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Compliant Technology and Ontologic Purveyor, as an Adjuvant Import for Social CommunityAdjuvant Import for Social Community

Repurposing and Teleoscostopic Re‐Renderings in Statistical Knowledge Data UnderpinningsStatistical Knowledge Data Underpinnings

Troy Anderson (KnowledgePlex, Inc.), President/CEO,Knowledge Management and Interactive Applications Processing Unit

Organisation for Economic Co‐Operation and DevelopmentMay 26, 2008 (Updated May 25, 2008)

http://www knowledgeplex org www kplex org

Food and Drug Administration Modernization Act of 1997, 21 U.S.C. § 371(h) (establishing FDA good guidance practices as law); ‘‘Food and Drug Administration Modernization and Accountability Act of 1997,’’ S. R 105 43 t 26 (1997) ( i i b t bli k l d f d t FDA id d t l k f t ti f d ti f id d t d f ll i bli i t d

http://www.knowledgeplex.org www.kplex.orghttp://www.dataplace.org/area_overview/index.html?place=P26.b7cc1efb

e921b36efbfc016057757684&z=1

Rep. 105–43, at 26 (1997) (raising concerns about public knowledge of, and access to, FDA guidance documents, lack of a systematic process for adoption of guidance documents and for allowing public input, andinconsistency in the use of guidance documents); House Committee on Government Reform, ‘‘Non‐ Binding Legal Effect of Agency Guidance Documents,’’ H. Rep. 106–1009 (106th Cong., 2d Sess. 2000) (criticizing ‘‘back‐door’’ regulation); the Congressional Accountability for Regulatory Information Act, H.R. 3521, 106th Cong., § 4 (2000) (proposing to require agencies to notify the public of the non‐binding effect of guidance documents); Gen. Elec. Co. v. EPA, 290 F.3d 377 (D.C. Cir. 2002) (striking down PCB risk assessment guidance as legislative rule requiring notice and comment); Appalachian Power Co. v. EPA, 208 F.3d 1015 (D.C.Cir. 2000) (striking down emissions monitoring guidance as legislative rule requiring notice and comment); Chamber of Commerce v. Dep’t of Labor, 174 F.3d 206 (D.C. Cir. 1999) (striking down OSHA Directive as legislative rule requiring notice and comment); Administrative Conference of the United States, Rec. 92–2, 1 C.F.R. 305.92–2 (1992) (agencies should afford the public a fair opportunity to challenge the legality or wisdom of policy statements and to suggest alternative choices)

<intro>

Troy Andersony

Inc.Inc.

A Not‐For‐ProfitKnowledge‐Into‐

St ti tiStatistics CompanyCompany

20 minutes

Boil down

Five and a half years ofy

Freeing Harmonized Data to the PeopleFreeing Harmonized Data to the People

And how that helpsAnd how that helps

</intro>/

<body>y

Let’s break it downLet s break it down

Free Harmonized Data to the PeopleFree Harmonized Data to the People

<1>


Audience Matters

I don’t mean us

W ’ th i ht l tWe’re the right people to democratize data butdemocratize data, but

Communities Matter

People on the front lines of communities matter

http://commons.wikimedia.org/wiki/Image:Chicago_fire_fighters_walking.jpg

Turning Statistics into Knowledge for ourselves

The World Experts

Takes a while to trickle down to community leaders

Instead

If you solve for the lowest common denominator

And democratize data for your people

The Seven P’s:The Seven P s:PractitionersPractitionersPolicy MakersProfessors

PressPupilsPupils

ProfessionalsProfessionalsPublic

Everyone wins

The Rule is:

Ease of Use is Use

No matter the community

http://commons.wikimedia.org/wiki/Image:Noe_Valley_San_Francisco_4.jpg

No matter the situation

If the audience can engage statistics themselves

They can tell a lot of stories that we could never dream of

While there is only one…

There are potentiallyp y

Given the right toolsg

They can tell almost yas good stories

But…

Even the right target g gaudience

is still not enoughg

<2>


People will try to do good even if it meanseven if it means…

Reinventing the wheel

Not playing on a level p y gplaying field

And, not seek out experts or

bbest practices

One stop shopp p

For Affordable Housingg

The Seven P’s:The Seven P s:PractitionersPractitionersPolicy MakersProfessors

PressPupilsPupils

ProfessionalsProfessionalsPublic

Get what’s relevantGet what s relevant…

Make it easier to connectMake it easier to connect

Trusted advisor for what’s important in the news

And access to experts onlineAnd access to experts online

And we gave them all thatAnd we gave them all that…

And what did they say?And what did they say?

“We want more!!!”We want more!!!

LAMPLAMP

syndicatesyndicate

technology grantstechnology grants

“KnowledgePlex for Innovations in Public Governance”

Don’t Reinvent

Level the Playing Field

Disseminate ExpertiseDisseminate Expertise

So then what did they ysay?

“We want more!!!”We want more!!!

<3>


Feedback

Great broadly on the topic of affordable

housinghousing…

… not so great locallyg y

A lot of requests…q

Grant Reque$t$q $ $

Geographic I f tiInformation $ystem$$ystem$

Buy an ESRI licensey

Cha‐Chingg

Hire GIS engineerg

Cha‐Chingg

Buy Some Datay

Cha‐Chingg

Put it all on Oracle

$#*#@!$ @

Cha‐Chingg

GIS is expensivep

Strangelyg y

Buying the cowy g

Answers were free

Weren’t They?y

Invest Billions!

Brilliant People!p

They Care!y

Nonetheless

Until You Cross

That Last Mile

Is It Really Free?y

F D t !!Free Data!!

Does this data look free to you?

Not until this data is transformed

Can it be put to use in psomething like…

We do the harmonization

To save everyone timey

You need to do the same

Or at least let us help you

The steps aren’t easyp y

Code race combinationsCode race combinations (standardize to 1997‐2002,

identify multiracialidentify multiracial, identify mixed pairs)

C d dCode gender combinationscombinations

Create flags for high g gcost, etc.

Re‐code purpose (for some series) to be consistentseries) to be consistent with pre‐2004 purpose

Create base files

Adds place code based on majority tract adds standardmajority tract, adds standard MSA code for 1999 and 2005

Re‐code tracts back to 2000 TIGER standardsTIGER standards

(Broomfield, Clifton Forge)

Make corrections to incorrect source data (FFIECsource data (FFIEC

recommended to delete 1 lender/agency records in 2002;

Code for including loans at every level possible (county ifevery level possible (county if tract is invalid, state if county

is invalid, etc.)s a d, etc )

Merge each loan with lowest possible level of median income from HUD Area Median Income file (which is published for counties, states, US)to calculate relative income levels

Merge each loan with subprime lenders list (the only indicatorlenders list (the only indicator before 2004 for subprime lending) & flag these loans

Summarize all the "topic" combinations, calculated all thecombinations, calculated all the medians for each geography

For files before 2002, weight 1990 tracts to 20001990 tracts to 2000

tracts using NCDB weighting

Merge HMDA data with housing and income variables from Decennial census to create standardized &

gentrification indicators .

These aren’t made up psteps

Warning: These data areWarning: These data are produced by professionals in a p oduced by p o ess o a s acontrolled environment in the friendly confines of the Urban Institute DataPlace must insistInstitute. DataPlace must insist that no one attempt to re-enact pany stunt or activity performed for th b fit f h i i d tthe benefit of harmonizing data or they’ll reinvent the wheelthey ll reinvent the wheel

Freely Available

Is not

F l W k blFreely Workable

You may provide free y pdata

Make the source files free

As long as your g yaudience’s time is free

T t d lTo get a degree, learn SAS, SPSS, geospatialSAS, SPSS, geospatial semantics, metadata,

etc. etc. etc.

Then the Data’s Free

Imagineg

How many people in y p pour audience

% of Community that can do HMDA

Can't Can

<4>


Exists

Freely Available!y

Freely Workable!y

In a time…

Pre‐

Pre‐Mash‐Upp

Does a number of jobsj

to harmonize data

1) Data Aggregation) gg g

2) Data Cleaning3) Data Normalizing

4) Geographic Normalization5) Indicator Building

These steps from b fbefore

Code race combinationsCode race combinations (standardize to 1997‐2002,

identify multiracialidentify multiracial, identify mixed pairs)

C d dCode gender combinationscombinations

Create flags for high g gcost, hoepa, etc.