GIS 4006 - Module 12 - Neocartography & Google Earth

For this weeks module 12 we were tasked with utilizing our map deliverable created in module 10, the population dot density map of South Florida, and uploading it into Google Earth. This module was completed using ArcGIS Pro and Google Earth Pro. Layers were converted in arcgis pro using the layer to KML tool. I was able to convert my dot density layer after converting it to to a a single point layer by regenerating a new set of random points based on a newly calculated population field of one dot representing 20,000 individuals. Legend elements and extent frame were sniped using windows sniping tool and uploaded into Google Earth Pro as image overlay elements. A tour was created that visits the following place markers in South Florida with the 3D buildings feature enabled: Miami Metropolitan, Miami Downtown, Fort Lauderdale, Tampa Bay, Saint Petersberg, and Tampa Downtown.

A discussion post was made by me in regards to the future of GIS and volunteered graphic information.
GIS as a science is continuously evolving as more and more users become active participants in GIS data gathering techniques. One of such techniques is volunteered graphic information, VGI. This geospatial data is being uploaded by an enormous amount of people via the internet at an ever increasing rate. I personally am a fan of VGI and any improvements in general to GIS sciences and applications but it must be noted that VGI comes with some drawbacks, such as data integrity. Some work arounds to inaccurate public data could involve an administrative review or peer review process. I could see a system that implemented a ranking system based on data quality being a good source of VGI content.

GIS 4006 - Module 11 - 3D Mapping

This weeks module 11 had us mastering the art of 3 dimension feature creation utilizing Arcgis Pro and then transferring data into Google Earth Pro.
The first part of this module involved completing a 3d Visualization Using ArcGIS Pro course on ESRI's website. This training course gave a great overview of 3d mapping basics and walked me through three exercises involving: Craterlake, Oregon , Downtown San Diego with extrusions rendered as non realistic and realistic from different view points.

A Boston buildings feature file was then extruded to have elevation values. This was achieved by generating random points within the building shapefile (34,300 points), adding surface (elevation) information to those points, and summarizing per building (100 points per
building, 343 buildings). This generated a 3d feautre scene of the cityscape of Boston that was saved as a KMZ file and uploaded into Google Earth Pro.

Importing 3d layers into Google Earth has many useful applications but two that come to mind would be construction projects and delivery routes for businesses. Utilizing Arcgis pro to convert 3d scene features to KMZ files and uploading them into Google Earth would be an easy way to get information out to potential clients or customers that do not have access to gis specific software, such as arcgis pro. Google earth is accessible by anyone for free and has a much simpler user interface learning curve when compared to arcgis applications.

Finally a comparison of Charles Joseph Minard's map-graph was conducted on the original and a 3d representation of Napoleon's march to Moscow. The original shows the decreasing size of the Grande Armée as it marches to Moscow using a clever use of mapping techniques to not only show a geographic representation of where Napoleons army marched to, from Kowno to Moscow, but it also shows a symbolic representation of the size of the army through the use of diminishing weight. When viewed in 2d and 3d these elements are apparent, but 3d presents this information in a more dramatic way. Giving the army route a Z value allows a user to view the decline of Napoleon’s army in weight thickness and in depth, all from a birds eye view. The 3d map definitely pulls off an effective theme but it should be noted that the 2d map presents a far more legible account with more details, such as river names, city names, and temperature all being visible at a glance. I personally prefer the original 2d map by Minard.

GIS 4006 - Module 10 - Dot Mapping

For this weeks module 10 we were tasked with creating a dot density map of South Florida based on information supplied by the U.S. Census Bureau. Dot density mapping involves the use of dots as symbols to identify one or more occurrences. In the case of my map 1 dot is equivalent to 20,000 people. The number of dots change in proportion equivalent to the population field in the supplied census data excel file.

The major advantage of dot mapping is that it is an intuitive concept that a viewer with no map experience can comprehend. A quick glance at the legend to confirm the dot symbol representation and a viewer can see on the map where dots are grouped closely and where they are spread apart. These areas would intuitively register as high and low population areas to an individual with no mapping experience. Another advantage is that dot mapping shows how land is used. In the case of a population map high concentrations of dots indicate a highly populated.

Disadvantages to dot mapping would involve the appearance of the dots themselves. Dots that are weighted to low would barely be visible and dots weighted to high would overlap and obscure. Dots can also be misunderstood if a viewer does not acknowledge the legend. A single dot could be understood as being equivalent to a single person in the case of a population dot density map like in this module.

GIS 4006 - Module 9 - Flowline Mapping

For this weeks module 9 we were instructed to create a flowline map that highlights the immigration values for each continent of the worlds total immigration to the United States for 2008. This data was supplied by the US Department of Homeland Security. The type of flowline map that I created falls under the category of distributive flowline maps. This type of map handles data that is quantitative such as immigration values. Using adobe illustrator I was able to customize this flowline map with many appearance and style effects such as, inner glow effects to title and flowlines, 3d extrude and bevel effects to flowlines, and drop shadow effects to flowlines and continents. All of these effects allow for better visualization of map elements. Other essential map elements were added while adhering to cartographic design principles.

GIS 4006 - Module 8 - Isarithmic Mapping

For this weeks lab, module 8, we were tasked with creating a map that utilizes continuous tone and hypsometric tinting in order to display precipitation data for the state of Washington over a period of 30 years. 
A continuous tone symbology is one that is classless and represents individual points as colors or tones corresponding to values. This symbology is implemented into the Washington Precipitation map as square inches of rainfall with orange tones indicating low values and blue tones values indicating high values.
Hypsometric tinting is a symbology method for enhancing elevation. By assigning colors or tones to contour lines or lines of equal elevation relief can be visualized easier. Colors chosen are typically tones or shades for ground cover found at that given elevation. 
The PRISM group at Oregon State University compiled this point based data set for the entirety of Washington state from 1981 - 2010. The attached map was created utilizing arcgis pro and includes all essential map elements while taking into account cartographic design principles. 

GIS 4006 - Module 7 - Choropleth Mapping

For this weeks Module 7 we were tasked with creating a choropleth map for wine consumption in Europe. A choropleth map is a themetic map in which areas are shaded or patterned according to a their measurement value. For this map population density for Europe is represented by an orange color gradient scale with lighter colors representing lower pop. dens. values and darker colors representing higher pop. dens. values. I chose a manual interval classification method as I was able to set the class ranges based on attribute table data. Wine consumption is represented by a proportional symbology scale where smaller purple circles represent low wine consumption and larger purple circles represent high wine consumption. Utilizing arcmap and adobe illustrator I was able to apply these two forms of symbology along with all necessary map elements. I found arcmap to be more useful than arcgis pro when it came to labeling the countries as arcmap was able to perform labeling far faster than arcgis pro.

GIS 4006 - Module 6 - Data Classification

For this weeks Module 6, we were tasked with displaying data for Dade County, Florida in four different classification methods for symbology: Natural breaks, equal interval, quanitle, and standard deviation. Two maps were created that display the percentage of individuals above 65 for the Dade County area and the number of individuals above 65 normalized by the sq. miles field. I chose a color gradient that reflects increasing values through a "hot spot visualization" with lighter colors reflecting lower values and darker red colors reflecting higher values. Each of the four classification methods separates classes based on a unique calculation method but I feel quantile resulted in the best map for visualizing hot spot areas for senior residency. I would also present this information as a percentage as this allows for easier user comprehension and visualization.

GIS 4006 - Module 4 - Cartographic Design

For this weeks Module 4 we were asked to create a map of Ward 7, Washington DC that highlights the location of public schools. The goal of this weeks module was to create a visually pleasing map theme that allowed for the display of all map elements to be clear and legible. Utilizing Arcgis Pro I was able to just that. My map incorporates all aspects of Gestalt's principles and all essential map elements are present and positioned appropriately. Some features that I chose that made my map more visually appealing include but are not limited to: ordering the drawing order so that no focal elements are obscured, muted color choice that avoids sharp contrast, road features colored with varying degrees of gray % to appear visually distinct from the county background, and use of a white, 2pt halo around neighborhood labels to make them appear visually distinct from background elements. Overall this weeks module was a refresher for me but I did take away valuable knowledge in learning and understanding Gestalt's Principles of Perception. I feel like this knowledge will be valuable, not only in GIS, but in all design oriented projects.

GIS 4006 - Module 3 - Typography

For this weeks Module 3 lab we were tasked with creating a map using adobe illustrator arcgis pro that highlights 17 features in an area of the Florida Keys known as Marathon. After researching Marathon on the wiki page, (https://en.wikipedia.org/wiki/Marathon,_Florida), I learned that Marathon’s name originates from the Florida East Coast Railroad and that the Florida Keys historian Dan Gallager in his book "Florida's Great Ocean Railway" credits New York playwright Wiiter Bynner for naming Marathon.

The theme for this map is as follows; Font: Berlin Sans FB Demi Bold, Size: 18, Color:Town-Yellow, Airport-Red, Statepark-Green, Countryclub-Blue,  Stroke: White.  All features for this module were labeled using this technique with varying font size/color and leader lines. Leader lines, for this map, were drawn with the pen tool in adobe illustrator and the type on a path tool was used to type Font: Berlin Sans FB Demi Bold, Size: 12, Color: Aerial-Black and Hydrographic-Italicized Blue, into the corresponding features GPS coordinate locations.

Features that make my map unique would be a drop shadow applied to the marathon islands feature layers, a color coded font that aids in the location of Town, Airport, Statepark, and Country Club features accompanied by a color coded legend, and, finally, an aesthetic use of symbols, colors, and border frames to highlight a beautiful area such as Marathon, Florida.

GIS 4006 - Module 2 - Intro to Adobe Illustrator

For this weeks module 2 we were tasked with customizing our module 1 maps of the state of Florida. This weeks module allowed me to familarize myself with much of adobe illustators tool set and I feel like this intro lesson will be useful later on.

I used scripts in this lab exercise to replace all major cities symbols with the symbol for a primitive hut. I did this by selecting both the major cities object and the hut object from the layers panel and ran the supplied script. I also applied this same method to the capital object in order to replace it with a primitive sun symbol.

I added a 1.0 neatframe by drawing a rectangle and adjusting the stroke width and color. I also added a drop shadow to my counties object by selecting it in the layers panel and then choosing the effect tab > stylize > and drop shadow. I added images of Florida’s state flag, salt water fish, and fruit from the web. A custom north arrow was chosen from ArcGIS symbols and a legend was created with corresponding map symbols and elements.

GIS 4006 - Module 1 - Map Critique

For this weeks assignment, Module 1, we were tasked with finding two maps, one that exemplifies a well-designed map, and one that exemplifies a poorly designed map. I chose the following two maps and wrote the following evaluation synopsis for each.

Well-designed Map
Title: National Elevation Data Set Shaded Relief of the United States
Author: United States Geological Survey (USGS)
Theme: Elevation for the United States

I chose this map for the well-designed selection because I feel it conveys the best sense of topography elevation in a simple, clear manner. This map may lack map elements such as scales, legends, and quantitative data but I feel like that information is not necessary to accomplish what the original authors were trying to convey, a good representation of the United States elevation. I believe the first three Tuftesisms represent this map:

1. Graphical excellence is the well-designed presentation of interesting data – a matter of

substance, of statistics, and of design.

2. Graphical excellence consists of complex ideas communicated with clarity, precision, and

efficiency.

3. Graphical excellence is that which gives to the viewer the greatest number of ideas in the

shortest time with the least ink in the smallest space.

Poorly designed Map

Title: N/A

Author: N/A

Theme: Terrain and Collective Groups of Fantasy World.

 

 I chose this map for the poor design selection because although it does convey its intended theme it was assembled in an uncreative and artistically poor manner. The look and feel of this map is achieved minimally using an ordinary Mercator projection, which I would attribute to a lack of creativity. The setting for this map may be fictitious but that does not disqualify it from being a well-designed map. This map fails the following “Tufteisms”:


1. Graphical excellence is the well-designed presentation of interesting data – a matter of substance, of statistics, and of design.

2.Graphical excellence consists of complex ideas communicated with clarity,
precision, and efficiency.

And

20. The revelation of the complex. 

Here is an example of a fictitious map where the artist clearly devoted an enormous amount of artistic detail into, which can be appreciated.

GIS 4006 - Introduction

For the start of spring semester 2019 I have enrolled in the GIS 4006 course Computer Cartography to continue pursuing my minor in the UWF GIS program.

I have a bit of experience with the GIS program here at UWF and I can say that, overall, it has been a pleasing and challenging learning experience. In fact, the last project I did for special topics involved mapping food desert areas for Navarre, Fl which was interesting to apply GIS skills to learn something about my own home town.

My future goals involve pursuing a masters and eventually a job related to Geology/GIS for the U.S. Geological Survey.

Here's a link to my Story Map (Links to an external site)

 

GIS 4930 - Project 4 - Navarre Food Desert Analysis

    A food desert can be described as an area in a community that has succumbed to health degradation as a result of no nearby nutritional sources.

    For the final portion of Project 4 we were tasked with assembling all of our food desert data into one concise power point presentation dedicated to highlighting our food desert area and illustrating how it is effecting the community. I chose my hometown of Navarre, Florida for the study area to my Food Desert Analysis. Navarre is approximately 84.83 square miles in area and 9 grocery stores were identified for this analysis.
GIS programs involved in this analysis included utilizing: ArcMap, Qgis, Leaflet, and Mapbox. Methods involved creating shapefiles for food deserts and grocery stores in Navarre. Shapefiles were uploaded into Mapbox as tilesets and displayed in Leaflet. Arcmap was used to create the study area map. The most challenging technique in this project involved having to create the Navarre food desert shape file. A new Near.csv to reference object id’s within 1 mile and unique to Navarre had to be created along with a new set of centroids.
The data being displayed indicates that grocery stores in Navarre are restricted mainly to highway 98. This restriction creates food deserts the farther north you travel from highway 98. I used my local town and the data does not surprise me. I knew that most grocery stores were located either directly on highway 98 or within at least 1 mile.

Food Desert = 51.67 % of total population
Food Oasis = 48.32 % of total population

    From these results one might conclude that Navarre is suffering from a food desert issue but this is not necessarily true. Yes the majority of land cover in Navarre is designated as food desert but the accessibility of highway 98 needs to be taken into account. All of these grocery stores are located less than a mile from highway 98 the main life line of Navarre. The accessibility of these grocery stores allows for Navarre to not be effected as drastically by food desert areas in regards to health and well being. 

    Navarre is a rapidly growing tourist driven town. There is no way that Navarre is suffering from negative health effects brought upon by inner community food desert intensification. This analysis has revealed that Navarre is majority food desert by total population but the accessibility of grocery stores off sets these food deserts by being located directly on Highway 98, the life line of the Gulf Coast. This analysis has shed light onto areas that can potentially benefit from grocery store construction in Navarre, Florida but like I said before Navarre is not under a food desert threat.

GIS 4035 - Final Project - Estuary Comparison - Sediment Deposition

For the final project of Photo Interpretation and Remote Sensing we were tasked with performing manual or automated image processing techniques on remotely sensed data. The landsat imagery for this project was obtained from USGS.com utilizing their GIS database known as Earth Explorer. The imagery I chose was captured by the Sentinel II satellite. Preprocessing techniques for this project involved clipping, scaling and visualizing the imagery to assess for estuary location, bay shape, water color gradient, and tidal influx/outflux.Programs utilized to perform this analysis included Erdas Imagine and ArcMap. Utilizing Erdas Imagine I was able to perform a supervised classification for the study areas of Pond 6 and Saultsman Cove.
 

Utilizing ArcMap I was able to create the following map deliverable, a combination of two maps showing study area, supervised classification, state extent, and spectral Euclidean distance for the two selected estuaries. An overall dark Euclidean distance output being indicative of pixels that have a higher likelihood of being classified correctly. My map, displayed below, contains not only all deliverable map elements including scale bar, north arrow, study area extent, legends, credit, and author, but also class area and total area derived from tables created and imported from Excel after analyzing attribute table data added in Erdas for each respective study area. In conclusion, I found this project to be an accurate assessment for testing an array of GIS skills related to land use land classification. In performing this sediment deposition analysis I have learned that by analyzing pixel coloration one can determine sediment deposition accurately. Sediment deposition rate was assessed for by analyzing the area values for high, medium, and low sediment classifications for each respective study area, and Pond 6 was found to have a higher chance at silling up the quickest.

GIS 4930 - Project 4 - Analyze 2- Food Deserts

For this weeks portion of project 4 we were asked to create a webmap in leaflet for food deserts in an area of our choosing. I chose Navarre, FL, my hometown as the study area for my food desert analysis.
I obtained my data using www.fgdl.com I searched florida census and downloaded

       2010 U.S. CENSUS BLOCKS IN FLORIDA

CENBLK2010_AUG11

                

The extent of the processing I did involved clipping the data to a shapefile of santa rosa county then selecting for all object ID’s within Navarre in order to create a study area shapefile.

For the grocery store shapefile I used ArcGIS and the coordinate inquiry box in order to zoom to the coordinates of all grocery stores in Navarre. I obtained coordinates using google maps. Once the coordinate inquiry located  a grocery store location I would put a point using the ArcGIS editor on a newly created navarregrocerystores.shp.

The data being displayed indicates that grocery stores in Navarre are restricted mainly to highway 98. This restriction creates food deserts the farther north you travel from highway 98. I used my local town and the data does not surprise me. I knew that most grocery stores were located either directly on highway 98 or within at least 1 mile.

http://students.uwf.edu/atg6/GIS/navarre_fooddesert.html

GIS 4930 - Project 4 - Analyze - Food Deserts

For this weeks portion of Project 4 we were tasked with taking the food desert and grocery store shape file data we created last week and uploading them into two open source web map making programs, Mapbox and Leaflet.
Using mapbox I was able to design a layout with a graduated symbology split into five classes based on  fooddesert.shp's pop2000 field.

Using the quickstart guide, and the project procedures I was able to create a marker displaying 2 text lines, a circle in a food oasis area, and a 4 coordinate polygon in a food desert area.

Some problems that I ran into over the course of this weeks project include: not being able to locate the correct mapbox to leaflet link required to transfer the maps tileset. Therefore my fooddeserts and grocerystores files are not displayed on my leaflet map.

Link to mapbox map displaying tileset

GIS 4930 - Project 4 - Prepare - Food Deserts

"food desert"     
an urban area in which it is difficult to buy affordable or good-quality fresh food.
-dictionary.com

For this weeks portion of project 4 we were tasked with preparing two maps, using Qgis , showing areas in Escambia county that would be considered food areas or food deserts. Areas were subjected to a proximity selection based on nearness to grocery stores. The following deliverables were created in qgis: a map showing Escambia county and UWFs location and a map showing food deserts and food oasis with the project 4 study area as background. The following population statistics were calculated : food desert 60.14% of total pop, food oasis 39.85% of total pop.

GIS 4035 - Module 10 - Germantown, MD - Supervised Classification

This week we were tasked with another image classification project. For this project a 57.42 square mile study area located in Germantown, Maryland was subjected to a supervised classification. The following classification consisted of a recording to 8 categories. The 8 categories are as follows: Urban, Grass, Deciduous Forest, Mixed Forest, Fallow Field, Agriculture, Water, and Road. The following features were selected for visually and based on provided coordinates using a drawn polygon method and the region growing properties > at inquire > spectral euclidean value method. The spectral euclidean value and the neighborhood value were adjusted accordingly to obtain features in which there was minimal spectral overlap.
The attached map deliverable highlights the 8 feature categories and their corresponding areas in square miles. The Spectral Euclidean distance map shows a representation of spectral Euclidean distance with brighter areas being represented as values with a larger pixel difference thus having a higher likelihood for wrong classification. The inset spectral Euclidean Distance map is overall dark indicating correct classification.

GIS 4035 - Module 9 - Unsupervised Classification

This week we were tasked with performing an unsupervised classification of our very own UWF campus here in Pensacola, Florida. The unsupervised classification was ran using ERDAS.
UWFclass50.img was created with the following settings : classes 50, RGB 321, maximum iterations 25,
convergence threshold 0.950, skip factors x: 2 y: 2.  Attribute table data was opened and the following category names were applied to the following pixel features in the UWFclass50.img:
Trees – dark green, Grass – green or chartreuse, Buildings/Road – grey,
Shadows – black, Mixed – light green
All 50 classes were recolored and renamed T, G, B, S, or M
Area was manually calculated and determined to be .90 square miles. Impervious percentage
calculated as 25% and Pervious percentage calculated as 75%.

GIS 4930 - Project 3: Analysis - Statistical Analysis of Methamphetamine Laboratory Busts

For this week's portion of Project 3 we were tasked with running an Ordinary Least Square regression on our data compiled in the previous week.

An ordinary least square regression method involves comparing a dependent variable (meth lab density) and explanatory (independent) variables. The explanatory variables consist of census data and the following variables were chosen: Males, Females, Age_18_21, Age_22_29, Age_30_39, Age_40_49, Age_50_64, Age_65_ Up, Hsehld_1_M, Hsehld_1_F, HSE_Units, and Vacant. The following variables were chosen based on review of the following three resulting values: Probability, Coefficient, and VIF. Values with probability > 1.0 were immediately removed as these values indicate low statistical significance. Variable coefficient values were taken into account as they indicate the relationship between the dependent variable and the explanatory variables indicating a strong or weak relationship. VIF values indicate overrepresentation or overlapping data amongst similar explanatory variables. Variables Males, Females, and HSE_Units all had a high VIF value as they were overrepresented in the census data. This does not necessarily denote a negative aspect seeing as how all participants had to choose one of two choices, Male or Female. This overrepresentation indicated from high VIF values is to be expected.

Attached is my results for OSL results displayed as STDResidual on the study area map and my OSL results in table form