It’s the holidays and what is one thing that is on the minds of everyone? Shopping! Yes, and this year with the economy slumping people are trying to not only find the perfect gift but the perfectly-priced gift. As I myself have pondered this question a thought entered my head. What if I were to do my shopping in a state that has no sales/general tax? Yes, these states do exist and Finder! and Maker! have a dataset that show sales tax across the USA by state. The map is below:

The states that are a very light cream color (Oregon, Montana, Delaware, New Hampshire) are the states that have no sales/general tax. The darker the state the higher the sales tax rate is in that state.

Now my next question is this. If I am to go to one of these states to shop will I really end up saving more money? I may not be spending money for a sales tax but I certainly will be spending more money on gas to travel the extra distance. I will set up a hypothetical situation using Finder! and Maker! to see what my answer will be.

Let’s say I live in the lovely state of Washington in the city called Castle Rock. In Washington the sales tax is at a rate of 6.5%. Next door to me is my neighbor Oregon that has a 0% sales tax. Now on Finder! I can load major shopping centers that are around me in my area. The map below shows that I have two major shopping centers right by me that are relatively close off of Interstate 5, one in Centralia, WA (Centralia Shopping Center, 34.4 miles away) and the other in Portland, OR (Jantzen Beach SuperCenter, 50.4 miles). These will be the two places that I will compare and the map is below of the two with Castle Rock right in the middle. The map is shown below:

Now let’s do some math. My holiday shopping expenses look like this:

Wife = $70, Mom = $60, Dad = $60, Sisters = $120 = = Total of $310 on gifts

In Washington, with shopping tax this equals 310 x 6.5% tax = 20.15, 310 + 20.15 = $330.15. So the difference between the two states is $20.15.

Now let’s look at gas expenses:

Let’s say gas in Castle Rock is $2.00 a gallon and my car averages a rate of 25 mpg. If my round trip from Castle Rock to Centralia is 68.8 and my trip from Portland and back is 100.8 miles, then my gas costs will look like this.

Castle Rock to Centralia: 68.8/25 = 2.75 g x $2 = $5.50

Castle Rock to Portland: 100.8/25 = 4.03 g x $2 = $8.06

By going to Centralia I will end up saving 8.06 – 5.50 = $2.56

Now as we put these two savings figures together we see that overall our trip to Portland would be a wiser choice. You will spend more money on fuel ($2.56), but you will save much more on your shopping expenses ($20.15). Together it will provide us with a savings of $17.59.

I would like to mention that this is very hypothetical. Often, other circumstances (county taxes, municipal taxes, toll roads encountered, different mpg rates on the trips, and many others) may enter into the equation and change figures. All in all this might be a solution to save money, so create your own hypotheticals using Finder! and Maker! and see if it will help. Below are links to Finder! datasets that show major shopping centers (malls, outlet malls) in a few 0% sales tax states. Happy Holidays and good luck shopping!

• Oregon
• Delaware
• New Hampshire

Popularity: 13% [?]

With the college football season winding down and the National Championship coming up on January 8, 2009 between the Florida Gators and the Oklahoma Sooners, it would be interesting to know what college programs across the country are paying their head coaches. The following map shows a data collection, by all co-educational post-secondary institutions that receive Title IV funding. That basically means all colleges that participate in financial student aid programs that also offer athletic programs. The points represent colleges and what male head coaches are paid per university.

Click Finder! to view the dataset.

After viewing this above map, I was interested in finding out what the BCS top 25 college football team rankings and their head coaches were getting paid – comparatively to the other head coaches throughout the US. The map below shows the top 25 college football teams with orange proportion symbols and reveal, for the most part, that most of the colleges with highly paid coaches are universities that are succeeding at the highest level. The University of Texas, University of Florida, and University of Alabama show particularly high salaries.

Click Finder! to view the dataset

Popularity: 12% [?]

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: 13% [?]