Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Optimizing the Access to Healthcare Services in Dense Refugee Hosting Urban Areas: A Case for Istanbul

With over 3.5 million refugees, Turkey continues to host the world's largest refugee population. This introduced several challenges in many areas including access to healthcare system. Refugees have legal rights to free healthcare services in Turkey's public hospitals. With the aim of increasing healthcare access for refugees, we looked at where the lack of infrastructure is felt the most. Our study attempts to address these problems by assessing whether Migrant Health Centers' locations are optimal. The aim of this study is to improve refugees' access to healthcare services in Istanbul by improving the locations of health facilities available to them. We used call data records provided by Turk Telekom.Comment: version to submit for D4R competitio

Probing streets and the built environment with ambient and community sensing

Data has become an important currency in todays world economy. Ephemeral and real-time data from Twitter, Facebook, Google, urban sensors, weather stations, and the Web contain hidden patterns of the city that are useful for informing architectural and urban design

Industry in Motion: Using Smart Phones to Explore the Spatial Network of the Garment Industry in New York City

Industrial agglomerations have long been thought to offer economic and social benefits to firms and people that are only captured by location within their specified geographies. Using the case study of New York City’s garment industry along with data acquired from cell phones and social media, this study set out to understand the discrete activities underpinning the economic dynamics of an industrial agglomeration. Over a two week period, data was collected by employing the geo-locative capabilities of Foursquare, a social media application, to record every movement of fashion workers employed at fashion design firms located both inside and outside the geographical boundaries of New York City’s Garment District. This unique method of studying worker activity exposed the day-to-day dynamics of an industrial district with a precision thus far undocumented in literature. Our work suggests that having access to the cluster provides almost the same agglomeration economies as residing within its borders.Rockefeller Foundatio

How does socio-economic and demographic dissimilarity determine physical and virtual segregation?

It is established that socio-economic and demographic dissimilarities between populations are determinants of spatial segregation. However, the understanding of how such dissimilarities translate into actual segregation is limited. We propose a novel network-analysis approach to comprehensively study the determinants of communicative and mobility-related spatial segregation, using geo-tagged Twitter data. We constructed weighted spatial networks representing tie strength between geographical areas, then modeled tie formation as a function of socio-economic and demographic dissimilarity between areas. Physical and virtual tie formation were affected by income, age, and race differences, although these effects were smaller by an order of magnitude than the geographical distance effect. Tie formation was more frequent when destination area had higher median income and lower median age. We hypothesize that physical tie formation is more costly than a virtual one resulting in stronger segregation in the physical world. Economic and cultural motives may result in stronger segregation of relatively rich and young populations from their surroundings. Our methodology can help identify types of states that lead to spatial segregation and thus guide planning decisions for reducing its adverse effects