Understanding Urban Functionality from POI Space

Fan, Dongwan; Qin, Kun; Kang, Chaogui

doi:10.1109/geoinformatics.2018.8557122

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Other variables frequently used in the academic literature to delineate FUAs include population density (e.g., Muniz, Galindo & Garcia, 2003), number of businesses (e.g., Fan, Qin & Kang, 2018;Huang, 2016) and capital flows (Liu, Dong & Chi, 2010). This study also uses these variables to make it comparable to the existing literature as well as test the robustness of our empirical results.…”

Section: Literature Reviewmentioning

confidence: 91%

Deliniation of metropolitan areas in Poland: A functional approach

Gawrońska-Nowak¹,

Lis²,

Zadorozhna³

2022

Economics &Amp; Sociology

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elineation of urban functional areas helps policymakers and urban planners understand the connections between the core cities and areas surrounding them, and subsequently develop policies and solutions that can serve local populations. This article develops a readily applicable econometric method for delineation that considers functional aspects of cities and their surroundings. We perform delineation analysis using the data for 78 Polish core cities, grouping them by population size. Using the satellite data on lights emitted at night, population density, commuter numbers as well as the number of houses and apartments built in each commune, we apply a threshold regression model to determine the boundaries of functional urban areas. Our main results suggest that the mean radius of functional urban areas (FUAs) around the largest (most populous) cities is, on average, 21 km, while it is between 13 and 16 km for smaller cities. We then test how the econometric results compare with the perceptions of local inhabitants through a citizen science project (CSP) conducted as a robustness check.

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Section: Literature Reviewmentioning

confidence: 91%

Deliniation of metropolitan areas in Poland: A functional approach

Gawrońska-Nowak¹,

Lis²,

Zadorozhna³

2022

Economics &Amp; Sociology

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“…(2) City Cultural Vibrancy Index POI data are a widely used urban big data that can accurately reveal urban functions and their spatial location information for urban function inference [31]. It is highly flexible in terms of research scale [10].…”

Section: Urban Vibrancy Indicators (1) City Social Vibrancy Indexmentioning

confidence: 99%

How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment

Gong

Wang

et al. 2022

IJERPH

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Quantitative assessment of urban vibrancy is crucial to understanding urban development and promoting sustainability, especially for rapidly developing areas and regions that have experienced post-disaster reconstruction. Taking Dujiangyan City, the hardest-hit area of the earthquake, as an example, this paper quantifies the urban economic, social, and cultural vibrancy after reconstruction by the use of multi-source data, and conducts a geographic visualization analysis. The purpose is to establish an evaluation framework for the relationship between the urban built environment elements and vibrancy in different dimensions, to evaluate the benefits of post-disaster restoration and reconstruction. The results show that the urban vibrancy reflected by classified big data can not be completely matched due to the difference in the data generation and collection process. The Criteria Importance Though Inter-criteria Correlation and entropy (CRITIC-entropy) method is used to construct a comprehensive model is a better representation of the urban vibrancy spatial characteristics. On a global scale, comprehensive vibrancy demonstrates high continuity and a bi-center structure. In the old town, the distribution of various urban vibrancies show diffusion characteristics, while those in the new district demonstrated a high degree of aggregation, and the comprehensive vibrancy is less sensitive to land-use mixture and more sensitive to residential land.

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