Revisiting the Spatial Definition of Neighborhood Boundaries
Functional Clusters versus Administrative Neighborhoods
Bibliographic Data
| ID | 15318913 |
|---|---|
| Authors | Pablo Martí Ciriquián (0000-0002-6877-784X, University of Alicante, corresponding author), Leticia Serrano-Estrada (0000-0002-7466-1974, University of Alicante), Almudena Nolasco-Cirugeda (0000-0002-3653-1650, University of Alicante), Jesús López Baeza (0000-0002-4092-1782, Digital City Science, HafenCity University Hamburg, Germany) |
| Year | 2021 |
| Volume | 29 |
| Issue | 3 |
| Pages | 73-94 |
| Publication date | 2021-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Urban Technology (JOURNAL) |
| Journal identifiers | ISSN: 1063-0732 • E-ISSN: 1466-1853 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/10630732.2021.1930837 |
| OpenAlex | W3188601280 |
| Language | EN |
| Citations received | 15 |
| References cited | 42 |
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 facilities—through 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
Distribution (mathematics · Diversity (politics · Economic geography · Function (biology · Geography · Partition (number theory · Regional science · Sociology · Subdivision · Human Mobility and Location-Based Analysis · Land Use and Ecosystem Services · Urban Design and Spatial Analysis
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Rating places and crime prevention
Mobilities and the flexible boundaries of the neighbourhood. A test with crime data in Barcelona
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How would AI define neighborhood boundaries? A comparison with human-crowdsourced data
Urban metabolisms in context
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Crowdsourcing urban form and function
Delineating urban functional areas with building-level social media data
From Local to Global
Community detection in graphs
Social Media data
How Families Manage Risk and Opportunity in Dangerous Neighborhoods
Identification of tourist hot spots based on social networks
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City Compactness
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Using locative social media and urban cartographies to identify and locate successful urban plazas
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Neighborhoods
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Living Room
| Citations per year | 3.75 |
|---|---|
| Citation span | 2022 - 2026 (5) |
| Highly cited | No |