The complexity of modern scientific research requires advanced approaches to handle and analyse r... more The complexity of modern scientific research requires advanced approaches to handle and analyse rich and dynamic data. Organizations such as hospitals, hold a great number of health datasets which may consist of many individual records. Artificial Intelligence methodologies incorporate approaches for knowledge retrieval and pattern discovery, which have been proven to be useful for data analysis in various disciplines. Decision trees methods belong to knowledge discovery methodologies and use computational algorithms for the extraction of patterns from data. This work describes the development of an autonomous Decision Support System (" Dth 1.0 ") for the real-time analysis of health data with the use of decision trees. The proposed system uses a patient's dataset based on the patients' symptoms and other relevant information and prepares reports about the importance of the characteristics that determine the number of patients of a specific disease. This work presents the basic concept of decision trees, describes the design of a tree-based system and uses a virtual database to illustrate the classification of patients in a hypothetical intra-hospital case study.
Spatial microsimulation is a methodology aiming to simulate entities such as households , individ... more Spatial microsimulation is a methodology aiming to simulate entities such as households , individuals or businesses in the finest possible scale. This process requires the use of individual based microdatasets. The package presented in this work facilitates the production of small area population microdata by combining various datasets such as census data and individual based datasets. This package includes a parallel implementation of random selection with optimization to select a group of individual records that match a macro description. This methodological approach has been used in a number of topics ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding, and Morrison 2007). The development of the method over recent years is driving computational complexity to the edge as it uses modern computational approaches for the combination of data. The R package sms presented in this work uses parallel processing approaches for the efficient production of small area population microdata, which can be subsequently used for geographical analysis. Finally, a complete case study of fitting geographical data with the R package is presented and discussed. Individual based models are among the most popular tool sets for understanding and analyzing trends or patterns of a population (for a description of population models, see Caswell 2001). Microsimulation models can also be seen as a form of population projection model (Imhoff and Post 1998). Microsimulation methodologies may be used to create small area population microdata by combining datasets and then using the results for geographical analysis (for a description of the method see Ballas, Rossiter, Bethan, Clarke, and Dorling 2005). The microsimulation method has been used in the past by economists with successful results to generate data for individuals and then check the effects of public policies at the smaller ag
Spatial microsimulation is a methodology aiming to simulate entities such as house-
holds, indivi... more Spatial microsimulation is a methodology aiming to simulate entities such as house- holds, individuals or businesses in the finest possible scale. This process requires the use of individual based microdatasets. The package presented in this work facilitates the pro- duction of small area population microdata by combining various datasets such as census data and individual based datasets. This package includes a parallel implementation of random selection with optimization to select a group of individual records that match a macro description. This methodological approach has been used in a number of topics ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding, and Morrison 2007). The development of the method over recent years is driving com- putational complexity to the edge as it uses modern computational approaches for the combination of data. The R package sms presented in this work uses parallel processing approaches for the efficient production of small area population microdata, which can be subsequently used for geographical analysis. Finally, a complete case study of fitting geographical data with the R package is presented and discussed.
Spatial microsimulation models can be used to produce small area output for a deeper understandin... more Spatial microsimulation models can be used to produce small area output for a deeper understanding of inequality. Dynamic spatial microsimulation models can be used to model transitions such as leaving home, entering school, university, the labour market, etc. This chapter presents a dynamic spatial microsimulation approach to the analysis of educational inequalities. The method simulates individual units (potential students) over a period of time. This chapter describes a model that utilises the BHPS dataset to build a dynamic spatial microsimulation model for the analysis of social and spatial inequalities in educational attainment. Educational attainment is particularly suitable for the development and application of a dynamic spatial microsimulation model given the influence that education has on a person’s life outcomes. The dynamic spatial microsimulation model described in this chapter has been used in a case study to analyse social and spatial inequalities in higher education entry and attainment.
This paper presents a spatial microsimulation approach to the analysis of social and spatial ineq... more This paper presents a spatial microsimulation approach to the analysis of social and spatial inequalities in higher education attainment. The paper provides a brief review of microsimulation and spatial microsimulation, highlighting the paucity in applications aimed at the analysis of educational policy. It then briefly reviews the educational policy framework in Britain and discusses relevant application areas for spatial microsimulation. It also demonstrates how spatial microsimulation modelling can be used to generate educational policy-relevant outputs and to map and analyse social and spatial inequalities in educational attainment. The paper presents three educational policy scenarios and uses a spatial microsimulation model to assess their spatial and social impact in the region of Yorkshire and the Humber, UK. Finally, in the light of the model outputs and policy analysis scenarios, the paper discusses possible future extensions and policy applications. One of the major findings is the division in the participation of young people to higher education changes by where they live.
The complexity of modern scientific research requires advanced approaches to handle and analyse r... more The complexity of modern scientific research requires advanced approaches to handle and analyse rich and dynamic data. Organizations such as hospitals, hold a great number of health datasets which may consist of many individual records. Artificial Intelligence methodologies incorporate approaches for knowledge retrieval and pattern discovery, which have been proven to be useful for data analysis in various disciplines. Decision trees methods belong to knowledge discovery methodologies and use computational algorithms for the extraction of patterns from data. This work describes the development of an autonomous Decision Support System (" Dth 1.0 ") for the real-time analysis of health data with the use of decision trees. The proposed system uses a patient's dataset based on the patients' symptoms and other relevant information and prepares reports about the importance of the characteristics that determine the number of patients of a specific disease. This work presents the basic concept of decision trees, describes the design of a tree-based system and uses a virtual database to illustrate the classification of patients in a hypothetical intra-hospital case study.
Spatial microsimulation is a methodology aiming to simulate entities such as households , individ... more Spatial microsimulation is a methodology aiming to simulate entities such as households , individuals or businesses in the finest possible scale. This process requires the use of individual based microdatasets. The package presented in this work facilitates the production of small area population microdata by combining various datasets such as census data and individual based datasets. This package includes a parallel implementation of random selection with optimization to select a group of individual records that match a macro description. This methodological approach has been used in a number of topics ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding, and Morrison 2007). The development of the method over recent years is driving computational complexity to the edge as it uses modern computational approaches for the combination of data. The R package sms presented in this work uses parallel processing approaches for the efficient production of small area population microdata, which can be subsequently used for geographical analysis. Finally, a complete case study of fitting geographical data with the R package is presented and discussed. Individual based models are among the most popular tool sets for understanding and analyzing trends or patterns of a population (for a description of population models, see Caswell 2001). Microsimulation models can also be seen as a form of population projection model (Imhoff and Post 1998). Microsimulation methodologies may be used to create small area population microdata by combining datasets and then using the results for geographical analysis (for a description of the method see Ballas, Rossiter, Bethan, Clarke, and Dorling 2005). The microsimulation method has been used in the past by economists with successful results to generate data for individuals and then check the effects of public policies at the smaller ag
Spatial microsimulation is a methodology aiming to simulate entities such as house-
holds, indivi... more Spatial microsimulation is a methodology aiming to simulate entities such as house- holds, individuals or businesses in the finest possible scale. This process requires the use of individual based microdatasets. The package presented in this work facilitates the pro- duction of small area population microdata by combining various datasets such as census data and individual based datasets. This package includes a parallel implementation of random selection with optimization to select a group of individual records that match a macro description. This methodological approach has been used in a number of topics ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding, and Morrison 2007). The development of the method over recent years is driving com- putational complexity to the edge as it uses modern computational approaches for the combination of data. The R package sms presented in this work uses parallel processing approaches for the efficient production of small area population microdata, which can be subsequently used for geographical analysis. Finally, a complete case study of fitting geographical data with the R package is presented and discussed.
Spatial microsimulation models can be used to produce small area output for a deeper understandin... more Spatial microsimulation models can be used to produce small area output for a deeper understanding of inequality. Dynamic spatial microsimulation models can be used to model transitions such as leaving home, entering school, university, the labour market, etc. This chapter presents a dynamic spatial microsimulation approach to the analysis of educational inequalities. The method simulates individual units (potential students) over a period of time. This chapter describes a model that utilises the BHPS dataset to build a dynamic spatial microsimulation model for the analysis of social and spatial inequalities in educational attainment. Educational attainment is particularly suitable for the development and application of a dynamic spatial microsimulation model given the influence that education has on a person’s life outcomes. The dynamic spatial microsimulation model described in this chapter has been used in a case study to analyse social and spatial inequalities in higher education entry and attainment.
This paper presents a spatial microsimulation approach to the analysis of social and spatial ineq... more This paper presents a spatial microsimulation approach to the analysis of social and spatial inequalities in higher education attainment. The paper provides a brief review of microsimulation and spatial microsimulation, highlighting the paucity in applications aimed at the analysis of educational policy. It then briefly reviews the educational policy framework in Britain and discusses relevant application areas for spatial microsimulation. It also demonstrates how spatial microsimulation modelling can be used to generate educational policy-relevant outputs and to map and analyse social and spatial inequalities in educational attainment. The paper presents three educational policy scenarios and uses a spatial microsimulation model to assess their spatial and social impact in the region of Yorkshire and the Humber, UK. Finally, in the light of the model outputs and policy analysis scenarios, the paper discusses possible future extensions and policy applications. One of the major findings is the division in the participation of young people to higher education changes by where they live.
13ο Εθνικό Συνέδριο Χαρτογραφίας – “Χαρτογραφία στο Διαδίκτυο. Σύγχρονες Τάσεις και Προοπτικές.”
Περίληψη
Η γεωγραφική οπτικοποίηση τα τελευταία χρόνια ενσωματώνει τεχνολογίες διαδικτύου και δι... more Περίληψη
Η γεωγραφική οπτικοποίηση τα τελευταία χρόνια ενσωματώνει τεχνολογίες διαδικτύου και διαδικτυακές υπηρεσίες για την απεικόνιση, τη διάθεση και τη χρήση των τελικών χαρτογραφικών απεικονίσεων. Η διάδραση στην πλοήγηση και την εξερεύνηση της πληροφορίας δίδει στην τελική οπτικοποίηση την διαδραστικότητα που απαιτείται για την απεικόνιση νέων δομών και συσχετισμών στην γεωγραφική πληροφορία. Η μελέτη και η απεικόνιση οικονομικών δεδομένων αποτελεί μια διαδικασία η οποία αποσκοπεί στη διερεύνηση της γεωγραφικής διάστασης τους η οποία επιτυγχάνεται με την χωρικοποίηση διαδικασιών, συναλλαγών και στοιχείων οικονομικής φύσεως χρησιμοποιώντας μεθόδους γεωγραφικής ανάλυσης ανακαλύπτοντας νέες γεωγραφικές συσχετίσεις. Τα δεδομένα οικονομικών ροών παρουσιάζουν ιδιαίτερο ενδιαφέρον καθώς περιγράφουν σύνθετες δομές ενώ παράλληλα διατηρούν γεωγραφικές διαστάσεις. Η ποσοτικοποίηση και η ανάλυση τέτοιων δεδομένων γίνεται όλο και πιο επίκαιρη καθώς οι παγκόσμιες σύνθετες οικονομικές διαδικασίες καταγράφονται και αναλύονται ώστε να προσφέρουν υποστήριξη στα πλαίσια λήψης στρατηγικών αποφάσεων. Μέσα από την παρούσα εργασία αναδεικνύεται ο ρόλος της διαδικτυακής χαρτογραφικής οπτικοποίησης των αποτελεσμάτων γεωγραφικής ανάλυσης και μελέτης οικονομικών δεδομένων παγκόσμιας κλίμακας με σκοπό την ανάδειξη της γεωγραφικής τους διάστασης και των χωρικών συσχετίσεων που αυτά έχουν.
Abstract
Geovisualization last years incorporates internet technologies and online services as a mean for the delivery of the cartographic representations. The ability of the interaction, the navigation and the exploration leads to visualizations with high levels of interactivity for the visualization of the new structures and spatial relationships in geographic information. The geovisulalization of big economic databases is a process which aims to the exploration of the geographic dimension of economic transactions. Thus the use of geographic analysis methods and tools, and geoenrichment processes lead to the discovery of new geographic correlations into big economic data. The visualization of the geographic dimensions of economic data flows globally, has a key role in the description of the complex structures behind global trade. The quantification and analysis of such data is becoming more and more relevant as global complex economic processes are recorded and analysed, in order to support strategies for mergers and acquisitions. This study focuses in the creation of the appropriate online cartographic visualization tool for the representation of the geographical analysis results based on global economic datasets. This tool will help in understanding the geographical dimension and the spatial relationships that Merger Market’s database include for mergers and acquisitions in global scale .
Spatial Microsimulation: A Reference Guide for Users, 2012
Spatial microsimulation models can be used to produce small area output for a deeper understandin... more Spatial microsimulation models can be used to produce small area output for a deeper understanding of inequality. Dynamic spatial microsimulation models can be used to model transitions such as leaving home, entering school, university, the labour market, etc. This chapter presents a dynamic spatial microsimulation approach to the analysis of educational inequalities. The method simulates individual units (potential students) over a period of time. This chapter describes a model that utilises the BHPS dataset to build a dynamic spatial ...
Spatial microsimulation can simulate attributes of a population in the micro scale, helping to un... more Spatial microsimulation can simulate attributes of a population in the micro scale, helping to understand the impacts of government policy, in the lowest possible scale. Using data from the Census of Population which provides us with information on actual population characteristics of an area, as well as Panel microdata which provide us detailed information about individuals, it is possible to simulate a geographical area by generating small area microdata. This simulation offers understanding of detailed information of a ...
Understanding local impacts of national scale policy requires a clear and coherent knowledge abou... more Understanding local impacts of national scale policy requires a clear and coherent knowledge about the effects of the policy in the smallest possible scale. This is the individual or household effects from this policy. Microsimulation is a methodological approach which by simulating in individual level can produce a robust representation of a population under the effects of different policies or events. This understanding is valuable for policy makers, in order to understand the dynamics of a population as well as producing clear “what-if” ...
This report notes that, despite commitments by European Union (EU) Member States to promote equit... more This report notes that, despite commitments by European Union (EU) Member States to promote equity in education and training, major geographic disparities persist in educational opportunities and outcomes, across but also within EU Member States and regions. Its aim is to support policy makers in their efforts to design effective measures to redress these disparities. It contains over 100 maps that help visualise inequalities. It identifies the top 10 and bottom 10 EU regions for each of the indicators it examines.
Abstract This paper presents a spatial microsimulation approach to the analysis of social and spa... more Abstract This paper presents a spatial microsimulation approach to the analysis of social and spatial inequalities in higher education attainment. The paper provides a brief review of microsimulation and spatial microsimulation, highlighting the paucity in applications aimed at the analysis of educational policy. It then briefly reviews the educational policy framework in Britain and discusses relevant application areas for spatial microsimulation. It also demonstrates how spatial microsimulation modelling can be used to generate educational ...
Spatial microsimulation is a methodology aiming to simulate entities such as households, individu... more Spatial microsimulation is a methodology aiming to simulate entities such as households, individuals or businesses in the finest possible scale. This process requires the use of individual based microdatasets. The package presented in this work facilitates the production of small area population microdata by combining various datasets such as census data and individual based datasets. This package includes a parallel implementation of random selection with optimization to select a group of individual records that match a macro description. This methodological approach has been used in a number of topics ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding, and Morrison 2007). The development of the method over recent years is driving computational complexity to the edge as it uses modern computational approaches for the combination of data. The R package sms presented in this work uses parallel processing approaches for the efficient production of small area population microdata, which can be subsequently used for geographical analysis. Finally, a complete case study of fitting geographical data with the R package is presented and discussed.
Abstract. A synthetic representation of the entire population of the city of Leeds has been gener... more Abstract. A synthetic representation of the entire population of the city of Leeds has been generated from publicly available datasets. Using an event driven model which simulates discrete demographic processes, the population has been projected 25 years into the future. Whilst the approach is grounded in the methods of microsimulation, concepts from spatial interaction modelling and agent-based systems are incorporated in an innovative way.
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Publications by Dimitris Kavroudakis
holds, individuals or businesses in the finest possible scale. This process requires the use
of individual based microdatasets. The package presented in this work facilitates the pro-
duction of small area population microdata by combining various datasets such as census
data and individual based datasets. This package includes a parallel implementation of
random selection with optimization to select a group of individual records that match a
macro description. This methodological approach has been used in a number of topics
ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and
Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding,
and Morrison 2007). The development of the method over recent years is driving com-
putational complexity to the edge as it uses modern computational approaches for the
combination of data. The R package sms presented in this work uses parallel processing
approaches for the efficient production of small area population microdata, which can
be subsequently used for geographical analysis. Finally, a complete case study of fitting
geographical data with the R package is presented and discussed.
holds, individuals or businesses in the finest possible scale. This process requires the use
of individual based microdatasets. The package presented in this work facilitates the pro-
duction of small area population microdata by combining various datasets such as census
data and individual based datasets. This package includes a parallel implementation of
random selection with optimization to select a group of individual records that match a
macro description. This methodological approach has been used in a number of topics
ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and
Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding,
and Morrison 2007). The development of the method over recent years is driving com-
putational complexity to the edge as it uses modern computational approaches for the
combination of data. The R package sms presented in this work uses parallel processing
approaches for the efficient production of small area population microdata, which can
be subsequently used for geographical analysis. Finally, a complete case study of fitting
geographical data with the R package is presented and discussed.
Η γεωγραφική οπτικοποίηση τα τελευταία χρόνια ενσωματώνει τεχνολογίες διαδικτύου και διαδικτυακές υπηρεσίες για την απεικόνιση, τη διάθεση και τη χρήση των τελικών χαρτογραφικών απεικονίσεων. Η διάδραση στην πλοήγηση και την εξερεύνηση της πληροφορίας δίδει στην τελική οπτικοποίηση την διαδραστικότητα που απαιτείται για την απεικόνιση νέων δομών και συσχετισμών στην γεωγραφική πληροφορία. Η μελέτη και η απεικόνιση οικονομικών δεδομένων αποτελεί μια διαδικασία η οποία αποσκοπεί στη διερεύνηση της γεωγραφικής διάστασης τους η οποία επιτυγχάνεται με την χωρικοποίηση διαδικασιών, συναλλαγών και στοιχείων οικονομικής φύσεως χρησιμοποιώντας μεθόδους γεωγραφικής ανάλυσης ανακαλύπτοντας νέες γεωγραφικές συσχετίσεις. Τα δεδομένα οικονομικών ροών παρουσιάζουν ιδιαίτερο ενδιαφέρον καθώς περιγράφουν σύνθετες δομές ενώ παράλληλα διατηρούν γεωγραφικές διαστάσεις. Η ποσοτικοποίηση και η ανάλυση τέτοιων δεδομένων γίνεται όλο και πιο επίκαιρη καθώς οι παγκόσμιες σύνθετες οικονομικές διαδικασίες καταγράφονται και αναλύονται ώστε να προσφέρουν υποστήριξη στα πλαίσια λήψης στρατηγικών αποφάσεων. Μέσα από την παρούσα εργασία αναδεικνύεται ο ρόλος της διαδικτυακής χαρτογραφικής οπτικοποίησης των αποτελεσμάτων γεωγραφικής ανάλυσης και μελέτης οικονομικών δεδομένων παγκόσμιας κλίμακας με σκοπό την ανάδειξη της γεωγραφικής τους διάστασης και των χωρικών συσχετίσεων που αυτά έχουν.
Abstract
Geovisualization last years incorporates internet technologies and online services as a mean for the delivery of the cartographic representations. The ability of the interaction, the navigation and the exploration leads to visualizations with high levels of interactivity for the visualization of the new structures and spatial relationships in geographic information. The geovisulalization of big economic databases is a process which aims to the exploration of the geographic dimension of economic transactions. Thus the use of geographic analysis methods and tools, and geoenrichment processes lead to the discovery of new geographic correlations into big economic data. The visualization of the geographic dimensions of economic data flows globally, has a key role in the description of the complex structures behind global trade. The quantification and analysis of such data is becoming more and more relevant as global complex economic processes are recorded and analysed, in order to support strategies for mergers and acquisitions. This study focuses in the creation of the appropriate online cartographic visualization tool for the representation of the geographical analysis results based on global economic datasets. This tool will help in understanding the geographical dimension and the spatial relationships that Merger Market’s database include for mergers and acquisitions in global scale .
1.
Θεωρία: Christaller, Lösch, Von-Thünen, Ricardo,
Hotelling, Weber, Central Place Theory
2.
Μέθοδοι: G.I.S., factors rating, break even analysis, MCE,time/space proximity, spatial optimization, network centrality
3.
Αλγόριθμοι: p-median, p-center, constrained p-median and p-center, coverage problems, greedy algorithms, heuristic algorithms, location-allocation
4.
Εφαρμογές: Υποκαταστήματα, ΑΤΜ, Σταθμοί, Υπηρεσίες, Μονάδες, Επιχειρήσεις, ΕΚΑΒ, Δασαρχείο, Παρατηρητήρια