sportsman’s value mapping - esri · 2016. 1. 29. · sportsman’s value mapping leveraging web...
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Sportsman’s Value Mapping
Leveraging Web GIS for Science-based
Public Spatial Surveys
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Overview
• Introduction/Background Slides 1 – 10
• Implementation/Production Slides 11 – 22
• Lessons Learned Slides 23 – 32
• Summary Slides 32 - 35
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Where do you hunt and fish????
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Where do you hunt and fish????
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114,000 Square Miles22 Tribal Nations22,000 Private Landowners
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Land Management Complexity
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Multiple DemandsPressured Landscapes
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Trouble on the Horizon
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Sportsman’s Value Mapping Survey
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Sportsman’s Value Mapping Survey
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SVM Team• John Hamill - TRCP Arizona Representative• Loren Chase - Human Dimensions Specialist• Ryan Nosek - Web Developer and GIS
Infrastructure Specialist• Dean Pokrajac - Web Developer and GIS
Infrastructure Specialist• Sue Boe - GIS Analyst• Jim DeVos - Assistant Director, Wildlife Division• Joyce Francis - Habitat Branch Chief• Doug Cummings - Information Branch Chief• Richard Lawrence - GIS Program Manager
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SVM Design Requirements
• Web-based survey• Easy to use,
intuitive sketching of user-provided geographic features
• Collects attitude information
• Exit interview• GIS-based analysis• Contain costs
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SVM Design Requirements
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Species/GroupFRIEND HARVEST TROPHY
LESSCROWDED
USUAL SPOT
CLOSE TO HOME
DRAWN EASILY
LIMITED ACCESS
EASY TO HUNT SCENERY
Turkey
HighModerately High
MediumModerately Low
Low<Null> = Low
Waterfowl
Predators
Elk
White-tailed Deer
Squirrel
Javelina
Mule Deer
Bighorn Sheep
Pronghorn
Dove
Other Small Game
Quail
Cold-water Fish (Trout)
Warm-water Fish (bass, catfish, etc.)
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SVM Application DevelopmentEsri Application Template
ArcGIS API for Java Script
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SVM Infrastructure• INITIAL CONFIGURATION: 2 Amazon m3.large instance
types (2 CPU cores w/7.5 GB RAM)• Drawing/editing geographic features became CPU intensive• Dual instances were not sharing
properly (Cloud builder net yet available)
• FINAL HIGH DEMAND CONFIGURATION: 1 Amazon c4.2xlarge instance type (8 CPU cores w/ 15 GB RAM)
• FINAL LOW DEMAND CONFIGURATION: 1 c4.xlarge (4 CPU, 7.5 GB RAM
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Monitoring, Data Backup & Management
• Archives of data backups created weekly
• Mock analyses and summarization was continual
• Google Analytics used to monitor traffic
• Data backups performed nighly in Amazon S3
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Marketing & Communication
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Survey Implementation
• Randomly selected 10000 AZ hunting & fishing license holders between ages 18 and 65
• Scripted creation of user store and credentials
• Mailings contracted with 3rd party vendor (Prisma Graphics)– Phase 1 – 7500 postcards with login URL/credentials– Phase 2 - 7200 reminders– Phase 3 - 7000 reminders + incentives
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• Concept dev & testing – October 2014 – June 2015
• Implementation & User Testing - July - August 2015
• Released/begin Phase I – September 15, 2015
• Survey Closed – December 7, 2015
• Analysis planning occurred December 2015
• Analysis currently under way
• Publication as Story Map Spring 2016– http://www.azgfd.com/mapSurvey
Survey Timeline / Status
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Survey Implementation Challenges• Stage 1 – September 15, 2015
• Few issues• Minor and intermittent latency/sluggishness
• Stage 2 – October 12, 2015: 7200 cards to non-participants reminding of completion deadline• Few issues
• Stage 3 – November 16: Incentives add fuel to fire• Incentives• CPU performance• Application performance• Target of 1000 respondents exceeded• 15% response rate achieved
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• Weekly messaging and response decision-making
• Daily customer questions, complaints, and handling
• Configuration details, application adjustments, and infrastructure adjustments as needed
• Survey closed on December 7th – target of 1000 exceeded (n = 1213 respondents)
Troubleshooting Activities
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Lessons Learned & Preliminary Results
• Development– App templates saved time & provided agility– Java Script API for ArcGIS provided flexibility and
performance improvements– Design and database development were easily
implemented by designers/non-developer staff– Frequent user testing and an open mind proved
essential to survey success and acceptance
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• Infrastructure:– Cloud builder for Amazon cloud deployment is a must
– Drawing/editing geographic features = MUCHO CPU• 5-7 simultaneous users (10-25 users per hour per Google
Analytics) easily handled by 1 c4.xlarge (4 CPU, 7.5 GB RAM)• Cloud builder allows flexible configuration of automatic
scaling abilities of the Cloud• > 5-7 simultaneous users (> 10-25 users/hour per GA) was
better handled by 1 c4.2xlarge (8 CPU, 15 GB RAM)
– STRONGLY SUGGEST load testing for scalability/ failure loads when anticipating higher user loads
Lessons Learned & Preliminary Results
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• Communication / Marketing– For surveys, BEGIN WITH THE INCENTIVE– Tailor the target interests/age (demographic)– Get the word out – legitimize the survey– Aggressively help and exude optimism when
dealing with questions, issues, and complaints– Every response costs about $10; claim/legitimize
responses
Lessons Learned & Preliminary Results
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• New Ground – The public is capable of reasonably assisted, technical, geographic surveys:
• able to draw reasonable geographic features
• responsive to having their voice heard, but incentives MATTER AND ARE HIGHLY EFFECTIVE
• Taking time and care when attempting the unknown pays high dividends
Lessons Learned & Preliminary Results
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• Current analysis: - This survey is also breaking new grounds analytically:– “Shardiness Index” – perimeter / √(𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂):
quickly identifies mistakes to be removed from the response analysis set
– “Value Index”-based analysis – each polygon is weighted: 1 / √(𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂):
Lessons Learned & Preliminary Results
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• “Value Index”-based analysis
– Polygons joined using the union geoprocessing function
– Mathematically combined scores of contributing polygons
– Resulting polygons converted to 60m pixel resolution raster
Lessons Learned & Preliminary Results
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Lessons Learned & Preliminary Results
Analyses
• “Hot spot” that accounts for drawing care and specificity & controls against generality
• Layer interpreted as “hunter/angler value”• ELK: most complex of our 15 species/species
groups layers • 1000s of contributed polygons• > 11 million polygons after “union-ing”
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– Attitudes• Same polygonal approach, no weighting or raster• 1 map per attitude for 15 species/species groups• 150 total attitude maps• Strength of attitudes provided by High to Low scale• Map will attitude strength on “value index” at a given
location• Currently experimenting with display techniques
Lessons Learned & Preliminary Results
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• Future uses– New Mexico conservation orgs are developing a tool based on our
templates
– The Arizona tool may be reapplied in future years
– The Arizona tool may be retooled to include non-consumptive (but still highly valuable) wildlife and habitat valuation for activities such as:
• Birding
• Off-road recreation (2, 3 and 4 wheel)
• Photography
• Hiking
• Mountain Biking
• Others…
Lessons Learned & Preliminary Results
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• Conservation managers needed information about how our hunting and angling publics value state lands.
• Managers needed information about attitudes that hunters/anglers share about their most-valued hunting and fishing spots.
Summary
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• State and nongovernment conservation organizations partnered to create the first-ever, successful map-based survey
• Survey assessed hunter and angler values and attitudes about most-valued spots
• We used ArcGIS Online and ArcGIS Server platform technology available through Esri’s Enterprise License Agreement with AZGFD.
Summary
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• Use results to help raise awareness of hunting and fishing land values in Arizona,
• Better understand and address attitudes that create value for most-valued areas
• Provide hunters and anglers with a voice to be heard during land use discussions
Summary
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• Use the AZGFD Fish Portal (www.azgfd.com) and TRCP websites to share maps and results
• Use cloud-based map services to feed the portal and websites that tell the story
• Continue to experiment with maps and apps as ways of providing surveys/information
Summary
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