Predictive limitations of spatial interaction models: a non-Gaussian analysis

Hilton, Benjamin; Sood, Ashish; Evans, T. S.

doi:10.1038/s41598-020-74601-z

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Other works also seek to understand what are the best parameters to use to obtain the best predictions of human mobility with this model. For example, [80] concluded that appropriate choices of input data and fitted parameters improves the predictive power of spatial interaction models like GM.…”

Section: Models and Methodsmentioning

confidence: 99%

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

2021

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The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

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Section: Models and Methodsmentioning

confidence: 99%

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

2021

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“…Constraints can also be considered in gravity models, these can be at the origin, at the destination or at both [ 162 , 163 ]. Assessing the effectiveness of these models is ongoing research [ 164 ], in some cases the scale of the system plays an important role [ 165 ], while in some others, the results from the radiation and gravity models are in agreement when assessing interventions [ 166 ].…”

Section: Urban Mobilitymentioning

confidence: 99%

Recent advances in urban system science: Models and data

Arcaute

Ramasco

2022

PLoS ONE

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Cities are characterized by the presence of a dense population with a high potential for interactions between individuals of diverse backgrounds. They appear in parallel to the Neolithic revolution a few millennia ago. The advantages brought in terms of agglomeration for economy, innovation, social and cultural advancements have kept them as a major landmark in recent human history. There are many different aspects to study in urban systems from a scientific point of view, one can concentrate in demography and population evolution, mobility, economic output, land use and urban planning, home accessibility and real estate market, energy and water consumption, waste processing, health, education, integration of minorities, just to name a few. In the last decade, the introduction of communication and information technologies have enormously facilitated the collection of datasets on these and other questions, making possible a more quantitative approach to city science. All these topics have been addressed in many works in the literature, and we do not intend to offer here a systematic review. Instead, we will only provide a brief taste of some of these above-mentioned aspects, which could serve as an introduction to the collection ‘Cities as Complex Systems’. Such a non-systematic view will lead us to leave outside many relevant papers, and for this we must apologise.

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“…Literature regarding land changes has discussed distal flows can be regarded as urban land teleconnections that drive and respond to urbanization (Seto et al, 2012). The gravity model is the most common formula for quantifying spatial interactions (Yan and Zhou, 2018;Hilton et al, 2020). Specifically, it allows us to measure location-based relationships by integrating the distance decay functions with measures of the relative scale of geographical entities (Haynes and Fotheringham, 1985).…”

Section: Graph-based Flow Magnitude Calculationmentioning

confidence: 99%

Incorporation of intra-city human mobility into urban growth simulation: A case study in Beijing

et al. 2022

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The effective modeling of urban growth is crucial for urban planning and analyzing the causes of land-use dynamics. As urbanization has slowed down in most megacities, improved urban growth modeling with minor changes has become a crucial open issue for these cities. Most existing models are based on stationary factors and spatial proximity, which are unlikely to depict spatial connectivity between regions. This research attempts to leverage the power of real-world human mobility and consider intra-city spatial interaction as an imperative driver in the context of urban growth simulation. Specifically, the gravity model, which considers both the scale and distance effects of geographical locations within cities, is employed to characterize the connection between land areas using individual trajectory data from a macro perspective. It then becomes possible to integrate human mobility factors into a neural-network-based cellular automata (ANN-CA) for urban growth modeling in Beijing from 2013 to 2016. The results indicate that the proposed model outperforms traditional models in terms of the overall accuracy with a 0.60% improvement in Cohen's Kappa coefficient and a 0.41% improvement in the figure of merit. In addition, the improvements are even more significant in districts with strong relationships with the central area of Beijing. For example, we find that the Kappa coefficients in three districts (Chaoyang, Daxing, and Shunyi) are considerably higher by more than 2.00%, suggesting the possible existence of a positive link between intense human interaction and urban growth. This paper provides valuable insights into how fine-grained human mobility data can be integrated into urban growth simulation, helping us to better understand the human-land relationship.

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Predictive limitations of spatial interaction models: a non-Gaussian analysis

Cited by 5 publications

References 30 publications

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Recent advances in urban system science: Models and data

Incorporation of intra-city human mobility into urban growth simulation: A case study in Beijing

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