[R-sig-Geo] Spatial Autocorrelation Estimation Method

Tue Nov 5 22:40:00 CET 2019

Dear Roger,

Thank you for your reply. I disabled HTML; my e-mails should be now in plain text.

I will give a better context for my desired outcome.

I am taking Airbnb's listings information for New York City available on: http://insideairbnb.com/get-the-data.html

I save every listings.csv.gz file available for NYC (2015-01 to 2019-09) - in total, 54 files/time periods - as a YYYY-MM-DD.csv file into a Listings/ folder. When importing all these 54 files into one single data set, I create a new "date_compiled" variable/column.

In total, after the data cleansing process, I have a little more 2 million observations.

I created 54 timedummy variables for each time period available.

I want to estimate using a hedonic spatial timedummy model the impact of a variety of characteristics which potentially determine the daily rate on Airbnb listings through time in New York City (e.g. characteristics of the listing as number of bedrooms, if the host if professional, proximity to downtown (New York City Hall) and nearest subway station from the listing, income per capita, etc.).

My dependent variable is price (log price, common in the related literature for hedonic prices).

The OLS model is done.

For the spatial model, I am assuming that hosts, when deciding the pricing of their listings, take not only into account its structural and location characteristics, but also the prices charged by near listings with similar characteristics - spatial
autocorrelation is then present, at least spatial dependence is present in the dependent variable.

As I wrote in my previous post, I was willing to consider the neighbor itself as a neighbor.

Parts of my code can be found below:

########

## packages

packages_install <- function(packages){
 new.packages <- packages[!(packages %in% installed.packages()[, "Package"])]
 if (length(new.packages))
 install.packages(new.packages, dependencies = TRUE)
 sapply(packages, require, character.only = TRUE)
}

packages_required <- c("bookdown", "cowplot", "data.table", "dplyr", "e1071", "fastDummies", "ggplot2", "ggrepel", "janitor", "kableExtra", "knitr", "lubridate", "nngeo", "plm", "RColorBrewer", "readxl", "scales", "sf", "spdep", "stargazer", "tidyverse")
packages_install(packages_required)

# Working directory
setwd("C:/Users/User/R")

## shapefile_us

# Shapefile zips import and Coordinate Reference System (CRS) transformation
# Shapefile download: https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_zcta510_500k.zip
shapefile_us <- sf::st_read(dsn = "Shapefile", layer = "cb_2018_us_zcta510_500k")

# Columns removal
shapefile_us <- shapefile_us %>% select(-c(AFFGEOID10, GEOID10, ALAND10, AWATER10))

# Column rename: ZCTA5CE10
setnames(shapefile_us, old=c("ZCTA5CE10"), new=c("zipcode"))

# Column class change: zipcode
shapefile_us$zipcode <- as.character(shapefile_us$zipcode)

## polygon_nyc

# Zip code not available in shapefile: 11695
polygon_nyc <- shapefile_us %>% filter(zipcode %in% zips_nyc)

## weight_matrix

# Neighboring polygons: list of neighbors for each polygon (queen contiguity neighbors)
polygon_nyc_nb <- poly2nb((polygon_nyc %>% select(-borough)), queen=TRUE)

# Include neighbour itself as a neighbour
# for(i in 1:length(polygon_nyc_nb)){polygon_nyc_nb[[i]]=as.integer(c(i,polygon_nyc_nb[[i]]))}
polygon_nyc_nb <- include.self(polygon_nyc_nb)

# Weights to each neighboring polygon
lw <- nb2listw(neighbours = polygon_nyc_nb, style="W", zero.policy=TRUE)

## listings

# Data import
files <- list.files(path="Listings/", pattern=".csv", full.names=TRUE)
listings <- setNames(lapply(files, function(x) read.csv(x, stringsAsFactors = FALSE, encoding="UTF-8")), files)
listings <- mapply(cbind, listings, date_compiled = names(listings))
listings <- listings %>% bind_rows

# Characters removal
listings$date_compiled <- gsub("Listings/", "", listings$date_compiled)
listings$date_compiled <- gsub(".csv", "", listings$date_compiled)
listings$price <- gsub("\\$", "", listings$price)
listings$price <- gsub(",", "", listings$price)

## timedummy

timedummy <- sapply("date_compiled_", paste, unique(listings$date_compiled), sep="")
timedummy <- paste(timedummy, sep = "", collapse = " + ")
timedummy <- gsub("-", "_", timedummy)

## OLS regression

# Pooled cross-section data - Randomly sampled cross sections of Airbnb listings price at different points in time
regression <- plm(formula=as.formula(paste("log_price ~ #some variables", timedummy, sep = "", collapse = " + ")), data=listings, model="pooling", index="id")

########

Some of my id's repeat in multiple time periods.

I use NYC's zip codes to left join my data with the neighborhood zip code specific characteristics, such as income per capita to that specific zip code, etc.

Now I want to apply the hedonic model with the timedummy variables.

Do you know how to proceed? 1) Which package to use (spdep/splm)?; 2) Do I have to join the polygon_nyc (by zip code) to my listings data set, and then calculate the weight matrix "lw"?

Again, thank you very much for the help provided until now.

Best regards,
Robert

________________________________________
From: Roger Bivand <Roger.Bivand using nhh.no>
Sent: Tuesday, November 5, 2019 15:30
To: Robert R
Cc: r-sig-geo using r-project.org
Subject: Re: [R-sig-Geo] Spatial Autocorrelation Estimation Method

On Tue, 5 Nov 2019, Robert R wrote:

> I have a large pooled cross-section data set. I would like to
> estimate/regress using spatial autocorrelation methods. I am assuming
> for now that spatial dependence is present in both the dependent
> variable and the error term. My data set is over a period of 4 years,
> monthly data (54 periods). For this means, I've created a time dummy
> variable for each time period. I also created a weight matrix using the
> functions "poly2nb" and "nb2listw". Now I am trying to figure out a way
> to estimate my model which contains a really big data set. Basically, my
> model is as follows: y = γD + ρW1y + Xβ + λW2u + ε My questions are: 1)
> My spatial weight matrix for the whole data set will be probably a
> enormous matrix with submatrices for each time period itself. I don't
> think it would be possible to calculate this. What I would like to know
> is a way to estimate each time dummy/period separately (to compare
> different periods alone). How to do it? 2) Which package to use: spdep
> or splm? Thank you and best regards, Robert

Please do not post HTML, only plain text. Almost certainly your model
specification is wrong (SARAR/SAC is always a bad idea if alternatives are
untried). What is your cross-sectional size? Using sparse kronecker
products, the "enormous" matrix may not be very big. Does it make any
sense using time dummies (54 x N x T will be mostly zero anyway)? Are most
of the covariates time-varying? Please provide motivation and use area
(preferably with affiliation (your email and user name are not
informative) - this feels like a real estate problem, probably wrongly
specified. You should use splm if time make sense in your case, but if it
really doesn't, simplify your approach, as much of the data will be
subject to very large temporal autocorrelation.

If this is a continuation of your previous question about using
self-neighbours, be aware that you should not use self-neighbours in
modelling, they are only useful for the Getis-Ord local G_i^* measure.

Roger

>
>       [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo using r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo

--
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
https://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en