This page is actively being updated by the AGRC Cadastral Data group, look for more information here in the near future.Cadastral data is data related to formal survey reference systems and in Utah includes:
the Public Land Survey System (PLSS) townships, sections, section divisions, and corner points;
public land ownership records including: owning entity, administrative agency, and binding designations
private land ownership including: subdivisions and parcels
GPS base station locations
Many cadastral data layers are available for download from the Utah State Geographic Information Database (SGID) including parcel data from 27 of 29 counties.
James Wingate at Blue Stakes has compiled most of address coordinate system descriptions listed below. AGRC will update this list as most information is brought to our attention. If you have information or corrections to contribute, please post them here as a comment or email
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AGRC is working on revising a polygon feature class that approximates the boundaries of each local address coordinate system (ACS) and each quadrant (NE, NW, SE, SW) within each local ACS. The feature class should be available by the end of January.
- Compare Historic Preservation database city domain/list with AGRC geocode service's city names that are mapped to zip codes and devise a strategy forachieving operational agreement
- QC AGRC's City --> Zip code list to make sure we have the right representation of zip codes for each city name.Get most current from Steve or Barry
Notes on the building the overall process for address matching
First Crack at Geocoding Using AGRC's Geocoding Web Service (Barry):
The complete dataset of addresses Heidi gave me to geocode is here: I:\AGR6\barry_work\HistAddresses2.gdb
74989 out of 88243 geocoded with a score of 80 or better (85%)
1882 of those remaining geocoded with a score between 60 - 79.
It took the application about 1 hour 50 min to geocode 10,000 addresses. A total of approx 16 hours of processing time were needed for all 88,243 addresses.
The application I made to batch process the addresses added 5 new fields: -UTM_X -UTM_Y -MatchAddr (The address in the streets fc the returned match is based on) -Score -Note (used only for marking those municipalities that were not found in the municipalities list)
The geocoding application can handle either city or zip code as the zone. The original dataset did not have zip codes, so we added a field called "Zone" and calculated it to be either the city name if it was in the municipalities feature class in the SGID or the zip code that the place name from GNIS is within.
Heidi corrected most of the municipality names so they matched the spelling in the SGID municipalities, which is used by the web service to search for possible zip codes. There are still some (3439) that are not the same for whatever reason, including some that are "Unknown". The largest part of these, however, are in "Richfield". I'm not sure why those weren't found in the municipalities list. These will probably geocode with a fairly high score if corrected.
The "Comments" column in the original tables was removed since it did not get along with the delimited file formats I needed to use. It can be added back since the original ObjectID has been kept as a key since I was told that the ID field might not be unique.
Process components:
How to keep track of what's done, by whom, whether its worth keeping (how to build/keep metadata in some sort of standard)
