User:Pw271
"Maine E911" is an import of The Maine E911 dataset which is of type jeojson covering all of Maine, USA. The import is currently (as of March 2018) at the planning stage.
Import Data
Background
http://www.maine.gov/megis/catalog/
https://gis2.maine.gov/arcgis/rest/services/Location/Maine_E911_Addresses_Roads_PSAP/MapServer
OSM Data Files
TODO
Import Type
This should be a one time import.
Identify what method will be used for entering the imported data into the OSM database - e.g. API, JOSM, upload.py, etc.
Data Preparation
Data Reduction & Simplification
Describe your plans, if any, to reduce the amount of data you'll need to import.
Examples of this include removing information that is already contained in OSM or simplifying shapefiles.
Tagging Plans
Describe your plan for mapping source attributes to OSM tags.
Changeset Tags
Describe how you'll use changeset tags in the import.
Data Transformation
Describe the transformations you'll need to conduct, the tools you're using, and any specific configurations or code that will be used in the transformation.
Data Transformation Results
Post a link to your OSM XML files.
Data Merge Workflow
Team Approach
Describe if you'll be doing this solo or as a team.
References
List all factors that will be evaluated in the import.
Workflow
Detail the steps you'll take during the actual import.
Information to include:
- Step by step instructions
- Changeset size policy
- Revert plans
Conflation
Identify your approach to conflation here.
QA
The E911 data was compared to the OpenStreetMap data for the state of Maine. Of the 668671 buildings listed in the E911 database, 2547 matched street name, address number, and city. This is such a small number, since there are only 3449 buildings in Maine in OpenStreetMap that have complete addresses. For each of the matched buildings I calculated the distance between the coordinates from the 2 databases. Ideally, the distance would be zero for each building. I did this to quantify the quality of the data in the E911 database.
Here is a cumulative plot of the distances for each of the 2547 matched buildings:
The graph shows approximately 20% of the buildings are closer than 10 feet and 80% of the buildings are closer than 100 feet. I tried to analyze the worst 3 points (greater than 2800 feet in separation) and came to the conclusion that the E911 data was correct and the OpenStreetMap data was in error.
My conclusion is that the E911 data is of good quality.