Revisiting the Spatial Definition of Neighborhood Boundaries: Functional Clusters versus Administrative Neighborhoods

Abstract

This study revisits the debate surrounding the definition of neighborhood boundaries by addressing the disconnect between the city's Administrative Neighborhoods and its functional organization. A method is proposed for dividing the city into more meaningful units through the spatial distribution of urban activities by retrieving data from Google Places. The dataset was pre-processed and spatially divided into Functional Clusters. A comparison between functional and administrative subdivisions of the city was undertaken, from which three overall conclusions could be drawn. First, a function-based city partition allows economically active urban areas to become the neighborhood's center, thereby creating a polynuclear neighborhood structure that would potentially encourage greater cross-movement of people throughout the city. Second, the specialization of activities becomes more evident in Functional Clusters than in Administrative Neighborhoods. Third, access to up-to-date data makes possible a timely diagnosis of the quantity and diversity of urban activities-i.e., economic activities, services, and facilitiesthrough Google Places data. The value of this contribution is to inform urban decision-making and policies in order to better balance the provision of a neighborhood's economic activity.

Modeling Pedestrian Flows: Agent-Based Simulations of Pedestrian Activity for Land Use Distributions in Urban Developments

“…The consequent pedestrian flow produced by the amenities and services follows certain rules, in our two use cases, in which higher density and diversity of amenities tend to produce a higher density of pedestrians walking predominantly on a local scale, potentially producing a higher sense of locality in line with Martí et al [51] describing urban clusters of social activity following the aggregation of economic activity. The results presented in this research not only validate their approach but also quantify it with measurable indicators.…”

Continued Increases of Gross Primary Production in Urban Areas during 2000–2016

“…Theoretically, urban expansion within a gridcell started with an increase in urban impervious surface (UIS), which reduce urban-rural vegetation (URV) [21]. Urban land management also affects URV, including (i) land cover change (such as new lawns, open parks, and green-belts); (ii) vegetation species change and growth in suburban, periurban, and neighboring rural areas; and (iii) vegetation management (such as fertilization and irrigation) [29,30]. In recent years, many cities started to improve and expand urban vegetation and considered urban vegetation as one of major metrics for the society to address the ecological and environmental impacts of urbanization [31].…”

Google Places Enricher: A tool that Makes It Easy to Get and Enrich Google Places API Data

“…Finally, the enrichment of the data provided by Google Places Enricher can help different studies interested in data of establishments with detailed categories, such as the work by Martí et al [9]. Promoting the debate on the spatial definition of neighbourhood boundaries, the authors conducted a study using Google Places data.…”