Final Project ~ Analysis of Food Deserts within The City of Port St. Lucie, Fl.

The most challenging part of this project for me was using all the combinations of the open source software; ESRI has me spoiled.  After analyzing the data I have to again say I was surprised by the results, but certainly stand behind them.  I have learned that what may be a food oasis to me could be a desert for someone else within my same community due to their age, income, lack of transportation or any combination thereof.  Because I live in a well-developed area in the heart of the city with access to many things, including grocery stores I did not expect to find the number of food deserts illustrated on the map.  Once I studied the locations it made perfect sense as over the years the city has annexed more and more properties and outside contractors had begun development.  When the economy began to fail several years ago, development stopped, including commercial entities such as grocery stores which were planned in proximity to many of the developments.  I’m happy to say that trend is beginning to turn so I expect that the food deserts will be reduced over the next few years.

Here is a link to my Leaflet Map (best viewed on a large monitor to avoid panning):

http://students.uwf.edu/rh51/GIS4930_SpecialTopics/leaflet/leaflet_rh.html

This is the link to my final power point project:

http://students.uwf.edu/rh51/GIS4930_SpecialTopics/Hamaty-FinalProject_4930_audio.pptx

Project 4 Analyze Week 2 ~ Food Desert Analysis in the City of Port Saint Lucie Florida using both ArcDesktop and Open source software (QGIS, Mapbox and Leaflet)

This week combined what we learned in prepare week about Food Deserts and QGIS and Analyze Week 1 where we utilized Mapbox and Leaflet.  The challenge this week was to select our own city and combine the skills we learned in all of the software components to produce a final map in leaflet viewable via a link directed to our I:/ drive.  Here is the link to my leaflet map:  http://students.uwf.edu/rh51/GIS4930_SpecialTopics/leaflet/leaflet_rh.html

Here is an image of the map but I recommend using the leaflet link to access the geocoder and zoom functions:

The tiling process essentially takes a continuous image and breaks it into portions known as tiles.  Each tile is about 256x256 pixels and since they are placed sided by side they appear to be a seamless map to the web user.    By combining tiles and zoom levels the process is very effective for using in web mapping applications.  Tiled maps cache efficiently so that your web browser can quickly recreate them from the cache instead of having to download each time you want to view the map.  Because they progressively load from the outside to the inside you can go ahead and zoom in to a general area and the map will catch up as the loading process is not interrupted.  Tiles are also simple to use and formatted for numerous technologies such as servers, web, desktops and even mobile devices.

As part of our lab we provided answers to some excellent "food for thought" questions.  I have included those here for parties interested in more detail of the map making process.  

1.       Explain your data including the source, processing required (did you create it? How?), and information about the quality/credibility of your food deserts and grocery stores.  

Initially I downloaded as much data as I could think to get.  I prefer to start with more, evaluate the data and then choose what best fits the project.  I used a combination of data from the census.gov tiger site as well as FGDL.  Because I decided to use the Basic style provided in Mapbox I did not need to utilize the road files I had previously downloaded, nor any information regarding bodies of water.  The data that became most helpful was the census block data (2010), City limit polygon for the City of Port Saint Lucie, county boundary polygons and grocery store locations from google earth exported to a KML file.  Once I determined the data I intended to use, I selected a coordinate system that was available both in ArcDesktop and QGIS as QGIS did not contain the initial Fl State Plane East I wanted to use.  I settled upon the UTM Zone 17N (EPSG:26917) which was the best choice given my options in QGIS.  Once this determination was made I performed a batch project on my shp files in ArcCatalog and placed them in a UTM17N folder.  This became my working folder from which I used QGIS to create my census block clips to eliminate the riparian boundary portions of the properties.  I converted the KML to shp files in ArcMap and added my other files within UTM17N to review my data and confirm it was all in the correct projection.  I also made sure to set the projection in QGIS.  Within QGIS there was other data which needed to be created from existing datasets.  I clipped the Census Blocks for the entire state to the City of Port St. Lucie shp file and defined the results as my study area.  I then created Polygon Centroids for my Study area.  In ArcMap I created a near file using my imported Polygon Centroids by making them the input Feature and my Grocery Stores as the near feature.  Since the City of Port St. Lucie is an urban area I used a search Radius of 1 mile.  I opened the centroid.dbf and saved it as near.csv using excel.  I was then able to add this layer to QGIS using the Add Delimited Text Layer making sure to choose no geometry before selecting ok.  I then needed to join my near.csv to my Study Area.  This was completed using the add vector join and STFID as the join and target field and made sure to cache join layer in virtual memory.  I could then use the attribute table to select features using an expression.  Any near distance equal to -1 was a food desert and by inverting the selection the remaining items were food oases.  I also need to create some basic statistics using the Food Desert Layer and the POP2000 as the target field.  Once all my data was organized and symbolized I chose the Basic Style in Mapbox,, imported my tilesets, created my layers and applied symbolization and other changes to the basic style.  I recorded the color scheme I wanted to use via ColorBrewer in both HEX and RGB with an application of 5 classes for later use.  Within Mapbox I grouped my food desert layer and then duplicated until I had 5 copies.  I set the parameters for my 5 class Jenks natural breaks classification (determined in ArcMap) and used the RGB information from ColorBrewer to delineate the food desert breakdown.  I made sure to record my map position (center lat/long and zoom level) so that I could later use this information in my leaflet code, both via notepad text and then saved as html utf-8 encoding when complete.  Once all Mapbox edits were complete I published my mapbox and then obtained the leaflet URL to add to my leaflet code.  I edited all required information in my leaflet code: title, set view coordinates and zoom level, map size, geocoder coordinates representing the center of the city and of course pointing all file paths to the correct location.  (Since I work from my C drive they were locally pathed to the leaflet folder within the Analyze2 folder inside my Data folder.  I also confirmed that my leaflet_rh.html link worked properly and that my information displayed correctly.  I feel that my food desert results are fairly accurate as I used the most up to date data available.  Also as local resident, and city worker privy to development, I was able to determine that the pattern fit rather well.  The food deserts may have a slight overestimation as we have had recent growth and several new grocery stores built but all in all I’d give it at a minimum a 90% rating.  This is also due to Google Earth not having all of the new grocery store locations updated.

2.       Describe the data being represented. Are there trends you can identify?  If you used your local town to map, did you expect to see food deserts occurring where they are?  Does the data surprise you?

I used my local town, the City of Port Saint Lucie.    The data illustrated in my leafelet map shows the location of existing grocery stores (obtained from google earth) and the location of Food Deserts overlaid on a base map to visualize where they are located within the city.  Because I live in a well-developed area in the heart of the city with access to many things, including grocery stores I did not expect to find the number of food deserts illustrated on the map.  Once I studied the locations it made perfect sense as over the years the city has annexed more and more properties and outside contractors had begun development.  When the economy began to fail several years ago, development stopped, including commercial entities such as grocery stores which were planned in proximity to many of the developments.  I’m happy to say that trend is beginning to turn so I expect that the food deserts will lesson over the next few years.

Week 14/15 Group 1 GIS Certification online Portfolio

This week’s portfolio experience was much easier than I expected.  I was dreading pulling everything together from all or our semesters and incorporating it with my resume and experience.  The Wix site (www.wix.com) was an excellent free source which I would recommend to others who wanted to post a quick and easy online portfolio.  The link could be placed on your paper resume, or included in your electronic submission as many applications now require this method.  You can link objects within it so that users can explore as much or as little information as they would like.  Here is a link to what I produced:

http://rh5171.wixsite.com/hamaty-uwf2016

Analyze Week 1 QGISresults for use with Mapbox, Leaflet and HTML code for online map publication - Free but not always easy

This entire module has focused on using free openSourced GIS applications.  In prepare week we created and analyzed Food Deserts in Urban Escambia County.  We used the data we had created to make zip files for our food deserts, grocery stores and food oases respectively.  These zip files were imported into our MapBox style and symbology was created using color brewer.  We were careful to note the RGB and HEX values for the colors as we needed one set for Map box and the HEX values for Leaflet.  We grouped our food desert layers in Mapbox and the duplicated them to represent the classification style we had chosen.  I chose to use 5 Manual Breaks for ease of interpretation and what I felt to be an accurate representation of the data from prepare week.  Once I was satisfied with my Mapbox results I moved on to working with leaflet to create a publishable map template.  Using a tutorial from their website as an example we were able to copy the source code to notepad and then edit the paths, directories and commands as needed to produce both a text file and an HTML file with UTF-8 encoding.  This HTML was our resulting map.  During the notepad editing process we chose the leaflet option from the Develop with this style dialog box in Map Box for the map we had created.  This path was then pasted into the appropriate locations within the leaflet notepad text so that leaflet would read the URL of Mapbox and create the internal map link.  We then used leaflet (which uses open street map for its underlying basemap) to create our map opening location, labled city pop up, a circular food oasis and a polygon representing a food desert.  The latter two items could be hovered upon to see what they represented.  We created a legend within our notepad code and referred to our hex values for the assigned colors and our mapbox map for the associated values.  Our final step was to enable a find feature which utilized a geocoding plug in to locate specific areas on the map by choosing the magnifying symbol and typing in an address or city.

Here is the link to my published map:  http://students.uwf.edu/rh51/GIS4930_SpecialTopics/leaflet/webmapsource.html

Happy Belated GIS Day

I had spectacular hopes of doing this event while I was at my work conference after our “conference day” was done.  Clearly that was a silly idea as each day we were provided with a different evening event which included dinner.  Convincing non-gis folks into skipping dinner or paying for it elsewhere so we had a location where I could set up my computer and not have to shout over 1800+ people was out of the question.  When I returned home, with the flu I might add, I used my spouse and some other family members who were in town to listen as I reviewed my blogs with them.  I think they especially liked the surgical mask I wore to prevent spreading my germs.  Thank goodness for the 72” tv as I could keep my distance from them and project the laptop to the tv.  I knew one day I could rationalize that purchase!  Anyhow, I spent a few hours going over the maps we have been creating from the beginning of this program forward.  Most of them had never heard of GIS so it was exciting to see the spark when they made connections with what they see in the real world and how it comes together.  My brother, who lives in Manhattan, even got in on the mix as he had mailed me a political map from the New York Times and asked if this was done with GIS.  I had previously tortured him with my classwork when he last came to visit.  That was also a nice feature to show everyone.  They were all very interested in how GIS aided in preventing and responding to both natural and terrorist disasters as well as the effective use of statistical data to make predictions to aid police in crime fighting or determining better locations for new stations.  There was one aunt particularly interested in the map we did on Wine consumption in Europe, I believe she is now considering relocating abroad, lol.  Overall the event went well.  They were all excited to see what I’ve been doing locked away in the home office for the past year.  Because we live in South Florida, they liked the segment on hurricane tracking and analysis as well as the exercise on damage assessment.  They all just thought the tv studio had map backdrops and they just animated them with stuff.  They had not made the correlation that the data from tracking planes and other information was used to build a GIS model of the event that could be described pictorially.  I think they all left with a deeper appreciation for GIS and for those of us interested in continue to use and develop new concepts for mapping.    

Please see the attachment for one of the maps I explained using the big screen.

Utilizing open source GIS Software (QGIS) in Conjuction with ArcMap Analysis Toolbox Near Tool to evaluate the presence of Food Deserts in Urban Escambia County

The initial part of this project gave us an opportunity to practice using QGIS so that we could import data, symbolize layers and compose maps.  Also included where the steps to create essential map elements.  This portion of the lab was a bit clunky since it was new software, the directions didn't always match the options on my screen and it was very glitchy.  I upgraded to the newest version rather than the one we were required to download at the beginning of the semester.  This reduced quite a number of glitches, but did increase the difficulty of following the lab directions as many software improvements had been made and the terminology was slightly different.  None the less by the time I finished Portion A I was confident I could tackle Portion B.  This Portion focused on the effects of urban sprawl and grocery stores as they affected the community's ability to conveniently access food, especially with no motor transportation.  I created a map illustrating areas that contained food deserts (no grocery store within one mile) and food oases.  I also included a brief explanation of the terminology as well as the statistical summary.

Module 10 - Supervised Classification of Germantown, MD

The first portion of this lab allowed us to experiment with a supervised classification.  We used tools such as the signature editor, imported or created and AOI layer, used UTM coordinates to hone in on classification types and used the polygon method versus the grow/grow properties option to "train" the classification tool.  After our first attempt we used the histogram plots and Mean plots to evaluate the results of our spectral signature.  We set the signature colors to approximate true colors using a band combination that would have the least spectral confusion.We then applied and saved our signature file.  We then used the Classify-Supervised tool to classify our image.  We then merged multiple classes of a similar nature to narrow down our actual classes.  Once complete we generated a distance image and a recoded image.  We then used the attribute table for the recode image to add our class names and calculate the area.  It took me a few tries to get the entire process good results.  I then moved on to the actual assignment which was to perform a supervised classification of Germantown MD.  Here is my end result: