Project 4: (Weeks 1-3) Napa Best Company Sales and Marketing Project

The sequence of maps are the culmination of an analysis of sales in Napa County to select sales territories for sales representatives of Napa Best Company and ultimately, using Network Analyst tools, determining the optimum travel route for three sales representatives to target the top ten sales locations in their respective territories.

Project 3: Better Books' store site selection

The following link offers a PowerPoint presentation that details the site assessments and site selection for a proposed third store location for Better Books' Stores.

Market Analysis for Selection of Third Store Location for San Francisco's Better Books' stores

Project 3: Market Analysis (Week 2)

In this first map submission, there is a market comparison for the two existing stores to distinguish household patterns for book sales and the relative drive time to arrive at the store. The Steiner location will be used as a model store to perform the comparative analysis to select the best of the two proposed locations for the newest addition to the Best Books Store family.



The three prospective locations are identified in the following map.

Updated Map to include table

Oops...forgot the table.

Project 3: GIS and Economic Development - Data

This map compares four demographic parameters with the locations of the two Better Books stores and competitor store locations.




This map is the first stage in a market analysis for a bookstore company, Best Books, in San Francisco, CA. This map identifies the one mile market zone around each of the two store locations and also the relative percentages of homes with occupants who have pursued college.

Greenspace in Marin City --> Results Week!

Attached please find a PowerPoint presentation related to the work assignment in the Results Week of Project 2 (Special Topics in GIS)

PowerPoint presentation regarding Greenspace in Marin City, California and efforts to offset utility costs for the Marin City Center

Marin City, CA Greenspace (Project 2, Part 2 - Analysis)

Percent tree cover was determined for five neighborhoods in Marin City, California in an effort to demonstrate to the Marin City Manager of the importance of retaining and expanding the city's greenspace.



Calculations were performed to determine the carbon storage and carbon sequestration in the trees in each of the five neighborhoods.

Week 4: Marin City, CA Greenspace Study

The attached maps present imagery of Marin City, CA that has been reclassified to depict the regions throughout the city that are dominated by trees, grass, and impervious layers. The first map shows the location of Marin City by providing inset maps of both Marin County and the group of San Francisco Bay Area Counties.



The second map presents just the Marin City data frame that was generated in ArcInfo 9.3.1 so the classification can be more clearly evaluated or examined.



Metadata Screenshot

Analysis: Asthma Hospitalizations in San Francisco Related to Air Quality

Three maps, attached, describe the assessment of air quality in the San Francisco Bay Area counties and their relationship with asthma hospitalization rates.

Prepare: Air Pollution, Asthma, and Race in the San Francisco Bay Area

The following hyperlinks show the metadata that was created to correspond with four excel data files corresponding to air quality and human health parameters in the San Francisco Bay area counties.

Metadata for asthma hospitalization rates

Metadata for demographics in the San Francisco Bay Area

Metadata for ozone concentrations

Metadata for particulate matter concentrations

The two excel spreadsheets listed below correspond to the merging of: 1) asthma hospitalization data and demographics and, 2) atmospheric ozone and particulate matter concentrations.

Merged data files for asthma hospitalization and race

Merged data files for ozone and particulate matter

Process Summary for
Prepare I: Air Pollution, Asthma, and Race in the San Francisco Bay Area

Brian E. Rood
September 7, 2010


Objective:

To gather data from typical public-access sites that will be incorporated into a comprehensive GIS that will elucidate the relationship between air quality, human health, and race in the San Francisco Bay Area counties.

Data Gathering:

Demographic data was parsed out from the US Census 2000 website,(http://factfinder.census.gov/servlet/datasetmainpageservlet) to identify the racial composition of the San Francisco Bay Area counties.

Asthma hospitalization rates (sorted by race) were manually transcribed from the California County Asthma Hopitalization Chart Book (Data from 1998-2000), California Department of Health Services. 9/03.

Air quality data (ozone and particulate matter) were both downloaded from the Bay Area Air Quality Management District (BAAQMD)(http://www.baaqmd.gov)

Data Handling:

The downloaded data were copied into separate MS Excel spreadsheets.
The resulting spreadsheets were modified to a format that would be
suitable to merge into the attribute table of existing shapefiles provided
by Ms. Trisha Holtzclaw. FID numbers were incorporated into the
spreadsheets that would be matched with the pre-existing FID values in
the shapefiles.

Documentation:

The metadata for the spreadsheets were updates in ArcCatalog using the
metadata file editor function. In the meantime, I realized that it would be
most appropriate to first merge the demographic and asthma
hospitalization rate files because these data would ultimately be merged
with the county shapefile (polygons), and the ozone and particulate
should be merged because they would be joined with the air monitoring
stations layer (point shapefile). Once these spreadsheets were
appropriately merged, then the metadata files could be updated.

Future Expectation:

The collected and modified data will be examined and incorporated into
a comprehensive GIS that will help us better understand patterns and
correlations of air quality, race, and human health in the San Francisco
Bay Area counties.

Week 5: LIDAR Image of Pensacola, FL

Below is a re-worked LIDAR image of an area in Pensacola, FL.

Week 4: Remote Sensing - Classification

Attached are two maps of the Germantown, MD images after recoding and reclassification from an original image provided by Ms. Trisha Holtzclaw. Histogram values that resulted from the reclassification step are included in the images' legends.





Below is an explanation of the reason that there are two submitted maps. One presents the appropriate RGB 5,4,3 color code...the other shows the classes grouped based on similarity (i.e. all urban/residential grouped as one)...however, the colors were manually altered because the RGB 5,4,3 was not informative and did not distinguish dissimilar land characteristics.

Week 3: Remote Sensing (Orthorectification)

This week's map presents an orthorectified image of Pensacola, Florida. The process of orthorectification was based off of the coordinates of a USGS Quad topo map of Pensacola. Included is a copy of the table that shows the RMSE (root mean square error) associated with this calibration. The total RMSE was 0.54 pixels.

ERDAS is a ridiculously cryptic software...there is NO support for the user of the software, and for those who do not have the most current operating system, XPS is not a user-friendly file format.




Total RMSE = 0.54 pixels

Week 2: Remote Sensing (Bands Analysis)

The Week 2 laboratory assignment involved further investigation of the tools of band analysis and selection. Three maps were generated that related to specific pixel values. In this case they corresponded to a lake, a limestone quarry, and an estuary. Click the links to view these maps.

Map of lake

Map of limestone quarry filled with water

Map of shallow estuary showing basin bottom

Week 1: Remote Sensing - Intro to ERDAS

Attached is a link to a map of the runways at the Air Force Base in Pensacola, Florida. The map was produced using the ERDAS Imagine 2010 software.

Click here to view map

Week 12: SAT Scores Final Project

Attached is the figure (including caption) that I generated mindful of an general readership audience of a typical newspaper. This figure would prevent a standard black-and-white printing because of the critical need for color, however, this figure offers a suitable color scheme for a standard three-color press. I had to download a shapefile from the U.S. Geological Survey because the one that the class used for the Chloropleth Map assignment did not have any georeferencing (i.e. when I tried to insert a legend, it indicated that the country was approximately 2 miles long and the shapefile would not accept a new coordinate system). Further details about this figure will be submitted directly for review.



Here is my proposed caption because the one on my map is more than 50 words:
The one on the map is probably more suited to a magazine...my choice would be the Weekly Standard. :)

CAPTION:
National average SAT scores are compared among states by bar graphs that show deviation of each state from the average. Positive bars indicate state performance surpassing the average while negative bars indicate poorer performance. State participation rates (green shading) are lower in the mid-west where ACT exams predominate.

Week 11: Google Earth

The state of Ohio can boast its efforts to harness alternative energy sources, wind energy being a significant subset of these alternative technologies. The literature that I read through suggests that there are two optimum locations to set up windmill fields, Lake Erie off-shore sites and the northwest regions of the state. These are areas where wind velocities are sufficiently high to make energy production viable. Also, they are areas that would least impact Ohioans from turbine noise, ice shedding, and light flicker. I have identified an off-short location that is with the state jurisdiction of Ohio, is not going to affect shipping lanes, and will not be too close to shore where marshland duck hunting is very popular along with other recreational activities.



I found a map generator for Lake Erie in Ohio at http://www.dnr.state.oh.us/website/OCM_GIS/MapViewer_app/OCM_MainMap/dbGroupToc/myfiles/nsc_metadata.htm. If you link to the entire address, the website will bring you to tables of metadata that are not directly useful. However, by trimming down the web linke, a map viewer locator function opens (http://www.dnr.state.oh.us/website/OCM_GIS/MapViewer_app/OCM_MainMap). I activated layers related to shipping, navigation, and recreation and export the resulting map.



The wind velocities across Lake Erie are greatest on the south side of the lake (within the county boundaries of Ohio). So I found a bathymetric map of Lake Erie at http://www.ngdc.noaa.gov/mgg/greatlakes/lakeerie_cdrom/html/e_gmorph.htm. The location that I propose for the windmill field is off-shore, within the boundary limit of Cuyahoga County, OH (county of Cleveland, OH), and affords optimal wind velocities. The bathymetric map of the area suggests that the basin floor is between 20 and 25 meters below the water surface. This is certainly shallow enough for pilings to be driven down into the geologic base of the Niagara Escarpment to effectively anchor the windmills without incident. The windmill fields would not impact apparent shipping channels and the population would not be affected by ice shedding, noise, or light flicker. The only concern might be for the safety of migratory birds (duck and geese species), however, this is an issue that must be considered for all such operations.

Week 10: Isarithmic Maps

Attached is an isarithmic map of the mean annual precipitation in Georgia. The contour lines were based at 5" rainfall levels. I saw Brandon Isenhart's map while I was working on mine and I liked his legend...I thought it was effective...and resisting the temptation to imitate, I made a more traditional legend where there was not gap between the color symbols.

Week 9: Flow Maps (my better attempt)

Hi Trisha...please note that this is not intended to be my bonus exercise...I wanted to have a history of my own decisions about "good" maps and bad maps. Please use this post for scoring my Week 9 assignment...thanks! :)

Well, I finished my first map and posted it, and then I wanted to go back and make it better ... so I used this lab as an opportunity to delve into greater details with AI. I still find some of the program to be cryptic ... my only real gripe is that I cannot figure out why sometimes the zoom tool permits a "zoom out" option and other times a different dropdown window is activated that does not have the zoom in/zoom out function. ...I am on a mission to figure out the key strokes that toggle between these two dropdown windows ... because it is REALLY difficult to zoom back to the full extent if you can't activate the darn zoom out tool! :) On a more serious note, I think I have created a better flow map in the second go-around.

Week 9: Flow Maps

The attached map shows the relative influx of permananent legal U.S. citizens originating from the various regions of the world. The map-type is referred to as a flow map where the width of each flow-arrow is proportional to the relative numbers of individuals successfully moving from one place to another. I created a literally accurate legend based on the data from which the arrows were made because there were a manageable number of values (9) to put in the legend without the legend becoming too cumbersome. I learned much more about AI although I find many of the tools to be cryptic and hidden in less-than-helpful places. The program also seems to be inconsistent in many ways...for example, with the zoom tool, one time you can right click and access the "zoom out" function, but then, with what seem to be the same key strokes, there is a completely different drop-down menu and no clear way to "zoom out". Most good software packages have more than one way to do the same function...AI seems to be lacking in this flexibility and so you would truly have to become "expert" with its use before you could feel a reasonable level of comepetence with the software as a whole (still expecting good things though!).