This paper addresses the problem of the growing exposure of contemporary cities to natural hazards by discussing the theoretical, methodological and practical aspects of using the configurational approach as a framework to perform a variety of spatial analyses to better orient disaster management. It claims that enabling a quick assessment of the evolving spatial functioning of the urban grid would effectively contribute to support strategic decision-making and to make post-disaster planning decisions more explicit among stakeholders, thus boosting wider understanding and participation among the public. The paper starts with a brief review of some relevant work done by the research community to date, which highlights emergent opportunities for urban morphology studies and Space Syntax theory to trigger effective innovations in disaster management practice. Next, the paper proposes to adopt a fit-for-purpose analysis approach with the aim to achieve a higher procedural flexibility in the analysis workflow. This issue is treated with a special focus on the necessities of relief organisations which need to integrate and overlap numerous layers of information and consider the feasibility of the analysis by evaluating time and costs. The proposal considers the economy of the construction of the map to be fundamental for ensuring the feasibility of a quantitative spatial assessment in data scarce contexts such as cities affected by disasters. Moreover, it recognises that the unicity of the map is likely to enable a better communication among different stakeholders following a BIM-oriented model of cooperation, while allowing a faster response in multi-hazards scenarios. Consequently, the proposal challenges the idea of the existence of a uniquely correct way to translate reality into a model, but rather suggests using a set of simplification techniques, such as filtering, generalisation and re-modelling, on a single crowdsourced map of the urban street network to generate suitably customised graphs for subsequent analysis. This brings together two themes: the first concerns the modelling activity per se and how certain technicalities that seem minor facts can influence the final analysis output to a greater extent; the second regards the crowdsourcing of spatial data and the challenges that the use of collaborative datasets poses to the modelling tasks. In line with the most recent research trends, this paper suggests exploiting the readiness of the Open Street Map (OSM) geo-dataset and the improving computational capacities of open GIS tools such as QGIS, which has recently achieved a wider acceptance worldwide. To further speed up the analysis and increase the likeness of the configurational analysis method to be successfully deployed by a larger pool of professionals it also proposes to make use of a state-of-the-art Python library named OSMnx. In the end, the consequences of using Volunteered Geographic Information (VGI), open source GIS platforms and Python scripting to perform the analysis are illustrated in a set of suitable case studies.
Rapid configurational analysis using OSM data. Towards the use of Space Syntax to orient post-disaster decision making
Cutini V.;
2019-01-01
Abstract
This paper addresses the problem of the growing exposure of contemporary cities to natural hazards by discussing the theoretical, methodological and practical aspects of using the configurational approach as a framework to perform a variety of spatial analyses to better orient disaster management. It claims that enabling a quick assessment of the evolving spatial functioning of the urban grid would effectively contribute to support strategic decision-making and to make post-disaster planning decisions more explicit among stakeholders, thus boosting wider understanding and participation among the public. The paper starts with a brief review of some relevant work done by the research community to date, which highlights emergent opportunities for urban morphology studies and Space Syntax theory to trigger effective innovations in disaster management practice. Next, the paper proposes to adopt a fit-for-purpose analysis approach with the aim to achieve a higher procedural flexibility in the analysis workflow. This issue is treated with a special focus on the necessities of relief organisations which need to integrate and overlap numerous layers of information and consider the feasibility of the analysis by evaluating time and costs. The proposal considers the economy of the construction of the map to be fundamental for ensuring the feasibility of a quantitative spatial assessment in data scarce contexts such as cities affected by disasters. Moreover, it recognises that the unicity of the map is likely to enable a better communication among different stakeholders following a BIM-oriented model of cooperation, while allowing a faster response in multi-hazards scenarios. Consequently, the proposal challenges the idea of the existence of a uniquely correct way to translate reality into a model, but rather suggests using a set of simplification techniques, such as filtering, generalisation and re-modelling, on a single crowdsourced map of the urban street network to generate suitably customised graphs for subsequent analysis. This brings together two themes: the first concerns the modelling activity per se and how certain technicalities that seem minor facts can influence the final analysis output to a greater extent; the second regards the crowdsourcing of spatial data and the challenges that the use of collaborative datasets poses to the modelling tasks. In line with the most recent research trends, this paper suggests exploiting the readiness of the Open Street Map (OSM) geo-dataset and the improving computational capacities of open GIS tools such as QGIS, which has recently achieved a wider acceptance worldwide. To further speed up the analysis and increase the likeness of the configurational analysis method to be successfully deployed by a larger pool of professionals it also proposes to make use of a state-of-the-art Python library named OSMnx. In the end, the consequences of using Volunteered Geographic Information (VGI), open source GIS platforms and Python scripting to perform the analysis are illustrated in a set of suitable case studies.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
