If
we remove urban data from our Class 1 \ 2 mix, we see no change in trend.
Not exact matches
Like pollsters and
data scientists have been doing for decades, we normalize our
data against U.S. census
data, ensuring that our panel of millions accurately matches the U.S. population to
remove any age or gender bias (though
urban geographies are slightly over-represented in our panel).
Comparison of spatially gridded minimum temperatures for U.S. Historical Climatology Network (USHCN)
data adjusted for time - of - day (TOB) only, and selected for rural or
urban neighborhoods after homogenization to
remove biases.
------------------------------------ And here's what the proxies vs. the highly adjusted instrumental
data that have been hopelessly corrupted by
removing thousands of rural stations and keeping
urban stations, moving rural sites to airports, «mostly made up» SH sea surface temperatures, cooling down the 1930s and 1940s artificially to
remove 0.5 C from the early 20th century warming... look like.
What's more, NASA GISS takes explicit steps in their analysis to
remove any such spurious signal by normalizing
urban station
data trends to the surrounding rural stations.
There are too many potential sources of bias which are not accounted for, too many apples - to - oranges comparisons, and they can not draw any conclusions about
urban heat influences until their
data are homogenized and other non-climate influences are
removed.
He and his colleagues were examining
data gathered in rural areas, to
remove the distortion of measurements in
urban areas.