Alachua County Location Decision

This laboratory focused on the use of GIS to assist a prospective home buyer in selecting a location in Alachua County, Florida that satisfied four criteria: (1) proximity to North Florida Regional Medical Center, (2) proximity to the University of Florida, (3) area having a high percentage of the population in the 40 - 49 age range, and (4) areas with high median home values. The laboratory involved the analysis and generation of three maps, one of which included a weighted-overlay analysis. U.S. Census Bureau data was accessed and Joined to shapefiles to graphically present demographic data and housing data spatially. Numerous Spatial Analyst tools were used during this lab including: Feature-to-Raster, Reclassify, Euclidean Distance, and Weighted-Overly.  Model Builder was used to conduct tradeoff analyses based on the different percentages-of-influence of the four criteria.

Emergency Response to HAZMAT Incident

This map illustrates the use of ArcMap's Network Analyst Extension and associated tools. Network Analyst was used to analyze and determine highway detour routes and evacuation routes for a hypothetical hazardous material (HAZMAT) incident along I-280 in San Francisco.  Buffer zones were established around the incident site and road blocks were placed on I-280 and US 101. Census tract data for the San Francisco area was used to determine the number of affected households and individuals within the 0.5 mile buffer.  An evacuation helicopter landing site was also selected based on a review of the orthophoto base map imagery. Network Analyst rendered layers of detour and evacuation routes and provided turn-by-turn driving directions for each of the routes.

 

 

Assessment of Elementary School Site

This map was generated as part of a final project for the “Applications in GIS” course. The objective of the project was to determine if the site selected for a new elementary school in Alachua County (designated as school ‘H’) conformed to the Environmental Protection Agency (EPA) school siting guidelines.  GIS data was downloaded from several sources to conduct the analysis including: railway, transmission power line, land use, and major road shapefiles from  FGDL, wetlands data from the National Wetlands Inventory and SJRWMD, digital elevation data from USGS, 2010 census tract and demographic data from U.S. Census Bureau, soil data from the National Resource Conservation Service, and parcel, school zone, existing schools, and Gainesville city limit shapefiles from the Alachua County GIS Service Center, the Alachua County Department of Growth Management, and the City of Gainesville.  Assessment results determined that the proposed site did conform to EPA guidelines. An interactive version of this map can be accessed at ArcGIS Online at the following link: http://bit.ly/ojZFYR.

 

Washington DC Crime

This laboratory focused on the use of GIS in assessing the spatial distribution and types of crimes that occurred in Washington DC in August 2009. A table (*.csv file) was downloaded that contained the type and the geographic coordinates of crimes that occurred during the month.  The data was exported to a shapefile such that the crimes could be graphically displayed spatially.  The Spatial Analyst Kernal Density was used to quantify the concentration of particular crimes in the city.  The density layer was then categorically symbolized using the Natural Breaks classification scheme into five intervals.

Land Use/Land Cover and Ground Truthing

This map illustrates the development of a land use/land cover classification map based on digitizing recognized ground features in a natural color aerial photograph.  Classification was based on the standard USGS Level I and Level II classification system.  Even though schemes for higher classification exist, they are typically used for specialized projects.  Here, students were to create their own Level III classifications based on the features found in the study area.   Following the classification, 35 sample points (shown as small circles on the map) were selected as ground truth locations for the classified areas.  The sample points were chosen based on a stratified random sampling scheme, where samples were taken in each classification category.  However, fewer samples were taken in regions that exhibited little variability (e.g., marshes and near shore coastal waters) while a larger number of samples were taken on the land areas of interest.  The locations were "truthed" using Google's Street View feature.   Overall classification accuracy was then computed.