Spatial Hot Spots in Twitter

: In this lesson, we will learn and apply the Getis Ord hot spot statistic.

Wednesday, May 5

Lecture

Getis Ord handout
Getis Ord spreadsheet
spdep package localG man and localG page
ArcGIS Getis Ord
GeoDa Local G caution: this page appears to give an incorrect simpler version of the statistic from 1992. The software implements the full 1995 formula.
Casey Lilley’s spatial autocorrelation in R
Michael Minn’s basic point pattern analysis in R

Ord, J. K., and A. Getis. 1995. Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis 27 (4):286–306. DOI:10.1111/j.1538-4632.1995.tb00912.x
Rogerson, P. A. 2006. Statistical Methods for Geography: A Student’s Guide. SAGE Publications. (this has a new addition, 2020)

Before next Lab, make a copy of the 01-search-dorian.r script in your RE-Dorian repository and modify it to query your own set of data from Twitter and prepare it for mapping just as we have done here!
You may modify both the search terms and the geographic extent of the search.
Remember that you may have to let the query run for a few hours if there are a high number of results.
This type of analysis is highly dependent on trending twitter activity over the past week. Create a query that will result in many thousands of tweets in the United States.
You may also want a second query to establish a baseline of normal twitter activity not related to your search. This could be an identical query that simply removes the search term constraints, or an identical query that finds the inverse of the search terms (i.e. all tweets that do not contain any of the keyword search terms). This second query will enable you to calculate a normalized tweet difference index.

New Dorian-RE Repository has been updated, including:

Code to complete the full analysis in R
Sample code for saving and loading .RDS files for peace of mind and reproducibility
Spatially joining Twitter data to counties
Calculating and mapping the Getis-Ord statistic
Template report for the GIS analysis

I suggest that you:

Fork/clone a new copy of the new repository (either change the name of the old one, or change the name of the fork while creating it
Run through the analysis to refresh your memory and understand the new code
Note any code parameters that may need to change to suit your own Twitter search data
Implement your own version of spatial Twitter anlaysis and write up a report

Due: Tuesday May 11

Ideas for further (introductory-level) Twitter analysis, Michael Kearney’s intro to rtweet