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Mobility Patterns and User Dynamics in Racially Segregated Geographies of US Cities

Abstract. In this paper we try to understand how racial segregation of the geographic spaces of three major US cities (New York, Los Angeles and Chicago) affect the mobility patterns of people living in them. Collecting over 75 million geo-tagged tweets from these cities during a period of one year beginning October 2012 we identified home locations for over 30,000 distinct users, and prepared models of travel patterns for each of them. Dividing the cities' geographic boundary into census tracts and grouping them according to racial segregation information we try to understand how the mobility of users living within an area of a particular predominant race correlate to those living in areas of similar race, and to those of a different race. While these cities still remain to be vastly segregated in the 2010 census data, we observe a compelling amount of deviation in travel patterns when compared to artificially generated ideal mobility. A common trend for all races is to visit areas populated by similar race more often. Also, blacks, Asians and Hispanics tend to travel less often to predominantly white census tracts, and similarly predominantly black tracts are less visited by other races.

Keywords: Mobility patterns, racial segregation, Twitter.

1 Introduction

Sociologists and economists have long been trying to understand the influence of racial segregation in the United States, on various social aspects like income, education, employment and so on. Every decennial census has hinted the gradually decreasing residential racial segregation in many major metropolitans, but, it is important to continually analyze the effects and how they change over time to have a better understanding of today's social environment.

In this paper, we try to understand if, and how, racial segregation affect the way people move around in large metropolitans. Ubiquitously available data from geo-location based sharing services like Twitter poses a prudent source of real-time spatial movement information. Coalescing users belonging to racially predominant geographic areas with their mobility patterns, we analyze to find variations in travel to areas of similar and dissimilar races. We also build generalized models of ideal human mobility and create a corpus of travel activity analogous to the actual data. Comparing the actual mobility of users to the ideal models, we look for bias and interesting behavior patterns and dynamics in three U.S. citiesNew York, Los Angeles and Chicago.

 
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