2026-09-20T12:13:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1002962026-06-06T00:49:33Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Cesar A. Hidalgo.
Ensenat, Elisa Castaner
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2015-12-16T15:54:07Z
2015-12-16T15:54:07Z
2015
2015
http://hdl.handle.net/1721.1/100296
930615801
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.
69 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Beyond city size : characterizing and predicting the location of urban amenities
Beyond city size : the spatial laws of urban micro-agglomerations
Characterizing and predicting the location of urban amenities
Spatial laws of urban micro-agglomerations.
Thesis
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<Abstract>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.</Abstract>
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