A co-worker was interested in segmenting a list of data points, and I went down a rabbit hole on one dimensional segmentation. I found an article on the Jenk’s natural breaks optimization on Wikipedia. I found another article that had some examples. This is used to bin data points so that clusters are always binned together. There is an iterative method that takes unordered data, but this implementation just sorts the data before binning.
In this post I’ll discuss how to plot data points on a shapefile. In a previous post I discussed how to install basemap using pip, the package manager for Python. Since
basemap is an extension of
matplotlib, we have a lot of familiar plotting functions and options at our disposal. Of particular importance is the ability to use projection data in plotting the shapefile, and plotting the data points.
I heard about a new (to me) tool for viewing shapefiles on this site. It required the installation of
basemap which is a part of
matplotlib, but the installation turned out to be a little tricky. I found a solution at the blog, I Lessen Data, which pointed me to another interesting post on mapping, So You’d Like to Make a Map Using Python.
In this post I’ll consider performing a local hypothesis test for a difference in means with spatial data. I do not know if this is the optimal way to go about this sort of thing, but I have not yet found another solution. I think the best way to describe the problem is to consider the artificial data, and then wade through the code.
In this post I’ll discuss creating and altering shapefiles, and converting point sets from one coordinate reference system to another. I’ll also touch on scripting these tasks for large data sets. I’ll begin with the installation of Quantum GIS and Python for manipulating geographical data. I mainly use QGIS for visualizing and building shapefiles, and I use OSGeo4W from the command line for adding/converting shapefile projections, and converting point sets from one CRS to another.