scholarly journals Assessment of Surface Water Quality Using Multivariate Analysis: Case Study of the Crati River, Italy

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2214 ◽  
Author(s):  
Giuseppina Ioele ◽  
Michele De Luca ◽  
Fedora Grande ◽  
Giacomina Durante ◽  
Raffaella Trozzo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Urban Areas ◽  
Local Population ◽  
Surface Water Quality ◽  
Principal Component ◽  
Data Set ◽  
Two Samples ◽  
Water Vulnerability ◽  
High Concentrations

The water vulnerability of the Crati river (Calabria, Italy), was assessed by applying chemometric methods on a large number of analytical parameters. This study was applied to a data set collected in the years 2015–2016, recording 30 physical–chemical and geological parameters at 25 sampling points, measured both for water and for sediments. The processing of the data by principal component analysis (PCA) allowed for highlighting the influence of the components most responsible for pollution. The accumulation of heavy metals in the water was detected only in two samples near the source of the river. On the contrary, their concentration values in the sediments exceeded the legal limit in several sites, probably due to their proximity to urban areas. In this case, high concentrations of chromium, mercury and nickel were detected both at the mouth of the river and along the valley. Lead was only detected in one sediment sample. The multivariate analysis techniques proved to be very useful to completely characterize the areas surrounding a river course and facilitate the development of a risk map to monitor health risks to the local population.

scholarly journals Application of multivariate statistical techniques in the water quality assessment of Danube river, Serbia

2015 ◽  
Vol 41 (4) ◽  
pp. 96-103 ◽  
Author(s):  
Danijela Voza ◽  
Milovan Vukovic ◽  
Ljiljana Takic ◽  
Djordje Nikolic ◽  
Ivana Mladenovic-Ranisavljevic
Keyword(s):  
Water Quality ◽  
Water Quality Management ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Water Quality Monitoring ◽  
Danube River ◽  
Multivariate Techniques ◽  
Data Set ◽  
Political Issue

AbstractThe aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA) in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.

scholarly journals Human Capital, Skills, and Uneven Intraurban Employment Growth: The Case of Göteborg, Sweden, 1990–2008

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Jonathan Borggren ◽  
Rikard H. Eriksson
Keyword(s):  
Human Capital ◽  
Urban Areas ◽  
Employment Growth ◽  
Local Population ◽  
Urban Dynamics ◽  
High Concentrations ◽  
Growth Differences ◽  
Severe Recession

Recent research has elucidated the role of talents to explain urban growth differences but it remains to be shown whether urban dynamics, such as human capital and a mixed local population, can be linked to intraurban employment growth. By use of a unique longitudinal database, we track the economic development through the lens of intraurban employment growth of a number of primary urban areas (PUA) in Göteborg, Sweden. Regarding factors influencing employment growth, we find that relative concentrations of human capital protect areas from rising unemployment during severe recession (1990–1993) and recovery (1990–2000) while the composition of skills is beneficial during recovery (1990–2000) and long-term growth (1990–2008). Our findings suggest that neither too high concentrations of creative occupations nor too low ones are beneficial. Thus, human capital drives much of the employment changes in relation to the recession and early transition from manufacturing to service but composition of skills is more relevant for explaining long-term intraurban employment growth.

Assessment of performance using MPSS based DEA

2015 ◽  
Vol 22 (4) ◽  
pp. 624-642 ◽  
Author(s):  
Subhadip Sarkar
Keyword(s):  
Principal Component ◽  
Original Data ◽  
Data Envelopment ◽  
Data Set ◽  
Content Type ◽  
Cost Strategy ◽  
A New Technique ◽  
Most Productive Scale Size ◽  
Practical Implications

Purpose – Identification of the best school among other competitors is done using a new technique called most productive scale size based data envelopment analysis (DEA). The paper aims to discuss this issue. Design/methodology/approach – A non-central principal component analysis is used here to create a new plane according to the constant return to scale. This plane contains only ultimate performers. Findings – The new method has a complete discord with the results of CCR DEA. However, after incorporating the ultimate performers in the original data set this difference was eliminated. Practical implications – The proposed frontier provides a way to identify those DMUs which follow cost strategy proposed by Porter. Originality/value – A case study of six schools is incorporated here to identify the superior school and also to visualize gaps in their performances.

scholarly journals MIGRATIONS, POWER AND URBAN SPACES IN SICILIJA THROUGH AN ETHNOGRAPHIC CASE STUDY

2020 ◽  
pp. 7-19
Author(s):  
Daria Settineri
Keyword(s):  
Urban Environment ◽  
Urban Area ◽  
Urban Areas ◽  
Local Population ◽  
Urban Spaces ◽  
Social Connections ◽  
Ethnographic Case Study ◽  
The Social ◽  
Extra State

In this article, the author, based on concrete factual material, explores the specifics of modern migration processes considered within an urban area localized in Palermo (Sicily). In the context of this complex heterotopic space, resorting to the conceptual apparatus of M. Foucault, this kind of rhizome, if we operate with the concepts of J. Deleuze and F.Guattari, the author analyzes the actions of various actors of power – local and transnational – which dominate in this closed socio-urban environment, outlined by the framework of certain city blocks, – formal and informal, institutionalized and not, state and extra-state, legal and illegal, political, social, ecclesiastical, economic, criminal, the objects of projection and manifestation of which are migrants (primarily illegal) concentrated in these urban areas, who coexist there with the local population. The author also studies reactions of “newcomers” to the factors that affect them, including their ways of understanding and familiarizing with of their new place of residence as a micro- and the macrocosm, in all the diversity and complexity of the social connections that permeate this habitat and the factors that affect it.

scholarly journals Urbanização extensiva e o processo de interiorização do estado de São Paulo: um enfoque contemporâneo

2013 ◽  
Vol 15 (2) ◽  
pp. 179
Author(s):  
Admir Antonio Betarelli Junior ◽  
Roberto Luís De Melo Monte-Mór ◽  
Rodrigo Ferreira Simões
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Urban Areas ◽  
Principal Component ◽  
Sao Paulo ◽  
Competitive Advantages ◽  
São Paulo ◽  
And Cluster Analysis ◽  
Contemporary Approach

O propósito deste trabalho é discutir a formação, produção e organização do espaço urbano no estado de São Paulo a partir do processo de interiorização da indústria paulista no final dos anos 1970. O lócus da análise é a indústria, uma vez que no enfoque contemporâneo o processo de industrialização sempre esteve articulado com a produção da espacialidade urbana. Conciliando o método diferencial-estrutural (shift-share), a Análise de Componentes Principais (ACP) e a análise de cluster, foi possível evidenciar que tal processo teve como resultado o fenômeno de urbanização extensiva. Os resultados “fotográficos” apontam que houve uma extensão virtual das condições gerais do tecido urbano-industrial de forma que centralidades polarizadoras e regiões circunvizinhas apresentam vantagens locacionais e competitivas, formando, assim, aglomerações urbanas no território paulista, principalmente, nas regiões beneficiadas pelo processo de interiorização da indústria. Palavras-chave: urbanização extensiva; análise multivariada; análise de cluster; método diferencial-estrutural; indústria; São Paulo. Abstract: The main aim of this paper is to discuss the formation, organization and production of urban areas in State of São Paulo (Brazil) in the variant of the process of industry’s internalization in the late ‘70s. As industrialization has always been linked to the production of urban spatiality in contemporary approach, the locus of analysis is the industry. Combining the method shift-share (Esteban-Marquillas), Principal Component Analysis (PCA) and cluster analysis, we noted evidence that this process has resulted in the phenomenon of extensive urbanization. The main findings of these applications (“photographic”) indicated that there was a virtual extension in general conditions of the urban-industrial fabric so that polarizing centralities and surrounding regions present locational and competitive advantages, forming, therefore, urban agglomerations in the territory of São Paulo, mainly in the regions benefiting with the process of industry’s internalization. Keywords: extensive urbanization; internalization of the industry; shift-share; multivariate analysis; São Paulo (Brazil).

scholarly journals Avaliação da qualidade das águas superficiais na circunscrição hidrográfica do rio Piranga utilizando análise estatística multivariada e não-paramétrica

2021 ◽  
Vol 14 (2) ◽  
pp. 694
Author(s):  
Micael De Souza Fraga ◽  
Laura Thebit de Almeida ◽  
Marcel Carvalho Abreu ◽  
Felipe Bernardes Silva ◽  
Guilherme Barbosa Reis ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Heavy Metals ◽  
Water Quality ◽  
Soil Erosion ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Minas Gerais ◽  
Data Set ◽  
Domestic Effluents

No estado de Minas Gerais, as campanhas de coleta e análise da qualidade da água nos corpos hídricos contemplam até 51 variáveis, o que dificulta a análise e interpretação desse conjunto de dados e a identificação das variáveis determinantes para a qualidade da água. Diante disso, o objetivo deste trabalho foi identificar as principais fontes de poluição, bem como o comportamento da qualidade da água ao longo do tempo de monitoramento, por meio de diferentes análises estatísticas na circunscrição hidrográfica do rio Piranga. Pelos resultados obtidos, a análise fatorial/análise de componentes principais apontou a alta susceptibilidade que a bacia apresenta à erosão do solo, a contaminação pelo lançamento de efluentes domésticos e a variabilidade da qualidade das águas em virtude dos metais pesados. As variáveis Escherichia coli, ferro dissolvido, fósforo total e manganês total apresentaram os valores de violação da classe de enquadramento mais críticos. A análise de tendência mostrou padrões diferentes para o índice de qualidade da água e para as variáveis mais relevantes para a qualidade da água. Dentre as variáveis que compõe o índice, destacam-se as tendências de aumento de nitrato em todas as estações analisadas. De maneira geral, os resultados mostraram que a qualidade da água na área de estudo varia em função da erosão do solo, do alto grau de contaminação por efluentes domésticos, da poluição difusa advinda das áreas agrícolas e dos metais pesados, sendo as variáveis de qualidade da água vinculadas a estes fatores as mais importantes. Surface water quality assessment in the hydrographic region of the Piranga River using multivariate and non-parametric statistical analysis ABSTRACTIn the state of Minas Gerais, campaigns to collect and analyze water quality in water bodies include up to 51 variables, which makes it difficult to analyze and interpret this data set and to identify the determining variables for water quality. Therefore, the objective of this work was to identify the main sources of pollution, as well as the behavior of water quality over the monitoring time, through different statistical analyzes in the hydrographic region of the Piranga River. Based on the results obtained, the factor analysis/principal component analysis out the high susceptibility that the hydrographic region presents to soil erosion, contamination by the release of domestic effluents and the variability of water quality due to heavy metals. The variables Escherichia coli, dissolved iron, total phosphorus and total manganese presented the most critical values of violation of the framework class. The trend analysis showed different patterns for the water quality index and for the most relevant variables for water quality. Among the variables that make up the index, the trends of nitrate increase in all analyzed stations stand out. In general, the results showed that the water quality in the unit varies depending on soil erosion, the high degree of contamination by domestic effluents, the diffuse pollution from agricultural areas and heavy metals, with water quality variables being linked to these factors the most important.Keywords: environmental analysis, Minas Gerais, water pollution, water resources.

scholarly journals AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 289-293 ◽  
Author(s):  
Y. Li ◽  
X. Hu ◽  
H. Guan ◽  
P. Liu
Keyword(s):  
Urban Areas ◽  
Spatial Clustering ◽  
Mean Shift ◽  
Computational Cost ◽  
Principal Component ◽  
Point Clouds ◽  
Lidar Data ◽  
Tensor Voting ◽  
Road Extraction ◽  
Data Set

The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for “Urban Classification and 3D Building Reconstruction” project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

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