Napa's Best Sales Territories and Routes

This was an interesting project for a ficticious company based in Napa County.  We created three sales territories using a variety of methods with the goal of equal distribution of sales prospects in terms of potential sales and number of prospects. 

We then created a route map for the top ten highest sales potential prospects for each sales territory starting from the salesperson's home.  The default map was then compared to a optimum route map created using Network Analyst. 

Napa's Best Wines Final Presentation

Better Books new store site selection - Report

Following is the link to my final power point presentation.  This was an interesting project for an area in which I lived.  I lived and worked on the Presidio of San Francisco and am very familiar with potential new store locations and existing store locations.  Better Books and the sales data was ficticious; however, the demographics came from 2003 Census Data. 

I would have liked to delve more into the marketing analysis of this project, e.g. which demographics most affect sales, how to interpret sales figures but just didn't have any more time.

Better Books Site Selection Presentation

Market Analysis & Available Sites

Better Books Market Analysis, San Francisco

Marin City Center - Part 3

This was a very interesting project that would have been amazing with some field work or some more data.  We first analyzed the land cover within the Marin City by trees, grass and impervious features using orthophotographs.  Carbon storage and sequestration was also analyzed based on some specific census block neighborhoods. 

The final portion of the project provided a conceptual plan of the city center.  We attempted to justify the expenditure for the new city center in conjuction with a tree planting project.  The goal was to create a plan that would offset the annual utilities of the city center complex by at least 50% with the energy savings of trees.  There was not nearly enough space within the less than 8 acre city center complex to plant enough trees to offset the utuility costs, so the remainder of trees would need to be planted around the city or on an additional piece of property purchased by the city. 


Marin City Center Final Report

Marin City Tree Study

The intent of the project was to convince the City of Marin to support the planting and maintenance of trees in areas that would benefit economically and environmentally to qualify for a federal grant under the Small Business Environment Stewardship Assistance Act of 2010.  The city would have to commit to a 25% match of the grant.

In this project I analyzed the land cover for selected neighborhoods within Marin City.  An 1 meter othorographic image was manually classified into 50 different classes of trees, grasses, and impermeable surfaces (buildings, pavement, etc).  The land use layer was then reclassified into 3 classes (trees, grass, impermeable). 

I determined the acreage of each class and the percent of trees for each of 5 neighborhoods.  Using the CITYgreen average mulipliers, I calculated the carbon storage and carbon sequestration rate.

Asthma Hositalization - San Francisco Bay Area

This was a three week project to identify a target population susceptible to asthma attacks requiring hospitalization in the San Francisco Bay Area.  Analysis was done using Census data for demographics and data on Ozone and Particulate Matter.  Alameda County was identified as having the highest Asthma hospitalization rates among the 9 counties in the Bay Area especially among the large African American population in the County.  This map shows the 4 area hospitals located near the most dense black population areas. 

Here is a link for a power point showing the steps I took in the analysis process and the many maps I created along the way.

http://students.uwf.edu/lrp7/Asthma.pptx

GIS & Public Health Metadata

Here are the links to the layers created or edited this week.

http://students.uwf.edu/lrp7/ams_metadata.htm
http://students.uwf.edu/lrp7/co_ozone_metadata.htm
http://students.uwf.edu/lrp7/co_particulate_metadata.htm
http://students.uwf.edu/lrp7/asthma_metadata.htm

LiDAR - Pensacola Beach, FL

This assisgnment was created in ArcMap instead of ERDAS Imagine so the process was much easier and enjoyable. 

ERDAS Imagine - Supervised Classification

ERDAS Imagine - Orthorectification

http://students.uwf.edu/lrp7/final_orthorect.xps

Major software crashing problems to get this exercise done. 

ERDAS Imagine - Spectral Bands Basics

http://students.uwf.edu/lrp7/Wk2Map1lrp7.xps
http://students.uwf.edu/lrp7/Wk2Map2lrp7.xps

http://students.uwf.edu/lrp7/Wk2Map3lrp7.xps


This assignment identified natural features by examining spikes in the histogram of multispectral imagery.  I'm still fighting the learning curve of the ERDAS Imagine learning curve.   Repetition is the key.   I created each of these maps three times.  First two because of my error of omission, third because the software decided to rotate my image and cut off my scale bar and bottom text.

ERDAS Challenge Assignment - Pensacola

http://students.uwf.edu/lrp7/Pensacola_lrp7.xps

This link is to the first "challege" map using ERDAS Imagine.  This assignment started out with flashbacks to Adobe Illustrator.  Not user friendly software.  Once we got the instructions to mirror what the server was giving us, things went more smoothly.  Looking forward to being an "Imagineer" and enjoying this new software.

Final Project - SAT Scores & Participation

SAT scores and participation by state based on college bound seniors in 2009.  Maximum score for the SAT is 2400.  These statistics do not represent the education quality within a state because of the extreme variations in participation.

Google Earth Project - Wind Farm Analysis

This project was to evaluate a wind farm near Bowling Green, OH then choose an appropriate site for a wind farm on the Great Lakes using Google Earth.  There were lots of links with additional information about average annual wind speeds, bird migration, etc.

Google Earth shows two wind machines located near a landfill 6 miles southwest of Bowling Green, OH. There are two residences between 1200 and 1500 feet from the wind farm. There are approximately 20 other structures within a .5 to .75 mile buffer. The average annual wind speed is between 6.5 and 7.0 mph. The elevation is 677 feet.

I couldn’t find any complaints from locals about the Bowling Green wind farm on the listed links. I assume there was a fuss at some point or the Department of Health study would not have been created. The Department of Health was very thorough and addressed every conceivable concern.

I selected a site 6 miles east of Caseville, Michigan (population 835) based on my site assessment index from the BERR planning criteria.

- Average annual wind speed of 7.5 mph--the highest in the state.

- Elevation of 619 was similar to Bowling Green

- Nearest residence to the site was approximately .5 miles with very few homes within a 1 miles radius.

- The large building near the site appears to be some type of agricultural processing plant with a reservoir. The plant could also be for livestock and the reservoir could quite possibly be for manure. If that is the case, the smell factor would make it similar to Bowling Green’s landfill.

- No shipping ports or routes are near the site

- Positioned approximately six miles from the coast so the beauty of the coast and natural habitats will not be affected.

- Bird migratory patterns for this area are light although there are numerous species in the region.

- Electrical output from two 1.8-megawatt turbines could provide for the electrical needs of the village of Caseville and rural area around the wind farm site.

I saved two different map scales in Google Earth then opened the .jpeg in Ai so I could add some wind contours, scale and additional text.

Isohyet Precipitation Map of Georgia

This was overall a fun lab.  I finally figured out the pen, pencil and blob strokes in Ai.  Lots and lots of practice.  My first map had gaps between the polygons.  The only advantage with that approach was the labeling on the polyline is clearly understood.  The labeling on the above map can be misinterpreted for the area above or below the line. 

I decided to color the map to clearly identify the precipitation ranges.  I selected and filled all counties with a color corresponding to the contours.  Then I created a new layer--blobs and went to work trying to color within the lines.  I blobbed over a bunch of county lines and couldn't move the lines above the blobs since the county fill colors would have had hierarchy, so I copied the county outlines and added them into a new layer above blobs.

I had some issues with sizing this map.  The original document seemed to have some oversized boundaries.  I found two extra scale bars lost below the state boundary.  I was able to re-scale the basemap to 150%.  I added a neatline which was clearly visible in Ai but did not show up on the export??  I'm not totally satisfied with my polygoning in the northeast part of the state but overall I think the final output is good.

Bonus Exercise

I had a difficult time choosing what map to try to “improve” with the examples given in the ESRI link. I chose the thematic map using 3D symbols. I chose my map from the proportional symbol lab not because I was dissatisfied with the outcome but because I wanted to try some new techniques.
In an effort to create 3D symbols on the map, I had to transform my data from linear to volumetric. I added a new field to the layer and calculated the field with the following VB expression:

       (([field] / 3.141592) * 0.75)^(1/3)

This created a range of numbers from 0 to 20. I was then able to symbolize the layer using either proportional or graduated symbols. I was not able to get a decent looking range of symbols using graduated so I chose proportional symbols. I still wasn’t satisfied with how ArcMap handled proportional symbols so I converted the symbols to graphics and customized the sizes to better suit the data. In the end the symbols did not look anything like 3D.

I created a simple legend by copying a symbol in data view and pasting in layout view then moving it into place. Again, my symbols looked nothing like the 3D examples but I liked how the text was displayed and how the symbols were slightly grouped. The symbols in the legend did form a hierarchy of first one copied and pasted was on the bottom. I tried using the graphics toolbar to move a symbol forward or backward without success.

The final technique I tried was deemphasizing the labels for the countries with very low consumption by changing to a lighter color. I converted the labels to annotation and selected a 70% gray color for the countries with low wine consumption. I think this simple technique worked well.

Although this exercise wasn’t a complete success, I did learn a couple of great new skills that may be more useful in a different application. I also now believe Adobe Illustrator is the way to go for a map like this.

Wk 9 - Immigration to the US Flow map

I'm actually starting to like Adobe Illustrator;)  This is a flow map showing immigration to the U.S. in 2007 by region.  I chose to break up the Americas region becuase it accounted for more than 40 percent of the total immigration and it was distributed  into 5 sub regions.  I experiemented with various projections in ArcMap and finally settled on Mercator.  I wanted to color ramp the regions so I added a "region" field in ArcCatalog then classified each country. 

 I wasn't able to master using the Pen tool in Ai but I found success with the Arc tool.  I still can't figure out how to draw text along a path; however, I'm not sure it would have been the best solution to identify the line values.  The add arrowhead feature in Ai is slick.  I used different percentages for different sized lines to get the look I wanted.  I'm definately a happier camper when I first get to control the data in ArcMap and use Ai for special affects and polish.

Wk 8 - Dot Density Map - Florida Housing Density

This map was created with Adobe Illustrator from an image provided.  This image wasn't of good quality and really needed the coastlines generalized so the complex geography wouldn't end up just a blur of ink. I was able to scale the image to 150 percent.  I tried to scale beyond that and crashed Ai everytime. 

I attempted to follow the lab instructions by taking the county with the highest housing density and adding as many dots as possible so dots just begin to coalesce.  The other requirement was all counties must have a dot if there is data.  Since, according to my calculations, Liberty County had 3.74 housing units per acre, the largest dot value couldn't exceed 3.74.  That meant 211 dots had to go into Pinelas County.  I wasn't close to fitting that many dots, so I copied and off set the four counties with the highest density and expanded them by 100 percent.  I also increased the dot size by 100 percent for those four counties.  This allowed me me fill the counties according to good dot density placement while still being able to see a single dot on the map.  Bi-focals didn't help this project.  There are 1807 dots on this map.

I chose to leave the three filters (wetlands, lakes, rivers) off my finished map.  I think the filters detracted from the finished map and made it difficult to see the pattern of the dots.  I'm glad this lab wasn't earlier in the semester--it definately made me feel like I earned a Spring Break.

Wine Consumption in Europe - Proportional Circle

This was a project I could tip my wine glass to. Love Wine! I am continuing to gain control of Adobe Illustrator but still lacked several skills to achieve what I wanted on this map. My skills with Excel have also improved allowing me to work quickly through the calculations. Data for Germany was missing from the spreadsheet so I went to the Wine Institute web site. The data didn't match up exactly with what was provided but it was close enough for this type of product. I also discovered the consumption was based on hectoliter (000). I searched on-line for a quick conversion and learned the French drink nearly a billion gallons of wine per year. 

 I used the suggested Europe Lambert Conformal projection.  I selected a color ramp for the countries from ArcMap to go with my red wine theme and only made a few modifications in Ai.  I lost a neat line somewhere within my layers.  I will definately spend more time on organizing layers as suggested.  I am having issues with controlling the area surrounding my map when I export a file.  The white border around my neatline is often uneven.

Population Change in the US by Census Division

This map was created in Adobe Illustrator from a copy of the Population change by state map.  I had selected a gray scale from the swatches layers in AI before I read the instruction about using the edit color command and choosing gray scale.  The way I used the gray scale was more difficult but had the same end result. I chose to label the divisions to clarify the four divisions from North Central to New England.

The challenge for this project was manipulating the data for the percentage of change and class breaks in Excel.  It wasn't difficult just very time consuming creating the Census Divisions from the list of states.  The data is broken down using equal interval classes.  Equal interval is a poor choice for this data because it leaves one interval class unused and lumps four other division that are colocated into one class.  I wasn't sure how much liberty to take on decimal points in the percentages.  I wanted to use two decimal points but would have ended up with a duplicate to and from number so I ended up with two and three decimal points.

I learned from the reading to not have any spaces between the color blocks in the legend.  I'm curious if ArcMap gives that option in the legend styles.

Choropleth Map of Population Change by State

This is a choropleth map depicting the population change in the US by state based on 1990 and 2000 Census data.  Most of the work was done in ArcMap then the file was exported to Adobe Illustrator for some finishing touches.  The CONUS map was created using WGS 84 projection.  I created a new data frame for Alaska and used NAD 83 Alaska Albers projection to get a better shaped feature. 

The data is broken down using natural breaks.  Is it "Viva Las Vegas" that is causing the population boom in Nevada?  I'm still experimenting with exporting/importing my final image.  I tried to follow Jennifer Slye's suggestion of using Picasso but haven't figured out how to copy the html data.

I'm slowly getting the hang of Adobe Illustrator and was able to create some lead lines for my state labels.  I really like the color options with AI.

Hispanic Population in Southern Florida

This map was created with Adobe Illustrator.  This was a challenging project to balance the three shapes on the page while placing the emphasis on the chorpleth map of Southern Florida.  My lack of skill with AI is still keeping me from achieving a final product for which I am satisfied.  I am staying positive as I am seeing the possibilites with this program.  I chose complimentary colors for the USA map and highlighted section of the Florida map.  I'm hoping there is no confusion with similar colors used in the legend.

 As I was recoloring the various counties I noticed some white gaps between some of the counties.  I would correct this with topology rules in ArcGIS.  I am not sure the easiest way to fix those in AI.

Florida Keys - Labeled with Adobe Illustrator

The learning curve begins with Adobe Illustrator.  This was a frustrating project because AI is nothing like any program I have ever used.  I prefer a hands on tutorial instead of trying to view a video and use help files. 

This was a difficult area to try to label especially without knowing any of the tricks of AI. I need lead lines for several of the labels to clearly show the area they represent.  I wasn't able to follow the guidance from the book reference placing text within an area and not having it too crowded.  I felt the harbors had to include the labels within the area and didn't want the text size too small.

Escambia County Population - Quantile Classification

I chose the quantile classification because it was the only classification that broke up the data in the Northern part of the county.  It seems to have a better balance than the other views.  Also, the data had a positive skew which makes quantile a good option.

Escambia County Population Map - 4 Classification Views

Map Critique - Bad Map

This sign is on State Route 28 in East Wenatchee as you enter a bridge construction zone. The map is actually meant for a recreation trail that is not readily visible from the highway. Recognizing this as a DOT sign and the fact you are driving on a state highway, one assumes the sign is meant for motorists. The title “YOU ARE HERE” is too large and should be text placed under the asterisk. A title like “Pedestrian Path Detour” should have been used. The map should also be rotated 90 for proper orientation.

Map Critique - Good Map

I like the layout of this map with the orthophoto outside the analysis area balancing the colors. The map has an effective locator map and good legend. I’m sure this map met its intended purpose by clearly showing the need for diligence in protecting children from sex offenders because they live amongst us.

Ready to go

Let's make maps!