Sunday, April 30, 2017

GIS Final Project

The very last assignment for Intro to GIS was an assessment of the Bobwhite-Manatee Transmission Line, which was constructed several years ago in Manatee and Sarasota Counties in southwest Florida. The object of the final project was to take the preferred corridor for the transmission line and analyze its impact on the environment and communities to determine whether it met the criteria its placement was supposedly based on.

The criteria assessed for this assignment were selected from a list used in the real-life planning of the transmission line. They were:

Does the preferred corridor avoid large areas of environmentally sensitive land? (Conservation lands and wetlands were analyzed for this project.)
Does the preferred corridor avoid homes? (This analysis involved digitizing possible houses from aerial imagery, as well as calculating the number of land parcels impacted.)
Does the preferred corridor avoid schools/daycares?
And is the preferred corridor feasible in terms of length and cost?

All analysis was performed in ArcMap. Analysis of each of the first three criteria primarily used either the Clip tool (with the preferred corridor as the clip shape) or a Select By Location query to identify impacted features. For the second and third criteria, a 400 foot buffer around the preferred corridor was created in ArcMap in order to include features in proximity to the transmission line as well as those directly impacted by the corridor. Length and cost were calculated based on the estimated centerline of the corridor, using ArcMap's measurement tool and a provided equation, respectively.

For more details and results (not to mention the maps), my final presentation and write-up are available online:
PowerPoint
Presentation Transcript

The short version: The transmission line is an estimated 25 miles long and impacts a fairly minimal amount of sensitive land, a relatively low number of homes and parcels, and no schools or daycares. In other words, it basically does a good job of fulfilling the criteria.

Monday, April 17, 2017

Cartographic Skills Module 12: Neocartography and Google Earth

In the final module for Cartographic Skills, we discussed the changing nature of cartography in the 21st century, given advances in technology, new applications, and especially VGI, or volunteered geographic information, meaning spatial information submitted by the public as opposed to collected and compiled by professional cartographers. 

For the lab assignment, we looked at ways to explore and share data using Google Earth, which is, of course, freely available to the public and relatively easy to use. The first part of the lab was to return to the dot density map created two modules ago and export it in KML format. KML files can then be opened in Google Earth, overlaid on the standard Google Earth imagery, and shared with other Google Earth users. As a method of simply viewing map layers, this is much more accessible than ArcMap.


In the second part of the lab, we used Google Earth's tour feature to record a tour of places in south Florida. Google Earth allows users to create placemarks that make it easy to zoom in or out and to transition from one view to another, so that you can navigate to a series of places easily. After you've recorded a tour, you can save it along with your map layers and export a package that can, again, be shared with other Google Earth users, who can play the tour as well as explore your map on their own. 

The record tour function automatically records every move you make while navigating around the map, so I found it a little tricky to do, as I had a hard time zooming, rotating, and panning smoothly while looking around a stop on the tour. Trying to get the scenes you want without giving the viewer motion sickness might take some practice! My tour for this assignment definitely wasn't perfect. Still, it's a very cool feature that I didn't know how to use before, and I'm now full of ideas for how to use it  in the future for sharing maps and places.

Friday, April 7, 2017

Cartographic Skills Module 11: 3D Mapping

This week we studied 3D mapping in Cartographic Skills as well. For the first section of the lab, we went through an ESRI training course on the basics of visualizing 3D data in ArcScene, including data types, how to set base heights and extrude features, and how to manipulate lighting. For me personally, the hardest part about adjusting to 3D scenes is how to navigate them successfully, but that's probably due at least in part to the fact that I'm currently working on a laptop with just a trackpad. I assume you get better control with a mouse!

For the rest of the lab assignment, we worked with data on building footprints in downtown Boston. Given the building footprints and a LiDAR dataset of the same area, we extracted height information for the buildings from the LiDAR file by taking a series of sample points and then using the average z value for each building footprint as the height for that building. With that data, we could display the buildings in 3D in ArcScene. Then we exported that layer as a KML file and opened it in Google Earth. The end result looked like this:


3D mapping is useful for urban planning, simulations, modeling industrial activity, visualizing environmental changes, or any application where it's helpful to view terrain in a similar way as it appears in the real world. It can also be helpful in viewing multiple datasets simultaneously, whereas the same map in 2D might get too crowded or complex to be useful. 3D maps can also be used to display data with z-values whose meaning isn't inherently spatial. For example, things like temperature, population, and property values can be visualized in 3D.

Some advantages of 3D maps are their versatility and the ability to explore complex spatial relationships, as well as to simulate viewsheds/lines-of-sight and what an area would look like at different times of day or year. However, care has to be taken to display height or depth data accurately, or to make it clear what information is being presented in the case of data like property values, as it could be easy for the viewer to misunderstand what they're seeing. It can also be hard to see certain features if the map covers a large geographic area or if some features block others from view. This problem is somewhat avoidable if the viewer has the ability to zoom in and out and navigate around the map, but if the map must be viewed outside of ArcGIS, exporting multiple static views from different angles or scales might be necessary to show a complete picture of the data.

Thursday, April 6, 2017

GIS Week 13: Georeferencing and ArcScene

For the final module in Intro to GIS, we got to explore georeferencing and mapping in 3D. For this lab assignment we were given two aerial images of the UWF campus that had no spatial reference, and georeferenced them (in other words, assigned real-world coordinates) based on existing shapefiles of campus buildings and roads. We then edited those shapefiles to digitize two features (one building and one road) that were present in the photos but not yet in the shapefiles. The results are below, along with a second map showing the location of UWF's eagle nest in relation to those features. This second map also shows the easement buffers created to protect the nest, which were drawn using ArcMap's multiple ring buffer tool.


 After all that, we took our first stab at using GIS in 3D. The building and roads shapefiles and the aerial imagery were imported into ArcScene and draped over a 30-meter DEM. The buildings were also extruded based on the heights listed in their attribute table, and then the 3D aspect of the entire scene was exaggerated somewhat to make the features stand out more. The final scene was exported as an image so that the layout could be finalized in ArcMap, as ArcScene doesn't allow for the creation of additional map elements like legends.

Sunday, April 2, 2017

Cartographic Skills Module 10: Dot Mapping

This week we explored dot mapping, which uses small dots to indicate occurrences of specific geographic phenomena. Each dot represents a set number of instances of the phenomenon being mapped, and the number of dots in an enumeration unit is proportional to the number of real-world phenomenon in the same area. Dot mapping is a good way of visualizing spatial distribution, as it brings out patterns nicely, especially if care is taken to place dots in areas where the phenomenon in question is most likely to occur, by using other geographic features that affect the phenomenon as a guide to dot placement.

As an example, the lab assignment this week uses dots to represent the population of southern Florida. We used census counts, with counties as the enumeration unit. In my map, below, each dot represents 20,000 people. The map was created in ArcMap with some finishing touches applied in Adobe Illustrator. (And I've realized after the fact that the title should say "population distribution" rather than "population density"--oops!)

Without additional input from the mapmaker, ArcMap places dots randomly throughout enumeration units. So, we used an additional layer (not shown in the final map) extracted from a landcover type dataset to have ArcMap place dots only within urban land areas--in other words, developed areas where we know people actually live. This gives us a more realistic and accurate distribution map that allows us to see where the human population is concentrated (with the highest density occurring along the southeastern coast around Miami) and where it is more dispersed (such as much of the interior of this part of the state).