The Open Data Smart City: A Case Study of Smart City Progress through Open Data Initiatives

Smart cities, cities that are supported by an extensive digital infrastructure of sensors, databases and intelligent applications, have become a major area of academic, governmental and public interest. Simultaneously, there has been a growing interest in open data, the unrestricted use of organizational data for public viewing and use. Drawing on Science and Technology Studies (STS), Urban Studies and Political Economy, this thesis examines how digital processes, open data and the physical world can be combined in smart city development, through the qualitative interview-based case study of a Southern Ontario Municipality, Anytown. The thesis asks what are the challenges associated with smart city development and open data proliferation, is open data complimentary to smart urban development; and how is expertise constructed in these fields? The thesis concludes that smart city development in Anytown is a complex process, involving a variety of visions, programs and components. Although smart city and open data initiatives exist in Anytown, and some are even overlapping and complementary, smart city development is in its infancy. However, expert informants remained optimistic, faithful to a technologically sublime vision of what a smart city would bring. The thesis also questions the notion of expertise within the context of smart city and open data projects, concluding that assertions of expertise need to be treated with caution and scepticism when considering how knowledge is received, generated, interpreted and circulates, within organizations.

Imputing accelerometer non-wear time when assessing children's moderate-to-vigorous physical activity

BACKGROUND: Moderate-to-vigorous physical activity (MVPA) is an important determinant of children’s physical health, and is commonly measured using accelerometers. A major limitation of accelerometers is non-wear time, which is the time the participant did not wear their device. Given that non-wear time is traditionally discarded from the dataset prior to estimating MVPA, final estimates of MVPA may be biased. Therefore, alternate approaches should be explored. OBJECTIVES: The objectives of this thesis were to 1) develop and describe an imputation approach that uses the socio-demographic, time, health, and behavioural data from participants to replace non-wear time accelerometer data, 2) determine the extent to which imputation of non-wear time data influences estimates of MVPA, and 3) determine if imputation of non-wear time data influences the associations between MVPA, body mass index (BMI), and systolic blood pressure (SBP). METHODS: Seven days of accelerometer data were collected using Actical accelerometers from 332 children aged 10-13. Three methods for handling missing accelerometer data were compared: 1) the “non-imputed” method wherein non-wear time was deleted from the dataset, 2) imputation dataset I, wherein the imputation of MVPA during non-wear time was based upon socio-demographic factors of the participant (e.g., age), health information (e.g., BMI), and time characteristics of the non-wear period (e.g., season), and 3) imputation dataset II wherein the imputation of MVPA was based upon the same variables as imputation dataset I, plus organized sport information. Associations between MVPA and health outcomes in each method were assessed using linear regression. RESULTS: Non-wear time accounted for 7.5% of epochs during waking hours. The average minutes/day of MVPA was 56.8 (95% CI: 54.2, 59.5) in the non-imputed dataset, 58.4 (95% CI: 55.8, 61.0) in imputed dataset I, and 59.0 (95% CI: 56.3, 61.5) in imputed dataset II. Estimates between datasets were not significantly different. The strength of the relationship between MVPA with BMI and SBP were comparable between all three datasets. CONCLUSION: These findings suggest that studies that achieve high accelerometer compliance with unsystematic patterns of missing data can use the traditional approach of deleting non-wear time from the dataset to obtain MVPA measures without substantial bias.