For music collectors, knowing which neighborhoods hold the most access to record shops is an extremely an important piece of information. My goal was to create
a map that could highlight the largest cluster of record stores within a single neighborhood and then determine walking distances to those stores in order to determine a perfect location for a residence. After acquiring addresses of record stores in and around Los Angeles, I was able to build a text file with clean location data to be imported.
This process of sorting and cleaning the data can be slow and tedious though helps us to know more about the finer operations and particularities within GIS. Once all the cleaned data was formatted into Excel, I was able to geocode my record store businesses’ addresses onto my map using an address locator. After determining my locations, I buffered the locations for walking distances. By buffering the geocoded stores I would be able to locate the ideal place to live for a music fan. Given that Los Angeles neighborhoods can be variable in terms of housing, walking distances could act as a literal buffer for unpredictability in housing availability.
Without the use of geocoding as a technique, a map such as this would be very difficult and time consuming to produce. The manual importation of the location data would greatly lengthen the time for maps such as mine and, in some cases, completely rule out more ambitious point based maps. I found that the use of geocoding helped me to easily answer my questions of: where are all the record stores located?; which neighborhoods had the most record stores?; and what areas around them would be ideal housing for music fans in terms of walking distance?
