Sportsman’s Value Mapping - ESRI · 2016. 1. 29. · Sportsman’s Value Mapping Leveraging Web...

Sportsman’s Value Mapping

Leveraging Web GIS for Science-based

Public Spatial Surveys

Esri SW User Conference January 18, 2016 1

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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QuestionsThank You!

rlawrence@azgfd.gov