Mohammad-Reza Namazi-Rad

Generating a Dynamic Synthetic Population – Using an Age-Structured Two-Sex Model for Household Dynamics

Generating a reliable computer-simulated synthetic population is necessary for knowledge processing and decision-making analysis in agent-based systems in order to measure, interpret and describe each target area and the human activity patterns within it. In this paper, both synthetic reconstruction (SR) and combinatorial optimisation (CO) techniques are discussed for generating a reliable synthetic population for a certain geographic region (in Australia) using aggregated- and disaggregated-level information available for such an area. A CO algorithm using the quadratic function of population estimators is presented in this paper in order to generate a synthetic population while considering a two-fold nested structure for the individuals and households within the target areas. The baseline population in this study is generated from the confidentialised unit record files (CURFs) and 2006 Australian census tables. The dynamics of the created population is then projected over five years using a dynamic micro-simulation model for individual- and household-level demographic transitions. This projection is then compared with the 2011 Australian census. A prediction interval is provided for the population estimates obtained by the bootstrapping method, by which the variability structure of a predictor can be replicated in a bootstrap distribution.

Applying a novel combination of techniques to develop a predictive model for diabetes complications

Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models.

Scene-adaptive configuration of two cameras using the correspondence field function

In a free viewpoint video system, the scene is captured by a number of cameras and it would be desirable to optimize the configuration of cameras, such as their location or orientation, to improve the rendering quality. This paper introduces a mathematical representation of the multi-camera geometry, called the correspondence field (CF), which can be used to quantify the suitability of a camera configuration for a given arrangement of objects in the scene. The correspondence field describes the spatial topology of the intersecting rays of cameras, arranged as a number of layers or surfaces in the field of view of cameras. The paper derives the topology of CF for certain camera arrangements and analyzes the impact of changes in camera location or orientation on this topology. It demonstrates that CF can be used to find the optimum camera configuration for a given objective. It also presents simulation results of this method using our light field simulator.

Alternative Planning and Land Administration for Future Smart Cities [Leading Edge]

The long-term planning approach with its central aim of changing the urban form using zoning regulations and improving transportation may no longer be the only conceivable solution. Participants in planning and land development processes formulating plans today are predominantly land developers, entrepreneurs, and landlords. However, community groups are increasingly active participants, providing a counterbalance to the profit-driven agenda of corporations. Explores that landscape for current smart city development.

A Semi-Empirical Determination of Perceived Liveability

Liveability is a concept closely related to the notion of well-being and refers to the environmental conditions that contribute to the quality of life, alongside individual features. Subjective and objective measurements of liveability are both of considerable practical and theoretical importance. A survey is conducted in this paper by which individuals tend to shape their preferences according to six factors describing various aspects of living conditions: (1) home, (2) neighbourhood, (3) transport, (4) entertainment, (5) services and (6) work. The survey data helps us to work out some indicators representing the perceived liveability in the targeted areas. A linear mixed model is used to explore possible relationships between objective factors and perceived liveability. A model-based estimate of liveability index can be then calculated for each non-sampled individual based on his/her socio-demographic characteristics and area of living.

Constrained optimization of average arrival time via a probabilistic approach to transport reliability

To achieve greater transit-time reduction and improvement in reliability of transport services, there is an increasing need to assist transport planners in understanding the value of punctuality; i.e. the potential improvements, not only to service quality and the consumer but also to the actual profitability of the service. In order for this to be achieved, it is important to understand the network-specific aspects that affect both the ability to decrease transit-time, and the associated cost-benefit of doing so. In this paper, we outline a framework for evaluating the effectiveness of proposed changes to average transit-time, so as to determine the optimal choice of average arrival time subject to desired punctuality levels whilst simultaneously minimizing operational costs. We model the service transit-time variability using a truncated probability density function, and simultaneously compare the trade-off between potential gains and increased service costs, for several commonly employed cost-benefit functions of general form. We formulate this problem as a constrained optimization problem to determine the optimal choice of average transit time, so as to increase the level of service punctuality, whilst simultaneously ensuring a minimum level of cost-benefit to the service operator.

Synthetic Population Initialization and Evolution-Agent-Based Modelling of Population Aging and Household Transitions

A synthetic population (SP) aims at faithfully reproducing actual social entities, individuals and households, and their characteristics as described in a population census. Depending on the quality and completeness of the input data sets, as well as the number of variables of interest and hierarchical levels (usually, individual and household), a reliable SP should be able to reflect the actual physical social entities, with their characteristics and specific behavioural patterns. This paper presents a methodology to construct a reliable dynamic synthetic population for the Illawarra Region-Australia. The two main components in the population synthesizer presented in this paper are initialization and evolution. Iterative proportional fitting procedure (IPFP) is presented to help with the initialization of the population using 2011 Australian census. Then, population aging and evolution is projected using an agent-based modeling (ABM) technique over ten years.

Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts

Abstract Estimating population counts for multidimensional tables based on a representative sample subject to known marginal population counts is not only important in survey sampling but is also an integral part of standard methods for simulating area-specific synthetic populations. In this article several estimation methods are reviewed, with particular focus on the iterative proportional fitting procedure and the maximum likelihood method. The performance of these methods is investigated in a simulation study for multidimensional tables, as previous studies are limited to 2 by 2 tables. The data are generated under random sampling but also under misspecification models, for which sample and target populations differ systematically. The empirical results show that simple adjustments can lead to more efficient estimators, but generally, at the expense of increased bias. The adjustments also generally improve coverage of the confidence intervals. The methods discussed in this article along with standard error estimators, are made freely available in the R package mipfp. As an illustration, the methods are applied to the 2011 Australian census data available for the Illawarra Region in order to obtain estimates for the desired three-way table for age by sex by family type with known marginal tables for age by sex and for family type.

Hybrid method for building extraction in vegetation-rich urban areas from very high-resolution satellite imagery

Abstract. A continuous update of building information is necessary in today’s urban planning. Digital images acquired by remote sensing platforms at appropriate spatial and temporal resolutions provide an excellent data source to achieve this. In particular, high-resolution satellite images are often used to retrieve objects such as rooftops using feature extraction. However, high-resolution images acquired over built-up areas are associated with noises such as shadows that reduce the accuracy of feature extraction. Feature extraction heavily relies on the reflectance purity of objects, which is difficult to perfect in complex urban landscapes. An attempt was made to increase the reflectance purity of building rooftops affected by shadows. In addition to the multispectral (MS) image, derivatives thereof namely, normalized difference vegetation index and principle component (PC) images were incorporated in generating the probability image. This hybrid probability image generation ensured that the effect of shadows on rooftop extraction, particularly on light-colored roofs, is largely eliminated. The PC image was also used for image segmentation, which further increased the accuracy compared to segmentation performed on an MS image. Results show that the presented method can achieve higher rooftop extraction accuracy (70.4%) in vegetation-rich urban areas compared to traditional methods.

An unconstrained statistical matching algorithm for combining individual and household level geo-specific census and survey data

Abstract The Population Census is an important source of statistical information in most countries that is capable of producing reliable estimates of population characteristics for small geographic areas. One limitation of a census is that there are many population characteristics that cannot be collected due to respondent burden or cost. This means that statistical agencies have to conduct population based surveys to provide social, economic and demographic characteristics for a target population which are not captured by a large-scale census. These surveys are usually capable of producing direct estimates at the national level and high level regions but often cannot produce reliable estimates for smaller areas. Due to the increasing demand for comprehensive statistical information not only at the national level but also for sub-national domains, there is a wide discussion in the literature about the use of statistical techniques that combine survey with census data to provide more detailed, finer-level estimates. Where censuses and sample surveys are based on the same reporting units, statistical matching techniques can be employed to link the records from survey and census data where exact matching of reporting units is impossible due to confidentiality restrictions. These techniques can then provide the detailed social, economic and demographic information required for small areas. An approach is developed in this paper in which a close-to-reality synthetic population of individuals and households is generated from available census tables using an iterative proportional updating (IPU) method. Statistical matching using a nearest neighbour method is then used to impute survey data to the individuals and households in the synthetic population. To evaluate this approach, 2011 Bangladesh census data is used to generate a district-specific synthetic population of individuals and households. Matching is then performed by imputing the nearest possible records among the 2011 Bangladesh Demographic and Health Survey to estimate the wealth index for each household within the synthetic population. The results show that using the method presented in this paper helps with achieving more representative estimates (comparing with direct survey estimates) particularly for areas with small sample sizes where not many population units with different socio-demographic characteristics are included.