Local Geary c in R

Ali Syed [email protected] Geographical Sciences and Economics majors, College of the University of Chicago

Sihan Mao [email protected] Master in Public Policy Candidate, the University of Chicago Harris School of Public Policy

June 12, 2019

Overview

R is well equipped with spatial operation tools. Spatial statistics, however, are not fully integrated into R environment. The objective of this project is to develop an R Notebook Tutorial to compute and visualize univariate local Geary c statistic. Success of this project would entail that the notebook tutorial is workable and effective enough to replicate the GeoDa local Geary c functions.

Scope

The following scope describes the work to develop the local Geary c function in R. The team shall conduct research on:

• the design of local Moran’s I and local Geary c¹,
• localmoran.R script in spdep repository²,
• current available local Moran’s I tutorial in R³.

Sample Data Description

Dataset Description: The dataset is the test dataset of tutorial for calculating local Geary c in R. The test requires a continuous variable as the attribute of polygons. The dataset includes Airbnb rents and boundaries of community areas in Chicago.

Type: Polygon shapefile; Observations: 77; Variables: 3

Variables to be included:

• community - name of community area
• AREAID – ID number associated with community area
• price_pp – price per person of Airbnb

Data Table:

Variable	Type	Example	Source
community	string	Kenwood	Chicago Data Portal
AREAID	int	39	Chicago Data Portal
price_pp	double	77.991453	Inside Airbnb

Geometry:

Chicago community boundaries

Citation:

1. Anselin, Luc. 1995. “Local Indicators of Spatial Association — LISA.” Geographical Analysis 27: 93–115.

2. Bivand, Roger. 2018. "r-spatial/spdep/localmoran." Github repository link: https://github.com/r-spatial/spdep/blob/master/R/localmoran.R.

3. Lansley,Guy and James Cheshire. 2016. "An Introduction to Spatial Data Analysis and Visualisation in R." Consumer Research Data Centre.

4. GeoDa Center (https://geodacenter.github.io/data-and-lab//airbnb_Chicago-2015/), based on data from Chicago Data Portal.

5. Inside Airbnb (http://insideairbnb.com/get-the-data.html).

Future Work

Build up permutation and signficance filter function in R to clone permutation settings in GeoDa. Potential reference is the geary python script in pysal repository.