title: A Case Study: Analyzing City Vitality with Four Pillars of Activity-Live, Work, Shop, and Play.
abstract: This case study evaluates and tracks vitality of a city (Seattle), based on a data-driven approach, using strategic, robust, and sustainable metrics. This case study was collaboratively conducted by the Downtown Seattle Association (DSA) and CDO Analytics teams. The DSA is a nonprofit organization focused on making the city of Seattle and its Downtown a healthy and vibrant place to Live, Work, Shop, and Play. DSA primarily operates through public policy advocacy, community and business development, and marketing. In 2010, the organization turned to CDO Analytics ( cdoanalytics.org ) to develop a process that can guide and strategically focus DSA efforts and resources for maximal benefit to the city of Seattle and its Downtown. CDO Analytics was asked to develop clear, easily understood, and robust metrics for a baseline evaluation of the health of the city, as well as for ongoing monitoring and comparisons of the vitality, sustainability, and growth. The DSA and CDO Analytics teams strategized on how to effectively assess and track the vitality of Seattle and its Downtown. The two teams filtered a variety of data sources, and evaluated the veracity of multiple diverse metrics. This iterative process resulted in the development of a small number of strategic, simple, reliable, and sustainable metrics across four pillars of activity: Live, Work, Shop, and Play. Data during the 5 years before 2010 were used for the development of the metrics and model and its training, and data during the 5 years from 2010 and on were used for testing and validation. This work enabled DSA to routinely track these strategic metrics, use them to monitor the vitality of Downtown Seattle, prioritize improvements, and identify new value-added programs. As a result, the four-pillar approach became an integral part of the data-driven decision-making and execution of the Seattle community's improvement activities. The approach described in this case study is actionable, robust, inexpensive, and easy to adopt and sustain. It can be applied to cities, districts, counties, regions, states, or countries, enabling cross-comparisons and improvements of vitality, sustainability, and growth.
authors.affiliation: 1 Pine Street Group L.L.C. , Seattle, Washington.
authors.forename: Matt
authors.initials: M
authors.lastname: Griffin
authors.affiliation: 2 Nordstrom, Inc. , Seattle, Washington.
authors.forename: Blake W
authors.initials: BW
authors.lastname: Nordstrom
authors.affiliation: 3 Downtown Seattle Association , Seattle, Washington.
authors.forename: Jon
authors.initials: J
authors.lastname: Scholes
authors.affiliation: 4 City of Seattle, Office of the Mayor , Seattle, Washington.
authors.forename: Kate
authors.initials: K
authors.lastname: Joncas
authors.affiliation: 5 ZGF Architects L.L.P. , Seattle, Washington.
authors.forename: Patrick
authors.initials: P
authors.lastname: Gordon
authors.affiliation: 3 Downtown Seattle Association , Seattle, Washington.
authors.forename: Elliott
authors.initials: E
authors.lastname: Krivenko
authors.affiliation: 6 CDO Analytics, Seattle Children's Hospital , Seattle, Washington.|7 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington.|8 Biomedical Informatics Program, Stanford University , Palo Alto, California.|9 Data-Enabled Life Sciences Alliance (DELSA) , Seattle, Washington.
authors.forename: Winston
authors.initials: W
authors.lastname: Haynes
authors.affiliation: 6 CDO Analytics, Seattle Children's Hospital , Seattle, Washington.|7 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington.|9 Data-Enabled Life Sciences Alliance (DELSA) , Seattle, Washington.|10 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington.
authors.forename: Roger
authors.initials: R
authors.lastname: Higdon
authors.affiliation: 6 CDO Analytics, Seattle Children's Hospital , Seattle, Washington.|7 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington.|9 Data-Enabled Life Sciences Alliance (DELSA) , Seattle, Washington.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Stewart
authors.affiliation: 6 CDO Analytics, Seattle Children's Hospital , Seattle, Washington.|9 Data-Enabled Life Sciences Alliance (DELSA) , Seattle, Washington.|10 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington.
authors.forename: Natali
authors.initials: N
authors.lastname: Kolker
authors.affiliation: 6 CDO Analytics, Seattle Children's Hospital , Seattle, Washington.|7 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington.|9 Data-Enabled Life Sciences Alliance (DELSA) , Seattle, Washington.|10 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Montague
authors.affiliation: 6 CDO Analytics, Seattle Children's Hospital , Seattle, Washington.|7 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington.|9 Data-Enabled Life Sciences Alliance (DELSA) , Seattle, Washington.|10 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington.|11 Departments of Biomedical Informatics and Medical Education and Pediatrics, School of Medicine, University of Washington , Seattle, Washington.|12 Department of Chemistry and Chemical Biology, College of Science, Northeastern University , Boston, Massachusetts.
authors.forename: Eugene
authors.initials: E
authors.lastname: Kolker
mesh_terms: D002958:City Planning / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D019982:Organizational Case Studies; D014861:Washington
keywords: big data analytics; machine learning; metrics; modeling; predictive analytics; urban research and planning