5.14 Management Pitfalls 262
5.15 Communications 265
5.16 Project Review Meetings 274
5.17 Project Management Bottlenecks 275
5.18 Cross-Cutting Skills 276
5.19 Active Listening 277
5.20 Project Problem-Solving 278
5.21 Brainstorming 288
5.22 Project Decision-Making 293
5.23 Predicting the Outcome of a Decision 301
5.24 Facilitation 303
5.25 Handling Negative Team Dynamics 306
5.26 Communication Traps 307
5.27 Proverbs and Laws 309
5.28 Human Behavior Education 311
5.29 Management Policies and Procedures 312
Problems 318
Case Studies
The Trophy Project 327
Communication Failures 329
McRoy Aerospace 332
The Poor Worker 333
The Prima Donna 334

The Team Meeting 335
Leadership Effectiveness (A) 337
Leadership Effectiveness (B) 341
Motivational Questionnaire 347
6 MANAGEMENT OF YOUR TIME AND STRESS 355
6.1 Understanding Time Management 356
6.2 Time Robbers 356
6.3 Time Management Forms 358
x CONTENTS
ftoc.qxd 1/3/13 3:50 PM Page x
6.4 Effective Time Management 359
6.5 Stress and Burnout 360
Problems 363
Case Study
The Reluctant Workers 364
7 CONFLICTS 365
7.1 Objectives 366
7.2 The Conflict Environment 367

7.3 Types of Conflicts 368
7.4 Conflict Resolution 371
7.5 Understanding Superior, Subordinate, and Functional
Conflicts 372
7.6 The Management of Conflicts 374
7.7 Conflict Resolution Modes 375
Problems 379
Case Studies
Facilities Scheduling at Mayer Manufacturing 382
Telestar International 383
Handling Conflict in Project Management 384
8 SPECIAL TOPICS 391
8.1 Performance Measurement 392
8.2 Financial Compensation and Rewards 399
8.3 Critical Issues with Rewarding Project Teams 405
8.4 Effective Project Management in the Small Business
Organization 408
8.5 Mega Projects 410
8.6 Morality, Ethics, and the Corporate Culture 411
8.7 Professional Responsibilities 414
8.8 Internal Partnerships 417
8.9 External Partnerships 418

8.10 Training and Education 420
8.11 Integrated Product/Project Teams 422
8.12 Virtual Project Teams 424
8.13 Breakthrough Projects 427
Contents xi
ftoc.qxd 1/3/13 3:50 PM Page xi
xii CONTENTS
8.14 Managing Innovation Projects 427
8.15 Agile Project Management 430
Problems 437
Case Study
Is It Fraud? 440
9 THE VARIABLES FOR SUCCESS 443
9.1 Predicting Project Success 444
9.2 Project Management Effectiveness 448
9.3 Expectations 449
9.4 Lessons Learned 450
9.5 Understanding Best Practices 451
9.6 Best Practices versus Proven Practices 458
Problems 460

Case Study
Radiance International 460
10 WORKING WITH EXECUTIVES 463
10.1 The Project Sponsor 464
10.2 Handling Disagreements with the Sponsor 474
10.3 The Collective Belief 475
10.4 The Exit Champion 476
10.5 The In-House Representatives 477
10.6 Stakeholder Relations Management 478
10.7 Politics 486
Problems 488
Case Studies
Corwin Corporation 491
The Prioritization of Projects 499
The Irresponsible Sponsors 500
Selling Executives on Project Management 502

ftoc.qxd 1/3/13 3:50 PM Page xii
11 PLANNING 505
11.1 Validating the Assumptions 508
11.2 Validating the Objectives 509
11.3 General Planning 510
11.4 Life-Cycle Phases 513
11.5 Proposal Preparation 516
11.6 Kickoff Meetings 516
11.7 Understanding Participants’ Roles 519
11.8 Project Planning 519
11.9 The Statement of Work 521
11.10 Project Specifications 526
11.11 Milestone Schedules 528
11.12 Work Breakdown Structure 529
11.13 WBS Decomposition Problems 536
11.14 Work Breakdown Structure Dictionary 540
11.15 Role of the Executive in Project Selection 541
11.16 Role of the Executive in Planning 546
11.17 The Planning Cycle 546
11.17 Work Planning Authorization 547
11.19 Why Do Plans Fail? 548
11.20 Stopping Projects 549
11.21 Handling Project Phaseouts and Transfers 550
11.22 Detailed Schedules and Charts 551
11.23 Master Production Scheduling 554
11.24 Project Plan 556
11.25 Total Project Planning 561
11.26 The Project Charter 565
11.27 Project Baselines 566
11.28 Verification and Validation 570
11.29 Requirements Traceability Matrix 571

11.30 Management Control 572
11.31 The Project Manager–Line Manager Interface 575
11.32 Fast-Tracking 577
11.33 Configuration Management 578
11.34 Enterprise Project Management Methodologies 579
11.35 Project Audits 582
Problems 586
12 NETWORK SCHEDULING TECHNIQUES 597
12.1 Network Fundamentals 600
Contents xiii
ftoc.qxd 1/3/13 3:50 PM Page xiii
12.2 Graphical Evaluation and Review Technique (GERT) 604
12.3 Dependencies 605
12.4 Slack Time 606
12.5 Network Replanning 612
12.6 Estimating Activity Time 616
12.7 Estimating Total Project Time 617
12.8 Total PERT/CPM Planning 618
12.9 Crash Times 620
12.10 PERT/CPM Problem Areas 623
12.11 Alternative PERT/CPM Models 626
12.12 Precedence Networks 627
12.13 Lag 630
12.14 Scheduling Problems 632
12.15 The Myths of Schedule Compression 632

12.16 Understanding Project Management Software 634
12.17 Software Features Offered 634
12.18 Software Classification 636
12.19 Implementation Problems 637
12.20 Critical Chain 638
Problems 643
Case Studies
Crosby Manufacturing Corporation 656
The Invisible Sponsor 658
13 PROJECT GRAPHICS 661
13.1 Customer Reporting 662
13.2 Bar (Gantt) Chart 663
13.3 Other Conventional Presentation Techniques 670
13.4 Logic Diagrams/Networks 673
Problems 675
14 PRICING AND ESTIMATING 677
14.1 Global Pricing Strategies 678
14.2 Types of Estimates 679
14.3 Pricing Process 682
14.4 Organizational Input Requirements 684
14.5 Labor Distributions 686
xiv CONTENTS

ftoc.qxd 1/3/13 3:50 PM Page xiv
14.6 Overhead Rates 690
14.7 Materials/Support Costs 692
14.8 Pricing Out the Work 695
14.9 Smoothing Out Department Man-Hours 696
14.10 The Pricing Review Procedure 698
14.11 Systems Pricing 700
14.12 Developing the Supporting/Backup Costs 701
14.13 The Low-Bidder Dilemma 705
14.14 Special Problems 705
14.15 Estimating Pitfalls 706
14.16 Estimating High-Risk Projects 707
14.17 Project Risks 708
14.18 The Disaster of Applying the 10 Percent
Solution
to Project Estimates 712
14.19 Life-Cycle Costing (LCC) 714
14.20 Logistics Support 719
14.21 Economic Project Selection Criteria: Capital Budgeting
720
14.22 Payback Period 720
14.23 The Time Value of Money 721
14.24 Net Present Value (NPV) 722
14.25 Internal Rate of Return (IRR) 723

14.26 Comparing IRR, NPV, and Payback 724
14.27 Risk Analysis 724
14.28 Capital Rationing 725
14.29 Project Financing 726
Problems 730
Case Study
The Estimating Problem 734
15 COST CONTROL 737
15.1 Understanding Control 741
15.2 The Operating Cycle 744
15.3 Cost Account Codes 745
15.4 Budgets 750
15.5 The Earned Value Measurement System (EVMS) 752
15.6 Variance and Earned Value 754
15.7 The Cost Baseline 773
15.8 Justifying the Costs 775
15.9 The Cost Overrun Dilemma 778
15.10 Recording Material Costs Using Earned Value

Measurement 779
15.11 The Material Accounting Criterion 782
Contents xv
ftoc.qxd 1/3/13 3:50 PM Page xv
15.12 Material Variances: Price and Usage 783
15.13 Summary Variances 784
15.14 Status Reporting 785
15.15 Cost Control Problems 792
15.16 Project Management Information Systems 793
15.17 Enterprise Resource Planning 793
15.18 Project Metrics 794
15.19 Key Performance Indicators 800
15.20 Value-Based Metrics 806
15.21 Dashboards and Scorecards 812
15.22 Business Intelligence 815
15.23 Infographics 816
Problems 820

Case Studies
The Bathtub Period 838
Franklin Electronics 839
Trouble in Paradise 841
16 TRADE-OFF ANALYSIS IN A PROJECT ENVIRONMENT
845
16.1 Methodology for Trade-Off Analysis 848
16.2 Contracts: Their Influence on Projects 865
16.3 Industry Trade-Off Preferences 866
16.4 Conclusion 869
17 RISK MANAGEMENT 871
17.1 Definition of Risk 873
17.2 Tolerance for Risk 875
17.3 Definition of Risk Management 876

17.4 Certainty, Risk, and Uncertainty 877
17.5 Risk Management Process 883
17.6 Plan Risk Management (11.1) 884
17.7 Risk Identification (11.2) 885
17.8 Risk Analysis (11.3, 11.4) 892
17.9 Qualitative Risk Analysis (11.3) 897
17.10 Quantitative Risk Analysis (11.4) 903
17.11 Probability Distributions and the Monte Carlo Process
904
17.12 Plan Risk Response (11.5) 913
xvi CONTENTS
ftoc.qxd 1/3/13 3:50 PM Page xvi
17.13 Monitor and Control Risks (11.6) 919
17.14 Some Implementation Considerations 920
17.15 The Use of Lessons Learned 921
17.16 Dependencies Between Risks 925
17.17 The Impact of Risk Handling Measures 930
17.18 Risk and Concurrent Engineering 933

Problems 940
Case Studies
Teloxy Engineering (A) 948
Teloxy Engineering (B) 948
The Risk Management Department 949
18 LEARNING CURVES 953
18.1 General Theory 954
18.2 The Learning Curve Concept 954
18.3 Graphic Representation 956
18.4 Key Words Associated with Learning Curves 958
18.5 The Cumulative Average Curve 958
18.6 Sources of Experience 960
18.7 Developing Slope Measures 963
18.8 Unit Costs and Use of Midpoints 964
18.9 Selection of Learning Curves 965
18.10 Follow-On Orders 966
18.11 Manufacturing Breaks 966
18.12 Learning Curve Limitations 968
18.13 Prices and Experience 968

18.14 Competitive Weapon 970
Problems 972
19 CONTRACT MANAGEMENT 975
19.1 Procurement 976
19.2 Plan Procurements 978
19.3 Conducting the Procurements 981
19.4 Conduct Procurements: Request Seller Responses 983
19.5 Conduct Procurements: Select Sellers 983
19.6 Types of Contracts 987
19.7 Incentive Contracts 991
19.8 Contract Type versus Risk 994
Contents xvii
ftoc.qxd 1/3/13 3:50 PM Page xvii
19.9 Contract Administration 995

19.10 Contract Closure 998
19.11 Using a Checklist 999
19.12 Proposal-Contractual Interaction 1000
19.13 Summary 1003
Case Studies
The Scheduling Dilemma 1009
To Bid or Not to Bid 1011
The Management Reserve 1012
20 QUALITY MANAGEMENT 1015
20.1 Definition of Quality 1017
20.2 The Quality Movement 1019
20.3 Comparison of the Quality Pioneers 1022
20.4 The Taguchi Approach 1023
20.5 The Malcolm Baldrige National Quality Award 1026
20.6 ISO 9000 1027
20.7 Quality Management Concepts 1029
20.8 The Cost of Quality 1032
20.9 The Seven Quality Control Tools 1035
20.10 Process Capability (CP) 1052

20.11 Acceptance Sampling 1054
20.12 Implementing Six Sigma 1054
20.13 Lean Six Sigma and DMAIC 1056
20.14 Quality Leadership 1057
20.15 Responsibility for Quality 1058
20.16 Quality Circles 1058
20.17 Just-In-Time Manufacturing (JIT) 1059
20.18 Total Quality Management (TQM) 1061
21 MODERN DEVELOPMENTS IN PROJECT MANAGEMENT
1069
21.1 The Project Management Maturity Model (PMMM) 1070
21.2 Developing Effective Procedural Documentation 1074
21.3 Project Management Methodologies 1078
21.4 Continuous Improvement 1079
21.5 Capacity Planning 1080
21.6 Competency Models 1082
21.7 Managing Multiple Projects 1084
21.8 End-of-Phase Review Meetings 1085
xviii CONTENTS

ftoc.qxd 1/3/13 3:50 PM Page xviii
Case Study
Honicker Corporation 1086
22 THE BUSINESS OF SCOPE CHANGES 1089
22.1 Need for Business Knowledge 1091
22.2 Timing of Scope Changes 1092
22.3 Business Need for a Scope Change 1093
22.4 Rationale for Not Approving a Scope Change 1094
Case Study
Kemko Manufacturing 1094
23 THE PROJECT OFFICE 1097
23.1 Present-Day Project Office 1098
23.2 Implementation Risks 1099
23.3 Types of Project Offices 1100

23.4 Networking Project Management Offices 1101
23.5 Project Management Information Systems 1101
23.6 Dissemination of Information 1103
23.7 Mentoring 1104
23.8 Development of Standards and Templates 1105
23.9 Project Management Benchmarking 1105
23.10 Business Case Development 1106
23.11 Customized Training (Related to Project Management)
1107
23.12 Managing Stakeholder Relations 1108
23.13 Continuous Improvement 1109
23.14 Capacity Planning 1109
23.15 Risks of Using a Project Office 1110
23.16 Project Portfolio Management 1111
Case Study
The Project Management Lawsuit 1116
24 MANAGING CRISIS PROJECTS 1119
24.1 Understanding Crisis Management 1119
24.2 Ford versus Firestone 1121
24.3 The Air France Concorde Crash 1122
24.4 Intel and the Pentium Chip 1123

24.5 The Russian Submarine Kursk 1123
24.6 The Tylenol Poisonings 1124
Contents xix
ftoc.qxd 1/3/13 3:50 PM Page xix
24.7 Nestlé’s Marketing of Infant Formula 1127
24.8 The Space Shuttle Challenger Disaster 1129
24.9 The Space Shuttle Columbia Disaster 1130
24.10 Victims Versus Villains 1131
24.11 Life-Cycle Phases 1132
24.12 Project Management Implications 1133
25 FUTURE OF PROJECT MANAGEMENT 1135
25.0 Changing Times 1135
25.1 Complex Projects 1139
25.2 Complexity Theory 1144
25.3 Scope Creep 1145
25.4 Project Health Checks 1151
25.5 Managing Troubled Projects 1155

26 THE RISE, FALL, AND RESURRECTION OF IRIDIUM:
A PROJECT MANAGEMENT PERSPECTIVE 1167
26.1 Naming the Project “Iridium” 1169
26.2 Obtaining Executive Support 1170
26.3 Launching the Venture 1170
26.4 The Iridium System 1172
26.5 The Terrestrial and Space-Based Network 1172
26.6 Project Initiation: Developing the Business Case 1173
26.7 The “Hidden” Business Case 1175
26.8 Risk Management 1175
26.9 The Collective Belief 1177
26.10 The Exit Champion 1177
26.11 Iridium’s Infancy Years 1178
26.12 Debt Financing 1181
26.13 The M-Star Project 1182
26.14 A New CEO 1183
26.15 Satellite Launches 1183
26.16 An Initial Public Offering (IPO) 1184
26.17 Signing Up Customers 1184
26.18 Iridium’s Rapid Ascent 1185
26.19 Iridium’s Rapid Descent 1187
26.20 The Iridium “Flu” 1191
26.21 Searching for a White Knight 1192

26.22 The Definition of Failure (October, 1999) 1192
26.23 The Satellite Deorbiting Plan 1193
26.24 Iridium is Rescued for $25 Million 1194
26.25 Iridium Begins to Grow 1194
xx CONTENTS
ftoc.qxd 1/3/13 3:50 PM Page xx
26.26 Shareholder Lawsuits 1195
26.27 The Bankruptcy Court Ruling 1195
26.28 Autopsy 1196
26.29 Financial Impact of the Bankruptcy 1197
26.30 What Really Went Wrong? 1198
26.31 Lessons Learned 1200
26.32 Conclusion 1202
Appendix A.

Exploring urban social media Selfiecity and On Broadway .docx

Recommended

Recommended

More Related Content

Similar to Exploring urban social media Selfiecity and On Broadway .docx

Similar to Exploring urban social media Selfiecity and On Broadway .docx (20)

More from mecklenburgstrelitzh

More from mecklenburgstrelitzh (20)

Recently uploaded

Recently uploaded (20)

Exploring urban social media Selfiecity and On Broadway .docx