Social Connectivity and Disaster Resilience: An opportunity for improved mapping and measurement using call detail records

Reducing the risk of populations to disaster is a key priority for those working within sustainable development, as highlighted by global policies including the Sustainable Development Goals and the Sendai Framework for Disaster Risk Reduction. Consequently, there is a need to understand where disaster risk is at its greatest, yet its quantification has proven difficult. Disaster risk is a function of the likely occurrence and exposure of a hazard, the vulnerability of the population to the hazard, and their (in)ability to prepare for, absorb and build back from the adverse impacts of the hazard, often understood as their resilience. The quantification of the latter two aspects, vulnerability and resilience, is not straightforward, with both having multiple definitions as well as approaches to their measurement. Within the wider resilience field, an alternative approach to its measurement is evolving, which specifically focuses on social networks as the unit of analysis. The premise is that greater social connectivity will directly enhance resilience, can be evaluated through a singular approach, and can be quantified using social network analysis. This approach has however been limited by the availability of data at substantive spatial and temporal scales.This PhD proposes that there is a significant opportunity to utilise Call Detail Records (CDRs), the metadata generated from the use of a mobile phone, to address these data limitations. The overall aim of this thesis is to assess the feasibility of using CDRs to create a social connectivity dataset that can be used specifically within disaster resilience estimation for disaster risk reduction. To substantiate the creation of this dataset from CDRs, the theoretical framework behind using social connectivity for disaster resilience estimation is first established, including a systematic review that evaluates the importance of social networks for disaster risk reduction in Nepal. The thesis then accounts for the representativeness of the CDR dataset through analysing the changing geo-demographics of mobile phone ownership in Nepal. In the last decade, household ownership has grown substantially Nepal across different socio-economic groups, whilst individual ownership stood at 82% in 2016. As a result, the CDR dataset is likely to be representative of a substantial cross-section of Nepal’s population. The feasibility of using CDRs to represent real-world social networks is then addressed by mapping the spatial distribution of the social communities detected within the CDR network. The study finds that the social communities are spatially concentrated; within these distributions, geographic communities, such as towns and cities, can be identified.The thesis then evaluates whether CDRs can be used for improved mapping and measurement of social connectivity for disaster resilience and risk estimation, creating a social connectivity index using novel CDR data and social network analysis. The index and its variables show that there are clear geographical patterns to social connectivity, with the peri-urban middle Hill regions expected to demonstrate the greatest resilience due to their sizeable and strong bonding and bridging networks. The thesis then addresses the limitations of each of the analyses presented and identifies future opportunities for further research. The thesis concludes that CDRs and the emerging body of literature on social connectivity and social network analysis present a significant opportunity to rethink the current methods of measurement of disaster resilience for disaster risk reduction