Tuesday, 10 December 2013

Book Review: Learning Geospatial Analysis with Python by Joel Lawhead

I decided to read this book since I've been doing maps using R. Hence it is better to learn the literature and science behind mapping and how to do a proper analysis on it. In addition, I would like to see what Python can offer in this discipline.

The book has 10 chapters contained in a 364 pages. The first three chapters was a long reading, not much on coding, but rather on discussions of introduction to Geospatial Analysis.


I like the idea that the author spent three chapters talking about the overall story (I would say) of Geospatial Analysis. Just a preview, the first chapter is of course the introduction; second is the data types, which surprisingly has a variety of formats; and third is all about the libraries and packages used in the said study. I am familiar with ArcGIS and QGIS, but this book lets you aware with other tools as well.

The simple illustration that complements the discussion is very helpful in telling the overall story of the subject. And the step-by-step tutorials are easy to follow (just what the book's description suggest), this is one thing that Packt books never frustrates me.

Monday, 2 December 2013

R: Explore ARIMA(2, 2, 2) subclass family on Shiny

I've been thinking that it might be better to explore the Box-Jenkins ARIMA (Autoregressive Integrated Moving-Average) three-iterative modelling on Shiny. So here is what I got, this app is intended for ARIMA(2, 2, 2) subclass family only.

The app has six tabs, and these are:
  1. Historical Plot;
  2. Identification;
  3. Estimation;
  4. Diagnostic;
  5. Forecast; and
  6. Data
The first tab is where the time plot of the simulated time series, the series can be simulated from different subclass family of ARIMA(2, 2, 2). The order is assigned using the controls in the side panel. The values of the parameters are set in the text field right below the plot. So for example, the ARIMA(1, 1, 1) has two text fields for AR (Autoregressive) and MA (Moving-Average) parameters as shown below. The default parameter for all models is 0.3.

The next tab is Identification, this is the first stage of Box-Jenkins iterative modelling. Here, the model is identified using the correlograms, Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF).