Yesterday a graduate student asked about my farmers’ market analysis, because she is TA-ing a university course on data collection methods and research. Her questions reminded me to alert her to the fact that data patterns are not necessarily constant across scales. For example, farmers’ market correlations may be seen in a global or national dataset but may not be prevalent at the local city level. Conversely, patterns seen at the local city level may not be seen in the national or global map.
Furthermore, focusing on local exceptions instead of global or national regularities, may be more meaningful, especially if the data are a high enough resolution to provide adequate insight. While I’m not sure if the farmers’ market dataset from the USDA will show patterns at a local level, I’m sure that it is a good thing to try. This approach also allows a more intricate data quality assurance, because with fewer datapoints (less than 20 per city), they can easily be verified and added to as needed if one is looking at just a single city.
This discussion reminds all of us analysts that “it might be incorrect to assume that the results obtained from the whole data set represent the situation in all parts of the study area.”* I’d be happy to hear your thoughts on this.
*See Quantitative Geography by A. Stewart Fotheringham, Chris Brunsdon, and Martin Charlton for further reading (p11 especially).