My data collection strategy could
have been improved had I checked over my data more carefully. Halfway
through my spatial analysis I realized that I was missing data due to the
misinterpretation of the codes in my large data table. I had over 100
fields in my data table because I was hoping to also involve job status (which
has many components to it) in my analysis. However I was unable to so
because I was missing these crucial fields in my data table (such as education
and employment). For some reason when I collected data from Rob's program,
it took triplets of my data and crashed midway through the processing. I
checked the data as soon as I got it and it appeared to be all there but because
I was unfamiliar with all of the codes there were several important fields
missing. So I restarted my project from the beginning because I did
not want to manually enter 299 values for each missing field.
The metadata used for this project was from a secondary source so the reliability is questionable to some degree. For example, the metada for the landuse data was poor and so the accuracy of this data is questionable.
The steps designed for my analysis did provide the results I had hoped for in that the healthy communities are located near parks. Though, income also influences where people can afford to live, it also is positively linked to health (ACPH, 1999).
The metadata used for this project was from a secondary source so the reliability is questionable to some degree. For example, the metada for the landuse data was poor and so the accuracy of this data is questionable.
The steps designed for my analysis did provide the results I had hoped for in that the healthy communities are located near parks. Though, income also influences where people can afford to live, it also is positively linked to health (ACPH, 1999).
