Characterisation and comparison of spatial patterns in urban systems: A case study of U.S. cities

Huynh, Hoai Nguyen; Makarov, Evgeny; Legara, Erika Fille; Monterola, Christopher; Chew, Lock Yue

doi:10.1016/j.jocs.2017.12.001

Cited by 16 publications

(9 citation statements)

References 24 publications

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“…Using the measures of mass and space decoherence to quantify spatial patterns of points, 3 regions could be highlighted, namely highly coherent (ǫ A , ǫ ξ 0.15), coherent (0.15 ǫ A , ǫ ξ 0.5) and decoherent (ǫ A 0.5 or ǫ ξ 0.5). When the points are decoherent, their pattern can further classified as clustered or dispersed if one of the two measures is significantly smaller than the other (same reasoning for σ A and σ ξ in [12]).…”

Section: Application To Real Urban Locations Datamentioning

confidence: 98%

“…It should be noted that the discussion so far has been concerned with the measure of size (or mass) of the clusters formed in the continuum percolation process. As have been previously shown [12,13], the area of clusters, i.e. their spatial extent, provides a different perspective to understand the (relative) spatial arrangement of points in a domain.…”

Section: E Mass Decoherence Vs Space Decoherencementioning

confidence: 99%

“…Among them, percolation has proved to be a powerful and useful tool to study urban morphology [11]. In recent years, percolation method has becoming increasingly popular in analysing the spatial organisation of places in urban systems at various scales, from city [12,13], to nation [14] and inter-country level [15]. The application of percolation in such studies has so far been mainly concerned with studying the evolution of the giant cluster formed when the distance threshold ρ for inter-point interaction [16] changes.…”

Section: Introductionmentioning

confidence: 99%

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Spatial point pattern and urban morphology: Perspectives from entropy, complexity, and networks

Huynh

2019

Phys. Rev. E

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Spatial organisation of physical form of an urban system, or city, both manifests and influences the way its social form functions. Mathematical quantification of the spatial pattern of a city is, therefore, important for understanding various aspects of the system. In this work, a framework to characterise the spatial pattern of urban locations based on the idea of entropy maximisation is proposed. Three spatial length scales in the system with discerning interpretations in terms of the spatial arrangement of the locations are calculated. Using these length scales, two quantities are introduced to quantify the system's spatial pattern, namely mass decoherence and space decoherence, whose combination enables the comparison of different cities in the world. The comparison reveals different types of urban morphology that could be attributed to the cities' geographical background and development status.

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Section: Application To Real Urban Locations Datamentioning

confidence: 98%

Section: E Mass Decoherence Vs Space Decoherencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial point pattern and urban morphology: Perspectives from entropy, complexity, and networks

Huynh

2019

Phys. Rev. E

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“…It can help in applications like traffic control management by detecting crowded regions. [13] gives a detailed analysis of the different spatial patterns found in city, and provides a case study on the cities of the U.S. A topic modeling based approach is used in [8], [12]to find the POIs (places of interests) and identify various functional zones or different type of regions (for example educational areas, recreational areas etc.) in a city.…”

Section: Related Workmentioning

confidence: 99%

Inferring Location Types With Geo-Social-Temporal Pattern Mining

et al. 2020

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With a rapid growth in the global population, the modern world is undergoing a rapid expansion of residential areas, especially in urban centres. This continuously demands for increased general services and basic amenities, which are required according to the kind of population associated with the places. The advent of location-based online social networks (LBSNs) has made it much easier to collect voluminous data about users in different locations or spatial regions. The problem of mining location types from the LBSN data is largely unexplored. In this paper, we propose a pattern mining approach, using the geo-social-temporal data collected from LBSNs, to infer types of different locations. The proposed method first mines frequent co-located users and user components from an LBSN and then performs a temporal pattern analysis to finally categorize the locations. Extensive experiments are conducted on two real datasets that demonstrate the efficacy of the proposed method in terms of mean reciprocal rank (MRR), visualisations, and insights. The resulting inference mechanism would be very useful in several application domains including urban planning, billboard placement, tour planning, and geo-social event planning.INDEX TERMS Location based social networks, Spatial data mining, Co-located friendships, Geo-socialtemporal patterns

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“…Piovani et al (2017) apply road network percolation at the mesoscopic scale of London metropolitan area, in relation with a retail location model. In spatial statistics, this method can be used to characterize the spatial morphology of point patterns (Huynh et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Multi-dimensional Urban Network Percolation

Raimbault

2019

Preprint

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Network percolation has recently been proposed as a method to characterize the global structure of an urban system form the bottom-up. This paper proposes to extend urban network percolation in a multi-dimensional way, to take into account both urban form (spatial distribution of population) and urban functions (here as properties of transportation networks). The method is applied to the European urban system to reconstruct endogenous urban regions. The variable parametrization allows to consider patterns of optimization for two stylized contradictory sustainability indicators (economic performance and greenhouse gases emissions). This suggests a customizable spatial design of policies to develop sustainable territories.

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Characterisation and comparison of spatial patterns in urban systems: A case study of U.S. cities

Cited by 16 publications

References 24 publications

Spatial point pattern and urban morphology: Perspectives from entropy, complexity, and networks

Spatial point pattern and urban morphology: Perspectives from entropy, complexity, and networks

Inferring Location Types With Geo-Social-Temporal Pattern Mining

Multi-dimensional Urban Network Percolation

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