This module helps you build an intuition for and understanding of datums in spatial and geospatial networks, within the broader context of network design. It covers the following: The concept of a datum and the meaning of the term datum defect A suite of examples meant to help you make sure you understand the problems…
This module teaches you about hypothesis testing through some super practical spatial and geospatial examples. I want you to come out the other end familiar with how and why we set up hypothesis tests, and how to carry them out yourself. In many ways, two-tailed hypothesis testing follows directly from the concept of confidence intervals…
Confidence intervals, the sampling distribution, and the Central Limit Theorem are things I find are not very well understood by people new to inferential statistics. This module is an insightful and practical to these topics meant to help you succeed in these topic areas. It covers: The concept of the sampling distribution, something you’ve seen…
This module introduces you to the concepts and methods required to assess the quality of a spatial or geospatial network. It includes perspectives on and approaches for quality assessment using measures of precision and measures of reliability, and covers the following: A brief treatment of the trade offs in network design between precision, reliability, and…
In this lab, you will build on what you did in the previous lab in order to do an adjustment of the small trilateration network you designed there. You will need to reduce the observations to the mapping plane, in turn requiring you to code the related functions. Like other labs in the series, this…
This is a relatively short module that provides an introductory treatment of how we reduce observations for use in a network adjustment. It includes: An explanation of why pre-processing and the reductions of observations are needed An overview of different reductions of observations including meteorological, gravimetric and geometric reductions A detailed treatment of how to…
This module is about local measures of precision and accuracy of coordinates in spatial and geospatial networks. It covers the following: An intuitive review of precision vs. accuracy The notion of using variance-covariance functions to predict the precision of a network An introduction to correlation and it’s calculated (from data and from the variance-covariance matrix)…
Description and objectives As you go through this and your related courses, you’ll learn a lot as it relates to the fundamental concepts of geodetic networks and their application. This project provides an introduction to the application of those fundamentals in different important areas of geospatial engineering. In the context of this course, the project is…
You’re already familiar with the topic of functional modeling and how it fits within the bigger picture of our framework for probability, statistics, and estimation for spatial applications introduced here. This module helps you take that understand to the next level. In this module: We’re going to go a little deeper on functional modeling We’ll…
Statistical tables! They’re often not fully understood and yet they’re pretty amazing if you know what you’re doing and really understand what the underlying probability distribution function is all about. That’s what this module is for. In this module: We’re going to take a look at the normal probability distribution function – also called the…
In this lab, students learning about spatial networks will build on what they did in the previous lab in order to carryout a pre-analysis for the design of a small trilateration network. Like other labs in the series, it’s about bringing spatial networks to life for yourself and understanding them as deeply as possible. The…
This module takes a look at the geodetic model and the role of gravity field on geodetic networks, i.e. spatial networks measured on the surface of the earth. It includes treatments of: The role and importance of gravity in geospatial networks The notion of equipotential surfaces and the geoid Heights above the geoid and the…
As we’ve seen, the goal in inferential statistics is to to reach conclusions and make decisions beyond the data you have available to you, which most often implies that you’re inferring something about a population based on a sample you took. In business, this might mean carrying out a marketing survey to infer something about…
Welcome! Courses in statistics often dive right into some of the topics you’re going to see in this module. But I find it much more timely to bring them at this point, now that you’ve got intuition, key concepts, and foundational frameworks in hand. Here, we’re going to look at the following:: Populations and samples…
This module introduces the three key math models for geospatial applications. These are: The functional model The measurement model The stochastic model Examples are provided to bring these to life and to illustrate their applications. And a framework is presented that helps you level up your understanding of how engineers carry out measurement, estimation, analysis,…
This short module introduces the geodetic modeling and estimation framework we will use in spatial networks. The framework itself and my notes on the topic can be downloaded here: In the first lesson below, connections are drawn to what you know already. In the second lesson, the full framework is described – both as a…
Welcome! This module helps you build on what you already know by getting into our first few examples of some networks, and by building out five observation equations that are among the key building blocks when using least squares in the context of geodetic and spatial networks. You’ll learn to derive and implement these observation…
This is a foundational lab for students learning about spatial networks. The goal is to bridge from theory to practice so you can build your tools and skill set, and code your own networks library. Like the labs to follow, it’s about bringing spatial networks to life for yourself and understanding them as deeply as…
Welcome! This is a set of review topics for anyone wanting a refresher as they embark on the Spatial Networks course. It includes self-paced video-based lessons on functional modeling, linearization, and an introduction to least squares and error propagation. These are optional because you should already be familiar with this material, so they are being…
Welcome! I love this topic! When I teach it in person, I give my students a tape measure and everyone gets the chance to go out into the hallway outside of the classroom and try their hand at measuring the width of that hallway. They’re tasked with measuring it as carefully as possible and our…
The following schedules are for my university students:
Probability, Statistics, and Estimation for Spatial Applications »
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