I’ve also received a number of questions this semester about animal observation and tracking data. Since I usually study people and not animals, I was a bit out of my element and had some homework to do. If you’ve ever watched nature shows, you’ve seen scientists tagging animals with collars or bands to track them by radio or satellite, or setting up cameras to record them. Many scientists upload their GPS coordinate data into publicly accessible repositories for others to download and use.
I’ve written a short, three-part document that I’ve posted on our tutorials page: GIS Data Sources for Wildlife Tutorial. In the first part, I provide summaries, links, and guidance on using large portals like Movebank and Zoatrack that include many species from all over the world (wild and domestic), as well a government repositories including NOAA’s National Center for Environment Information Geoportal and the National Park Service’s Data Store. The second part focuses on search strategies, crawling the web and combing through academic literature in library databases to find additional data. Since these datasets are highly diffuse, it’s worth going beyond the portals to see what else you can discover.
I describe how you can add and visualize this data in QGIS and ArcGIS Pro in the third and final part. Wildlife data comes packaged in a number of formats; in some cases you’ll find shapefiles or geodatabases that you can readily add and visualize, but more often than not the data is packaged in a plain CSV / TXT format. This requires you to plot the coordinates (X for longitude, Y for latitude) to create a dot map of the observations. Data files will often contain a number of individual animals, which can be uniquely identified with a tag ID, allowing you to symbolize the points by category so you have a different color or symbol for each individual. Alternatively, there might be separate data files for each individual, that you could add and symbolize differently. The files will contain either a sequential integer or a timestamp that indicates the order of the observations. With one field that indicates the order and another that identifies each individual, you can use a Points to Line or Points to Path tool to generate lines (tracks or trajectories) from the points (observations or detections).
You can see where dingos in Queensland, Australia are going in the screenshot below, which displays individual observation points, and the screenshot in the header of this post where the points were connected to form paths. I obtained the data from ZoaTrack and used QGIS for mapping. Check out the tutorial for details on how to find and map your favorite animals.
Over the course of this academic year I’ve helped many students find GIS data related to coastal storms and flooding in the US. There’s a ton of data available, particularly from NOAA, but there are so many projects and initiatives that it can be tough to find what you’re looking for. So I’ll share a few key resources here.
NOAA’s DigitalCoast is a good place to start; it’s a catalog of federal, state, and US territory projects and websites that provide both spatial and non-spatial datasets related to coastal storms and flooding. You can filter by place and data type; there are even a few global sources. Most of the projects I mention below are cataloged there.
Given the size of many of these datasets, the ArcGIS File Geodatabase is often used for packaging and distribution. Once you’ve downloaded and unzipped one, it looks like a folder with lots of subfolders and files. If you’re an ArcGIS user, use the Catalog pane to browse your file system and add a connection to the database / folder to access its contents. If you’re a QGIS user, use the Data Manager and on the Vector tab change the source type from File to Directory. In the Source Type dropdown you can choose OpenFileGDB, and browse and select the database, which appears as a folder. Once you hit the Add button, you’ll be prompted to choose the features in the DB that you wish to add to the project.
FEMA Flood Hazards and Disasters
The FEMA flood maps are usually the first thing that comes to mind when folks set out to find data on flooding, but good luck finding their GIS data. I’ve searched through their main program site for the National Flood Hazard Layer and followed every link, but can’t for the life of me find the connection to the page that has actual GIS data; there are map viewer tools, scanned paper maps, web mapping services, and everything else under the sun.
If you want FEMA flood data in a GIS format: GO HERE! This is the record in data.gov for the National Flood Hazard Layer. The links at the bottom include this one: Download Seamless Nationwide NFHL GIS data. The data is packaged in an ArcGIS File Geodatabase, with one polygon feature class for flood zones. They’re categorized into 100 and 500 year zones, open water bodies, areas outside of flood zones, and areas outside flood zones protected by levees. The pic below illustrates 100 and 500 year zones overlaid on the OpenTopoMap.
FEMA also has a GIS data feed for current and historical emergencies and disasters, that are available in a variety of formats both spatial and non-spatial. These are county-level layers that indicate where disaster areas were declared and what kind of funding or assistance is / was available.
NOAA Sea Level Rise
The FEMA maps assess both past events and current conditions to model the likelihood of flooding in a 100 or 500 year period for a major storm event. A different way of looking at flooding is to consider sea level rise due to climate change, where the impact of sea level rise is measured in different increments. Instead of the impact of a one-shot event, this illustrates potential long term change. NOAA’s Sea Level Rise (SLR) viewer allows you to easily visualize the impact of sea level rise in 1 foot increments, between 1 and 10 feet. You can download the data by US state or territory for coastal areas. There are separate downloads for sea level rise, rise depth, the confidence intervals for the models, as well as DEMs and flood frequency. The sea level rise data is package in an ArcGIS file geodatabase, with two sets of files (a low estimate and high estimate) in one foot increments. An example of 6 feet in sea level rise is shown below.
NOAA National Hurricane Center
Beyond showing the general impact of flooding or sea level rise, you can also look at the track of individual hurricanes and tropical storms. The National Hurricane Center’s GIS data page provides historical forecasts – the projected path and cone of storms, windspeeds, storm surges, etc. You choose your year, then can choose a storm, and then a particular day. You can use this data to see how the forecasts evolved as the storm moved. When we’re in hurricane season, you can also see what the circumstances are day by day for tracking new storms.
If you want to see what actually happened (as opposed to a forecast), you can dig through the data page and browse the different options. There’s the Tropical Cyclone Report (TCR) which provides “information on each tropical cyclone, including synoptic history, meteorological statistics, casualties and damages, and the post-analysis best track (six-hourly positions and intensities). Tropical cyclones include depressions, storms and hurricanes.” The default page shows you the Atlantic, but you can swap to Eastern or Central Pacific using the link at the top. Storms are listed alphabetically (and thus by date) and your format options are shapefile or KML. There’s a map at the bottom that depicts and labels all the storms for that season. You actually get four shapefiles in a download; a point file that contains a number of measurements, a line file for the storm track, a polygon file for the radius of the storm, and another polygon with the wind swath. The layers for 2021’s Tropical Storm Henri are illustrated below.
GIS data for the storms begins in 2010 with KMZ files (which you’ll need to convert in ArcGIS or QGIS to make them useful beyond display purposes), and shapefiles appear in 2015. Further back in time are just PDF reports and map scans.
If you really want to go back and time and get all the tracks at once, there’s the HURDAT2 database; one for the Atlantic (1851 to present) and another for the Pacific (1949 to present). It’s a csv file that contains coordinates for the track of every storm, which you can process to create a geospatial file using a points to line tool. Or – you can grab a version where that’s already been created! The International Best Track Archive for Climate Stewardship (IBTrACS) keeps a running CSV and shapefile of all global storms. Scroll down and choose shapefile (CSV is another option). The download page is just a list of files – you can choose points or lines, storms by ocean (East Pacific, North Atlantic, North Indian, South Atlantic, South Indian, South Pacific, West Pacific), or grab everything in lists that are: active, everything (ALL), last 3 years, or since 1980. Below is an example of all storms in the North Atlantic – there are quite a lot (see below)! You get storm speed and direction, wind speed and direction, coordinates, and identifiers associated with the storm as points and lines. A subset of this data for the 2021 season is displayed in the feature image at the top of this post.
How About the Weather?
There are many places you can go for this and the best source depends on the use case. More often than not, I end up using the Local Climatological Database. Choose a geographic type, then a specific area, and you’ll see all the weather stations in this area. Add them to the cart, and then view the cart once you have all the stations you want. On the next screen choose an output format (CSV or TXT fixed width) and a date range. You submit an order and wait a bit for it to be compiled, and are notified by email when it’s ready for download. Mixed in this CSV are records that are monthly, daily, and hourly, so after downloading you’ll want to extract just the period you’re interested in. Data includes temperature, precipitation, dew point, wind speed and direction, humidity, barometric pressure, and cloud cover.
Some processing is required to make these files GIS ready. Each record represents an observation at a station at a given point in time, so if you plot these “as is” the likely idea is you’re making an illustrated time series of some sort, as you’ll have tons of observations plotted on a few spots (where the stations are). If this isn’t desirable, then you’ll filter records to create extracts for just a given point in time, maybe separate features for each time period. For monthly summaries you can pivot time to columns, to create a column for each month and indicator. This would be impractical for daily or hourly summaries, unless you’re focusing on a single month for the former or day / week for the latter (otherwise you’ll have a bazillion columns).
Annoyingly, the CSV option doesn’t include any of the station information in the download (like the standard WBAN ID, name, longitude, latitude, and elevation) except for one unique identifier. I know that this information was all included in the past, and am not sure why it was dropped. The TXT version includes the station info, but fixed-width files are a pain to work with. If you are working with a small number of stations, you can pull the station info individually by previewing the station on the download screen (click on the station title or little eye symbol). The five digit WBAN number is included as the last 5 digits of the identifier in the CSV, so you can identify and relate each one. If you don’t want to mess with copying and pasting, you can generate a second extract for all the stations for just a single day and download that in the TXT format, and then parse just the station columns and associate them with your main table.
There are multiple ways that you can create extracts for this data beyond the example I just provided, available from the main data tools page. For a more refined search you can select the summary period (yearly, monthly, daily, hourly) and targeted variables in advance. There are also FTP options for bulk downloads.
One thing that surprises folks who are new to working with this data, is that there aren’t many weather stations. For the LCD, my home state of Delaware only has three, one in each county. The entire City of New York only has three as well, at each of the airports and one in Central Park. If you’re not interested in points and want areas, then you would need to gather a significant number of stations and do interpolation. Or – use data that’s already modeled. I mentioned PRISM at Oregon State in a previous post, as a nice source for national US rasters of temperature and precipitation that you can generate for dailies, monthlies, and normals.
Here’s a fun post to close out the year. During GIS-based research consultations, I often help people understand the importance of coordinate reference systems (or spatial reference systems if you prefer, aka “map projections”). These systems essentially make GIS “work”; they are standards that allow you to overlay different spatial layers. You transform layers from one system to another in order to get them to align, perform specific operations that require a specific system, or preserve one aspect of the earth’s properties for a certain analysis you’re conducting or a map you’re making.
Wrestling with these systems is a conceptual issue that plays out when dealing with digital data, but I recently stumbled across a physical manifestation purely by accident. During the last week of October my wife and I rented a tiny home up in the Catskill Mountains in NY State, and decided to go for a day hike. The Catskills are home to 35 mountains known collectively as the Catskill High Peaks, which all exceed 3,500 feet in elevation. After consulting a thorough blog on upstate walks and hikes (Walking Man 24 7), we decided to try Windham High Peak, which was the closest mountain to where we were staying. We were rewarded with this nice view upon reaching the summit:
While poking around on the peak, we discovered a geodetic survey marker from 1942 affixed to the face of a rock. These markers were used to identify important topographical features, and to serve as control points in manual surveying to measure elevation; this particular marker (first pic below) is a triangulation marker that was used for that purpose. It looks like a flat, round disk, but it’s actually more like the head of a large nail that’s been driven into the rock. A short distance away was a second marker (second pic below) with a little arrow pointing toward the triangulation marker. This is a reference marker, which points to the other marker to help people locate it, as dirt or shrubbery can obscure the markers over time. Traditional survey methods that utilized this marker system were used for creating the first detailed sets of topographic maps and for establishing what the elevations and contours were for most of the United States. There’s a short summary of the history of the marker’s here, and a more detailed one here. NOAA provides several resources for exploring the history of the national geodetic system.
Triangulation Survey Marker
Reference Survey Marker
When we returned home I searched around to learn more about them, and discovered that NOAA has an app that allows you to explore all the markers throughout the US, and retrieve information about them. Each data sheet provides the longitude and latitude coordinates for the marker in the most recent reference system (NAD 83), plus previous systems that were originally used (NAD 27), a detailed physical description of the location (like the one below), and a list of related markers. It turns out there were two reference markers on the peak that point to the topographic one (we only found the first one). The sheet also references a distant point off of the peak that was used for surveying the height (the azimuth mark). There’s even a recovery form for submitting updated information and photographs for any markers you discover.
NA2038’DESCRIBED BY COAST AND GEODETIC SURVEY 1942 (GWL)
NA2038’STATION IS ON THE HIGHEST POINT AND AT THE E END OF A MOUNTAIN KNOWN
NA2038’AS WINDHAM HIGH PEAK. ABOUT 4 MILES, AIR LINE, ENE OF HENSONVILLE
NA2038’AND ON PROPERTY OWNED BY NEW YORK STATE. MARK, STAMPED WINDHAM
NA2038’1942, IS SET FLUSH IN THE TOP OF A LARGE BOULDER PROTRUDING
NA2038’ABOUT 1 FOOT, 19 FEET SE OF A LONE 10-INCH PINE TREE. U.S.
NA2038’GEOLOGICAL SURVEY STATION WINDHAM HIGH PEAK, A DRILL HOLE IN A
NA2038’BOULDER, LOCATED ON THIS SAME MOUNTAIN WAS NOT RECOVERED.
For the past thirty plus years or so we’ve used satellites to measure elevation and topography. I used my new GPS unit on this hike; I still chose a simple, bare-bones model (a Garmin eTrex 10), but it was still an upgrade as it uses a USB connection instead of a clunky serial port. The default CRS is WGS 84, but you can change it to NAD 83 or another geographic system that’s appropriate for your area. By turning on the tracking feature you can record your entire route as a line file. Along the way you can save specific points as way points, which records the time and elevation.
Moving the data from the GPS unit to my laptop was a simple matter of plugging it into the USB port and using my operating system’s file navigator to drag and drop the files. One file contained the tracks and the other the way points, stored in a Garmin format called a gpx file (a text-based XML format). While QGIS has a number of tools for working with GPS data, I didn’t need to use any of them. QGIS 3.4 allows you to add gpx files as vector files. Once they’re plotted you can save them as shapefiles or geopackages, and in the course of doing so reproject them to a projected coordinate system that uses meters or feet. I used the field calculator to add a new elevation column to the way points to calculate elevation in feet (as the GPS recorded units in meters), and to modify the track file to delete a line; apparently I turned the unit on back at our house and the first line connected that point to the first point of our hike. By entering an editing mode and using the digitizing tool, I was able to split the features, delete the segments that weren’t part of the hike, and merge the remaining segments back together.
Original way points and track plotted in QGIS, with erroneous line
Using methods I described in an earlier post, I added a USGS topo map as a WMTS layer for background and modified the symbology of the points to display elevation labels, and voila! We can see all eight miles of our hike as we ascended from a base of 1,791 to a height of 3,542 feet (covering 1,751 feet from min to max). We got some solid exercise, were rewarded with some great views, and experienced a mix of old and new cartography. Happy New Year – I hope you have some fun adventures in the year to come!
Stylized way points with elevation labels and track displayed on top of USGS topo map in QGIS
You must be logged in to post a comment.