Risk-Sensitive Land-Use Planning (RSLUP) is a critical process for integrating disaster-risk management (DRM) considerations into land-use planning, for which first it is necessary to understand the existing risk sensitivity. This study aims to develop a GIS-based multi-criteria zoning approach to mapping earthquake-risk sensitivity of land use at local level. The methodology comprises two main steps. Firstly, the spatial earthquake-risk sensitivity of land use is evaluated based on some risk themes related to development, land use, and emergency facilities. Secondly, earthquake-risk sensitivity is mapped by overlaying the spatial risk-theme maps, using weights determined through the Analytical Hierarchical Process (AHP). Overall, four earthquake-risk-sensitive zones were identified in the study area, ranging from high-risk-sensitive zones to low-risk-sensitive zones. This research contributes to the theory and practice of DRM by proposing a multi-criteria risk-sensitivity assessment and mapping methodology. The findings demonstrate that the risk-sensitivity map provides policymakers with a guide to identify “where” and “why” RSLUP interventions are necessary. This will serve as a decision-support system to formulate the “what” and “how” of the RSLUP at the local level, reflecting the risk sensitivity accordingly and thereby ensuring sustainability.

Local government bodies and other concerned agencies in developing countries spend a considerable amount of money on rural road development. However, in the absence of any robust and systematic methodology, road development largely relies on ad-hoc decisions and subjective judgement of public officials. Such a decision-making process often leads to inefficient resource allocation bypassing equity and long-term societal benefits. Although there are some established methodologies for road network planning, complexities exist in applying those methods. First, most of the established methods are not suitable for rural road development, particularly regarding the volume and nature of traffic on them. Second, some methods are highly complex and lack practical applicability. Third, road development planning should not be top-down alone but ensure the participation of local stakeholders. Given these limitations, this study proposes a methodology—Rural Road Planning and Prioritisation Model (RPPM). It consists of two major components (i) developing a core network in participation with local stakeholders and (ii) prioritisation of roads based on Cost-Benefit Analysis (CBA) and Multi-Criteria Analysis (MCA). The proposed method is piloted in one district, and a web-based software is also developed for practical implementation by the Local Government Engineering Department (LGED), Bangladesh. The paper also discusses the results of the pilot study.

Risk-Sensitive Land Use Planning (RSLUP) is the process of mainstreaming disaster risk reduction and management parameters in land use planning. To ensure the effectiveness and sustainability of RSLUP, it is necessary to identify and understand the existing risk sensitivity of the land use plan. This research aims to develop a GIS-based multi-criteria zoning approach for mapping earthquake risk sensitivity of land use at local level. The methodology applied in this research is comprised of two steps. Firstly, assessment of the spatial earthquake risk sensitivity of the land use of an area based on the risk themes and corresponding risk attributes including both natural characteristics as well as built environment factors. Secondly, mapping of earthquake risk sensitivity by overlaying the spatial risk attribute maps based on weights determined through the Analytical Hierarchical Process (AHP). The research findings show that risk sensitivity map provides a guideline for policymakers to understand “where” and “why” RSLUP interventions are necessary. This research contributes to the theory and practices of disaster risk reduction and management by proposing a risk sensitivity assessment and mapping methodology. This will facilitate RSLUP at local level as a decision support system ensuring sustainability by reflecting the risk sensitivity accordingly and effectively.