Hi Margaret,
The Point Distance tool will do this for you, but you will need an Advanced license.
You can use the Near tool to calculate the distance from the tobacco stores to the nearest park and then the nearest school or you can use the Generate Near Table tool if you would like the distance to the nearest 3 parks and the nearest 3 schools (or any other number). You could then average in the table or use the Dissolve tool to average based on each tobacco store and join back to the tobacco retailers layer.
That would be useful .... if one had the advanced license Near—Help | ArcGIS for Desktop
I am also guessing wildly that you don't have the Network Analyst extension so that you could get a true measure of the travel along roadways and/or paths to better reflect the actual distances.
Perhaps you state what you have in terms of software. Programmning skills??
You could do simple thinks for kindof quantitative assessments, like point counts within mult-ring buffers etc. If you had the spatial analyst extension, you could determine euclidean distances along raster representations along roadways. At present you are somewhat limited in your options.
I tried using euclidean distance (cell size 250 ft) from city centers because I could not get all the the park features for the entire study area ( which I had to expand in order to run OLS because we only have 22 census tracts and to run any regression you need at least n=30). Then executed the spatial join tool to join and create a new variable average distance to incorporated city centers for each census tract. Tobacco retail is generally located closer to city centers ( like all retail ). I really thought I needed this variable ( or some other spatial reference variable like average distance to highway) in order to have a get a good OLS regression model that would identify significant socio-economic ( data from census) disparities caused by density variation of tobacco retail.Turns out tobacco density has a significant negative correlation with median income and positive correlation with higher density of people with a disability (adj R-squared of .80). However, the all models it tried over and under predicted in all the high tobacco density census which is what we are trying to explain.
Then I found and tried a completely different approach (I would just focus on the 22 census tracts in our county). First I calculated the tobacco retail per 1,000 residents in the county. Then identified the tracts that had a rate higher the county rate. These tracts are considered to have high density compared to the tracts that had a lower rate than the county rate.I calculated the average of the following socio-economic indicators for high rate tracts and low rate tracts: percent below poverty, median income, percent in labor force, gini index, percent uninsured, percent white, percent household with one + under 18 years, average house size, percent male, percent female, percent 15-19 year old, percent 20-24 year old, median age, percent disabled, percent veterans. I then calculated the difference between the two averages for each soci-economic indicator. Differences greater than 4% were highlighted as potential disparities. I would then perform a two sample test ( if possible) to determine if the differences in the high and low rate tracts are statistically significant. This method is more effective in my opinion.
Comments and advice is welcome.
Unfortunately both the Near Table tool and Near tool require an Advanced license.
You could try using Linear Referencing, which only requires a Basic license. To do that you would repeat these steps separately for the schools and then the parks:
1. First add a unique value field to the polygons that can be copied to another feature class, like a Long field that is calculated to be equal to the ObjectID of the polygons.
2. Convert your polygons into lines by copy/pasting them into a lines feature class with an identical schema to your polygons.
3. You then can use the Create Routes to convert the polygon outlines to Routes. The Polygon ID would be your Route ID for each route.
4. Make sure the points have a unique ID field, then use the Locate Features Along Routes with the closest route option unchecked, the distance option checked, and a large search tolerance for the tobacco shop points.
5. Use the Summary Statistics tool to summarize the Point IDs and Route IDs as the case fields and get the minimum distance value in the summary to eliminate duplicate route locations along curved portions of the route.
6. The minimum distance values in the output could be averaged for each tobacco shop point based on the unique ID field using the Summary Statistics tool (works under a Basic license). The Case fields should be just the Point IDs and summarize the Mean of the distance.
7. Join the summary back to the points and calculate over your averages.
and for some more background How do you interpret "nested" standard deviational ellipse? making the use of the network analyst capabilities more pertinent since the paths taken will be along roads/etc