Classic and spatial shift-share analysis of state-level employment change in Brazil

This paper combines classic and spatial shift-share decompositions of 1981 to 2006 employment change across the 27 states of Brazil. The classic shift-share method shows higher employment growth rates for underdeveloped regions that are due to an advantageous industry-mix and also due to additional job creation, commonly referred to as the competitive effect. Alternative decompositions proposed in the literature do not change this broad conclusion. Further examination employing exploratory spatial data analysis (ESDA) shows spatial correlation of both the industry-mix and the competitive effects. Considering that until the 1960s economic activities were more concentrated in southern regions of Brazil than they are nowadays, these results support beta convergence theories but also find evidence of agglomeration effects. Additionally, a very simple spatial decomposition is proposed that accounts for the spatially-weighted growth of surrounding states. Favourable growth in northern and centre-western states is basically associated with those states’ strengths in potential spatial spillover effect and in spatial competitive effect

Relationship between exports, imports, and economic growth in France: evidence from cointegration analysis and Granger causality with using geostatistical models

This paper introduces a new way of investigating linear and nonlinear Granger causality between exports, imports and economic growth in France over the period 1961-2006 with using geostatistical models (kiriging and inverse distance weighting). Geostatistical methods are the ordinary methods for forecasting the locations and making map in water engineerig, environment, environmental pollution, mining, ecology, geology and geography. Although, this is the first time which geostatistics knowledge is used for economic analyzes. In classical econometrics there do not exist any estimator which have the capability to find the best functional form in the estimation. Geostatistical models investigate simultaneous linear and various nonlinear types of causality test, which cause to decrease the effects of choosing functional form in autoregressive model. This approach imitates the Granger definition and structure but improve it to have better ability to investigate nonlinear causality. Results of both VEC and Improved-VEC (with geostatistical methods) are similar and show existance of long run unidirectional causality from exports and imports to economic growth. However the F-statistic of improved-VEC is larger than VEC indicating that there are some exponential and spherical functions in the VEC structure instead of the linear form.Granger causality; Exports; Imports; Economic growth; Geostatistical model; Kiriging; Inverse distance weighting; Vector auto-regression; France

Classification and Mapping of Recreation and Ecotourism Areas in West Virginia

Travel and tourism are recognized as the largest and fastest growing economic sector in the world. Different recreational and tourism sites can provide different types of activities based on their unique characteristics. Like any other form of tourism, the growth of ecotourism is dependent on the flow of visitors and therefore, marketing for a destination requires identifying various characteristics of the destination and preferences of stakeholders. The main aim of this dissertation is to classify and map recreation and ecotourism areas in West Virginia. The dissertation is presented in the form of three essays. The first essay classifies and maps classes of Recreation Opportunity Spectrum (ROS) in the state and examines its relationship with the travel and tourism generated revenues. Results showed that most of the areas in the state are Rural (R) followed by Semiprimitive Nonmotorized (SPNM) and Roaded Natural (RN). Visitors\u27 travel spending was significantly associated with the urban class. The second essay identifies and maps forest-based ecotourism areas in the state using six different criteria and visitors\u27 preferences. Pairwise comparison of Analytic Hierarchy Process (AHP) was used to compute the criteria weights from questionnaire survey of visitors. Significant variations were found in visitors\u27 preferences. Areas under Class IV and Class V of naturalness continuum of both weighted and unweighted ecotourism maps covered more than half of the state\u27s area, suggesting higher prospects for promoting forest-based ecotourism in the state. The results also indicated that each class changed in size when visitors\u27 preferences were applied. The third essay performs sensitivity analysis of the criteria weights derived from visitors and experts\u27 survey and maps the robust suitable areas for forest-based ecotourism areas in the state. Similar to essay two, pairwise comparison of AHP was used to compute criteria weights from experts. Results indicated that about one third of the state\u27s area was highly suitable and not sensitive to the variations of criteria weights. The finding of this dissertation demonstrated ROS classes and forest-based ecotourism areas in the state which could provide helpful information to the resource managers and policy makers in terms of recreation and tourism development, marketing, and promotion. Results of the study were mapped using Geographic Information System (GIS) and Geographic Data Analysis (GeoDa) software

Predictive modeling of PV energy production: How to set up the learning task for a better prediction?

In this paper, we tackle the problem of power prediction of several photovoltaic (PV) plants spread over an extended geographic area and connected to a power grid. The paper is intended to be a comprehensive study of one-day ahead forecast of PV energy production along several dimensions of analysis: i) The consideration of the spatio-temporal autocorrelation, which characterizes geophysical phenomena, to obtain more accurate predictions.ii) The learning setting to be considered, i.e. using simple output prediction for each hour or structured output prediction for each day. iii) The learning algorithms: We compare artificial neural networks, most often used for PV prediction forecast, and regression trees for learning adaptive models. The results obtained on two PV power plant datasets show that: taking into account spatio/temporal autocorrelation is beneficial; the structured output prediction setting significantly outperforms the non-structured output prediction setting; and regression trees provide better models than artificial neural networks

A geographic knowledge discovery approach to property valuation

This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely data driven and does not require previous knowledge of the area applied. To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm. For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the investigation of relationships between spatial entities beyond first degree connectivity. A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality

Genome-wide profiling of chromosome interactions in Plasmodium falciparum characterizes nuclear architecture and reconfigurations associated with antigenic variation.

Spatial relationships within the eukaryotic nucleus are essential for proper nuclear function. In Plasmodium falciparum, the repositioning of chromosomes has been implicated in the regulation of the expression of genes responsible for antigenic variation, and the formation of a single, peri-nuclear nucleolus results in the clustering of rDNA. Nevertheless, the precise spatial relationships between chromosomes remain poorly understood, because, until recently, techniques with sufficient resolution have been lacking. Here we have used chromosome conformation capture and second-generation sequencing to study changes in chromosome folding and spatial positioning that occur during switches in var gene expression. We have generated maps of chromosomal spatial affinities within the P. falciparum nucleus at 25 Kb resolution, revealing a structured nucleolus, an absence of chromosome territories, and confirming previously identified clustering of heterochromatin foci. We show that switches in var gene expression do not appear to involve interaction with a distant enhancer, but do result in local changes at the active locus. These maps reveal the folding properties of malaria chromosomes, validate known physical associations, and characterize the global landscape of spatial interactions. Collectively, our data provide critical information for a better understanding of gene expression regulation and antigenic variation in malaria parasites

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

Periodic pattern mining from spatio-temporal trajectory data

Rapid development in GPS tracking techniques produces a large number of spatio-temporal trajectory data. The analysis of these data provides us with a new opportunity to discover useful behavioural patterns. Spatio-temporal periodic pattern mining is employed to find temporal regularities for interesting places. Mining periodic patterns from spatio-temporal trajectories can reveal useful, important and valuable information about people's regular and recurrent movements and behaviours. Previous studies have been proposed to extract people's regular and repeating movement behavior from spatio-temporal trajectories. These previous approaches can target three following issues, (1) long individual trajectory; (2) spatial fuzziness; and (3) temporal fuzziness. First, periodic pattern mining is different to other pattern mining, such as association rule ming and sequential pattern mining, periodic pattern mining requires a very long trajectory from an individual so that the regular period can be extracted from this long single trajectory, for example, one month or one year period. Second, spatial fuzziness shows although a moving object can regularly move along the similar route, it is impossible for it to appear at the exactly same location. For instance, Bob goes to work everyday, and although he can follow a similar path from home to his workplace, the same location cannot be repeated across different days. Third, temporal fuzziness shows that periodicity is complicated including partial time span and multiple interleaving periods. In reality, the period is partial, it is highly impossible to occur through the whole movement of the object. Alternatively, the moving object has only a few periods, such as a daily period for work, or yearly period for holidays. However, it is insufficient to find effective periodic patterns considering these three issues only. This thesis aims to develop a new framework to extract more effective, understandable and meaningful periodic patterns by taking more features of spatio-temporal trajectories into account. The first feature is trajectory sequence, GPS trajectory data is temporally ordered sequences of geolocation which can be represented as consecutive trajectory segments, where each entry in each trajectory segment is closely related to the previous sampled point (trajectory node) and the latter one, rather than being isolated. Existing approaches disregard the important sequential nature of trajectory. Furthermore, they introduce both unwanted false positive reference spots and false negative reference spots. The second feature is spatial and temporal aspects. GPS trajectory data can be presented as triple data (x; y; t), x and y represent longitude and latitude respectively whilst t shows corresponding time in this location. Obviously, spatial and temporal aspects are two key factors. Existing methods do not consider these two aspects together in periodic pattern mining. Irregular time interval is the third feature of spatio-temporal trajectory. In reality, due to weather conditions, device malfunctions, or battery issues, the trajectory data are not always regularly sampled. Existing algorithms cannot deal with this issue but instead require a computationally expensive trajectory interpolation process, or it is assumed that trajectory is with regular time interval. The fourth feature is hierarchy of space. Hierarchy is an inherent property of spatial data that can be expressed in different levels, such as a country includes many states, a shopping mall is comprised of many shops. Hierarchy of space can find more hidden and valuable periodic patterns. Existing studies do not consider this inherent property of trajectory. Hidden background semantic information is the final feature. Aspatial semantic information is one of important features in spatio-temporal data, and it is embedded into the trajectory data. If the background semantic information is considered, more meaningful, understandable and useful periodic patterns can be extracted. However, existing methods do not consider the geographical information underlying trajectories. In addition, at times we are interested in finding periodic patterns among trajectory paths rather than trajectory nodes for different applications. This means periodic patterns should be identified and detected against trajectory paths rather than trajectory nodes for some applications. Existing approaches for periodic pattern mining focus on trajectories nodes rather than paths. To sum up, the aim of this thesis is to investigate solutions to these problems in periodic pattern mining in order to extract more meaningful, understandable periodic patterns. Each of three chapters addresses a different problem and then proposes adequate solutions to problems currently not addressed in existing studies. Finally, this thesis proposes a new framework to address all problems. First, we investigated a path-based solution which can target trajectory sequence and spatio-temporal aspects. We proposed an algorithm called Traclus (spatio-temporal) which can take spatial and temporal aspects into account at the same time instead of only considering spatial aspect. The result indicated our method produced more effective periodic patterns based on trajectory paths than existing node-based methods using two real-world trajectories. In order to consider hierarchy of space, we investigated existing hierarchical clustering approaches to obtain hierarchical reference spots (trajectory paths) for periodic pattern mining. HDBSCAN is an incremental version of DBSCAN which is able to handle clusters with different densities to generate a hierarchical clustering result using the single-linkage method, and then it automatically extracts clusters from a hierarchical tree. Thus, we modified traditional clustering method DBSCAN in Traclus (spatio-temporal) to HDBSCAN for extraction of hierarchical reference spots. The result is convincing, and reveals more periodic patterns than those of existing methods. Second, we introduced a stop/move method to annotate each spatio-temporal entry with a semantic label, such as restaurant, university and hospital. This method can enrich a trajectory with background semantic information so that we can easily infer people's repeating behaviors. In addition, existing methods use interpolation to make trajectory regular and then apply Fourier transform and autocorrelation to automatically detect period for each reference spot. An increasing number of trajectory nodes leads to an exponential increase of running time. Thus, we employed Lomb-Scargle periodogram to detect period for each reference spot based on raw trajectory without requiring any interpolation method. The results showed our method outperformed existing approaches on effectiveness and efficiency based on two real datasets. For hierarchical aspect, we extended previous work to find hierarchical semantic periodic patterns by applying HDBSCAN. The results were promising. Third, we apply our methodology to a case study, which reveals many interesting medical periodic patterns. These patterns can effectively explore human movement behaviors for positive medical outcomes. To sum up, this research proposed a new framework to gradually target the problems that existing methods cannot handle. These include: how to consider trajectory sequence, how to consider spatial temporal aspects together, how to deal with trajectory with irregular time interval, how to consider hierarchy of space and how to extract semantic information behind trajectory. After addressing all these problems, the experimental results demonstrate that our method can find more understandable, meaningful and effective periodic patterns than existing approaches