Week 3 Lab Geocoding

     I love to eat. Unfortunately I am a poor UCLA student living under a tight budget so when I eat out I want to make sure the food is not only tasty but cheap as well. So my goal in this lab was to find out the best location to live in the LA area where the best cheap eats are within walking distance.
     First, I had to compile a list of 70 cheap restaurants in the LA county area. To do this, I used the website www.yelp.com to find the best rated restaurants under the "cheap" category meaning one meal is ten dollars or under. Not only were the restaurants compiled in my map under the cheap category but they also received a minimum yelp rating of 4 out of 5 possible points. Next I created an excel file shown above that listed the restaurants along with address, city, state, and zipcode. Although yelp had all the spatial information needed to geocode the restaurants, I decided to omit food trucks because they had no permanent location that I could plot. After importing the excel file, I was able to geocode using the composite_us.loc and match all of the restaurants after fixing one tied data(which is why there are only 69 restaurants listed). Finally I zoomed to the highest density of best cheap eats and created a ringed 2 mile buffer around each restaurant.
     Creating a buffer allowed me to figure out that the best place to live if I wanted to settle in a location within walking distance of the cheap eats is near northern downtown LA area close to Burbank. This is the area with the densest cheap eats and is the area shown on the inset map. If I wanted to extend my problem, I would have also plotted potential cheap apartments in the LA area along with a buffer to show the best potential living places near the restaurants.
     Although compiling spatial data into excel and finding the best cheap eats was time consuming, learning how to present data via geocoding was rewarding. I believe geocoding locations of importance is something clients/companies would pay money for. Geocoding locations also seems to interest many people, that is why services such as Google Maps are becoming so popular. Looking at an address book of cheap eat restaurants is very dull but being able to transform that list into a visual map via geocoding is much more informative by allowing viewers to see spatial relationships. Without geocoding, I would not have been able to solve my problem as fast and as effectively.