Our group collectively conducted traditional research, in search of scholarly information pertaining to the spread of resources across Los Angeles. We specifically researched education and food resources; where there are a lot or a little of these resources, and the reasons why there is excess or deficits in certain areas in LA. 

Not all of the research we conducted is directly related to Los Angeles, but we can see important patterns of how and why resources (such as education and food) are geographically dispersed in other large metropolitan cities, and use these patterns to help our understanding of resources in LA. 

All in all, the goal of our research was to find out which areas of Los Angeles have solid resources and which areas do not, along with the factors contributing to why certain areas have more or better resources than others (factors such as per-capita income). 

Based on that research, we found that there is a positive correlation between the number of healthy food stores and the income level of the citizens of that area. There are more healthy food stores in high income neighborhoods, while there are more fast food stores in low income neighborhoods. 

One article discussed how fast food restaurants are located in close proximity to high schools and other educational institutions that are in low income, high commercial neighborhoods (Simon). Another article that we analyzed discussed a study that was done in New York that revealed how the types of food businesses or stores substantially differentiate between neighborhoods with different socioeconomic status. The study found that there were less supermarkets, smaller grocery stores, fewer produce (fruit and vegetable) markets, natural food stores, and bakeries in poorer neighborhoods compared to richer neighborhoods (Moore). 

Although we found a clear relationship between food resources and income levels in the research we conducted, we did not see this relationship in our own data. We did not see as clear of a relationship between the median income levels of each council district and the number of grocery stores in each district. 

In the future, we might want to narrow down the data that we use specifically to health food and fast food stores only. We might not have found results that matched with our research because the scope of our data (the number of grocery stores) was too broad.