Various types of essential data are collected at different phases of the infrastructure life cycle. For major infrastructure assets, these data are commonly available and tend to be of high quality. At the local level however, the quality of infrastructure data is deficient in terms of completeness, timeliness, and currency. Citizen science offers a nonconventional but potentially effective approach for improving the quality of infrastructure data in local communities. Hence, this journal article formatted dissertation aims to assess infrastructure data management practices and provide solutions to enhance the availability and quality of these data, especially for local communities. The research objectives are to: (a) assess the maturities of infrastructure data in public agencies, (b) identify key quality characteristics of infrastructure data and provide a framework to aid in producing high-quality infrastructure monitoring data through citizen science methods, (c) assess the validity and reliability of citizen-generated infrastructure monitoring data, focusing on stormwater drainage infrastructure, and (d) develop a guide for monitoring drainage infrastructure in local communities using volunteer citizen scientists. Each of these objectives serves as the central theme for each article in this dissertation. In the first study, a quantitative method was developed and applied to assess infrastructure data management practices of public agencies with a focus on transportation agencies. The data management areas assessed included data stewardship, storage and warehousing, and integration. The second study discussed the potential of interdisciplinary citizen science projects to enhance infrastructure monitoring data and decision-support models for local communities. Factors that can influence the quality of infrastructure monitoring data were also discussed. The third study assessed the reliability and validity of drainage infrastructure monitoring data collected using citizen scientists in field trials. Reliability was assessed relative to trained inspectors' data while validity was assessed relative to trained inspectors' data and data obtained from LiDAR survey. Results obtained further established the applicability of citizen science to infrastructure monitoring. Finally, experience garnered from iterative design of protocol and implementation of field trials were analyzed. The analysis formed the basis for recommendations offered to aid collection of drainage infrastructure monitoring data in local communities.
Various types of essential data are collected at different phases of the infrastructure life cycle. For major infrastructure assets, these data are commonly available and tend to be of high quality. At the local level however, the quality of infrastructure data is deficient in terms of completeness, timeliness, and currency. Citizen science offers a nonconventional but potentially effective approach for improving the quality of infrastructure data in local communities. Hence, this journal article formatted dissertation aims to assess infrastructure data management practices and provide solutions to enhance the availability and quality of these data, especially for local communities. The research objectives are to: (a) assess the maturities of infrastructure data in public agencies, (b) identify key quality characteristics of infrastructure data and provide a framework to aid in producing high-quality infrastructure monitoring data through citizen science methods, (c) assess the validity and reliability of citizen-generated infrastructure monitoring data, focusing on stormwater drainage infrastructure, and (d) develop a guide for monitoring drainage infrastructure in local communities using volunteer citizen scientists. Each of these objectives serves as the central theme for each article in this dissertation. In the first study, a quantitative method was developed and applied to assess infrastructure data management practices of public agencies with a focus on transportation agencies. The data management areas assessed included data stewardship, storage and warehousing, and integration. The second study discussed the potential of interdisciplinary citizen science projects to enhance infrastructure monitoring data and decision-support models for local communities. Factors that can influence the quality of infrastructure monitoring data were also discussed. The third study assessed the reliability and validity of drainage infrastructure monitoring data collected using citizen scientists in field trials. Reliability was assessed relative to trained inspectors' data while validity was assessed relative to trained inspectors' data and data obtained from LiDAR survey. Results obtained further established the applicability of citizen science to infrastructure monitoring. Finally, experience garnered from iterative design of protocol and implementation of field trials were analyzed. The analysis formed the basis for recommendations offered to aid collection of drainage infrastructure monitoring data in local communities.
