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The ParkServe™ Team – September 2016 Strategies and Tips for Large Scale Data Collection and Automation The Trust for Public Land’s ParkServe TM
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Transcript of 2016 conservation track: strategies and tips for large scale data collection and automation by...
The ParkServe™ Team – September 2016
Strategies and Tips for Large Scale Data Collection and AutomationThe Trust for Public Land’s ParkServeTM
5/17/2016
Presentation Agenda
Overview: The Trust for Public Land as an organization
Context: ParkScore® and ParkServe™
Strategies and Tips: Collection, Creation, Loading and Modeling
Conclusions: Best Practices, Roadblocks and Limitations
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Our Mission
The Trust for Public Land creates parks and protects land for people, ensuring healthy, livable communities for generations to come.
The Trust for Public Land’s ParkScore®
• Taking ParkScore® to scale
• Focuses on park accessibility metric for all urban areas in the US
What is ParkServe™?
By May 1, 2018 ParkServe aims to map parks and park access for:• All 3,573 US Census 2010 defined Urban Areas-Urban Clusters
– 13,931 US Census 2010 places (including 12,762 Cities)
– 2,494 of 3,143 US counties (70%)
ParkServe at 100% would summarize 10 minute walk park access for:
– Over 80% of Americans – Over 80% of American households earning less than $35,000 per year – Over 90% of all persons of color
What is ParkServe™?
Based on 2015 US Population Estimates—284,301,095 people live within Block Groups intersecting Urban Area-Urban Cluster areas of 318,536,439 total US population (Sources: US Census Bureau 2010 Census Urban Areas and Place boundaries, and Esri, Inc. Business Analyst 2015 Demographic Dataset).
What is ParkServe™?4 Components of Completion
1. Data Collection2. Data Creation3. Data Loading & Preliminary Modeling4. Data Verification & Final Modeling
Iterative Approach to Completion
ParkServeTM Collection
ParkServeTM Collection
ParkServeTM Data Creation
ParkServeTM Reporting
ParkServeTM Reporting
ParkServeTM Data Loading
ParkServeTM Modeling
ParkServeTM Data Verification
• Data Model Evolution– Start simple and add complexity as necessary
• Data Collection Workflow– More testing/development before scaling up– Speed/Progress vs. Efficiency
• Complex automation– Everything built from scratch– Learning process for whole team
ParkServeTM Roadblocks and Limitations
