On Spatial and Geographical Tools

From arcGIS Server 9.3 Help

From arcGIS Server 9.3 Help

As a spatial and visual learner, I’ve been curious about GIS (Geographical Information Systems) since I first learned about it during my time as a public health program evaluator, several years ago. Although our evaluation team never built a GIS database (while I was there), we discussed its potential in relation to some of our projects. For example, how could this database system add to our project evaluating fruit and vegetable food quality in select distributors across urban Honolulu? We also looked at street walkability and measured pavement quality (if a sidewalk existed), lighting, buffers between the walking space and road, signage, among other features.

In the larger public health context, I find spatial mapping an invaluable tool. Ian Gregory brings up a sample project investigating infant mortality rates from 19th century Britain in this blog post, which compares urban and rural locations and their changing rates of infant mortality. I also remember seeing conference presentations that visually represented increasing obesity rates in America, by state, over the past century. While these “obesity maps” of America were jarring, the data served largely to visualize a health epidemic, which then served as a segue into a discussion on particular interventions to combat the situation. In these instances, the visual map, which looked at geographical and temporal factors, served little more than a shock factor. It would have been more interesting and perhaps more meaningful to layer additional variables, such as socioeconomic factors, urban versus suburban versus rural sprawl, access to types of food establishments, etc. As Richard White argues in What is Spatial History? tools for spatial history are a means of doing research, not the end point.

While statistical tools can tell us the correlation between BMI and percent likelihood of chronic fast-food consumption, a geographical relationship between communities with overweight and obese individuals with their lived environment offer additional insight. For example, it can show us the ratio between types of food establishments and grocery stores. It can also show us whether those areas are conducive for walking (safety, lighting, buffers from road, etc). A geographical relationship may also show us a historic relationship between increasing rates of obesity with increasing rates of encroaching corporate food industries. Does it matter how many blocks away you live from certain establishments? Is there even a relationship at all with BMI and fast-food restaurant proximity? With geographical datasets, a new set of questions can be asked to commonly researched projects.

For my own project, which I mentioned in my last post, I’m wondering how I can create a spatial or geographical component to it or whether that would even contribute to my research. I’m dealing a lot with language and discourse around white middle class women’s aging bodies in postwar magazine articles. I’m not entirely sure how spatial history can be part of my research tool-set (for this project) but I’ll keep open to the possibility.

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