30 Day Map Challenge 2024

Last year I made a static map that showed earthquake epicenters for some of the biggest quakes in Utah. I created this map using earthquake and fault data from UGRC from 1850-2020. This year I took it up a notch, creating an interactive map in ArcGIS Experience Builder, which I demonstrate in the video below. Using the same data, this map allows users to select individual points, showing the date, time, and magnitude of the earthquake felt. The left column highlights some of the larger earthquakes in Utah and the right column allows you to filter based on magnitude for easier navigation. This map will be a part of our Putting Down Roots Experience, which will be live soon! For now check out the PDF version here: https://earthquakes.utah.gov/putting-down-roots/ 

You can also check out our hazards portal to learn more about risks near you!

https://lnkd.in/eQZvkRZV

Sticking with the earthquake theme from yesterday, today I created an interactive earthquake fault map with ArcGIS Experience Builder, also using data from UGRC. This map highlights faults in Southern Utah, allowing users to select a fault and see age, slip rate, dip direction, slip sense, mapping, and more info. This is demonstrated in the video below. This map will also be a part of our Putting Down Roots Experience coming soon! For now check out the PDF version here: https://earthquakes.utah.gov/putting-down-roots/

I made this map using polygon data from the U.S. Fish and Wildlife Service.This map highlights different types of wetland and riparian areas around Utah Lake. To see more information about these areas, visit our Utah Wetlands interactive map here! https://wetlands.geology.utah.gov/index.html 

Inspired by the game “The Settlers of Catan,” I made a map highlighting energy resources in Utah! You can find more information and data at our Utah Energy Resources experience here: https://geology.utah.gov/apps/energy-resources/?page=Home 

One last map from our Putting Down Roots Experience! This interactive map takes you on a journey through a possible scenario for implementing an earthquake early warning system in Utah. This is demonstrated in the video below. The experience starts out at the fault rupture, then moves you towards a sensor, which transmits data to an earthquake alert center and then alerts high-priority partners. Technology like this is already being implemented in states like California, Oregon, and Washington!  

I decided to take my dust map from last year and expand it for the entire state of Utah! This took quite a bit of work, finding Landsat 8 imagery for the same time of year and mosaicking everything in ArcGIS Pro, but I think it turned out great! Below is my map description from last year. Keep an eye out for an upcoming Survey Notes article in the next year to learn more about dust hazards in Utah :) 

Dust storms are an increasingly prevalent problem in Utah. Dust emitted from playas has a variety of potentially harmful effects on the environment and human health, causing diseases such as valley fever, asthma, and pneumonia in humans, earlier snowmelt and decreased runoff from mountain snowpack, and carries many organisms and metals that can affect air quality and water resources. Understanding dust sources will help us mitigate the hazard moving forward.

To identify areas mostly likely to be dust sources, we considered soil type, slope, and vegetation cover using NDVI. We expect soils with smaller grain sizes, like silts and clays, to be more available for wind transport. Similarly, we expect large, flat regions with no vegetation to have the greatest potential for dust production.

I created a model with ModelBuilder in ArcGIS Pro that uses a 30m DEM, polygon soil data from UGRC, and Landsat 8 imagery from EarthExplorer to identify likely dust sources. Twelve Landsat 8 images (RED and NIR) were taken from Summer 2020, mosaicked in ArcGIS Pro, and used to calculate NDVI. The 30m DEM was used to identify slopes <5%, and the resulting NDVI, slope, and selected soils were reclassified and weighted against each other for the final output shown here.

Today I made a vintage looking map using a 30m DEM, roads, cities, and water data from UGRC. I used more muted colors to mimic the old vintage map look. 

Today I used data from HDX (Humanitarian Data Exchange) to map tropical storm Eleanor off the coast of Madagascar earlier this year. This storm brought a lot of rain and heavy winds to the islands of Mauritius and Reunion, but luckily neither suffered any severe damage. 

Today I used Dall-E to attempt to create a geologic map of Utah. I uploaded the accurate geologic map as a reference, then I used the following prompt (which was also AI generated):  

“A geologic map of the state of Utah, detailed geological formations, vibrant colors showcasing different rock layers, complex patterns, geographical shapes, high contrast between sediment types, labels and legends in precise typography, clear delineation of fault lines, background featuring an abstract representation of Utah's landscape, (ultra-detailed), (4K), visually informative and engaging. Only show the state of Utah, no other surrounding states.”

It honestly didn’t do too bad! The colors match up pretty well on the right side of the state, and some of the overall shapes seem to line up with the geologic map provided. We definitely still have a long way to go with AI and geologic maps, but this was a fun experiment for today!

To see a REAL geologic map of Utah, check out our interactive map portal here! https://geomap.geology.utah.gov/ 

Today I drew a map of Banda Niera, a collection of remote islands in Indonesia I visited 5 years ago as part of an undergraduate research opportunity. The map shows forts built all over the islands built by the Dutch, back when this was the Dutch East Indies. This was easily the most beautiful place I’ve ever been! 

I made today’s map using public data from IBCAO and NOAA. I really enjoyed playing around with vintage styles and colors on Day 7, so I tried to incorporate a little bit of that here. 

Today's map shows the fascinating history of ancient Lake Bonneville, illustrating how dramatically this prehistoric lake has transformed over “time and space.” The map highlights various historical shorelines with corresponding timestamps and includes bathymetric contours to depict the lake’s depth. For added perspective, I've overlaid modern cities and roads. If you currently live in Utah, would your home be submerged if Lake Bonneville still existed today?

A special shoutout to Buck Ehler for letting me use his Lake Bonneville data! 

And check out my coworker Paul Inkenbrandt’s storymap here to learn more about Lake Bonneville!  https://storymaps.arcgis.com/stories/f5011189bdc94545b9231d56e4ffc1e4 

In an effort to move to more open source technology, today I made an REM (relative elevation model) on QGIS, following the tutorial below. There was definitely a learning curve going from ArcGIS Pro to QGIS, but I’m excited to learn more and make more beautiful maps here in the future!

The map below shows Carson River in Nevada. REMs are really cool because they can help you visualize river features that may be harder to discern from an aerial photo or DEM. 

https://dancoecarto.com/creating-rems-in-qgis-the-idw-method 

Today I played around with the gradient tool for this world map. I liked that it made every country look like an agate, one of my favorite things to rockhound in Utah! 

Today I decided to showcase one of 10 geologic maps that I made as part of my thesis a couple years ago. I studied dust from various locations across the United States and looked at the geochemistry and mineralogy of these samples, trying to determine likely sources and create unique ‘fingerprints’ for these different regions. For the location below we were able to see that nearby White Sands National Park had a strong influence in the mineralogy of our spring samples when the prevailing wind direction came from the SW. In the fall when the primary wind direction was from SSE, we saw a dramatic change in the mineralogy. If you’d like to learn more about the methods we used and other maps and figures I made, check out our paper recently published in Aeolian Research! 

Today I decided to go simple with a population density map of Utah. To make things a little more interesting, I used the Multiply Layer Blend tool and combined the census data with a Utah hillshade basemap. 

This is a map I created with fellow student Eric Chambers during my undergraduate degree at BYU. Our goal was to map the maximum flood extent during the rainy season and track the resulting animal migrations in Zakouma National Park. We were able to produce an accurate flood extent map of the park, and when combined with migration data for elephants and lions, we found that the elephants migrated great distances to be closer to the water. The lions on the other hand were more localized in their migration patterns. Our resulting maps were also useful in helping Zakouma National Park establish permanent roads throughout the park with no risk of washing out during the wet season. 

We used Landsat-8 images taken from September 10, 2020 (wet season) and April 11, 2017 (dry season). Each of these dates included 2 Landsat images, which we mosaicked together to cover our entire study area. Our mosaicked Landsat-8 imagery was created using the Seamless Mosaic tool in ENVI. Our final result included a mosaicked scene representing the wet season in 2020, and a mosaicked scene representing the dry season in 2017. We used the Modified Normalized Difference Water Index (MNDWI) to classify the Landsat 8 (L8) scenes for water. We used the Coastal/Aerosol band of L8 (Band 1) to classify clouds. The images were examined to find suitable threshold values for each date. GPS collar data obtained from Chiara Fraticelli was used to visualize animal movements in September 2020. 

Today I played around with the ESRI Flood Simulation tool. I learned about this at the ESRI UC this year and thought it looked super interesting! I decided to run it for Moab, Utah, an area that can experience a lot of flooding during monsoon season at the end of the summer. For the model parameters I based them off this article here from a flood in 2022 (https://www.sltrib.com/news/2022/08/21/deluge-moab-could-be-100-year/). This estimated between 1 and 1.5 inches of rainfall in the span of 20 minutes, putting part of Main Street under 3 feet of water! In the screenshots below you can see the darker blue areas are under almost a meter of water, which is consistent with the flood in 2022. I had a lot of fun playing around with this tool! The next thing I want to try is seeing if I can apply similar simulations to flash flooding in slot canyons in Utah.  Models like this could help us better understand flood hazards before disaster strikes! 

Today I made a typography map on ArcGIS Pro of my hometown neighborhood in Springville, Utah. This took quite a bit of time, but I love how it turned out! 

Today I played around with OpenStreetMap 3D Buildings and Trees. I was pretty impressed with the level of detail for really any part of the world, especially when it came to the trees! I ultimately decided to make a quick map of BYU campus, but I encourage you all to play around with this! It’s really fun to zoom around your hometown or different monuments around the world! 

Do you say “soda,” “pop,” or “coke” when referring to your favorite carbonated beverage? This is the conflict I chose to map today! Check out your state and see if you agree with the results! I used data from https://popvssoda.com/statistics/USA.html to make this map. 

Today I used data from OpenStreetMap and ESRI to create a land vs water map. It’s cool to see just how much of our earth is covered in water! 

Today I made a map of the Aspen Grove hike up Mount Timpanogos. This is a hike I do every year with my family and is a great memory we have together. I labeled parts of the trail with different milestones we refer to while on the hike, such as the various waterfalls, Emerald Lake, and the boulder field. The shape of the map is Mount Timpanogos itself! 

Today I used circles to show national park distribution and popularity. The most visited national park this year was Great Smoky Mountains in Tennessee with 13.3 million visitors. The least visited national park was Gates of the Arctic in Alaska with only 11,045 visitors. What’s your favorite national park to visit? I’m personally a big fan of Capitol Reef in Utah. 

Data: https://www.dirtinmyshoes.com/us-national-parks-by-visitation/ 

Today I made a map showing Holocene lava flows in Iceland. This country is very tectonically active and produces a lot of ‘heat’ from volcanic eruptions. 

Check out Iceland’s most recent eruption here: https://www.nbcnews.com/now/video/iceland-volcano-erupts-for-the-seventh-time-this-year-225274437606 

Data: https://www.ni.is/is/rannsoknir/landupplysingar/skilmalar 

Have you ever wanted your very own world map dice? Well look no further! Using the cube map projection I created this fun little craft. Let me know if you end up printing it out and putting it together! 

This is a map I made for the AASG meeting in Park City, Utah this year (https://geology.utah.gov/aasg-2024/). While the top map is more general, the bottom map shows a more detailed overview of Park City. I referenced OpenStreetMap for many of the buildings and roads here. 

Today I decided to map the Mississippi River Delta using data from the National Hydrography Dataset Plus High Resolution.

Data: https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/water/the-most-detailed-map-of-us-waters-that-youve-ever-seen/ 

Today I used building and transportation data from Overture to create this map of New York City! I exported a python script from their latest release into QGIS and created the layout below. I’m still learning how to use QGIS, so this was another fun way to play around with open source data! 

Definitely my favorite map this month :) I've loved doing the map challenge again this year and am grateful for all your encouragement and support! I can't wait to see what next year brings!