Dobson presentation nys geo summit for slideshare

The Briefing That Follows

• Was an invited presentation at the New York State Geospatial Summit 2011– Held in Skaneateles, New York

• Presentation by Dr. Mike Dobson, TeleMapics– I appreciated the invitation to speak and the

assistance provided by– Bill Johnson (Deputy Director, Office of Cyber Security,

NYS Division of Homeland Security & Emergency Services (DHSES))

Last Night, did the bed in your hotel room -

Have these sheets?

Then, You Need To Know About

Crowdsourcing AKA

VGI

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Crowdsourcing

• Today, I hope to cover– Why crowdsourcing should be of interest to

builders of spatial databases– What is accomplished by adapting

crowdsourcing as a tool in database compilation

– Issues related to using crowdsourcing in map database compilation

Besides its many benefits

• Crowdsourcing is, unfortunately, a form of disintermediation– And Mapping/GIS Professional may be the

ones disintermediated– You may want to pay attention to this topic

• So you can tell your boss what he or she doesn’t know that you do (or will by the end of this talk)

Map Errors

• It used to be that most errors on maps were the result of– Compilation/edit errors– Printing errors

• Yep, maps used to be printed.• Guess we showed those lithography and printing

press guys.

How We Doing?

Common Map Errors

Common Map Errors

New Types Of Map Errors

• Now we have map errors resulting from– Database synchronization error– Conflation errors– Over-reliance on sophisticated software

systems that do not yet have all the capabilities of expert systems

• Of course, there are few mapping experts left, so this problem may never be resolved

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Common Map Errors

Common Map Errors

Map Updating V.1

Map Updating V. 2

Map Updating V. 2a

Common Map Errors

Common Map Errors

Common Map Errors

Okay – You Get The Point

• How do we fix this?– Spend more money (more than you can say)

• Not likely

– Employ crowdsourcing to harness the• Wisdom of crowds• Local knowledge• Social Media

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Social Networking

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Crowdsourcing Provides

• Content that is produced and voluntarily contributed by users of web sites and digital media

Crowdsourcing/VGI

• Architecture of participation– Based on

• Sharing• Cooperation• Collective action

– Not based on• Coordinated management• Operational budgets• Overarching business structure• Specific use cases

The Crowd Part

• As Linus Torvald of Linux fame said of “open software” efforts– “With more eyes all bugs become shallow”

• Let’s recast this and say that in crowdsourced map compilation efforts

• “With enough eyes all map errors become noticeable and candidates for correction”

• If, that is, the eyes belong to locals who know the details of the geography in question

Crowdsourcing

• Based on the wisdom of crowds– Finding a way to discover the knowledge that

a group has about geographical space

Crowdsourcing

• What makes a crowd wise?– Diversity of opinion– Independence– Decentralization– Aggregation (finding a way to help people

work together)

Crowdsourcing

• The wisdom of crowds– If a group satisfies these conditions, its

judgment is likely to be accurate – Each person’s guess, you might say, has two

components: information and error – If you can subtract the error, and you’re left

with the information

Crowdsourcing/VGI

• Data quality in this type of system is influenced by– The “law” of large numbers

• “…With more eyes, map errors become shallow”

– The “always editable” status of the database– Presumption that the database is self-healing

over time– The tension between “openess” in collection

and “standardization of requirements for data entry and attributes

Okay

• If that is how crowdsourcing systems are supposed to work, how well have we done using it for the compilation of map databases?

Crowdsourcing And Maps

Crowdsourcing And Maps

Crowdsourcing And Maps

Structural Problems With Crowdsourcing

Issues for Crowdsoucring

Sub-Issues Weaknesses

1. How many people will contribute?

b. How many collaborators are capable of contributing valid data?

c. Not enough credible data gathers to validate the data.

Structural Problems With Crowdsourcing

Issues for Crowdsoucring

Sub-Issues Weaknesses

2. Where are contributors located?

b. How does this distribution match the desired map coverage?

c. Not enough distribution/coverage to meet the need forcomprehensive, current data.

No reliable way to redirect their spatial focus

Structural Problems With Crowdsourcing

Issues for Crowdsoucring

Sub-Issues Weaknesses

3. How long does it take to get quality results over large areas of coverage?

b. Number and distribution of contributors not manageable.

c. Unknown, but lack of progress will become a significant problem for contributors.

Even when you get the data, the lack of standards and effective quality control is a significant problem.

Comparison

Meridian 2 OSM 03/08 OSM 03/09 OSM 10/09

Total Length (m)

261,678,924 169,608,142, 267,950,190 324,127,812

Total LengthWith Attributes

205,621,010 54,581,704 95,332,646 123,632,213

Number of “complete cells

112,385 30,702 57,622 73,092

Number of ‘complete” cells with attributes

92,467 6,670 13,595 19,719

Haklay and Ellul 2010

What Does This Tell Us About Crowdsourcing And Maps?

• Crowdsourcing may not effectively harness the wisdom of crowds– Lacks diversity– May lack independence– May lack decentralization– May lack local knowledge

So What Do We Do?

Crowdsourcing Is Better When Hybridized

• Like Oreos and Milk

• But what you need to mix with it with is– Authority/Trust– Verification/editing– Local Knowledge

• Interpreted or observed

Best Model

• Augment existing government or commercial database with user generated content and supplement these updates with professionally compiled data

• See Swiss Topo, Google, Navteq, TomTom, etc.

Crowdsourcing/VGI

• The hybrid form includes– Use of crowdsourcing as part of an ensemble

of map data compilation tools that normally includes

• Field work/observation• Data mining• Use of imagery• Conflation• Data editing• Data QC/QA

Crowdsourcing/VGI

• The hybrid form may include– Active input

• Is a change to a map database made by a contributor as the result of a process in which they must take the initiative to identify the desired change and take purposeful action to convey the change to the provider of the spatial database

– The best example of this method would be TomTom’s map share software

Active Crowdsourcing

Crowdsourcing/VGI

• The hybrid form may include– Passive input

• Is a term generally denoting the use of GPS devices to collect what is known as floating path or probe data

• In essence, the PND or GPS receiver used by the contributor tracks their paths of movement (usually vehicular) during their daily journeys and automatically reports the data back to a repository

Passive Crowdsourcing

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Relative Accuracy

Data Compilation Techniques

020406080

100120140160180200

C C + D C +D +A C +D +P C+D+P+A

Rel

ativ

e A

ccu

racy

Google’s Build Approach

Google’s Revise Approach

Google’s Approach

The Benefits of Products/Services/Purpose in Map Updating

Google’s Approach

• Isn’t always as elegant as it might be– Often suffers from lack of local knowledge

• Data collection– Especially in respect to attribution

• Data editing– Software blunders

• Market Driven• Editing authority

– Official Google editors are not local ( at least not in U.S.) and this is a major weakness of the system.

Use Map Maker – Learn Languages

Conclusion

• Google does not yet have it quite right, but they have put together most of the critical elements required to hybridize crowdsourcing.

• Even so, Crowdsourcing is not without its limitations.

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Crowdsourcing

• Pure crowdsourcing will not be the successor to producers of navigation databases.

• Hybrid Crowdsourcing will be of advantage when it is used as a supplementary method in map database creation.

So what do we need to do?

• Embrace technology and adapt crowdsourcing to fit within an authoritative model.

Find A Way to Merge These Situations

Government/Commercial

• Training in compilation• Specialization• Staff size limited• Research budget limited• Salaried

Crowdsourced

• Self-selection• Local experience (or not)• Contribution pool

potentially unlimited• Research hours limited to

free time• Avocation – contributing

is the reward

Make this model work

So what else do we need to do?

• Innovate in a manner that produces systems where more of the crowd contributes Local Knowledge rather than labor (digitizing imagery)

• Further leverage the potential strengths of mining the crowd’s behavior

Solicit the Crowd to Solve Your Problems

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Use Social Networking Tools

Use Dilithium Crystals

And Get Details

And Addresses

Provide Powerful Technology For Field Agents

Can we do it?

• I suspect you will have to do it, but it will be a big challenge.

• What are the odds?

Consider this

• Odds are irrelevant

Yeah, Right.

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For More

• See our blog Exploring Local

• http://blog.telemapics.com