Project 2: GIS and Landscape Design: Part 2 Analyze

Project 2 in GIS 4930 Special Topics in GIS focuses on GIS and Landscape Design/Management. The scenario involves using a GIS to answer questions related to Small Business Environmental Stewardship Assistance Act of 2010 which encourages communities to plant trees in urban neighborhoods in order to revitalize communities.

In the preparation phase ortho-images of Marin City, CA were reclassified to identify three distinct classes of ground cover - Trees, Grasses and Impervious Surfaces. Once reclassification was completed using ArcGIS Spatial Analysis tools, the analyze phase focused on looking at calculating percent tree cover, carbon storage and carbon sequestration for five selected neighborhoods within the city area. The analysis performed is a good demonstration of the way a GIS can be used to derive quantitative spacial information about an area from a basic image.

Using the reclassified image, each neighborhoods ground cover classifications were extracted allowing for the calculation of the area, acreage and percent of total coverage for each class of land cover. The data from those calculations was used to calculate estimated carbon storage and carbon sequestration values for each neighborhood based on CITYGreen methods and equations. The result is the posted map which shows each of the five neighborhoods statistics allowing for the easy determination of which areas in Marin City are more likely to benefit from tree planting programs.

Project 2: Prepare

Project 2 in GIS 4930 Special Topics in GIS focuses on GIS and Landscape Design/Management. The scenario involves using a GIS to answer questions related to Small Business Environmental Stewardship Assistance Act of 2010 which encourages communities to plant trees in urban neighborhoods in order to revitalize communities. The act requires matching funds to be provided by the city and the City Manager is requesting a study to show some quantitative reasons to fund the program. Specifically, the GIS is being used to classify ground cover in Marin City, California to try to answer three questions: 1) How does the stewardship program currently benefit the city in terms of energy savings. 2) How does the program benefit the city in terms of carbon stored. 3)How can the program help to identify areas within the city that would benefit from planting more trees.

The first step in the process was to reclassifying ortho-images of Marin City into classified raster images that can be used to determine three general types of land cover classifications: Trees, Grasses and Impervious Surfaces. As part of the preperation process the following base map was created showing Marin County and Marin City with the area to be classified:

Special Topics Project 1 Report Phase

The posted maps were created as part of the first project in the University of West Florida On-line GIS certification course, Special Topics in GIS (GIS 4048). The project was a health study of air pollution, asthma and race in the San Francisco bay area. The study focused on looking at asthma hospitalization rates in the nine counties that make up the bay area to look for correlations between factors such as race and air quality.

The study had three separate analytical parts that required their own deliverables. Each of the maps posted here was part of one of those studies and they are being presented in the order of the study.

Public Health Analysis: Part 1 Demographics

The first part of the study was actually a separate study that took priority when the project leaders were notified that funds were available to help uninsured populations. The goal was to determine if there were any correlations between certain possible poverty indicators such as unemployment, race and single mother-hood and the uninsured population.









Public Health Analysis: Part 2 A Closer Look at Asthma

The second part of the study looked at the relation ship of race and air quality factors to the rate of asthma hospitalizations in the bay area. The goal was to try to determine if there was any correlation between these factors, determine which ones and locate both the target population and target county where funds would best be allocated to hospitals likely to receive the most asthma hospitalizations.









Public Health Analysis: Part 3

Part 3 of the study proceeds under the assumption that the previous phases of the study have shown a population most at risk of asthma hospitalization and the county that is the most likely to experience impacts from increased hospitalizations from the targeted population. The goal of this portion of the study was to look at where targeted asthma sufferers may suffer due to point sources of pollution and which hospitals are most likely to be utilized by the targeted population. The study mapped sources of pollution such as Toxic Release Index (TRI) point locations and roadways, hospitals and the distribution of the targeted population at the Census Tract level. These factors were compared via weighted overlays to identify the most likely "hot" zones where the proximity to a hospital, the pollution factors and the targeted population could most likely lead to increased use necessitating the need for an increase in staffing and funding.

Special Topics Project 1: Analyze Phase Post 1

The posted map was completed as part of the University of West Florida On-Line GIS Certification Program class, GIS 4930/5945 Special Topics in GIS. The Special Topics class is focusing on one geographic area, the San Francisco Bay Area, and studying various problems faced by city GIS managers and analysts in that area over the course of the semester.

The first class project focuses on GIS and Public Health by looking at possible race and environmental factors that may be related to asthma hospitalization rates in the nine counties that comprise the San Francisco Bay Area.

The posted map shows county asthma hospitalization rates per 10,000 people and it forms a base map that can be used for later p[arts of the study:



The map is a fairly straightforward thematic choropleth map that highlights the counties showing the highest rate of hospitalizations per 10,000 people in the bay area, specifically Alameda County. The map also shows some hospitals that are within the study area that are likely to be receiving many of the hospitalized patients.

The map was fairly easy to construct using ESRI ArcMap GIS software, the biggest challenges being choosing a suitable color scheme of the county data and a background that highlighted the map data. I annotized the County labels for this map so that I could move them to open areas of the map and fit in the legend and other pertinent map data while keeping the scale as large as possible.

Special Topics in GIS Project 1: Air Pollution, Ashtma and Race in San Francisco Bay Area

The following links were created for the first assignment in the University of West Florida's 2010 GIS Certification Program on-line course, Special Topics in GIS which began in the Fall 2010 semester.

The Special Topics class focuses on longer term projects centered on s specific area - the San Francisco Bay Area. The first project focuses on public health via a study that uses GIS to examine whether there is any correlation between asthma admittance rates in the nine counties that comprise the San Francisco bay area, air quality (based on ozone and particulate matter data) and racial demographics. The study initiators are local county hosptial officials who are looking into data to help develop resource allocation budgets for the county hospital system.

The prepare phase of the project required preparing demographic, air quality and asthma admittance data from various sources such as publications and the internet into a format that coud be utilized by the GIS and connected to spatial data utilized by the GIS (in this case the county geospatial data).

As all the GIS datasets were being created or altered from exiosting datasets, one of the project requirements was to update the metadata associated with each data file to reflects it's relevence to the current project and to provide future possible users of the data a way to understand what data was incorporated in the study for validation purposes.

What is posted here are the metadata files for all the datasets anticipated to be used in the project:

Demographics
Asthma Rates
Monthly Ozone
Particulate Matter

Bay Area Counties Shapefile
Bay Area Hospitals Shapefile
Bay Area Monitoring Stations Shapefile

In addition to ther preperaption of the data sets and the metadata, the Prepare phase required the production of a preliminary Process summary outlining the steps taken from inception to completion of the study and presentation of the data.

Module 5 Challenge: LiDAR - Pensacola Beach Section

The map image below was created using ArcGIS with Light Detection and Ranging (LiDAR) data collected from an airplane over a section of Pensacola Beach, Florida. The image was created for the Week 5 LiDAR Module Challenge in the 2010 University of West Florida Online GIS Certification program class, Photo Interpretation and Remote Sensing (GIS 4035L).



To create the image, the LiDAR data was converted into a point file using the Add XY Data tool in ArcGIS and then exported as a shape-file which was then converted to a raster file using the IDW Spatial Analyst tool. The rater image was given a stretched symbology to enable the identification of features and then symbolized using color scheme showing contrasting colors for low to high elevations, in this case darker blue tones for the lower elevations and orange-red for the higher elevations. The contrasting tones contribute to the ability to identify features of which there where three types we were asked to highlight - a road, sand dunes and water. To show these features, a new polygon layer was added and each feature was outlined in the polygon layer and then symbolized separately after adding a feature identification field to the polygon layer. The map was then laid out with a grid to show the projection system used for the original LiDAR data set.

Everything seemed to go pretty smoothly for the challenge, the biggest issue being deciding how to add a header to the original dataset to make sure ArcMap could identify the X, Y and Z columns. The ID polygons took some time to get correct but only because I kept closing them too soon and had to redo them.

Module 4 Challenge: Supervised Classification

The link posted here is to a map created using ERDAS IMAGINE 2010 software as part of the University of West Florida On-line GIS Certification program class, Photo Interpretation and Remote Sensing (GIS4035/L). The link requires Internet Explorer to open and display properly.

Germantown Maryland

The image is a Supervised Maximum Likelihood classification from a satellite image of Germantown, Maryland classified for 14 land use types identified by spectral signatures identified using the ERDAS IMAGINE software. The process involved creating a unique spectral signature for each land use type by first creating an area of interest polygon around a specific known feature on the map in an attempt to capture pixel values that are unique to the feature and thus to the type of land use. Once the distinct signatures were created the software could reclassify the entire image using the signatures to show each of the land use type.

In theory the task was not too complicated. However, knowing when a specific signature covered enough pixels in the image was not an easy task, particularly after the assignment was changed to require that each land use contain pixel values in a certain range. In the end I got 12 of 14 within the proper ranges and was only off on the other two by a relatively small amount of pixels. However, after reclassifying signatures multiple times and regenerating the classified map 16 times, I feel I gave a tremendous effort to accomplish a task that just did not seem worthwhile as each successive classification once a certain point had been reached seemed to have a negligible effect on the image as a whole. In that respect I'm satisfied with the map regardless off the possible mark downs for these two classes that were only slightly out of the challenge parameters.