A Multi-Criteria Analysis GIS Tool for Measuring the Vulnerability of the Residential Stock Based on Multidimensional Indices

There is extensive scientific evidence showing that the characteristics of the urban and residential environment directly affect people’s quality of life and health. In this framework, numerous building renovation policies have been developed in Europe, mainly focused on improving energy efficiency. However, we are dealing with a multifactorial and multicausal phenomenon of a complex system where competent institutions need quantitative diagnosis mechanisms that consider this holistic vision when making decisions and prioritizing interventions. Regarding this, the present research develops the potential of the multi-criteria methodology in a first proposal, which integrates social, energy, environmental and spatial aspects linked to the relationship between housing and the effects on the health of its inhabitants. It is a multidimensional method based on systematized and exportable vulnerability indices, which applies indicators that have been calculated using cadastral data and a typomorphological characterization of the residential stock. The analysis of the results through geostatistical techniques of autocorrelation and clustering applied to the case study of Donostia-San Sebastián shows that the proposed methodology is effective in achieving the objectives set. The associated GIS tool has proved to be agile and replicable.This work was supported by the Diputación Foral de Gipuzkoa under Grant 2020-CIEN-000049-01

Identification of relationship between housing difficulty and property values in urban areas

The objective of the present work is to use statistical data to identify territorial zones characterized by the correlation between urban access to services and quality of housing and the value of property ownership. While poverty is widely accepted to be an inherently multi-dimensional concept, it has proved very difficult to develop measures that both capture this multidimensionality and make comparisons over time and space easy. With this in mind, we attempt to apply a Total Fuzzy and Relative (TFR) approach, based on a fuzzy measure of the degree of association of an individual to the totality of the poor and an approach of semantic distance (Munda, 1995), based on the definition of a “fuzzy distance” as a discriminating reference to rank the availability to property in real estate market, as complement of urban poverty, in a specific case (the Italian City of Bari)

Identification of relationship between housing difficulty and property values in urban areas

The objective of the present work is to use statistical data to identify territorial zones characterized by the correlation between urban access to services and quality of housing and the value of property ownership. While poverty is widely accepted to be an inherently multi-dimensional concept, it has proved very difficult to develop measures that both capture this multidimensionality and make comparisons over time and space easy. With this in mind, we attempt to apply a Total Fuzzy and Relative (TFR) approach, based on a fuzzy measure of the degree of association of an individual to the totality of the poor and an approach of semantic distance (Munda, 1995), based on the definition of a “fuzzy distance” as a discriminating reference to rank the availability to property in real estate market, as complement of urban poverty, in a specific case (the Italian City of Bari)

Identification of relationship between housing difficulty and property values in urban areas

The objective of the present work is to use statistical data to identify territorial zones characterized by the correlation between urban access to services and quality of housing and the value of property ownership. While poverty is widely accepted to be an inherently multi-dimensional concept, it has proved very difficult to develop measures that both capture this multidimensionality and make comparisons over time and space easy. With this in mind, we attempt to apply a Total Fuzzy and Relative (TFR) approach, based on a fuzzy measure of the degree of association of an individual to the totality of the poor and an approach of semantic distance (Munda, 1995), based on the definition of a “fuzzy distance” as a discriminating reference to rank the availability to property in real estate market, as complement of urban poverty, in a specific case (the Italian City of Bari)

Hydro-meteorological risk assessment methods and management by nature-based solutions

Hydro-meteorological risk (HMR) management involves a range of methods, such as monitoring of uncertain climate, planning and prevention by technical countermeasures, risk assessment, preparedness for risk by early-warnings, spreading knowledge and awareness, response and recovery. To execute HMR management by risk assessment, many models and tools, ranging from conceptual to sophisticated/numerical methods are currently in use. However, there is still a gap in systematically classifying and documenting them in the field of disaster risk management. This paper discusses various methods used for HMR assessment and its management via potential nature-based solutions (NBS), which are actually lessons learnt from nature. We focused on three hydro-meteorological hazards (HMHs), floods, droughts and heatwaves, and their management by relevant NBS. Different methodologies related to the chosen HMHs are considered with respect to exposure, vulnerability and adaptation interaction of the elements at risk. Two widely used methods for flood risk assessment are fuzzy logic (e.g. fuzzy analytic hierarchy process) and probabilistic methodology (e.g. univariate and multivariate probability distributions). Different kinds of indices have been described in the literature to define drought risk, depending upon the type of drought and the purpose of evaluation. For heatwave risk estimation, mapping of the vulnerable property and population-based on geographical information system is a widely used methodology in addition to a number of computational, mathematical and statistical methods, such as principal component analysis, extreme value theorem, functional data analysis, the Ornstein–Uhlenbeck process and meta-analysis. NBS (blue, green and hybrid infrastructures) are promoted for HMR management. For example, marshes and wetlands in place of dams for flood and drought risk reduction, and green infrastructure for urban cooling and combating heatwaves, are potential NBS. More research is needed into risk assessment and management through NBS, to enhance its wider significance for sustainable living, building adaptations and resilience

Mountains in a flat world: Why proximity still matters for the location of economic activity

Thomas Friedman (2005) argues that the expansion of trade, the internationalization of firms, the galloping process of outsourcing, and the possibility of networking is creating a 'flat world': a level playing field where individuals are empowered and better off. This paper challenges this view of the world by arguing that not all territories have the same capacity to maximize the benefits and opportunities and minimize the risks linked to globalization. Numerous forces are coalescing in order to provoke the emergence of urban 'mountains' where wealth, economic activity, and innovative capacity agglomerate. The interactions of these forces in the close geographical proximity of large urban areas give shape to a much more complex geography of the world economy.

Value in Places and Places in Systems

In a context in which universities and creative practices are used as part of placemaking, this working paper looks at place-based approaches to evaluations which leads it to consider places as systems. Indeed, the key message is that capturing the value of place-based interventions is difficult, not just because of the ‘standard’ methodological issues arising for evaluation, but because places are both parts of systems and are systems themselves and it is not clear how their boundaries can be defined. This -the paper argues - has some interesting implications, including that ‘franchising’ of solutions across different places is not always possible because localities develop in a path-dependent way. Secondly, systems thinking - bringing to the fore the issues of frames, boundaries and stakeholders - makes visible ‘the orders of worth’ in evaluation practice. This means that a number of evaluative criteria co-exist for any given place at any given time and, rather than recording or representing, evaluating is about making choices about which frames, boundaries and stakeholder are documented and which are marginalised. This raises questions about the limits of the outcomes-based and objectives-driven evaluation approaches in relation to places, not just because cause-effect attribution is difficult in complex social environments but also because the value co-creation which underpins place-based projects cannot be ‘bounded’ in the way required by outcomes-based evaluation against fixed objectives. Equally importantly, the question ‘whose values and which stakeholders’ inevitably arises. This calls for supplementing those standard approaches with more open-ended forms of mapping, tracing and narrating. These considerations are presented in the paper against the backdrop of changing conceptions about the role of universities in place-making

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.