Quantifying Urban Centrality: A Simple Index Proposal And International Comparison
By: PEREIRA, Rafael Henrique Moraes
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Contributor(s): NADALIN, Vanessa | MONASTERIO, Leonardo | ALBUQUERQUE, Pedro Henrique Melo.
Material type: 
BookSeries: Texto para Discussão ; 1675a.Publisher: Brasília : IPEA, 2012Description: 32 p.Online resources: Acesso Abstract: This study introduces a new measure of urban centrality. It identifies distinct urban structures from different spatial patterns of jobs and resident population. The proposed urban centrality index constitutes an extension of the spatial separation index (MIDELFART-KNARVIK et al., 2000). It is suggested that urban structure should be more accurately analyzed by considering a centrality scale (varying from extreme monocentricity to extreme polycentricity) rather than a binary variable (monocentric or polycentric). The proposed index controls for differences in size and shape of the geographic areas for which data is available, and can be calculated using different variables, such as employment and population densities and trip generation rates. The properties of the index are illustrated in simulated artificial data sets. Simulation results for hypothesized urban forms are compared to other similar measures proposed by previous literature. The index is then applied to the urban structure of four different metropolitan areas: Pittsburgh and Los Angeles in the United States; São Paulo, Brazil; and Paris, France, The index is compared to other traditional spatial agglomeration measures, such as global and local Moran`s I, and density gradient estimations
This study introduces a new measure of urban centrality. It identifies distinct urban structures from different spatial patterns of jobs and resident population. The proposed urban centrality index constitutes an extension of the spatial separation index (MIDELFART-KNARVIK et al., 2000). It is suggested that urban structure should be more accurately analyzed by considering a centrality scale (varying from extreme monocentricity to extreme polycentricity) rather than a binary variable (monocentric or polycentric). The proposed index controls for differences in size and shape of the geographic areas for which data is available, and can be calculated using different variables, such as employment and population densities and trip generation rates. The properties of the index are illustrated in simulated artificial data sets. Simulation results for hypothesized urban forms are compared to other similar measures proposed by previous literature. The index is then applied to the urban structure of four different metropolitan areas: Pittsburgh and Los Angeles in the United States; São Paulo, Brazil; and Paris, France, The index is compared to other traditional spatial agglomeration measures, such as global and local Moran`s I, and density gradient estimations
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