A graph based approach for functional urban areas delineation

Abstract

In an increasingly urbanized world, where cities are changing continuously, it is essential for policy makers to have access to regularly updated decision-making tools for an effective management of urban areas. An example of these tools is the delineation of cities into functional areas which provides knowledge on high spatial interaction zones and their socioeconomic composition. In this paper, we presented a method for the structural analysis of a city, specifically for the determination of its functional areas, based on communities detection in graph. The nodes of the graph correspond to geographical units resulting from a cartographic division of the city according to the road network. The edges are weighted using a Gaussian distance-decay function and the amount of spatial interactions between nodes. Our approach optimize the modularity to ensure that the functional areas detected have strong interactions within their borders but lower interactions outside. Moreover, it leverages on POIs' entropy to maintain a good socioeconomic heterogeneity in the detected areas. We conducted experiments using taxi trips and POIs datasets from the city of Porto, as a study case. Trough those experiments, we demonstrate the ability of our method to portray functional areas while including spatial and socioeconomic dynamics.

Delineating functional regions from road networks: The case of South Korea

“…In recognition of these limitations, more recent studies have tested alternative geospatial big data sources, such as point-of-interest coordinates (Gao et al, 2017; Niu and Silva, 2021), taxi/transit service usage (Houssou et al, 2019; Wu et al, 2019), socio-media check-in (Cai et al, 2017; Jia et al, 2019; Poorthuis, 2017), smart-card transactions (Sulis et al, 2018; Zhong et al, 2014), and cellular-phone signals (Chen et al, 2020; Ratti et al, 2010). These alternatives may help reduce the limitations inherent in conventional survey-based flow data.…”

“…Despite its merit, the sole focus of a morphological approach on physical forms weakens the rationale to treat the identified clusters as functional regions built on connectivity. Geospatial big data sources—such as those on points of interest, taxi/transit services, social media usage, smart-card transactions, and cellular-phone signals—have also been increasingly tested as an alternative to conventional survey-based flow data (e.g., Cai et al, 2017; Houssou et al, 2019; Niu and Silva, 2021; Ratti et al, 2010; Sulis et al, 2018). However, the applicability of such data is bounded by data availability in both spatial and temporal dimensions, and approximating spatial interdependency from big data may also be questioned as to its validity and robustness.…”