In January, a record 4 million people are expected to gather in Washington, DC to take part in the Inauguration celebration. Hotels near the area sold out nearly the day after Obama was elected. Luckily, where traditional lodging failed, Craigslist saved the day. People all around Washington, DC (and I mean all around, including from places like Baltimore which is nearly 40 miles away) have been so kind as to offer up their couches, rooms, whole houses, and even offices to those in need of a place to stay for the event. That is, if you are willing to sell your first born child!

Prices for a room are ranging from $50 per night to above $4,000. It is quite common to see rooms rented for an average of $2,000 per night. In fact hundreds of DC entrepreneurs a day are jumping for the opportunity to make their month’s rent in one night of sleeping on a friend’s couch so they can rent their apartment. While the prices are often unbelievable, many offers include breakfast, a ride to the metro, and even babysitting.

We thought it would be interesting to see where these people’s room/homes were, if they were actually located anywhere near to the inauguration site (or DC for that matter) and how much they were charging based on their location.

We took a 3 day sample of ads from Craigslist, geo-coded them and then added some attributes based on prices and amenities. You can find this dataset in Finder!.

This first map shows the entire DC metro area and beyond to demonstrate the distribution of rooms for rent.

The next two maps show the DC area with rooms based on price per room, per night, as well as metro stops. It seems like there is very little relationship between the location of the room and the price. Go figure!

So if you are still looking for a place to stay, or if you want to check out how much you can get away with charging for your floor and a sleeping bag, check out this dataset in Maker!.

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(maps made by Emily Sciarillo)

The question of who is allowed to purchase and possess firearms has been debated in state legislatures all across the country. Some want more restrictions and some want fewer restrictions, and every state has its own unique set of rules. Debates rage on and it seems that any amount of restrictions, high or low, will not keep everybody happy. Here, at Fortiusone we see ourselves as an unbiased party that simply wants to present facts. So we thought we would take a look into the heated topic and see if creating more restrictions was for the best, for the worst, or if it even mattered at all.

The first thing we did was create a dataset in Finder! that scored each state’s leniency toward the amount of restrictions put in place when purchasing and/or possessing a firearm. The dataset can be found here:

http://finder.geocommons.com/overlays/7897

We compared firearm restrictions on age, criminal background, and type of weapons across all states in the USA. We gave different point values for the severity of the restriction. Higher numbers were the result of tougher enforcement and lesser values were the result of lesser enforcement. A full rundown of how this point system was systematically determined can be found in the dataset description. The one important value that we obtained was a summation of all these different values for each state that we deemed the State Firearm Restrictive Value. The higher a state’s State Firearm Restrictive Value (the bigger the orange circle on the map) the less lenient the state was in their firearm restrictions. The map is below:

Now that we have this data we decided to pair it up with crime rate data across the country by state. The two areas of crime that we focused on were the amount of firearm related murders per capita by state and burglary rates per 100,000 inhabitants within the state. First we will look at firearm related murders. We decided to use this crime category because it gives us a great sense of the how serious firearm crime is in a state. The dark areas on the map below represent the states that display high rates of firearm related murder per capita.

The link to this dataset can be found at:

http://finder.geocommons.com/overlays/7902

and the map is below paired with the State Firearm Restrictive Values.

Now we will look at burglary rates (per 100,000 inhabitants) within the state paired with the State Firearm Restriction Values the map below. We decided that this would be a good category because it is often said that increases in gun ownership might lead to less burglary. Some on the other hand find this to be false. All in all it is another debatable firearm ownership topic that we can explore.

The link to the dataset can be found at:

http://finder.geocommons.com/overlays/7896

What can we conclude from observing the two sets of crime data with the State Firearm Restrictive Values? There is no show of a strong correlation in either case. When running a correlation formula between the data of firearm murders and restriction values you get a value of .1155888. When running the same correlation between burglary rates and restriction values you get a value of -.0144564. With values so close to zero it is easy to determine that a distinct correlation between the two values does not exist in either case.

Basically low levels of restrictions are found in states where crimes rates are high and also where crime rates are low. You can also find high levels of restrictions found in states where crime rates are both high and low. The results vary greatly. To conclude, it is perhaps wise to say that crime rates are not the sole factor when putting gun restrictions into state legislation.

Popularity: 13% [?]

On one of many flights this week I was asked the question, “what would you do with the $700 billion of bailout money?” Not an easy question to answer and there has been lots of arm chair quarterbacking on the topic. I’m hardly an expert on financial policy, but in short this was my layover induced answer.

There seem to be two fundamental problems, of many, worsening our current economic quagmire. 1) The housing bubble pushed home prices to levels most working Americans could not afford and to keep the bubble going the financial community became very creative with mortgages and how the risk associated with them was calculated. The end result was lots of people in houses they could not really afford and very little transparency in the risk this created in the financial markets. There is a lot more to the story but for the sake of brevity we’ll leave it at that. 2) Credit liquidity in the current market has almost ossified causing our collective economic gears to come to a rattling halt. Wall Street freaks…the media freaks…the consumer freaks (no spending)…sales of goods plummet…Wall Street freaks again…media fuels more freaking…rinse and repeat.

To break the cycle it would seem logical that liquidity needs to be injected into the market. A lot of pundits have looked at this being solved by the government buying up the bad assets, giving capital to the banks in return for equity stakes, and several other derivative plans. While all these ideas have their merits and risks the idea I exposed on the plane was slightly different. Back to the core issues - I saw the biggest failing being lack of market transparency and a fundamental mismatch between supply and demand in the housing market. So how could we restore transparency to the market while getting people in homes they can actually afford thus freeing capital for consumer spending and financial investment.

My answer was a foreclosure clearing house. This may be Polly Anna and not feasible, but it made for a fun intellectual exercise. There has been lots of talk around providing bail outs to people whose homes are foreclosing, but even this will be short term and will not solve the fundamental problem that they are in a home they cannot afford. The only real solution is to put these individuals and families into homes they can afford. The easy credit and risk shell game that banks ran has created a basic mismatch of people buying supply with demand they did not really have.

The clearing house is a simple idea of providing a transparent market place where people can trade down to houses they can afford and have new loans guaranteed to do so. The loans could be guaranteed by the government but competed for by the banks. Banks that already have the mortgages on existing properties could have the choice of refinancing the house so the owner could afford the payments (that would be their own risk calculation) or entering the home into the clearing house. Also the home owner could have the choice to enter their home into the clearing house if they would like to trade down voluntarily.

The clearing house itself could run like many of the existing home real estate market places matching buyers and sellers (Zillow, Trullia. RedFin etc.). In fact the government could probably contract with one of the sites to run the technology side of the clearing house at a reasonable cost. Once a person’s home was identified for purchase they would then be free to look for a new home in the clearinghouse they could afford. The government backing would allow loans to be made so the individual, now free of the foreclosed home, could buy a new home they could afford. Banks would still compete to provide the best rate and terms to new owner, but the risk would all be transparent to the government since they would be providing financial backing and to the owners so they were not mislead into buying more house than they could afford (again).

In theory this should introduce liquidity back into the market and with a little time put liquidity back into the consumer market since the majority of a person’s paycheck would no longer be going to a mortgage. The market would be transparent again but not run or partially owned by the government. I would argue that it was not capitalism or the market economy that broke during this financial crisis, but a loss of transparency and a resulting hiding of risk. In fixing the crisis the government’s role should be ensuring transparency in the market place so that it can function effectively. My idea is most likely off the deep end, but I do hope government action is centered around restoring transparency and restoring liquidity to the market. If you were Sec. Paulson for a day what would you do with $700 billion? There are no shortage of smart people around the globe. Can we crowdsource an answer?

Popularity: 19% [?]

James Fee joins in and shares his insight on supporting ESRI’s Geodatabase format and how a File Geodatabase can be shared efficiently. He agrees that the more file formats supported by a GeoData application, the more likely people will use it.

The election rallied much excitement, perhaps due in part to several compelling mapping implementations. The media, for example CNN, turned to maps to present data regarding the election. Maps compiled included locations of candidate rallies and the country’s standings (color-coded in red vs. blue). We even provided our own analysis post-election. (And maybe the most well know, SNL’s Magic Map….)

Jeff Thurston discusses GIS implementation across large energy companies, specifically at Saudi Aramco and BP. Saudi Aramco has 15 GIS units where contractors and numerous amounts of sensors that feed SCADA systems are all dynamically linked through GIS. As for BP, the company embarked on an innovation strategy that seeks to embed GIS and spatial information across the company. Thurston states he knows ‘of a few operations using GIS at the scale and complexity of Saudi Aramco’ and has seen ‘few companies attempt to extend the application of GIS in strategic role beyond practical and operational considerations.’

Google Maps now offers a feature that enables you to download your search results as a waypoint into your GPS system. The feature supports Garmin, TomTom and Pioneer. Make sure you have the correct software installed on your computer.

The KML Handbook by Josie Wernecke is now available for pre-order. Wernecke is a Google tech writer and explains the various elements and features of KML in her brand new book, including topics like Regionation and View Based Refresh.

Popularity: 17% [?]