A Study on the Development of Regional Marine Industry in China

The 21st century is a century belonging to the oceans, and the ocean plays a vital role in economic and social development. China is rich in marine resources, mainly distributed among eleven marine coastal provinces and cities. With the constant development of the ocean, the marine industries grow rapidly. In China, the “marine industry” refers to the production of developing, utilizing and protecting the ocean, which is also divided into a primary marine industry, a secondary marine industry, and a tertiary marine industry (China Marine Statistical Yearbook, 2017). To study the development of regional marine industries will drive the growth of the regional land economy. As there is a strong correlation between the development of the marine industry and the creation of shore-based organization, it is of great significance to analyze the current status of the regional marine industry in China, which has profound effect to theory and practice for exploring the future development of the regional marine industry. Against this background, this study firstly defines and analyzes the meaning of the marine industry and marine industrial competitiveness by combing the previous literature, and analyzing the overview of regional marine industry and the development of the three marine industries in China. Secondly, this study compares the development of regional marine industry from the perspective of competitiveness and establishes the evaluation index system which includes six first-class targets and sixteen second-class targets. Through MATLAB software, the entropy method is applied to evaluate the competitiveness of regional marine industries. Then, disparities found in regional marine industrial development are analyzed for their reasons. Thirdly, this study establishes the panel data model to analyze the factors influencing the development of regional marine industries in China. Regional gross ocean products (GROP) represent the dependent variable whilst the labor factor, capital factor, technological factor, and environmental factor represent the independent variables. This study also compares these factors, deemed as key influencing elements for the regional development of the three marine industries and the international competency. In general, this study gives the conclusions from two perspectives. On the one hand, there are development disparities in the regional marine industry in China which are seen by comparing the regional marine industrial competitiveness. It is shown that Guangdong, Shandong, and Shanghai have stronger competitive advantages. On the other hand, Guangxi, Hebei and Hainan lack competitive advantages. In addition, marine economic capacity, marine human resources, and marine technology occupy major shares in the evaluation of regional marine industrial competitiveness. Additionally, this study finds that labor, technology, capital, and environment have an impact on the development of the regional marine industries. Labor (that is the ocean-related employed), technology, research funds (one of the capital factors), and marine pollution treatments (one of the environmental factors) have a significant positive influence on development of regional marine industry. At the same time, these selected factors each affect the regional development of China’s three marine industries and international competency to a different extent. Combining the actual development of regional marine industries in China with the results of empirical analyses, this study puts forward suggestions to enhance the development of the regional marine industries in China.Chapter 1 Introduction 1 1.1 Background of Research 1 1.2 Purposes of Research 2 1.3 Methodology 3 1.4 Outline of Research 4 Chapter 2 Basic Concepts and Literature Review 6 2.1 Basic Concepts 6 2.1.1 Marine industry 6 2.1.2 Industrial competitiveness 8 2.1.3 Industrial cluster 10 2.2 Literature Review 11 2.2.1 Literature review on the competitiveness of marine industry 11 2.2.2 Literature review on the development of marine industry 17 Chapter 3 The Development of Chinese Marine Industry 26 3.1 The Current Situation of Chinese Marine Industry System 26 3.2 The Development of Chinese Regional Marine Industries 28 3.2.1The division of Chinese marine regions 28 3.2.2 The marine industry distribution in regions 30 3.3 Structure of the Chinese Marine Industry 35 3.3.1 Chinese primary marine industry 35 3.3.2 Chinese secondary marine industry 36 3.3.3 Chinese tertiary marine industry 42 3.3.4 Obstacles of China’s three marine industries 44 Chapter 4 A Comparative Analysis on Regional Marine Industry in China 49 4.1 Factors Influencing the Marine Industrial Competitiveness 49 4.2 The Evaluation Index System 55 4.2.1 The purpose of the establishment of the index system 55 4.2.2 The principles of the establishment of index system 56 4.2.3 The establishment of the evaluation index system 59 4.3 Empirical Analysis 65 4.3.1 The selection of evaluation methods 65 4.3.2 Data source 69 4.3.3 Results 70 4.4 Summary 92 Chapter 5 Empirical Tests for the Development of Regional Marine Industries in China 95 5.1 The Selection of Variables and Data Source 96 5.1.1 The selection of variables 96 5.1.2 Data source 99 5.2 Empirical Tests 101 5.2.1 Regression estimation of regional marine industry (GROP) 101 5.2.2 Regression estimation of primary marine industry 108 5.2.3 Regression estimation of secondary marine industry 110 5.2.4 Regression estimation of tertiary marine industry 112 5.2.5 Regression estimation on international competency 115 5.3 Summary 116 Chapter 6 Conclusions 120 6.1 Research Findings 120 6.2 Implications 121 6.3 Further Study 123 References 124Docto

Variety and regional economic growth in the Netherlands

In economic theory, one can distinguish between variety as a source of regional knowledge spillovers, called Jacobs externalities, and variety as a portfolio protecting a region from external shocks. We argue that Jacobs externalities are best measured by related variety (within sectors), while the portfolio argument is better captured by unrelated variety (between sectors). We introduce a methodology based on entropy measures to compute related variety and unrelated variety. Using data at the COROP level for the period 1996-2002, we find that Jacobs externalities enhance employment growth, while unrelated variety dampens unemployment growth. Productivity growth, by contrast, can be explained by traditional determinants including investments and R&D expenditures. Implications for regional policy in The Netherlands follow.evolutionary economic geography, new economic geography, economic variety

Analysis of socio-ecohydrological factors affecting water security, liveability and sustainability : a case study of the Cirebon metropolitan region, West Java, Indonesia

Water security, liveability, and sustainability are important concepts in development. These concepts can help planners and managers to construct and achieve an equilibrium of socio-ecohydrological systems over a long period. This study, in the context of balanced urban development, seeks to better understand socioe-cohydrological issues, challenges, options, and strategies for achieving water security, liveability, and sustainability concerning urbanisation and climate change. This includes assessments of the urban and peri-urban environment and communities, and multi-level government institutions. Water security in this study is defined in the context of a water insecure region as “insufficient accessibility and capability of water sources and services to satisfy the household needs for health, livelihood, ecosystem, and production, coupled with inadequate acceptability and adaptive capacity of households to deal with the ecohydrological changes that impact liveability and sustainability”. Liveability is defined in this study as “dynamic interactions between water, people, and the environment as a function of biophysical and socio-economic subsystems in one urban system”, while sustainability is defined as “long-term liveability that is ensured via planning approaches and environmental management interventions”. Water security was assessed in the context of socio-ecohydrological change based on (i) the experiences of communities in the access to water and sanitation infrastructures; (ii) the acceptability of water risks from ecohydrological change; (iii) the capability of ecosystem and institutional services to satisfy the needs for health, livelihood, ecosystem, and production; and (iv) adaptive capacity in dealing with the impacts resulting from socio- ecohydrological change. Liveability was assessed based on the communities’ perceptions of the most important aspects for liveability, liveability aspects that they are most satisfied with, and liveability aspects that they are least satisfied with, in the urban and peri-urban areas. The results were categorised within four themes: ecosystem, urban, peri-urban, and human services. Sustainability was assessed by combining observed landuse/ hydrological/ climate data and the perceptions of climate change vis-à-vis ecohydrological changes and coping strategies. The study combined place and human-based approaches to assess these three thematic areas combining qualitative and quantitative data for finding interconnection and trade-off for achieving balanced urban development (BUD). Based on the in-depth case study of Cirebon Metropolitan Region (CMR) in Indonesia, this study explored (i) socio-economic and physical environments of the region including watersheds within the Cimanuk-Cisanggarung River Basin; (ii) community perspectives at different urbanisation levels; and (iii) multi-level government perspectives. This study presents seven analytical frameworks related to different aspects of work reported in this thesis: (i) delineate peri-urban areas; (ii) quantify rural-urban interface ecohydrology; (iii) understand urbanisation impacts on urban and peri-urban ecohydrological based liveability; (iv) identify perceived liveability of urban and peri-urban communities in the context of socio-ecohydrology; (v) classify issues and factors impacting household water insecurity in the context of socio-ecohydrological change; (vi) understand sustainability challenges concerning climate change and urbanisation in the urban system, and identify appropriate adaptation supports for sustaining water security and liveability; (vii) identify the complexity and uncertainty involved in assessing water security, liveability, and sustainability, and to find the linkages between urban and perixvii urban communities, urban and peri-urban ecosystems, and cross-scale institutions for achieving BUD

Bio-Ecological Diversity vs. Socio-Economic Diversity: A Comparison of Existing Measures

This paper aims to enrich the standard toolbox for measuring diversity in economics. In so doing, we compare the indicators of diversity used by economists with those used by biologists and ecologists. Ecologists and biologists are concerned about biodiversity: the diversity of organisms that inhabit a given area. Concepts of species diversity such as alpha (diversity within community), beta (diversity across communities) and gamma (diversity due to differences among samples when they are combined into a single sample) have been developed (Whittaker, 1960). Biodiversity is more complex than just the species that are present, it includes species richness and species evenness. Those various aspects of diversity are measured by biodiversity indices such as Simpson’s Diversity Indices, Species Richness Index, Shannon Weaver Diversity Indices, Patil and Taillie Index, Modified Hill’s Ratio. In economics, diversity measures are multi-faceted ranging from inequality (Lorenz curve, Gini coefficient, quintile distribution), to polarisation (Esteban and Ray, 1994; Wolfon, 1994, D’Ambrosio (2001)) and heterogeneity (Alesina, Baqir and Hoxby, 2000). We propose an interdisciplinary comparison between indicators. We review their theoretical background and applications. We provide an assessment of their possible use according to their specific properties.Diversity, Growth, Knowledge

Evaluation for Core Competence of Private Enterprises in Xuchang City Based on an Improved Dynamic Multiple-Attribute Decision-Making Model

Because Deng’s grey relational degree is inconspicuous, Deng’s relational degree with an exponential function is first presented. Then, we demonstrate that improved Deng’s relational degree is more conspicuous than the original model. Then, we construct a multiple-attribute decision-making model, based on improved Deng’s relational degree with multiple stages, and a method for determining the weight of the index is also developed. Finally, the core competence of private enterprises in Henan province is analyzed, illustrating the validity and feasibility of the improved model

Methods for detecting spatial clustering of economic activities using micro-geographic data

This PhD thesis consists of three self-contained but related essays on the topic of empirical assessment of spatial clusters of economic activities within a micro-geographic framework. The tendency of economic activities to be concentrated in a specific territory is well recognized, starting at least from the seminal studies by Alfred Marshall (Marshall, 1920). This spatial behaviour is not fortuitous; by concentrating in some areas firms enjoy a number of advantages, which then have implications for local economic growth and regional disparities and, as a consequence, are object of study in the fields of economics, geography and policy making. It has been recognized, however, that a major obstacle to further comprehension of the agglomeration phenomena of firms is the lack of a method to properly measure their spatial concentration. The most traditional measures employed by economists, indeed, are not completely reliable. Their most relevant methodological limit lies in the use of regional aggregates, which are built by referring to arbitrary definitions of the spatial units (such as provinces, regions or municipalities) and hence introduce a statistical bias arising from the chosen notion of space. This methodological problem can be tackled by using a continuous approach to space, where data are collected at the maximum level of spatial disaggregation, i.e. each firm is identified by its geographic coordinates, say (x, y), and spatial concentration is detected by referring to the distribution of distances amongst economic activities. The main purpose of the dissertation is to contribute to the development of this kind of continuous space-based measures of spatial clustering. The scientific context and motivation are outlined in depth in the first three chapters. Then the first essay introduces the space–time K-function empirical tool, proposed in spatial statistical literature, into economic literature in order to detect the geographic concentration of industries while controlling for the temporal dynamics that characterize the localization processes of firms. The proposed methodology allows to explore the possibility that the spatial and temporal phenomena, producing the observed pattern of firms at a given moment of time, interact to provide space–time clustering. The presence of significant space–time interaction implies that an observed pattern cannot be explained only by static factors but that we should also consider the dynamic evolution of the spatial concentration phenomenon. Indeed, for example, new firm settlements may display no spatial concentration if we look separately at each moment of time and yet they may present a remarkable agglomeration if we look at the overall resulting spatial distribution after a certain time period. In general, without knowing the temporal evolution of the phenomenon under study it is not possible to identify the mechanism generating its spatial structure. As a matter of fact, different underlying space–time processes can lead to resulting spatial patterns which look the same. The methodology is illustrated with an application to the analysis of the spatial distribution of the ICT industries in Rome (Italy), in the long period 1920–2005. The problem of disentangling spatial heterogeneity and spatial dependence phenomena when detecting for spatial clusters of firms is the topic of the second essay, “Measuring industrial agglomeration with inhomogeneous K-function: the case of ICT firms in Milan (Italy)”. Spatial clusters of economic activities can be the result of two distinct broad classes of phenomena: spatial heterogeneity and spatial dependence. The former arises when exogenous factors lead firms to locate in certain specific geographical zones. For instance, firms may group together in certain areas in order to exploit favourable local conditions, such as the presence of useful infrastructures, the proximity to the communication routes or more convenient local taxation systems. The phenomenon of spatial dependence, which is often of direct scientific interest, occurs instead when the presence of an economic activity in a given area attracts other firms to locate nearby. For instance, the presence of firms with a leading role encouraging the settlement of firms producing intermediate goods in the same area or the incidence of knowledge spillovers driving industrial agglomerations. This essay suggests a parametric approach based on the inhomogeneous K-function that allows to assess the endogenous effects of interaction among economic agents, namely spatial dependence, while adjusting for the exogenous effects of the characteristics of the study area, namely spatial heterogeneity. The approach is also illustrated with a case study on the spatial distribution of the ICT manufacturing industry in Milan (Italy). The third paper is titled “Weighting Ripley’s K-function to account for the firm dimension in the analysis of spatial concentration”. In the methodological context of the continuous space-based measures of spatial clustering, firms are identified as dimensionless points distributed in a planar space. In realistic circumstances, however, firms are generally far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital and so on. This implies that a high level of spatial concentration can occur, for example, because many small firms cluster in space, or few large firms (in the limit just one firm) cluster in space. A proper test for the presence of spatial clusters of firms should thus consider the impact of the firm dimension on industrial agglomeration. For this respect, the third essay develops a methodology based on an extension of the K-function considering firm size as a weight attached to each of the points representing the firms’ locations

Methods for detecting spatial clustering of economic activities using micro-geographic data

This PhD thesis consists of three self-contained but related essays on the topic of empirical assessment of spatial clusters of economic activities within a micro-geographic framework. The tendency of economic activities to be concentrated in a specific territory is well recognized, starting at least from the seminal studies by Alfred Marshall (Marshall, 1920). This spatial behaviour is not fortuitous; by concentrating in some areas firms enjoy a number of advantages, which then have implications for local economic growth and regional disparities and, as a consequence, are object of study in the fields of economics, geography and policy making. It has been recognized, however, that a major obstacle to further comprehension of the agglomeration phenomena of firms is the lack of a method to properly measure their spatial concentration. The most traditional measures employed by economists, indeed, are not completely reliable. Their most relevant methodological limit lies in the use of regional aggregates, which are built by referring to arbitrary definitions of the spatial units (such as provinces, regions or municipalities) and hence introduce a statistical bias arising from the chosen notion of space. This methodological problem can be tackled by using a continuous approach to space, where data are collected at the maximum level of spatial disaggregation, i.e. each firm is identified by its geographic coordinates, say (x, y), and spatial concentration is detected by referring to the distribution of distances amongst economic activities. The main purpose of the dissertation is to contribute to the development of this kind of continuous space-based measures of spatial clustering. The scientific context and motivation are outlined in depth in the first three chapters. Then the first essay introduces the space–time K-function empirical tool, proposed in spatial statistical literature, into economic literature in order to detect the geographic concentration of industries while controlling for the temporal dynamics that characterize the localization processes of firms. The proposed methodology allows to explore the possibility that the spatial and temporal phenomena, producing the observed pattern of firms at a given moment of time, interact to provide space–time clustering. The presence of significant space–time interaction implies that an observed pattern cannot be explained only by static factors but that we should also consider the dynamic evolution of the spatial concentration phenomenon. Indeed, for example, new firm settlements may display no spatial concentration if we look separately at each moment of time and yet they may present a remarkable agglomeration if we look at the overall resulting spatial distribution after a certain time period. In general, without knowing the temporal evolution of the phenomenon under study it is not possible to identify the mechanism generating its spatial structure. As a matter of fact, different underlying space–time processes can lead to resulting spatial patterns which look the same. The methodology is illustrated with an application to the analysis of the spatial distribution of the ICT industries in Rome (Italy), in the long period 1920–2005. The problem of disentangling spatial heterogeneity and spatial dependence phenomena when detecting for spatial clusters of firms is the topic of the second essay, “Measuring industrial agglomeration with inhomogeneous K-function: the case of ICT firms in Milan (Italy)”. Spatial clusters of economic activities can be the result of two distinct broad classes of phenomena: spatial heterogeneity and spatial dependence. The former arises when exogenous factors lead firms to locate in certain specific geographical zones. For instance, firms may group together in certain areas in order to exploit favourable local conditions, such as the presence of useful infrastructures, the proximity to the communication routes or more convenient local taxation systems. The phenomenon of spatial dependence, which is often of direct scientific interest, occurs instead when the presence of an economic activity in a given area attracts other firms to locate nearby. For instance, the presence of firms with a leading role encouraging the settlement of firms producing intermediate goods in the same area or the incidence of knowledge spillovers driving industrial agglomerations. This essay suggests a parametric approach based on the inhomogeneous K-function that allows to assess the endogenous effects of interaction among economic agents, namely spatial dependence, while adjusting for the exogenous effects of the characteristics of the study area, namely spatial heterogeneity. The approach is also illustrated with a case study on the spatial distribution of the ICT manufacturing industry in Milan (Italy). The third paper is titled “Weighting Ripley’s K-function to account for the firm dimension in the analysis of spatial concentration”. In the methodological context of the continuous space-based measures of spatial clustering, firms are identified as dimensionless points distributed in a planar space. In realistic circumstances, however, firms are generally far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital and so on. This implies that a high level of spatial concentration can occur, for example, because many small firms cluster in space, or few large firms (in the limit just one firm) cluster in space. A proper test for the presence of spatial clusters of firms should thus consider the impact of the firm dimension on industrial agglomeration. For this respect, the third essay develops a methodology based on an extension of the K-function considering firm size as a weight attached to each of the points representing the firms’ locations

Managing Traffic Data through Clustering and Radial Basis Functions

Due to the importance of road transport an adequate identification of the various road network levels is necessary for an efficient and sustainable management of the road infrastructure. Additionally, traffic values are key data for any pavement management system. In this work traffic volume data of 2019 in the Basque Autonomous Community (Spain) were analyzed and modeled. Having a multidimensional sample, the average annual daily traffic (AADT) was considered as the main variable of interest, which is used in many areas of the road network management. First, an exploratory analysis was performed, from which descriptive statistical information was obtained continuing with the clustering by various variables in order to standardize its behavior by translation. In a second stage, the variable of interest was estimated in the entire road network of the studied country using linear-based radial basis functions (RBFs). The estimated model was compared with the sample statistically, evaluating the estimation using cross-validation and highest-traffic sectors are defined. From the analysis, it was observed that the clustering analysis is useful for identifying the real importance of each road segment, as a function of the real traffic volume and not based on other criteria. It was also observed that interpolation methods based on linear-type radial basis functions (RBF) can be used as a preliminary method to estimate the AADT.This research was funded by The University of the Basque Country (UPV/EHU), Call for Innovation Projects “IKD i3 Laborategia” (Call 1-2020, 2019/20)

Assessing sustainability in cities : a complexity science approach to the concept of happiness for the urban environment

Where we live affects all aspects of our life and thus our happiness. In recent years, and now for more than half of the Earth’s population, our place of residence or activity has been increasingly transformed into an urban one. We start our quest for happiness using bibliometric research to investigate it framework as scientists constructed it during the past years. We detect that while the impact of happiness studies has grown in importance during the last twenty years, happiness-related concepts find it difficult to penetrate the urban studies field of studies. We map the temporal evolution of both happiness and urban studies fields into dynamic networks obtained by paper keywords co-occurrence analysis. We identify the main concepts of “urban happiness” field and their capacity to agglomerate into coherent thematic clusters. We present a one-parameter spatial network model to reproduce the changes in the topology of these networks. Results explain the evolution and the level of interpenetration of these two fields as a function of “conceptual” distances, mapped into Euclidean ones. Complex networks science appears as a valid alternative to other approaches (i.e., co-frequency matrix of bibliometric analysis), and opens the way for the systematic study of other academic fields in terms of complex evolving networks. We then present a methodology based on Max-Neef, et al. (1991) “human scale development” paradigm to measure current levels of Quality of Life (QoL) for urban environments. We use the fundamental human needs as our study domains. Drawing on the cases of Vila de Gràcia neighbourhood and Virreina square of Barcelona, we assess their fulfilment with a set of questions reflecting the subjective dimension of QoL. We use two consecutive processes to sort questions into needs: a qualitative involving local communities and/or expert groups, and a quantitative involving the definition of weights for each question and per need. We add objective indicators to reflect the objective dimension of QoL. We compare the two dimensions and define an integrative QoL. We identify intervention axes for a potential improvement in the results. We argue that this method can be used to define more holistic urban quality indexes to improve decision making processes, policies and plans. It is a tool to enhance bottom-up approaches and processes of urban analysis to create more liveable places for the dwellers. Next, we present a methodology based on weighted networks and dependence coefficients aimed at revealing connectivity patterns between categories. Using the same case studies and human needs as our categories we show that diverse spatial levels present different and nontrivial patterns of need emergence. A numerical model indicates that these patterns depend on the probability distribution of weights. We suggest that this way of analysing the connectivity of categories (human needs in our case study) in social and ecological systems can be used to define new strategies to cope with complex processes, such as those related to transition management and governance, urban-making, and integrated planning. We conclude our journey with applications that show the strength of collective response regarding social matters. We study dwellers perceptions through the following cases: experimental activities in the public space, discourse analysis and reaction on emerging urban phenomena such as the massive migration of population in the Mediterranean during 2015.Donde vivimos afecta todos los aspectos de nuestra vida y, por lo tanto, nuestra felicidad. En los últimos años, y para más de la mitad de la población de la Tierra, nuestro lugar de residencia o actividad se transforma a uno urbano. Comenzamos nuestra búsqueda de la felicidad aplicando investigación bibliométrica para investigar el marco su tal como lo construyeron los científicos durante los últimos años. Detectamos que si bien el impacto de los estudios de la felicidad ha crecido en importancia durante los últimos veinte años, los conceptos relacionados con la felicidad tienen dificultades en penetrar el campo de los estudios urbanos. Mapeamos la evolución temporal de los campos de felicidad y estudios urbanos en redes dinámicas obtenidas mediante análisis de coocurrencia de palabras clave en artículos científicos. Identificamos los conceptos principales del campo de "felicidad urbana" y su capacidad para aglomerarse en grupos temáticos coherentes. Presentamos un modelo de red espacial de un parámetro para reproducir los cambios en la topología de estas redes. Los resultados explican la evolución y el nivel de interpenetración de estos dos campos en función de las distancias "conceptuales", mapeadas en euclidianas. La ciencia de redes complejas aparece como una alternativa válida a otros enfoques (p.e., matriz de frecuencia conjunta de análisis bibliométrico) y abre el camino para el estudio sistemático de otros campos académicos en términos de redes complejas en evolución. A continuación presentamos una metodología basada en el paradigma de Max-Neef, et al. (1991) de "desarrollo a escala humana" para medir los niveles actuales de calidad de vida en entornos urbanos. Utilizamos las necesidades humanas fundamentales como nuestros campos de estudio. Basados en los casos del barrio de Vila de Gràcia y la plaza Virreina de Barcelona, evaluamos el cumplimiento de un conjunto de preguntas que reflejan la dimensión subjetiva de la calidad de vida. Utilizamos dos procesos consecutivos para clasificar las preguntas en necesidades: una cualitativa que involucra a las comunidades locales y / o grupos de expertos, y una cuantitativa que involucra la definición de pesos para cada pregunta y por necesidad. Agregamos indicadores objetivos para reflejar la dimensión objetiva de la calidad de vida. Comparamos las dos dimensiones y definimos una calidad de vida integrativa. Identificamos ejes de intervención para conseguir una posible mejora en los resultados. Argumentamos que este método puede usarse para definir índices de calidad urbana más holísticos para mejorar los procesos, políticas y planes de toma de decisiones. Es una herramienta para dinamizar los enfoques desde la base (bottom-up) y los procesos de análisis urbano para crear lugares más vivibles para los habitantes. Seguimos con una metodología basada en redes ponderadas y coeficientes de dependencia destinados a revelar patrones de conectividad entre categorías. Usando los mismos casos de estudio y las necesidades humanas como nuestras categorías, mostramos que diversos niveles espaciales presentan patrones de emergencia diferentes y no triviales. Un modelo numérico indica que estos patrones dependen de la distribución de probabilidad de los pesos. Sugerimos que esta forma de analizar la conectividad de las categorías (necesidades humanas en nuestro caso de estudio) en los sistemas socio-ecológicos se puede utilizar para definir nuevas estrategias para hacer frente a procesos complejos, como los relacionados con la gestión de la transición y la gobernanza, la construcción urbana y planificación integrada. Concluimos nuestro viaje con aplicaciones que muestran la fuerza de la respuesta colectiva en asuntos social. Estudiamos percepciones de habitantes a través de los siguientes casos: actividades experimentales en el espacio público, análisis del discurso y reacción ante fenómenos urbanos emergentes, como la migración masiva en el Mediterraneo durante 201

Assessing sustainability in cities : a complexity science approach to the concept of happiness for the urban environment

Where we live affects all aspects of our life and thus our happiness. In recent years, and now for more than half of the Earth’s population, our place of residence or activity has been increasingly transformed into an urban one. We start our quest for happiness using bibliometric research to investigate it framework as scientists constructed it during the past years. We detect that while the impact of happiness studies has grown in importance during the last twenty years, happiness-related concepts find it difficult to penetrate the urban studies field of studies. We map the temporal evolution of both happiness and urban studies fields into dynamic networks obtained by paper keywords co-occurrence analysis. We identify the main concepts of “urban happiness” field and their capacity to agglomerate into coherent thematic clusters. We present a one-parameter spatial network model to reproduce the changes in the topology of these networks. Results explain the evolution and the level of interpenetration of these two fields as a function of “conceptual” distances, mapped into Euclidean ones. Complex networks science appears as a valid alternative to other approaches (i.e., co-frequency matrix of bibliometric analysis), and opens the way for the systematic study of other academic fields in terms of complex evolving networks. We then present a methodology based on Max-Neef, et al. (1991) “human scale development” paradigm to measure current levels of Quality of Life (QoL) for urban environments. We use the fundamental human needs as our study domains. Drawing on the cases of Vila de Gràcia neighbourhood and Virreina square of Barcelona, we assess their fulfilment with a set of questions reflecting the subjective dimension of QoL. We use two consecutive processes to sort questions into needs: a qualitative involving local communities and/or expert groups, and a quantitative involving the definition of weights for each question and per need. We add objective indicators to reflect the objective dimension of QoL. We compare the two dimensions and define an integrative QoL. We identify intervention axes for a potential improvement in the results. We argue that this method can be used to define more holistic urban quality indexes to improve decision making processes, policies and plans. It is a tool to enhance bottom-up approaches and processes of urban analysis to create more liveable places for the dwellers. Next, we present a methodology based on weighted networks and dependence coefficients aimed at revealing connectivity patterns between categories. Using the same case studies and human needs as our categories we show that diverse spatial levels present different and nontrivial patterns of need emergence. A numerical model indicates that these patterns depend on the probability distribution of weights. We suggest that this way of analysing the connectivity of categories (human needs in our case study) in social and ecological systems can be used to define new strategies to cope with complex processes, such as those related to transition management and governance, urban-making, and integrated planning. We conclude our journey with applications that show the strength of collective response regarding social matters. We study dwellers perceptions through the following cases: experimental activities in the public space, discourse analysis and reaction on emerging urban phenomena such as the massive migration of population in the Mediterranean during 2015.Donde vivimos afecta todos los aspectos de nuestra vida y, por lo tanto, nuestra felicidad. En los últimos años, y para más de la mitad de la población de la Tierra, nuestro lugar de residencia o actividad se transforma a uno urbano. Comenzamos nuestra búsqueda de la felicidad aplicando investigación bibliométrica para investigar el marco su tal como lo construyeron los científicos durante los últimos años. Detectamos que si bien el impacto de los estudios de la felicidad ha crecido en importancia durante los últimos veinte años, los conceptos relacionados con la felicidad tienen dificultades en penetrar el campo de los estudios urbanos. Mapeamos la evolución temporal de los campos de felicidad y estudios urbanos en redes dinámicas obtenidas mediante análisis de coocurrencia de palabras clave en artículos científicos. Identificamos los conceptos principales del campo de "felicidad urbana" y su capacidad para aglomerarse en grupos temáticos coherentes. Presentamos un modelo de red espacial de un parámetro para reproducir los cambios en la topología de estas redes. Los resultados explican la evolución y el nivel de interpenetración de estos dos campos en función de las distancias "conceptuales", mapeadas en euclidianas. La ciencia de redes complejas aparece como una alternativa válida a otros enfoques (p.e., matriz de frecuencia conjunta de análisis bibliométrico) y abre el camino para el estudio sistemático de otros campos académicos en términos de redes complejas en evolución. A continuación presentamos una metodología basada en el paradigma de Max-Neef, et al. (1991) de "desarrollo a escala humana" para medir los niveles actuales de calidad de vida en entornos urbanos. Utilizamos las necesidades humanas fundamentales como nuestros campos de estudio. Basados en los casos del barrio de Vila de Gràcia y la plaza Virreina de Barcelona, evaluamos el cumplimiento de un conjunto de preguntas que reflejan la dimensión subjetiva de la calidad de vida. Utilizamos dos procesos consecutivos para clasificar las preguntas en necesidades: una cualitativa que involucra a las comunidades locales y / o grupos de expertos, y una cuantitativa que involucra la definición de pesos para cada pregunta y por necesidad. Agregamos indicadores objetivos para reflejar la dimensión objetiva de la calidad de vida. Comparamos las dos dimensiones y definimos una calidad de vida integrativa. Identificamos ejes de intervención para conseguir una posible mejora en los resultados. Argumentamos que este método puede usarse para definir índices de calidad urbana más holísticos para mejorar los procesos, políticas y planes de toma de decisiones. Es una herramienta para dinamizar los enfoques desde la base (bottom-up) y los procesos de análisis urbano para crear lugares más vivibles para los habitantes. Seguimos con una metodología basada en redes ponderadas y coeficientes de dependencia destinados a revelar patrones de conectividad entre categorías. Usando los mismos casos de estudio y las necesidades humanas como nuestras categorías, mostramos que diversos niveles espaciales presentan patrones de emergencia diferentes y no triviales. Un modelo numérico indica que estos patrones dependen de la distribución de probabilidad de los pesos. Sugerimos que esta forma de analizar la conectividad de las categorías (necesidades humanas en nuestro caso de estudio) en los sistemas socio-ecológicos se puede utilizar para definir nuevas estrategias para hacer frente a procesos complejos, como los relacionados con la gestión de la transición y la gobernanza, la construcción urbana y planificación integrada. Concluimos nuestro viaje con aplicaciones que muestran la fuerza de la respuesta colectiva en asuntos social. Estudiamos percepciones de habitantes a través de los siguientes casos: actividades experimentales en el espacio público, análisis del discurso y reacción ante fenómenos urbanos emergentes, como la migración masiva en el Mediterraneo durante 2015Postprint (published version