Overview

As humans, we are predisposed to seeing patterns when there may not necessarily be any. This week, you will be using a mixture of qualitative and quantitative methods to examine the spatial distribution of crimes.

Figure 3.0: Examples of visualizations of the crime data

Click for a text description of the Examples of visualizations image.

This image provides examples of outputs from the analysis you will undertake in this lesson. It includes a boxplot separated by crime type, bar charts of crime over time, the output of a K-function analysis, a histogram, an interactive map of crime made with Leaflet, a mean center analysis output, and a kernel density estimate map.

Credit: Blanford, 2019

Learning Outcomes

At the successful completion of Lesson 3, you should be able to:

1. describe and provide examples of simple deterministic and stochastic spatial processes;
2. list the two basic assumptions of the independent random process (i.e., no first or second order effects);
3. outline the logic behind derivation of long run expected outcomes of the independent random process using the quadrat counts for a point pattern as an example;
4. outline how the idea of a stochastic process might be applied to line, area and field objects.
5. define point pattern analysis and list the conditions necessary for it to work well;
6. explain how quadrat analysis of a point pattern is performed and distinguish between quadrat census and quadrat sampling methods;
7. outline kernel density estimation and understand how it transforms point data into a field representation;
8. describe distance-based measures of point patterns (mean nearest neighbor distance and the G, F and K functions);
9. describe how the independent random process and expected values of point pattern measures are used to evaluate point patterns, and to make statistical statements about point patterns;
10. explain how Monte Carlo methods are used when analytical results for spatial processes are difficult to derive mathematically;
11. justify the stochastic process approach to spatial statistical analysis;
12. discuss the merits of point pattern analysis and outline the issues involved in real world applications of these methods.

Checklist

Lesson 3 is one week in length. (See the Calendar in Canvas for specific due dates.) The following items must be completed by the end of the week. You may find it useful to print this page out first so that you can follow along with the directions.

Questions?

Please use the 'Discussion - Lesson 3' Forum to ask for clarification on any of these concepts and ideas. Hopefully, some of your classmates will be able to help with answering your questions, and I will also provide further commentary where appropriate.