In my previous post, I showed how to prepare the data for a bivariate choropleth map using PostGIS and QGIS. I also indicated that there is a website that shows an ArcGIS tool to do it. But, this actually turns into a good opportunity to illustrate some Python, and how to create the bivariate data using Arcpy.
Arcpy is certainly not as terse as SQL, but it does get the job done, and rather easily. We just have to think about the project a little differently. The code below is a Script tool that I created.
import arcpy, math, numpy
fc = arcpy.GetParameterAsText(0)
numrecs = int(arcpy.GetCount_management(fc).getOutput(0))
fields = arcpy.ListFields(fc, "bimode")
if len(fields) != 1:
arcpy.AddField_management(fc, "bimode", "text", 3)
f1 = arcpy.GetParameterAsText(1)
f2 = arcpy.GetParameterAsText(2)
fields = ['bimode',f1,f2]
var1 = arcpy.UpdateCursor(fc, sort_fields=f1)
i=1
for row in var1:
row.setValue("bimode",str(int(math.ceil((float(i) / float(numrecs)) * 3.0))))
var1.updateRow(row)
i=i+1
var2 = arcpy.UpdateCursor(fc, sort_fields=f2)
i=1
for row in var2:
row.setValue("bimode",row.getValue("bimode") + "." + str(int(math.ceil((float(i) / float(numrecs)) * 3.0))))
var2.updateRow(row)
i=i+1
Today I want to introduce you to another one of my students, Meghan Murphy. Meghan is an outstanding student, and one of the top undergraduates I have ever worked with (I know, I say that a lot, but they just keep getting better and better). Even as a Sophomore, Meghan was always helping other students out, even the Seniors – students would seem to wait for Meghan to organize everyone together to study for upcoming exams.
I just got the new build of Radian Studio, and now that it can directly read geodatabases, you can link directly to the geodatabase from inside of Radian and perform Radian spatial queries on it. In this video, I’m linking directly to an ESRI geodatabase, creating a small map display of the data, performing a spatial clip of two vector layers, and returning the results. In my 
This week my 
Noah Krach. Noah is an amazing undergraduate. Recently, we lost one of our graduate research associates, and Noah stepped in to provide technical support on a National Science Foundation project in Lake Victoria. Without missing a beat, Noah was all over the project, and he used his time in my class to create an Arcpy tool to extract, translate, and load (ETL) gigabytes of Landsat imagery. This tool does a lot, and I can’t even begin to describe all he did, you’ll simply have to watch and learn.
Jessica Molnar. Like Caitlin, Jessica is another student you’ve seen before. She’s got such a big heart, and is always looking for ways to apply GIS to humanitarian and ecological solutions. In this project, Jessica created an Arcpy application to identify locations for community gardens in Baltimore City with special consideration for locations within food deserts, near churches and schools, and on suitable soils for growing food. Jessica’s program also found those locations that were already owned by the City, but were vacant. Let’s hope the City makes use of this to build a more beautiful Baltimore (BTW, Jessica wrote her program to work in any location in the State of Maryland, so any community can use this tool!). I think Jessica may eventually roll this into a cloud based solution – hey Jessica, I think we found a project for graduate school!
John Tilghman. John’s family owns an orthodontist practice, and John decided to use PostGRES/PostGIS along with a number of other different Python packages to perform market area analysis. John integrated PostGRES, Google, and the Pygal libraries to create the first stages of a geodashboard to assess the effectiveness of marketing strategies, and other metrics. In the video, you’ll also see how he created a distance decay algorithm in SQL to determine at what point customers drop off from visiting the practice. With just a little bit of information (addresses and marketing strategies), John was able to extract a ton of business information – in fact, our guests from ESRI were surprised the John wasn’t already a business major!
Josh Young. Josh created an Arcpy script to assemble tons of location based data that might be useful for someone thinking about moving to a particular location. Now, in Josh’s case, he chose location based data he deemed important for the neighborhood (download speeds, elementary school, crime statistics, distance to the downtown, etc.). But ultimately, what Josh has shown us is how to create a template that integrates multiple Python packages and online data to provide very useful information.
gisadvising 