Self-Organizing Maps and the US Urban Spatial Structure

This article considers urban spatial structure in US cities using a multi- dimensional approach. We select six key variables (commuting costs, den- sity, employment dispersion/concentration, land-use mix, polycentricity and size) from the urban literature and define measures to quantify them. We then apply these measures to 359 metropolitan areas from the 2000 US Census. The adopted methodological strategy combines two novel techniques for the social sciences to explore the existence of relevant pat- terns in such multi-dimensional datasets. Geodesic self-organizing maps (SOM) are used to visualize the whole set of information in a meaningful way, while the recently developed clustering algorithm of the max-p is applied to draw boundaries within the SOM and analyze which cities fall into each of them. JEL C45, R0, R12, R14. Keywords Urban spatial structure, self-organizing maps, US metropolitan areas

Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems

[EN] With the advance of new technologies and emergence of the concept of the smart city, there has been a dramatic increase in available information. Water distribution systems (WDSs) in which databases can be updated every few minutes are no exception. Suitable techniques to evaluate available information and produce optimized responses are necessary for planning, operation, and management. This can help identify critical characteristics, such as leakage patterns, pipes to be replaced, and other features. This paper presents a clustering method based on self-organizing maps coupled with k-means algorithms to achieve groups that can be easily labeled and used for WDS decision-making. Three case-studies are presented, namely a classification of Brazilian cities in terms of their water utilities; district metered area creation to improve pressure control; and transient pressure signal analysis to identify burst pipes. In the three cases, this hybrid technique produces excellent results. © 2018 Elsevier Ltd. All rights reserved.This work is partially supported by Capes and CNPq, Brazilian research agencies. The use of English was revised by John Rawlins.Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, J. (2018). Hybrid SOM+k-Means Clustering to Improve Planning, Operation and Management in Water Distribution Systems. Environmental Modelling & Software. 106:77-88. https://doi.org/10.1016/j.envsoft.2018.02.013S778810

Competitiveness of the Forest Sector in the EU Candidate Countries - Cluster Analysis

This study, initiated within the framework of IIASA's Young Scientists Summer Program 2001, investigates some key issues related to the enlargement and, in particular, the competitiveness of forest-related industries in the candidate countries. The main contribution of this study is its holistic approach to discern various forms of industrial competitiveness in selected candidate countries. Moreover, the objective is to investigate how the observed patterns of competitiveness have evolved during the transition process so far, giving some implications of the models of restructuring and integration of the European forest sector as a whole

CLUSTERING THE HETEROGENITY OF EU URBAN PERFORMANCES

Cities represent today the intrinsic socio-economic complexity of local systems. Looking at the performances of urban systems enable us to explaining the main factors of territorial development. By moving from the theory of "progressive systems", and assigning to the cities some of this theory's properties, it is possible to outline a methodological perspective to capture the emerging phenomena describing the cities' performances. Keeping this view in mind, the aim of the paper is facing the intrinsic socio-economic complexity and heterogeneity of cities within the EU integration policies.. In order to better qualify this issue, we provide a multidimensional scaling approach, as a quantitative method useful to compare the several urban performances by letting a cluster evidence among the EU cities emerge.Urban trajectories, progressive system, multidimensional scaling.

A Fuzzy Clustering Approach to the Key Sectors of the Spanish Economy.

La búsqueda de los sectores clave de una economía ha sido y es uno de los temas más recurrentes del análisis input-output. Además de su liderazgo para impulsar el desarrollo, concepto demasiado amplio e impreciso, un sector puede ser clave desde una determinada perspectiva y menos o nada importante desde alguna otra diferente. También, probablemente, puede serlo para varias cuestiones a la vez, en distinto grado. Por tanto, se necesita realizar una aproximación de análisis de conglomerados difuso. En este trabajo se propone un enfoque multidimensional para clasificar a los sectores productivos de la tabla input-output española de 1995, basándose en tres grupos de variables: las relacionadas con su integración productiva, su peso específico en la economía y su dinámica económica. Además, se incorpora al análisis el nivel tecnológico, que por ser variable categórica plantea problemas metodológicos especiales. Todas estas cuestiones se abordan aplicando un análisis clúster robusto y difuso que arroja como resultado una clasificación de sectores ilustrativa del papel que juega cada uno de ellos en la economía española

Design of homogenous territorial units: a methodological proposal

One of the main questions to solve when analysing geographically added information consists of the design of territorial units adjusted to the objectives of the study. In fact, in those cases where territorial information is aggregated, ad-hoc criteria are usually applied as there are not regionalization methods flexible enough. Moreover, and without taking into account the aggregation method applied, there is an implicit risk that is known in the literature as Modifiable Areal Unit Problem (MAUP) (Openshaw, 1984). This problem is related with the high sensitivity of statistical and econometric results to different aggregations of geographical data, which can negatively affect the robustness of the analysis. In this paper, an optimization model is proposed with the aim of identifying homogenous territorial units related with the analyzed phenomena. This model seeks to reduce some disadvantages found in previous works about automated regionalisation tools. In particular, the model not only considers the characteristics of each element to group but also, the relationships among them, trying to avoid the MAUP. An algoritm, known as RASS (Regionalization Algorithm with Selective Search) it also proposed in order to obtain faster results from the model. The obtained results permit to affirm that the proposed methodology is able to identify a great variety of territorial configurations, taking into account the contiguity constraint among the different elements to be grouped.

Comparative Coastal Risk Index (CCRI): A multidisciplinary risk index for Latin America and the Caribbean

As the world's population grows to a projected 11.2 billion by 2100, the number of people living in low-lying areas exposed to coastal hazards is projected to increase. Critical infrastructure and valuable assets continue to be placed in vulnerable areas, and in recent years, millions of people have been displaced by natural hazards. Impacts from coastal hazards depend on the number of people, value of assets, and presence of critical resources in harm's way. Risks related to natural hazards are determined by a complex interaction between physical hazards, the vulnerability of a society or social-ecological system and its exposure to such hazards. Moreover, these risks are amplified by challenging socioeconomic dynamics, including poorly planned urban development, income inequality, and poverty. This study employs a combination of machine learning clustering techniques (Self Organizing Maps and K-Means) and a spatial index, to assess coastal risks in Latin America and the Caribbean (LAC) on a comparative scale. The proposed method meets multiple objectives, including the identification of hotspots and key drivers of coastal risk, and the ability to process large-volume multidimensional and multivariate datasets, effectively reducing sixteen variables related to coastal hazards, geographic exposure, and socioeconomic vulnerability, into a single index. Our results demonstrate that in LAC, more than 500,000 people live in areas where coastal hazards, exposure (of people, assets and ecosystems) and poverty converge, creating the ideal conditions for a perfect storm. Hotspot locations of coastal risk, identified by the proposed Comparative Coastal Risk Index (CCRI), contain more than 300,00 people and include: El Oro, Ecuador; Sinaloa, Mexico; Usulutan, El Salvador; and Chiapas, Mexico. Our results provide important insights into potential adaptation alternatives that could reduce the impacts of future hazards. Effective adaptation options must not only focus on developing coastal defenses, but also on improving practices and policies related to urban development, agricultural land use, and conservation, as well as ameliorating socioeconomic conditions

Heterogeneous agglomeration

Many prior treatments of agglomeration either explicitly or implicitly suppose that all industries agglomerate for the same reasons, with traditional Marshallian (1890) factors affecting all industries similarly. An important instance of this approach is the extrapolation of the agglomeration experience of one key sector or cluster to the larger economy. Another is the pooling of data to look at common tendencies in agglomeration. This paper uses UK establishment level data on coagglomeration to document heterogeneity across industries in the microfoundations of agglomeration economies. The pattern of heterogeneity that we document is consistent with both traditional Marshallian theories and with alternative approaches that emphasize the adaptive and organizational aspects of agglomeration. *Disclaimer: This work was based on data from the Business Structure Database and the Quarterly UK Labour Force Survey, produced by the Office for National Statistics (ONS) and supplied by the Secure Data Service at the UK Data Archive. The data are Crown Copyright and reproduced with the permission of the controller of HMSO and Queen's Printer for Scotland. The use of the data in this work does not imply the endorsement of ONS or the Secure Data Service at the UK Data Archive in relation to the interpretation or analysis of the data. This work uses research datasets which may not exactly reproduce National Statistics aggregates