Module Spatial Statistics
The latest module “Spatial Statistics” was of great interests for me, since I currently do a lot of statistics with R for my internship project. I analyze the influence of the spatial variables on harvest costs. R gives out great reports with lots of numbers, which I am not always sure how to interpret. So I was very excited to get a more fundamental knowledge in the field of statistics.
Also I was glad to read in the instructor’s welcoming email that he puts an emphasis on the theoretical and methodological background of the used tools. That was indeed the case during the model. We used mainly ArcGIS Geostatistical Analyst extension for the spatial analysis and the necessary steps to get the results were very well documented for us. This way we could focus on the outputs of the tool, rather then learning another tool. In general the Geostatistical Analyst is pretty intuitive. Also we used SPSS for analysis. I used it already a little bit during my bachelors degree and was hoping that we use R this time. I use R a lot for my work projects, since it integrates better with Python and is Open-Source.
So what was the module about?
The first lecture was a great refresher of statistical terms. Thinks I heard many times before. But as with many things, the more often one hears it and thinks about it, the more one understands it. Slowly I am getting the hang of all those statistical terms.
From there we moved on and learned the differences between estimate statistics and test statistics, and about autocorrelation.
Next we got to Point Pattern Analysis. One of it’s methods is for instance the Nearest Neighbor Analysis. It is used a lot in crime statistics, to test if certain crimes show spatial clustering. The typical example is of course, that the crime rate is higher in low-income neighborhoods. We had to analyze, if the spatial distribution of Salzburg’s citizens eduction level is random or if it is possible to find a pattern. For example, are people with university degrees clustered in a certain neighborhood, or are they spread out all over the city.
On we went with the topic of variography. Variography has the interesting assumption, that two points that are close to each other, take on close values because these values were generated under similar physical conditions (Isaaks and Srivastava, 1989). We put that to use right away with another analysis of Salzburg’s population. Using ArcGIS Geostatistical Analyst we had to examine if there is a spatial autocorrelation of Non-EU citizens in the city of Salzburg. For instance the trend analysis below shows, the majority of Non-EU citizens live in the centre of Salzburg.
We stayed around that topic and learned about the connection between Variography and Interpolation. Towards the end of the module we finally came to Regression Analysis. The topic I was especially interested since that is exactly what I do with my harvest cost research. With a multivariate regression analysis we had to test the following hypotheses:
Furnishing of an apartment is related to its marked value, ownership situation, the size of the apartment, and how many people live there.
The analysis was done with the very user friendly program SPSS. We had to try out a forward and backward selection and discuss the output differences.
The last lecture and assignment was about clustering: How they are created, how to interpreted them, necessary preconditions, etc.
For me the use case examples in the assignments were very concrete and therefor made the often abstract statistical methods a lot more accessible. The newly learned knowledge will be of great help for future statistical analysis.