GIS4930 Module 4 - Final Presentation (Food Deserts PPT)

Weeks of working with open source software have come together finally. Working with QGIS, Tilemill, MapBox, and Leaflet has been quite an experience, and definitely was enough to develop preference for some software over others. The creation of a web map essentially from scratch was a challenge initially but easier to do as time and experience increased. Completing an analysis for food deserts in my hometown (Marshall, Michigan) was fun as well as interesting, since it produced some results which I had not expected. Along the way, much more was learned about food deserts, their impact, and potential solutions. One of my favorite finds was the USDA's Food Access Research Atlas which can be found here with additional explanatory information available here. The USDA's site enables anyone to investigate food deserts by census tract.

The culmination of the project was the assembling of all the information and experiences into a PowerPoint presentation complete with audio which was a new experience. A link to the presentation is included here, along with a screenshot and link to the web map created for this project.

Food Deserts in the City of Hospitality: An Analysis of Marshall, Michigan and Vicinity

(PowerPoint Presentation)

Food Deserts of Marshall, Michigan

(Web Map)

Screenshot of web map created with QGIS, Tilemill, MapBox, and Leaflet

For the time being that is enough about food deserts. Now it's time to move on to food desserts.

GIS4930 Module 4 - Tiling & Basemap Creation

Compilation of the grocery store layer included referencing several online sources plus verifying the information in Google Streetview. Online sources included Yellow Pages, Manta, and Google Maps which turned out not to be very reliable. Two grocery stores were shown in completely wrong locations. This was verified by personal knowledge and double-checking street view. Verifying grocery stores in the outlying rural areas involved more research to verify whether they were full-service or convenience stores (street view, store websites or descriptions, and reviews were used). Grocery stores outside the study area were included, because of the 10-mile marker used to define rural food deserts. (http://www.ers.usda.gov/data-products/food-access-research-atlas/documentation.aspx) Addresses for the grocery stores were tabulated in Excel, saved as csv file, and geocoded before performing the Near analysis to determine which urban census tract centroids exceeded the 1-mile marker and which rural census tract centroids exceeded the 10-mile distance from a grocery store.

Centroids of the census tracts were calculated to provide a comparison point for the one-mile marker food desert determinant (urban) and 10-mile marker (rural). Interestingly, the centroid for one of the rural census tracts landed inside one of the city census tracts. Both city census tracts were considered to be food deserts which surprised me. If one of the grocery stores had been located on the east end of town or farther south, at least one of the city census tracts probably would not have been classified as a food desert. The grocery stores are both located near the western edge of town (and extreme NW part of the larger urban census tract). Because of the town size and number of grocery stores, this type of analysis might not be as informative as anticipated. Perhaps an evaluation by census block would be more appropriate to really define the at-risk areas, or an assessment by residents’ age and mobility/transportation issues could be done. I was surprised that at least one of the two southern rural census tracts was not a food desert. However, the grocery stores in that area are of a smaller nature and do not carry stock nearly as comprehensive as a chain grocery store would. So while not technically food deserts, these rural areas might still be prone to a lower-quality diet unless supplemented with home-grown produce.

The process of creating a web map of food deserts included tiling the shapefiles described above. Tiling a shapefile enables more rapid loading of layers into a web map because the layers are displayed in smaller portions (tiles). In TileMill each of the data layers for this map was centered in a tight-fitting bounding box with a centralized marker. The zoom level was adjusted so that the resulting tiles would be 10MB. Each of these map layers had up to 19 tiles. The default world layer was removed by editing the code, and the symbology (colors, opacity) for the layers was adjusted as needed. Each layer was exported for use in Mapbox and by default was sent off to a folder not of my choosing. I opted to re-organize those layers into project folders prior to adding them to the map in Mapbox. The method of saving layers or projects in both TileMill and Mapbox was not very intuitive for me (save/don't save, save again, export, etc.), so several times some steps had to be repeated in order to ensure that I had the desired product. As so often happens, this was just enough exposure to new software to let me know how much there is to learn! The screenshot below shows the food deserts, census tract boundaries, and grocery stores added to the revised basemap in Mapbox.

Food Deserts in Marshall, Michigan
with Census Tract Boundaries and Grocery Stores
Created with TileMill and Mapbox

GIS4930 Module 4 - Tilemill & Leaflet

The exploration of opensource software for GIS purposes continued this week with the focus on Tilemill and Leaflet. Shapefiles were imported into Tilemill which already had a basemap in place. Arcmap was used to classify the food deserts by population (QGIS could have been used also) before selecting the color scheme in Color Brewer. This color scheme was entered into the Tilemill source code. The Leaflet code editing was terribly confusing, but with a lot of referencing previous instructions, double-checking coding changes, verifying paths, and re-reading of instructions, I made it through. OpenCage Geocoder was utilized to find latitude and longitude for specific addresses. Markers and shapes with popup text boxes were edited in the code. Layer groups were created and the ability to toggle layers on or off was coded. A geocoder was added to the source code for finding locations. Interestingly enough, it went to locations within a few blocks of the input addresses, but not to the exact address location specified. Its reliability indicates that it should be investigated further. Throughout the process of editing the Leaflet code, the text and html files were saved incrementally, keeping previous copies in case something really got messed up during the editing process. The gods of coding must have been with me this week though, because I made it through all the Tilemill and Leaflet coding without any errors. What a lovely surprise that was!

Here's the link to the final web map: Food Deserts of Southeast Escambia County which opens up this:

Food Deserts of Southeast Escambia County

GIS4930 Module 4 - Intro to QGIS (Prepare Week)

Prepare week for this module started off with an introduction to QGIS, an open source, desktop GIS. The similarities between QGIS and ArcGIS are significant, but there are several differences as well. QGIS did reveal a few quirks, one being that an added layer did not show up in its correct location, but what could possibly be bad about UWF being in the Bahamas at this time of year? UWF's location was easily corrected by removing and re-adding the layer to the map. A difficulty I encountered was not being able to find adequate help resources (in a timely manner) for issues I experienced while using QGIS. Adding and adjusting bar scales was much more involved in QGIS than it is in ArcGIS. The use of data frames in the composer was a particular challenge for me. Frequently my data frame would be void of information after changes were made elsewhere in the composer. Hopefully more experience will alleviate those issues.

All that being said, a definite advantage to QGIS is that it's free and available to anyone. This would be beneficial for small businesses and non-profits which do not have the financial resources available to invest in ArcGIS.

University of West Florida

(Introductory QGIS Map)

For this module's project, QGIS was used to perform several processes which have been utilized in ArcGIS. These included re-projection, clipping, selecting by attributes, selection by polygon (this turned out to be a challenge), and centroid generation. From this information, food deserts were calculated based on whether the centroids of the census blocks were within 1 mile of a grocery store.

Statistics were also generated in QGIS after using an edit session to delete unnecessary columns in the attribute table for the food desert shapefile and again for the non-food desert shapefile. The results of these processes are shown in the following map of Food Deserts and Non-Food Desert areas in Pensacola, Florida.

Food Deserts and Non-Food Deserts of Pensacola

Escambia County, Florida

GIS4035 Module 4 - Ground Truthing and Accuracy Assessment

Land Use / Land Cover Classification Accuracy
Pascagoula, Mississippi

This module included a lot of completely new material for me. Although the lab exercise was straightforward and rather easy, the preparatory information was rather overwhelming. The lab exercise itself involved using last module's classification map and adding sample points. The actual land use or land cover for these sample points was determined using Google Maps Street View where possible or zooming in on Google Maps aerial view. This was compared to the classification selected last week for the same points. The results were tabulated for accuracy. Best accuracy for this map was determined for non-forested wetlands, probably because they were so easy to distinguish. Barren lands had lowest accuracy because of having classified open areas as barren lands when a better classification would have been herbaceous rangeland. The lab itself was a fun exercise. The resultant map with sample points is shown here.

GIS4048 Module 4: Natural Hazards - Hurricanes

Just in time for the beginning of hurricane season, this module presented an opportunity to work as a contract employee of the Federal Emergency Management Agency (FEMA). Of interest to FEMA in this scenario was an inventory of structures with structure damage, wind damage or inundation as a result of Hurricane Sandy’s landfall in New Jersey.

Although only classified as a Category 1 hurricane at landfall on the New Jersey coast, Hurricane Sandy was a catastrophic storm due to its enormous size and other factors which amplified the storm surge. States all along the Atlantic coast were impacted to some degree by Hurricane Sandy. Ten states and the District of Columbia were declared major disaster areas by FEMA, making them eligible for funding from the President's Disaster Relief Fund which is managed by FEMA and other federal agencies.

To meet FEMA’s objectives, the track of Hurricane Sandy was plotted. This was done by adding an Excel file to the map and exporting the data to a geodatabase. The Points to Line tool was used to show Hurricane Sandy’s track. That information is shown along with wind speed and barometric pressure on the map below along with the states and district that were declared disaster areas by FEMA. Graticules were added to this map as well. 4° intervals were selected for the graticules since 2° intervals are too closely spaced and 5° intervals are not as easily interpolated as 4° intervals.

Hurricane Sandy Track and
States/District with Major Disaster Declarations

Geodatabase datasets were created for mosaicking two separate sets of aerial imagery (before and after Hurricane Sandy) and imagery effects tools were utilized for comparing pre- and post-hurricane imagery. Attribute domains were created and used to catalog damage in order to compile an inventory of structures along one block of a street in New Jersey. The difficulty with this part of the exercise was determining the degree of damage. Without seeing elevation views of the structures, the extent of any damage was hard to assess. The following map shows the damage assessment and the pre- and post-imagery of structures which were cataloged along one block. 

Hurricane Sandy Damage Assessment
Coolidge Avenue, Toms River Township, New Jersey (2012)

Information from maps such as these would assist FEMA and other organizations to evaluate the extent of damage after a hurricane and to determine how best to allocate resources.

GIS4102 Module 4: Python Fundamentals Part II

The focus of this week's exercise and assignment was continuing practice of and developing familiarity with Python scripting. The emphasis was on importing modules, troubleshooting code (many opportunities arise for this in my particular case), creating while loops and conditional statements, adding comments, and iterating variables within loops. The first portion of the assignment involved the import of a module and troubleshooting the code provided for a dice game with random number generation.

The script-creating portion of this assignment included developing a while loop to add 20 random integers between 0 and 10 to a list. From that list one number was eliminated and the number of times it had occurred was printed in a message. For random-generations in which the number did not occur, a different message was printed. The screenshots included here show an example of each message:

The Number 5 Eliminated from List
and Associated Message

The Number 5 Does Not Occur in List
with Associated Message

The error message returned at the second while loop I initially coded was a bit deceptive, notifying me that my variable was not in the list although it could be printed out. After trying to troubleshoot it in one manner, I finally tried another tactic which solved the problem. Those while loops certainly do want to have an exit condition in order to process.

Throughout the script, descriptive comments were added. After the script was completed, header comments were included to identify the script name, author, date, and a brief description of the script. This was a great practice exercise and assignment.

GIS3015 Module 4 Lab: Typography

This lab focused on utilizing Adobe Illustrator to create a map of the Middle Keys of Florida. The emphasis of this exercise was on typography.  The importance of layer organization cannot be stressed enough!  I found myself re-arranging and prioritizing my layers, making adjustments as I discovered issues here and there.  Over time, I anticipate that this will become even more logical and easier to complete. Being new to AI, I experienced some difficulties caused by inadvertent key strokes and who knows what else. These were all resolved, except for being unable to draw a straight-line path on which I could place text.  No online source of information was located that could help me eliminate the error message which kept popping up, so that will require further investigation if it happens again in a different document. Practicing letter and word spacing, kerning and leading during this assignment helped me to understand the advantages of those practices better.  Creating a legend in ArcMap is preferable to creating one in AI, in my opinion.  By the end of it all, a map of the Middle Keys of Florida showing cities and select features was produced.

Map of Middle Keys of Florida showing cities and other features

GIS4043 ArcGIS Online and Map Packages

This lab focused on developing and submitting map packages to ArcGIS online. It has been the most time-consuming lab to date for me.  One map package featured climbing areas and points of interest in Yosemite Valley, while the other focused on the study of trees in the Aguirres Springs drainage.  These procedures are valuable, but I can see that much more practice is needed, so that far less time is spent on referencing multiple sources of help to determine exactly what is needed and how that can be accomplished.  This lab also reinforced that knowing exactly what outcome, result or product is expected must be very clear.  If the goal is not readily determined, prompt clarification must be requested.  This would be of benefit to the producer as well as the client.  The ability to use map packages will be very beneficial, and I'm looking forward to the time when completing similar tasks will be second-nature.

Submittal:  Overview of Map and Tile Packages

Submittal:  Optimize a Map Package