scholarly journals Assessing Socioeconomic Vulnerability after a Hurricane: A Combined Use of an Index-Based approach and Principal Components Analysis

2020 ◽  
Vol 12 (4) ◽  
pp. 1452
Author(s):  
Neiler Medina ◽  
Yared Abayneh Abebe ◽  
Arlex Sanchez ◽  
Zoran Vojinovic
Keyword(s):  
Census Data ◽  
Principal Component ◽  
Decision Makers ◽  
Small Island ◽  
Socioeconomic Vulnerability ◽  
Combined Use ◽  
Assessment Approach ◽  
Components Analysis ◽  
Neighbourhood Scale

Small Island Developing States (SIDS) are vulnerable to sea-level rise and hydro-meteorological hazards. In addition to the efforts to reduce the hazards, a holistic strategy that also addresses the vulnerability and exposure of residents and their assets is essential to mitigate the impacts of such hazards. Evaluating the socioeconomic vulnerability of SIDS can serve the purpose of identification of the root drivers of risk. In this paper, we present a methodology to assess and map socioeconomic vulnerability at a neighbourhood scale using an index-based approach and principal component analysis (PCA). The index-based vulnerability assessment approach has a modular and hierarchical structure with three components: susceptibility, lack of coping capacities and lack of adaptation, which are further composed of factors and variables. To compute the index, we use census data in combination with data coming from a survey we performed in the aftermath of Irma. PCA is used to screen the variables, to identify the most important variables that drive vulnerability and to cluster neighbourhoods based on the common factors. The methods are applied to the case study of Sint Maarten in the context of the disaster caused by Hurricane Irma in 2017. Applying the combined analysis of index-based approach with PCA allows us to identify the critical neighbourhoods on the island and to identify the main variables or drivers of vulnerability. Results show that the lack of coping capacities is the most influential component of vulnerability in Sint Maarten. From this component, the “immediate action” and the “economic coverage” are the most critical factors. Such analysis also enables decision-makers to focus their (often limited) resources more efficiently and have a more significant impact concerning disaster risk reduction.

Targeting the water-poor through water poverty mapping

Water Policy ◽  
2004 ◽  
Vol 6 (5) ◽  
pp. 397-411 ◽  
Author(s):  
James Cullis ◽  
Dermot O Regan
Keyword(s):  
Water Supply ◽  
Census Data ◽  
Decision Makers ◽  
Data Sources ◽  
Poverty Index ◽  
Poor Households ◽  
Water Poverty Index ◽  
Development Policies ◽  
Poverty Mapping

This paper shows how water poverty mapping using census data and the Water Poverty Index can be used to identify effectively the most water-poor households in a region for the targeting of water supply development policies and projects. The main findings come from a case study conducted in the Estcourt municipal district in South Africa where simple water poverty maps were developed using readily available data sources at three different scales: enumerator area, place names and sub-catchment. The efficiency of targeting the most water-poor households using the different scales of water poverty maps were measured by comparing both the inclusion and exclusion rates of targeting and comparing them with other similar targeting studies. The distribution of water poverty within a community was also compared with the results of a detailed household questionnaire conducted as part of the broader development of the water poverty index (WPI). The main conclusion from the study is that water poverty mapping is a strong visual extension of the WPI that has great potential for providing a practical way for water management authorities and decision makers to identify and target the most water poor households and monitoring the impacts and tangible benefits of water supply development policies.

scholarly journals Applying Roadmapping and Conceptual Modeling to the Energy Transition: A Local Case Study

2021 ◽  
Vol 13 (7) ◽  
pp. 3683
Author(s):  
Gerrit Muller
Keyword(s):  
Conceptual Modeling ◽  
Energy Transition ◽  
Decision Makers ◽  
Policy Makers ◽  
Combined Use ◽  
Local Case ◽  
Transition Policy ◽  
Climate Crisis ◽  
Global Transition

The climate crisis requires a global transition toward sustainable practices. In this transition, policy makers face the challenge to take along a wide variety of stakeholders with own interests, needs, and concerns. This research explores the combined use of conceptual models and roadmapping to facilitate understanding, communication, reasoning, and decision-making between a large heterogeneous set of stakeholders. We apply these methods, in the form of action research, in several smaller research projects at a small town in the Netherlands. We find that the combination of conceptual modeling and roadmapping facilitates discussions between heterogeneous stakeholders on complex transition problems, such as the energy transition, at a local scale. However, we see a significant gap in the way of thinking and communicating between experts and decision-makers, which requires additional means to connect them.

scholarly journals An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
City Planning ◽  
Family Income ◽  
Census Data ◽  
Negative Impact ◽  
Human Life ◽  
Principal Component ◽  
Economic Parameters

The United Nations estimates that the global population is going to be double in the coming 40 years, which may cause a negative impact on the environment and human life. Such an impact may instigate increased water demand, overuse of power, anthropogenic noise, etc. Thus, modelling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. This study aims to investigate the ability of using remote sensing and Geographic Information System (GIS) techniques to model the UEQ with a case study in the city of Toronto via deriving different environmental, urban and socio-economic parameters. Remote sensing, GIS and census data were first obtained to derive environmental, urban and socio-economic parameters. Two techniques, GIS overlay and Principal Component Analysis (PCA), were used to integrate all of these environmental, urban and socio-economic parameters. Socio-economic parameters including family income, higher education and land value were used as a reference to assess the outcomes derived from the two integration methods. The outcomes were assessed through evaluating the relationship between the extracted UEQ results and the reference layers. Preliminary findings showed that the GIS overlay represents a better precision and accuracy (71% and 65%), respectively, comparing to the PCA technique. The outcomes of the research can serve as a generic indicator to help the authority for better city planning with consideration of all possible social, environmental and urban requirements or constraints.

scholarly journals An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
City Planning ◽  
Family Income ◽  
Census Data ◽  
Negative Impact ◽  
Human Life ◽  
Principal Component ◽  
Economic Parameters

The United Nations estimates that the global population is going to be double in the coming 40 years, which may cause a negative impact on the environment and human life. Such an impact may instigate increased water demand, overuse of power, anthropogenic noise, etc. Thus, modelling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. This study aims to investigate the ability of using remote sensing and Geographic Information System (GIS) techniques to model the UEQ with a case study in the city of Toronto via deriving different environmental, urban and socio-economic parameters. Remote sensing, GIS and census data were first obtained to derive environmental, urban and socio-economic parameters. Two techniques, GIS overlay and Principal Component Analysis (PCA), were used to integrate all of these environmental, urban and socio-economic parameters. Socio-economic parameters including family income, higher education and land value were used as a reference to assess the outcomes derived from the two integration methods. The outcomes were assessed through evaluating the relationship between the extracted UEQ results and the reference layers. Preliminary findings showed that the GIS overlay represents a better precision and accuracy (71% and 65%), respectively, comparing to the PCA technique. The outcomes of the research can serve as a generic indicator to help the authority for better city planning with consideration of all possible social, environmental and urban requirements or constraints.

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

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