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!.

Popularity: 14% [?]

(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: 14% [?]

Considering that the US produces 602.3 billion pounds of corn crop annually, I thought it would be interesting to map where most of our corn is coming from in the US. The type of corn, maize, which is the one most widely produced is a cousin to sweet corn that is a high-sugar variant. 99 percent of the US corn crop is actually the starchy, tough plant, known as maze. The following map shows the values, in metric tons (MT) of corn produced by each state. By clicking on a state you can see how much it produces.

If you are wondering where most of our corn is going, then here are a couple of the largest products of corn:

Feed= 333.2 billion pounds

Ethanol= 179.2 billion pounds

High fructose corn syrup= 28 billion pounds

Sweet corn (ears, canned, and frozen)= 5.8 billion pounds

Popularity: 7% [?]

I’m not really the romantic type but every now and again I feel it is nice to show the lady I love that she is special to me. Unfortunately, this is not always the easiest task and planning for a special night out can be frustrating. After working at FortiusOne for over a year it dawned on me that Finder! and Maker! can be used easily in making plans for my special lady.

So far my plan looks like this. On my way home from work I want to stop and get flowers, arrive home with flowers in hand, hop with my lady onto the metro, find a very nice restaurant in DC, and go to a show of some sort. My big concern is that I want places that are relatively close to one another and along the metro line. Let’s see how I do.

First I’ll start with flowers. Since I take the Metro to work, I need to find a florist that is somewhere along my commute home. My first task is to map florists in DC and compare those locations with my metro ride home. I found a website called www.locateaflowershop.com, located all the florists that they had listed in Washington DC, and mapped them using a geocoder at www.gpsvisualizer.com. I then created a map of all the DC Metro Stations that are on my daily Metro Commute. The map below shows my metro ride with the orange dots being all the flower shop locations in DC and the black squares as metro stations.

I found a shop right next to the Woodley Park-Zoo/Adams Morgan station. By combining the two sets of data, I am easily able to find the ideal place to get flowers.

Now I must decide on dinner and a show. My first decision will be the show. I came across the website for the Washington Performing Arts Society, which gives a listing of venue sites. I went ahead and mapped these locations using the geocoder at gpsvisualizer (dark squares). I then paired this with a listing of the Washingtonian Top 100 Restaurants of 2008, which I again mapped by lat/lon through gpsvisualizer (yellow dots). Along with this, I mapped out the metro ride I would take from my place at Grosvenor-Strathmore to Union Station on the Red Line (white dots) and a dinner and a show in Washington DC.

From observing what I have mapped, it appears my best bet would be to see a show at the Harman Center for the Arts because of the vast amount of top restaurants in its surrounding area. Also the Gallery Place – Chinatown stop is about two blocks away and will be a short walk from the metro stop to the show.

Many other elements could be added or taken away from this situation to best find the ideal night out for you. All you need are locations of events and/or venues that you want to go to. Then you load this data onto Finder!, map all of these spots together onto Maker!, and then visually view the data to make decisions for your perfect night out.

Popularity: 7% [?]