GIS 4930: Special Topics; Module 4: Open Source Analyze Part 1

This is the first of two weeks of analyze type work.   Although this week's effort mostly focused on analyzing how the data would be presented in a few weeks.  This was accomplished by utilizing web mapping tools and applications, which is built around presenting maps to the public in an open sourced manner.  This week we are transforming some of the prep work created last week into the web forum in preparation of distributing it to the masses.  The overall objectives this week are as followed:

1. Navigate through, and add layers to Tilemill

2. Gain familiarity with Leaflet

3. Use tiled layers and plug-ins in a web map

The main theme to all of the above objectives before looking at them individually is that they are open source!  That means anyone has the ability to acquire them, learn about them, and in most cases contribute to the community with them. 

Tilemill is an interactive mapping software predominately used by cartographers and journalists to create interactive maps for sharing with the public.  Leaflet is a javascripting utility which allows you to code html web maps for display, much like the one linked below. The layer tiling mentioned in the last objective was accomplished with some basic html code using Notepad, and shared on a webmapping host. 

The Web Map I created displays the end result of this week's efforts.  It combines the objectives mentioned above with the data we looked at last week for food deserts in the Pensacola, FL area.  Every feature or option on this map falls into one of the objectives above.  However, after thoroughly reading through the instructions numerous times, ensuring I didn't skip a step, I was unable to get the tiled layer function to work properly.  My legend is visible, however, there is not an option to turn on or off the layers.  I think there may have been a step missing from the instructions.  Nevertheless, I was able to get the find function to appear in the lower left.  The points, polygons, and circle are also very specific.  Each of these elements is an individual block or segment of code which was pre-thought out to contribute to the map in this specific manner.  This was all done to get familiar with these applications and get ready to present my own specific area exploring food deserts in a couple weeks.   

GIS 4930: Special Topics; Project 4 - Open Source Prep

Welcome to the beginning of the last multi-week module in Special Topics in GIS.  The focus for the remainder of the class is on Food Deserts and their increasing proliferation due to urbanization and expansion of the markets/grocers containing wholesome and nutritious foods to include fresh vegetables and fruits as well as other produce. The second large aspect of this project is that all preparation, analysis, and reporting for the focus area will be done using open source software.  As the certificate program as a whole draws to a close, it is a good introduction into what is available outside of ESRI's ArcGIS suite of applications. This week I specifically used Quantum GIS (QGIS) to build the base map and do the initial processing of Food Desert data for the Pensacola area of Escambia County, Florida. The overall objectives going into this week are listed below:

1. Perform basic navigation through QGIS

2. Learn about the differences of data processing with multiple data sets and geoprocessing tools in QGIS, while employing multiple data frames and similar functionality. 

3. Experience the differences of map creation with the QGIS specific Print Composer

Below is a map not unlike many of the others I have created using ArcMap.  That is, in fact, the point of one huge aspect of this project.  There is open source, defined as free to use software which you can personally suggest improvements for update and redistribution to the masses.  These open source applications perform quite similar tasks and produce similar outputs as those in ArcMap.  QGIS is one of these options.  Given the background in ArcGIS from the rest of this certificate program, there is not a steep learning curve in picking up QGIS and running with it.  There are definitely differences, but with little instruction it becomes very intuitive just like ArcMap.  Now you might be wondering, if these two softwares are so similar, then why wouldn't everyone choose QGIS over any ESRI related products?  There are still advanced tools and spatial analysis functions in ArcGIS that are beyond this software.  For the basic to moderate tasks, they can definitely be done in QGIS. But sometimes there will be no substitute for the processing ease and power of ArcGIS.  
Back to the map provided, what you're looking at is two frames, or two sides of the same information.  You are presented with both Food Deserts and Food Oasis by census tract for the Pensacola area of Escambia County.  These deserts were calculated by comparing the centroid (geographic center) of a census tract with its distance to a grocery store. Tracts without a grocery store providing fresh produce are said to be in a Food Desert. The average person in these areas have to travel further to obtain fruits and vegetables.  When doing so, other closer, less healthy alternatives might be taking precedence for these people.  Ultimately, those with less access are likely to be less healthy overall and that is the issue we are starting to get into with this subject.  

Keep checking for the next installments of analysis as we continue to look at this issue. The area shown below is just for example purposes.  As the project moves forward, my analysis and results will focus on Rockledge, Florida, which is located in Brevard County. 

GIS 4930: Special Topics; Project 3: Stats Analyze Week

Welcome to the continuation of our look at statistical analysis with ArcMap.  Recall that the theme being explored with statistics is methamphetamine lab busts around Charleston, West Virginia.  These past few weeks of analysis have been the bulk of the work for this project.  The overall objectives of the analysis portion are to review and understand regression analysis basics, and a couple key techniques.  Define what the dependent and independent variables are for the study as they apply to the regression analysis.  Perform (multiple renditions) of an Ordinary Least Squares (OLS) regression model.  Finally, complete 6 statistical sanity checks based on the OLS model outcomes.  

In the previous post, we looked at a big overview of the area that is being analyzed.  There are 54 lab busts from the 2004-2008 time frame taken from the DEA's National Clandestine Laboratory data.  Decennial census data from 2000 and 2010 at the census tract level was spatially joined to these 54 lab busts.  The data was then normalized into  a percentage by the census tract into 31 categories for analysis in the OLD model.  These 31 categories of data were then fed into the model and systematically removed while analyzing their affect on the model.  Ultimately as good of a model as possible was arrived at with some results shown below. 

This is a generated output depicting the OLS results created in ArcMap. Key things to note from the table is that there are only nine variables being incorporated into the OLS model of the original 31.  How were variables removed you might ask?  There are six checks, or questions, to answer to determine the validity of a variable's use in the OLS model: does an independent variable help or hurt the model; is the relationship to the dependent variable as expected; are there redundant explanatory variables; is the model biased; are there variables missing or unexplained residuals; how well does the model predict the dependent variable?  The first three of these were generally grouped into one solid check for determining if a variable should stay or go.  The remaining checks were applied to the model results as a whole.  As long as a variable had a coefficient that wasn't near zero, a probability lower than 0.4, and a VIF less than 7.5, it could stay.  After looking at this data table, it's time to transition to the visual interpretation, shown below. 

This map depicts the standard residual for the OLS model depicted in the table.  It symbolizes areas using a standard deviation style outlook.  However, rather than wanting a more Gaussian curve style of data showing some of every color, you ideally want values to be in the +/- 0.5 range because that is said to be highly accurate.  Darker browns indicate areas that the model predicted less meth labs actually were.  Whereas, darker blues indicate high value areas where the model expected more meth labs than those that were actually present.  

This week's focus was not to describe the data results, but to accomplish the analysis leading up to it.. 

GIS 4930: Special Topics; Project 3: Stats Prepare

Throughout the next few weeks, we will be delving deep into the clandestine, the dangerous and ultimately bad world of drugs.  Specifically we will be examining the role of GIS statistical analysis as it applies to aiding law enforcement with determining ideal locations to find methamphetamine labs.  Meth has been around since the early 1920's and have been illegal since the 60's, which drove the illicit trade underground.  Meth labs have been found in every state, but surprisingly only in about half of the country's counties.  Over the next few weeks, we will be analyzing two different counties of West Virginia, Putnam and Kanawha. These counties are credited with 187 meth lab busts from 2004-2008.  Chances are, we all know someone who has been impacted through drugs, or drug use, or at minimum you can see it all too prevalent on the news. The idea behind this lab is to examine the socioeconomic trend information that can aid in determining where meth labs are most likely and be able to give that information to local law enforcement agencies. The end deliverable for this year will be a scientific paper discussing the issue and analysis being done on the study area shown below. 

As stated earlier, the study area is of the Charleston vicinity in West Virginia and is home to 187 meth lab busts.  This information has already been summarily broken down into a meth lab density by census tract shown in the main map provided below.  This essentially means the total number of busts per census tract was divided by the area of the tract to provide us with density values seen in the legend.  This map also provides a basic overview of the subject counties and provides state context as well.  

GIS 4930: Special Topics - Module 2: MTR Report

Honestly, I absolutely hated this MTR assignment.  Throughout these past couple of weeks, we were supposed to be working as a group to complete the final project.  Even though I signed up for a group, I felt like I wasn't a part of one.  I would ask the group leader questions but would not receive a response in a timely manner.  Basically, I felt like I was "out of the loop" throughout the entire assignment.  If I had any extra time to work on this assignment, I would most definitely start from the beginning.  I'm really disappointed with the decisions I made in regards to Non-MTR and MTR during the Analyze week a few weeks back.  

GIS 4930: Special Topics; Project 2: MTR Analyze

This week we were tasked with classifying areas of Mountaintop Removal (MTR) and NonMTR on four landsat images for our group's study site.  There are four groups to sign up for in which I decided to become a part of Group 1. The fact that there are four landsat images and four members in my group, we all took responsibility for classifying one image.  

The first step was to create a single raster dataset of seven landsat bands that pertained to my image with the Composite Bands tool in ArcMap.  Next, the Extract by Mask tool was used to create a raster of portion of the composite landsat that fell within the study area.  At this point, the image was ready to be classified into two categories: MTR and NonMTR.  This step was accomplished by using a new software known as ERDAS.  Within ERDAS, the Unsupervised Classification tool was selected, creating 50 classes from the masked image.  Following this, the MTR and NonMTR pixels were selected and titled as such in the attribute table.  I then saved the layer and added it into ArcMap.  The raster was then reclassified with all areas other than the MTR areas as No Data. This was then converted into a polygon.  My results from this assignment are shown below. 

River banks, roads, and flat mountainous regions have similar spectral reference characteristics as MTR sites, so a portion of the red area is not accurate. This will be corrected next week during the Report Week assignment. 

GIS 4930: Special Topics; Project 2 - MTR Prep

The next few weeks will be spent looking at Special Applications in GIS that can be used to analyze mountain top removal (MTR). MTR and valley filling are a common practice most particularly dealing with coal mining. The Appalachian Mountain chain in the mis-eastern United States is an area that is particularly affected with this form of mining.  The mining essentially involves peeling away the surface of the earth including trees, brush, soil to get at the rocky layer beneath to harvest away the precious coal. This is the premise of the project throughout the next few weeks. The first part of week's assignment was to create a basemap for the study area that I will be exploring during these next few weeks. The project as both an individual and group component.  Deliverables, like the basemap shown below, still have to be done independently. However, much of the upcoming analysis will be broken down into manageable chunks to be completed in groups resulting in a final group presentation. 
The basemap below provides an overview of the study area, and displays the DEM, streams, and basin for Group 1, which is the group I have chosen to work with.  Many things have been done to the original DEM layer to show the elevation, streams, and basins as shown below.  Essentially, a mosaic raster was made out of 4 DEM sections, which was then clipped to the study area.  From there, multiple tools were applied to the mosaic to generate the streams and basins. Using the Fill tool, I was able to fill the holes in the pixel database.  This makes it so when running a subsequent flow analysis, there aren't holes for the "flowing water" to go into.  Flow direction is applied to see how and where water would or should move given the overall contours of the elevation slopes.  From there, a calculation is ran to determine what actually correlates to a running stream.  This calculation funnels into a conditional statement tool identifying areas that should be streams.  Finally, a feature class is created from that entire process and then displayed appropriately.  
We were also asked to create a Map Story, which displays the six stages of mountaintop removal. We were also asked to create a Story Map Journal, which is the building blocks in progress towards a final compilation for the project. 

GIS 4930: Special Topics - Project 1: Network Analyst Results

In preparation for Hurricane Oscar, four products were created to help the community.  The target audience does not have a GIS background.  The first product communicates evacuation routes from Tampa General Hospital to two local hospitals, Memorial Hospital and St. Joseph’s Hospital.  An informative pamphlet was developed for distribution to patients and their families.  The pamphlet includes a plan showing two evacuation routes, evacuation timing, emergency contacts, as well as location details of each hospital.  The pamphlet is designed to inform patients and their families on the evacuation process.

Evacuation routes were created utilizing Network Analyst within ArcMap.  Data was retrieved from the University of West Florida, Florida Division of Emergency Management, and the Florida Geographic Data Library.  Directions for both destinations were extracted using the Network Analyst function within ArcMap. 

The pamphlet clearly depicts evacuation routes and other important information, however, it does not include local shelters near the destination hospitals.  It also doesn’t provide alternate routes for family members coming from major highways.  Regardless, this pamphlet would give me confidence that the hospital was taking good care of my loved one.

The second product communicates emergency supply routes to the delivery crew and emergency workers.  Grayscale maps were developed showing the distribution of emergency supplies by the U.S. Army National Guard to the three local storm shelters: Tampa Bay Blvd Elementary, Middleton High School, and Oak Park Elementary.  These three maps serve as an emergency supply route plan, with detailed directions for each location.  

Using Network Analysis, three separate maps were created to provide detailed information showing the routes from the U.S. National Guard Armory to the three local storm shelters.  The routes have been divided into several linear sections to provide drivers with a clear understanding.  Directions were extracted using the Network Analyst function within ArcMap.  An inset map provides an overview of the detailed routes. 

While the maps are affective in providing navigation details, they do not include contact data or timing on when supplies should be delivered.  The maps also don’t include addresses of the starting or destination points.  Regardless, they are adequate for their intended purpose. 

The third product displays multiple evacuation routes from downtown Tampa to the nearest local shelter, and is intended for distribution by television and newspapers.  Close up images of the routes are provided, as well as an inset map displaying the full route.  Text advises drivers on general precautions. 

Using Network Analysis and Adobe Illustrator, routes were created from 15 zones to the shelter. Color codes help the public determine the recommended route.  Streets and major roads along the routes are labeled accordingly and arrows provide directional information. 

While the map clearly depicts the evacuation routes for the downtown Tampa Bay area, it does not provide detailed driving directions, contact information, or the address of the shelter destination.  Nevertheless, local residents should be able to find the evacuation routes.  The map shown below displays the emergency supply route from the National Guard Armory to the Oak Park Elementary Shelter.

The fourth product, as shown below, depicts shelter locations and will be distributed to the general public by television and newspapers.  The area is divided into three zones, each with a designated shelter.  Informational text lists the shelter names, addresses, predictions on hurricane landfall, and safety precautions in the event of flooding. 

ArcMap was used to create the map showing the zones and shelter locations.  Major state roads and highways were labeled accordingly.  Illustrator was used to make it aesthetically pleasing and add textual information. 

The map serves its purpose in communicating the nearest shelter locations to the general public.  It would be helpful to provide contact information for the three shelter locations.  

Overall, I really enjoyed this first project and am pleased with how all of my maps turned out. However, the creation of the maps depicting the four different scenarios was extremely time consuming.  Nevertheless, I'm looking forward to creating more maps throughout the upcoming projects. 

GIS 4930: Special Topics - Week 2: Analyze

After creating a basemap of the study area and determining the potential flood zones, an evacuation route map was created based on four different scenarios.  Using the Network Analyst toolbar in ArcMap, I was able to define optimal evacuation routes to allow the transfer of patients from one hospital to another, deliver emergency supplies to shelters, transfer citizens to the nearest shelter location, and finding the nearest shelter for local residents.

The first scenario mentioned is the evacuation of patients from Tampa General Hospital on Davis Islands.  The hospital is located at the northern tip of Davis Islands, a small, residential area that was created with sediments dredged during the creation of the nearby canals.  Due to the very low elevation of the islands, the hospital will almost certainly be subjected to heavy flooding during the coming storm.  A route was created to evacuate all patients to other local hospitals before the hurricane hits.  The hospitals that were chosen to accept patients is the Memorial Hospital of Tampa and the St. Joseph’s Hospital.  These two routes were created by using the Network Analyst toolbar and ensuring that the Impedance was set to Seconds for calculating the routes.

The second scenario displays the distribution of emergency supplies by the U.S. Army National Guard to three storm shelters.  Emergency supplies will be delivered to the U.S. Army National Guard armory, located at Howard Ave. and Gray St. on the west side of the river.  Once the supplies reach the armory, National Guard troops will be tasked with delivering the supplies to the local storm shelters.  However, the supplies may not reach the armory before the storm hits, so drivers will have to travel to the shelters while avoiding flooded roadways.  Three new routes were created to assist the drivers and the National Guard troops to safely deliver emergency supplies to the shelters (Tampa Bay Blvd, Middleton High School, and Oak Park Elementary).  These routes were created using the same tools used in scenario one. 

The third scenario displays the creation of multiple evacuation routes for downtown Tampa.  Since the downtown area of Tampa is heavily populated, routes were created to assist the general public in evacuating to the nearest shelter location as quickly as possible.  This was accomplished by using the Scaled Cost function, which works by multiplying the Impedance attribute by the Scaled Cost attribute.  A New Closest Facility was created using the Network Analyst toolbar.  Using this, I was able to classify a point layer as Incidents, which shows all of the locations in downtown Tampa that need to be evacuated.  As shown in the map attached, the final destination for all evacuation routes is the Middleton High School shelter.

The fourth, and final, scenario shows residents which of the three shelters is closest to them by drive time.  The objective in finding the nearest shelter is to aid residents in getting to their designated shelter as quickly as possible and help alleviate confusion.  This was achieved by creating a New Service Area within the Network Analyst toolbar.  The area surrounding Tampa Bay Blvd Elementary is shown in light green, the area surrounding Middleton High School is shown in light red, and the area surrounding Oak Park Elementary is shown in light yellow. 

GIS 4930: Special Topics - Project 1: Network Prep

Welcome to week one of my Special Topics in GIS class.  This class is made up of four real life style projects, with topics that cover multiple weeks.  The order of the projects go as followed: project preparation during the first week, analysis throughout the second, and presentation during the third.
Throughout this first project, we looked at Network Analysis and how it can be used to prepare the citizens of Tampa Bay from a Hurricane that's about to make landfall.  Since this first assignment was the prepare week, the main objective was to acquire the data necessary for a base map and further analysis.  The data was all provided by UWF, which includes: a Digital Elevation Model (DEM), streets, point data files for fire departments, police departments, hospitals, a National Guard Armory drop location, and schools designated as shelters.  All of this data was used in compiling the base map shown below. The biggest aspect to this weeks project was preparing the potential flood zones.  This was done by reclassifying the original DEM into appropriately usable elevation increments.  The reclassified DEM was then converted into a polygon feature class for easier processing. It was discovered that all areas that have less than 6 feet of elevation will most likely have flooding.  To show this even further, a "Flood Zone" feature class was created to display the areas that are at risk for flooding.  The Flood Zone layer is shown with low opacity to better display the variations of elevation.  
The map below shows the Tampa Bay area as it relates to most likely flood zones, with transportation arteries that would be affected.