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