It has been some time since the last time I blogged here. This Spring semester has been busier than I anticipated, between classes, working on my dissertation proposal, and my Research Assistantship. I should give myself more credit, though. In this academic year, I have presented work at 3 conferences and 1 symposium, drafted a manuscript, and submitted funding applications. I guess I just didn’t have the bandwidth to sit and write a blogpost.
Now that the semester is over (hooray!), I am working more at integrating mapping, GIS into my research process. Handling and processing large datasets is a skill I have honed over time. Between my political economy of corruption course at the University of Chicago and the non-profit work and freelance journalism I’ve done over the years, I have a level of competence in processing, analyzing, and writing about large data sets. That particular skill was extremely useful in last semester’s “Census Data class”- as it was called. It was an Urban Planning course- entitled Urban and Regional Analysis- that emphasized mapping and analyzing economic and demographic data, and ultimately, synthesizing reports about a particular region of study.
This summer, I will be learning Python with emphasis on scripting for ArcGIS. This is just a first step in a career-long learning process. It just makes sense to know more about mapping with GIS- beyond “point, click, and hope for the best.” The first time I mapped healthcare provider availability using Kernel Density estimates, I did not know how to interpret the map or the equations used to produce said estimates. Similarly, I conducted hours of research on Moran’s I
More recently, I tested out the 2-Step Floating Catchment Area (2SFCA) method (ESRI Tutorial here), a method developed by Luo and Wang (2003), using data sets that I created. The 2SFCA method calculates supply-to-demand ratios based on “floating” catchment areas. The first step computes the supply-to-demand ratio within a supplier’s service area, defined as a threshold travel time or distance from supplier locations, calculating a supplier-to-demand ratio within the defined catchment area. The second step calculates the supplier-to-demand ratio for each demand location- usually a specified travel time or distance from census tract centroids that are population-weighted. The second step sums up the supply-to-demand ratios between overlapping service areas, producing a measure of accessibility for a demand locations, which may have more than one supply location. In the end, I produced a map that looked a lot like the Kernel Density map I produced hours before, but it was well worth learning the process and the reasoning behind the method.
Back to what I was saying though… my summer will be pretty busy. I have my RAship, preparation for qualifying exams, and manuscript revisions. There will be time for fun, of course! I have already put in good time in the garden, planting tomatoes, basil, ancho chili, Swiss chard, okra, strawberries, onions, lavender, red long beans, blue lake Bush beans, peas, chives, dill, cilantro, amaranth, burgundy beans, acorn squash, zucchini, and crookneck squash. And I can happily report that my perennial garden (tulips, veronicas, hydrangeas, yarrow…) is thriving without any work on my part.
- Luo W, Wang F. (2003) Measures of spatial accessibility to healthcare in a GIS environment: synthesis and a case study in Chicago region. Env Plann B. 2003;30: 865–884.
- Luo W, Qi Y. (2009). An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health and Place.15: 1100–1107. pmid:19576837
- Luo, J. (2014). Integrating the Huff Model and Floating Catchment Area Methods to Analyze Spatial Access to Healthcare Services. Transactions In GIS, 18(3), 436-448. doi:10.1111/tgis.12096
- Wang, F. (2012). “Measurement, Optimization, and Impact of Health Care Accessibility: A Methodological Review.” Annals of The Association of American Geographers 102, no. 5: 1104-1112