Studying the social dynamics of a city on a large scale has traditionally been a challenging endeavor, often requiring long hours of observation and interviews, usually resulting in only a partial depiction of reality. To address this difficulty, we introduce a clustering model and research methodology for studying the structure and composition of a city on a large scale based on the social media its residents generate. We apply this new methodology to data from approximately 18 million check-ins collected from users of a location-based online social network. Unlike the boundaries of traditional municipal organizational units such as neighborhoods, which do not always reflect the character of life in these areas, our clusters, which we call Livehoods, are representations of the dynamic areas that comprise the city. We take a qualitative approach to validating these clusters, interviewing 27 residents of Pittsburgh, PA, to see how their perceptions of the city project onto our findings there. Our results provide strong support for the discovered clusters, showing how Livehoods reveal the distinctly characterized areas of the city and the forces that shape them.
Authors are Justin Cranshaw, Raz Schwartz, Jason Hong, and Norman Sadeh
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Planners and social psychologists have suggested that the recognizability of the urban environment is linked to peo- ple’s socio-economic well-being. We build a web game that puts the recognizability of London’s streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram’s). We col- lect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of Lon- don based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway pas- sengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where “web science” meets the “smart city” agenda.
Setting The Stage For Empirical Research In Virtual Social Networksvia fCh
This work sets the theoretical stage for future empirical investigations of the socio-economic dynamics in virtual social networks (VSN), at member and network entrepreneur levels. In so doing, I start by identifying the empirical loci, Flickr and LinkedIn, justify and describe the choices. A model of the utility function of the generic VSN member is followed by descriptions of the value dynamics for each VSN, which in turn create the premise for an economic model of VSN as platforms for multi-sided markets. The model is built by considering several theoretical angles from the perspectives of microeconomics and sociology of networks. The contribution is in extending theoretical and empirical insights across disciplines and fields with the idea of exploring and understanding the emerging VSN-phenomenon.
Migrating Department of Education Web Mapping App to AWS EC2Blue Raster
The U.S. Department of Education (ED) participated in the Federal Geographic Data Committee's (FGDC) GeoCloud Program in 2012. The GeoCloud initiative provides selected agencies an Amazon Web Services (AWS) hosting platform to on-ramp their geospatial applications. ED migrated its on-premises ArcGIS for Server for the School District Demographic Data System (SDDS) Map Viewer (http://nces.ed.gov/surveys/sdds) to Amazon EC2. SDDS is publicly available and allows access to information about demographics, social characteristics, and economics of children and school districts from the National Center for Education Statistics (NCES). Using GeoCloud, ED gained experience with cloud-based Windows 2008R2 Server and Esri ArcGIS 10.1 for Server platform. It has been almost one year now and we'll reflect on various lessons learned including planning, security/hardening, AWS console, server configuration, reliability, licensing, and backup strategy. We will discuss the current state of our server deployments and future plans for ED in the Cloud.
Livehoods: Understanding cities through social mediaJustin Cranshaw
Extended version of ICWSM 2012 presentation introducing our work on http://livehoods.org.
To reference this work, cite the Cranshaw et al paper: The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City.
Psychological Maps 2.0: A Web Engagement Enterprise Starting in LondonGabriela Agustini
Planners and social psychologists have suggested that the recognizability of the urban environment is linked to peo- ple’s socio-economic well-being. We build a web game that puts the recognizability of London’s streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram’s). We col- lect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of Lon- don based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway pas- sengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where “web science” meets the “smart city” agenda.
Setting The Stage For Empirical Research In Virtual Social Networksvia fCh
This work sets the theoretical stage for future empirical investigations of the socio-economic dynamics in virtual social networks (VSN), at member and network entrepreneur levels. In so doing, I start by identifying the empirical loci, Flickr and LinkedIn, justify and describe the choices. A model of the utility function of the generic VSN member is followed by descriptions of the value dynamics for each VSN, which in turn create the premise for an economic model of VSN as platforms for multi-sided markets. The model is built by considering several theoretical angles from the perspectives of microeconomics and sociology of networks. The contribution is in extending theoretical and empirical insights across disciplines and fields with the idea of exploring and understanding the emerging VSN-phenomenon.
Migrating Department of Education Web Mapping App to AWS EC2Blue Raster
The U.S. Department of Education (ED) participated in the Federal Geographic Data Committee's (FGDC) GeoCloud Program in 2012. The GeoCloud initiative provides selected agencies an Amazon Web Services (AWS) hosting platform to on-ramp their geospatial applications. ED migrated its on-premises ArcGIS for Server for the School District Demographic Data System (SDDS) Map Viewer (http://nces.ed.gov/surveys/sdds) to Amazon EC2. SDDS is publicly available and allows access to information about demographics, social characteristics, and economics of children and school districts from the National Center for Education Statistics (NCES). Using GeoCloud, ED gained experience with cloud-based Windows 2008R2 Server and Esri ArcGIS 10.1 for Server platform. It has been almost one year now and we'll reflect on various lessons learned including planning, security/hardening, AWS console, server configuration, reliability, licensing, and backup strategy. We will discuss the current state of our server deployments and future plans for ED in the Cloud.
Livehoods: Understanding cities through social mediaJustin Cranshaw
Extended version of ICWSM 2012 presentation introducing our work on http://livehoods.org.
To reference this work, cite the Cranshaw et al paper: The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
Maps of the living neighborhoods - a study of Genoa through social mediaMarna Parodi
A proposal for the application to the city of Genoa of “Livehoods”, a urban computing project started in 2012 by Carnegie Mellon University (http://livehoods.org/).
Livehoods analyses data generated on smartphones by Foursquare a location based social network. Foursquare allow users to check-in in a venue, e.g. a shop, a theatre, a swimming pool. Data are aggregated into clusters that display the activity patterns of people dwelling in a certain area. Livehoods maps capture characteristics of the urban habitat that are well perceived by the people, but usually hardly if at all represented by traditional maps. In Genoa, this research could be have as object of study the area of Fiumara and its surroundings, with an analysis of the relation of the institutional borders of the area, with reference to the original urban requalification plan as well, and the dynamic borders traced by Livehoods.
An Introduction to the mobile context and mobile social software, which explores the topics of the mobile context and its role on what I referred to as People-centric mobile computing.
Privacy, Ethics, and Big (Smartphone) Data, at Mobisys 2014Jason Hong
Keynote talk I gave at the Mobile and Cloud Workshop at Mobisys 2014. I talk about my experiences and reflections on privacy, focusing on (1) Urban Analytics, (2) Google Glass, and (3) PrivacyGrade.
[Digest] Eigenbehaviors- identifying structure in routineHsing-chuan Hsieh
1. Preface
-People’s daily routines are typically coupled with routines across other temporal scales
-E.g., going out on the town with friends on Saturday nights, or spending time with family during the December holidays.
-So are animals (daily and seasonal cycle)
2. Purpose: present a methodology-- “eigenbehaviors” to quantify these universal patterns in the behavior of individuals and communities within a social network.
-Eigenbehaviors- the principal components of an individual’s behavioral dataset.
For full information, please refer to the original paper:
http://realitycommons.media.mit.edu/pdfs/eigenbehaviors.pdf
Jonathan Lenaghan, VP of Science and Technology, PlaceIQ at MLconf ATL 2016MLconf
Discerning Human Behavior from Mobility Data: Mobility data encompasses many elements, including location history, latitude coordinates, longitude coordinates, anonymized mobile device IDs, and timestamps. Such data are generated, for instance, by automobile navigation applications and by the mobile advertising ecosystem. Typical sources of mobility data contain extensive inaccuracies that result from a variety of sources, ranging from shortcomings in location services on mobile devices to the intentional misrepresentation of spatial coordinates by bad ecosystem actors. In this talk, we describe a production data pipeline, Darwin, which analyzes the location quality of mobility data to measure how accurately a set of mobility data represents true movement patterns. Darwin uses a number of measures that are ultimately combined into two quality scores: hyper-locality and clusterability. These measurements include techniques from information theory, the mean number of spatial clusters, the compactness of the clusters, and the differences between the empirical distribution of digits in the spatial coordinates and reference distributions.
This talk discusses advanced computationally assisted reasoning about large interaction-dominated systems and addresses the role of involve details of huge numbers and levels of intricate interactions in current fields of research.It was delivered at the SMART Infrastructure Facility by Professor Chris Barrett on September 26, 2012. For more detail, see http://goo.gl/gLp7c.
CityFlocks: Designing Social Navigation for Urban Mobile Information Systemskavasmlikon
CityFlocks is a mobile system enabling visitors and new residents in a city to tap into the knowledge and experiences of local residents, so as to gather information about their new environment. Its design specifically aims to lower existing barriers of access and facilitate social navigation in urban places. This paper presents a design case study of a mobile system prototype that offers an easy way for information seeking new residents or visitors to access tacit knowledge from local people about their new community. In various user tests we evaluate two general user interaction alternatives – direct and indirect social navigation – and analyse under what conditions which interaction method works better for people using a mobile device to socially navigate urban environments. The outcomes are relevant for the user interaction design of future mobile information systems that leverage off of a social navigation approach.
Spatial statistics presentation Texas A&M Census RDCCorey Sparks
The purpose of this workshop is twofold. A primary goal is to provide researchers with a basic overview of spatial analysis. A secondary goal is to give attention to issues in GIS and spatial analysis that may be relevant to researchers planning to work with location data and unique geographies in confidential data sets in the Texas Census Research Data Center.
The workshop will consist of three sessions. Each session will be led by Dr. Corey Sparks, Assistant Professor at UTSA's College of Public Policy. Dr. Spark's research focuses on statistical demography, Geographic Information Systems and the application of modern statistical methods to problems in demography and health. His teaching interests focus on use and application of advanced statistical techniques including hazards analysis, multivariate methods and spatial statistics in human population analysis.
The simulation produced was not necessarily provided by the communit.docxjoshua2345678
The simulation produced was not necessarily provided by the community which is participating in the simulation. Simulation, in this regard, can be a tool to evaluate and learn from the experience of the simulation. A simulation is a tool for understanding the behavior of the environment and how to interact with the situation. The simulation of an experiment is also a tool when this is needed. It can be useful when we are performing an analysis to make corrections or change the results of this experiment. Simulation is useful when we receive from it what we originally wanted to understand from the target the evaluation of the simulation performed by expectations, perspectives and also the knowledge of the community that practices in a particular field. (Doran, J, 2018)
Methods to assess the quality of simulations:
The Standard View: The Standard View method relies on a realist perspective because it refers to the observability of reality to compare the real with simulated data produced by the simulation. To make the model reliable for evaluating the Standard View, the observer must know when the mock objects were being observed, and how long it took them to reach their present state. In particular, there is not an excellent model to evaluate the Standard View of reality based on a continuous-time model; that is, an observer has to know when the events took place in question. It seems to the non-interval model that we typically use to consider the Standard View would work for evaluating the Standard View. (Valli, K, 2015)
The Constructivist View: Reality is simply an expression of subjective desires, thoughts, and feelings of the observer. The world is merely a means to achieve these desires and passions. The individual creates his world through thoughts, feelings, and actions. Reality is only an expression, not a cause, of the individual's subjective experiences. There is no external cause. (Mitchell, J. P, 2015)
The User Community View: there is something original in this evaluation, particularly the consumers, their plans, and a something out beyond, which everybody points to, relying on any bestowed object also having a real analysis regarding it. The User Community is an entity with an extremely high level of participation. A part of this is to support the idea of the User community, which seems to be a part of this, a design in itself, because the content in front of it, the users that consume it, makes this an object for study. (Tuominen, J, 2015)
2
In the above situation I would state Standard view is the excellent practice to evaluate zoning for a location of 7, 50,000 occupants as a result of the elements age, race, training and revenue status.
Researchers discovered the evidence that restrictive zoning raises cabin costs; in beach the front urban communities, just as in provincial areas and town neighbourhoods around the country. In the match that the district zoning law is stricter, at that point the area is increasing.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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Similar to The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City, at ICWSM 2012
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
Maps of the living neighborhoods - a study of Genoa through social mediaMarna Parodi
A proposal for the application to the city of Genoa of “Livehoods”, a urban computing project started in 2012 by Carnegie Mellon University (http://livehoods.org/).
Livehoods analyses data generated on smartphones by Foursquare a location based social network. Foursquare allow users to check-in in a venue, e.g. a shop, a theatre, a swimming pool. Data are aggregated into clusters that display the activity patterns of people dwelling in a certain area. Livehoods maps capture characteristics of the urban habitat that are well perceived by the people, but usually hardly if at all represented by traditional maps. In Genoa, this research could be have as object of study the area of Fiumara and its surroundings, with an analysis of the relation of the institutional borders of the area, with reference to the original urban requalification plan as well, and the dynamic borders traced by Livehoods.
An Introduction to the mobile context and mobile social software, which explores the topics of the mobile context and its role on what I referred to as People-centric mobile computing.
Privacy, Ethics, and Big (Smartphone) Data, at Mobisys 2014Jason Hong
Keynote talk I gave at the Mobile and Cloud Workshop at Mobisys 2014. I talk about my experiences and reflections on privacy, focusing on (1) Urban Analytics, (2) Google Glass, and (3) PrivacyGrade.
[Digest] Eigenbehaviors- identifying structure in routineHsing-chuan Hsieh
1. Preface
-People’s daily routines are typically coupled with routines across other temporal scales
-E.g., going out on the town with friends on Saturday nights, or spending time with family during the December holidays.
-So are animals (daily and seasonal cycle)
2. Purpose: present a methodology-- “eigenbehaviors” to quantify these universal patterns in the behavior of individuals and communities within a social network.
-Eigenbehaviors- the principal components of an individual’s behavioral dataset.
For full information, please refer to the original paper:
http://realitycommons.media.mit.edu/pdfs/eigenbehaviors.pdf
Jonathan Lenaghan, VP of Science and Technology, PlaceIQ at MLconf ATL 2016MLconf
Discerning Human Behavior from Mobility Data: Mobility data encompasses many elements, including location history, latitude coordinates, longitude coordinates, anonymized mobile device IDs, and timestamps. Such data are generated, for instance, by automobile navigation applications and by the mobile advertising ecosystem. Typical sources of mobility data contain extensive inaccuracies that result from a variety of sources, ranging from shortcomings in location services on mobile devices to the intentional misrepresentation of spatial coordinates by bad ecosystem actors. In this talk, we describe a production data pipeline, Darwin, which analyzes the location quality of mobility data to measure how accurately a set of mobility data represents true movement patterns. Darwin uses a number of measures that are ultimately combined into two quality scores: hyper-locality and clusterability. These measurements include techniques from information theory, the mean number of spatial clusters, the compactness of the clusters, and the differences between the empirical distribution of digits in the spatial coordinates and reference distributions.
This talk discusses advanced computationally assisted reasoning about large interaction-dominated systems and addresses the role of involve details of huge numbers and levels of intricate interactions in current fields of research.It was delivered at the SMART Infrastructure Facility by Professor Chris Barrett on September 26, 2012. For more detail, see http://goo.gl/gLp7c.
CityFlocks: Designing Social Navigation for Urban Mobile Information Systemskavasmlikon
CityFlocks is a mobile system enabling visitors and new residents in a city to tap into the knowledge and experiences of local residents, so as to gather information about their new environment. Its design specifically aims to lower existing barriers of access and facilitate social navigation in urban places. This paper presents a design case study of a mobile system prototype that offers an easy way for information seeking new residents or visitors to access tacit knowledge from local people about their new community. In various user tests we evaluate two general user interaction alternatives – direct and indirect social navigation – and analyse under what conditions which interaction method works better for people using a mobile device to socially navigate urban environments. The outcomes are relevant for the user interaction design of future mobile information systems that leverage off of a social navigation approach.
Spatial statistics presentation Texas A&M Census RDCCorey Sparks
The purpose of this workshop is twofold. A primary goal is to provide researchers with a basic overview of spatial analysis. A secondary goal is to give attention to issues in GIS and spatial analysis that may be relevant to researchers planning to work with location data and unique geographies in confidential data sets in the Texas Census Research Data Center.
The workshop will consist of three sessions. Each session will be led by Dr. Corey Sparks, Assistant Professor at UTSA's College of Public Policy. Dr. Spark's research focuses on statistical demography, Geographic Information Systems and the application of modern statistical methods to problems in demography and health. His teaching interests focus on use and application of advanced statistical techniques including hazards analysis, multivariate methods and spatial statistics in human population analysis.
The simulation produced was not necessarily provided by the communit.docxjoshua2345678
The simulation produced was not necessarily provided by the community which is participating in the simulation. Simulation, in this regard, can be a tool to evaluate and learn from the experience of the simulation. A simulation is a tool for understanding the behavior of the environment and how to interact with the situation. The simulation of an experiment is also a tool when this is needed. It can be useful when we are performing an analysis to make corrections or change the results of this experiment. Simulation is useful when we receive from it what we originally wanted to understand from the target the evaluation of the simulation performed by expectations, perspectives and also the knowledge of the community that practices in a particular field. (Doran, J, 2018)
Methods to assess the quality of simulations:
The Standard View: The Standard View method relies on a realist perspective because it refers to the observability of reality to compare the real with simulated data produced by the simulation. To make the model reliable for evaluating the Standard View, the observer must know when the mock objects were being observed, and how long it took them to reach their present state. In particular, there is not an excellent model to evaluate the Standard View of reality based on a continuous-time model; that is, an observer has to know when the events took place in question. It seems to the non-interval model that we typically use to consider the Standard View would work for evaluating the Standard View. (Valli, K, 2015)
The Constructivist View: Reality is simply an expression of subjective desires, thoughts, and feelings of the observer. The world is merely a means to achieve these desires and passions. The individual creates his world through thoughts, feelings, and actions. Reality is only an expression, not a cause, of the individual's subjective experiences. There is no external cause. (Mitchell, J. P, 2015)
The User Community View: there is something original in this evaluation, particularly the consumers, their plans, and a something out beyond, which everybody points to, relying on any bestowed object also having a real analysis regarding it. The User Community is an entity with an extremely high level of participation. A part of this is to support the idea of the User community, which seems to be a part of this, a design in itself, because the content in front of it, the users that consume it, makes this an object for study. (Tuominen, J, 2015)
2
In the above situation I would state Standard view is the excellent practice to evaluate zoning for a location of 7, 50,000 occupants as a result of the elements age, race, training and revenue status.
Researchers discovered the evidence that restrictive zoning raises cabin costs; in beach the front urban communities, just as in provincial areas and town neighbourhoods around the country. In the match that the district zoning law is stricter, at that point the area is increasing.
Similar to The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City, at ICWSM 2012 (20)
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- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
A tale of scale & speed: How the US Navy is enabling software delivery from l...
The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City, at ICWSM 2012
1. School of Computer Science
Carnegie Mellon University
@livehoods | livehoods.org
Utilizing Social Media to Understand
the Dynamics of a City
Justin Cranshaw Raz Schwartz Jason I. Hong Norman Sadeh
jcransh@cs.cmu.edu razs@andrew.cmu.edu jasonh@cs.cmu.edu sadeh@cs.cmu.edu
2. Neighborhoods
• Neighborhoods provide order to the chaos of the city
• Help us determine: where to live / work / play
• Provide haven / safety / territory
• Municipal government: organizational unit in resource
allocation
• Centers of commerce and economic development.
Brands.
• A sense of cultural identity to residents
3. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
What comes to
mind when you
picture your
neighborhood?
4. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
The Image of a
NeighborhoodYou’re probably not imagining
this.
5. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
The Image of a
Neighborhood
What you’re imagining most
likely looks a lot more like this.
Every citizen has had long associations with
some part of his city, and his image is
soaked in memories and meanings.
---Kevin Lynch, The Image of
a City
6. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Kevin Lynch, 1960 Stanley Milgram, 1977
Studying Perceptions:
Cognitive Maps
7. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Two Perspectives
“Politically constructed” “Socially constructed”
Neighborhoods have fixed
borders defined by the city
government.
Neighborhoods are organic,
cultural artifacts. Borders are
blurry, imprecise, and may be
different to different people.
8. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Collective Cognitive Maps
“Socially constructed”
Neighborhoods are organic,
cultural artifacts. Borders are
blurry, imprecise, and may be
different to different people.
Can we discover
automated ways of
identifying the “organic”
boundaries of the city?
Can we extract local
cultural knowledge from
social media?
Can we build a collective
cognitive map from data?
9. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
We seek to leverage location-based mobile
social networks such as foursquare, which let
users broadcast the places
they visit to their
friends via
check-ins.
Observing the City:
SmartPhones
11. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Hypothesis
• The character of an urban area is defined not just
by the the types of places found there, but also
by the people who make the area part of their
daily routine.
• Thus we can characterize a place by observing
the people that visit it.
• To discover areas of unique character, we
should look for clusters of nearby venues that
are visited the same people
The moving elements of a city, and in particular the people and
their activities, are as important as the stationary physical parts.
---Kevin Lynch, The Image of a
City
12. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Clustering The City
• Clusters should be of geographically
contiguous foursquare venues.
• Clusters should have a distinct character from
one another, perceivable by city residents.
• Clusters should be such that venues within a
cluster are more likely to be visited by the
same users than venues in different clusters.
19. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Social Venue Similarity
We can get a notion of how similar
two places are by looking at who has
checked into them.
is a vector where the
component counts the number of
times user
checks in to venue .
We can think of as the bag of
checkins to venue .
We can then look at the cosine
similarity between venues.
Problems With This Approach
(1)The resulting similarity
graph is quite sparse
•Similarity tends to be
dominated by “hub” venues
20. are the m nearest geographic
neighbors to venue i.
Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Venue Affinity Matrix
Restricting to nearest
neighbors overcomes bias by
“hub” venues such as
airports, and it adds
geographic contiguity.
is a small positive
constant that overcomes
sparsity in pairwise co-
occurrence data.
We derive an affinity matrix A that blends
social affinity with spatial proximity:
21. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Spectral Clustering
Ng, Jordon, and Weiss
(1)D is the diagonal degree
matrix
•
•
•Find , the k
smallest evects’s of
• and let
- be the
rows of E
•Clustering with
KMeans induces a
clustering of
the original data.
22. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Post Processing
• We introduce a post processing step to clean
up any degenerate clusters
• We separate the subgraph induced by each
into connected components, creating new
clusters for each.
• We delete any clusters that span too large a
geographic area (“background noise”) and
reapportion the venues to the closest non-
degenerate cluster by (single linkage)
geographic distance
23. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Related Livehoods
To examine how the Livehoods are related to one
another, for each pair of Livehoods, we
compute a similarity score.
For Livehood the vector
is the bag of check-ins to .
Each component measures
for a given user the number of
times has checked in to
any venue in .
24. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Data
25. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
The Data
• Foursquare check-ins are by default private
• We can gather check-ins that have been
shared publicly on Twitter.
• Combine the 11 million foursquare check-ins
from the dataset Chen et al. dataset [ICWSM
2011] with our own dataset of 7 million checkins
gathered between June and December of
2011.
• Aligned these Tweets with the underlying
foursquare venue data (venue ID and venue
category)
27. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Currently maps of New York, San Francisco Bay, Pittsburgh, and
Montreal
33. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
How do we evaluate this?
• Livehoods are different from
neighborhoods, but how? In our evaluation,
we want to characterize what Livehoods are.
• Do residents derive social meaning from the
Livehoods mapping?
• Can Livehoods help elucidate the various forces
that shape and define the city?
• Quantitative (algorithmic) evaluation
methods fall far short of capturing such
concepts.
34. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Evaluation
• To see how well our algorithm performed, we
interviewed 27 residents of Pittsburgh
• Residents recruited through a social media
campaign, with various neighborhood groups
and entities as seeds
• Semi-structured Interview protocol explored the
relationship among Livehoods, municipal borders,
and the participants own perceptions of the city.
• Participants must have lived in their
neighborhood for at least 1 year
35. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Interview Protocol
• Each interview lasted approximately one hour
• Began with a discussion of their backgrounds in relation
their neighborhood.
• Asked them to draw the boundaries of their
neighborhood over it (their cognitive map).
• Is there a “shift in feel” of the neighborhood?
• Municipal borders changing?
• Show Livehoods clusters (ask for feedback)
• Show related Livehoods (ask for feedback)
37. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
38. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
39. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
Carson Street runs along the length of
South Side, and is densely packed
with bars, restaurants, tattoo parlors, and
clothing and furniture shops. It is the
most popular destination for nightlife.
40. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
South Side Works is a recently built,
mixed-use outdoor shopping mall,
containing nationally branded apparel
stores and restaurants, upscale
condominiums, and corporate offices.
41. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
There is an small, somewhat older
strip-mall that contains the only super
market (grocery) in South Side. It also
has a liquor store, an auto-parts store, a
furniture rental store and other small
chain stores.
42. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
The rest of South Side is
predominantly residential, consisting of
mostly smaller row houses.
43. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
My Cognitive Map of South Side
44. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
The Livehoods of South Side
LH8
LH9LH7
LH6
I’ll show the evidence in support of the Livehoods
clusters in South Side, and will describe the forces that shape
the city that the Livehoods highlight.
45. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
Demographic
Differences
“Ha! Yes! See, here is my division!
Yay! Thank you algorithm! ... I
definitely feel where the South
Side Works, and all of that is, is a
very different feel.”
LH8
LH9LH7
LH6
LH8 vs LH9
46. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
Architecture
&Urban Design
“from an urban standpoint it is a
lot tighter on the western part
once you get west of 17th or
18th [LH7].”
LH8
LH9LH7
LH6
LH7 vs LH8
47. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
“Whenever I was living down on 15th Street
[LH7] I had to worry about drunk people
following me home, but on 23rd [LH8] I need to
worry about people trying to mug you... so it’s
different. It’s not something I had anticipated,
but there is a distinct difference between the
two areas of the South Side.”
Safety
LH8
LH9LH7
LH6
LH7 vs LH8
48. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
South Side Pittsburgh
Demographic
Differences
“There is this interesting mix of people there I
don’t see walking around the neighborhood.
I think they are coming to the Giant Eagle
from lower income neighborhoods...I always
assumed they came from up the hill.”
LH8
LH9LH7
LH6
LH6
50. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Conclusions
• Throughout our interviews we found very
strong evidence in support of the clustering
• Interviews showed that residence found strong
social meaning behind the Livehood clusters.
• We also found that Livehoods can help shed
light on the various forces that shape people’s
behavior in the city, the city including
demographics, economic factors, cultural
perceptions and architecture.
51. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Limitations
• Most Livehoods had real social meaning to
participants, but no algorithm is perfect. There are
certainly Livehoods that don’t make sense.
• There are obvious biases to using foursquare data.
However this a limitation to the data, not our
methodology.
• Some populations are left out (the digital divide)
• We don’t want to overemphasize sharp divisions
between Livehoods. In reality neighborhoods blend
into one another.
• This is not comparative work. We’re not making the
claim that ours model is the best model for capturing
the areas of a city, only that its a good model.
52. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Future Work
• Comparative Work
• Our model works well, but it’s just one way to
accomplish the end goal.
• It would be fascinating to compare how
different model variations segment the city
differently.
53. Mobile Commerce Lab,Mobile Commerce Lab, Carnegie Mellon UnivCarnegie Mellon Universityersity
@livehoods | livehoods.org
Thanks!Please explore our maps at livehoods.orgYou can also find us on on Facebook and Twitter
@livehoods.
Justin Cranshaw Raz Schwartz Jason I. Hong Norman Sadeh
jcransh@cs.cmu.edu razs@andrew.cmu.edu jasonh@cs.cmu.edu sadeh@cs.cmu.edu
School of Computer Science
Carnegie Mellon University
@livehoods | livehoods.org