In this paper, we demonstrate three unique use cases of LiDAR data and processing, which can be implemented in an urban metropolis to determine the challenges that are associated with climate change. LiDAR data for the City of Toronto were collected in April 2015 with a density of 10 points/m 2 . We utilized both a digital terrain model and a bare earth digital elevation model in this work. The first case study estimated storm water, in which we compared flow accumulation values and catchment areas generated with a 20-m DEM and a 1-m LiDAR DEM. The finer resolution DEM demonstrated that the urban street features play a significant role in flow accumulation by directing flows. Urban catchment areas were found to occur on spatial scales that were smaller than the 20-m DEM cell size. For the second case study, the solar potential in the City of Toronto was calculated based on the slope and aspect of each land parcel. According to area, 56% of the city was found to have high solar potential, with 33% and 11% having medium and low solar potential. For the third case study, we calculated the building heights for 16,715 high-rise buildings in Toronto, which were combined with ambulance and fire emergency response times required to reach the base of the building. All buildings that had more than 17 stories were within a 5-min response time for both fire and ambulance services. Only 79% and 88% of these buildings were within a 3-min response time for ambulance and fire emergencies, respectively. LiDAR data provides a highly detailed record of the built urban environment and can provide support in the planning and assessment of climate change resilience activities.
Qualitative GIS is a relatively new methodological approach for analyzing and visualizing qualitative data within a geographic context. Qualitative data can take many forms, including interviews, documents, photographs, and audio and video clips. Content analysis for example, is an effective qualitative method for analyzing text-based data. We argue that basic concepts, (i.e. how to store data, data requirements, visualization techniques, and modes of analysis) within qualitative GIS have not been adequately defined, rendering difficult the replication of work performed and hindering the development of incremental knowledge in the field. Database management systems provide a means for storing, managing, and analyzing qualitative GIS data. A standardized and well-designed open source database system provides a mechanism for qualitative GIS projects, ensuring consistency and project replication. Qualitative GIS data stored in a database allows for additional visualization options, such as geographic word clouds. To demonstrate the concepts we performed content analysis on Master Transportation Plans from Calgary and Montreal using SpatiaLite, an open source database system. We developed Structured Query Language (SQL) queries to generate and populate groups and theme tables within the SpatiaLite database. We present our database design and queries in the hopes that they will help others conducting qualitative GIS research.
The power of technology poses opportunities and risks when it is focused on addressing urban issues and masked as civic participation. • The model of implementation for technological solutions can determine if a city is actually smart and adheres to a democratic model of governance. • Until the citizen is included and co-designs the smart city, outcomes will do little more than propagate uncertainty and bias in a rapidly changing industry of smart city technologies. In this paper, the policies, projects, and promises of "smart" initiatives at the City of Toronto are evaluated, as they manifest through a technological convergence between local government services and an increased focus on citizen services through data-driven mediums. Through direct participant observation and formal interviews, a robust understanding of the internal institutional dynamics, the perspectives citizens in the "smart city," and the operational disconnects in governance, policy, and practice has been gained. Our case study on the City of Toronto provides an account of how and from where these smart motivations for increasing a data-driven engagement with the public have arisen over the past several years. In doing so, we identify key characteristics that both enable and hinder the existing smart city in the forms of access to open data, the use of increased computational methods, and the engagement of public services through digital space as requirements for the future of participatory governance. We argue that instituting appropriate policies and engaging citizens to co-design and participate in the planning processes are essential to ensuring an inclusive, modern, and open smart city.
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