GIS II: Advanced GIS
This collection of GIS lab reports showcases my hands-on experience using ArcGIS Pro to solve real-world spatial problems. From dasymetric population mapping and network analysis to spatial autocorrelation and clustering, each lab demonstrates applied techniques in geospatial analysis. These projects reflect my growing expertise in spatial data processing, model building, and cartographic visualization, developed through coursework in Portland State University’s GIS Certificate program.
Raster Analysis Tools in ArcGIS
This first lab focuses on foundational GIS mapping skills, introducing core concepts such as symbology, data classification, and map layout design. It served as a baseline exercise to build fluency in ArcGIS Pro’s interface and basic cartographic communication.
2. ArcGIS ModelBuilder - Finding a site for a new school
In this lab, I explored raster data processing and spatial modeling through tools like Con, Slope, and Majority Filter. The exercise emphasizes conditional logic and neighborhood analysis in raster datasets. I also constructed and documented a geoprocessing workflow in ModelBuilder, highlighting the value of automation and consistency in GIS.
3. Dasymetric Mapping
This lab focuses on dasymetric population mapping using land cover data to refine census block group population estimates. I calculated zonal statistics, examined raster cell values, and estimated the population of the Beaverton Creek subwatershed. The exercise demonstrates how integrating remote sensing and demographic data can improve spatial accuracy in population studies.
4. Network Analyst
This lab introduces ArcGIS Network Analyst tools to model transportation access and service areas. I analyzed travel times with and without barriers, evaluated location-allocation strategies (e.g., Maximize Attendance), and discussed spatial interaction models like Huff. This exercise connects GIS analysis to real-world infrastructure and planning applications such as emergency response and retail siting.
5. Pattern Analysis
In this lab, I employed spatial statistics tools to evaluate clustering and spatial autocorrelation in demographic data. Using techniques like Average Nearest Neighbor, Global and Local Moran’s I, and Getis-Ord Gi*, I interpreted spatial patterns in variables such as youth population, unoccupied housing, and Hispanic population share.
GIS for Planners