Inferring land use from mobile phone activity

Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations.Comment: To be presented at ACM UrbComp201

Coupling Human Mobility and Social Ties

Studies using massive, passively data collected from communication technologies have revealed many ubiquitous aspects of social networks, helping us understand and model social media, information diffusion, and organizational dynamics. More recently, these data have come tagged with geographic information, enabling studies of human mobility patterns and the science of cities. We combine these two pursuits and uncover reproducible mobility patterns amongst social contacts. First, we introduce measures of mobility similarity and predictability and measure them for populations of users in three large urban areas. We find individuals' visitations patterns are far more similar to and predictable by social contacts than strangers and that these measures are positively correlated with tie strength. Unsupervised clustering of hourly variations in mobility similarity identifies three categories of social ties and suggests geography is an important feature to contextualize social relationships. We find that the composition of a user's ego network in terms of the type of contacts they keep is correlated with mobility behavior. Finally, we extend a popular mobility model to include movement choices based on social contacts and compare it's ability to reproduce empirical measurements with two additional models of mobility

Modeling the adoption of innovations in the presence of geographic and media influences

While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reflecting real geography or influence from mass media forces. In this article, we show these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homopholy both amongst individuals with similar propensities to adopt a technology and geographic location are critical to reproduce features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves

Simulated adoption treating the media as endogenous and increasing with the number of adopters.

<p>(a.) Shows simulated new users per week (normalized to the maximum over the period) as well as normalized media volume each week. (b.) A comparison of all model scenarios is shown. Traditional models, models which do not include media influence are capable of predicting adoption in early periods, but dramatically underestimate total adoption. Including endogenous media effects allows us to make adoption predictions that more closely resemble real data.</p

Plots of weekly adoption for select cities.

<p>(a.) Time series display the number of new U.S. Twitter users for three separate locations (Ann Arbor, MI, Denver, CO, and Arlington, VA) from mid-March 2006 through late-August 2009, normalized by the largest weekly increase in Denver users. (b.) Shows a plot of the cumulative fraction of each city's user base normalized by the total number of users in Denver, CO.</p

Plots of weekly national adoption.

<p>(a.) The number of new U.S. Twitter users is plotted for each week, normalized by the maximum weekly increase during the entire period of data collection. (b.) The cumulative total number of U.S. Twitter users is plotted for for the same time period. Google search and news volumes are normalized such that the maximum value is 1.</p

Verification of the basic SI model.

<p>Four different transmission rates are displayed, each run 500 times and averaged. The bands surrounding the average value are bounds containing 75%, and 95% of simulation runs.</p

Simulated critical mass achievement times are compared to times measured from Twitter data.

<p>We find spatially embedded friendships are necessary to reproduce the inter-city spread of Twitter.</p

Temporal snapshots of critical mass achievement at locations across the US.

<p>For snapshot, the smaller, gray markers indicate locations that have already reached critical mass. The larger, black markers denote locations that achieved critical mass during that week. We note that locations achieving critical mass at very early times are clustered around Twitter's birthplace, San Francisco, CA, suggesting local word-of-mouth diffusion. There are, however, a few locations on the other side of the country, namely the suburbs of Boston, MA that are equally early in adoption, contrasting local diffusion with the flattening effects of the Internet.</p