A Predictive Model of Urban Stock and Activity: 1. Theoretical Considerations

1975 ◽  
Vol 7 (8) ◽  
pp. 965-979 ◽  
Author(s):  
M A O Ayeni
Keyword(s):  
Spatial Structure ◽  
Predictive Model ◽  
Dynamic Model ◽  
Predictive Models ◽  
Urban Systems ◽  
Urban Spatial Structure ◽  
Static Models ◽  
Theoretical Considerations

Urban systems are dynamic and hence require predictive models that incorporate the element of time more explicitly. Comparative static models of the Lowry type may be embellished by the use of the entropy-maximizing methodology and by slight reformulations of the equations by the introduction of simple lags. The result is a quasi-dynamic or dynamic model of urban spatial structure. This is the first of two papers, in which the issues involved in the construction of predictive models are discussed and the equation systems of the model developed. The operationalization of the concepts and the empirical development of the model for a Nigerian city will be discussed in the paper to follow.

scholarly journals Complexity of Functional Urban Spaces Evolution in Different Aspects: Based on Urban Land Use Conversion

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Guolei Zhou ◽  
Chenggu Li ◽  
Yanjun Liu ◽  
Jing Zhang
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Spatial Structure ◽  
Central City ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Spaces ◽  
Urban Systems ◽  
Urban Spatial Structure ◽  
Rapid Urbanization

The evolution of urban spatial structure and urban land use is a topical issue in urban studies. The analysis of the complexity of functional urban spaces evolution is valuable for a deeper understanding of the changes in urban spatial structure. Taking the central city of Changchun as the study area, the paper uses the urban land conversion method to analyze the spatial and temporal characteristics of functional urban spaces evolution in different aspects. The study found that the evolution of functional urban spaces presents significant spatial and temporal differences in different stages and different aspects. There is a close relationship between functional urban spaces evolution and scale. As the scale becomes smaller, the spatial differences and patterns of functional urban spaces evolution become more complex. In the context of rapid urbanization, the mutual replacement of functional urban spaces is frequent, which is not conducive to the sustainable development of urban space as a whole. This study will deepen the understanding of the evolution of urban spatial structure and the complexity of urban systems and provide theoretical support for the optimization and sustainable development of urban spaces.

The Dynamics of Urban Spatial Structure: Some Exploratory Results Using Difference Equations and Bifurcation Theory

1981 ◽  
Vol 13 (12) ◽  
pp. 1473-1483 ◽  
Author(s):  
M Clarke ◽  
A G Wilson
Keyword(s):  
Difference Equation ◽  
Spatial Structure ◽  
Dynamic Model ◽  
Difference Equations ◽  
Bifurcation Theory ◽  
Equation Model ◽  
Urban Spatial Structure ◽  
Hypothetical System ◽  
Simple Difference ◽  
Bifurcation Behaviour

It is demonstrated that a simple difference equation model, which exhibits complex bifurcation behaviour, can be used to represent change in urban retailing and residential systems. These submodels are combined to form a rudimentary dynamic model of urban spatial structure. A sample of exploratory results are presented for a 169-zone hypothetical system.

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

2020 ◽  
pp. 129-134
Keyword(s):  
Predictive Model ◽  
Unmanned Aerial Vehicle ◽  
Predictive Models ◽  
Navigation System ◽  
Error Compensation ◽  
Compensation Scheme ◽  
Radar Images ◽  
Aerial Vehicle ◽  
System Errors ◽  
Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

Characterizing the urban spatial structure using taxi trip big data and implications for urban planning

2021 ◽  
Author(s):  
Haibo Li ◽  
Xiaocong Xu ◽  
Xia Li ◽  
Shifa Ma ◽  
Honghui Zhang
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Spatial Structure ◽  
Urban Spatial Structure

scholarly journals The Hanshin (Osaka-Kobe)-Earthquake, Its Disasters and Reconstruction in Relation to the Urban Spatial Structure.

1996 ◽  
Vol 105 (4) ◽  
pp. 516-519
Author(s):  
Mitsunori SAITO ◽  
Munenori SAWA ◽  
Shizuaki SHIBUYA ◽  
Shinichi TAKAHASHI ◽  
Kenn YAMAZAKI
Keyword(s):  
Spatial Structure ◽  
Urban Spatial Structure ◽  
Kobe Earthquake

O-203 Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization (IVF) cycles

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
J A Ortiz ◽  
R Morales ◽  
B Lledo ◽  
E Garcia-Hernandez ◽  
A Cascales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Predictive Models ◽  
Maternal Age ◽  
The Other ◽  
Predictor Variables ◽  
Learning Models ◽  
Male Factor ◽  
Factors Associated ◽  
Machine Learning Models

Abstract Study question Is it possible to predict the likelihood of an IVF embryo being aneuploid and/or mosaic using a machine learning algorithm? Summary answer There are paternal, maternal, embryonic and IVF-cycle factors that are associated with embryonic chromosomal status that can be used as predictors in machine learning models. What is known already The factors associated with embryonic aneuploidy have been extensively studied. Mostly maternal age and to a lesser extent male factor and ovarian stimulation have been related to the occurrence of chromosomal alterations in the embryo. On the other hand, the main factors that may increase the incidence of embryo mosaicism have not yet been established. The models obtained using classical statistical methods to predict embryonic aneuploidy and mosaicism are not of high reliability. As an alternative to traditional methods, different machine and deep learning algorithms are being used to generate predictive models in different areas of medicine, including human reproduction. Study design, size, duration The study design is observational and retrospective. A total of 4654 embryos from 1558 PGT-A cycles were included (January-2017 to December-2020). The trophoectoderm biopsies on D5, D6 or D7 blastocysts were analysed by NGS. Embryos with ≤25% aneuploid cells were considered euploid, between 25-50% were classified as mosaic and aneuploid with >50%. The variables of the PGT-A were recorded in a database from which predictive models of embryonic aneuploidy and mosaicism were developed. Participants/materials, setting, methods The main indications for PGT-A were advanced maternal age, abnormal sperm FISH and recurrent miscarriage or implantation failure. Embryo analysis were performed using Veriseq-NGS (Illumina). The software used to carry out all the analysis was R (RStudio). The library used to implement the different algorithms was caret. In the machine learning models, 22 predictor variables were introduced, which can be classified into 4 categories: maternal, paternal, embryonic and those specific to the IVF cycle. Main results and the role of chance The different couple, embryo and stimulation cycle variables were recorded in a database (22 predictor variables). Two different predictive models were performed, one for aneuploidy and the other for mosaicism. The predictor variable was of multi-class type since it included the segmental and whole chromosome alteration categories. The dataframe were first preprocessed and the different classes to be predicted were balanced. A 80% of the data were used for training the model and 20% were reserved for further testing. The classification algorithms applied include multinomial regression, neural networks, support vector machines, neighborhood-based methods, classification trees, gradient boosting, ensemble methods, Bayesian and discriminant analysis-based methods. The algorithms were optimized by minimizing the Log_Loss that measures accuracy but penalizing misclassifications. The best predictive models were achieved with the XG-Boost and random forest algorithms. The AUC of the predictive model for aneuploidy was 80.8% (Log_Loss 1.028) and for mosaicism 84.1% (Log_Loss: 0.929). The best predictor variables of the models were maternal age, embryo quality, day of biopsy and whether or not the couple had a history of pregnancies with chromosomopathies. The male factor only played a relevant role in the mosaicism model but not in the aneuploidy model. Limitations, reasons for caution Although the predictive models obtained can be very useful to know the probabilities of achieving euploid embryos in an IVF cycle, increasing the sample size and including additional variables could improve the models and thus increase their predictive capacity. Wider implications of the findings Machine learning can be a very useful tool in reproductive medicine since it can allow the determination of factors associated with embryonic aneuploidies and mosaicism in order to establish a predictive model for both. To identify couples at risk of embryo aneuploidy/mosaicism could benefit them of the use of PGT-A. Trial registration number Not Applicable

Sign in / Sign up

Export Citation Format

Share Document