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5 Results and Discussion

With the artificial data being a representation of ideal movement pattern one should follow, the comparison with actual movements yield a number of interesting results. Figure 3 shows, for each of the three cities, fraction of visits by people living in white, black, Asian and Hispanic tracts, alongside the simulated ideal fractions. As clearly visible, the actual movement patterns of users deviate significantly from the expected behavior.

A noticeable trend for any given race is that visits are always higher to tracts of similar race, with the difference from simulated visits for blacks, Asians and Hispanics being very high, going up to four times for Asians. The difference is not as high for whites, however, both the actual and simulated fractions are noticeably larger than visits to any other races. This is explained by the fact that there are far too many white tracts compared to other races, and white segregation clusters are large as well. While visiting tracts of other races the actual and simulated data are very close except in New York and Chicago where the actual visits by people living in white tracts to black tracts are over 50% less than what ideally should have been. Likewise, people living in black tracts in all three cities would visit white tracts less often, while their visits to Asian and Hispanic tracts does not skew much from the artificial data. Hispanics in New York and Chicago visits blacks less often than expected, but it is just the opposite in Los Angeles.

In general, visits to white tracts by blacks, Asians and Hispanics are always much lower than simulated. The only exception to this trend is for Asians in Chicago, where there are very few tracts with a majority Asian population, meaning, it could simply be a bias in sample size. The visits to predominantly black tracts by other races is also lower than the artificial data, although there is an interesting exception in Los Angeles where Asians and Hispanics visit blacks more often than expected.

It is fascinating to see that all races are biased towards areas of identical race, and tend to keep away from others. It is also interesting to note that these trends do not resonate equally in all cities. For example, blacks in New York and Los Angeles, would visit Hispanics close to or even more than expected, but in Chicago the fraction of visits is less.

6 Conclusion

In this paper we try to understand the effects of racial segregation on mobility patterns of people living in three major U.S. metropolitansNew York City, Los Angele and Chicago. We assembled a dataset comprising of human entities, their home locations and daily movement data by accumulating geo-tagged tweets from these cities and performing simple preprocessing steps. The human entities were combined with geographic entities, in this case census tract polygons, and each user was associated with a particular race homologous to the race with majority population in that tract, as calculated from the 2010 census data. Building parameterized models of human mobility for these cities we generated synthetic data to compare with the actual movement of people. We observed significant effects of racial segregation on people's mobility, leading to some interesting observations.

Although racial segregation in the U.S. has been decreasing in the past few decades the major metropolitans are still vastly segregated. People living within tracts of any particular race are biased towards other races and tend to visit tracts of similar race more often. However, the difference in visits to other races is not evenly distribute. Blacks, Asians and Hispanics usually have a higher percentage of difference in their visits to white tracts, and similarly, black tracts are less visited by other races. Within these patterns we also observed some variations among the three cities, for example, the higher than expected visits to black tracts in Los Angeles.

Our approach allows to use readily available geo-location based data from Twitter to model human mobility and investigate effects of geographic and sociological constraints. However, this approach is far from being perfect and opens up numerous avenues for future research. For instance, census tracts have people from different races living in them, but human entities are designated to the race with majority population, when in reality they may belong to a different race. Another assumption we made was the uniform distribution of direction of travel. It would be interesting to introduce skews in the distribution according to the presence of geographic barriers.


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