Mobility Measures

Geographic mobility is a celebrated feature of American life. Deciding where to live is seen not only as a key personal freedom, but also a means of economic advancement. Millions of Americans move each year over great distances. But while this right to travel is safeguarded by the Constitution, these mobility decisions are not entirely free. In terms of moving long distances, employment and family reasons are central, and a regime of employment and family law “mobility measures” play a significant role in regulating why and how we move. This Article first sets forth this new framework of “mobility measures,” which are constituted by employment law sorting (moving across employers and space for employment purposes) and family law clustering (moving with a legally defined portable family unit). These mobility measures not only enable and facilitate long-distance moves, including with subsidies to the tune of billions of dollars a year, but they motivate these moves to take a particular form: to move for employment purposes, only taking our nuclear family with us. In this way, we are encouraged by the law to move, yet the law prevents us from mitigating the disruption caused by the move. So while mobility has its benefits, this Article argues that it has underappreciated costs. Long-distance moves destroy place-specific investments with our closest supporters that are crucial for everyday functions, as well as economic productivity. These relationship and economic costs harm all long-distance movers, but weigh particularly heavily on one group — women. This toxic combination of employment sorting and family clustering makes mobility more problematic than it needs to be. This Article closes by offering new ways of substantially altering employment sorting and family clustering to optimize the balance between the two and reap more benefits from mobility with fewer costs. These reforms would soften sorting while expanding clustering, and at the same time encourage certain forms of mobility (particularly to cities) that would permit a more optimal combination of sorting and clustering

Ordering Metro Lines by Block Crossings

A problem that arises in drawings of transportation networks is to minimize the number of crossings between different transportation lines. While this can be done efficiently under specific constraints, not all solutions are visually equivalent. We suggest merging crossings into block crossings, that is, crossings of two neighboring groups of consecutive lines. Unfortunately, minimizing the total number of block crossings is NP-hard even for very simple graphs. We give approximation algorithms for special classes of graphs and an asymptotically worst-case optimal algorithm for block crossings on general graphs. That is, we bound the number of block crossings that our algorithm needs and construct worst-case instances on which the number of block crossings that is necessary in any solution is asymptotically the same as our bound

An In-Place Sorting with O(n log n) Comparisons and O(n) Moves

We present the first in-place algorithm for sorting an array of size n that performs, in the worst case, at most O(n log n) element comparisons and O(n) element transports. This solves a long-standing open problem, stated explicitly, e.g., in [J.I. Munro and V. Raman, Sorting with minimum data movement, J. Algorithms, 13, 374-93, 1992], of whether there exists a sorting algorithm that matches the asymptotic lower bounds on all computational resources simultaneously

Insertion Sort is O(n log n)

Traditional Insertion Sort runs in O(n^2) time because each insertion takes O(n) time. When people run Insertion Sort in the physical world, they leave gaps between items to accelerate insertions. Gaps help in computers as well. This paper shows that Gapped Insertion Sort has insertion times of O(log n) with high probability, yielding a total running time of O(n log n) with high probability.Comment: 6 pages, Latex. In Proceedings of the Third International Conference on Fun With Algorithms, FUN 200

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?

Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases

Neighbourhood choice and neighbourhood reproduction

Although we know a lot about why households choose certain dwellings, we know relatively little about the mechanisms behind their choice of neighbourhood. Most studies of neighbourhood choice focus only on one or two dimensions of neighbourhoods: typically poverty and ethnicity. In this paper we argue that neighbourhoods have multiple dimensions and that models of neighbourhood choice should take these dimensions into account. We propose the use of a conditional logit model. From this approach we can gain insight into the interaction between individual and neighbourhood characteristics which lead to the choice of a particular neighbourhood over alternative destinations. We use Swedish register data to model neighbourhood choice for all households which moved in the city of Uppsala between 1997 and 2006. Our results show that neighbourhood sorting is a highly structured process where households are very likely to choose neighbourhoods where the neighbourhood population matches their own characteristics. We find that income is the most important driver of the sorting process, although ethnicity and other demographic and socioeconomic characteristics play important roles as well.PostprintPeer reviewe

Two Combinatorial Models with identical Statics yet different Dynamics

Motivated by the problem of sorting, we introduce two simple combinatorial models with distinct Hamiltonians yet identical spectra (and hence partition function) and show that the local dynamics of these models are very different. After a deep quench, one model slowly relaxes to the sorted state whereas the other model becomes blocked by the presence of stable local minima.Comment: 23 pages, 11 figure