All assets types extracted form the San Diego City Iq system
cityiq.io-objects-san_diego-5. Modified 2020-04-12T16:44:03
Resources
- assets. All assets
- locations. All locations: walkways, traffic lanes and parking zones
- walkways. Walkways
- traffic_lanes. Traffic lanes
- parking_zones. Parking zones
- cameras. Cameras
- mics. Microphones
- nodes. Nodes
- em_sensors. Electro-magnetic sensors
- env_sensors. Environmental sensors
Documentation
These datafile are extracts of the assets and locations from the San Diego CityIQ system. Refer to the [CityIQ developer documentation](<https://ie-cities-docs.run.aws-usw02-pr.ice.predix.io/#c_overview _of_general_apis.html>) for details about these data records. The data are extracted using the cityiq Python package. See the ExtractAssets.ipynb notebook for the extract process.
Versions
- Development version
- Development version
- First release
- Added tract and community names
- Rebuild with recent data, and broke out asset and location types
Contacts
- Wrangler
Packages
- zip https://library-metatab-org.s3.amazonaws.com/cityiq.io-objects-san_diego-5.zip
- xlsx https://library-metatab-org.s3.amazonaws.com/cityiq.io-objects-san_diego-5.xlsx
- s3 s3://library.metatab.org/cityiq.io-objects-san_diego-5.csv
- csv https://library-metatab-org.s3.amazonaws.com/cityiq.io-objects-san_diego-5.csv
- source https://github.com/sandiegodata-projects/cityiq.io.git
Accessing Packages in Metapack
import metapack as mp
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/cityiq.io-objects-san_diego-5.zip')
# Create Dataframes
assets_gdf = pkg.resource('assets').geoframe()
locations_gdf = pkg.resource('locations').geoframe()
walkways_gdf = pkg.resource('walkways').geoframe()
traffic_lanes_gdf = pkg.resource('traffic_lanes').geoframe()
parking_zones_gdf = pkg.resource('parking_zones').geoframe()
cameras_gdf = pkg.resource('cameras').geoframe()
mics_gdf = pkg.resource('mics').geoframe()
nodes_gdf = pkg.resource('nodes').geoframe()
em_sensors_gdf = pkg.resource('em_sensors').geoframe()
env_sensors_gdf = pkg.resource('env_sensors').geoframe()
Data Dictionary
assets | locations
assets
| Column Name | Data Type | Description |
|---|---|---|
| assetuid | string | |
| assettype | string | |
| parentassetuid | string | |
| mediatype | string | |
| events | string | What events the asset generates |
| community_name | string | Name of San Diego planning community where asset is located |
| tract_geoid | string | Geoid of the tract where the asset is located |
| roadsegid | integer | SANDAG road segment id. Links to the SANDAG roads data |
| speed | integer | Speed limit on the road segment |
| oneway | string | Is the road segment one way? |
| abloaddr | integer | Absolute lowest address on road segment |
| abhiaddr | integer | Absolute highest address on road segment |
| rd30full | string | Full street name |
| geometry | string | WKT Geometry of parking zone |
locations
| Column Name | Data Type | Description |
|---|---|---|
| locationuid | string | |
| locationtype | string | |
| parentlocationuid | string | |
| community_name | string | Name of San Diego planning community where location is located |
| tract_geoid | string | Geoid of the tract where the location is located |
| roadsegid | integer | SANDAG road segment id. Links to the SANDAG roads data |
| speed | integer | Speed limit on the road segment |
| oneway | string | Is the road segment one way? |
| abloaddr | integer | Absolute lowest address on road segment |
| abhiaddr | integer | Absolute highest address on road segment |
| rd30full | string | Full street name |
| geometry | string |
References
Urls used in the creation of this data package.
- metapack+https://library-metatab-org.s3.amazonaws.com/sangis.org-roads-sandiego-1.zip#all_roads. Roads in San Diego
- metapack+https://library-metatab-org.s3.amazonaws.com/sandiegodata.org-geography-2018.csv#tract_boundaries. San Diego tracts and communities
- metapack+https://library-metatab-org.s3.amazonaws.com/sandiegodata.org-geography-2018.csv#sd_community_boundaries. San Diego tracts and communities
Packages
- zip https://library-metatab-org.s3.amazonaws.com/cityiq.io-objects-san_diego-5.zip
- xlsx https://library-metatab-org.s3.amazonaws.com/cityiq.io-objects-san_diego-5.xlsx
- s3 s3://library.metatab.org/cityiq.io-objects-san_diego-5.csv
- csv https://library-metatab-org.s3.amazonaws.com/cityiq.io-objects-san_diego-5.csv
- source https://github.com/sandiegodata-projects/cityiq.io.git
Accessing Packages in Metapack
import metapack as mp
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/cityiq.io-objects-san_diego-5.zip')
# Create Dataframes
assets_gdf = pkg.resource('assets').geoframe()
locations_gdf = pkg.resource('locations').geoframe()
walkways_gdf = pkg.resource('walkways').geoframe()
traffic_lanes_gdf = pkg.resource('traffic_lanes').geoframe()
parking_zones_gdf = pkg.resource('parking_zones').geoframe()
cameras_gdf = pkg.resource('cameras').geoframe()
mics_gdf = pkg.resource('mics').geoframe()
nodes_gdf = pkg.resource('nodes').geoframe()
em_sensors_gdf = pkg.resource('em_sensors').geoframe()
env_sensors_gdf = pkg.resource('env_sensors').geoframe()