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


Where do you hunt and fish????


114,000 Square Miles22 Tribal Nations22,000 Private Landowners


Land Management Complexity


Multiple DemandsPressured Landscapes


Trouble on the Horizon


Sportsman’s Value Mapping Survey


Sportsman’s Value Mapping Survey


https://arcgis.azgfdportal.com/svmoteromesa/

https://arcgis.azgfdportal.com/svmoteromesa/

https://www.youtube.com/watch?v=l0u_9nsfskU

https://www.youtube.com/watch?v=l0u_9nsfskU

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


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


SVM Design Requirements


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.)


SVM Application DevelopmentEsri Application Template

ArcGIS API for Java Script


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


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


Marketing & Communication


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


• 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


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


• 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


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


• 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



• 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



• 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



• 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):



• “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




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”


– 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



• 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…



• 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


• 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


• 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


• 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


http://www.azgfd.com/

QuestionsThank You!

[email protected]

sportsman’s value mapping - esri · 2016. 1. 29. · sportsman’s value mapping leveraging web...

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