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Migrants play a central role in the economy and society of most developing countries and are primary drivers of economic mobility among poor and rural households. The decision to migrate is one of the most important economic decisions an individual can make. On the one hand, social networks play a crucial role in influencing people's migration decision. On the other hand, as migrants adapt to a new environment, their social network evolves. My research seeks to shed light on the influence of social networks on migration, as well as the influence of migration on social networks. This dissertation answers three questions on migration and social networks using large-scale social network data: (1) What are the roles of migrants in connecting global social networks? (2) How do social networks affect people's decision to migrate? (3) How do migrants' social networks evolve over the migration process?In the first chapter, I explore in detail the relationship between international social ties and global migration. Social ties form the bedrock of the global economy and international political order. Yet prior empirical studies have been constrained by a lack of granular data on the interconnections between individuals. In this study, using several billion domestic and international Facebook friendships, I find that long-term migration accounts for roughly 83% of international ties on Facebook. By computing the average shortest path length in a social graph with and without migrants, I find that migrants effectively decrease the length of the average shortest path, and act as conduits for more shortest paths than non-migrants.The second chapter studies how social networks influence an individual’s decision to migrate. Two distinct mechanisms through which social networks provide utility to migrants are disambiguated: first, that networks provide migrants with access to information, for instance about jobs and conditions in the destination; and second, that networks act as a safety net for migrants by providing material or social support. I use a massive `digital trace' dataset to link the migration decisions of millions of individuals to the topological structure of their social networks. The main analysis indicates that the average migrant derives more utility from `interconnected’ networks that provide social support than from `extensive’ networks that efficiently transmit information.In the third chapter, I develop and validate a novel and general approach to detecting migration events in trace data. The most common `frequency-based' approach to inferring migration events often results in mis-classifications. The novel approach accurately classifies migrations, and also provides more granular insight into migration spells and types than what are captured in standard survey instruments.The fourth chapter examines how migrants' social networks change over the migration and settlement process based on the migration events and dates that were detected in the third chapter. I characterize changes in network structure before and after migration by observing the evolving social networks of a nation's worth of migrants. I find stark and systematic changes in this structure: within two months of migrating, migrants cease communication with nearly half of their former contacts in their place of origin; these `lost' relationships are almost exactly offset by the 55% increase in new connections with people in the destination. I also show that friendship persistence and loss is highly predictable: the social ties most likely to persist are those that have frequent communication.As a whole, the chapters in this dissertation develop methods and theories to understand the interaction between migration and social networks. It lays the groundwork for future researchers answering questions in migration and social networks using population-scale digital trace data.

Real estate projects are developed excessively in China in this decade. Many new housing districts are built, but they far exceed the actual demand in some cities. These cities with a high housing vacancy rate are called ghost cities. The real situation of vacant housing areas in China has not been studied in previous research. This study, using Baidu positioning data, presents the spatial distribution of the vacant housing areas in China and classifies cities with a large vacant housing area as cities or tourism sites. To the best of our knowledge, it is the first time that we detected and analyzed the ghost cities in China at such fine scale. To understand the human dynamic in ghost cities, we select one city and one tourism sites as cases to analyze the features of human dynamics. This study illustrates the capability of big data in sensing our cities objectively and comprehensively., added references for Case Study; corrected typos; revised argument in Introduction; added a sentence to explain the second-tier and third tier cities in Result; added two sentences to introduce the background of the study in Conclusion; added two people in the Acknowledgements; added a coauthor for his contribution in designing the algorithms of home-work detection and migration calculation