Abstract Title

Adapting to Climate Change Using Data Science in Local Governments

Additional Funding Sources

This project was made possible by the NSF Idaho EPSCoR Program and by the National Science Foundation under Award No. OIA-1757324. Additional funding was received from IBEST, a bioinformatics center within University of Idaho.

Abstract

Data science-skills (DSS)—a mix of computational, statistical, data task, and domain knowledge and competencies (Overton and Kleinschmit, Forthcoming) used to transform data to insight (Provost and Fawcett 2013)—improve a city government’s capacity to adapt to climate change by leveraging technological innovation and big data. Unfortunately, little is known about the current state of DSS in city governments. This project is designed to fill this gap in knowledge through two specific objectives: (1) determine the current levels of DSS in city governments and (2) identify the barriers preventing the acquisition of DSS. Since no data on the subject exists, a unique survey instrument will be designed and fielded to city managers to collect data. The project hypothesizes that the level of most DSS in cities are low and that leadership bias, government characteristics, and limited resources prevent city’s from acquiring DSS necessary to adapt to climate change. This poster focuses on the timeline of the project, the selection process of the cities, and the early development of the survey questions.

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Adapting to Climate Change Using Data Science in Local Governments

Data science-skills (DSS)—a mix of computational, statistical, data task, and domain knowledge and competencies (Overton and Kleinschmit, Forthcoming) used to transform data to insight (Provost and Fawcett 2013)—improve a city government’s capacity to adapt to climate change by leveraging technological innovation and big data. Unfortunately, little is known about the current state of DSS in city governments. This project is designed to fill this gap in knowledge through two specific objectives: (1) determine the current levels of DSS in city governments and (2) identify the barriers preventing the acquisition of DSS. Since no data on the subject exists, a unique survey instrument will be designed and fielded to city managers to collect data. The project hypothesizes that the level of most DSS in cities are low and that leadership bias, government characteristics, and limited resources prevent city’s from acquiring DSS necessary to adapt to climate change. This poster focuses on the timeline of the project, the selection process of the cities, and the early development of the survey questions.