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More Geospatial Analysis with SAS

Started ‎10-21-2016 by
Modified ‎08-03-2021 by
Views 2,380

Editor's note: SAS programming concepts in this and other Free Data Friday articles remain useful, but SAS OnDemand for Academics has replaced SAS University Edition as a free e-learning option. Hit the orange button below to start your journey with SAS OnDemand for Academics:


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Picking up where we left off with the last Free Data Friday post (and keying off Chris Hemedinger‘s comment), I wanted to go a little deeper into the geospatial analysis of the campground data we last analyzed.Using SAS University Edition to analyze campground data.jpg


Get the data

Download the data from here, and I recommend you copy / paste the list of short forms from the site into your code for easy reference. 



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Getting the data ready

As I mentioned last week, other than merging the data into a single Excel file, there wasn’t much I had to do.  Before I imported it, I used the Excel “delete duplicates” tool (which removed the 1 record I mentioned above) and then imported it into the University Edition environment.


The results

First, let me address what Chris mentioned in his comment, that the GEODIST function allows you to use your data or provide the latitudes / longitudes and calculate the distance (in kilometers or miles).  This is a much easier way to do it!


Here’s the code, more or less the same with the exception of the Distance calculations.  This new code is much easier to read, and more efficient from a resource perspective.


proc sql;
geodist(lat, lon, 43.670, -79.386, 'K') as dist_K,
geodist(lat, lon, 43.670, -79.386, 'M') as dist_M,
name, prov_state, town
from work.IMPORT
where amenities like '%FT%'
and amenities like '%NP%';


Here’s the results. You can see that they're the same as last week’s, so from a validity and reliability aspect, we’re good!


Image 1.png


The other analytical technique I wanted to show is taking a table and joining it onto itself.  This is useful when you want to compare the values to themselves, and in the example I have, I’m calculating the distance between each campsite to every other campsite.  I have put limits on the data to keep the running time to a minimum, and enough data is returned to show what I’m referring to.


Here's the full code (we'll go through it, I just want you to see the whole thing):


proc sql;
create table work.test as
select as name1, a.prov_state as prov_state1, as name2, b.prov_state as prov_state2,
geodist(, a.lon,, b.lon, 'K') as dist_K
from work.import a , work.import b
and geodist(, a.lon,, b.lon, 'K')>0
and geodist(, a.lon,, b.lon,'K')<100;
select * from work.test where dist_K<30;


In breaking the code down, looking at the FROM statement first, you’ll see I have the work.import table referenced twice.  SQL as a language has a useful feature called aliasing, which enables you to refer to a table as something else; in this case, I’m using a and b.  This allows SAS to distinguish between the tables, columns, and values. 



from work.import a , work.import b


Next, the select statement has the list of the variables I want.


select as name1, a.prov_state as prov_state1, as name2, b.prov_state as prov_state2,
geodist(, a.lon,, b.lon, 'K') as dist_K

You’ll note that this time, I have a. or b. in front of the variable names – this is because the same columns appear in both tables (because it’s the same table twice) and I have to distinguish which is which.  You’ll see that I’ve done the same with the GEODIST function as well; a notable difference is that in previous examples, I’ve used the 43.670, -79.386 coordinates as the second point.  In this example, because I’m comparing each camp to every other camp, I use the LAT and LON from each instance of the table. 


Finally, I’m creating a new table (work.test) here:


create table work.test as


and as I mentioned I’m limiting the data in the Where clause:


and geodist(, a.lon,, b.lon, 'K')>0
and geodist(, a.lon,, b.lon,'K')<100;


First, I’m saying where the names do not match (I don’t want Jones’ Camp Ground compared to itself).  I’m also saying where the distance is greater than 0 (if A -> B is 15, I don’t need to know that B -> A is -15).  Greater than 0 also removes the possibility of campsites that have been entered twice but with slightly different names (Jones’ Camp Ground and Jones Camp Site, for example).  The first criteria would have skipped this, and the >0 criteria would remove them.  The final criteria is to limit the distance to <100 kilometers. 


Because I have put this data into a temporary table (work.test), I need to now display what I’ve done, and that’s the single line query at the bottom. 


select * from work.test where dist_K<30;


I added the distance <30 kilometers criteria just to speed up the query, but to give a variety of results.


Because the data is sorted alphabetically by PROV_STATE then by Camp, the sites in Alberta come up first.  You’ll see there seems to be quite a few camps within close proximity to a camp called Anthony Henday, which may be of interest to someone that is looking to do some travelling around the area.


Image 2.png



Now it’s your turn!


Did you find something else interesting in this data? Share in the comments. I’m glad to answer any questions.



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Last update:
‎08-03-2021 01:50 PM
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