The urban environment influences the way people live and shape their everyday lives, and microclimate sensitive design can enhance the use of urban streets and public spaces. Innovative approaches to urban microclimate design will become more important as the world’s population becomes ever more urban, and climate change generates more variability and extremes in urban microclimatic conditions. However, established methods of investigation based upon conventions drawn from building services research and framed by physiological concepts of thermal comfort may fail to capture the social dynamics of urban activity and their interrelationship with microclimate. This research investigates the relationship between microclimate and urban culture in Christchurch, New Zealand, based upon the concept of urban comfort. Urban comfort is defined as the socio-cultural (therefore collective) adaptation to microclimate due to satisfaction with the urban environment. It involves consideration of a combination of human thermal comfort requirements and adaptive comfort circumstances, preferences and strategies. A main methodological challenge was to investigate urban comfort in a city undergoing rapid physical change following a series of major earthquakes (2010-2011), and that also has a strongly seasonal climate which accentuates microclimatic variability. The field investigation had to be suitable for rapidly changing settings as buildings were demolished and rebuilt, and be able to capture data relevant to a cycle of seasons. These local circumstances meant that Christchurch was valuable as an example of a city facing rapid and unpredictable change. An interpretive, integrative, and adaptive research strategy that combined qualitative social science methods with biophysical measures was adopted. The results are based upon participant observation, 86 in-depth interviews with Christchurch residents, and microclimate data measurements. The interviews were carried out in a variety of urban settings including established urban settings (places sustaining relatively little damage) and emerging urban settings (those requiring rebuilding) during 2011-2013. Results of this research show that urban comfort depends on adaptive strategies which in turn depend on culture. Adaptive strategies identified through the data analysis show a strong connection between natural and built landscapes, combined with the regional outdoor culture, the Garden City identity and the connections between rural and urban landscapes. The results also highlight that thermal comfort is an important but insufficient indicator of good microclimate design, as social and cultural values are important influences on climate experience and adaptation. Interpretive research is needed to fully understand urban comfort and to provide urban microclimate design solutions to enhance the use of public open spaces in cities undergoing change.
When disasters and crises, both man-made and natural, occur, resilient higher education institutions adapt in order to continue teaching and research. This may necessitate the closure of the whole institution, a building and/or other essential infrastructure. In disasters of large scale the impact can be felt for many years. There is an increasing recognition of the need for disaster planning to restructure educational institutions so that they become more resilient to challenges including natural disasters (Seville, Hawker, & Lyttle, 2012).The University of Canterbury (UC) was affected by seismic events that resulted in the closure of the University in September 2010 for 10 days and two weeks at the start of the 2011 academic year This case study research describes ways in which e-learning was deployed and developed by the University to continue and even to improve learning and teaching in the aftermath of a series of earthquakes in 2010 and 2011. A qualitative intrinsic embedded/nested single case study design was chosen for the study. The population was the management, support staff and educators at the University of Canterbury. Participants were recruited with purposive sampling using a snowball strategy where the early key participants were encouraged to recommend further participants. Four sources of data were identified: (1) documents such as policy, reports and guidelines; (2) emails from leaders of the colleges and academics; (3) communications from senior management team posted on the university website during and after the seismic activity of 2010 and 2011; and (4) semi-structured interviews of academics, support staff and members of senior management team. A series of inductive descriptive content analyses identified a number of themes in the data. The Technology Acceptance Model 2 (Venkatesh & Davis, 2000) and the Indicator of Resilience Model (Resilient Organisations, 2012) were used for additional analyses of each of the three cases. Within the University case, the cases of two contrasting Colleges were embedded to produce a total of three case studies describing e-learning from 2000 - 2014. One contrast was the extent of e-learning deployment at the colleges: The College of Education was a leader in the field, while the College of Business and Law had relatively little e-learning at the time of the first earthquake in September 2010. The following six themes emerged from the analyses: Communication about crises, IT infrastructure, Availability of e-learning technologies, Support in the use of e-learning technologies, Timing of crises in academic year and Strategic planning for e-learning. One of the findings confirmed earlier research that communication to members of an organisation and the general public about crises and the recovery from crises is important. The use of communication channels, which students were familiar with and already using, aided the dissemination of the information that UC would be using e-learning as one of the options to complete the academic year. It was also found that e-learning tools were invaluable during the crises and facilitated teaching and learning whilst freeing limited campus space for essential activities and that IT infrastructure was essential to e-learning. The range of e-learning tools and their deployment evolved over the years influenced by repeated crises and facilitated by the availability of centrally located support from the e-Learning support team for a limited set of tools, as well as more localised support and collaboration with colleagues. Furthermore, the reasons and/or rate of e-learning adoption in an educational institution during crises varied with the time of the academic year and the needs of the institution at the time. The duration of the crises also affected the adoption of e-learning. Finally, UC’s lack of an explicit e-learning strategy influenced the two colleges to develop college-specific e-learning plans and those College plans complemented the incorporation of e-learning for the first time in the University’s teaching and learning strategy in 2013. Twelve out of the 13 indicators of the Indicators of Resilience Model were found in the data collected for the study and could be explained using the model; it revealed that UC has become more resilient with e-learning in the aftermath of the seismic activities in 2010 and 2011. The interpretation of the results using TAM2 demonstrated that the adoption of technologies during crises aided in overcoming barriers to learning at the time of the crisis. The recommendations from this study are that in times of crises, educational institutions take advantage of Cloud computing to communicate with members of the institution and stakeholders. Also, that the architecture of a university’s IT infrastructure be made more resilient by increasing redundancy, backup and security, centralisation and Cloud computing. In addition, when under stress it is recommended that new tools are only introduced when they are essential.
