Peers, Neighborhoods and Immigrant Student Achievement - Evidence from a Placement Policy

We examine to what extent immigrant school performance is affected by the characteristics of the neighborhoods that they grow up in. We address this issue using a refugee placement policy which provides exogenous variation in the initial place of residence in Sweden. The main result is that school performance is increasing in the number of highly educated adults sharing the subject’s ethnicity. A standard deviation increase in the fraction of high-educated in the assigned neighborhood raises compulsory school GPA by 0.9 percentile ranks. Particularly for disadvantaged groups, there are also long-run effects on educational attainment.Peer effects; Ethnic enclaves; Immigration; School performance

Introducing Systems Approaches in Health Behavioral Research

Health behaviors, such as smoking and unhealthy eating, are among the top leading preventable risk factors of non-communicable diseases; still more than 20% of the global population smokes, around 13% is obese, approximately a quarter of the adult population does not fulfill the guidelines for physical activity, and less than a quarter of the population meets the recommendations of fruit and vegetable consumption. The prevalence of unhealthy behaviors is consistently larger among individuals in lower socioeconomic groups as compared to higher socioeconomic groups. Determinants of hea

A knowledge discovery approach to urban analysis

Enhancing our knowledge of the complexities of cities in order to empower ourselves to make more informed decisions has always been a challenge for urban research. Recent developments in large-scale computing, together with the new techniques and automated tools for data collection and analysis are opening up promising opportunities for addressing this problem. The main motivation that served as the driving force behind this research is how these developments may contribute to urban data analysis. On this basis, the thesis focuses on urban data analysis in order to search for findings that can enhance our knowledge of urban environments, using the generic process of knowledge discovery using data mining. A knowledge discovery process based on data mining is a fully automated or semi-automated process which involves the application of computational tools and techniques to explore the “previously unknown, and potentially useful information” (Witten & Frank, 2005) hidden in large and often complex and multi-dimensional databases. This information can be obtained in the form of correlations amongst variables, data groupings (classes and clusters) or more complex hypotheses (probabilistic rules of co-occurrence, performance vectors of prediction models etc.). This research targets researchers and practitioners working in the field of urban studies who are interested in quantitative/ computational approaches to urban data analysis and specifically aims to engage the interest of architects, urban designers and planners who do not have a background in statistics or in using data mining methods in their work. Accordingly, the overall aim of the thesis is the development of a knowledge discovery approach to urban analysis; a domain-specific adaptation of the generic process of knowledge discovery using data mining enabling the analyst to discover ‘relational urban knowledge’. ‘Relational urban knowledge’ is a term employed in this thesis to refer to the potentially ‘useful’ and/or ‘valuable’ information patterns and relationships that can be discovered in urban databases by applying data mining algorithms. A knowledge discovery approach to urban analysis through data mining can help us to understand site-specific characteristics of urban environments in a more profound and useful way. On a more specific level, the thesis aims towards ‘knowledge discovery’ in traditional thematic maps published in 2008 by the Istanbul Metropolitan Municipality as a basis of the Master Plan for the Beyoğlu Preservation Area. These thematic maps, which represent urban components, namely buildings, streets, neighbourhoods and their various attributes such as floor space use of the buildings, land price, population density or historical importance, do not really extend our knowledge of Beyoğlu Preservation Area beyond documenting its current state and do not contribute to the interventions presented in the master plan. However it is likely that ‘useful’ and ‘valuable’ information patterns discoverable using data mining algorithms are hidden in them. In accordance with the stated aims, three research questions of the thesis concerns (1) the development of a general process model to adapt the generic process of knowledge discovery using data mining for urban data analysis, (2) the investigation of information patterns and relationships that can be extracted from the traditional thematic maps of the Beyoğlu Preservation Area by further developing and implementing this model and (3) the investigation of how could this ‘relational urban knowledge’ support architects, urban designers or urban planners whilst developing intervention proposals for urban regeneration. A Knowledge Discovery Process Model (KDPM) for urban analysis was developed, as an answer to the the first research question. The KDPM for urban analysis is a domain-specific adaptation of the widely accepted process of knowledge discovery in databases defined by Fayyad, Piatetsky-Shapiro, and Smyth (1996b). The model describes a semi-automated process of database formulation, analysis and evaluation for extracting information patterns and relationships from raw data by combining both GIS and data mining functionalities in a complementary way. The KDPM for urban analysis suggests that GIS functionalities can be used to formulate a database, and GIS and data mining can complement each other in analyzing the database and evaluating the outcomes. The model illustrates that the output of a GIS platform can become the input for a data mining platform and vice versa, resulting in an interlinked analytical process which allows for a more sophisticated analysis of urban data. To investigate the second and third research questions, firstly the KDPM for urban analysis was further developed to construct a GIS database of the Beyoğlu Preservation Area from the thematic maps. Then, three implementations were performed using this GIS database; the Beyoğlu Preservation Area Building Features Database consisting of multiple features attributed to the buildings. In Implementation (1), the KDPM for urban analysis was used to investigate a variety of patterns and relationships that can be extracted from the database using three different data mining methods. In Implementations (2) and (3), the KDPM for urban analysis was implemented to test how the knowledge discovery approach through data mining proposed in this thesis can assist in developing draft plans for the regeneration of a run-down neighbourhood in the Beyoğlu Preservation Area (Tarlabaşı). In Implementation (2), the KDPM for urban analysis is implemented in combination with an evolutionary process to apply a regeneration approach developed by the author; a computational process which generates draft plans for ground floor use, user-profile and tenure-type allocation was developed. In Implementation (3), students applied the KDPM for urban analysis during the course of an international workshop. The model enabled them to explore site-specific particularities of Tarlabaşı that would support their urban intervention proposals. Among the outputs of the thesis three of them are considered as utilizable outputs that distinguish this thesis from previous studies: The KDPM for urban analysis. Although there have been other studies which make use of data mining methods and techniques combined with GIS technology, to the best of our knowledge no previous research has implemented a process model to depict this process and used the model to extract ‘knowledge’ from traditional thematic maps. Researchers and practitioners can re-use this process model to analyze other urban environments. The KDPM for urban analysis is, therefore, one of the main utilizable outputs of the thesis and an important scientific contribution of this study. The Beyoğlu Preservation Area Building Features Database. A large and quite comprehensive GIS database which consists of 45 spatial and non-spatial features attributed to the 11,984 buildings located in the Beyoğlu Preservation Area was constructed. This database is one of the original features of this study. To the best of our knowledge, there are no other examples of applications of data mining using such a comprehensive GIS database, constructed from a range of actual micro-scale data representing such a variety of features attributed to the buildings. This database can be re-used by analysts interested in studying the Beyoğlu Preservation Area. The Beyoğlu Preservation Area Building Features Database is therefore one of the main utilizable outputs of the thesis and represents a scientific contribution to the research material on the Beyoğlu Preservation Area. A computational process which generates draft plans for ground floor use, user-profile and tenure-type allocation, using GIS and data mining functionalities with evolutionary computation. This output of the thesis was generated by Implementation (2), which aimed to investigate Research Question (3). The overall process involved the successive application of Naïve Bayes Classification, Association Rule Analysis and an Evolutionary Algorithm to a subset of the Beyoğlu Preservation Area Building Features Database representing the Tarlabaşı neighbourhood. Briefly, the findings of the data mining analysis were used to formulate a set of rules for assigning ground floor use information to the buildings. These rules were then used for fitness measurements of an Evolutionary Algorithm, together with other fitness measurements for assigning user-profile and tenure-type information (defined by the author according to the regeneration approach developed by the author). As a result, the algorithm transformed the existing allocation of the ground floor use in the buildings located in Tarlabaşı in accordance with the given rules and assigned user-profile and tenure type information for each building. This computational process demonstrated one way to use the data mining analysis findings in developing intervention proposals for urban regeneration. A similar computational process can be implemented in other urban contexts by researchers and practitioners. To the best of our knowledge, no prior research has used data mining analysis findings for fitness measurements of an Evolutionary Algorithm in order to produce draft plans for ground floor use, user-profile and tenure-type allocation. This is, therefore, the most original scientific contribution and utilizable output of the thesis. As a result of the research, on the basis of the data that is available in the thematic maps of the Beyoğlu Preservation Area, the potential of a knowledge discovery approach to urban analysis in revealing the relationships between various components of urban environments and their various attributes is demonstrated. It is also demonstrated that these relationships can reveal site-specific characteristics of urban environments and if found ‘valuable’ by the the targeted researchers and practitioners, these can lead to the development of more informed intervention proposals. Thereby the knowledge discovery approach to urban analysis developed in this thesis may help to improve the quality of urban intervention proposals and consequently the quality of built environments. On the other hand, the implementations carried out in the thesis also exposed the major limitation of the knowledge discovery approach to urban analysis through data mining, which is the fact that the findings discoverable by this approach are limited by the relevant data that is collectable and accessible

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Bayesian Approaches to Learning from Data how to Untangle the Travel Behavior and Land Use Relationships

The body of research on land use and travel behavior relationships reaches widely different conclusions with results varying even when considering a single author. The hypothesis of this research is that these differences arise, in part, from the fact that the vast majority of these studies do not address all the theoretical travel behavior tenets and are therefore ad-hoc in nature. An inductive approach to the study of the relationships between land use and travel behavior, prior to carrying out traditional deductive studies, can help improve the outcomes by providing an opportunity to identify and test such relationships. With data sourced from the 2001 National Household Travel Survey Add-On, supplemented with local land use data, this study uses heuristic search algorithms to evaluate relationships hidden in the data without these being framed, a priori, by specific statistical constructs. Bayesian scoring is used to evaluate and compare the results from actual data collected for the Baltimore Metropolitan Area with the set of predominant conceptual frameworks linking travel behavior and land use obtained from the literature. Results show that socioeconomic factors and land use characteristics act in a nested fashion, one in which socioeconomic factors do not influence travel behavior independently of land use characteristics. The land use travel behavior connection is specifically strong only for particular combinations of socioeconomic characteristics and a land use mix which includes both moderate residential densities and a significant amount of commercial opportunities. The study also finds that the heuristic search approach to derive relationships between land use and travel behavior does work, that this technique needs to be fine tuned for the proper use of spatially explicit data, and that although the research outputs are an unbiased representation of the land use travel behavior relationships, they need proper interpretation, especially in light of persisting theoretical questions still driving this research field. The study concludes that an inductive approach to the analysis of the relationships between land use and travel behavior provides valuable knowledge of the data that can be used to better formulate deductive studies, so that the two methodologies are complementary to each other

beyond WASTESCAPES:

Resource consumption mostly overcomes the embedded capacity of global ecosystems, which are self-regenerating until they reach the point of the planet’s limits. Moreover, the consumption of virgin resources and raw materials is strictly related to a consequent production of waste, which is negatively affecting both human health and other various spatial conditions. In addition to this, the temperature of the globe is predicted to rise even more in the next century, which might lead to food shortages, water scarcity, and even conflicts. Studies show that if this model of growth goes on, there will be the need of almost an additional planet Earth (in terms of resources) for us to be able to continue to survive. This condition of scarcity also regards land itself, which is understood as a non-renewable resource. Issues regarding linear metabolism, unsustainable resource consumption, abandonment, vacancies, and also the depletion of fertile soil, are caused by various rapid urbanization processes that can generate wastescapes. These can be generated in the form of unused, abandoned, polluted, or (socially) problematic areas. The unsustainability of this linear model of growth is self-evident, because it represents a significant threat for environmental sustainability, human health, and happiness. Many initiatives around the world are currently in the process of moving towards circularity. However, the recycling of wastescapes is still an important knowledge gap in the current definition of a circular economy, with the latter mostly only focusing on the recycling of material resources in contemporary cities. What can be done to integrate the regeneration of wastescapes with the principles of a circular economy? Can we envision a spatial dimension of circularity by going beyond just recycling of material waste to improve citizens’ quality of life and wellbeing? Could this be achieved through the preservation of both the availability of natural resources and the ability of eco-systems to regenerate themselves, without exceeding the global ecological overshoot

Türk şehirleri için mekânsal suç analizi ile suç önleme stratejileri geliştirme: Keçiören örneği

The place-based crime prevention notion comes from the idea that the human behavior is influenced by the environment, so it is possible to prevent crime before it happens by controlling and managing the environment with a proper design. To create a more secure environment and better quality of life, it is necessary to analyze the physical and nonphysical factors that affect crime victimization in order to develop crime prevention strategies. In the thesis, a spatial model is developed to analyze the physical and nonphysical parameters of crime victimization in Turkish cities to develop place-based strategies for crime prevention. Five neighborhoods of Keçiören Municipality in Ankara is selected as the study area, concerning its typical urban structure of Turkish cities and the crime victimization problem. The analysis is performed for non-physical parameters at the macroscale, which defines 98 small statistical areas within 5 neighborhoods. Non-physical parameters are defined as socioeconomic variables, precautions taken against crime, and the perception of security. The micro analysis evaluates the relationship of physical parameters in a smaller representation unit as buildings, road segments and three different zones for buildings on the main roads, buildings behind the main roads, and buildings in the hinterland. The physical parameters are defined as the building density on road segments, target accessibility, the degree of road network, and building properties like the number of floors, the use of building, the availability of gardens, parcel walls, a defined entrance, the side of entrance, facing the public realm, and the availability of elevation differences in the building. The data used for the macro analysis are derived from a victim survey with 1744 samples applied to the households about their socio-economic status, the precaution methods they use, their attitude towards crime and the perception of security, and victimization for different crime types. The survey was prepared by Düzgün (2006) and funded by the State Planning Organization in 2007, under the name of the project “Developing Crime Prevention Strategies Based on vi Spatial Analysis in Urban Area”. In the macro analysis, the Socio-Economic Status index (SES), precaution, security, and victimization indexes are created by a multivariate statistical model, the Principle Component Analysis. The correlation between crime victimization and three different indexes are analyzed and the relationship between population density and land use and different crime victimization types is evaluated. In the micro analysis burglary victimization and physical parameters are evaluated for smaller representation units. Finally, the physical and non-physical variables are statistically tested with the regression analysis and with the results, place-based strategies are suggested to prevent crime in the study area and in Turkish cities.Ph.D. - Doctoral Progra