Last night in the CNN/Youtube Republican debate, he charmed the audience with his quick wit and clever oratory. When asked whether Jesus would support the death penalty, Huckabee responded without pause: “Jesus was too smart to ever run for office.” Thunderous applauses of approval from the audience also followed many of his answers to probing questions from Youtubers and host, Anderson Cooper.

In the pivitol Iowa polls, the “Huckster” is currently neck-in-neck with Romney: the long time leader of the pack in that state. Since July, the Arkansas Governor’s poll ratings in Iowa have shot up dramatically; other Republican presidential hopefuls have seen diminishing support over the same time period.

The graph below shows monthly average ratings from major political polls taken by organizations like Gallup, the American Research Group and the America Reporter and Strategic Vision (source of average poll results: www.usaelectionpolls.com). The results of a poll conducted by Rasmussen Reports on November 27, 2007 are also shown on the figure. Link here to see the data in Swivel.

Average Percent Support by Month by Candidate

What about the rest of the country? The maps below show average percent support for Romney and Huckabee for the month of November; brighter shades of red indicate higher percentages. Not surprising, Romney’s greatest share is in Utah. But, he also has strong backing in New Hampshire, another political bellweather state like Iowa. The top five states for Romney, not including Iowa, are: Utah (65%), New Hampshire (33%), Michigan (25%), Nevada (21%) and South Carolina (21%).

Percent Support for Romney

Huckabee’s strongest shares of support, outside of the Hawkeye state and presumably Arkansas, appear to be in the South and Southeast. His top five states, according to November polls and excluding Iowa, are: Texas (16%), Missouri (12%), North Carolina (12%), South Carolina (12%) and Wisconsin (11%). And BTW, those states share something in common with Iowa: they are all places where Huckabee is gaining swift momentum.

Percent Support for Huckabee

Popularity: 7% [?]

The Realtytrac 3d Quarter foreclosure numbers released this week paint a grim picture for the nation’s housing market. Unprecedented foreclosure rates were reported for numerous cities. In Stockton, CA, the leader of the pack, an astounding 1 in 31 households filed for foreclosure last quarter.

What is also unsettling is the geographic pervasiveness of the problem. As James Saccacio, the CEO of the Realtytrac noted this week: “…increasing foreclosure activity was not limited to just a few hot spots.”

The maps below show foreclosure rates and trends in filings for the top 98 largest metropolitan areas (lower 48 states), based on Realtytrac’s numbers. Pan across the maps to identify hotspots by city or zoom out to get a more general spatial perspective. Top rankings are provided beneath each map.

Foreclosure Rates

Stockton, CA ~ 1 in 31

Detroit/Livonia/Dearborn, MI ~ 1 in 33

Riverside/San Bernadino, CA ~ 1 in 43

Sacramento, CA ~ 1 in 48

Las Vegas/Paradise, NV ~ 1 in 48

Percent Change in Property Foreclosure Files (2nd to 3d Quarter 2007)

Richmond, VA ~ 224%

Wilmington, DE-NJ ~ 202%

Springfield, MA ~ 151%

Boston/Quincy, MA ~ 146%

Cambridge/Newton/Framington, MA ~ 132%

Percent Change in Property Foreclosure Files (3d Quarter 2006 to 3d Quarter 2007)

Bethesda/Frederick/Gaithersburg, MD ~ 1640%

Cambridge/Newton/Framington, MA ~ 1552%

Boston/Quincy, MA ~ 1274%

Springfield, MA ~ 1169%

Essex, MA ~ 994%

Popularity: 13% [?]

1 = 100? It is according to the Drug Quantity Table, where 1 gram of crack cocaine equals 100 grams of powder cocaine; part of the drug equivalency formulae born out of the 80s overblown media hype and of mandatory minimum sentencing law passed by the Congress in 1986. Although prison sentence for trafficking/possession of 10 gram of crack cocaine is not 100 times that for 1 Kilo of powder cocaine, it can still lead to a jail sentence that is nearly 10 times longer. However, that is not the only problem with the sentence that is partly determined by the Sentencing Guidelines and a so called Base Offense Level (BOL) table. It so happens that trafficking in small amount of cocaine occurs mainly in inner-city neighborhoods and by relatively young African-Americans. And yes, they form the bulk (85%) of prison population that is serving very long jail sentences.

The U.S. Sentencing Commission (USSC), an independent body within Justice dept that is charged with developing prison sentencing guidelines, is trying to address the disparity between the longer jail sentences for crack cocaine offense and those for other illegal drugs such as powder cocaine. The USSC decided to decrease crack cocaine related BOL by two. For eg., a formerly level 16 BOL (with a mandatory prison term of 2 years) is now 14 and thus could lead to zero jail-time. This has been widely commented and welcomed by Federal Public and Community Defenders, NAACP, ACLU and other civic groups.

However, that is not what has riled up the Administration’s Justice Dept., it is the USSC’s proposal to make the changes in BOL retroactive. They object vehemently to many findings from the USCC’s report titled Analysis of the Impact of the Crack Cocaine Amendment If Made Retroactive, especially they argue that it would lead to overburdening of the district courts with the petitions from thousands of inmates who are eligible to reduction in prison sentence and other issues such as overcrowding of half-way houses, increase of workload for the U.S. Martials, and release of violent criminals into localities that could suffer from renewed drug trade and violence associated with that, especially in the light of the FBI statistics showing increasing rates of violent crimes over the last three years.

It is not clear when the USSC is likely to vote on the issue of retroactive reduction of crack cocaine sentences, whenever that happens, it would lead to release of large number of inmates. Therefore we at FortiusOne thought that the public may want to discover the geographic dimension of the possible early release of the crack cocaine inmates…Below is a heat map of crack prisoners who are eligible for release after one year. Explore on GeoCommons data for other time periods along with number of defendants by the type drug offenses and the average/median prison sentences by federal district courts for drug crimes.

Crack offenders eligible for early release within a year

Popularity: 39% [?]

Making the News

Here’s an eye-catching statistic: “Foreclosures cost neighbors 223 billion dollars.” This statistic comes from a study just released by the Center for Responsible Lending and its drawing a lot of attention. In their study, they take an unprecedented look at the spillover effects of the recent explosion in foreclosures (2005-2006). They look specifically at the devaluation in property values that the neighbors of those properties are likely to incur and the losses to communities as a result of depreciating property tax bases.

The numbers coming out of the study are ominous. They cite that over 44 million homes in the United States will experience property devaluation as a result of foreclosures in their neighorhoods. Fourty-two counties in the United States can expect to see their property tax base erode by more than $1 billion. And households located in proximity to lost properties could see the value of their property decrease by $5,000, on average.

What parts of the country will get hardest hit?

To examine this, the county level statistics statistics in the Center for Responsible Lending study were geocoded and the hotspots mapped. It should be noted that while their analysis is based on census tract data, the numbers presented in their report are at the county and state level. Further, they provide statistics for only those counties contained in Metropolitan Statistical Areas (MSAs). A full description of the data and the methodology they employ can be found here on their website.

The map below shows which parts of the country could see large property devaluations and tax base erosion as a result of foreclosure spillovers. The top ten counties ranked in order are: Los Angeles, Ca; Cook County, Il; Kings, NY; Miami-Dade, FL; Queens, NY; Orange, CA; Bronx, NY; Broward, FL; Maricopa, AZ; and New York, NY. Los Angeles county clearly dominates: It’s total devaluation is nearly double that of the second ranked Cook county.

Pan around the map to see the other hotspots, in the Chicago area, the northeast and Florida.

Total Property Devaluation from Foreclosure Spillover Effects

If you are a homeowner, you don’t want to live in the following counties: Kings; NY, Hudson, NY; Queens, NY; Miami-Dade, FL; Bronx, NY; Los Angeles, CA; Manassas Park, VA; Passaic, NJ; New York, NY; and Prince Georges, MD. Those are are the top ten counties ranked by average property value loss per household affected by the spillovers. The map below shows a richer illustration of the geographic aspects of the problem.

Average Decrease in Property Value Per Household Affected

A Slightly Different Look At Things

Where can a single foreclosure be expected to result in the largest impact on property values? To get at this, the study’s numbers on total property devaluation and houses lost due to foreclosures were used to create an index: property tax erosion per foreclosure.

The answer: New York, NY. On average, every foreclosure in this area can be expected to result in a 18.8 million dollar decline in the county’s tax base, due to spillover effects alone! The top ten counties, according to the index, are:

New York, NY ~ $18,824,604

Kings, NY ~ 3,189,975

San Francisco, CA ~ 2,806,025

Bronx, NY ~ 2,744,213

Queens, NY ~ 1,801,715

Hudson, NY ~ 1,459,685

Alexandria, VA ~ 1,362,766

District of Columbia, DC ~ 1,127,907

Arlington, VA ~ 1,106,435

Suffolk, MA ~ 1,040,268

Popularity: 15% [?]

Figured I would keep on the crowdsourced data theme going with some myths I’ve seen crop up in many people’s perception of crowdsourced data and its reliability. First lets take a step back and look at a definition of crowdsourcing, ” [the] act of taking a job traditionally performed by an employee or contractor, and outsourcing it to an undefined, generally large group of people, in the form of an open call.” The fact this “group” is not paid or under contract leads many to believe what they produce cannot be trusted. I think this general assumption leads to a number of myths about crowdsourced data.

Crowdsourced Data and Official Data are Mutually Exclusive

There is a common perception (especially from traditional data providers) that data comes from an official source and is guaranteed accurate or it is crowdsourced and you have no clue if it is accurate or not. Encyclopedia Britannica articles come from an official source and Wikipedia is crowdsourced. NAVTEQ street data comes from an official source and OpenStreetMaps is crowdsourced. We can trust Encyclopedia Britannica and NAVTEQ because we pay them to provide us an accurate product, but we are not sure if we can trust Wikipedia and OpenStreetMap because we do not have a contract for them and any willy nilly crazy person could enter bad data. The issue is seen in black and white - non-trusted and trusted.

In reality crowdsourcing is a tool to collect data. Sometimes it is an end in and of itself like Wikipedia and OpenStreetMap. Other times it is an enabler - like voting news stories from third party sources on Digg. Digg does not user generate the stories but crowdsources the determination of which stories are most worth reading. More recently Tom Tom has used crowdsourced data to enhance their official base data. Perhaps the greatest potential of the crowdsourcing model is a hybrid working with traditional/official data sets. Not only mixing the two together, but using crowdsourcing to enhance the accuracy and validity of existing official data. For instance a map of toxic dumping sites from the EPA is interesting by itself, but it is imminently more valuable if you can add your own data of the schools, playgrounds, and friend’s houses your kids play at. Secondly, if you would like to add evidence to the map supporting the damage caused by the dumping site or add evidence showing the dumping site has been cleaned up then everyone has better context for the original data set and its validity. In both cases crowdsourcing is being used to enhance existing data and does not stand by itself.

Official Data is Automatically Accurate than Crowdsourced Data is Not

Their is a pervasive myth that if data comes from an official source or has official metadata then it must be accurate. Vice versa if it is crowdsourced it must be inaccurate. The truth of the matter is official data and metadata has inaccuracies and crowdsourced data has inaccuracies. In fact the vast vast majority of data in the world has inaccuracies. To quote Chris (our beloved Heretic Alpaca and CTO), “your data sucks and my data sucks - now that we have that settled we can go do something.” The fact that people think corwdsourced data is inaccurate is truly a good thing because they think about what they are consuming and are looking to see if there are problems. The beauty is that when they find problems they can actually go and fix them. The worst thing about official data is that we blindly assume that everything is perfect and when we find that perfection lacking there is no recourse to fix it.

Metadata is the Panacea

Many a GIS wonk has preached without metadata geographic information is just content. Once there is metadata the professionals have entered the room and all concerns evaporate. When people ask me about metadata in GeoCommons, especially our government customers, I say sure we can include your metadata. We can even make it mandatory to include metadata before inclusion if that is your preference, but just having metadata we do not think is sufficient. Metadata can often be anonymous and there is seldom repercussions or rewards if you are sloppy and quick putting in your metadata or thorough and diligent. When you fuse metadata with a crowdsourcing approach there can now be accountability. I create and contribute the data and that data is attached to me. You can click on the source and you get my profile. If the data rocks - kudos and praise for me, if the data blows - everyone knows I was the slacker who put it in.

Recently I did some digging back into the arguments around FGDC metadata when it came out in the early nineties. The standards was not without criticism and suggestions for improvement (Dutton 1994), “The metadata standard is per force formulated from a producer’s perspective. It is, one assumes, the responsibility of data producers to document published datasets, and there is not much consumers can do other than to offer feedback on the adequacy of the organization, usability and quality of datasets they acquire.” We now have the technological means by which to address what could not be addressed then, yet we are to ensconced in the statas quo and dogma to embrace the opportunity to improve the system.

Crowdsourcing is the Wild West of Data

Crowdsourcing is often conflated with “no rules” or “anything goes”, thus leading to a perception of not being trustworthy. While you can crowdsource with no rules it does not mean you are not allowed to have rules. Further, those rules can result in highly trusted content. Think of academic publishing, one of the most successful crowdsourced experiments of all time. Anyone can submit an article have it reviewed by a group of peers anonymously and published in some of the most trusted publications on the planet. No one pays me to publish an article. I have no economic incentive for the data in my paper to be accurate yet I would trust information from “The New England Journal of Medicine” way before I would anything out of the Encyclopedia Britannica. But…you say…academic journal are written by professionals! Not necessarily true - anyone can submit to an academic journal. You need no pedigree, and articles have been published by undergraduates that have no degree at all. The same kid with a Facebook page and 254 friends. Academic journals are trusted because of the peer driven culture that surrounds them, not economic incentive or accuracy standards that must be adhered to. A crowdsourced system can be highly trustworthy depending on how it is structured and the rules that are put in place. I do believe there is a trade off between the number of rules and requirements and the level of participation and innovation in a crowdsourced system. The more rules and requirements the higher the level of trust, but the less participation and possible innovation. Those that can maximize trust and participation in a crowdsourced application will be those who succeed.

Conclusion

In short I think crowdsourced data and tools often get an undeserved stereotype. People tend to lump it all together instead of looking at opportunities to leverage a new tool to enhance their competitiveness. I think this is often the result of fearful knee jerk reactions. Crowdsourcing does have the ability to disintermediate market places, but those who figure out how to harness that to their advantage will be the ones who succeed. Defensive criticism is usually a sign you are strategically headed in the wrong direction.

Popularity: 9% [?]