4 research outputs found
Delineating urban functional zones using mobile phone data: A case study of cross-boundary integration in Shenzhen-Dongguan-Huizhou area
As cities continuously expand and with the emergence of mega-city regions, the urban functional zones (UFZs) have spread beyond their original administrative boundaries. An accurate and updated delineation of the UFZs is crucial for assessing the functional integration between cities within a mega-city region. Mobility data provides a chance to depict the UFZs from actual human activities at a finer spatial scale. Existing studies mostly adopted network-based approaches relying on the topological relationship but ignoring spatial factors, causing the lack of sensitivity in detecting the cross-cities integration of the functional region. This research proposed a novel regionalisation algorithm that redraws non-overlap boundaries of urban functional zones based on the commuting origin-destination matrix, representing the spatial interactions within cities and cross-cities. In particular, functional zones are drawn by searching for the best partition with the best goodness of fitting in the hierarchical spatial interaction model. The algorithm was applied to a case study of a mega-city region, Shenzhen-Dongguan-Huizhou (SDH) area in China using mobile phone signalling data. By adopting two different settings, this model evaluated the current status and predict the future trend of urban integration respectively. The results show the current boundary of UFZs in the SDH area almost coincides with administrative boundaries. Meanwhile, the results of long-term predictions might be utilised by policymakers to give more attention to the areas near the Dongguan-Huizhou boundary to promote industry cooperation and avoid mismatches between the functional and administrative regions
Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data
The unprecedented urbanization in China has dramatically changed the urban
spatial structure of cities. With the proliferation of individual-level
geospatial big data, previous studies have widely used the network abstraction
model to reveal the underlying urban spatial structure. However, the
construction of network abstraction models primarily focuses on the topology of
the road network without considering individual travel flows along with the
road networks. Individual travel flows reflect the urban dynamics, which can
further help understand the underlying spatial structure. This study therefore
aims to reveal the intra-urban spatial structure by integrating the road
network abstraction model and individual travel flows. To achieve this goal, we
1) quantify the spatial interaction relatedness of road segments based on the
Word2Vec model using large volumes of taxi trip data, then 2) characterize the
road abstraction network model according to the identified spatial interaction
relatedness, and 3) implement a community detection algorithm to reveal
sub-regions of a city. Our results reveal three levels of hierarchical spatial
structures in the Wuhan metropolitan area. This study provides a data-driven
approach to the investigation of urban spatial structure via identifying
traffic interaction patterns on the road network, offering insights to urban
planning practice and transportation management
How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery
The Attention Restoration Theory (ART) presents a theoretical framework with
four essential indicators (being away, extent, fascinating, and compatibility)
for comprehending urban and natural restoration quality. However, previous
studies relied on non-sequential data and non-spatial dependent methods, which
overlooks the impact of spatial structure defined here as the positional
relationships between scene entities on restoration quality. The past methods
also make it challenging to measure restoration quality on an urban scale. In
this work, a spatial-dependent graph neural networks (GNNs) approach is
proposed to reveal the relation between spatial structure and restoration
quality on an urban scale. Specifically, we constructed two different types of
graphs at the street and city levels. The street-level graphs, using sequential
street view images (SVIs) of road segments to capture position relationships
between entities, were used to represent spatial structure. The city-level
graph, modeling the topological relationships of roads as non-Euclidean data
structures and embedding urban features (including Perception-features,
Spatial-features, and Socioeconomic-features), was used to measure restoration
quality. The results demonstrate that: 1) spatial-dependent GNNs model
outperforms traditional methods (Acc = 0.735, F1 = 0.732); 2) spatial structure
portrayed through sequential SVIs data significantly influences restoration
quality; 3) spaces with the same restoration quality exhibited distinct spatial
structures patterns. This study clarifies the association between spatial
structure and restoration quality, providing a new perspective to improve urban
well-being in the future.Comment: 33 pages, 7 figures, Under revie
Development clusters for small places and rural development for territorial cohesion?
This article proposes an alternative policy development approach for territories encompassing rural areas with small urban settlements or ‘small places’, which normally suffer from lagging territorial development trends. The proposed ‘Development Clusters for Small Places’ approach draws on the potential of all places to further their development via municipal clustering, based on four analytic dimensions: (i) existing functional areas; (ii) similarities in economic circularity and specialisation; (iii) presence of ongoing territorial and governance cooperation processes; and (iv) spatial physical connectivity and accessibility. Besides a theoretical overview of this policy approach, the article analyses concrete examples of its potential implementation in two case studies: Alentejo in Portugal and Innlandet in Norway. The findings highlight the potential advantages of municipal clustering over current mainstream regional development rationales to implement endogenous rural development in a supra-municipal scale, thus increasing institutional thickness and policy influence towards a more territorial cohesive region.info:eu-repo/semantics/publishedVersio