scholarly journals Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karen McCulloch ◽  
Nick Golding ◽  
Jodie McVernon ◽  
Sarah Goodwin ◽  
Martin Tomko

AbstractUnderstanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.

Author(s):  
Ali Diab ◽  
Andreas Mitschele-Thiel

The 5th Generation (5G) of mobile communication networks is being developed to address the demands and business contexts of 2020 and beyond. Its vision is to enable a fully mobile and connected society and also to trigger socio-economic transformations in ways eventually unimagined today. This means that the physical world to be covered with planned 5G networks including communication networks, humans and objects is becoming a type of an information system. So as to improve the experience of individuals, communities, societies, etc. within such systems, a thorough comprehension of intelligence processes responsible of generating, handling and controlling those data is fundamental. One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have crucial role in forthcoming communication technologies. Human mobility patterns models can be categorized into synthetic, trace-based and community-based models. Synthetic models are largely preferred and widely applied to simulate mobile communication networks. They try to capture the patterns of human movements by means of a set of equations. These models are traceable, however, not capable of generating realistic mobility models. The key idea of trace-based models is the exploitation of available measurements and traces achieved in deployed systems to reproduce synthetic traces characterized by the same statistical properties of real traces. A main drawback of trace-based modeling of human patterns is the tight coupling between the trace-based model and the traces collected, the network topology deployed and even the geographic location, where the traces were collected. This is why the results of various trace-based models deviate clearly from each other. Sure, this prohibits the generalization of trace-based models. When one also considers that the traces themselves are rarely available, one can understand why synthetic models are preferred over trace-based ones. Community-based modeling of human movements depends on the fact stating that mobile devices are usually carried by humans, which implies that movement patterns of such devices are necessarily related to human decisions and socialization behaviors. So, human movement routines heavily affect the overall movement patterns resulting. One of the major contributions in this context is the application of social networks theory to generate more realistic human movement patterns. The chapter highlights the state of art and provides a comprehensive investigation of current research efforts in the field of trace- and social-based modeling of human mobility patterns. It reviews well-known approaches going through their pros and cons. In addition, the chapter studies an aspect that heavily relates to human mobility patterns, namely the prediction of future locations of users.


2017 ◽  
Vol 13 ◽  
pp. 8-24
Author(s):  
Zbigniew Zioło

The processes of technological  progress create new opportunities for economic, social and cultural growth, shape new relations between economic  entities and their environment,  and influence changes in the determinants  of entrepreneurship development.  These processes vary significantly in certain geographic locations, characterised by an enormous  diversity of natural, social, economic and cultural structures. As a consequence, this creates different opportunities  and different conditions for the development of entrepreneurship in certain spatial scales, from the continental scale, through national and regional to local scales. The article presents complex conditions  for the development of entrepreneurship, highlights its limitations resulting from institutional  barriers, and the importance of knowing the mechanisms of mutual relations between spatial systems and the influence of control instruments. The quality of central and local government authorities is of particular significance here, which do not always properly use the mechanisms of rational business support. A serious barrier to the development of entrepreneurship is the low quality of social capital, manifested in a lack of trust in institutional authorities and reluctance to engage in entrepreneurship and business development. The conclusions point out that further research should be developed that will take into account changing business conditions, with a defined strategic goal of raising the quality and standard of living, international competitiveness of the country and products in different market categories.


2006 ◽  
Vol 14 (1) ◽  
pp. 73-95 ◽  
Author(s):  
Keith Davids ◽  
Chris Button ◽  
Duarte Araújo ◽  
Ian Renshaw ◽  
Robert Hristovski

2018 ◽  
Vol 31 ◽  
pp. 23 ◽  
Author(s):  
Pascal Le Floc'h ◽  
Michel Bertignac ◽  
Olivier Curtil ◽  
Claire Macher ◽  
Emilie Mariat-Roy ◽  
...  

This study considers how to reconcile different spatial scales to find the best common denominator to be used as an ecosystem-based management unit. For this, two fishery production zones differing ecologically, economically, legally and institutionally were investigated. The first case study is located within French territorial waters, in a MPA created in 2007- the Parc Naturel Marin d'Iroise (PNMI). The second case study, the Bay of Biscay, covers both territorial waters and the French exclusive economic zone. The paper adopts a multidisciplinary approach. Relevant questions concern how marine space is shared between exploited species and fishing fleets, especially the spatial mobility strategies they employ. An assessment of the institutional system established for the PNMI contributes to the discussion of changes in coastal space use. It is obvious that the area in need of protection, defined on the basis of essential fish habitats, does not solely concern the fisheries located within the coastal zone. Experiments conducted by scientists and professionals in the Bay of Biscay provide other key points for the discussion in terms of what institutional frameworks to promote.


2017 ◽  
Author(s):  
Abigail C. Snyder ◽  
Robert P. Link ◽  
Katherine V. Calvin

Abstract. Hindcasting experiments (conducting a model forecast for a time period in which observational data is available) are rarely undertaken in the Integrated Assessment Model (IAM) community. When they are undertaken, the results are often evaluated using global aggregates or otherwise highly aggregated skill scores that mask deficiencies. We select a set of deviation based measures that can be applied at different spatial scales (regional versus global) to make evaluating the large number of variable-region combinations in IAMs more tractable. We also identify performance benchmarks for these measures, based on the statistics of the observational dataset, that allow a model to be evaluated in absolute terms rather than relative to the performance of other models at similar tasks. This is key in the integrated assessment community, where there often are not multiple models conducting hindcast experiments to allow for model intercomparison. The performance benchmarks serve a second purpose, providing information about the reasons a model may perform poorly on a given measure and therefore identifying opportunities for improvement. As a case study, the measures are applied to the results of a past hindcast experiment focusing on land allocation in the Global Change Assessment Model (GCAM) version 3.0. We find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs, such as GCAM, that require global supply to equal global demand at each time period. Additionally, the deviation measures examined in this work successfully identity parametric and structural changes that may improve land allocation decisions in GCAM. Future work will involve implementing the suggested improvements to the GCAM land allocation system identified by the measures in this work, using the measures to quantify performance improvement due to these changes, and, ideally, applying these measures to other sectors of GCAM and other land allocation models.


2020 ◽  
Author(s):  
Chang He ◽  
Cai-Hua Xiong ◽  
Ze-Jian Chen ◽  
Wei Fan ◽  
Xiao-Lin Huang

Abstract Background: Upper limb exoskeletons have drawn significant attention in neurorehabilitation because of anthropomorphic mechanical structure analogous to human anatomy. Whereas, the training movements are typically underorganized because most exoskeletons only control the movement of the hand in space, without considering rehabilitation of joint motion, particularly inter-joint postural synergy. The purposes of this study were to explore the application of a postural synergy-based exoskeleton (Armule) reproducing natural human movements for robot-assisted neurorehabilitation and to preliminarily assess its effect on patients' upper limb motor control after stroke. Methods: We developed a novel upper limb exoskeleton based on the concept of postural synergy, which provided five degrees of freedom (DOF) , natural human movements of the upper limb. Eight participants with hemiplegia due to a first-ever, unilateral stroke were recruited and included. They participated in exoskeleton therapy sessions 45 minutes/day, 5 days/week for 4 weeks, with passive/active training under anthropomorphic trajectories and postures. The primary outcome was the Fugl-Meyer Assessment for Upper Extremities (FMA-UE). The secondary outcomes were the Action Research Arm Test(ARAT), modified Barthel Index (mBI) , and exoskeleton kinematic as well as interaction force metrics: motion smoothness in the joint space, postural synergy error, interaction force smoothness, and the intent response rate. Results: After the 4-weeks intervention, all subjects showed significant improvements in the following clinical measures: the FMA-UE ( p =0.02), the ARAT ( p =0.003), and the mBI score ( p <0.001). Besides, all subjects showed significant improvements in motion smoothness ( p =0.004), postural synergy error ( p =0.014), interaction force smoothness ( p =0.004), and the intent response rate ( p =0.008). Conclusions: The subjects were well adapted to our device that assisted in completing functional movements with natural human movement characteristics. The results of the preliminary clinical intervention indicate that the Armule exoskeleton improves individuals’ motor control and activities of daily living (ADL) function after stroke, which might be associated with kinematic and interaction force optimization and postural synergy modification during functional tasks. Clinical trial registration: ChiCTR, ChiCTR1900026656; Date of registration: October 17, 2019. http://www.chictr.org.cn/showproj.aspx?proj=44420


2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Khalid BENAMOR ◽  
Wissam ABAI ◽  
Lamdjed BOUZIDI

Despite positive results quantitative variables have had in predicting the future of companies alongside their predictive ability of companies’ financial position, the remarkable increase in bankruptcies of companies without any early detection and the consequent damage to the economy in general and to companies in particular, highlighted the need to make up for quantitative variables luck in terms of predictive significance in the process of predicting companies’ financial position, which made studies interested in this field react by emphasizing variables of descriptive nature. The aim of this study is to appreciate aspects of variables of descriptive nature and the extent of their contribution to the prediction of financial position of Algerian companies, using as a case study descriptive variables data of the risk scoring technique with application on a sample data of 15 companies operating in Algiers. This study concluded that descriptive variables contribute significantly to the prediction of the financial position of Algerian companies.


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