Tag Archives: Maps

Change SP Object Polygon Rendering Order in R

The Problem

I have a geoJSON file that was made by combining many (as in ~250) geoJSON files each containing a single polygon in R.  The polygons are of varying size and can overlap in space.  When I open the file in a GIS, often the smaller polygons are hidden under the larger ones, making displaying the data challenging, if not impossible.

Map showing few polygons when there should be many

I know there are more polygons than are visible in this area, so they must be hiding under the larger polygons.

Map showing larger polygons obscuring smaller polygons

With transparency set to 60% for each polygon (see the Draw Effects dialog in QGIS for the fill symbol layer), you can see that smaller polygons are hiding under larger ones.

The Goal

I would prefer that the polygons stack up so that the largest is on the bottom and the smallest is on the top.  This requires that I change the rendering order based on the area of each polygon.

The Quick Fix

QGIS has an option to control the rendering order.  Open the Layer Properties; go to the Style tab; check the box for “Control feature rendering order”; click the button on the right with the A Z and an arrow to enter the expression you want (I would order by area for example).

Why isn’t this the right solution for me?  I’m sharing a user-contributed dataset.  One of the goals is that anyone can use it.  When polygons are obscured, it makes the dataset just a little harder to use and understand, which means people won’t like using it.  Another goal is that anyone with a reasonable understanding of GIS can contribute.  If I have to write a bunch of instructions on how to visualize the data before they can add to the dataset, people are less likely to contribute.

Map showing all the polygons expected.

Now I can see all of the polygons because the larger ones are on the bottom and the smaller ones are on top.

My Solution

Hat tip to Alex Mandel and Ani Ghosh for spending a couple of hours with me hammering out a solution.  We used R because I already had a script that takes all of the polygons made by contributors and combines them into one file.  It made sense in this case to add a few lines to this post-processing code to re-order the polygons before sending the results to the GitHub repository.

What you need to know about rendering order & SP Objects

The order in which items in an SP object are rendered is controlled by the object’s ID value.  The ID value is hidden in the ID slot nested inside the polygons slot.  If you change these values, you change the order items are rendered.  ID = 1 goes first, ID =2 goes on top of 1, 3 goes on top of 2, and so on.  So for my case, assigning the IDs based on the area will get me the solution.

How

This Stack Exchange Post on re-ording spatial data was a big help in the process.  Note that every SP object should have the slots and general structure I used here.  There’s nothing special about this dataset.  If you’d like the dataset and another copy of the R code, however, it is in the UC Davis Library’s AVA repository.

#load the libraries you'll need
library(raster)
library(geojsonio)
library(rgdal)

### FIRST: Some context about how I made my dataset in the first place

# search in my working directory for the files inside the folders 
# called "avas" and "tbd"
avas <- list.files(path="./avas", pattern = "*json$", full.names = "TRUE")
tbd <- list.files(path="./tbd", pattern = "*json$", full.names = "TRUE")

#put the two lists into one list
gj <- c(avas, tbd)

#read all the geojson files & create an SP object
vects <- lapply(gj, geojson_read, what="sp")

#combine all the vectors together. bind() is from the raster package.
#probably could just rbind geojson lists too, but thats harder to plot
all <- do.call(bind, vects)

#Change any "N/A" data to nulls
all@data[all@data=="N/A"]<- NA


### SECOND: How I did the sorting

#Calculate area of polygons - needed for sorting purposes
# the function returns the value in the area slot of each row
all@data$area<-sapply(slot(all, "polygons"), function(i){slot(i, "area")})

#add the row names in a column - needed for sorting purposes
all$rows<-row.names(all)

#Order by area - row names & area are needed here
# decreasing = TRUE means we list the large polygons first
all<-all[match(all[order(all$area, decreasing = TRUE),]$rows, row.names(all@data)),]

#add new ID column - essentially you are numbering the rows 
# from 1 to the number of rows you have but in the order of 
# largest to smallest area
all$newid<-1:nrow(all)

#assign the new id to the ID field of each polygon
for (i in 1:nrow(all@data)){
 all@polygons[[i]]@ID<-as.character(all$newid[i])}

#drop the 3 columns we added for sorting purposes (optional)
all@data<-all@data[,1:(ncol(all@data)-3)]

#write the data to a file in my working directory
geojson_write(all, file="avas.geojson", overwrite=TRUE, convert_wgs84 = TRUE)
Advertisements

Making of a Moon Tree Map

29_CleanUp.png

I’m presenting a workflow for finishing maps in Inkscape at FOSS4G North America this year (2016). To really show the process effectively, I made a map and took screenshots along the way.

The Data

I decided to work with Moon Tree location data.  It’s quirky and interesting… and given that this is a geek conference I figured the space reference would be appreciated.  A few months ago I learned about Moon Trees watching an episode of Huell Howser on KVIE Public Television and then visited the one on the California State Capitol grounds.  I later learned from my aunt that my grandfather was a part of the telemetry crew that retrieved the Apollo 14 mission that carried the seeds that would become the Moon Trees, so there’s something of a connection to this idea.  Followers of my research also know that I’m a plant person, particularly plant geography.  So this seemed like the perfect dataset.  I was fortunate to find that Heather Archuletta had already digitized the locations of public trees and made them available in KML format.

Data Processing

The KML format is great for some applications (particularly Google maps, for which it was designed) but it poses some challenges.  I spent several hours… maybe more than I want to admit… formatting the .dbf to make the shapefile more useful for my purposes.  I created columns and standardized the content.  The map does not present all the data available (um… duh.).  It was challenge enough getting all this onto one page.

Yes, Inkscape is Necessary

You can’t make this map in QGIS completely.  I mean, normally you can make some fantastic maps in QGIS, but this one is actually not possible.  Right now, QGIS can’t handle having map frames with different projections.  I tried, but I found that even when the map composer looked right, the export in all three export options changed the projection and center of each frame to match that of the last active frame.  So I ended up with a layout with three zoom levels centered on Brazil… interesting, but not what I had in mind.  So I exported an .svg file three times from the map composer – one for each map frame – and put them together in Inkscape.

Sneaky Cartography

One of the methods I often use in my maps is to create subtle blurred halos behind text or icons that might otherwise get lost on a busy background.  I don’t like when the viewer sees the halos (maybe it’s from teaching ArcMap far too many years at universities).  It’s not quite a pet peeve, but I think there’s often better ways to handle busy backgrounds and readability.  My blog, my soapbox.  Can you spot them?  There are a couple in the map and in the final slide of the pitch video.  It doesn’t look like much, but I promise the text is easier to read.

The texture on the continents is the moon.  I clipped a photo of the moon using the continent outlines.  I liked the idea of trees on the moon.

Icons

The icons are special to me.  I’ve been really wanting to make a map using images from Phylopic and I thought this was the perfect opportunity… but… but… no one had uploaded outlines for any of the species I needed.  So I made them and uploaded them.  So if you want an .svg of these, help yourself.  If, however, you need dinosaurs, they’ve got you covered.

Watch it happen:

My pitch video captures the process from start to finish:

 Want more open source cartography?

Come to FOSS4G North America and see my and several other talks focused on cartography.  I’ll cover methods and tools in Inkscape common for cartography.


Favorite Cartographers: Erwin Raisz

Normal people don’t have favorite cartographers, but I think many geographers do.  I’ve simply fallen in love with the maps of Erwin Raisz.  How?  It’s not a name you hear too often, if at all, in the world of geography, it’s true.  I was introduced to his work by a writing assignment in one of my geography core classes for my PhD.  We were asked to write essays about famous geographers and directed to a series of volumes in the library containing short biographies of geographers.  I just started flipping through pages, found a cartographer, and went to work researching for my essay.  This is what I found:

Examples of Rasz's physiography from his book, Principles of Cartography.

Raisz developed this style of drawing maps that shows the landforms not as abstract symbols but as they look in real life.  They are seemingly more art than science, but appearances are deceiving in this respect because the man was a masterful cartographer.

An example of Raisz's style applied to the state of Michigan.

The detail in these maps and the amount of information is astounding.  This is really what I strive for in my own products – the combination of art and science that makes the subject feel so easy to understand, but conveys so much information.

Erwin Raisz's Armadillo Projection

And finally, if all the prettiness didn’t win you over yet, how can you not love the sense of humor in making a map projection in the shape of an armadillo?