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Anthrome data is delivered in 5 minute resolution or roughly a 10KM cell –at the equator. In addition, there are historical estimates for 1900, 1800 and 1700. The work depends upon the HYDE 3.1 dataset and Landscan population.  The Anthromes  are  shown below in the legend

I started with a rather simple test. Let’s go the code
require(BerkeleyEarth)

Anthromes1900 <- file.choose()
Anthromes2000 <- file.choose()

Ant1900 <- raster(Anthromes1900)
Ant2000 <- raster(Anthromes2000)

lonlat <- cbind(Stations\$Lon, Stations\$Lat)

Stations <- cbind(Stations, Anthrome1900 =  extract(Ant1900,y = lonlat),
Anthrome2000 =  extract(Ant2000,y= lonlat))

Urban <- Stations[which(Stations\$Anthrome2000 == 12 | Stations\$Anthrome2000 ==11),]

Pretty simple. I use the BerkeleyEarth function to read in the stations. Then I read in two rasters: one containing Anthromes for 1900 and the other for the year 2000. Then I extract the values for each station given its Longitude and Latitude. Then I select only those stations which were Urban or mixed settlements in the year 2000. That reduces the 36,000 Berkeley Earth stations to around 8000. Their geographic distribution looks like this.

Next, we read in the data, window it to 1900 to 2011 and then we use “intersectInvData()” to make sure that the stations in the inventory match those in the data. After that we calculate weights and then “solve” a least squares problem.

Data <- windowArray(Data, start = 1900, end = 2011.99)

Data <- intersectInvData(Urban, Data)

weights <- inverseDensity(Data\$Inventory,Data\$Array)

Temps <- solveTemperature(Data\$Array,weights)

That’s it!  The variable Temps now holds the temperature anomalies for all the urban anthromes. Next, I decided to segregate the Urban Anthromes into 2 classes: those that were already urban in 1990 and those that were not. So, we have two classes. In one class the stations were urban in 1900 and remained urban in 2000. In the other class, they were non urban in 1900 and become urban in 2000. I solve the least squares equation for both sub classes and difference them.Think about what you expect the answer to be. In short, you will have two curves. The black curve is the temperature anomaly of all the urban stations that remained urban. The red line is the anomaly of the non urban stations that became urban.

The black line represents those stations that began as Urban and ended as Urban. The red line is stations that began as non urban and became urban. There is a tiny positive trend (.0007)  in this difference. What does that mean?  You tell me. Did you expect stations that went from non urban to urban to show more warming? I did. Can this negative result be explained by appealing to a variety of suppositions? Sure. As always with these things more research required.