I have the results for spatial clustering, in this results I have the id for some cities in USA. I would like to show this clustering results on a nice map. Is this feasible in R?
Yes, this is feasible.
You need to map the city ids to geographical data, then visualize it.
With the extensive drawing capabilities of R, this is not very hard; there are several R packages that will do the heavy lifting, and tutorials to guide you. Just pick whatever package you prefer.
We cannot give you a complete source, of course, because we don't know what kind of ids you have. For example many people use zip codes, others use FIPS ids, etc.
Related
Very new here, hi, postgraduate student who is tearing their hair out.
I have inherited a dataset of secondary data collected from research papers on species populations and their genetic diversity and have been adding more appropriate data to this sheet in preparation to perform some analyses. Part of the analysis will include subsetting the data by biome type to create comparisons between the biomes, and therefore I've been cleaning up and trying at add this information to the data I've added. I have latlong coordinates for each population (in degrees decimals) and it appears that the person working on this before me was able to use these to determine the biome for each point, specifically following the Olson et al. (2001)/WWF 14 biome categorisation, but at this point I'll take anything.
However I have no idea how this was achieved and truly can't find anything to help. After googling just about every combination of "r package biomes WWF latitude longitude species assign derive convert" that you can think of, the only packages that I have located are non functioning in my version of RStudio (e.g. biomeara, ggbiome), leaving me with no idea if they'd even work, and all other pages that I have dragged up seem to already have biome data included with their dataset. Other research papers I have found describe assigning biomes based on latlong coords and give 0 steps on how to actually achieve this. Is it possible in R? Am I losing my mind? Does anyone know of a way to do this, whether in R or not, and that preferably doesn't take forever as I have over 8000 populations to assess? Many thanks!
I would like to compare a satellite pseudo-image generated by the WRF model and a real satellite image with the R-package named Spatstat. However, I do not know how to begin. I have read that it is possible to carry out a spatial pattern comparison but I do not know which function I should use. I have two temperature images in a predefined domain and I would like to know if the comparison is carried out point-by-point over those images or I have to facilitate the model output and satellite data. In that case, how should I do that? Is there any available script?
Thanks in advance.
Kind regards, Lara.
The spatstat package is designed mainly to analyse spatial patterns of point locations (like the pattern of locations of accidents that occurred in a given year). It does have some basic facilities for comparing pixel images. If A and B are two pixel images, you can do arithmetic by typing e.g. mean(A-B) or mean(abs(A-B)). There are also some image metrics (distances between images) available. Please visit the spatstat.org website for more information.
background
i have some private survey data that contains a column of confidential information: the geographic location of the survey respondents. under no circumstances can this information be released.
as is common in survey research, in order for users to correctly calculate a variance on my survey data set, those users will either need that geographic location (unacceptable) or, alternatively, a set of replicate weights. i can create that set of replicate weights; however, it's quite easy to look at the correlations between those weights and back-calculate which of the survey respondents share the same geographic location. that is also unacceptable.
to help me with this question, you don't have to be familiar with replicate weights -- just think of them as a few columns of strongly-correlated clustered data.
i understand that if i want to maintain that clustering, an evil data user will always have semi-decent guesses at who shares geographic locations; i just want to make that guessing game less precise. on the un-obfuscated replicate weights, an evil data user can figure out 100% of the cases.
request
i am looking for a technique that
prevents the public use file users from easily deducing the shared geographic location off of the correlations between my replicate weights variables
does not obliterate the correlations between my columns of data (the replicate weights variables)
can be implemented on an R data.frame object without a major time investment
i say shared because the evil user might not know where the location is, but they might know if two survey respondents are from the same location -- an unacceptable possibility.
what i have tried
i don't really want to re-invent the wheel here. i am looking for r syntax, an r package, or anything else that would be relatively straightforward to implement. i've found one, two, three, four papers describing techniques that would all be suitable for my purposes; unfortunately, none of the authors have been willing to share actual code to implement them.
i can do simple things like add and subtract random values to my replicate weights columns according to a normal distribution, but i'd prefer to rely on the work of someone who understands privacy issues better than i do.
thanks!!!!
i have written this nine-step tutorial to walk through the process in an attempt to answer my own question. i am not an expert in the field of privacy/confidentiality and would love to hear both feedback about this idea and also other ideas. thanks!
http://www.asdfree.com/2014/09/how-to-provide-variance-calculation-on.html
I have a csv of the following format:
person, location, time_of_day, money_spent
I've been going through and seeing how to format data to make it work with the more popular libraries (see: https://sites.google.com/site/daishizuka/toolkits/sna/sna_data), but they seem to be focused on various formats of expressing the connectedness between each member.
I would like to express extra dimensionality to my network by, say, coloring the nodes and connectors different colors according to time_of_day met, or change size of the various dots by money_spent. Can someone give me some guidance as to how I can do that with an implementation of network graphs in R?
I can figure out how to preprocess my data such that it is compatible; I'm just not getting how to implement things to the liking of the SNA libraries such as igraph.
The networkDynamic R package provides data structures for dynamic networks and some basic functionality for importing and manipulating this type of data. You should then be able to do analysis with network, sna, or igraph packages (disclaimer: I'm one of the maintainers of networkDynamic)
Lately I have seen some cool examples of mapping in R and wanted to give this a shot. I currently have ArcView at work, but my spatial join is not working correctly (most likely user error).
Objective: I need a list of countries and what World Region they belong to. I have two layers (one country detail, the other region detail) and wanted to join the world region assignment onto each country. The join isn't working, so i figured I would come to the R community.
What are my options? This is my first attempt at doing any mapping in R and maybe there is an easier/better solution. Eventually I want to take lat/long data and map it as well.
Any insight will be much appreciated.
Brock
See the Spatial task view on CRAN, and packages like maps/mapdata, sp, rgdal, raster, blighty, rworldmap, RgoogleMaps, etc.
Do you have shapefiles you want to read? First get rgdal installed, or look at other options like maptools and shapefiles if that is difficult on your platform. Read functions in these packages will provide Spatial*DataFrame objects.
For information on the Spatial classes:
library(sp)
vignette("sp")
spatstat also has a lot of support for spatial data, and another vignette for converting to / from sp:
library(spatstat)
vignette("shapefiles")
The PBSmapping package is another good place to start. They have pretty extensive documentation and a great reference manual as well.