Roadway/School Exposures Data: Plans for Exploring, Downloading, and Processing the Data

[lstillwe]

3/16/18 - Attended meeting with Ann Moss Joyner from Cedar Grove Institute for Sustainable Communities. She will be providing road data (to Kara). I will take a look at that as soon as available.

[lstillwe]

@karafecho - Do we want to place any limits on search radius? For instance, if primary or secondary road is more than a mile away, do we still need to know the exact distance? This may save same time in the search process, if I can limit the size of the search radius.

[lstillwe]

Have written most of logic for distance to primary and secondary road centerline data from the federal Tiger Line data for all 50 US states. Will update with search radius if deemed appropriate. The schedule for this task is currently on track - about 80% done.

[karafecho]

I briefly reviewed the literature on roadway exposures, including this article. I think you can probably restrict the search to 500 meters (roughly .3 miles). Does that work for you @lstillwe?

[lstillwe]

Yes - will do!

[karafecho]

If you need to restrict the search even further (<500 meters), we probably can. Just let me know @lstillwe.

[karafecho]

Contacted Ann for 'best' metric for right-of-way data.

[karafecho]

Documentation on US Census Bureau TIGER/Line Shapefiles, 2015:

Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

Documentation on roadway data:

• The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the
• U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents
• a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data
• set, or they can be combined to cover the entire nation

[karafecho]

Current binning strategy for the DDCR Service:

Distance in meters from primary household to nearest major road/highway (1 = 0-49, 2 = 50-99, 3 = 100-199, 4 = 200-299, 5 = 300-499, 6 = >=500 meters)

[karafecho]

WRT roadway data, Lisa will be creating an API for a nationwide Translator Roadway Exposures Service. We are not pursuing the right-of-way data due to limitations with the data and redundancy with the roadway data. Out of scope for Translator project.

[karafecho]

Considering expanding Roadway Exposures Service to include school data, School data can be found in GreenTeam_CedarGroveKFBS Google folder. These data will be very useful for the Translator project, especially our long-terms goals of accounting for mobility when considering exposures and health outcomes. Lisa and Kara will discuss how best to use the data to expand existing Translator Services or create a new one. Initial thought is to expand the Roadway Exposures Service such that given an input latitude/longitude (and eventually time), the service would return distance to nearest major roadway/highway, distance to nearest public school, and distance from nearest public school to major roadway/highway.

[karafecho]

School data have been received from Cedar Grove. The data are from the National Center for Educational Statistics, 2015-2016 (most recent year available). Data on public schools contain addresses, and thus can be mapped to geocodes (Lisa wrote a script to do this); data on private schools do not contain addresses, and thus cannot be used.