Search Results for: Map

Ash Stands On State Forests

For this map I queried the NYS DEC STANDS database to make a map of state reforestation lands that ash are the dominant tree species on. Ash you can see, there are ash stands on most state lands, with white ash most common in the southern part of the state, with a mix of green and black ash on state lands primarily north of the Adirondack Park. For viewing of the polygon layer, you may want to switch to satellite view.

Purple/White = White Ash Stand
Green/Green = Green Ash Stand
Blue/Dark Gray = Black Ash Stand

Data Source: DEC State Land Forest Stands. DEC State Land Forest Stands Polygon data showing forest cover types delineated by state foresters on DEC state land. Balloons added to make easier to see. http://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1356

What I’m learning with my Facebook Map Atlas of the Day 🗺

What I’m learning with my Facebook Map Atlas of the Day 🗺

Since mid-January, I have been posting a new Map Atlas of the Day to my Personal Facebook account. Most of those maps are created with QGIS, using data I’ve been able to pull from a variety of sources but a lot from the tidycensus R package and exported to an GPKG. I tried to spend less then 10-15 minutes each day on the map.

Each day creating the map, it makes me look for ways to automate processes, pull data together quickly. Often I discover a new problem with the layout, forcing me to dig a little deeper into the documentation, learning a new function or technique in QGIS. As time is tight, I often try to do things as quickly. Once you do things constantly, you get quicker and quicker each day, and find ways to take shortcuts. You find ways to improve beauty, make things more visually attractive.

One stop shop for Exchange, Transform, Load – best reason for making maps with R 🗺

One stop shop for Exchange, Transform, Load – best reason for making maps with R 🗺

While the R programming may not be considered a GIS map making program, it does one thing exceptionally well – exchange, transform and load data onto map. It really is a one stop shop. A few lines of code handles it all, and the code can be reused from map to map.

Traditionally before I got into R, I would manually download data, clean it in LibreOffice Calc aka Excel then link it in QGIS to a shapefile. That’s a lot of clicking and repetitive work that can easily be automated and repeated in R Studio, and run over and over again. Often in a fraction of the time, sometimes even less if I’m just updating the map with the latest days data or just making minor transformation of the data.

But with R it’s one stop shopping. You can often the load the data directly into R by either using read_csv on an internet dataset like from data.ny.gov, then process it in the same pipeline using mutate or summarize, join the data using inner_join, then plot it directly on the map using ggplot. No flipping through windows, mingling datatypes manually, clicking through join options or style options in QGIS. The default palettes and options in ggplot are often attractive, and needing only minor adjustment in codes.

And generally I’m happy with the output of ggplot for map making. Usually the output is consistent and easy to read, although sometimes things like title strings get chopped off and I need to adjust the parameters. But it’s just code and usually just requires me to change a line or two of the code before proceeding. The output is SVG, and often very good quality, with files small and easy to compress as compressed SVG for sharing on the blog.

When you shouldn’t use GIS software for mapping

When you shouldn’t use GIS software for mapping

Many GIS professionals and hobbyists will turn to their favorite GIS program, namely QGIS or ArcGIS for making maps. I did that for many years. But I’m seeing the real disadvantages to using GIS software for creating basic cholorpath maps and diagrams compared to using a programming language like R or Python.

GIS software is point and click. You don’t need to learn a programming language, although certainly knowing Python can help automate map creation. But the problem with GIS software is its point and click. You can create maps, but it’s often involves many extra steps including going to a website to download data, then manipulate data in an external program like Microsoft Excel or Open Office Calc, or maybe Python and Pandas then open and join the data in your GIS program. Sure you can save your data, your project and templates for reuse but it’s a lot of extra steps. Atlases and scripting can automate part of the process but still it’s a lot of pointing and clicking and manual work.

The other problem with GIS created maps is it so tempting to lard them up with excessive layers, formatting and details that are totally unnecessary for basic cholorpath maps that are about readability and not styling. Just because it’s easy to add a ton of layers and details with your GIS program doesn’t mean you should. Often good chloropath maps are simple, easy to read at a glance. They don’t need a million layers.

QGIS can export maps as SVG vector files for fast and compact web display. But the SVG files it creates isn’t nearly as fast or simple as those created by ggplot2. It’s easy to mess up layouts slightly in the layout composer in QGIS and not find it out until later. Often maps created by R and ggplot2 just look good out of the box with minimal tweaking. And it’s super easy to reuse code between R projects by just cutting and pasting.

For a lot of mapping and diagrams you can’t beat using R and ggplot2. But it’s not for everything. Complicated topographic maps with many layeds or those that involve custom feature creation and plotting are best done in GIS software. Advanced labeling, formatting or 3D rendering is best done in GIS software. But for basic map plotting for figure creation nothing really beat R and ggplot2.