Analytics On Demand users’ access to useful bonus apps keeps growing! Ten are now available in the Free Apps library. Here are descriptions of the most recently added:
Create a Service Area allows users to input an excel worksheet that contains the branch name and a geography code on each individual row. The geography code needs to be either a block group, a census tract, or a zip code. Each row has to be consistent in terms of the geography type selected as you can not mix and match (use block group, census tract and zip code in the same worksheet).
What will be output is a .kml spatial file that can be used to define a library service area without checking the locations on the geography drop downs. In place of this, you can simply have to upload their spatial file. Then save each ‘custom’ geography to a hard drive so it can be used later.
This accommodates libraries that have multiple branches with unique service area definitions/boundaries. For example, King County in Washington has 48 branches, each defined by block groups. They have approximately 1,000 block groups assigned. It would take them a considerable amount of time to create these in Google maps, ESRI or even DemographicsNow.
Convert Flat File to AOD Format convert flat files (e.g. TXT or TSV) with delimiters to a format that can be consumed by other Analytics On Demand apps.
Test My Patron Data allows users to submit a file of their patron data (that would be used for Patron Profiles) and receive an analysis of the file in terms of how many records were considered ‘good’ and how many were considered ‘bad’. It also measures how many of the ‘good’ records (that passed the USPS CASS validation phase) passed the Experian ConsumerView matching file (if the user checks that box that says “Apply Experian HH data…”. In addition, should the user wish to insert activity dates in to a blank field, they can do that (and have a choice as to which date they want to insert – oldest, newest, today). A user can run their file through this, save the ‘good’ data file, and then use that ‘good’ data file to run through the app. They can download the ‘bad data’ file and try to clean up the bad data to increase their output results.