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Statistical Analysis of Spatial Data [LOOM.02.035], Year 2018

Aim

Passing this course will help in preparing a research project, reading scientific literature and writing thesis. Most of computational exercises here are introducing an online spatial data calculator.

Arrangement

This is a web-based course. Open an exercise, read it and the instructions below the text. If the instructions are not complete enough, first, try to find answer from web resources. If the task is still not understandable, ask the mentor to improve the instructions and after that make an appointment for consultations. Any support in improving this application is appreciated.

Exam

If you are satisfied with the result calculated automatically from points earned by sending answers from this application, announce your wish to get this result entered to the Study Information System. In case of a higher ambitions, agree a time for written examination. Examination takes up to 4 hour, the questions are from the same themes but not the same.

Excercises

The exercises and questions of this subject are grouped to themes, themes to domains. Theme page can be opened by clicking on the theme name in the list below. There are three options for viewing an exercise text and instructions (for some exercises also data files and tutorial videos) — click the exercise number in the list, input exercise number, or select theme and then the exercise in the table. In the first case, right click enables to select if to open the exercise detailed view in the same window, in a new page or new tab. Not all exercises can be solved only by looking at instructions. Some individual thinking and interpretation of results is needed as well.

Answers to the exercises and questions can be sent from the exercise detailed view. After submitting the answer, the logged in student will see the expected answer and answers sent by other students. The submitted answer can be updated and corrected after evaluation by the mentor. Select the row you intend to comment in the table of sent answers to open tools for sending a comment or update. All logged in students can send comments and updates to the answers of other students.

In case of computation exercises, add the software and method name to the answer — in some cases, different applications use different computational algorithms and produce different results from the same data.

The sequence of exercise answering is free.

Software

Spatial Data Calculator

R spatial

Contact

Kalle Remm; phone 52 90513; in Tartu: Vanemuise 46-229; in Elva: Järve 4A.

Summary

Log in to see the results.

Themes

Basic statistics and classification

Here are some themes to revise some basic topics in statistics.

Basic Statistics

Cluster Analysis

Spatial Clustering

Point patterns

Point pattern analysis deals with the spatial arrangements of point objects, which can belong to one type or to different types. In the latter case, location of object relative to objects of another type is the key question. Web sources: Wikipedia, R tutorial, Velazquez et al. 2015. An overview of point pattern anaysis in ecology is in Wiegand_Moloney_2004.

Point Pattern

Relation Between Point Patterns

Thinning

Lines and directions

We can transform a linear pattern to a point pattern by placing points on lines, and then point pattern analysis methods can be applied. More specific topics of linear pattern analysis are intersection, route planning, generalization of directions, line simplification and connecting of lines.

Lines

Directions

Local statistics

Here are exercises for calculating statistics locally including values in proximity of a location on a surface. For simpler cases like in these exercises, the Local Statistics page in Spatial Data Calculator should fit well. Estonian version of the local statistics page includes option to log in using credentials from this course and to use more detailed data layers licenced to the employees of the Institute of Ecology and Earth Sciences.

Local Statistics (categorical variable)

Local Statistics (numerical variable)

Local Statistics Along a Line

Correspondence and correlation

The term correlation is often used to mark any statistical relationship. In a stricter sense, it is a mutual relationship between numerical variables, while correspondence is a mutual relationship between nominal variables. A relationship is spatial if its properties depend on absolute or relative location (distance between observation sites).

Correspondence of Categorical Surfaces

Interpolation

Spatial Correlation

Spatial Autocorrelation

Spatial models

Unlike to the correlations, covariance and correspondence, statistical models are not reversible. The statistical models are created and used to predict the value of a dependent variable (response variables) using values of explanatory variables (independent variable, covariate). Spatial models include variable(s) representing location. The model is not spatial if location coordinates are recorded for each observation but are not included into the model .

Trend Surface

Variography and Kriging

Distribution and Suitabity Modelling

Model validation

Pattern generation

Here are exercises on planning field observation sites and generating theoretical (landscape) patterns.

Creating a Point Pattern

Questions on theory

This domain contains questions on theory. The answers can be found from the forth, fifth and sixth chapter of the textbook, which (unfortunately for foreigners) is in Estonian. Alternative sources are referred at each question, although learning a local language should be possible during a couple of months.

Exploratory Spatial Data Analysis

Spatial Models

Pattern Creation

Contributions

You can send up to five answers to each question in this theme. Of course, you can also present more than one proposal within one answer.  All proposals are appreciated. The same questions are also in examination sets. It is enough to cite the ID number of your answer during examination.

Student Contribution