Beyond city size : characterizing and predicting the location of urban amenities

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Title as it appears in M...

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Bibliographic Details
Main Author: Ensenat, Elisa Castaner
Other Authors: Cesar A. Hidalgo.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2015
Subjects:
Online Access:http://hdl.handle.net/1721.1/100296
Description
Summary:Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Title as it appears in MIT Commencement Exercises program, June 5, 2015: Beyond city size : the spatial laws of urban micro-agglomerations. Cataloged from student-submitted PDF version of thesis. === Includes bibliographical references (pages 67-69). === Intercity studies have shown that a city's characteristics -ranging from infrastructure to crime-scale as a power of its population. These studies, however, have not been extended to the intra-city scale, leaving open the question of how urban characteristics are distributed within a city. Here we study the spatial organization of one important urban characteristic: its amenities, such as restaurants, cafes, and libraries. We use a dataset summarizing the position of more than 1.2 million amenities disaggregated into 74 distinct categories and covering 47 U.S. cities to show that: (i) the spatial distribution of amenities within a city is characterized by dense agglomerations of amenities (which we call micro-clusters), (ii) that unlike in the intercity case, size is a poor predictor of the amenities of each type that locate in each micro-cluster, and (iii) that the number of amenities of each type in a micro-cluster is better predicted using information on the collocation of amenities observed across all micro-clusters than using the micro-cluster's size. Finally, we use these findings to create a recommendation algorithm that suggests amenities that are missing in a micro-cluster and can inform the efforts of developers and planners looking to construct and regulate the development of new and existing neighborhoods. === by Elisa Castaner Ensenat. === M. Eng.