Final Report for GIS 4035 - Photo Interpretation & Remote Sensing

I have definitely lost my mind...but I know where it is. My final project for GIS 4035 - Photo Interpretation & Remote Sensing, was to apply remote sensing data and image processing techniques that I studied and used during this semester. I utilized concepts, data types, processes, and techniques I learned in ERDAS Imagine, and ArcMap. What an experience this has been! My project was to examine the Lake Tahoe basin area and determine the change, if any, in healthy vegetation from the period of 1999 to 2010. The guiding question for my research is: How much has healthy vegetation decreased in the Lake Tahoe basin area from 1999 to 2010?

It was a fun project and I certainly have learned a great deal this semester. Please see my Final Report at the link below. Here is my final map:

Lake Tahoe:1999 to 2010 -Decrease in Healthy Vegetation

Brevard County Food Desert - Palm Bay & West Melbourne

This has been a crazy-fun experience. My last project for GIS 4930, Special Topics in Geographic Information Systems is to create a webmap using Open Source GIS tools. The subject of this last project continues to be Food Deserts. For my project, I selected two cities in Brevard County: Palm Bay and West Melbourne.I used QGIS (Quantum GIS) as a FREE and Open Source, ArcMap -substitute to conduct most of my analysis. I had to use ESRI's ArcMap for the Near tool to compute where the Food Desert and Food Oasis existed relative to my cities above. After running the tool in ArcMap, the QGIS table looked like the below:



I used the 2010 US Census data. The first red circle represents the Census Tract information; the second circle is POP2010. I used the tract information to compute the centroids of the population so that I could measure a Euclidian distance of one mile from the grocery store layer. The last column is the Near_Dist data derived from ArcMap – Like I said, I could not use QGIS to do everything I wanted – This Near column is the result of executing the Near Tool with a one mile search. Any item that returned a -1 is farther than one mile from the grocery store and hence in a Food Desert by my definition.

      

I used Google Earth to collect the Grocery Store information and the US Census data to compute population centroids. I used a number of FREE Open Source GIS tools, such as QGIS, Mapbox, and Leaflet, and I gained valuable experience doing so. Below is my QGIS and Mapbox maps.

My Mapbox Link: Mapbox Brevard Food Desert

The data I created is very good and highly useful for the purpose of determining Food Deserts. I believe that my data fairly represents the Food Desert situation in my study area.

Here is my final presentation for GIS 4930:

Link to My Presentation: My Brevard County Food Deserts Presentation

It has been a pleasure conducting this brief study and I look forward to many more years of using free and open source tools for GIS analysis.

My Raven GIS Portfolio

I have assembled a collection of my GIS works and created a website to host and showcase my skills, experience and a resume for all to view. I would like to share this website with you and would appreciate if you visit this site.


Raven GIS Portfolio


This is still a Development website and I plan to add more info as I progress and gain more GIS experience. Check back later to see what I have added.




Raven

Raven GIS Portfolio

Mapbox and Brevard County Food Deserts

This week we continue work on our Food Desert maps using open source GIS tools. I learned an incredible amount about Mapbox and Leaflet, but I am no where as comfortable with these Open Source tools as I am with ArcMap. Nonetheless, this has been a great learning experience.

I used data from Google Earth to collect and compile my grocery stores layer and I used the population centroids, based on the US Census data for both Palm Bay and West Melbourne. The US Census data indicated that the census blocks were farther apart as you traveled south from the center of Palm Bay. This meant to me that the population was much less dense. As I reviewed the aerials of south Palm Bay I discovered this area is very rural. If I did this lab exercise again, I would use a greater distance from a grocery store, perhaps five miles, for south Palm Bay, however, I would have to determine a definition to confidently say where Palm Bay turns rural.
I gained valuable experience using TileMill and then loading my Tiles (file extension = .mbtiles) to Mapbox.  However, the link above tells us that TileMill is no longer in active development; looks like I'll be using Mapbox Studio Classic instead.As an Open Source program, Mapbox is constantly changing, so this made the lab a little more challenging. In the end I was able to compile a Mapbox Dataset that had a basemap and both my Food Desert and Grocery Store layers. Below is what my map looks like at this point.

As you can see, there appears to be a no grocery stores south of Malabar Road. That is why so much of Palm Bay is a Food Desert. I suspect that more grocery stores may move into south Palm Bay in the future.

Open Source tools and Food Deserts

What a week. Happy after Thanksgiving to everyone. This week was about open source GIS tools. I learned about TileMill and Leaflet. Great tools for building web maps. I had a challenge to get all the tools on Leaflet to work properly for me. Tools such as the Layer Control or Geocoder. Leaflet is an html based program that allows you to upload maps using Tiles (Tilemill) and customize them by essentially copying the html code. However, even individuals that have extensive experience with code struggle with getting the correct, code, spacing, punctuation, and the case of letters. Needless to say, this week was a challenge. Given more time and coffee, I am sure I will enjoy using both TileMill and Leaflet in the future.

The objective this week was to develop a map showing Food Desert locations surrounding Pensacola Florida. Below is the link to my Food Desert map. It is a work still under construction. Please check back later to see what features and tools I have added.

Pensacola Food Desert

GIS Day - Brevard County

I have been conducting my internship with Brevard County Survey & Mapping Dept. In past years, the Brevard County Commissioners have passed a decree recognizing GIS Day (here's a link to the 2012 GIS Day Proclamation). So far this year, I have not seen nor heard that a decree was passed. So, with no official activities for GIS Day at Brevard County Government Center, I spent my day discussing how to compute Geometry and input coordinates on my new "Speed Hump" map. I had been working on a project for the Transportation Dept mapping all the Speed Humps in Brevard County - all 459 of them...and Iwas at the point of conclusion so I was working on completing my map. It is a good feeling to actually complete a project for a "customer."

Open Source GIS: Food Deserts

This week we are working with an open source GIS tool called QGIS (Quantum GIS). Open source programs are a great way to work as a community toward a common goal. This week we are preparing data to compile a Food Desert map of Southern Escambia county. The final project will be to create a Food Desert map of an area of my choice. A food desert is defined as an urban area with a grocery store or fresh food source, that is greater than 1 mile away and in a rural area, a grocery store located greater than 10 miles away. I took the data from Escambia County and the US Census data for this area and applied a layer for grocery stores. Below are the Food Desert maps I produced. 

The first QGIS map shows Escambia County in relation to all Counties in Florida. 

  The second map created in QGIS shows the relative  location of Escambia County and the Study Area in the inset, and the two data frames with the study area that depict the Food Desert and Food Oasis locations:

Supervised Classification: Germantown, MD

What a week it has been. In Photo Interpretation, we continued learning about classification of aerial images. This week was Supervised Classification. Using ERDAS Imagine, and a raster image of Germantown, MD, I created my own Spectral Signature and Supervised Classification. I used the AOI Seed tool (Drawing tab, Insert Geometry group, press the Grow dropdown arrow, then click Growing Properties) to designate eight areas (pixels) on the image that were of a particular type of land cover, including:

Urban/residential  

Grasses 

Deciduous Forest

Mixed Forest (Deciduous/Conifer)  

Fallow Field

Agriculture

Roads

Water

I used Mean Plots and Histograms to evaluate where there was Spectral Confusion. More accurately, I was looking for areas where the Mean Plot lines did not converge or which bands had the most separation. I discovered that my Plots had the most spectral separation in Bands 1 -2- and 3. Then I used the  Maximum Likelihood tool to classify my image after setting color to "Approximate true color."   

The result of my work including a Distance Map is shown below.

Statistical Analysis:Ordinary Least Square – Meth Lab Indicators

This week we continue to work on our project of Statistical Analysis. I used ArcMap and the Spatial Statistics tools available to me: ArcToolbox, > Spatial Statistics Tools> Modeling Spatial Relationships> Ordinary Least Squares. Regression analysis is the most commonly used statistic in the social sciences, hence I used the OLS tool for this study. In this lab exercise I am examining the relationship between the existence of Meth Labs (Meth Lab Density), my dependent variable, and certain socio-economic factors. I ran the Ordinary Least Square tool in ArcMap more than 22 times to remove extraneous variables from the 2010 US Census data so that I could finally arrive at the six explanatory (independent variables) that I will use in my study report.

My explanatory variables are listed below:

The objective of running the OLS tool so many times was to create a valid model that would aid in the prediction of the presence of Meth Labs. This involved examining a variety of statistics, listed above, in a methodical way to evaluate six separate Checks:

     1. Are your independent variables helping or hurting?

     2. Are the relationships what I expected?

     3. Are there redundant explanatory variables?

     4. Is your model biased?

     5. Are any important independent variables missing?

     6. How well is the model predicting the dependent variable?

After reviewing each "Check" above and after sifting through approximately 70 US Census attributes, I arrived at my destination with data that still needs to be evaluated. But, I can say at this point that I have six explanatory variables that show some relationship to the prediction of Meth Lab Density.

Unsupervised Classification

This week I learned about the two ways to classify pixels on an image: Supervised and Unsupervised. For a Supervised classification, I would identify "training pixels" to use to identify the rest of the features on the image I was working with. For Unsupervised classification, the pixels are simply divided into a number of classes depending on the number you specify. The tools to accomplish this in ArcMap are: Iso Cluster Tool and the Maximum Likelihood Classification tool

We also used ERDAS Imagine to classify pixels. In ERDAS, the classification process was more tedious but generally worked the same. In the Raster tab, under the Classification group, click the Unsupervised button, then select Unsupervised Classification.

I performed an Unsupervised Classification using an aerial of the UWF Campus. I made five classes, named them and designated colors for each class:

1. Grass – green  

2. Trees – dark green 

3. Buildings/Road – grey 

4. Shadows – black 

5. Mixed - orange

The process was long but I am happy with the results. Additionally, I created another column or field for "Area" and then I totaled the area as "Permeable" or "Impermeable" and listed the percentages of each on my final map.  

Thermal & Multispectral Analysis - Barren Soil, Guayaquil, Ecuador

For this lab I conducted an analysis of a particular feature using Thermal & Multispectral tools. The primary goal was to use both ERDAS Imagine and ArcMap to adjust band combinations to highlight a feature of my choice. I created a composite image in ArcMap using a LANDSAT ETM image of Coastal Ecuador.  (Data Management Tools > Raster > Raster Processing > Composite Bands). At first I examined each band separately. For Band 6, the Thermal Band, the image remained mostly the same as in all the previous bands, only it appeared more “fuzzy.” The thermal return indicated that my feature was quite warm. This told me that it was a material (bare earth) that was emitting a good deal of heat. Knowing that I was viewing a feature very near the Equator, I surmised that this feature was emitting retained heat from the sun. I could also venture to say that this image was not taken at first daylight since the bare soil was already at least "warm".  I would guess the bare soil had absorbed at least some sun energy and therefore the time of day was at least past mid-morning. Additionally, I could not detect any significant shadows so this also told me the sun was probably at a high angle overhead. After viewing each band separately, I used various band combinations to further examine the image including:

False color – 4-3-2

Natural color – 3-2-1

Healthy vegetation – 4-5-1

Near IR, Mid IR, Red – 4-5-3

I finally decided to use the Band combination of 7-4-2 to display the final map analysis. This combination provides a "natural-like" appearance of many features. Healthy vegetation is bright green, grasslands appear green, pink areas represent barren soil, oranges and browns represent sparsely vegetated areas. Hence, the feature I selected in this map of Guayaquil, Ecuador is a patch of barren soil surrounded by healthy vegetation and water.

Statistical Analysis with ArcGIS - Meth Labs in Charleston, WV

This week was "Prepare" week for Statistical Analysis with ArcGIS. Below is my basemap of the two counties in West Virginia we will be examining using Statistical tools in ArcGIS. The map depicts Putnam and Kanawha counties and a simple distribution of Meth Lab Seizures, courtesy of the DEA and the National Clandestine Laboratory Register.

I took 2010 US Census data for this area and computed percentages for various categories, including, single female head of household  (pcnt_FCHLD), My hypothesis is that where there is a high (or elevated) percentage of single female head of household, there will be a corresponding high probability of a meth lab. I will test this hypothesis using ArcGIS Statistical Analysis tools. There are various other conditions that make it likely for a meth lab to exist, however, pcnt_FCHLD is the primary attribute I will use in my study. It is my goal that the results may be useful to DEA and local authorities whose objective is to seize Meth Labs and arrest anyone involved in the production of Methamphetamine.      

Multispectral Anaysis

This week in Photo Interpretation we continued our study of image enhancement with multispectral analysis. This was a great week where I learned how to use:

1) Examine the histogram for shapes and patterns in the data.
2) Visually examine the image as grayscale for light or dark shapes and patterns.
3) Visually examine the image as multispectral, changing the band combinations to make certain features stand out. (This one was a blast!)
4) Use the Inquire Cursor to find the exact brightness value of a particular area (The Inquire tool was my favorite).

I worked through several tasks in ERDAS Imagine to accomplish the above.  The first Exercise was using Histograms in ERDAS. Manipulating Breakpoints was a challenge for me. Though I understand the principle, getting the correct image was difficult. Exercise 2, was Using Spectral Characteristics. My main take-away is:

a.      The band combination, Red: 4, Green: 3, Blue: 2 is called

      Near Infrared.

b.      The band combination Red: 5, Green: 4, Blue: 3 is called

      Short Wave Infrared.

c.       The band combination Red: 3, Green: 2, Blue: 1 is called

      True Color.

These band combinations help to highlight certain features and make it easier for us to find or identify important or otherwise, elusive features on a map. Exercise 3, Band Ratios - Creating Indices was very interesting to me. I created a Normalized Differential Vegetation Index (NDVI) that helps to distinguish clearcut areas. I could have used this tool for identification of MTR Mining sites in Special Projects!

The last Exercise, was to find 3 "Mystery" features matching pixel criteria as specified below.

The first feature was:  In Layer_4 there is a spike between pixel values of 12 and 18. Name the type of feature responsible for this and locate an example of it on the map. I used all four of the techniques above and with some trial and error I identified a large body of water as this feature:

I selected a True Color band combination to display this feature.

 The next "Mystery" feature was: A small spike in layers 1-4 around pixel value 200, and B) a large spike between pixel values 9 and 11 in Layer_5 and Layer_6. I selected a snow-covered mountain range as this "Mystery" feature.

I selected a Short Wave Infrared band combination to highlight the snow on these mountains.

The "Mystery" feature here is Mount Olympus in Washington State. The mix of dark, small, bare rocks on the mountain tops made this identification more challenging.

The third and final "Mystery" feature was to identify a certain type of water feature: Layers 1-3 become much brighter than normal; layer 4 becomes somewhat brighter, and layers 5-6  remain unchanged. 

Using the four techniques from above, finding this water feature was a difficult hunt. I thought the type of water might be near shore, so it would be a mix of rocks, shallow water and perhaps some turbidity. But the more I searched using the Inquire tool I became convinced the feature could be a stream or river. The Queets River, along its banks seemed to exhibit the above traits. So, this is the feature I selected.

  

I displayed this feature using a Near Infrared band combination, 4-3-2, The Queets River in Washington State, is located on the Olympic Peninsula, mostly within the Olympic National Park. This river empties into the Pacific Ocean. 

Mountaintop Removal:Report Week & MTR Story Map Journal

This week in Special Projects, we concluded our module on Mountaintop Removal (MTR) mining sites. We took a deep look at the data by processing Raster images of the Appalachian Coal Mining Region. To do this, we were split into several groups so that we could analyze Landsat images that would reveal MTR sites in this region. I was a member of Group 3 and I analyzed Landsat image  LT50190342010243EDC00. Specifically, I processed this image using ERDAS Imagine to re-classify areas that were MTR or Non-MTR and I used the Multipart to Singlepart tool to create several more polygons for analysis. After classifying the MTR sites using ERDAS Imagine, I conducted an accuracy check by generating 30 random points and comparing those points to a 2010 aerial image of the same region. My accuracy check yielded 28 out of 30 points that were True or correct which gave me a 93% accuracy. My results were a slight decrease in the total acreage of MTR mining sites. Though this is possible due to restoration, the decrease in amount of MTR acres does not provide remediation for the source of drinking water for the families who live in this region. There is a correlation between MTR mining and poor health, fish kill, lower bird hatchings, and poor water quality.

There are six stages to MTR mining: Clearing, Blasting, Digging, Dumping Waste, Processing, and Reclamation. During the blasting phase it is not unusual to cause violent shaking to families homes located miles away from the MTR site, nor is it unusual to find coal dust coated on the their homes. In my opinion, it is time to end MTR mining. As a nation, we have many other forms of energy available to us and we should make use of these alternate energy sources. The Coal Mining region located in the Appalachian Mountains would benefit immensely if we converted from MTR mining to wind, solar or other methods for producing electricity. Changing from MTR mining to solar energy, would be another great way to create jobs and provide America a lasting and renewable energy source.

Please see my Map Journal - MTR Mining: Streams and Basins

Lab 6: Spatial Enhancement

This week in Photo Interpretation we took a look at downloading data from USGS to obtain a Landsat Archive of Landsat 4-5 TM. The objective of this lab was to use various image enhancement tools and techniques to improve the quality of your image.

I used ERDAS Imagine to begin my spatial enhancement. The first technique I used was the Fourier Transformation. A Fourier Transformation is a mathematical technique for separating an image into its various spatial frequency components. I had a Landsat image that contained striping and the Fourier Transformation afforded me a method to remove that striping. The next tool I used was the Wedge button. This was a challenge to get the correct amount of coverage and not delete too much detail of the image. I also used the LowPass button that seemed to smooth the image a bit more. After completing these tasks in ERDAS Imagine, I switched to ArcMap to try a few more tools to enhance my image. I tried other filters and changing the brightness and contrast. In the end, I built pyramids for my images and this seemed to add a slight bit more contrast to the image. Below is my final map.

Mountain Top Removal: Analyze Week

This week is a continuation of Mountain Top Removal. The main objective for this week was to Reclassify an MTR raster. To accomplish this, we used a Landsat image and manipulated it using ERDAS Imagine. I also learned about SkyTruth, a non-profit organization that provides  remote sensing and digital mapping technology using satellite images and other visual technologies, to provide information and data to environmental advocates, the media, and the public.  

My task was to examine a digital multispectral image that represents a portion of our study area. Additionally, this week we signed up for small groups. I am in Group 3 and we have two Landsat images to work with:

LT50190332010243EDC00.tar

LT50190342010243EDC00.tar

Using ArcMap I was able to use the Training Sample Manager to sample various polygons from the image and classify them as MTR or NonMTR. This could be a tedious task but I found it mildly entertaining.  I created the following histograms and scatter plots to check the accuracy of my sampling:

 

Looks pretty good to me. After this, I used ERDAS Imagine to create an AOI layer and then use the "Grow tool" to select pixels that were in an MTR area. Conducting this analysis took time for many of the tools to process the data. Once again, this was a fun exercise even though I had to wait patiently between each step of processing data to arrive at my final product that I then exported back to ArcMap to complete.  Below is my final re-classified MTR image 

ERDAS Imagine - Remote Sensing & EMR

This week I learned about ERDAS Imagine.  This is a remote sensing application for GIS. The ERDAS Imagine program allows us to manipulate raster images to extract information. In this lab we were using a raster of Washington State to classify the types of land cover such as vegetation, water, bare earth, etc. To do this, initially we began with some calculations of electromagnetic radiation (EMR). The chart below shows the electromagnetic spectrum:

There were a few formulas involved such as Maxwell’s wave theory: C = λν and Planck Relation: Q = hν 

But the real point of this lab was to learn about ERDAS Imagine and how to process a raster image to understand the types of land cover depicted. A very useful feature was the ability to switch between TM False Natural Color and TM False Color IR. The False Natural Color gives the bare ground a pink color which adds a sharp contrast making it easier to discern from the green forested areas.

Overall this was a fun Lab but I am sure I will need more practice with ERDAS Imagine to feel as comfortable with it as I do with ArcMap. Below is the map I created for this week's Lab. It shows an area in Washington State that I have re-classified by using the Inquiry Box and manipulating of the ERDAS Attribute Table.

  

Mountain Top Removal and Map Stories

This week I worked on a map concerning Mountain Top Removal for coal mining in the Appalachian mountain region. The EPA defines Mountain Top Removal as:

“Mountaintop removal/valley fill is a mining practice where the tops of mountains are removed, exposing the seams of coal. Mountaintop removal can involve removing 500 feet or more of the summit to get at buried seams of coal. The earth from the mountaintop is then dumped in the neighboring valleys.” (ilovemountains).

I used several tools to make this map. The bulk of the work was in creating the Hydrology Dataset from the Mosaic Group Raster Data. There were plenty of steps involved including:

- The Fill tool — This step modifies the raster to prevent water flow from pooling.
- The Flow Direction tool — This process assigns each pixel a value representing the direction of water flow across each cell.
- The Flow Accumulation tool — For each cell in the raster, this tool calculates the total number of other cells that flow into it.
- Calculate 1% of the pixels in the image —  A generally accepted condition is that the Flow Accumulation value must exceed 1% of the total number of pixels in the raster. If more than 1% of the total pixels in the raster flow into an area, it is probably a stream.
- The Con tool — This tool defines the threshold of flow accumulation values that qualify something as a stream
- Stream to Feature — This tool creates polyline vector features from the Con tool output raster

This was a lot of work but I am glad I used all the above tools for the exposure and practice. Below is my basemap.

Another skill I worked on this week was to create a Story Map and a Story Map Journal. The subject for both stories concerned the 6 Stages of Mountain Top Removal:

- Stage 1: Clearing 

- Stage 2: Blasting

- Stage 3: Digging 

- Stage 4: Dumping waste 

- Stage 5: Processing

- Stage 6: Reclamation 

You can find My Story Map at: Mountain Top Removal

I also created a Story Map Journal, but this is still a work in progress. If you would like to take a look at it now, go here: Mountain Top Removal: Streams and Basins

Ground Truth: Error Matrix & Overall Accuracy

This week I modified my map from last week, Land Use Land Classification. The challenge last week was to not use any tool that would help you verify the type of land I was classifying. I could only use the aerial image (.tif) of Pascagoula, MS. and make my best guess at what type of land I was classifying. This week, the challenge was to take an unbiased sampling of the land I classified and compute the overall accuracy. Under normal circumstances, you might travel to the location and conduct fieldwork to determine the correct land use/classification by conducting a Random, Stratified Random, or Systematic Sampling. Since I could not actually travel to Pascagoula, the tool of choice was Google Maps - Street View. This is an excellent proxy for actually being there.

Another tool I created and used was an Error Matrix. I conducted a virtual Random Sampling of my map starting at the North West border and working my way South to cover the entire study area and I marked as "True" or "False" the areas I had previously classified. I used Google Maps to confirm what I classified was correct - or not. I found that I miss-identified a few land types such as:

- A Commercial / Industrial area (Category 15, USGS Andersonian framework for national land use and land cover) - What looked like an Industrial area to me turned out to be a shopping center.
- A Residential area (category 11, USGS Andersonian framwork) - I correctly identified most of the residential areas, but one area I classified as residential turned out to be a cemetery!

My Overall Accuracy was 76%. Here's the map I created.

   

Network Analysis: Report Week for Tampa Evacuation

It has been a very long week. Lots of excitement and drawing of Storm Drains with Brevard County GIS Dept. I am learning a great deal and keeping busy. For this Special Topics assignment, I completed my work on the Tampa Bay Evacuation project by compiling a Pamphlet that could be used to inform relatives and hospital patients of the Tampa General Hospital of critical information in the event of an evacuation. Below is one page of the pamphlet I created.

Another Network Analysis I performed was to compute multiple evacuation routes from downtown Tampa to Middleton High School Shelter. This involved setting-up the Network Analysis to load or account for fifteen different locations in the downtown Tampa area. Here;s my results:

Another Network Analysis I ran was to generate multiple polygons to find the closest shelter, there were three to consider, for residents in the Tampa area. The analysis was interesting  because I cannot remember using this tool before though I do recall using the Multiple Ring Buffer. This was better. Here's my map for the Closest Shelter:

Land Use Land Cover: Pascagoula, Mississippi

Another fun week! This week I worked with the USGS Andersonian system for classification of  Land Use Land Cover. I continue to hone my skills for using, Shape/Size, Shadows, Patterns, and Association, for identifying various land features. In this exercise, we used the USGS Anderson framework for naming Land features. The area we examined was Pascagoula, MS. This location has loads of water features. Digitizing the water or the river, was my biggest challenge. I took a large cut at trying to draw my initial polygon around as much water as I could only to have the polygon distort or completely disappear. I finally got the Create Features to work by digitizing smaller areas at a time. This work can be tedious, but I enjoyed it very much. As it happens, I am working on a similar project at my internship with Brevard County, where I am digitizing and updating storm drain pipes throughout the county.    

Network Analysis Week 2 - Tampa Evacuation Routes

This week it was time to analyze our data from the Prepare Week for the Tampa Evacuation exercise. The objective was to find the closest shelter for residents in specific areas in Tampa. Additional objectives included finding the best routes to supply the Shelters from the Tampa Armory and where and how to best evacuate patients from Tampa General Hospital located on Davis Island.

I learned a lot using the Network Analyst Toolbar and the Network Analyst Window. I created a new Network Dataset and set-up the network using Time (seconds) as the Scaled Cost to be evaluated relative to the route selection. I also used the Flood Zones layer (grid code) to group the Scaled Cost to be evaluated in the route selection. I created additional polygons to form the three sections around the Shelter facilities where residents would evacuate to in the event of flooding. I believe this was the most important message to convey, not that a hospital evacuation nor delivery of needed supplies from the Armory aren't important, it's just that I felt informing thousands of potentially panicking people (the triple "P" threat) where to go for safe shelter seems more important to me. Below is the map I created this week.

GIS 4035 - Visual Interpretation

This has been another busy week. The Lab for Photo Interpretation and Remote Sensors was actually a three for one.

The first Exercise for Visual Interpretation was to use Tone and Texture to discern various features using an aerial photo. I used an aerial photo that showed features such as a runway, housing subdivisions, water and vegetation. Tone is defined as the  brightness or darkness of an area and the first part of the lab had us classify areas from very light, to very dark. Using the "Convert Graphics to Features" went smoothly for the first Exercise, but I had some technical challenges for the second Exercise. Another objective of this lab was to look critically at the features you are given in an aerial photo so that you can distinguish the subtle differences between organic and man-made features. Below is the map I created for Exercise 1:

  Exercise 2 consisted of Identifying Features by using Shape & Size, Shadows, Patterns and Association. The objective here was to use all available information to ensure you correctly identified the object or feature you were looking at. As I said above, I had some technical challenges with part 2 with the "Convert Graphics to Features" option. It  seemed as though each time I needed to convert my selection (Shape & Size, Shadows, Patterns and Association) the option was greyed-out and not available. I tried Starting an Editing session and Stopping Editing; I tried opening and closing AcrMap. I walked away for a while and when I came back, the Convert option was still not available. Somehow, I did get the Convert Graphics to Features to work....mostly. But I do not know why the option was greyed-out.  Below is my map for Exercise 2:

The last portion of the Lab, Interpreting Color, had us use two .tif's to compare the indicated color of features using True Color and False Color. False Color is also called Color Infrared. I found various features including land, water, healthy vegetation and distressed vegetation. The colors I found matched closely, but were not exactly the same as what the "legend" for "False Color IR vs True Color" stated in the Lab instructions.

GIS 4930 Special Topics: Project1 Network Analyst

I know I had a Summer break but where did the time go? It feels like it's been a very long time since I have done Lab Work, while at the same time, where did the Summer Break go to?

This week, I start GIS 4930 Special Topics, with a Network Analyst project. The project, is broken into three one week phases - good thing too.  So, this week, I begin with reviewing my data and commencing the "Prepare Week." The following weeks I will actually conduct the "Analysis" and write the "Report."

The scenario is that I have been hired by the city of Tampa Bay to conduct an analysis of roads that could be used for evacuation in the event a hurricane causes flooding. I will use the following information:

1. Classified DEM polygon layer (with appropriate color scheme)
2. Flood Zone layer with appropriate color scheme
3. Hospitals, police stations, fire stations, shelters, national guard armory
4. Roads

This week's objective was to organize and prepare by assembling and preparing all my data. I used a Clipping tool (MassClip) and Re-projection tool (MassProject) and I re-classified a DEM and converted it from a Raster to Polygon. The Python work was very cool as I just finished GIS 5103, Python Programming; I could actually understand and appreciate the Tools written using Python. The last part of the exercise was to make a map package and share my map with a colleague. This included writing an email to let this person know what I had done and solicit feedback. I think writing the email was a challenge since I did not know the level of detail required for this fictitious colleague. Here's what my "Prepare map" looks like at this point. I can always spend much more time on any lab and I am sure I will tweek this map as I conduct the next Phase of Project1 - Analysis Week.

GIS5103 - Sharing Tools

I have somehow made it to the last Module of GIS 5103 - GIS Programming, otherwise known as "Python."  I must admit, I have learned more than I could absorb and the journey has been a hoot. I have learned more about coding and how to use Python for GIS applications than I ever thought possible. 

In this last Module we covered how to create a tool and then securely share it. The basic process to make a script tool involves writing the script, making a toolbox and then "importing" the script to the toolbox. In this way, you can share the tool and even provide a password to securely transmit the tool to colleagues.  Below is the tool dialog window for the tool I created:

This tool takes an input feature class and adds random points then places a buffer around the points. When successfully run, the output of the tool looks like this:

Another cool thing we learned was the basic steps to make Python scripts or other files visible in ArcCatalog, First, you must add the file extension (PY in the case of Python files) to the File Types tab. First, select “Customize” from the main menu with ArcCatalog open; select “ArcCatalog Options” from the dropdown menu and then select the File Types tab. From this point you can select “New Types” and from the list, (I had no list and had to manually type in my file extension) select the file extension you want to add. 

As I said, I learned quite a bit during this course. I enjoyed the entire semester and all aspects of learning how to use Python. My challenge each week had been and remains, that I could spend countless hours going over a script and there always seemed to be at least one item that caused me to stall; I just could not get that last block of code to run properly like in Module 8, “Working with Geometries.” I was off to a good start with that script but I just could not get the darn thing to work properly.  On the other hand, one of the best things about this course was the “Discussion Board” and the “Helping Each Other” section of each Module. I don’t feel as though I posted too many questions, but I guarantee you that I read each and every question and answer, and that is the only reason I was able to accomplish as much as I did. I know we are all in this together and I couldn’t be happier about the company I am keeping nowadays. Thank you to everyone who posted a question and answer and occasionally “a glimpse of the working code.”  Good luck to all of us in our future GIS endeavors.    

GIS4048 Final Project-Brevard County Solar Project

This Final Project was awesome! This project was a lot of work but it was a great experience. The task was to conduct a Location Decision analysis for a subject of your choice. I was excited to tackle this project and I decided to do my Location Decision analysis for a proposed Solar Center in Brevard County. I was partially influenced to select this subject because I feel strongly that we should aggressively pursue Solar Energy production and the fact that I live in Brevard County.

My "client" was the "Special Project Coordinator for Brevard County, Mr. Estrella Sol (Star Sun, pretty clever, if I do say so myself). Brevard County, through new leadership, wants to become known as the "Solar Coast" as well as the "Space Coast." At least, that is the scenario I invented for this Final Project. In reality, FPLis building new Solar Centers in DeSoto, Charlotte and Manatee Counties, and I am saying that Brevard County wants to be a part of this opportunity.  The criteria for this Solar center is:

                                   #1 The parcel must be owned by Brevard County

                                   #2 The parcel must be within 1 mile of a major road

                                   #3 The parcel must be at least 1000 yards from schools

                                   #4 The parcel must be greater than 100 acres

                                   #5 The parcel should avoid disturbing areas of 

                                        environmentally sensitive lands 

For this project I used, Geoprocessing tools such as Clip, Buffer, Euclidean Distance, ModelBuilder and Weighted Analysis. The data I used was as follows:

 


I was also fortunate to have the Brevard County Appraiser website available for my reference. I was able to view aerials of the Parcels I was considering for this project.

After establishing the environment and setting my projection, I used a couple of Select By Attribute searches to limit the field of possible Parcels for consideration; Brevard County has 287,853 parcels . I selected Parcels owned by Brevard County (Select By Attribute > ONAME = Brevard County; this gave me 1806 parcels) and then Parcels greater than 100 Acres (this left me with only 48 parcels). Then, I used ModelBuilder to run  a process to find the Parcels that are near (within 1 mile of) a Major Road, I-95.

  
The last portion of the Project Analysis was to create a Weighted Analysis model. I used the Feature to Raster tool on three datasets:

Parcels_100Acres to ConvertParcelsBC100

MajorRoads_I95 to ConvertMajorRoads

BC_schools_selection to ConvertSchools

The Weighed Analysis model produced a map that seemed to favor the largest parcels, but it was not as conclusive as I had hoped.  

Finally, I was able to use the Brevard County Appraiser's website to review aerials of several of the Parcels. I found some Parcels that looked like they met the criteria, but the aerial image showed that the parcel was actually underwater; easy to eliminate that parcel from consideration. In the end I was able to find three great parcels that met all the criteria including avoiding sensitive lands.  Here's my final map and a link to my final presentation:

This exercise and the entire course of GIS 4048 Applications in GIS, has been very illuminating. I thoroughly enjoyed studying the different GIS applications such as HomeIand Security, Natural hazards and Urban Planning. Once again, I feel like I could have spent even more time working on this final project and all of the projects throughout the semester. If I had more time on this Final Project, I could have provided a written report that included all the information from the Brevard website and I believe this would have been greatly valuable to my customer, Mr. Estrella Sol.  Also, if I had found data about soil type and slope, that too would have been very useful. We take for granted, living in Florida, that slope is not a factor, however, I know, for instance, that in the Tallahassee area “rolling hills” seem to be the norm. A solar site on the wrong side of a hill would be disastrous (better use hillside shading for this analysis). I know I have learned a lot from this course; now my challenge is to continue to use the skills I have gained. To that end I am doing volunteer work with the Brevard County GIS Department and I hope to find full-time employment in the not-too-distant-future.