[R-sig-Geo] How to objectively subset cities by population

[Thiago V. dos Santos]

Dear Dr. Gräler,
Thanks for your contribution. I very much enjoyed the clustering suggestion, and it seems to be available in R's leaflet through the "markerClusterOptions" command. It could solve my problem, so I will take a closer look at that.
Regarding your first suggestion, can you point me out some example that uses the overlaid grid approach?
Thanks, -- Thiago V. dos Santos
PhD studentLand and Atmospheric ScienceUniversity of Minnesota


On Thursday, July 27, 2017, 3:00:55 AM CDT, Dr. Benedikt Gräler <b.graeler at 52north.org> wrote:

Dear Thiago,

if you want them spatially evenly distributed, you could overlay a grid 
and select the largest per grid box - or maybe more intuitive, select 
the largest per predefined administrative areas (counties/postal 
codes/...). This could also change based on zoom-level. An alternative 
is to group sensors and expand and zoom in by clicking on the group (see 
e.g. [1]).

HTH,

  Ben

[1] http://sensorweb.demo.52north.org/client/#/map


On 27/07/2017 06:09, Thiago V. dos Santos via R-sig-Geo wrote:
> Dear all,
> 
> I have temperature records of nearly 1200 locations in southern Brazil.
> 
> I am writing a shiny app that will show an interactive map with the locations plotted as circles, where the user can click a location to see its temperature time series.
> 
> However, if I show all the locations in the map, it will look really bad, too cramped.
> 
> Therefore, in an attempt to make the map look a bit cleaner, I am trying to think of an objective way to subset the locations. My initial approach would be to show only the "largest" locations, i.e. the ones with a population above a certain threshold.
> 
> The problem is: the distribution of the population is so positively skewed that I am having a hard time determining the optimal cutoff point.
> 
> Does anybody here know any tool or method, possibly spatial, that can assist me with this analysis?
> 
> These are the locations I am working with:
> 
> #-------------------------------
> # Download and summarize
> locs <- read.csv("https://www.dropbox.com/s/ykdd8x1mlc76klt/locations.csv?raw=1")
> hist(locs$Population)
> summary(locs$Population)
> 
> # Convert to spatial points and plot
> require(sp)
> coordinates(locs) <- cbind(locs$Lon , locs$Lat)
> plot(locs)
> bubble(locs,"Population")
> #-------------------------------
> 
> Thanks in advance,
>  -- Thiago V. dos Santos
> 
> PhD student
> Land and Atmospheric Science
> University of Minnesota
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> 
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-- 
Dr. Benedikt Gräler
52°North Initiative for Geospatial Open Source Software GmbH
Martin-Luther-King-Weg 24
48155 Muenster, Germany

E-Mail: b.graeler at 52north.org
Fon: +49-(0)-251/396371-39
Fax: +49-(0)-251/396371-11

http://52north.org/
Twitter: @FiveTwoN

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